Abstract: A computing system initializes a score for each chatbot profile of a plurality of chatbot profiles. The chatbot profiles correspond to different personas. For each chatbot profile, the computing system collects biometric response data for a user while the user has an interaction session with the chatbot profile. The computing system updates the score for the chatbot profile based on the biometric response data for the user collected while the user has the interaction session with the chatbot profile. The computing system ranks the chatbot profiles based on the scores and selects a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles.

Abstract: Various embodiments provide for decentralized crowd sourced generation of recommendation data objects. An example apparatus receives, originating from an external computing device, a recommendation data object request, the recommendation data object request comprising a user identifier and one or more user attributes. The example apparatus may further retrieve, based on a predictive recommendation model, one or more therapy identifiers associated with a therapy efficacy score exceeding a therapy efficacy score threshold for attributes of a first attributes set associated with a first cluster identifier, the first attributes set comprising one or more of the one or more user attributes.

Abstract: Systems and methods are configured to enable guided interaction with query assistant software using brainwave data. In various embodiments, a client device presents a query assistant user interface to a monitored end-user that describes a query associated with a plurality of response options and an intended physiological action for each response option. Accordingly, one or more user monitoring data objects associated with the monitored end-user are received that include user brainwave monitoring data objects. These user monitoring data objects are processed using one or more response designation machine learning models to generate response designators based on the user monitoring data objects that includes a physiological response designator describing a selected intended physiological action that is deemed to be related to the user monitoring data objects. Accordingly, a user response is then determined based on the response designators and the user interface may be updated based on the user response.

Abstract: Various embodiments provide for decentralized crowd sourced generation of recommendation data objects. An example apparatus receives, originating from an external computing device, a recommendation data object request, the recommendation data object request comprising a user identifier and one or more user attributes. The example apparatus may further retrieve, based on a predictive recommendation model, one or more therapy identifiers associated with a therapy efficacy score exceeding a therapy efficacy score threshold for attributes of a first attributes set associated with a first cluster identifier, the first attributes set comprising one or more of the one or more user attributes.

Abstract: A computing system initializes a score for each chatbot profile of a plurality of chatbot profiles. The chatbot profiles correspond to different personas. For each chatbot profile, the computing system collects biometric response data for a user while the user has an interaction session with the chatbot profile. The computing system updates the score for the chatbot profile based on the biometric response data for the user collected while the user has the interaction session with the chatbot profile. The computing system ranks the chatbot profiles based on the scores and selects a chatbot profile from the plurality of chatbot profiles for a subsequent interaction session with the user based on the ranking of the chatbot profiles.

Abstract: Systems and methods are configured to enable guided interaction with query assistant software using brainwave data. In various embodiments, a client device presents a query assistant user interface to a monitored end-user that describes a query associated with a plurality of response options and an intended physiological action for each response option. Accordingly, one or more user monitoring data objects associated with the monitored end-user are received that include user brainwave monitoring data objects. These user monitoring data objects are processed using one or more response designation machine learning models to generate response designators based on the user monitoring data objects that includes a physiological response designator describing a selected intended physiological action that is deemed to be related to the user monitoring data objects. Accordingly, a user response is then determined based on the response designators and the user interface may be updated based on the user response.

Abstract: This disclosure relates generally to systems and methods of operating systems and more particularly to systems and methods for a smart operating system for integrating dynamic case management into a process management platform. In one embodiment, a computer-implemented dynamic case management method includes creating a plurality of lightweight stateless computing processes; placing the processes in a WAIT state; receiving a request to initiate a process instance corresponding to a lightweight stateless process; placing at least one of the processes in an EXECUTING state; processing the process instance by the processes placed in the EXECUTING state; determining a next process for the process instance; and routing the process instance to the next process.

Abstract: The present disclosure relates to methods of systems for process modeling. Embodiments of the present disclosure may store a user interface framework in a memory allocated to a browser application in the computer. The user interface framework may provide a user interface to a user operating the computer to perform process modeling. Some embodiments may also render the user interface using a rendering engine associated with the browser application and capture one or more process modeling events received as input by the user. The one or more process modeling events may be associated with one or more process models. In addition, some embodiments may store the one or more process modeling events as model data in the memory allocated to the browser application and render the one or more process models on the user interface based on at least a subset of the model data.

Abstract: A method, device and non-transitory computer readable medium that implements error detection and recovery includes receiving from one or more agents monitoring one or more subsystem processes of a business process operating in a cloud based architecture an identification of an error condition in at least one of the subsystem processes. Additionally, any associated information or data necessary to execute the at least one of the subsystem processes with the identified error condition is received. An error recovery process for the at least one of the subsystem processes with the identified error condition is executed by the application management computing device. The recovered at least one of the subsystem processes with the identified error condition is reinitiated using the received information or data corresponding to the at least one of the subsystem processes with the identified error condition.

Abstract: A method, device and non-transitory computer readable medium that implements error detection and recovery includes receiving from one or more agents monitoring one or more subsystem processes of a business process operating in a cloud based architecture an identification of an error condition in at least one of the subsystem processes. Additionally, any associated information or data necessary to execute the at least one of the subsystem processes with the identified error condition is received. An error recovery process for the at least one of the subsystem processes with the identified error condition is executed by the application management computing device. The recovered at least one of the subsystem processes with the identified error condition is reinitiated using the received information or data corresponding to the at least one of the subsystem processes with the identified error condition.

Abstract: The present disclosure relates to methods of systems for allocating a call from a user to an agent. Embodiments of the disclosure may determine a set of sentiment indicators associated with the user from one or more acoustic parameters of the call. In addition, embodiments of the disclosure may select a candidate agent to handle the call based on the set of sentiment indicators and a sentiment handling capability associated with the candidate agent. Moreover, embodiments of the disclosure may allocate the call to the candidate agent.

Abstract: The present disclosure relates to methods of systems for allocating a call from a user to an agent. Embodiments of the disclosure may determine a set of sentiment indicators associated with the user from one or more acoustic parameters of the call. In addition, embodiments of the disclosure may select a candidate agent to handle the call based on the set of sentiment indicators and a sentiment handling capability associated with the candidate agent. Moreover, embodiments of the disclosure may allocate the call to the candidate agent.

Abstract: Systems, methods, and non-transitory computer-readable media for a cloud based selectively scalable architecture (CBSSA) that may be used for selective and automatic up-scaling and downscaling of individual sub systems are disclosed. In this architecture sub systems may also be extended and added onto the system architecture independently without impacting the other sub systems. Hardware and Software provisioning techniques may be achieved at runtime using the APIs of the cloud infrastructure.

Abstract: This disclosure relates generally to Error! Reference source not found, and more particularly to systems and methods for a smart operating system for integrating dynamic case management into a process management platform. In one embodiment, a computer-implemented dynamic case management method includes creating a plurality of lightweight stateless computing processes; placing the processes in a WAIT state; receiving a request to initiate a process instance corresponding to a lightweight stateless process; placing at least one of the processes in an EXECUTING state; processing the process instance by the processes placed in the EXECUTING state; determining a next process for the process instance; and routing the process instance to the next process.

Abstract: The present disclosure relates to methods of systems for process modeling. Embodiments of the present disclosure may store a user interface framework in a memory allocated to a browser application in the computer. The user interface framework may provide a user interface to a user operating the computer to perform process modeling. Some embodiments may also render the user interface using a rendering engine associated with the browser application and capture one or more process modeling events received as input by the user. The one or more process modeling events may be associated with one or more process models. In addition, some embodiments may store the one or more process modeling events as model data in the memory allocated to the browser application and render the one or more process models on the user interface based on at least a subset of the model data.