Nuance Communications, Inc.

Sense Labs, Inc.

International Business Machines Corporation

Scansoft, Inc.

Jeffrey N Marcus

Michael Stuart Phillips

Christopher M Micali

Ryan T Houlette

Amy E Ulug

David Andrew Mauro

Real Tremblay

Jerome Tremblay

Charles H Rutledge

Jean-Francois Fortier

Francois Berleur

Ashish Verma

Kenneth W Church

Shajith I Mohamed

Prasanta Kumar Ghosh

William Bridges Smith, Jr

Identification of an intent of a conversation can be useful for real-time or post-processing purposes. According to example embodiments, a method, and corresponding apparatus of identifying at least one intent-bearing utterance in a conversation, comprises determining at least one feature for each utterance among a subset of utterances of the conversation; classifying each utterance among the subset of utterances, using a classifier, as an intent classification or a non-intent classification based at least in part on a subset of the at least one determined feature; and selecting at least one utterance, with intent classification, as an intent-bearing utterance based at least in part on classification results by the classifier. Through identification of an intent bearing utterance, a call center for example, can provide improved service for callers through, for example, more effective directing of a call to a live agent.

System and method for identification of intent segment(s) in caller-agent conversations

Electrical usage of devices in a building may be monitored to provide information about the operation of the devices to a user. The information communicated to a user may include historical information that is retrieved from a server and real-time information that is received from a power monitoring device. The historical information may be transmitted to the user device over a first network connection where the historical information is retrieved using an identifier received from the user device. The real-time information may be received from a power monitoring device and transmitted to the user device over a second network connection.

Communication of historical and real-time information about devices in a building

Selecting a grammar for use in a machine question-answering system, such as a Natural Language Understanding System, can be difficult for non-experts in such grammars. A tool, according to an example embodiment, can compare annotations of sample sentences, performed correctly by a human, the annotations having intents and mentions, against annotations performed by multiple grammars. Each grammar can be scored, and the system can select the best scored grammar for the user. In one embodiment, a method of selecting a grammar includes comparing manually-generated annotations against machine-generated annotations as a function of a given grammar among multiple grammars. The method can further include applying scores to the machine-generated annotations that are a function of weightings of the intents and mentions. The method can additionally include recommending whether to employ the given grammar based on the scores.

Method and apparatus for selecting among competing models in a tool for building natural language understanding models

Designing a natural language understanding (NLU) model for an application from scratch can be difficult for non-experts. A system can simplify the design process by providing an interface allowing a designer to input example usage sentences and build an NLU model based on presented matches to those example sentences. In one embodiment, a method for initializing a workspace for building an NLU system includes parsing a sample sentence to select at least one candidate stub grammar from among multiple candidate stub grammars. The method can include presenting, to a user, respective representations of the candidate stub grammars selected by the parsing of the sample sentence. The method can include enabling the user to choose one of the respective representations of the candidate stub grammars. The method can include adding to the workspace a stub grammar corresponding to the representation of the candidate stub grammar chosen by the user.

Initializing a workspace for building a natural language understanding system

Electrical usage of devices in a building may be monitored to provide information about the operation of the devices to a user. The information communicated to a user may include historical information and real-time information. In determining the real-time information, it may be desired to provide the information quickly and use a smaller set of features that are available more quickly. In determining the historical information, it may be desired to provide more accurate information and use a larger set of features. A device may compute real-time information and historical information using different sets of features.

Determining information about devices in a building using different sets of features

A system and method for providing an easy-to-use interface for verifying semantic tags in a steering application in order to generate a natural language grammar. The method includes obtaining user responses to open-ended steering questions, automatically grouping the user responses into groups based on their semantic meaning, and automatically assigning preliminary semantic tags to each of the groups. The user interface enables the user to validate the content of the groups to ensure that all responses within a group have the same semantic meaning and to add or edit semantic tags associated with the groups. The system and method may be applied to interactive voice response (IVR) systems, as well as customer service systems that can communicate with a user via a text or written interface.

Call steering data tagging interface with automatic semantic clustering

Methods and systems for setting selected automatic speech recognition parameters are described. A data set associated with operation of a speech recognition application is defined and includes: i. recognition states characterizing the semantic progression of a user interaction with the speech recognition application, and ii. recognition outcomes associated with each recognition state. For a selected user interaction with the speech recognition application, an application cost function is defined that characterizes an estimated cost of the user interaction for each recognition outcome. For one or more system performance parameters indirectly related to the user interaction, the parameters are set to values which optimize the cost of the user interaction over the recognition states.

Method and apparatus for setting selected recognition parameters to minimize an application cost function

An interactive user interface is described for setting confidence score thresholds in a language processing system. There is a display of a first system confidence score curve characterizing system recognition performance associated with a high confidence threshold, a first user control for adjusting the high confidence threshold and an associated visual display highlighting a point on the first system confidence score curve representing the selected high confidence threshold, a display of a second system confidence score curve characterizing system recognition performance associated with a low confidence threshold, and a second user control for adjusting the low confidence threshold and an associated visual display highlighting a point on the second system confidence score curve representing the selected low confidence threshold. The operation of the second user control is constrained to require that the low confidence threshold must be less than or equal to the high confidence threshold.

Interface for setting confidence thresholds for automatic speech recognition and call steering applications

Method and system are provided for performing random confirmation in a speech system. When a speech recognition result is received with an associated confidence score indicating a level of confidence with respect to the speech recognition result, a confirmation decision is made in terms of whether a confirmation is to be carried out based on the confidence score. The confirmation decision may be made in a random confirmation mode. A confirmation may be performed when the confirmation decision is to carry out a confirmation on the speech recognition result.

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Newton, MA, United States of America

Jeffrey N Marcus