Facebook, Inc.

Nuance Communications, Inc.

Alexandre Lebrun

Laurent Nicolas Landowski

Willy Blandin

Jason Edward Weston

Robert Franklin Daniel

Jeremy Harrison Goldberg

Yoram Talmor

Martin Jean Raison

Deniz Demir

Antoine Bordes

Matthew Logan Schmid

Allan Grinshtein

Roee Vulkan

Callixte Cauchois

An email-like user interface displays a list of user logs determined based on user-specified list criteria to user logs received in a natural language (NL) training environment. The list comprise a subset of the received user logs in order to minimize the number of actions required to configure and train the NL configuration system in a semi-supervised manner, thereby improving the quality and accuracy of NL configuration system. To determine a list of user logs relevant for training the user logs can be filtered, sorted, grouped and searched within the email-like user interface. A training interface to a network of instances that comprises a plurality of NL configuration systems leverages a crowd-sourcing community of developers in order to efficiently create a customizable NL configuration system.

Email-like user interface for training natural language systems

A crowdsourcing based community platform includes a natural language configuration system that predicts a user's desired function call based on a natural language input (speech or text). The system provides a collaboration platform to configure and optimize quickly natural language systems to leverage the work and data of other developers, thus minimizing the time and data required to improve the quality and accuracy of one single system and providing a network effect to reach quickly critical mass of data. An application developer can provide training data for training a model specific to the developer's application. The developer can also obtain training data by forking one or more other applications so that the training data provided for the forked applications is used to train the model for the developer's application.

Crowd sourced based training for natural language interface systems

Techniques to response to user requests using natural-language machine learning based on example conversations are described. In one embodiment, an apparatus may comprise a bot application interface component operative to receive an example-interaction repository, the example-interaction repository comprising a plurality of example user-to-bot interactions; and an interaction processing component operative to submit the example-interaction repository to a natural-language machine learning component; receive a sequence model from the natural-language machine learning component in response to submitting the example-interaction repository; and perform a user-to-bot conversation based on the sequence model. Other embodiments are described and claimed.

Techniques to respond to user requests using natural-language machine learning based on example conversations

Techniques to predictively respond to user requests using natural language processing are described. In one embodiment, an apparatus may comprise a client communication component operative to receive a user service request from a user client; an interaction processing component operative to submit the user service request to a memory-based natural language processing component; generate a series of user interaction exchanges with the user client based on output from the memory-based natural language processing component, wherein the series of user interaction exchanges are represented in a memory component of the memory-based natural language processing component; and receive one or more operator instructions for the performance of the user service request from the memory-based natural language processing component; and a user interface component operative to display the one or more operator instructions in an operator console. Other embodiments are described and claimed.

Techniques to predictively respond to user requests using natural language processing

The present disclosure relates to systems, methods, and devices for providing personal assistant service via messaging. In particular, one or more embodiments receive a request for personal assistant service in an electronic message from a user, assign the request to an agent, determine an intent of the request, and configure an agent user interface to include one or more options in accordance with the determined intent of the request.

Providing personal assistant service via messaging

Systems, methods, and non-transitory computer-readable media can acquire an incoming message via a communication system. Access to a preconfigured message template can be provided. A command to generate an outgoing message based on the preconfigured message template can be acquired. The outgoing message generated based on the preconfigured message template can be transmitted via the communication system. The outgoing message can be transmitted as a response to the incoming message.

Systems and methods for providing messages based on preconfigured messages templates

A Live Chat (LC) system, apparatus, and method combine virtual agent (VA) and LC in a manner enabling a user to receive resolution of a contact request at a reduced cost and enhancing both the user and LC operator's experience. VAs may source response content for easier or more frequent user queries, whereas VAs may transfer a user query to an LC operator whenever a user query becomes too complicated, enabling the LC to source the response content. From the user's point of view, escalation from the VA to the LC operator happens in the very same user screen view used to contact the VA, enhancing the user's experience by providing seamless escalation. Conversation history between the user and the VA or formatted data related thereto may be provided to the LC operator, expediting the LC operator's response time and reducing a number of interactions needed to resolve the user's query.

Growing community of inventors

Palo Alto, CA, United States of America

Alexandre Lebrun