Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Office action is in response to Applicant’s amendment submitted on January 20, 2026.
Claims 1-20 are pending in the application.
Response to Arguments/Remarks
Claim Rejections - 35 USC § 101
Claims 1-20 were rejected under 35 U.S.C. 101 because the claimed invention was directed to an abstract idea without significantly more.
The rejection of claims 1-20 have been withdraw in view of Applicant’s Remarks and the amendments made to the claims.
Claim Rejections - 35 USC § 103
Claims 1-3, 5, 7, 10, 12-15, and 18 were rejected under 35 U.S.C. 103 as being unpatentable over Patel et al. US Patent Publication No. 2023/0054815 in view of Betge-Brezetz et al. US Patent Publication No. 2003/0221005.
The amendments to claims 1, 12, and 18 have overcome the rejection. Therefore, the prior rejection has been withdrawn, and new grounds of rejection are made in this Office action. The new grounds of rejection are necessitated by Applicant's amendment, and accordingly, this Office action is made Final.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1-3, 5, 7, 10, 12-15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Patel et al. US Patent Publication No. 2023/0054815 (“Patel”) in view of Betge-Brezetz et al. US Patent Publication No. 2003/0221005 (“Betge-Brezetz”) and Patil et al. US Patent Publication No. 2020/0366563 (“Patil”).
Regarding claim 1, Patel discloses a device, comprising:
a processing system including a processor; and a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:
receiving first network monitoring data indicative of a first operational status of a plurality of applications (para. [0019] alert labeling system 420 may receive a first alert from a first application. para. [0035] receive alerts from more than one application and from different applications);
processing the first network monitoring data (para. [0031] request to assign a rating label of 1, 2, or 3. request may include instructions regarding the requirements for each rating. para. [0032] special labels may include a high severity status label to indicate that the alert is associated with a high severity incident);
training a model based on the first network monitoring data and the plurality of the first processed results to obtain a trained model (para. [0034] train a machine learning model to associate the first rating label with the first alert. alert labeling system 420 may feed the machine learning model with alerts and associated labels);
processing, according to the trained model, second network monitoring data indicative of a second operational status of the plurality of network devices to obtain a plurality of second processed results (para. [0036] determine, using the machine learning model, whether the second alert is similar to the first alert. para. [0037] associate the first rating label with the second alert).
Patel does not teach determining a network service assurance (NSA) objective.
Patel discloses receiving first network monitoring data indicative of a first operational status of a plurality of applications and/or a system but not expressly a plurality of network devices.
Patel discloses processing the first network monitoring data but not according to the NSA objective to obtain a plurality of first processed results.
Patel discloses prioritizing alerts but does not expressly teach prioritizing the second network monitoring data according to the plurality of the second processed results.
Patel teaches training the machine learning model. Patel does not teach wherein the trained model incorporates new network types, protocols, devices, or previously unobserved network conditions resulting in improved model accuracy.
Betge-Brezetz discloses determining a network service assurance (NSA) objective (para. [0041] evaluation criteria. para. [0042] SLA or SLS); receiving first network monitoring data indicative of a first operational status of a plurality of network devices (para. [0032] receives at least some of the service data from the network and preferably PD data representative of the performances of the network or at least one portion of the latter, as well as the alarms NEA transmitted by the equipment of the network. transmitted by the routers and interfaces of the network); and processing the first network monitoring data according to the NSA objective to obtain a plurality of first processed results (para. [0039] estimate after each violation the severity of each SLS violation according to one or several parameter. evaluation criteria are defined by an expert of the operator, for example in the form of rules (or "policies")); prioritizing the second network monitoring data according to the plurality of the second processed results (para. [0046] each alarm message associated one or more several levels of severity (of violation) is then delivered. para. [0067] alarm messages in accordance with their classification). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel with Betge-Brezetz’s disclosure. One of ordinary skill in the art would have been motivated to do so because Patel is concerned with business impact of alerts and prioritizes alerts such that important alerts are flagged and directed to users. Betge-Brezetz is similarly directed to prioritizing alerts based on severity of the alerts, and it would have been beneficial to have received and managed network alarms from network devices that affect customers, including detection violations of SLA and/or SLS.
Patil discloses a trained model that incorporates new network types, protocols, devices, or previously unobserved network conditions resulting in improved model accuracy (para. [0045] CMC 118 can be trained, using a desired machine learning or artificial intelligence (AI) technology, to learn to identify or determine device types of devices (e.g., device 104), communication protocols associated with… devices. para. [0104] training component 616 can apply the training data to the machine learning and/or AI engine 620. para. [0105] training component 616 can generate or obtain supplemental training data regarding such new types of devices and/or updates to such certain devices. para. [0097] defined data management criteria can specify or indicate that data having or associated with characteristics that indicate the processing and communication of the data has to satisfy certain conditions relating to latency, quality of service (QoS). para. [0107] based at least in part on the training of the machine learning and/or AI engine 620, the CMC 604 can determine respective characteristics of the respective devices to facilitate data management of data communicated by the respective devices via the core network, in accordance with the defined data management criteria). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Patil’s disclosure of training the machine learning model. One of ordinary skill in the art would have been motivated to do so because Patel discloses training a machine learning model, and it would have been beneficial to similarly provide the capability to identify new data by supplementing, refining training, learning, and/or knowledge of the machine learning model (para. [0105]).
Regarding claim 12, Patel teaches a method, comprising:
receiving, by the processing system, first network monitoring data indicative of a first operational status of a plurality of applications (para. [0019] alert labeling system 420 may receive a first alert from a first application. para. [0035] receive alerts from more than one application and from different applications);
evaluating, by the processing system, the first network monitoring data to obtain a plurality of first prioritized results (para. [0031] request to assign a rating label of 1, 2, or 3. request may include instructions regarding the requirements for each rating. para. [0032] special labels may include a high severity status label to indicate that the alert is associated with a high severity incident);
training, by the processing system, a predictive model based on the first network monitoring data and the plurality of first prioritized results to obtain a trained predictive model (para. [0034] train a machine learning model to associate the first rating label with the first alert. alert labeling system 420 may feed the machine learning model with alerts and associated labels); and
processing, by the processing system and according to the trained predictive model, second monitoring data indicative of a second operational status of the plurality of network devices to obtain a plurality of second prioritized result (para. [0036] determine, using the machine learning model, whether the second alert is similar to the first alert. para. [0037] associate the first rating label with the second alert).
Patel does not teach identifying a network operations objective.
Patel discloses receiving first network monitoring data indicative of a first operational status of a plurality of applications and/or a system but not expressly a plurality of network devices.
Patel discloses prioritizing the first network monitoring data according to the network operations objective to obtain first prioritized results but not according to the network operations objective.
Patel discloses prioritizing alerts but does not expressly teach wherein the network monitoring data is prioritized according to the second prioritized results.
Patel teaches training the machine learning model. Patel does not teach wherein the trained model incorporates new network types, protocols, devices, or previously unobserved network conditions resulting in improved model accuracy.
Betge-Brezetz discloses determining a network service assurance (NSA) objective (para. [0041] evaluation criteria. para. [0042] SLA or SLS); receiving first network monitoring data indicative of a first operational status of a plurality of network devices (para. [0032] receives at least some of the service data from the network and preferably PD data representative of the performances of the network or at least one portion of the latter, as well as the alarms NEA transmitted by the equipment of the network. transmitted by the routers and interfaces of the network); and processing the first network monitoring data according to the NSA objective to obtain a plurality of first processed results (para. [0039] estimate after each violation the severity of each SLS violation according to one or several parameter. evaluation criteria are defined by an expert of the operator, for example in the form of rules (or "policies")); prioritizing the second network monitoring data according to the plurality of the second processed results (para. [0046] each alarm message associated one or more several levels of severity (of violation) is then delivered. para. [0067] alarm messages in accordance with their classification). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel with Betge-Brezetz’s disclosure. One of ordinary skill in the art would have been motivated to do so because Patel is concerned with business impact of alerts and prioritizes alerts such that important alerts are flagged and directed to users. Betge-Brezetz is similarly directed to prioritizing alerts based on severity of the alerts, and it would have been beneficial to have received and managed network alarms from network devices that affect customers, including detection violations of SLA and/or SLS.
Patil discloses a trained model that incorporates new network types, protocols, devices, or previously unobserved network conditions resulting in improved model accuracy (para. [0045] CMC 118 can be trained, using a desired machine learning or artificial intelligence (AI) technology, to learn to identify or determine device types of devices (e.g., device 104), communication protocols associated with… devices. para. [0104] training component 616 can apply the training data to the machine learning and/or AI engine 620. para. [0105] training component 616 can generate or obtain supplemental training data regarding such new types of devices and/or updates to such certain devices. para. [0097] defined data management criteria can specify or indicate that data having or associated with characteristics that indicate the processing and communication of the data has to satisfy certain conditions relating to latency, quality of service (QoS). para. [0107] based at least in part on the training of the machine learning and/or AI engine 620, the CMC 604 can determine respective characteristics of the respective devices to facilitate data management of data communicated by the respective devices via the core network, in accordance with the defined data management criteria). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Patil’s disclosure of training the machine learning model. One of ordinary skill in the art would have been motivated to do so because Patel discloses training a machine learning model, and it would have been beneficial to similarly provide the capability to identify new data by supplementing, refining training, learning, and/or knowledge of the machine learning model (para. [0105]).
Regarding claim 18, Patel teaches a non-transitory machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
obtaining first network monitoring data indicative of a first operational status of a plurality of applications (para. [0019] alert labeling system 420 may receive a first alert from a first application. para. [0035] receive alerts from more than one application and from different applications);
prioritizing the first network monitoring data to obtain first prioritized results (para. [0031] request to assign a rating label of 1, 2, or 3. request may include instructions regarding the requirements for each rating. para. [0032] special labels may include a high severity status label to indicate that the alert is associated with a high severity incident);
training a predictive model based on the first network monitoring data and the first prioritized results to obtain a trained predictive model (para. [0034] train a machine learning model to associate the first rating label with the first alert. alert labeling system 420 may feed the machine learning model with alerts and associated labels); and
evaluating, according to the trained predictive model, second monitoring data indicative of a second operational status of the plurality of network devices to obtain second prioritized results (para. [0036] determine, using the machine learning model, whether the second alert is similar to the first alert. para. [0037] associate the first rating label with the second alert).
Patel does not teach identifying a network operations objective.
Patel discloses receiving first network monitoring data indicative of a first operational status of a plurality of applications and/or a system but not expressly a plurality of network devices.
Patel discloses prioritizing the first network monitoring data according to the network operations objective to obtain first prioritized results but not according to the network operations objective.
Patel discloses prioritizing alerts but does not expressly teach wherein the network monitoring data is prioritized according to the second prioritized results.
Patel teaches training the machine learning model. Patel does not teach wherein the trained model incorporates new network types, protocols, devices, or previously unobserved network conditions resulting in improved model accuracy.
Betge-Brezetz discloses determining a network operations objective (para. [0041] evaluation criteria. para. [0042] SLA or SLS); receiving first network monitoring data indicative of a first operational status of a plurality of network devices (para. [0032] receives at least some of the service data from the network and preferably PD data representative of the performances of the network or at least one portion of the latter, as well as the alarms NEA transmitted by the equipment of the network. transmitted by the routers and interfaces of the network); and prioritizing the first network monitoring data according to the network operations objective to obtain a plurality of first processed results (para. [0039] estimate after each violation the severity of each SLS violation according to one or several parameter. evaluation criteria are defined by an expert of the operator, for example in the form of rules (or "policies")); the network monitoring data is prioritized according to the second prioritized results (para. [0046] each alarm message associated one or more several levels of severity (of violation) is then delivered. para. [0067] alarm messages in accordance with their classification). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel with Betge-Brezetz’s disclosure. One of ordinary skill in the art would have been motivated to do so because Patel is concerned with business impact of alerts and prioritizes alerts such that important alerts are flagged and directed to users. Betge-Brezetz is similarly directed to prioritizing alerts based on severity of the alerts, and it would have been beneficial to have received and managed network alarms from network devices that affect customers, including detection violations of SLA and/or SLS.
Patil discloses a trained model that incorporates new network types, protocols, devices, or previously unobserved network conditions resulting in improved model accuracy (para. [0045] CMC 118 can be trained, using a desired machine learning or artificial intelligence (AI) technology, to learn to identify or determine device types of devices (e.g., device 104), communication protocols associated with… devices. para. [0104] training component 616 can apply the training data to the machine learning and/or AI engine 620. para. [0105] training component 616 can generate or obtain supplemental training data regarding such new types of devices and/or updates to such certain devices. para. [0097] defined data management criteria can specify or indicate that data having or associated with characteristics that indicate the processing and communication of the data has to satisfy certain conditions relating to latency, quality of service (QoS). para. [0107] based at least in part on the training of the machine learning and/or AI engine 620, the CMC 604 can determine respective characteristics of the respective devices to facilitate data management of data communicated by the respective devices via the core network, in accordance with the defined data management criteria). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Patil’s disclosure of training the machine learning model. One of ordinary skill in the art would have been motivated to do so because Patel discloses training a machine learning model, and it would have been beneficial to similarly provide the capability to identify new data by supplementing, refining training, learning, and/or knowledge of the machine learning model (para. [0105]).
Regarding claim 2, Patel in view of Betge-Brezetz and Patil teach the device of claim 1, wherein the first network monitoring data comprises a first plurality of network alarms (Patel: para. [0035] receive alerts from more than one application and from different applications. Betge-Brezetz: para. [0039] alarm messages).
Regarding claim 3, Patel in view of Betge-Brezetz and Patil teach the device of claim 2, wherein the processing the first network monitoring data further comprises: associating a severity with each alarm of the first plurality of network alarms (Patel: para. [0031] request to assign a rating label of 1, 2, or 3. para. [0032] special labels may include a high severity status label to indicate that the alert is associated with a high severity incident. Betge-Brezetz: para. [0039] estimate after each violation the severity of each SLS violation according to one or several parameterable evaluation criteria).
Regarding claim 5, Patel in view of Betge-Brezetz and Patil teach the device of claim 1, wherein the receiving the first network monitoring data further comprises: receiving messages determined according to a network monitoring protocol (Patel: para. [0019] monitoring systems monitor applications’ health. para. [0035] receive alerts from more than one application and from different applications).
Regarding claim 7, Patel in view of Betge-Brezetz and Patil teach the device of claim 1, wherein the model comprises one of a machine learning model or artificial intelligence (AI) model (Patel: para. [0041] train the machine learning model to associate the second rating label with the second alert. supervised or unsupervised training).
Regarding claim 10, Patel in view of Betge-Brezetz and Patil teach the device of claim 1, wherein the training the model comprises unsupervised learning (Patel: para. [0041] train the machine learning model to associate the second rating label with the second alert. supervised or unsupervised training).
Regarding claim 13, Patel in view of Betge-Brezetz and Patil teach the method of claim 12, wherein the receiving, by the processing system, the first network monitoring data further comprises: receiving, by the processing system, messages determined according to a network monitoring protocol (Patel: para. [0019] monitoring systems monitor applications’ health. para. [0035] receive alerts from more than one application and from different applications).
Regarding claim 14, Patel in view of Betge-Brezetz and Patil teach the method of claim 12, wherein the predictive model comprises one of a machine learning model or artificial intelligence (AI) model (Patel: para. [0041] train the machine learning model to associate the second rating label with the second alert. supervised or unsupervised training).
Regarding claim 15, Patel in view of Betge-Brezetz and Patil teach the method of claim 12, wherein the training the predictive model comprises unsupervised learning (Patel: para. [0041] train the machine learning model to associate the second rating label with the second alert. supervised or unsupervised training).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Betge-Brezetz, Patil and Yaron et al. US Patent Publication No. 2025/0258912 (“Yaron”).
Regarding claim 4, Patel does not teach the device of claim 1, wherein each second processed result of the plurality of the second processed results is associated with a network response activity of a plurality of network response activities to obtain a plurality of associations, and wherein the prioritizing the second network monitoring data is based on the plurality of associations.
Yaron discloses each second processed result of a plurality of the second processed results is associated with a network response activity of a plurality of network response activities to obtain a plurality of associations, and prioritizing network monitoring data based on the plurality of associations. (para. [0059] alert priority may be further determined based on efficiency of remediation actions. initial priority. para. [0060] prioritization model is further applied to application actionability features for the alerts to be prioritized. prioritization model may then be applied to the determined actionability features. resulting priority would, in such an embodiment, also account for actionability such that alerts which are more actionable are prioritize). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Yaron’s disclosure. One of ordinary skill in the art would have been motivated to do so because Patel is concerned with business impact of alerts and prioritizes alerts such that important alerts are flagged and directed to users. It would have been beneficial to implemented Yaron in order to have prioritized alerts based on actions in order to have prioritized actionable alerts.
Claims 11, 17, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Betge-Brezetz, Patil, and Sawhney et al. US Patent Publication No. 2023/0004835 (“Sawhney”).
Regarding claim 11, Patel does not teach the device of claim 1, wherein the training the model further comprises: automatically recognizing features to obtain recognized features, wherein the recognized features expedite the training of the model.
Sawhney discloses automatically recognizing features to obtain recognized features, wherein the recognized features expedite training of the model (para. [0039] “alert attribute” refers to data, text, identifiers, metadata, or other alert related characteristics or features that are extracted from alert related datasets and used to create a responder prediction training corpus. para. [0045] “responder prediction training corpus” refers to data objects that are configured to train the one or more machine learning models of the responder prediction server system. para. [0081] alert extractor unit 113 extracts one or more alert related datasets from an alert monitoring service tool 151. para. [0083] alert attributes extracted from the alert related datasets may comprise an alert identifier). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Sawhney’s disclosure of automatically recognizing features to obtain recognized features, wherein the recognized features expedite training of the model. One of ordinary skill in the art would have been motivated to do so in order to have further trained the model to recognize different attributes of alerts to determine similarity of alerts and provide labeling of alerts.
Regarding claim 17, Patel does not teach the method of claim 12, wherein the training the predictive model further comprises: automatically recognizing features to obtain recognized features, wherein the recognized features expedite the training of the predictive model.
Sawhney discloses automatically recognizing features to obtain recognized features, wherein the recognized features expedite training of the model (para. [0039] “alert attribute” refers to data, text, identifiers, metadata, or other alert related characteristics or features that are extracted from alert related datasets and used to create a responder prediction training corpus. para. [0045] “responder prediction training corpus” refers to data objects that are configured to train the one or more machine learning models of the responder prediction server system.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Sawhney’s disclosure of automatically recognizing features to obtain recognized features, wherein the recognized features expedite training of the model. One of ordinary skill in the art would have been motivated to do so in order to have further trained the model to recognize different attributes of alerts to determine similarity of alerts and provide labeling of alerts.
Regarding claim 20, Patel does not teach the non-transitory machine-readable medium of claim 18, wherein the training the predictive model further comprises: automatically recognizing features to obtain recognized features, wherein the recognized features expedite the training of the predictive model.
Sawhney discloses automatically recognizing features to obtain recognized features, wherein the recognized features expedite training of the model (para. [0039] “alert attribute” refers to data, text, identifiers, metadata, or other alert related characteristics or features that are extracted from alert related datasets and used to create a responder prediction training corpus. para. [0045] “responder prediction training corpus” refers to data objects that are configured to train the one or more machine learning models of the responder prediction server system.). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Sawhney’s disclosure of automatically recognizing features to obtain recognized features, wherein the recognized features expedite training of the model. One of ordinary skill in the art would have been motivated to do so in order to have further trained the model to recognize different attributes of alerts to determine similarity of alerts and provide labeling of alerts.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Betge-Brezetz, Patil, and Fenoglio et al. US Patent Publication No. 2021/0184915 (“Fengolio”).
Regarding 6, Patel does not teach the device of claim 5, wherein the network monitoring protocol comprises a simple network monitoring protocol (SNMP).
Fenoglio discloses providing data using simple network monitoring protocol (para. [0045] network data collection platform 304 may receive a variety of data feeds that convey collected data 334 from the devices. data feeds may comprise, but are not limited to, management information bases (MIBS) with Simple Network management Protocol (SNMP)v2. para. [0067] service that monitors a network detects a plurality of anomalies in the network. service uses data regarding the detected anomalies as input to one or more machine learning models. para. [0079] prioritize the anomaly detection alerts generated by anomaly detector(s) 406 and send these alerts to output and visualization interface). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Fenoglio’s disclosure of using SNMP. One of ordinary skill in the art would have been motivated to do so because Fenoglio is similar directed prioritizing alerts, and it would have been beneficial to support different protocols including SNMP, which is widely supported across devices for providing real-time data and provides simplified management.
Claim 8-9, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Patel in view of Betge-Brezetz, Patil, and Lasso et al. US Patent Publication No. 2025/0168056 (“Lasso”).
Regarding claim 8, Patel does not teach the device of claim 7, wherein the AI model comprises a neural network.
Lasso discloses an AI model comprising a neural network (para. [0045] machine learning algorithm or model can be any machine learning algorithm or model or combination thereof, including but not limited to… neural network. model can be any generative artificial learning model or algorithm. para. [0048] training service 321 can train a machine learning model or algorithm to identify anomalies). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Lasso’s disclosure of implementing an AI model comprising a neural network. One of ordinary skill in the art would have been motivated to do so for benefits of supporting different types of models as disclosed by Lasso, and it would have been beneficial to an AI model comprising a neural network to handle complex data as well as to recognize new data such as new alerts.
Regarding claim 9, Patel does not teach the device of claim 1, wherein the model comprises generative artificial intelligence.
Lasso discloses a model comprising generative artificial intelligence (para. [0045] machine learning algorithm or model can be any machine learning algorithm or model or combination thereof, including but not limited to… neural network. model can be any generative artificial learning model or algorithm. para. [0048] training service 321 can train a machine learning model or algorithm to identify anomalies). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Lasso’s disclosure of implementing an AI model comprising a neural network. One of ordinary skill in the art would have been motivated to do so for benefits of supporting different types of models as disclosed by Lasso, and it would have been beneficial to an AI model to recognize new data such as new alerts.
Regarding claim 16, Patel does not teach the method of claim 12, wherein the predictive model comprises generative artificial intelligence.
Lasso discloses a model comprising generative artificial intelligence (para. [0045] machine learning algorithm or model can be any machine learning algorithm or model or combination thereof, including but not limited to… neural network. model can be any generative artificial learning model or algorithm. para. [0048] training service 321 can train a machine learning model or algorithm to identify anomalies). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Lasso’s disclosure of implementing an AI model comprising a neural network. One of ordinary skill in the art would have been motivated to do so for benefits of supporting different types of models as disclosed by Lasso, and it would have been beneficial to an AI model to recognize new data such as new alerts.
Regarding claim 19, Patel does not teach the non-transitory machine-readable medium of claim 18, wherein the predictive model comprises generative artificial intelligence.
Lasso discloses a model comprising generative artificial intelligence (para. [0045] machine learning algorithm or model can be any machine learning algorithm or model or combination thereof, including but not limited to… neural network. model can be any generative artificial learning model or algorithm. para. [0048] training service 321 can train a machine learning model or algorithm to identify anomalies). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Patel and Betge-Brezetz with Lasso’s disclosure of implementing an AI model comprising a neural network. One of ordinary skill in the art would have been motivated to do so for benefits of supporting different types of models as disclosed by Lasso, and it would have been beneficial to an AI model to recognize new data such as new alerts.
Examiner’s Note
The following prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Porter et al. US Patent Publication No. 2024/0422050 (para. [0065] machine learning model is trained using examples of device behaviour on the network corresponding to the same device. when a new device is connected to the network for the first time, there is an initial training phase period in which data relating to the device behaviour is collected and used to train the machine learning model)
Jensen et al. US Patent Publication No. 2024/0205178 (para. [0061] online machine learning engine may observe new conditions that are detected and new actions taken by IT administrators in real-time, and further train itself based on the detected conditions, the taken action)
Nalam et al. US Patent Publication No. 2025/0342098 (para. [0027] agents 103 continuously monitor the user devices for changes in operational data and the data collection engine 120 continuously retrieves data from the agents 103. machine learning algorithms are continuously trained and re-trained (e.g., in multiple iterations) with new data comprising new device dispositions…regarding the new device dispositions to improve the accuracy of the machine learning algorithms over time).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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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/JOSHUA JOO/Primary Examiner, Art Unit 2445