Abstract: Detection of crisis situations using multiple classifiers can enable fast and efficient processing of input phrases to determine if an abatement protocol should be executed. An input phrase can be passed through a pattern matching classifier and then through a trained machine learning classifier. If neither classifier identifies a crisis, the workflow can continue as usual. However, if a crisis is identified, confirmation of the crisis situation can be sought and used to further update one or both of the classifiers. If the crisis is confirmed, emergency information and crisis management tools can be presented to the user, among other mitigating actions. If the crisis is not confirmed, a prompt can be presented to the user to discuss the trigger phrase associated with the trigger signal.

Abstract: A chatbot capable of empathic engagement with a user is disclosed. An identified trend in a user's mood or goals between a first time and a second time can be associated with open input (e.g., open text string input) from the user. As the user's mood or goals continue to be tracked, a subsequent trend can be identified that is the same as, similar to, different from, or opposite to the first identified trend. The user can then be automatically engaged based on the open input associated with the first identified trend. In an example, a user may input thoughts or reasons why they have been having a positively trending mood over a duration of time. The chatbot can then repeat or otherwise use those same thoughts or reasons to engage the user empathically when the chatbot detects that the user is experiencing a negatively trending mood.

Abstract: A chatbot capable of empathic engagement with a user is disclosed. An identified trend in a user's mood or goals between a first time and a second time can be associated with open input (e.g., open text string input) from the user. As the user's mood or goals continue to be tracked, a subsequent trend can be identified that is the same as, similar to, different from, or opposite to the first identified trend. The user can then be automatically engaged based on the open input associated with the first identified trend. In an example, a user may input thoughts or reasons why they have been having a positively trending mood over a duration of time. The chatbot can then repeat or otherwise use those same thoughts or reasons to engage the user empathically when the chatbot detects that the user is experiencing a negatively trending mood.

Abstract: A therapy platform can leverage mood tracking to personalize delivery of therapy. User input data is collected to generate one or more mood scores that change over time. Tracked mood scores can include an overall mood score (e.g., ?100 to 100 where ?100 represents a strongly negative or “bad” mood and 100 represents a strongly positive or “happy” mood) and/or enumerated mood scores (e.g., numerical scores tracking individual, enumerated moods, such as “anxious,” “sad,” “tired,” “happy,” and the like). In some cases, an overall mood score can be made up of enumerated mood scores. Tracked mood scores can be used to inform the selection of what therapy to provide. For example, a first therapy tool may be selected for delivery when the user evidences a first set of mood scores, whereas a second therapy tool may be selected when the user evidences a second set of mood scores.

Abstract: A therapy platform can leverage mood tracking to personalize delivery of therapy. User input data is collected to generate one or more mood scores that change over time. Tracked mood scores can include an overall mood score (e.g., ?100 to 100 where ?100 represents a strongly negative or “bad” mood and 100 represents a strongly positive or “happy” mood) and/or enumerated mood scores (e.g., numerical scores tracking individual, enumerated moods, such as “anxious,” “sad,” “tired,” “happy,” and the like). In some cases, an overall mood score can be made up of enumerated mood scores. Tracked mood scores can be used to inform the selection of what therapy to provide. For example, a first therapy tool may be selected for delivery when the user evidences a first set of mood scores, whereas a second therapy tool may be selected when the user evidences a second set of mood scores.

Abstract: A chatbot capable of empathic engagement with a user is disclosed. An identified trend in a user's mood or goals between a first time and a second time can be associated with open input (e.g., open text string input) from the user. As the user's mood or goals continue to be tracked, a subsequent trend can be identified that is the same as, similar to, different from, or opposite to the first identified trend. The user can then be automatically engaged based on the open input associated with the first identified trend. In an example, a user may input thoughts or reasons why they have been having a positively trending mood over a duration of time. The chatbot can then repeat or otherwise use those same thoughts or reasons to engage the user empathically when the chatbot detects that the user is experiencing a negatively trending mood.

Abstract: Detection of crisis situations using multiple classifiers can enable fast and efficient processing of input phrases to determine if an abatement protocol should be executed. An input phrase can be passed through a pattern matching classifier and then through a trained machine learning classifier. If neither classifier identifies a crisis, the workflow can continue as usual. However, if a crisis is identified, confirmation of the crisis situation can be sought and used to further update one or both of the classifiers. If the crisis is confirmed, emergency information and crisis management tools can be presented to the user, among other mitigating actions. If the crisis is not confirmed, a prompt can be presented to the user to discuss the trigger phrase associated with the trigger signal.