Response to an Amendment
This office action is a response to a communication made on 01/16/2026.
Claims 3 and 16 are canceled.
Claim 21 is new.
Claims 1, 9 and 14 are currently amended.
Claims 1-2, 4-15 and 17-21 are pending for this application.
Response to Arguments
Applicant: Applicant’s arguments, see remarks on page 10-11, filed 01/16/2026, applicant argues that, “Mann, Shen, Krishnaswamy and Kushnir does not suggest or fail to teach the amended claims 1, 9, and 14 recite, in relevant part, "the one or more service providers comprising a plurality of individual people" and "determining, by the second machine learning model, one or more of the plurality of individual people of the one or more service providers associated with the failure of the 5G network component."
Examiner: Applicant's arguments filed 01/16/2026 have been fully considered but they are not persuasive. Examiner respectfully disagrees.
Shen teaches the one or more service providers comprising a plurality of individual people because Col-14, II. 63-65, teaches applied to an incident or root cause may be related to automatic scaling of an application, service, or device rollout; third-party vendor (service provider) involved, see Fig. 5, step 512, Col-15, II. 33-47 and II. 60-67, teaches the responsible person or team information (i.e. plurality of individual people) 512 may include various other types of data or information. For example, the responsible person or team may be a group of employees within a business, a person or persons in a business, or both (e.g., two responsible people within a responsible business group may be designated). In various embodiments, multiple responsible persons/groups may be assigned to different actions. For example, a corrective action may be assigned to a first person or team and a preventative action may be assigned to a different person or team. In another example, a person or team may be assigned to actually complete a task or action item, while another person or team may be assigned as being responsible for ensuring that the task or action item actually gets completed… the system may also transmit a message to a responsible person or team based on the information generated at 518 for the new incident. Such a message may include any of the data generated at 518. For example, the message may include action data related to the new incident, which may include one or more specific actions to be taken by the responsible person or team determined at 518.
However, Mann in view of Shen, and further in view of Krishnaswamy remain silent on determining, by the second machine learning model, one or more of the one or more service providers associated with the failure of the 5G network component.
Kushnir teaches determining, by the second machine learning model, one or more of the one or more service providers associated with the failure of the 5G network component because ¶0016 and ¶0018, teaches Individual components of computer networks may fail outright or function at a level inadequate to bear the demands of the network. Although degradation of network service is most often caused by the failure or inadequacy of network components… The efforts of service providers to remedy network problems require a determination of the root cause or root causes of each problem, …a statistical model (i.e. second machine learning) may be applied to historical benchmark data to accurately infer the causes of network problems.
For more clarification, please see the rejection below.
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.
Claim(s) 1, 4, 6-14 and 17-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mann et al. (US 2020/0372415), hereinafter “Mann” in view of Shen et al. (US 11188411), hereinafter “Shen” in view of Krishnaswamy et al. (US 2021/0243839), hereinafter “Krishnaswamy”, and further in view of Kushnir et al. (US20160162346A1), hereinafter “Kushnir”.
With respect to claim 1, Mann discloses a method, comprising:
accessing, by a computing device (¶0033, client device 110), data associated with a failure of a network component (¶0039, teaches the incident data may further include root causes determined by the root cause analyzer 130. ¶0061, teaches the incident parameters may include, but are not limited to, identified root causes of incidents, times of incidents, locations of incidents (e.g., locations within a system or network), systems or components affected by incidents (e.g., hardware components or software sub-systems);
providing, by the computing device (¶0033, client device 110), the data associated with the failure of the network component to a first machine learning model (¶0031, teaches a root cause for an incident may be determined by a machine learning model trained to identify root causes of incidents, ¶0071, teaches component-based (i.e., correlation of incident patterns across different components)), wherein the first machine learning model is configured to determine and output data indicating a root cause of the failure (¶0031, teaches a root cause for an incident may be determined by a machine learning model trained to identify root causes of incidents, ¶0042, teaches the root cause analyzer 130 may be configured to output a rich root cause analysis report indicating information related to identification of the suitable insights for an incident and any recommendations for addressing the incident based on the suitable insights, ¶0046, teaches the recognized incident patterns are processed to determine a root cause of an incident);
generating, by the computing device ((¶0033, client device 110), a service ticket comprising data indicating the root cause of the failure of the network component and the respective destinations associated with the root cause of the failure of the 5G network component (¶0036, teaches each ticket includes at least a textual description of an issue. The issue may be indicative of an incident (e.g., an incident for which a root cause is determined by the root cause analyzer 130), ¶0061, teaches the incident parameters may include, but are not limited to, identified root causes of incidents, times of incidents, locations (i.e. respective destination) of incidents (e.g., locations within a system or network));
receiving, by the second machine learning model (Mann, ¶0111, teaches the historical root causes relate to respective historical incidents indicated by the historical tickets (i.e. second machine learning model is trained on historical tickets) determined, for example, as described herein above with respect to FIG. 2), the data indicating the root cause of the failure of the 5G network component (¶0061, teaches the incident parameters may include, but are not limited to, identified root causes of incidents, times of incidents, locations (i.e. destination) of incidents (e.g., locations within a system or network), ¶0115, teaches for a set of one or more of the historical tickets, the historical root causes are analyzed, and the same or similar historical root causes are determined for each set of historical tickets);
determining, by the second machine learning model (Mann, ¶0111, teaches the historical root causes relate to respective historical incidents indicated by the historical tickets (i.e. second machine learning model is trained on historical tickets) determined, for example, as described herein above with respect to FIG. 2), one or more service providers and respective destinations based at least in part on the data indicating the root cause, the one or more service providers associated with the root cause of the failure of the 5G network component (¶0061, teaches the incident parameters may include, but are not limited to, identified root causes of incidents, times of incidents, locations (i.e. destination) of incidents (e.g., locations within a system or network), ¶0115, teaches for a set of one or more of the historical tickets, the historical root causes are analyzed, and the same or similar historical root causes are determined for each set of historical tickets, ¶0117, teaches root causes and tickets may be received from root cause analyzer and ticketing systems (e.g., ITSM systems), wherein ITSM is service provider).
However, Mann remain silent on providing, by the computing device, data indicating the root cause of the failure to a second machine learning model, wherein the second machine learning model is configured to determine and output data indicating one or more service providers and respective destinations of the one or more service providers associated with the root cause of the failure of the 5G network component, the one or more service providers comprising a plurality of individual people; transmitting, by the computing device, the service ticket to the respective destinations of the one or more service providers associated with the root cause of the failure.
Shen discloses providing, by the computing device (Fig. 1, computing devices 101-104), data indicating the root cause of the failure to a second machine learning model (Col-17, II. 25-28, teaches the root cause data may indicate what is believed to be one or more actual causes of each of the plurality of historical incidents. This may be input to train the machine learning algorithm so that the machine learning algorithm may learn to determine root causes for future incidents based on new incident data input into a trained mode, see Col-18, II. 6-11, teaches different type of machine learning algorithms, artificial intelligence (AI) algorithms, neural networks, etc. may be used as the untrained machine learning algorithm or as part of the untrained machine learning algorithm. Such algorithms may be used to produce a trained model), wherein the second machine learning model is configured to determine and output data indicating one or more service providers and respective destinations of the one or more service providers associated with the root cause of the failure of the 5G network component (Col-4, II. 52-61, teaches performing root cause analysis of computing incidents using machine learning described herein may include using incident data associated with historical incidents of downtime or interrupted service of computing applications, devices, and/or services. Such historical incident data may include information about each historical incident, such as when an incident took place; what applications, devices, and/or services (i.e. oner or more service providers) were affected; how many users and/or locations (i.e. respective destinations) were impacted by the incident, Col-8, II. 61-64, teaches device that is equipped to communicate over a wired and / or wireless communication medium ( e.g. , NFC , RFID , NBIOT , 9 3G , 4G , 5G , GSM , GPRS , WiFi , WiMax , CDMA , satellite , ZigBee , etc.. Col-17, II. 25-28, teaches the root cause data may indicate what is believed to be one or more actual causes of each of the plurality of historical incidents. This may be input to train the machine learning algorithm so that the machine learning algorithm may learn to determine root causes for future incidents based on new incident data input into a trained mode), the one or more service providers comprising a plurality of individual people (Col-14, II. 63-65, teaches applied to an incident or root cause may be related to automatic scaling of an application, service, or device rollout; third-party vendor (service provider) involved, see Fig. 5, step 512, Col-15, II. 33-47 and II. 60-67, teaches the responsible person or team information (i.e. plurality of individual people) 512 may include various other types of data or information. For example, the responsible person or team may be a group of employees within a business, a person or persons in a business, or both (e.g., two responsible people within a responsible business group may be designated). In various embodiments, multiple responsible persons/groups may be assigned to different actions. For example, a corrective action may be assigned to a first person or team and a preventative action may be assigned to a different person or team. In another example, a person or team may be assigned to actually complete a task or action item, while another person or team may be assigned as being responsible for ensuring that the task or action item actually gets completed… the system may also transmit a message to a responsible person or team based on the information generated at 518 for the new incident. Such a message may include any of the data generated at 518. For example, the message may include action data related to the new incident, which may include one or more specific actions to be taken by the responsible person or team determined at 518 );
transmitting, by the computing device, the service ticket to the respective destinations of the one or more service providers associated with the root cause of the failure (Col-15, II. 60-67 and Col-16, II. 1-5, teaches transmit a message (i.e. service ticket) to a responsible person or team (i.e. respective destinations) based on the information generated at 518 for the new incident. Such a message may include any of the data generated at 518. For example, the message may include action data related to the new incident, which may include one or more specific actions to be taken by the responsible person or team determined at 518. In this way, the determination of corrective and/or preventative actions related to a new incident, the determination of who should be responsible for taking those corrective and/or preventative actions, and transmission of a message notifying those responsible for the action(s) may all be automatically performed by the systems, Col-4, II. 52-61, teaches performing root cause analysis of computing incidents using machine learning described herein may include using incident data associated with historical incidents of downtime or interrupted service of computing applications, devices, and/or services. Such historical incident data may include information about each historical incident, such as when an incident took place; what applications, devices, and/or services (i.e. oner or more service providers) were affected; how many users and/or locations (i.e. respective destinations) were impacted by the incident).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mann’s historical root causes relate to respective historical incidents indicated by the historical tickets with data indicating the root cause of the failure to a second machine learning model, wherein the second machine learning model is configured to determine and output data indicating one or more service providers and respective destinations of the one or more service providers associated with the root cause of the failure of the 5G network component, the one or more service providers comprising a plurality of individual people, transmitting, by the computing device, the service ticket to the respective destinations of the one or more service providers associated with the root cause of the failure of Shen, in order to assess which service providers or destinations are most likely affected by the failure (Shen).
However, Mann in view of Shen remain silent on the 5G network component comprising a user plane function, a control plane function, a radio unit component, or some combination thereof.
Krishnaswamy discloses the 5G network component comprising a user plane function, a control plane function, a radio unit component, or some combination thereof (¶0017, ¶0052, and ¶0059, teaches typical 3GPP fifth generation (5G) C-RAN, wherein radio units (Rus) are interfaced together to enable and implement RAN functions… Fifth Generation (5G) standards support a wide variety of applications…In the system 100, interfaces denoted with “-c” or simply “c” (illustrated with dashed lines) provide control plane connectivity, while interfaces denoted with “-u” or simply “u” (illustrated with solid lines) provide user plane connectivity …5G configuration (e.g., FIG. 1B) includes a DU 105 with L1 and L2 user plane and control plane, which creates more interfaces and failure points within the C-RAN 100B. The PRANmon component 107 is particularly useful for 5G configurations because it gathers diagnostic information across different layers, elements, and components to provide real-time (or near real-time) diagnosis and policy-based action to address system performance issues).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mann’s in view of Shen’s communication over 5G network with a user plane function, a control plane function, a radio unit component, or some combination thereof of Krishnaswamy, in order to serve a distinct functional purpose, and optimize deployments for latency, performance, and cost efficiency based on use cases (Krishnaswamy, ¶0052 and ¶0063).
Shen teaches one or more of the plurality of individual people because see Fig. 5, step 512, Col-15, II. 33-47 and II. 60-67, teaches the responsible person or team information (i.e. plurality of individual people) 512 may include various other types of data or information. For example, the responsible person or team may be a group of employees within a business, a person or persons in a business, or both (e.g., two responsible people within a responsible business group may be designated). In various embodiments, multiple responsible persons/groups may be assigned to different actions. For example, a corrective action may be assigned to a first person or team and a preventative action may be assigned to a different person or team . However, Mann in view of Shen, and further in view of Krishnaswamy remain silent on determining, by the second machine learning model, one or more of the one or more service providers associated with the failure of the 5G network component, outputting, by the second machine learning model, data indicating at least one of the one or more service providers, the one or more individuals, and the respective destinations.
Kushnir discloses determining, by the second machine learning model, one or more of the one or more service providers associated with the failure of the 5G network component (¶0016 and ¶0018, teaches Individual components of computer networks may fail outright or function at a level inadequate to bear the demands of the network. Although degradation of network service is most often caused by the failure or inadequacy of network components… The efforts of service providers to remedy network problems require a determination of the root cause or root causes of each problem, …a statistical model (i.e. second machine learning) may be applied to historical benchmark data to accurately infer the causes of network problems);
outputting, by the second machine learning model, data indicating at least one of the one or more service providers, the one or more individuals, and the respective destinations (¶0016 and ¶0018, teaches Individual components of computer networks may fail outright or function at a level inadequate to bear the demands of the network. Although degradation of network service is most often caused by the failure or inadequacy of network components… The efforts of service providers to remedy network problems require a determination of the root cause or root causes of each problem, …a statistical model (i.e. second machine learning) may be applied to historical benchmark data to accurately infer the causes of network problems, ¶0022, teaches enhancing the ranking output by the model).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mann’s identified root causes of incidents, locations (i.e. destination) of incidents (e.g., locations within a system or network) in view of Shen’s communication over 5G network in view of Krishnaswamy’s system with determining, by the second machine learning model, one or more individuals of the one or more service providers associated with the failure of the 5G network component, outputting, by the second machine learning model, data indicating at least one of the one or more service providers, the one or more individuals, and the respective destinations of Kushnir, in order to determine which service providers and possibly which specific individual(s) working for those service providers are linked to the network failure (Kushnir, ¶0016 and ¶0018).
For claim 9, it is a system claim corresponding to the method of claim 1. Therefore claim 9 is rejected under the same ground as claim 1.
For claim 14, it is a non-transitory computer readable medium claim corresponding to the method of claim 1. Therefore claim 14 is rejected under the same ground as claim 1.
With respect to claims 4 and 17, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir discloses the method and the computer readable medium of claims 1 and 14, further comprising:
receiving, by the computing device, a feedback ticket associated with the service ticket (Mann, ¶0085, teaches the features may also include parameters related to corrective actions utilized for different incidents, textual or numerical feedback of insights for previous incidents, or both), the feedback ticket comprising a first accuracy rating corresponding to the root cause of the failure of the 5G network component and a second accuracy rating corresponding to at least one of the one or more service providers, the respective destinations, and the one or more individuals (Mann, ¶0063, teaches since the predictive tickets are generated based on internal IT systems data rather than based on symptoms of issues experienced by end users, the predictive tickets allow for more accurate identification of root causes and, accordingly, more accurate selections of recommendations for address issues, than existing solutions, ¶0112, teaches the machine learning algorithm 830 is configured to perform deep learning based on the historical root causes and historical tickets in order to learn a description that describes a root cause most accurately, most precisely, or both, ¶0085, teaches the features may also include parameters related to corrective actions utilized for different incidents, textual or numerical feedback of insights for previous incidents, or both);
retraining, by the computing device, the first machine learning model based at least in part on the first accuracy rating (Mann, ¶0106, teaches the created suitability model may be refined continuously as new data is received, ¶0130, teaches the learning phase may continue during the operational phase such that machine learning models may be continuously refined as new data is received without departing from the scope of the disclosure. Accordingly, the suitability model may be adapted to new incidents and resulting tickets); and
retraining, by the computing device, the second machine learning model based at least in part on the second accuracy rating (Mann, ¶0106, teaches the created suitability model may be refined continuously as new data is received. In an embodiment, the historical data matching and approximation of predictive functions may be performed only once initially, and may be repeated only as desired to determine, e.g., newly added insights, newly detected incidents, and so on, ¶0111, teaches the historical root causes relate to respective historical incidents indicated by the historical tickets (i.e. second machine learning model is trained on historical tickets) determined, for example, as described herein above with respect to FIG. 2. The machine learning algorithm is configured to correlate between the historical root causes and the historical tickets, ¶0130, teaches the learning phase may continue during the operational phase such that machine learning models may be continuously refined as new data is received without departing from the scope of the disclosure. Accordingly, the suitability model may be adapted to new incidents and resulting tickets).
With respect to claims 6, 10 and 19, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir discloses the method, the system and the computer readable medium of claims 1, 9 and 14, wherein the first machine learning model is trained at least in part on historical error logs (Mann, ¶002, teaches the historical data set includes machine-generated textual data, where anomalies in the machine-generated textual data may be indicative that an incident has occurred or will occur, ¶0054, teaches all incident patterns are generated from machine-generated textual data which are automatically processed and classified into statistical metrics. For example, an error log reported by a load balancer may be: “Error(s) found in configuration file, ¶0111, teaches the machine learning algorithm is configured to correlate between the historical root causes and the historical tickets).
With respect to claims 7, 11 and 20, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir discloses the method the system and the computer readable medium of claims 1, 9 and 14, wherein the second machine learning model is trained at least in part on historical service tickets and/or on service provider data (Mann, (¶0111, teaches the historical root causes relate to respective historical incidents indicated by the historical tickets (i.e. second machine learning model is trained on historical tickets) determined, for example, as described herein above with respect to FIG. 2. The machine learning algorithm is configured to correlate between the historical root causes and the historical tickets).
With respect to claim 8, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir discloses the method of claim 1, further comprising:
accessing, by the computing device (Mann, ¶0033), data associated with a second failure of a second 5G network component (Mann, ¶0039, teaches the incident data may further include root causes (i.e. first and second failures) determined by the root cause analyzer 130, ¶0061, teaches the incident parameters may include, but are not limited to, identified root causes of incidents (i.e. second failures), times of incidents, locations of incidents (e.g., locations within a system or network), systems or components affected by incidents (e.g., hardware components or software sub-systems);
providing, by the computing device, the data associated with the second failure of the second 5G network component to the first machine learning model, such that the first machine learning model outputs data indicating a root cause of the second failure (Mann, ¶0031, teaches a root cause for an incident may be determined by a machine learning model trained to identify root causes of incidents (i.e. second failures), ¶0071, teaches component-based (i.e., correlation of incident patterns across different components)), wherein the first machine learning model is configured to determine and output data indicating a root cause of the failure (Mann, ¶0031, teaches a root cause for an incident may be determined by a machine learning model trained to identify root causes of incidents (i.e. first and second failures), ¶0042, teaches the root cause analyzer 130 may be configured to output a rich root cause analysis report indicating information related to identification of the suitable insights for an incident and any recommendations for addressing the incident based on the suitable insights, ¶0046, teaches the recognized incident patterns are processed to determine a root cause of an incident);
providing, by the computing device, the data indicating the root cause of the second failure of the second 5G network component to the second machine learning model such that the second machine learning model outputs data indicating one or more service providers and respective destinations associated with the root cause of the second failure of the second 5G network component (Shen, Col-4, II. 52-61, teaches performing root cause analysis of computing incidents using machine learning described herein may include using incident data associated with historical incidents of downtime or interrupted service of computing applications, devices, and/or services. Such historical incident data may include information about each historical incident, such as when an incident took place; what applications, devices, and/or services (i.e. oner or more service providers) were affected; how many users and/or locations (i.e. respective destinations) were impacted by the incident, Col-17, II. 19-21 and II. 25-28, teaches a computing device may input the root cause data associated with the plurality of historical incidents (i.e. second failure) into the machine learning algorithm for training, the root cause data may indicate what is believed to be one or more actual causes of each of the plurality of historical incidents… This may be input to train the machine learning algorithm so that the machine learning algorithm may learn to determine root causes for future incidents based on new incident data input into a trained mode,);
determining, by the computing device, the failure of the 5G network component and the second failure of the second 5G network component share a common root cause and are associated with the one or more service providers and respective destinations (Mann, ¶0053, teaches the root cause analyzer 130 is configured to group together alerts which have a common cause into one incident. In yet another embodiment, the root cause analyzer 130 is further configured to report any alert remaining after the root cause analysis with additional information related to the associated incident or incidents, ¶0061, teaches the incident parameters may include, but are not limited to, identified root causes of incidents, times of incidents, locations (i.e. respective destination) of incidents (e.g., locations within a system or network), Shen, Col-18, II. 60-65, teaches a new incident may be related to a same application, device, or service for which historical incidents have already occurred, or the new incident may be related to a different application, device, or service than those for which historical incident data was used to train the machine learning algorithm);
generating, by the computing device, a single service ticket comprising data indicating the common root cause of the failure of the 5G network component and the second failure of the second 5G network component, and indicating the respective destinations (Mann, ¶0036, teaches each ticket includes at least a textual description of an issue. The issue may be indicative of an incident (e.g., an incident for which a root cause is determined by the root cause analyzer 130), ¶0061, teaches the incident parameters may include, but are not limited to, identified root causes of incidents, times of incidents, locations (i.e. respective destination) of incidents (e.g., locations within a system or network); and
transmitting, by the computing device, the single service ticket to the respective destinations of the one or more service providers associated with the common root cause (Shen, Col-15, II. 60-67 and Col-16, II. 1-5, teaches transmit a message (i.e. service ticket) to a responsible person or team (i.e. respective destinations) based on the information generated at 518 for the new incident (i.e. second failure). Such a message may include any of the data generated at 518. For example, the message may include action data related to the new incident, which may include one or more specific actions to be taken by the responsible person or team determined at 518. In this way, the determination of corrective and/or preventative actions related to a new incident, the determination of who should be responsible for taking those corrective and/or preventative actions, and transmission of a message notifying those responsible for the action(s) may all be automatically performed by the systems, Col-4, II. 52-61, teaches performing root cause analysis of computing incidents using machine learning described herein may include using incident data associated with historical incidents of downtime or interrupted service of computing applications, devices, and/or services. Such historical incident data may include information about each historical incident, such as when an incident took place; what applications, devices, and/or services (i.e. oner or more service providers) were affected; how many users and/or locations (i.e. respective destinations) were impacted by the incident).
With respect to claim 12, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir discloses the computing system of claim 9, wherein the computing system is implemented on a distributed cloud-based architecture (Mann, ¶0064, teaches the ticket predictor 160 may reside in a cloud computing platform, a datacenter, and the like, Shen, see Fig. 2, step 225).
With respect to claims 13 and 18, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir discloses the computing system of claim 9, wherein the 5G network is a standalone 5G network implemented on a distributed cloud-based architecture (Mann, ¶0064, teaches the ticket predictor 160 may reside in a cloud computing platform, a datacenter, and the like, Shen, see Fig. 2, step 225, and Col-4, II. 26-29, teaches computing systems / platforms with associated devices are configured to operate in the distributed network environment , communicating with one another over one or more suitable data communication networks).
With respect to claim 21, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir discloses the method of claim 1, wherein the plurality of individual people comprises at least one of an engineer, a project manager, or a team lead (Shen, see Fig. 5, step 512, Col-15, II. 33-47 and II. 60-67, teaches the responsible person or team information (i.e. responsible person or team would be an engineer or project manager or team lead) 512 may include various other types of data or information. For example, the responsible person or team may be a group of employees within a business, a person or persons in a business, or both (e.g., two responsible people within a responsible business group may be designated). In various embodiments, multiple responsible persons/groups may be assigned to different actions. For example, a corrective action may be assigned to a first person or team and a preventative action may be assigned to a different person or team. In another example, a person or team may be assigned to actually complete a task or action item, while another person or team may be assigned as being responsible for ensuring that the task or action item actually gets completed… the system may also transmit a message to a responsible person or team based on the information generated at 518 for the new incident. Such a message may include any of the data generated at 518. For example, the message may include action data related to the new incident, which may include one or more specific actions to be taken by the responsible person or team determined at 518).
Claim(s) 2 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mann in view of Shen in view of Krishnaswamy, and further in view of Cuddihy et al. (US 5463768), hereinafter “Cuddihy”.
With respect to claims 2 and 15, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir discloses the method, the computer readable medium of claims 1, and 14, further comprising:
receiving, by the first machine learning model (Mann, ¶0031 teaches machine learning model), an error log associated with the failure of the 5G network component (Mann, ¶0054, teaches an error log reported by a load balancer may be: “Error(s) found in configuration file: letclhaproxy/haproxy.cfg” and an error reported by an application server may be: “Connection pool is full. discarding connection: [ConnectionName], path name: [PathName]”. Thus, the root cause analyzer 130 is configured to determine the root cause of any incident by merely processing input machine-generated textual data);
outputting, by the first machine learning model (Mann, ¶0031 teaches machine learning model), the data indicating the root cause of the failure of the 5G network component (Mann, ¶0042, teaches the root cause analyzer 130 may be configured to output a rich root cause analysis report indicating information related to identification of the suitable insights for an incident).
However, Mann in view of Shen, and further in view of Krishnaswamy remain silent on identifying, by the first machine learning model, a line in the error log that corresponds to the failure of the 5G network component; determining, by the first machine learning model, the root cause of the failure, based at least in part on the line in the error log.
Cuddihy discloses identifying, by the first machine learning model (Col-1, II. 56-57, teaches Other diagnostic systems have used artificial neural networks to correlate data in order to diagnose machine faults), a line in the error log that corresponds to the failure of the 5G network component (Col-4, II. 62-63, teaches In step 44, a line from a first error log A is read);
determining, by the first machine learning model (Col-1, II. 56-57, teaches Other diagnostic systems have used artificial neural networks to correlate data in order to diagnose machine faults), the root cause of the failure, based at least in part on the line in the error log (Col-4, II. 62-63, teaches In step 44, a line from a first error log A is read, Col-7, II. 47-49, teaches the fault(s) associated with the case(s) found are then considered diagnoses of the malfunction represented by the new error log 58).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mann’s identified root causes of incidents in view of Shen’s 5G network with identifying, by the first machine learning model, a line in the error log that corresponds to the failure of the 5G network component; determining, by the first machine learning model, the root cause of the failure, based at least in part on the line in the error log of Cuddihy, in order to enhance the network’s reliability, performance, and maintenance efficiency, supporting the critical infrastructure that 5G network provide (Cuddihy).
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Mann in view of Shen in view of Krishnaswamy in view of Kushnir, and further in view of Shimizu et al. (US 2004/0073574), hereinafter “Shimizu”.
With respect to claim 5, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir discloses the method of claim 1, however, Mann in view of Shen in view of Krishnaswamy, and further in view of Kushnir remain silent on wherein the service ticket comprises a link to user data identified by the computing device.
Shimizu discloses wherein the service ticket comprises a link to user data identified by the computing device (¶0020-¶0021, teaches a service portal 17 is provided with a user profile database 19 in which the profiles of the users who use the information service system are stored…Each of the information tickets 25 contains link information 31 indicating appropriate links to service providers 32 which provide respective services about the object identified by the UOI... A link 29 to such service usage is embedded in each information ticket 25).
Therefore, it would be obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Mann’s in view of Shen’s, and further in view of Krishnaswamy’s system with wherein the service ticket comprises a link to user data identified by the computing device of Shimizu, in order to enhance authentication, streamline processes, improve security, ensure compliance and provide personalize support (Shimizu).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/GOLAM MAHMUD/Examiner, Art Unit 2458
/UMAR CHEEMA/Supervisory Patent Examiner, Art Unit 2458
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