Abstract: An automated dynamic message categorization system is provided and includes first, second and third processing units. The first processing unit is configured to generate a user interface (UI) and to present the UI to a user. The second processing unit is configured to pull information from a first textual element which has been entered into the UI, to identify second textual elements that are relevant to the first textual element based on the pulled information and to extract textual element identifiers from the second textual elements. The third processing unit is configured to generate, for each extracted textual element identifier, a confidence score describing a degree of correlation between each extracted textual element identifier and the first textual element. The first processing unit is further configured to present to the user each extracted textual element identifier with a corresponding confidence score as a selectable option via the UI.

Abstract: An automated dynamic message categorization system is provided and includes first, second and third processing units. The first processing unit is configured to generate a user interface (UI) and to present the UI to a user. The second processing unit is configured to pull information from a first textual element which has been entered into the UI, to identify second textual elements that are relevant to the first textual element based on the pulled information and to extract textual element identifiers from the second textual elements. The third processing unit is configured to generate, for each extracted textual element identifier, a confidence score describing a degree of correlation between each extracted textual element identifier and the first textual element. The first processing unit is further configured to present to the user each extracted textual element identifier with a corresponding confidence score as a selectable option via the UI.

Abstract: A method, a processing device, and a computer program product are provided. Discussions in threads are analyzed to learn patterns that occur before at least one respective additional party joins the discussions in the threads. A model of conversation states is created based on the analyzing. Thread updates and new threads are periodically analyzed, based on the model, to determine if a corresponding triggering condition occurred with respect to any new respective discussions of the threads or the new threads. The at least one respective additional party is notified to participate in a respective new discussion when the periodically analyzing determines that the corresponding triggering condition occurred with respect to the respective new discussion in a corresponding thread or a corresponding new thread.

Abstract: A method, a processing device, and a computer program product are provided. Discussions in threads are analyzed to learn patterns that occur before at least one respective additional party joins the discussions in the threads. A model of conversation states is created based on the analyzing. Thread updates and new threads are periodically analyzed, based on the model, to determine if a corresponding triggering condition occurred with respect to any new respective discussions of the threads or the new threads. The at least one respective additional party is notified to participate in a respective new discussion when the periodically analyzing determines that the corresponding triggering condition occurred with respect to the respective new discussion in a corresponding thread or a corresponding new thread.

Abstract: An automated dynamic message categorization system is provided and includes first, second and third processing units. The first processing unit is configured to generate a user interface (UI) and to present the UI to a user. The second processing unit is configured to pull information from a first textual element which has been entered into the UI, to identify second textual elements that are relevant to the first textual element based on the pulled information and to extract textual element identifiers from the second textual elements. The third processing unit is configured to generate, for each extracted textual element identifier, a confidence score describing a degree of correlation between each extracted textual element identifier and the first textual element. The first processing unit is further configured to present to the user each extracted textual element identifier with a corresponding confidence score as a selectable option via the UI.

Abstract: An automated dynamic message categorization system is provided and includes first, second and third processing units. The first processing unit is configured to generate a user interface (UI) and to present the UI to a user. The second processing unit is configured to pull information from a first textual element which has been entered into the UI, to identify second textual elements that are relevant to the first textual element based on the pulled information and to extract textual element identifiers from the second textual elements. The third processing unit is configured to generate, for each extracted textual element identifier, a confidence score describing a degree of correlation between each extracted textual element identifier and the first textual element. The first processing unit is further configured to present to the user each extracted textual element identifier with a corresponding confidence score as a selectable option via the UI.

Abstract: A method, a processing device, and a computer program product are provided. Discussions in threads are analyzed to learn patterns that occur before at least one respective additional party joins the discussions in the threads. A model of conversation states is created based on the analyzing. Thread updates and new threads are periodically analyzed, based on the model, to determine if a corresponding triggering condition occurred with respect to any new respective discussions of the threads or the new threads. The at least one respective additional party is notified to participate in a respective new discussion when the periodically analyzing determines that the corresponding triggering condition occurred with respect to the respective new discussion in a corresponding thread or a corresponding new thread.

Abstract: A method, a processing device, and a computer program product are provided. Discussions in threads are analyzed to learn patterns that occur before at least one respective additional party joins the discussions in the threads. A model of conversation states is created based on the analyzing. Thread updates and new threads are periodically analyzed, based on the model, to determine if a corresponding triggering condition occurred with respect to any new respective discussions of the threads or the new threads. The at least one respective additional party is notified to participate in a respective new discussion when the periodically analyzing determines that the corresponding triggering condition occurred with respect to the respective new discussion in a corresponding thread or a corresponding new thread.