Abstract: Techniques are described for invoking and switching between chatbots of a chatbot system. In some embodiments, the chatbot system is capable of routing an utterance received while a user is already interacting with a first chatbot in the chatbot system. For instance, the chatbot system may identify a second chatbot based on determining that (i) such an utterance is an invalid input to the first chatbot or (ii) that the first chatbot is attempting to route the utterance to a destination associated with the first chatbot. Identifying the second chatbot can involve computing, using a predictive model, separate confidence scores for the first chatbot and the second chatbot, and then determining that a confidence score for the second chatbot satisfies one or more confidence score thresholds. The utterance is then routed to the second chatbot based on the identifying of the second chatbot.

Abstract: Techniques are described for invoking and switching between chatbots of a chatbot system. In some embodiments, the chatbot system is capable of routing an utterance received while a user is already interacting with a first chatbot in the chatbot system. For instance, the chatbot system may identify a second chatbot based on determining that (i) such an utterance is an invalid input to the first chatbot or (ii) that the first chatbot is attempting to route the utterance to a destination associated with the first chatbot. Identifying the second chatbot can involve computing, using a predictive model, separate confidence scores for the first chatbot and the second chatbot, and then determining that a confidence score for the second chatbot satisfies one or more confidence score thresholds. The utterance is then routed to the second chatbot based on the identifying of the second chatbot.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for identifying an intent for an utterance based on semantic framing. For an input utterance, a semantic frame is generated. The semantic frame includes semantically relevant grammatical relations and corresponding words identified in the utterance. The semantically relevant grammatical relations define context and relationships of words in the utterance. The semantic frame is used to identify an intent for the utterance, based on an intent model. The intent model maps features to corresponding words for a given intent. The semantic frame is compared to a plurality of intent models, and a best-matching intent model is used to identify the intent for the utterance.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for identifying an intent for an utterance based on semantic framing. For an input utterance, a semantic frame is generated. The semantic frame includes semantically relevant grammatical relations and corresponding words identified in the utterance. The semantically relevant grammatical relations define context and relationships of words in the utterance. The semantic frame is used to identify an intent for the utterance, based on an intent model. The intent model maps features to corresponding words for a given intent. The semantic frame is compared to a plurality of intent models, and a best-matching intent model is used to identify the intent for the utterance.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for detecting that there are multiple intents represented in an utterance and then matching each detected intent to an intent associated with a chatbot in a chatbot system. In certain embodiments, a chatbot system receives an utterance from a user. A language of the utterance is determined and a set of rules identified for the language of the utterance. The utterance is parsed to extract information relating to the sentence structure of the utterance. The set of one or more rules is used to (1) determine whether the utterance is formed of two or more parts that each correspond to a separate intent of a user, and (2) split the utterance into the two or more parts for separate processing including matching of each user intent to an intent configured for a chatbot.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for identifying an intent for an utterance based on semantic framing. For an input utterance, a semantic frame is generated. The semantic frame includes semantically relevant grammatical relations and corresponding words identified in the utterance. The semantically relevant grammatical relations define context and relationships of words in the utterance. The semantic frame is used to identify an intent for the utterance, based on an intent model. The intent model maps features to corresponding words for a given intent. The semantic frame is compared to a plurality of intent models, and a best-matching intent model is used to identify the intent for the utterance.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for identifying an explicit invocation of a chatbot and determining an input for the chatbot being invoked. In certain embodiments, explicit invocation analysis involves detecting an invocation name in an utterance. The invocation name is an identifier assigned to a particular chatbot. In response to detection of the invocation name, the utterance is refined for input to the particular chatbot by determining which parts of the utterance, if any, contain relevant information for the particular chatbot and generating a new utterance, using the relevant parts of the utterance, for processing by the particular chatbot. The refining can involve removal of a portion of the utterance associated with the invocation name.

Abstract: Techniques are described for invoking and switching between chatbots of a chatbot system. In some embodiments, the chatbot system is capable of routing an utterance received while a user is already interacting with a first chatbot in the chatbot system. For instance, the chatbot system may identify a second chatbot based on determining that (i) such an utterance is an invalid input to the first chatbot or (ii) that the first chatbot is attempting to route the utterance to a destination associated with the first chatbot. Identifying the second chatbot can involve computing, using a predictive model, separate confidence scores for the first chatbot and the second chatbot, and then determining that a confidence score for the second chatbot satisfies one or more confidence score thresholds. The utterance is then routed to the second chatbot based on the identifying of the second chatbot.

Abstract: The present disclosure relates to chatbot systems, and more particularly, to techniques for detecting that there are multiple intents represented in an utterance and then matching each detected intent to an intent associated with a chatbot in a chatbot system. In certain embodiments, a chatbot system receives an utterance from a user. A language of the utterance is determined and a set of rules identified for the language of the utterance. The utterance is parsed to extract information relating to the sentence structure of the utterance. The set of one or more rules is used to (1) determine whether the utterance is formed of two or more parts that each correspond to a separate intent of a user, and (2) split the utterance into the two or more parts for separate processing including matching of each user intent to an intent configured for a chatbot.