Microsoft Technology Licensing, LLC

Semantic Machines, Inc.

David Leo Wright Hall

Daniel Louis Klein

Jesse Daniel Eskes Rusak

Jordan Rian Cohen

Daniel Lawrence Roth

Adam David Pauls

Percy Shuo Liang

Laurence Steven Gillick

Jason Andrew Wolfe

Jacob Daniel Andreas

Pengyu Chen

Joshua James Clausman

David Ernesto Heekin Burkett

Michael Jack Newman

Jayant Sivarama Krishnamurthy

Steven Andrew Wegmann

Andrew Volpe

Taylor Darwin Berg-Kirkpatrick

Alexander J Kolmykov-Zotov

John P Bufe, Iii

Alan Xinyu Guo

Yuchen Zhang

Jean Crawford

Linda Kathleen Arsenault

Charles Clayton Wooters

Jordan R Cohen

A method of adapting a computerized assistant program to satisfy an updated constraint. The method comprises maintaining a dialogue history including a first utterance that indicates an initial constraint. The method further comprises receiving a second utterance indicating a new constraint that conflicts with the initial constraint. The method further comprises recognizing a revision function statement parametrized by a reference to an initial computerized assistant program configured to satisfy the initial constraint, and a reference to the new constraint. The method further comprises executing instructions derived from the revision function statement to return a revised computerized assistant program that is configured to satisfy the new constraint.

Updating constraints for computerized assistant actions

A method of extending a conversational computing interface comprises obtaining data-flow traces indicating computational steps that occur based on execution of a nonnative skill implemented in a nonnative programming language of the conversational computing interface. A native computer-executable plan is automatically generated. The generated native computer-executable plan represents the data-flow traces in a native programming language of the conversational computing interface. The conversational computing interface is automatically trained based on the native computer-executable plan and a conversational event associated with the native computer-executable plan.

Training conversational computing interfaces with traced programs

A method for training a dialogue learning model includes presenting, via a user interface of a computing device, an utterance and a list of actions based on the utterance. A selection of an action from the list of actions is received via the user interface. A designated span of the utterance is received via the user interface. The selected action and the designated span of the utterance is provided to a computing system for training the dialogue learning model.

Expandable dialogue system

A method of leveraging a dialogue history of a conversational computing interface to execute an updated dialogue plan. The method comprises maintaining an annotated dialogue history of the conversational computing interface. The annotated dialogue history includes a plurality of traced steps defining a data-flow including input data used to execute a context-dependent operation and output data recorded from a previous execution of the context-dependent operation. The method further comprises recognizing an updated dialogue plan including a prefix of executable steps and an updated executable step following the prefix. The method further comprises automatically computer-recognizing that the prefix of executable steps of the updated dialogue plan matches a corresponding prefix of traced steps in the annotated dialogue history. The method further comprises re-using the data-flow from the prefix of traced steps in the annotated dialogue history to automatically determine input data of the updated executable step.

Leveraging dialogue history in updated dialogue

A method includes receiving an utterance at a computerized automated assistant system, and detecting, via a date/time constraint module of the computerized automated assistant system, one or more constraints in the utterance associated with a date or time. The utterance is associated with a domain. The method further comprises generating, via the date/time constraint module, a periodic set for each of the one or more constraints associated with the date or time, and combining, via the date/time constraint module, the one or more periodic sets. The method further comprises processing, via a dialogue manager module of the computerized automated assistant system, the combined periodic sets to determine an action, and executing the action at the computerized automated assistant system.

Inference on date time constraint expressions

An automated natural dialogue system provides a combination of structure and flexibility to allow for ease of annotation of dialogues as well as learning and expanding the capabilities of the dialogue system based on natural language interactions.

Robust expandable dialogue system

A method of extending a conversational computing interface. The method comprises executing a nonnative skill implemented in a nonnative programming language of the conversational computing interface. The method further comprises automatically computer-tracing computer operations performed by the nonnative skill during such execution. The method further comprises automatically computer-generating a native computer-executable plan representing the traced computer operations in a native programming language of the conversational computing interface.

Converting nonnative skills for conversational computing interfaces

A system that provides a sharable language interface for implementing automated assistants in new domains and applications. A dialogue assistant that is trained in a first domain can receive a specification in a second domain. The specification can include language structure data such as schemas, recognizers, resolvers, constraints and invariants, actions, language hints, generation template, and other data. The specification data is applied to the automated assistant to enable the automated assistant to provide interactive dialogue with a user in a second domain associated with the received specification. In some instances, portions of the specification may be automatically mapped to portions of the first domain. By having the ability to learn new domains and applications through receipt of objects and properties rather than retooling the interface entirely, the present system is much more efficient at learning how to provide interactive dialogue in new domains than previous systems.

Developer platform for providing automated assistant in new domains

A method of automatically delineating turns in a multi-turn dialogue between a user and a conversational computing interface. Audio data encoding speech of the user in the multi-turn dialogue is received. The audio data is analyzed to recognize, in the speech of the user, an utterance followed by a silence. The utterance is recognized as a last utterance in a turn of the multi-turn dialogue responsive to the silence exceeding a context-dependent duration dynamically updated based on a conversation history of the multi-turn dialogue and features of the received audio, wherein the conversation history includes one or more previous turns of the multi-turn dialogue taken by the user and one or more previous turns of the multi-turn dialogue taken by the conversational computing interface.

Automatic turn delineation in multi-turn dialogue

A method comprising recognizing a user utterance including an ambiguity. The method further comprises using a previously-trained code-generation machine to produce, from the user utterance, a data-flow program including a search-history function. The search-history function is configured to select a highest-confidence disambiguating concept from one or more candidate concepts stored in a context-specific dialogue history.

Ambiguity resolution with dialogue search history

A system that allows non-engineers administrators, without programming, machine language, or artificial intelligence system knowledge, to expand the capabilities of a dialogue system. The dialogue system may have a knowledge system, user interface, and learning model. A user interface allows non-engineers to utilize the knowledge system, defined by a small set of primitives and a simple language, to annotate a user utterance. The annotation may include selecting actions to take based on the utterance and subsequent actions and configuring associations. A dialogue state is continuously updated and provided to the user as the actions and associations take place. Rules are generated based on the actions, associations and dialogue state that allows for computing a wide range of results.

A method for configuring an automated dialogue system uses traces of interactions via a graphical user interface (GUI) for an application. Each trace includes interactions in the context of a plurality of presentations of the GUI. Elements of one or more presentations of the GUI are identified, and templates are associated with portions of the trace. Each template has one or more defined inputs and a defined output. For each template of the plurality of templates, the portions of the traces are processed to automatically configure the template by specifying a procedure for providing values of inputs to the template via the GUI and obtaining a value of an output. The automated dialogue system is configured with the configured templates, thereby avoiding manual configuration of the dialogue system.

Automatic spoken dialogue script discovery

A method for generating training data for training a natural language processing system comprises loading, into a computer memory, a computer-readable transcript representing an ordered sequence of one or more dialogue events. The method further comprises acquiring a computer-readable command describing an exemplary ordered subsequence of one or more dialogue events from the computer-readable transcript. The method further comprises re-parametrizing the computer-readable command with an alternative semantic parameter. The method further comprises generating an alternative ordered subsequence of one or more dialogue events based on the re-parametrized computer-readable command. The method further comprises outputting, to a data store, an alternative computer-readable transcript including the alternative ordered subsequence of one or more dialogue events, the alternative computer-readable transcript having a predetermined format usable to train the computerized assistant.

Training natural language system with generated dialogues

A method for generating a dialogue event in a natural language processing system comprises loading, into a computer memory, a computer-readable seed command describing an ordered sequence of two or more top-level dialogue events. A dialogue event includes a client utterance or a computerized assistant response. The seed command includes one or more sub-commands, each sub-command corresponding to a portion of the ordered sequence of two or more top-level dialogue events, and the focal sub-command of the one or more sub-commands being parametrized by a seed semantic parameter. The method further comprises re-parametrizing the focal sub-command by outputting a plurality of different re-parametrized focal sub-commands wherein, in each re-parametrized focal sub-command, the seed semantic parameter is replaced by one of a plurality of different synthetic semantic parameters. The method further comprises, for each of the plurality of different synthetic semantic parameters: saving a corresponding re-parametrized focal sub-command.

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Berkeley, CA, United States of America

David Leo Wright Hall