Abstract: Various embodiments of the present invention provide methods, apparatuses, computing devices, and/or the like that are configured to perform software application framework monitoring using alert signatures for the software applications that are generated by at least one of: (i) a conditional ensemble machine learning framework comprise one or more alert priority score generation machine learning models, one or more alert priority explanation generation machine learning models, and a conditional ensemble machine learning model that is configured to generate an explanation-inclusive alert signature if a deep-learning-based alert priority score generated by the alert priority score generation machine learning models is identical to a decision-tree-based alert priority designation generated by the alert priority explanation generation machine learning models, and (ii) a set of alert priority score adjustment models such as an entity-based alert priority score adjustment model, a temporal alert priority score adju

Abstract: Embodiments described herein relate to systems and methods for automatically generating content, generating API requests and/or request bodies, structuring user-generated content, and/or generating structured content in collaboration platforms, such as documentation systems, issue tracking systems, project management platforms, and other platforms. The systems and methods described use a network architecture that includes a prompt generation service and a set of one or more purpose-configured large language model instances (LLMs) and/or other trained classifiers or natural language processors used to provide generative responses for content collaboration platforms.

Abstract: Various embodiments of the present invention provide methods, apparatuses, computing devices, and/or the like that are configured to perform software application framework monitoring using alert signatures for the software applications that are generated by at least one of: (i) a conditional ensemble machine learning framework comprise one or more alert priority score generation machine learning models, one or more alert priority explanation generation machine learning models, and a conditional ensemble machine learning model that is configured to generate an explanation-inclusive alert signature if a deep-learning-based alert priority score generated by the alert priority score generation machine learning models is identical to a decision-tree-based alert priority designation generated by the alert priority explanation generation machine learning models, and (ii) a set of alert priority score adjustment models such as an entity-based alert priority score adjustment model, a temporal alert priority score adju

Abstract: Methods, systems, and computer readable medium for generating ticket requests are disclosed. The method includes retrieving a content item created at a social media platform in relation to an entity being serviced by an issue tracking system and determining a help-seeking intent associated with the content item based on textual data of the content item. The method further includes determining whether the help-seeking intent associated with the content item exceeds a threshold level of intent, and upon determining that the help-seeking intent associated with the content item exceeds the threshold level of intent: generating a ticket request based on the textual data of the content item, and communicating the ticket request to the issue tracking system.

Abstract: Method and computer readable medium for generating alerts are disclosed. The method includes retrieving a plurality of content items created or updated in a particular time period at a social media platform in relation to a software application of interest, and determining one or more sentiments associated with each of the plurality of content items. The one or more sentiments determined based on textual data of the plurality of content items. The method further includes determining whether a potential incident has occurred in relation to the software application of interest based at least on the one or more sentiments associated with the plurality of content items. In response to determining that a potential incident has occurred in relation to the software application of interest, the method further includes generating an alert message indicating the potential incident and communicating the alert message to a monitoring system.

Abstract: Various embodiments of the present invention provide methods, apparatuses, computing devices, and/or the like that are configured to perform software application framework monitoring using alert signatures for the software applications that are generated by at least one of: (i) a conditional ensemble machine learning framework comprise one or more alert priority score generation machine learning models, one or more alert priority explanation generation machine learning models, and a conditional ensemble machine learning model that is configured to generate an explanation-inclusive alert signature if a deep-learning-based alert priority score generated by the alert priority score generation machine learning models is identical to a decision-tree-based alert priority designation generated by the alert priority explanation generation machine learning models, and (ii) a set of alert priority score adjustment models such as an entity-based alert priority score adjustment model, a temporal alert priority score adju

Abstract: Various embodiments of the present invention provide methods, apparatuses, computing devices, and/or the like that are configured to perform software application framework monitoring using alert signatures for the software applications that are generated by at least one of: (i) a conditional ensemble machine learning framework comprise one or more alert priority score generation machine learning models, one or more alert priority explanation generation machine learning models, and a conditional ensemble machine learning model that is configured to generate an explanation-inclusive alert signature if a deep-learning-based alert priority score generated by the alert priority score generation machine learning models is identical to a decision-tree-based alert priority designation generated by the alert priority explanation generation machine learning models, and (ii) a set of alert priority score adjustment models such as an entity-based alert priority score adjustment model, a temporal alert priority score adju

Abstract: Systems and methods provide techniques for more effective and efficient predictive monitoring of a software application framework. For example, various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to enable effective and efficient predictive monitoring of a software application framework using alert signatures for the software application that are generated by using at least one of first-order alert classification machine learning models, temporal-sequential alert classification machine learning models, categorical-sequential alert classification machine learning models, alert classification machine learning models, and second-order alert classification machine learning models.

Abstract: Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured accurately and programmatically train a responder prediction machine learning model for generating response team predictions based on the systematic collection of one or more responder prediction training corpuses comprising one or more alert related datasets in a responder prediction server system. For example, the responder prediction server system may extract one or more alert attributes for each of the one or more alert related datasets for training one or more responder prediction machine learning models and/or one or more prioritization machine learning models. The responder prediction machine learning model and prioritization machine learning models may process one or more alerts, in real-time, to generate one or more response team prediction objects for rendering in a response team suggestion interface.