International Business Machines Corporation

Nuance Communications, Inc.

Liam David Comerford

Steve Richard White

Mahesh Viswanathan

David Nahamoo

David Carl Frank

Stephane Herman Maes

Marie Angelopoulos

Jane M Shaw

Ravi F Saraf

Ponani S Gopalakrishnan

Niranjan Mohanlal Patel

Daniel Mark Coffman

Steven E Molis

Edward Adam Epstein

Eric Gung-Hwa Lean

Steven V DeGennaro

Vincent Albert Bourgault

Michael Wayne Mirre

Peter Joseph Spellane

Akhileshwari N Chandra

Stephen Eric Levy

Burn Lewin Lewis

John D Crow

Ali Afzali Ardakani

Robert A Laff

Lawrence I Levy

Leonid Rachevksy

John S Harper

Ali Afzali-Ardakani

John Peter Karidis

Michael Alan Picheny

Michael John Brady

Chengjun Julian Chen

Stephen Joy Boies

Peder Andreas Olsen

Michael Daniel Monkowski

Peter James Brittenham

Peter G Ledermann

Frederick Jelinek

Peter S Zory, Jr

Hamed A Ellozy

Catalina M Danis

Barry C Goldstein

Susan L Spraragen

Thomas Allan Corbi

Satya Dharanipragada

Jacob Paul Ukelson

Vernon E Shrauger

Joseph J Laibinis

John D Gould

Paul Derek Fernhout

William Dennis Strohm

Ashileshwari N Chandra

A method and system for teaching an object of a deictic reference to a machine. A processor of the machine teaches the object of the deictic reference to the machine which results in the machine learning the object. The teaching includes: the processor finds an item in a region indicated by a physical pointing gesture, by the user, that points to the object; the processor shines a laser light on the item and in response, the processor receives a negative spoken indication from the user that the item shined on by the laser light is not the object; in response to the negative spoken indication from the user, the processor interacts with the user in an iterative procedure wherein the machine learns the object in a final iteration of the procedure. The processor stores the learned object in a storage repository.

User-guided teaching an object of a deictic reference to a machine

A machine, system and method for user-guided teaching of deictic references and referent objects of deictic references to a conversational system. The machine includes a system bus for communicating data and control signals received from the conversational system to the computer system, a data and control bus for connecting devices and sensors in the machine, a bridge module for connecting the data and control bus to the system bus, respective machine subsystems coupled to the data and control bus, the respective machine subsystems having a respective user interface for receiving a deictic reference from a user, a memory coupled to the system bus for storing deictic references and objects of the deictic references learned by the conversational system and a central processing unit coupled to the system bus for executing the deictic references with respect to the objects of the deictic references learned.

Machine, system and method for user-guided teaching of deictic references and referent objects of deictic references to a conversational command and control system

A machine, system and method for user-guided teaching and modifications of voice commands and actions to be executed by a conversational learning system. The machine includes a system bus for communicating data and control signals received from the conversational learning system to a computer system, a vehicle data and control bus for connecting devices and sensors in the machine, a bridge module for connecting the vehicle data and control bus to the system bus, machine subsystems coupled to the vehicle data and control bus having a respective user interface for receiving a voice command or input signal from a user, a memory coupled to the system bus for storing action command sequences learned for a new voice command and a processing unit coupled to the system bus for automatically executing the action command sequences learned when the new voice command is spoken.

Machine, system and method for user-guided teaching and modifying of voice commands and actions executed by a conversational learning system

Techniques are disclosed for overcoming errors in speech recognition systems. For example, a technique for processing acoustic data in accordance with a speech recognition system comprises the following steps/operations. Acoustic data is obtained in association with the speech recognition system. The acoustic data is recorded using a combination of a first buffer area and a second buffer area, such that the recording of the acoustic data using the combination of the two buffer areas at least substantially minimizes one or more truncation errors associated with operation of the speech recognition system.

Methods and apparatus for buffering data for use in accordance with a speech recognition system

Computing devices are disclosed which include a digitizer and a display, wherein the digitizer and the display are mounted on opposite surfaces of the device. For example, a computing device such as portable hand held device includes a display mounted on a front surface of the device and a digitizer is mounted on a rear surface of the device, wherein the digitizer receives a pointing action to be displayed on the display and wherein locations on a digitizer surface are mapped to locations on the display surface so that a user may easily indicate locations or objects on a screen without obscuring the user's view of the screen.

Interactive display device

Principles of the present invention provide techniques for automatically navigating through a telephone menu system. Illustrative techniques of the invention permit a user of a telephone menu system (e.g., IVR system) to record operations needed to navigate to a particular point in a menu tree of the telephone menu system and to either gather information or perform a transaction at that menu point. The information or transaction status may be reported to the user. Part of the act of training may be the association of a command with the navigation operations, after which the automated telephone menu navigation system of the invention will repeat the procedure, in correct synchrony with the telephone menu service, when the user says the command. In addition, the techniques of the invention detect failure or loss of synchronization or changes in the telephone menu.

Method and apparatus for automatic telephone menu navigation

A method for conversational computing includes executing code embodying a conversational virtual machine, registering a plurality of input/output resources with a conversational kernel, providing an interface between a plurality of active applications and the conversational kernel processing input/output data, receiving input queries and input events of a multi-modal dialog across a plurality of user interface modalities of the plurality of active applications, generating output messages and output events of the multi-modal dialog in connection with the plurality of active applications, managing, by the conversational kernel, a context stack associated with the plurality of active applications and the multi-modal dialog to transform the input queries into application calls for the plurality of active applications and convert the output messages into speech, wherein the context stack accumulates a context of each of the plurality of active applications.

Conversational computing via conversational virtual machine

A conversational computing system that provides a universal coordinated multi-modal conversational user interface (CUI)across a plurality of conversationally aware applications () (i.e., applications that 'speak' conversational protocols) and conventional applications (). The conversationally aware applications () communicate with a conversational kernel () via conversational application APIs (). The conversational kernelcontrols the dialog across applications and devices (local and networked) on the basis of their registered conversational capabilities and requirements and provides a unified conversational user interface and conversational services and behaviors. The conversational computing system may be built on top of a conventional operating system and APIs () and conventional device hardware (). The conversational kernel () handles all I/O processing and controls conversational engines (). The conversational kernel () converts voice requests into queries and converts outputs and results into spoken messages using conversational engines () and conversational arguments (). The conversational application API () conveys all the information for the conversational kernel () to transform queries into application calls and conversely convert output into speech, appropriately sorted before being provided to the user.

This invention is concerned with an electrically conductive polymer blend composition which is a liquid compatible blend, comprising a doped product formed form blending a first solution comprising a Lewis base electrically conductive polymer in undoped form in a first organic solvent with a second solution comprising a Lewis acid polymer dopant in a second organic solvent, wherein said Lewis acid polymer dopant dopes said Lewis base electrically conductive polymer in undoped form to obtain said electrically conductive polymer blend, the resulting doped conductive product being soluble in the combination of said first and said second organic solvents and mixable at the molecular level.

Electrically conductive polymeric materials and use thereof

A conversational computing system that provides a universal coordinated multi-modal conversational user interface (CUI) () across a plurality of conversationally aware applications () (i.e., applications that 'speak' conversational protocols) and conventional applications (). The conversationally aware maps, applications () communicate with a conversational kernel () via conversational application APIs (). The conversational kernel () controls the dialog across applications and devices (local and networked) on the basis of their registered conversational capabilities and requirements and provides a unified conversational user interface and conversational services and behaviors. The conversational computing system may be built on top of a conventional operating system and APIs () and conventional device hardware (). The conversational kernel () handles all I/O processing and controls conversational engines (). The conversational kernel () converts voice requests into queries and converts outputs and results into spoken messages using conversational engines () and conversational arguments (). The conversational application API () conveys all the information for the conversational kernel () to transform queries into application calls and conversely convert output into speech, appropriately sorted before being provided to the user.

A method for managing spoken language interface data structures and collections of user interface service engines in a spoken language dialog manager in a personal speech assistant. Interfaces, designed as part of applications, may by these methods be added to or removed from the set of such interfaces used by a dialog manager. Interface service engines, required by new applications, but not already present in the dialog manager, may be made available to the new and subsequently added applications.

Methods and apparatus for contingent transfer and execution of spoken language interfaces

A Personal Speech Assistant (PSA) is a computing apparatus which provides a spoken language interface to another apparatus to which it is attached by supporting execution of a conversational dialog manager and its supporting service engines. In operation, a PSA is connected to a device which provides some service to a user. Any &ldquo;appliance&rdquo; is a candidate for enhancement with the PSA. Devices such as, for example, video cassette recorders (VCRs) or Personal Digital Assistants (PDAs), which offer rich, but frequently difficult interfaces, may be made more useful by the integration of a PSA according to the invention. It is a preferred feature of a dialog manager used by the PSA that the user interface properties, in terms of the vocabulary the device understands, the informative prompts it provides, and other aspects of its conversational behavior, are all easily modified to correspond to the preferences or limitations of the user.

Personal speech assistant supporting a dialog manager

Growing community of inventors

Carmel, NY, United States of America

Liam David Comerford