Abstract: A natural language processing combination classifier is disclosed, leveraging an entailment classifier and optionally at least one of a pattern matching classifier and a trained machine learning (ML) classifier. Each of the different types of classifiers can be used to identify different categories of matches. For example, a pattern matching (e.g., regular expression) classifier may identify exact matches, an entailment classifier can obtain context-specific classifications based on likelihood of entailment to comparison data, and the SML classifier can obtain potential matches based on large-scale supervised training. The comparison data for the entailment classifier can be generated from small datasets, and can be readily updated without the need for retraining any machine learning models. Different types of classifiers can be processed using different logic to provide a user with the most appropriate response given a circumstance and given the user's open input.

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.