A method and apparatus is disclosed that transmits a plug-in module to a web browser of a client computer. The web browser displays web content in a main window. The plug-in module, when activated, causes the web browser to display a user interface to a user of the client computer. The user interface is separate from the main window of the web browser, displays at least a portion of the web content, and is configured to enable the user to operate on the displayed at least a portion of the web content.

In some implementations, a system may receive non-deterministic finite state automata (NFSA) to represent a set of dialog flows associated with a human-machine interface. The system may generate a deterministic finite state automaton (DFSA) that includes a minimum set of states that represents all dialog flows included in the set of dialog flows represented in the NFSA and does not represent any dialog flows that are not included in the set of dialog flows represented in the NFSA. The system may traverse the DFSA to identify a set of K paths that have a highest total weight based on a weight assigned to each transition in the DFSA. The system may prune the DFSA to remove any states and any transitions that do not belong to the set of K paths. The system may generate an output related to one or more subsets of the set of K paths.

A dialogue apparatus includes a speech recognition unit (1) configured to perform speech recognition on utterance input to generate a text corresponding to the utterance, a speech waveform corresponding to the utterance, and information regarding a length of sound of the utterance; a language understanding unit (2) configured to grasp contents of the utterance by using the text corresponding to the utterance; a dialogue management unit (3) configured to determine contents of a response corresponding to the utterance by using the content of the utterance; an utterance state extraction unit (4) configured to extract a state of the utterance by using the text corresponding to the utterance, the speech waveform corresponding to the utterance, and the information regarding the length of the sound of the utterance; a response state determination unit (5) configured to determine a state of the response according to the state of the utterance; a response sentence generation unit (6) configured to generate a response sentence by using the content of the response; and a speech synthesis unit (7) configured to synthesize speech corresponding to the response sentence with the state of the response taken into account.

A conversion table generation device includes a similar word extraction unit and a conversion table generation unit. The similar word extraction unit is configured to extract similar words similar to first words, for each of the first words included in a word group used in a dialogue. The conversion table generation unit is configured to associate any one of the first words with the extracted similar words, that are similar to the plurality of first words, as second words, on the basis of the priority, and generates a conversion table for voice recognition with the second word as a conversion source and the first word as a conversion destination.

A conversion table generation device includes a similar word extraction unit and a conversion table generation unit. The similar word extraction unit is configured to extract similar words similar to first words, for each of the first words included in a word group used in a dialogue. The conversion table generation unit is configured to associate any one of the first words with the extracted similar words, that are similar to the plurality of first words, as second words, on the basis of the priority, and generates a conversion table for voice recognition with the second word as a conversion source and the first word as a conversion destination.

To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.

To prevent intent classifiers from potentially choosing intents that are ineligible for the current input due to policies, dynamic intent classification systems and methods are provided that dynamically control the possible set of intents using environment variables (also referred to as external variables). Associations between environment variables and ineligible intents, referred to as culling rules, are used.

This application provides a network configuration method, apparatus, and system. The method includes: determining, based on a mapping relationship, that a first data node in a first YANG data model corresponds to a second data node in a second YANG data model, where the first data node and the second data node include a same indication operation, and the mapping relationship includes a correspondence between a data node in the first YANG data model and a data node in the second YANG data model; and generating a first packet based on the second data node.

This application provides a network configuration method, apparatus, and system. The method includes: determining, based on a mapping relationship, that a first data node in a first YANG data model corresponds to a second data node in a second YANG data model, where the first data node and the second data node include a same indication operation, and the mapping relationship includes a correspondence between a data node in the first YANG data model and a data node in the second YANG data model; and generating a first packet based on the second data node.

A computer system configured for autonomous learning of entity values is provided. The computer system includes a memory that stores associations between entities and fields of response data. The computer system also includes a processor configured to receive a request to process an intent; generate a request to fulfill the intent; transmit the request to a fulfillment service; receive, from the fulfillment service, response data specifying values of the fields; identify the values of the fields within the response data; identify the entities via the associations using the fields; store, within the memory, the values of the fields as values of the entities; and retrain a natural language processor using the values of the entities.