Cognitive triggering of human interaction strategies to facilitate collaboration, productivity, and learning

A computer triggering system for triggering a dialogue system to implement human interaction strategies configured to facilitate human interactions between and among selected persons in an environment, the computer triggering system configure to perform operations comprising:

• receiving input data about the environment;
• using machine learning (ML) algorithms of the computer triggering system to extract features from the input data;
• using the ML algorithms to analyze relationships among and between the features extracted from the input data to generate a person-A user-interaction-readiness model for person-A in the environment, wherein the person-A user-interaction-readiness model comprises a model of person-A's readiness to participate in a human interaction with other persons in the environment;
• using the ML algorithms to apply real-world data about person-A to the person-A user-interaction-readiness model to generate a first classification output comprising data representing person-A's readiness to participate in a human interaction with other persons in the environment;
• using the ML algorithms to analyze relationships among and between the features extracted from the input data to generate a person-B user-interaction-readiness model for person-B in the environment, wherein the person-B user-interaction-readiness model comprises a model that indicates person-B's readiness to participate in a human interaction with another person in the environment;
• using the ML algorithms to apply real-world data about person-B to the person-B user-interaction-readiness model to generate a second classification output comprising data representing person-B's readiness to participate in a human interaction with other persons in the environment;
• using the ML algorithms to extract features from the input data, the first classification output, and the second classification output;
• using the ML algorithms to analyze relationships among and between the features extracted from the input data, the first classification output, and the second classification output to generate a user-interaction-candidates group model for person-A and person-B in the environment, wherein the user-interaction-candidates group model comprises a model of person-A's and Person B's readiness to participate in a human interaction that includes person-A and person-B;
• using the ML algorithms to apply real-world data about person-A and person-B to the user-interaction-candidates group model to generate a third classification output comprising data representing person-A's readiness to participate in a human interaction with person-B in the environment, as well as person-B's readiness to participate in a human interaction with person-A;
• and
• based at least in part on the third classification output, triggering the dialogue system to initiate the implementation of human interaction strategies configured to facilitate human interactions between person-A and person-B in the environment.

A computer program product for triggering a dialogue system to implement human interaction strategies configured to facilitate human interactions between and among selected persons in an environment, the computer program product comprising a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor, causes the processor to perform a method comprising:

• receiving input data about the environment;
• using machine learning (ML) algorithms of the processor to extract features from the input data;
• using the ML algorithms to analyze relationships among and between the features extracted from the input data to generate a person-A user-interaction-readiness model for person-A in the environment, wherein the person-A user-interaction-readiness model comprises a model of person-A's readiness to participate in a human interaction with other persons in the environment;
• using the ML algorithms to apply real-world data about person-A to the person-A user-interaction-readiness model to generate a first classification output comprising data representing person-A's readiness to participate in a human interaction with other persons in the environment;
• using the ML algorithms to analyze relationships among and between the features extracted from the input data to generate a person-B user-interaction-readiness model for person-B in the environment, wherein the person-B user-interaction-readiness model comprises a model that indicates person-B's readiness to participate in a human interaction with another person in the environment;
• using the ML algorithms to apply real-world data about person-B to the person-B user-interaction-readiness model to generate a second classification output comprising data representing person-B's readiness to participate in a human interaction with other persons in the environment;
• using the ML algorithms to extract features from the input data, the first classification output, and the second classification output;
• using the ML algorithms to analyze relationships among and between the features extracted from the input data, the first classification output, and the second classification output to generate a user-interaction-candidates group model for person-A and person-B in the environment, wherein the user-interaction-candidates group model comprises a model of person-A's and Person B's readiness to participate in a human interaction that includes person-A and person-B;
• using the ML algorithms to apply real-world data about person-A and person-B to the user-interaction-candidates group model to generate a third classification output comprising data representing person-A's readiness to participate in a human interaction with person-B in the environment, as well as person-B's readiness to participate in a human interaction with person-A;
• and
• based at least in part on the third classification output, triggering the dialogue system to initiate the implementation of human interaction strategies configured to facilitate human interactions between person-A and person-B in the environment.