Abstract: A system and method for automating the training of enterprise customer response systems using a range of dynamic or generic data sets, used to gradually take human supervision and intervention out of the training process for enterprise WA and similar automated response engines, by training existing machine learning models or engines using a heuristic middleman annotation assistant that helps map generic/public/new datasets to the existing machine learning model or engine, and allowing for limited human oversight over the remaining unknown or badly classified data segments which is used to further teach the heuristic and classification model until the human oversight is no longer needed for the heuristic to learn and map newer datasets, thereby reducing human, dollar, and time costs, and improving automated response system efficiency.

Abstract: Representation-neutral dialogue systems and methods (“RNDS”) are described that include multi-application, multi-device spoken-language dialogue systems based on the information-state update approach. The RNDS includes representation-neutral core components of a dialogue system that provide scripted domain-specific extensions to routines such as dialogue move modeling and reference resolution, easy substitution of specific semantic representations and associated routines, and clean interfaces to external components for language-understanding (i.e., speech-recognition and parsing) and language-generation, and to domain-specific knowledge sources. The RNDS also allows seamless interaction with a community of devices.

Abstract: Representation-neutral dialogue systems and methods (“RNDS”) are described that include multi-application, multi-device spoken-language dialogue systems based on the information-state update approach. The RNDS includes representation-neutral core components of a dialogue system that provide scripted domain-specific extensions to routines such as dialogue move modeling and reference resolution, easy substitution of specific semantic representations and associated routines, and clean interfaces to external components for language-understanding (i.e., speech-recognition and parsing) and language-generation, and to domain-specific knowledge sources. The RNDS also resolves multi-device dialogue by evaluating and selecting among candidate dialogue moves based on features at multiple levels. Multiple sources of information are combined, multiple speech recognition and parsing hypotheses tested, and multiple device and moves considered to choose the highest scoring hypothesis overall.

Abstract: A system and method to interactively converse with a cognitively overloaded user of a device, includes maintaining a knowledge base of information regarding the device and a domain, organizing the information in at least one of a relational manner and an ontological manner, receiving speech from the user, converting the speech into a word sequence, recognizing a partial proper name in the word sequence, identifying meaning structures from the word sequence using a model of the domain information, adjusting a boundary of the partial proper names to enhance an accuracy of the meaning structures, interpreting the meaning structures in a context of the conversation with the cognitively overloaded user using the knowledge base, selecting a content for a response to the cognitively overloaded user, generating the response based on the selected content, the context of the conversation, and grammatical rules, and synthesizing speech wave forms for the response.

Abstract: A method and system are described to adapt instructions for performing a task by a user, which includes receiving generalized instructions for the task, selecting a content of the generalized instructions based on user-specific knowledge regarding the task, constructing utterances using the selected content, and conveying the utterances to the user.

Abstract: Representation-neutral dialogue systems and methods (“RNDS”) are described that include multi-application, multi-device spoken-language dialogue systems based on the information-state update approach. The RNDS includes representation-neutral core components of a dialogue system that provide scripted domain-specific extensions to routines such as dialogue move modeling and reference resolution, easy substitution of specific semantic representations and associated routines, and clean interfaces to external components for language-understanding (i.e., speech-recognition and parsing) and language-generation, and to domain-specific knowledge sources. The RNDS also resolves multi-device dialogue by evaluating and selecting among candidate dialogue moves based on features at multiple levels. Multiple sources of information are combined, multiple speech recognition and parsing hypotheses tested, and multiple device and moves considered to choose the highest scoring hypothesis overall.

Abstract: Representation-neutral dialogue systems and methods (“RNDS”) are described that include multi-application, multi-device spoken-language dialogue systems based on the information-state update approach. The RNDS includes representation-neutral core components of a dialogue system that provide scripted domain-specific extensions to routines such as dialogue move modeling and reference resolution, easy substitution of specific semantic representations and associated routines, and clean interfaces to external components for language-understanding (i.e., speech-recognition and parsing) and language-generation, and to domain-specific knowledge sources. The RNDS also allows seamless interaction with a community of devices.

Abstract: A system and method to interactively converse with a cognitively overloaded user of a device, includes maintaining a knowledge base of information regarding the device and a domain, organizing the information in at least one of a relational manner and an ontological manner, receiving speech from the user, converting the speech into a word sequence, recognizing a partial proper name in the word sequence, identifying meaning structures from the word sequence using a model of the domain information, adjusting a boundary of the partial proper names to enhance an accuracy of the meaning structures, interpreting the meaning structures in a context of the conversation with the cognitively overloaded user using the knowledge base, selecting a content for a response to the cognitively overloaded user, generating the response based on the selected content, the context of the conversation, and grammatical rules, and synthesizing speech wave forms for the response.

Abstract: A method and system are described to adapt instructions for performing a task by a user, which includes receiving generalized instructions for the task, selecting a content of the generalized instructions based on user-specific knowledge regarding the task, constructing utterances using the selected content, and conveying the utterances to the user.