Abstract: A method and system for automatically executing an action within a call center environment. Data is aggregated from multiple data sources into a combined data stream. One of the data sources is a source of data corresponding to at least one communication processed by the call center one of the data sources can be a source of external data representing activity that is external to the call center. The combined data stream is processed into successive batches of data corresponding to one or more communications between a call center agent and a communicating party received by the call center. A sensor data structure specifying at least one rule is applied to the batches of data. The at least one rule can include a machine learning model and a configuration data structure based on historical data from the multiple data sources. When it is determined that at least one of the batches satisfies the at least one rule, a notification message relating to the one or more communications is generated.

Abstract: Disclosed implementations use automated transcription and intent detection and an AI model to evaluate interactions between an agent and a customer within a call center environment. The evaluation flow used for manual evaluations is leveraged so that the evaluators can correct the AI evaluations when appropriate. Based on such corrections, the AI model can be retrained to accommodate specifics of the business and center—resulting in more confidence in the AI model over time.

Abstract: Disclosed implementations use automated transcription and intent detection and an AI model to evaluate interactions between an agent and a customer within a call center environment. The evaluation flow used for manual evaluations is leveraged so that the evaluators can correct the AI evaluations when appropriate. Based on such corrections, the AI model can be retrained to accommodate specifics of the business and center—resulting in more confidence in the AI model over time.

Abstract: Disclosed implementations use automated transcription and intent detection and an AI model to evaluate interactions between an agent and a customer within a call center environment. The evaluation flow used for manual evaluations is leveraged so that the evaluators can correct the AI evaluations when appropriate. Based on such corrections, the AI model can be retrained to accommodate specifics of the business and center—resulting in more confidence in the AI model over time.

Abstract: A method and system that can include providing a request matching service within a community, receiving a request to match a task request with a first profile of the community, calculating a set of multi-layered composite similarity scores, selecting at least one matched task request according to composite scores, and outputting at least one matched task request of the first profile.

Abstract: Systems, devices, and methods for displaying method content are described. In some embodiments, media content for display in a virtual three dimensional environment may be described. The virtual three dimensional environment including a representation of the identified media content may be generated. The generated virtual three dimensional environment may be displayed on a display device in communication with the first computing device. The virtual three dimensional environment may be displayed from a vantage point at a first location within the virtual three dimensional environment. Input modifying the virtual three dimensional environment may be detected. The virtual three dimensional environment may be updated in accordance with the detected input. The updated virtual three dimensional environment may be displayed on the display device.