Abstract: Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data.

Abstract: Systems and methods for handling dual modality communication between at least one user device and at least one server. The modalities comprise audio modalities and mechanical motion modalities. The server may be simultaneously connected to the user device via a data network and a voice network and simultaneously receive audio-based input and mechanical motion-based input.

Abstract: Customer support, and other types of activities in which there is a dialogue between two humans can generate large volumes of conversation records. Automated analysis of these records can provide information about high-level features of, for example, the workings of a customer service department. Analysis of these conversations between a customer and a customer-support agent may also allow identification of customer support activities that can be provided by virtual agents instead of actual human agents. The analysis may evaluate conversations in terms of complexity, duration, and sentiment of the participants. Additionally, the conversations may also be analyzed to identify the existence of selected concepts or keywords. Workflow characteristics, the extent to which the conversation represents a multi-step process intended to accomplish a task, may also be determined for the conversations.

Abstract: Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data.

Abstract: Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data.

Abstract: Customer support, and other types of activities in which there is a dialogue between two humans can generate large volumes of conversation records. Automated analysis of these records can provide information about high-level features of, for example, the workings of a customer service department. Analysis of these conversations between a customer and a customer-support agent may also allow identification of customer support activities that can be provided by virtual agents instead of actual human agents. The analysis may evaluate conversations in terms of complexity, duration, and sentiment of the participants. Additionally, the conversations may also be analyzed to identify the existence of selected concepts or keywords. Workflow characteristics, the extent to which the conversation represents a multi-step process intended to accomplish a task, may also be determined for the conversations.

Abstract: Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data.

Abstract: Customer support, and other types of activities in which there is a dialogue between two humans can generate large volumes of conversation records. Automated analysis of these records can provide information about high-level features of, for example, the workings of a customer service department. Analysis of these conversations between a customer and a customer-support agent may also allow identification of customer support activities that can be provided by virtual agents instead of actual human agents. The analysis may evaluate conversations in terms of complexity, duration, and sentiment of the participants. Additionally, the conversations may also be analyzed to identify the existence of selected concepts or keywords. Workflow characteristics, the extent to which the conversation represents a multi-step process intended to accomplish a task, may also be determined for the conversations.

Abstract: Customer support, and other types of activities in which there is a dialogue between two humans can generate large volumes of conversation records. Automated analysis of these records can provide information about high-level features of, for example, the workings of a customer service department. Analysis of these conversations between a customer and a customer-support agent may also allow identification of customer support activities that can be provided by virtual agents instead of actual human agents. The analysis may evaluate conversations in terms of complexity, duration, and sentiment of the participants. Additionally, the conversations may also be analyzed to identify the existence of selected concepts or keywords. Workflow characteristics, the extent to which the conversation represents a multi-step process intended to accomplish a task, may also be determined for the conversations.

Abstract: A conversation user interface enables users to better understand their interactions with computing devices, particularly when speech input is involved. The conversation user interface conveys a visual representation of a conversation between the computing device, or virtual assistant thereon, and a user. The conversation user interface presents a series of dialog representations that show input from a user (verbal or otherwise) and responses from the device or virtual assistant. Associated with one or more of the dialog representations are one or more graphical elements to convey assumptions made to interpret the user input and derive an associated response. The conversation user interface enables the user to see the assumptions upon which the response was based, and to optionally change the assumption(s). Upon change of an assumption, the conversation GUI is refreshed to present a modified dialog representation of a new response derived from the altered set of assumptions.

Abstract: Customer support, and other types of activities in which there is a dialog between two humans can generate large volumes of conversation records. Automated analysis of these records can provide information about high-level features of, for example, the workings of a customer service department. Analysis of these conversations between a customer and a customer-support agent may also allow identification of customer support activities that can be provided by virtual agents instead of actual human agents. The analysis may evaluate conversations in terms of complexity, duration, and sentiment of the participants. Additionally, the conversations may also be analyzed to identify the existence of selected concepts or keywords. Workflow characteristics, the extent to which the conversation represents a multi-step process intended to accomplish a task, may also be determined for the conversations.

Abstract: Systems and methods for modifying a computer-based speech recognition system. A speech utterance is processed with the computer-based speech recognition system using a set of internal representations, which may comprise parameters for recognizing speech in a speech utterance, such as parameters of an acoustic model and/or a language model. The computer-based speech recognition system may perform a first task in response to the processed speech utterance. The utterance may also be provided to a human who performs a second task based on the utterance. Data indicative of the first task, performed by the computer system, is compared to data indicative of a second task, performed by the human in response to the speech utterance. Based on the comparison, the set of internal representations may be updated or modified to improve the speech recognition performance and capabilities of the speech recognition system.

Abstract: Systems and methods for modifying a computer-based speech recognition system. A speech utterance is processed with the computer-based speech recognition system using a set of internal representations, which may comprise parameters for recognizing speech in a speech utterance, such as parameters of an acoustic model and/or a language model. The computer-based speech recognition system may perform a first task in response to the processed speech utterance. The utterance may also be provided to a human who performs a second task based on the utterance. Data indicative of the first task, performed by the computer system, is compared to data indicative of a second task, performed by the human in response to the speech utterance. Based on the comparison, the set of internal representations may be updated or modified to improve the speech recognition performance and capabilities of the speech recognition system.

Abstract: Systems and methods for modifying a computer-based speech recognition system. A speech utterance is processed with the computer-based speech recognition system using a set of internal representations, which may comprise parameters for recognizing speech in a speech utterance, such as parameters of an acoustic model and/or a language model. The computer-based speech recognition system may perform a first task in response to the processed speech utterance. The utterance may also be provided to a human who performs a second task based on the utterance. Data indicative of the first task, performed by the computer system, is compared to data indicative of a second task, performed by the human in response to the speech utterance. Based on the comparison, the set of internal representations may be updated or modified to improve the speech recognition performance and capabilities of the speech recognition system.

Abstract: A conversation user interface enables users to better understand their interactions with computing devices, particularly when speech input is involved. The conversation user interface conveys a visual representation of a conversation between the computing device, or virtual assistant thereon, and a user. The conversation user interface presents a series of dialog representations that show input from a user (verbal or otherwise) and responses from the device or virtual assistant. Associated with one or more of the dialog representations are one or more graphical elements to convey assumptions made to interpret the user input and derive an associated response. The conversation user interface enables the user to see the assumptions upon which the response was based, and to optionally change the assumption(s). Upon change of an assumption, the conversation GUI is refreshed to present a modified dialog representation of a new response derived from the altered set of assumptions.

Abstract: Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data.

Abstract: A conversation user interface enables users to better understand their interactions with computing devices, particularly when speech input is involved. The conversation user interface conveys a visual representation of a conversation between the computing device, or virtual assistant thereon, and a user. The conversation user interface presents a series of dialog representations that show input from a user (verbal or otherwise) and responses from the device or virtual assistant. Associated with one or more of the dialog representations are one or more graphical elements to convey assumptions made to interpret the user input and derive an associated response. The conversation user interface enables the user to see the assumptions upon which the response was based, and to optionally change the assumption(s). Upon change of an assumption, the conversation GUI is refreshed to present a modified dialog representation of a new response derived from the altered set of assumptions.

Abstract: Data having some similarities and some dissimilarities may be clustered or grouped according to the similarities and dissimilarities. The data may be clustered using agglomerative clustering techniques. The clusters may be used as suggestions for generating groups where a user may demonstrate certain criteria for grouping. The system may learn from the criteria and extrapolate the groupings to readily sort data into appropriate groups. The system may be easily refined as the user gains an understanding of the data.

Abstract: Systems and methods for modifying a computer-based speech recognition system. A speech utterance is processed with the computer-based speech recognition system using a set of internal representations, which may comprise parameters for recognizing speech in a speech utterance, such as parameters of an acoustic model and/or a language model. The computer-based speech recognition system may perform a first task in response to the processed speech utterance. The utterance may also be provided to a human who performs a second task based on the utterance. Data indicative of the first task, performed by the computer system, is compared to data indicative of a second task, performed by the human in response to the speech utterance. Based on the comparison, the set of internal representations may be updated or modified to improve the speech recognition performance and capabilities of the speech recognition system.

Abstract: Systems and methods for modifying a computer-based speech recognition system. A speech utterance is processed with the computer-based speech recognition system using a set of internal representations, which may comprise parameters for recognizing speech in a speech utterance, such as parameters of an acoustic model and/or a language model. The computer-based speech recognition system may perform a first task in response to the processed speech utterance. The utterance may also be provided to a human who performs a second task based on the utterance. Data indicative of the first task, performed by the computer system, is compared to data indicative of a second task, performed by the human in response to the speech utterance. Based on the comparison, the set of internal representations may be updated or modified to improve the speech recognition performance and capabilities of the speech recognition system.