Abstract: The present disclosure describes automated generation of early warning predictive insights derived from contextual analysis of user activity data of a distributed software platform. Predictive insights are automatically generated from analysis of user activity through implementation of trained artificial intelligence (AI) modeling. User activity data is accessed pertaining to user interactions by a plurality of users on a software data platform. The trained AI modeling generates a plurality of mobility determinations that identify changes in patterns of user behavior over a current temporal filter associated with the user activity data. The plurality of mobility determinations is curated using business logic rules that evaluate a relevance of the mobility determinations. One or more predictive insights may be generated and presented via a graphical user interface notification.

Abstract: The present disclosure describes automated generation of early warning predictive insights derived from contextual analysis of user activity data of a distributed software platform. Predictive insights are automatically generated from analysis of user activity through implementation of trained artificial intelligence (AI) modeling. User activity data is accessed pertaining to user interactions by a plurality of users a software data platform. The trained AI modeling generates a plurality of mobility determinations that identify changes in patterns of user behavior over a current temporal filter associated with the user activity data. The plurality of mobility determinations is curated using business logic rules that evaluate a relevance of the mobility determinations. One or more predictive insights may be generated and presented via a graphical user interface notification.

Abstract: The present disclosure describes automated generation of early warning predictive insights derived from contextual analysis of user activity data of a distributed software platform. Predictive insights are automatically generated from analysis of user activity through implementation of trained artificial intelligence (AI) modeling. User activity data is accessed pertaining to user interactions by a plurality of users a software data platform. The trained AI modeling generates a plurality of mobility determinations that identify changes in patterns of user behavior over a current temporal filter associated with the user activity data. The plurality of mobility determinations is curated using business logic rules that evaluate a relevance of the mobility determinations. One or more predictive insights may be generated and presented via a graphical user interface notification.

Abstract: Non-limiting examples of the present disclosure relate to automated presentation control during presentation of one or more digital documents. Processing operations described herein may be configured to work with any type of application/service, where examples described herein extend to presentation of any type of digital document(s) including slide-based presentations. Speech signals may be detected while a digital document is being presented. The detected speech signals may be converted to text. The converted text may be matched with contextual data (e.g., pre-processed content) associated with the digital document. Probability scoring for determination of a next placement position of a digital document may be generated based on the matching of converted text with the contextual data of the digital document. The next placement position may be selected based on the generated probability scoring. Display of the digital document may be automatically updated to reflect the selected next placement position.

Abstract: Non-limiting examples of the present disclosure relate to automated presentation control during presentation of one or more digital documents. Processing operations described herein may be configured to work with any type of application/service, where examples described herein extend to presentation of any type of digital document(s) including slide-based presentations. Speech signals may be detected while a digital document is being presented. The detected speech signals may be converted to text. The converted text may be matched with contextual data (e.g., pre-processed content) associated with the digital document. Probability scoring for determination of a next placement position of a digital document may be generated based on the matching of converted text with the contextual data of the digital document. The next placement position may be selected based on the generated probability scoring. Display of the digital document may be automatically updated to reflect the selected next placement position.