Abstract:
An in-call virtual assistant system monitors a real-time call, e.g., a call that is in progress, between multiple speakers, identifies a trigger and executes a specified task in response to the trigger. The virtual assistant system can be invoked by an explicit trigger or an implicit trigger. For example, an explicit trigger can be a voice command from one of the speakers in the call, such as “Ok Chorus, summarize the call” for summarizing the call. An implicit trigger can be an event that occurred in the call, or outside of the call but that is relevant to a speaker. For example, an event such as a speaker dropping off the call suddenly can be an implicit trigger that invokes the virtual assistant system to perform an associated task, such as notifying the remaining speakers on the call that one of the speakers dropped.
Abstract:
A call-modeling system models calls in real-time, with the goal of helping users, e.g., a sales representative and/or their managers, improve and/or guide the outcome of the calls. The call-modeling system generates real-time probabilities for possible outcomes of the conversation, as well as highlight specific on-call patterns, which may be either conducive or detrimental to a desired conversation outcome. The generated probabilities and highlighted patterns may be used by the sales representatives and/or their managers to either increase the probability of a desired outcome and/or optimize for call duration with a specific outcome.
Abstract:
A feedback identification system to automatically determine product feature requests by analyzing conversations of representatives with customers. The feedback identification system retrieves recordings of various conversations, extracts features of the conversations, and analyzes the features to determine a set of features that is indicative of a feature request. A feature request is a request for adding a specified functionality to a product. The set of features is analyzed to generate a feedback manifest, which includes the feature request (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.
Abstract:
A moment identification system automatically generates a playlist of conversations having a specified moment. A moment can be occurrence of a specific event or a specific characteristic in a conversation, or any event that is of specific interest for an application for which the playlist is being generated. For example, a moment can include laughter, fast-talking, objections, response to questions, a discussion on a particular topic such as budget, behavior of a speaker, intent to buy, etc., in a conversation. The moment identification system analyzes each of the conversations to determine if one or more features of a conversation correspond to a specified moment, and includes those of the conversations in the playlist having one or more features that correspond to the specified moment. The playlist may include a portion of a conversation that has the specified moment rather than the entire conversation.
Abstract:
An action item identification system automatically determines action items by analyzing conversations of representatives with customers. The action item identification system retrieves recordings of various conversations, extracts features of each of the conversations, and analyzes the features to determine a set of features that is indicative of an action item associated with the corresponding conversation. The set of features is further analyzed to generate the action item in an action item manifest (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.
Abstract:
A pattern recognition system (“system”) automatically determines conversation patterns that distinguish a first set of participants from a second set of participants. For example, a first set of participants can be top performing representatives and the second set of participants can be low performing representatives. The system analyzes a first set of recordings of the top performing representatives to extract a first set of features associated with the first set of recordings, and analyzes the first set of features to generate first pattern data that is indicative of a pattern of the conversation of the top performing representatives. Similarly, the system also generates second pattern data that is indicative of a pattern of the conversation of the low performing representatives. The system analyzes the first pattern data and the second pattern data to generate distinctive features that distinguish the first pattern from the second pattern.
Abstract:
The disclosure is directed to automatically determining action items by analyzing conversations of representatives with customers. An action item identification system retrieves recordings of various conversations, extracts features of each of the conversations, and analyzes the features to determine a set of features that is indicative of an action item associated with the corresponding conversation. The set of features is further analyzed to generate the action item in an action item manifest (a) as a summary of what is discussed in the conversations or (b) verbatim from the conversations.
Abstract:
The disclosure is directed to automatically determining conversation patterns that distinguish a first set of participants from a second set of participants. For example, a first set of participants can be top-performing representatives and the second set of participants can be low-performing representatives. A pattern recognition system analyzes a first set of recordings of the top-performing representatives to extract a first set of features associated with the first set of recordings, and analyzes the first set of features to generate first pattern data that is indicative of a pattern of the conversation of the top-performing representatives. Similarly, the pattern recognition system also generates second pattern data that is indicative of a pattern of the conversation of the low-performing representatives. The pattern recognition system analyzes the first pattern data and the second pattern data to generate distinctive features that distinguish the first pattern from the second pattern.
Abstract:
The disclosure is directed to analyzing voice conversations between participants of conversations and coordinating calls between participants, e.g., in order to influence an outcome of the voice conversation. For example, sales calls can be coordinated between specific sales representatives (“representatives”) and customers by routing a sales call from a customer to a specific sales representative, based on their voices and the content of the conversation, with the goal of positively influencing the outcome of the sales call. A mapping between sales representatives and customers that is set to maximize the probability for certain outcomes is generated. This mapping (or pairing) may be fed into either an automatic or manual coordination system that connects or bridges sales representatives with customers. The mapping may be generated either based on historic data or early-call conversation analysis, in both inbound and outbound calls.
Abstract:
The disclosure is directed to analyzing voice conversations between participants of conversations and coordinating calls between participants, e.g., in order to influence an outcome of the voice conversation. For example, sales calls can be coordinated between specific sales representatives (“representatives”) and customers by routing a sales call from a customer to a specific sales representative, based on their voices and the content of the conversation, with the goal of positively influencing the outcome of the sales call. A mapping between sales representatives and customers that is set to maximize the probability for certain outcomes is generated. This mapping (or pairing) may be fed into either an automatic or manual coordination system that connects or bridges sales representatives with customers. The mapping may be generated either based on historic data or early-call conversation analysis, in both inbound and outbound calls.