Abstract: Method and apparatus improves the quality of responses from an automatic dialogue system by dynamically adjusting response thresholds. More particularly, the automatic dialogue system may dynamically determine response threshold values in response to user feedback. The response threshold values may be used to evaluate a confidence value. The confidence value may be assigned to or otherwise associated with an input class, or user intent. The system may automatically adjust the response threshold values to provide a better user experience as the amount of user-interaction with the system increases.

Abstract: According to one embodiment, a method, computer system, and computer program product for retraining a classifier-based automatic dialogue system with recorded user interactions is provided. The present invention may include receiving recorded interactions, where the interactions are between a user and an automatic dialogue system; determining, based on the recorded interactions, whether to pair a given input with one or more classes; pairing inputs with one or more classes; assessing the reliability of the paired inputs and classes; determining whether the reliable paired inputs and classes can be consistently mapped; and merging all consistently mapped reliable pairs with an initial training set.

Abstract: A system, computer program product, and method are provided to automate a natural language processing system to facilitate an artificial intelligence platform defining a relationship between dialogue and post dialogue activity. Dialogue is detected and analyzed, including identification of key words and phrases within the dialogue. Post dialogue actions, including physical actuation of a hardware device and an associated temporal proximity of the action and the dialogue, are monitored. The hardware device receives an instruction from a processing unit that relates to the analyzed dialogue and the hardware device changes states and/or actuates another hardware device. The system constructs a hypothesis, i.e., a relationship from the identified key phrase drawn from the analyzed dialogue and the monitored post action dialogue. A dialogue tree containing identified terms and associated post dialogue actions is dynamically modified with one or more new identified terms and the associated post dialogue actions.

Abstract: Method and apparatus improves the quality of responses from an automatic dialogue system by dynamically adjusting response thresholds. More particularly, the automatic dialogue system may dynamically determine response threshold values in response to user feedback. The response threshold values may be used to evaluate a confidence value. The confidence value may be assigned to or otherwise associated with an input class, or user intent. The system may automatically adjust the response threshold values to provide a better user experience as the amount of user-interaction with the system increases.

Abstract: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Abstract: According to one embodiment, a method, computer system, and computer program product for retraining a classifier-based automatic dialogue system with recorded user interactions is provided. The present invention may include receiving recorded interactions, where the interactions are between a user and an automatic dialogue system; determining, based on the recorded interactions, whether to pair a given input with one or more classes; pairing inputs with one or more classes; assessing the reliability of the paired inputs and classes; determining whether the reliable paired inputs and classes can be consistently mapped; and merging all consistently mapped reliable pairs with an initial training set.

Abstract: A system, computer program product, and method are provided to automate a natural language processing system to facilitate an artificial intelligence platform defining a relationship between dialogue and post dialogue activity. Dialogue is detected and analyzed, including identification of key words and phrases within the dialogue. Post dialogue actions, including physical actuation of a hardware device and an associated temporal proximity of the action and the dialogue, are monitored. The hardware device receives an instruction from a processing unit that relates to the analyzed dialogue and the hardware device changes states and/or actuates another hardware device. The system constructs a hypothesis, i.e., a relationship from the identified key phrase drawn from the analyzed dialogue and the monitored post action dialogue. A dialogue tree containing identified terms and associated post dialogue actions is dynamically modified with one or more new identified terms and the associated post dialogue actions.

Abstract: According to one embodiment, a method, computer system, and computer program product for retraining a classifier-based automatic dialog system with recorded user interactions is provided. The present invention may include receiving recorded interactions, where the interactions are between a user and an automatic dialog system; determining, based on the recorded interactions, whether to pair a given input with one or more classes; pairing inputs with one or more classes; assessing the reliability of the paired inputs and classes; determining whether the reliable paired inputs and classes can be consistently mapped; and merging all consistently mapped reliable pairs with an initial training set.

Abstract: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Abstract: According to one embodiment, a method, computer system, and computer program product for retraining a classifier-based automatic dialogue system with recorded user interactions is provided. The present invention may include receiving recorded interactions, where the interactions are between a user and an automatic dialogue system; determining, based on the recorded interactions, whether to pair a given input with one or more classes; pairing inputs with one or more classes; assessing the reliability of the paired inputs and classes; determining whether the reliable paired inputs and classes can be consistently mapped; and merging all consistently mapped reliable pairs with an initial training set.

Abstract: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Abstract: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Abstract: Natural language processing of raw text data for optimal sentence boundary placement. Raw text is extracted from a document and subject to cleaning. The extracted raw text is examined to identify preliminary sentence boundaries, which are used to identify potential sentences in the raw text. One or more potential sentences are assigned a well-formedness score. A value of the score correlates to whether the potential sentence is a truncated/ill-formed sentence or a well-formed sentence. One or more preliminary sentence boundaries are optimized depending on the value of the score of the potential sentence(s). Accordingly, the processing herein is an optimization that creates a sentence boundary optimized output.

Abstract: A call center for routing of a call from a user to one of a plurality of agent stations of an organization, comprises: an interactive graphical display of information relating to the organization, the display for showing status information of at least one agent station; a device for enabling a user to select an agent station from the display; and, a device for effecting a communications link between the caller and the agent station. Further included is a device for receiving information about the organization and updating the graphical display of information.

Abstract: A method and processing system for the indexing of data for one or more users involving the steps of receiving primary data, delineating episode data from the primary data, generating media representations to correspond with the episode data, generating a table of contents from the episode data and media representations in real time, and storing and/or presenting the table of contents on a graphical user interface. Primary data may constitute audio data, video data, text data, combinations of these as well as any medium-generated data which may be in or converted to digital form. Episode data is generated on the basis of significant video images determined by scene changes and other information. In addition, episode data may be based on a significant audio change, delineated from the primary data, e.g. based on speaker changes and other information.

Abstract: A game is provided with a game board having a central space and an inner ring of spaces surrounding the central space. The game board has a plurality of wavy paths each defined by a series of spaces with these wavy paths extending outwardly from the inner ring of spaces to an outer peripheral region of the game board. The wavy paths defined by the series of spaces contact each other to provide for continuous paths extending from the outer peripheral region of the game board inwardly toward the inner ring of spaces as well as circumferentially around the latter along circumferential wavy paths while the wavy paths define between themselves barrier spaces separating the paths from each other. By way of a chance device it is possible for the different players to move player-identifying game pieces along the various spaces progressing along the wavy paths in various directions.