Abstract: Techniques for initiating system actions based on conversational content are disclosed. A system identifies a first conversational moment type. The first conversational moment type is defined by a first set of one or more conversational conditions. The system receives a user-selected action to be performed by the system in response to detecting conversational moments of the first conversational moment type. The system stores the user-selected action in association with the first conversational moment type. The system performs the user-selected action in response to detecting the conversational moments of the first conversational moment type.

Abstract: Techniques for initiating system actions based on conversational content are disclosed. A system identifies a first conversational moment type. The first conversational moment type is defined by a first set of one or more conversational conditions. The system receives a user-selected action to be performed by the system in response to detecting conversational moments of the first conversational moment type. The system stores the user-selected action in association with the first conversational moment type. The system performs the user-selected action in response to detecting the conversational moments of the first conversational moment type.

Abstract: Described techniques select portions of an audio stream for transmission to a trained machine learning application, which generates response recommendations in real-time. This real-time response is facilitated by the system identifying, selecting and transmitting those portions of the audio stream likely to be most relevant to the conversation. Portions of an audio stream less likely to be relevant to the conversation are identified accordingly and not transmitted. The system may identify the relevant portions of an audio stream by detecting events in a contemporaneous event stream, use a trained machine learning model to identify events in an audio stream, or both.

Abstract: Described techniques select portions of an audio stream for transmission to a trained machine learning application, which generates response recommendations in real-time. This real-time response is facilitated by the system identifying, selecting and transmitting those portions of the audio stream likely to be most relevant to the conversation. Portions of an audio stream less likely to be relevant to the conversation are identified accordingly and not transmitted. The system may identify the relevant portions of an audio stream by detecting events in a contemporaneous event stream, use a trained machine learning model to identify events in an audio stream, or both.

Abstract: A conversation may be monitored in real time using a trained machine learning model to identify a desired outcome of a conversation and generate one or more phrases for accomplishing the desired outcome. A confidence score may also be determined for one or more phrases that indicates a likelihood that the one or more phrases may help accomplish the desired outcome of the conversation. In some examples, a confidence score may be based on whether an agent, a caller, or both responded unfavorably to a similar phrase used previously in another conversation. In other examples, a confidence score corresponding to one or more phrases may be based on whether a prior conversation in which one or more similar phrases was used resulted in the desired outcome being accomplished.

Abstract: A conversation may be monitored in real time using a trained machine learning model. This real-time monitoring may detect attributes of a conversation, such as a conversation type, a state of a conversation, as well as other attributes that help specify a context of a conversation. Contextually appropriate behavioral targets may be provided by machine learning model to an agent participating in a conversation. In some embodiments, these “behavioral targets” are identified by applying a set of rules to the contemporaneously identified conversation attributes. The behavioral targets may be defined in advance prior to the start of a conversation. In this way, the machine learning model may be trained to associate particular behavioral target(s) with one or more conversation attributes (or collections of attributes). This facilitates the real-time monitoring of a conversation and contemporaneous guidance of an agent with machine-identified behavioral targets.

Abstract: A conversation may be monitored in real time using a trained machine learning model. This real-time monitoring may detect attributes of a conversation, such as a conversation type, a state of a conversation, as well as other attributes that help specify a context of a conversation. Contextually appropriate behavioral targets may be provided by machine learning model to an agent participating in a conversation. In some embodiments, these “behavioral targets” are identified by applying a set of rules to the contemporaneously identified conversation attributes. The behavioral targets may be defined in advance prior to the start of a conversation. In this way, the machine learning model may be trained to associate particular behavioral target(s) with one or more conversation attributes (or collections of attributes). This facilitates the real-time monitoring of a conversation and contemporaneous guidance of an agent with machine-identified behavioral targets.

Abstract: A conversation may be monitored in real time using a trained machine learning model. This real-time monitoring may detect attributes of a conversation, such as a conversation type, a state of a conversation, as well as other attributes that help specify a context of a conversation. Contextually appropriate behavioral targets may be provided by machine learning model to an agent participating in a conversation. In some embodiments, these “behavioral targets” are identified by applying a set of rules to the contemporaneously identified conversation attributes. The behavioral targets may be defined in advance prior to the start of a conversation. In this way, the machine learning model may be trained to associate particular behavioral target(s) with one or more conversation attributes (or collections of attributes). This facilitates the real-time monitoring of a conversation and contemporaneous guidance of an agent with machine-identified behavioral targets.

Abstract: Techniques for monitoring a conversation in real-time to detect attributes of a conversation, identifying a desired outcome of the conversation, and identifying voice modulations that may be applied to the agent's voice to help accomplish the desired outcome are disclosed. The system may identify voice modulations by comparing a current conversation to one or more prior conversations having desired outcomes similar to that of the current conversation. A trained machine learning model may select and apply voice modulations associated with accomplishing a desired outcome.

Abstract: A conversation may be monitored in real time using a trained machine learning model. This real-time monitoring may detect attributes of a conversation, such as a conversation type, a state of a conversation, as well as other attributes that help specify a context of a conversation. Contextually appropriate behavioral targets may be provided by machine learning model to an agent participating in a conversation. In some embodiments, these “behavioral targets” are identified by applying a set of rules to the contemporaneously identified conversation attributes. The behavioral targets may be defined in advance prior to the start of a conversation. In this way, the machine learning model may be trained to associate particular behavioral target(s) with one or more conversation attributes (or collections of attributes). This facilitates the real-time monitoring of a conversation and contemporaneous guidance of an agent with machine-identified behavioral targets.

Abstract: Described techniques select portions of an audio stream for transmission to a trained machine learning application, which generates response recommendations in real-time. This real-time response is facilitated by the system identifying, selecting and transmitting those portions of the audio stream likely to be most relevant to the conversation. Portions of an audio stream less likely to be relevant to the conversation are identified accordingly and not transmitted. The system may identify the relevant portions of an audio stream by detecting events in a contemporaneous event stream, use a trained machine learning model to identify events in an audio stream, or both.

Abstract: A conversation may be monitored in real time using a trained machine learning model to identify a desired outcome of a conversation and generate one or more phrases for accomplishing the desired outcome. A confidence score may also be determined for one or more phrases that indicates a likelihood that the one or more phrases may help accomplish the desired outcome of the conversation. In some examples, a confidence score may be based on whether an agent, a caller, or both responded unfavorably to a similar phrase used previously in another conversation. In other examples, a confidence score corresponding to one or more phrases may be based on whether a prior conversation in which one or more similar phrases was used resulted in the desired outcome being accomplished.

Abstract: A conversation may be monitored in real time using a trained machine learning model. This real-time monitoring may detect attributes of a conversation, such as a conversation type, a state of a conversation, as well as other attributes that help specify a context of a conversation. Contextually appropriate behavioral targets may be provided by machine learning model to an agent participating in a conversation. In some embodiments, these “behavioral targets” are identified by applying a set of rules to the contemporaneously identified conversation attributes. The behavioral targets may be defined in advance prior to the start of a conversation. In this way, the machine learning model may be trained to associate particular behavioral target(s) with one or more conversation attributes (or collections of attributes). This facilitates the real-time monitoring of a conversation and contemporaneous guidance of an agent with machine-identified behavioral targets.