AT&T Intellectual Property I, LP

Interactions Corporation

Nuance Communications, Inc.

At+t Corp.

Michael J Johnston

Srinivas Bangalore

Patrick John Ehlen

David Crawford Gibbon

Behzad Shahraray

Mazin E Gilbert

Zhu Liu

Ethan Selfridge

Bernard S Renger

Junlan Feng

Taniya Mishra

Dan Melamed

Andrea Basso

Hisao M Chang

Jay Gordon Wilpon

Harry Edward Blanchard

Brant Jameson Vasilieff

Derya Ozkan

Charles Douglas Blewett

Seyed Eman Mahmoodi

Ebrahim Kazemzadeh

Helen Hastie

Linda Ann Roberts

Horst Juergen Schroeter

Patrick Guy Haffner

David James Thomson

Alicia Abella

Vivek Kumar Rangarajan Sridhar

Jay Henry Lieske, Jr

Yoryos Yeracaris

Svetlana Stoyanchev

Hyuckchul Jung

James Dreher

John Leonard

Robert Lifgren

Phillip Gray

Remi Zajac

Minhua Chen

Badrinath Jayakumar

Sirnivas Bangalore

Structure of conversations between users and agents and/or systems is discovered and interactively displayed to analysts, thereby better supporting development of automated conversation handling systems for different domains. A corpus of prior dialogs of users with agents (without preexisting semantic labels indicating purposes for different parts of the dialogs) is taken as input, and embeddings are generated for textual units (e.g., rounds) of the dialogs. The embeddings are used to cluster the textual units, and the clusters and their relationships are visualized within a user interface that analysts may use to explore and fine-tune the structure of the conversations.

Induction of dialog flow within a conversational natural language system

A virtual assistant system for communicating with customers uses human intelligence to correct any errors in the system AI, while collecting data for machine learning and future improvements for more automation. The system may use a modular design, with separate components for carrying out different system functions and sub-functions, and with frameworks for selecting the component best able to respond to a given customer conversation.

Virtual assistant architecture for natural language understanding in a customer service system

A virtual assistant system for communicating with customers uses human intelligence to correct any errors in the system AI, while collecting data for machine learning and future improvements for more automation. The system may use a modular design, with separate components for carrying out different system functions and sub-functions, and with frameworks for selecting the component best able to respond to a given customer conversation. The system may have agent assistance functionality that uses natural language processing to identity concepts in a user conversation and to illustrate that concepts within a graphical user interface of a human agent so that the human agent can more accurately and more rapidly assist the user in accomplishing the user's conversational objectives.

Intelligent agent assistant for natural language understanding in a customer service system

Within an environment in which users converse at least partly with human agents to accomplish a desired task, a server assists the agents by identifying workflows that are most applicable to the current conversation. Workflow selection functionality identifies one or more candidate workflows based on techniques such as user intent inference, conversation state tracking, or search, according to various embodiments. The identified candidate workflows are either automatically selected on behalf of the agent, or are presented to the agent for manual selection.

Automated workflow selection for agent within an electronic conversation system

A computer-implemented method for providing agent assisted transcriptions of user utterances. A user utterance is received in response to a prompt provided to the user at a remote client device. An automatic transcription is generated from the utterance using a language model based upon an application or context, and presented to a human agent. The agent reviews the transcription and may replace at least a portion of the transcription with a corrected transcription. As the agent inputs the corrected transcription, accelerants are presented to the user comprising suggested texted to be inputted. The accelerants may be determined based upon an agent input, an application or context of the transcription, the portion of the transcription being replaced, or any combination thereof. In some cases, the user provides textual input, to which the agent transcribes an intent associated with the input with the aid of one or more accelerants.

Accelerating agent performance in a natural language processing system

Systems and methods are disclosed for determining a move driven by an interaction. In some embodiments, a processor determines an operational state of an interaction with a user based on parameter values of a data structure. The processor identifies a plurality of candidate moves for changing the operational state by determining a domain in which the interaction is occurring, retrieving a set of candidate moves that correspond to the domain from a knowledge graph, and adding the set to the plurality of candidate moves. The processor encodes input of the user received during the interaction into encoded terms, and determines a move for changing the operational state based on a match of the encoded terms to the set of candidate moves. The processor updates the parameter values of the data structure based on the move to reflect a current operational state led to by the move.

Dialog management using knowledge graph-driven information state in a natural language processing system

Disclosed herein are systems, methods, and non-transitory computer-readable storage media for processing multimodal input. A system configured to practice the method continuously monitors an audio stream associated with a gesture input stream, and detects a speech event in the audio stream. Then the system identifies a temporal window associated with a time of the speech event, and analyzes data from the gesture input stream within the temporal window to identify a gesture event. The system processes the speech event and the gesture event to produce a multimodal command. The gesture in the gesture input stream can be directed to a display, but is remote from the display. The system can analyze the data from the gesture input stream by calculating an average of gesture coordinates within the temporal window.

System and method for continuous multimodal speech and gesture interaction

Extracting, from user activity data, quantitative attributes and qualitative attributes collected for users having user profiles. The quantitative attributes and the qualitative attributes are extracted during a specified time period determined before the user activity data is collected. Values for the quantitative attributes and the qualitative attributes are plotted, and subsets of the user profiles are clustered into separate group of users based on the plotted values. Delivering a product related content to the groups of users based on the clustering.

User profile and its location in a clustered profile landscape

Methods, systems, devices, and media for creating a plan through multimodal search inputs are provided. A first search request comprises a first input received via a first input mode and a second input received via a different second input mode. The second input identifies a geographic area. First search results are displayed based on the first search request and corresponding to the geographic area. Each of the first search results is associated with a geographic location. A selection of one of the first search results is received and added to a plan. A second search request is received after the selection, and second search results are displayed in response to the second search request. The second search results are based on the second search request and correspond to the geographic location of the selected one of the first search results.

System and method for creating and sharing plans through multimodal dialog

Disclosed herein are systems, methods, and computer-readable storage media for improving speech recognition accuracy using textual context. The method includes retrieving a recorded utterance, capturing text from a device display associated with the spoken dialog and viewed by one party to the recorded utterance, and identifying words in the captured text that are relevant to the recorded utterance. The method further includes adding the identified words to a dynamic language model, and recognizing the recorded utterance using the dynamic language model. The recorded utterance can be a spoken dialog. A time stamp can be assigned to each identified word. The method can include adding identified words to and/or removing identified words from the dynamic language model based on their respective time stamps. A screen scraper can capture text from the device display associated with the recorded utterance. The device display can contain customer service data.

System and method for improving speech recognition accuracy using textual context

A system, method and computer-readable storage devices are disclosed for multi-modal interactions with a system via a long-touch gesture on a touch-sensitive display. A system operating per this disclosure can receive a multi-modal input comprising speech and a touch on a display, wherein the speech comprises a pronoun. When the touch on the display has a duration longer than a threshold duration, the system can identify an object within a threshold distance of the touch, associate the object with the pronoun in the speech, to yield an association, and perform an action based on the speech and the association.

System and method for initiating multi-modal speech recognition using a long-touch gesture

A speech interpretation module interprets the audio of user utterances as sequences of words. To do so, the speech interpretation module parameterizes a literal corpus of expressions by identifying portions of the expressions that correspond to known concepts, and generates a parameterized statistical model from the resulting parameterized corpus. When speech is received the speech interpretation module uses a hierarchical speech recognition decoder that uses both the parameterized statistical model and language sub-models that specify how to recognize a sequence of words. The separation of the language sub-models from the statistical model beneficially reduces the size of the literal corpus needed for training, reduces the size of the resulting model, provides more fine-grained interpretation of concepts, and improves computational efficiency by allowing run-time incorporation of the language sub-models.

Hierarchical speech recognition decoder

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Hoboken, NJ, United States of America

Michael J Johnston