Microsoft Technology Licensing, LLC

Ying Wang

Emmanouil Koukoumidis

Joseph Edwin Johnson, Jr

Hailong Mu

Di Li

Dustin Isaac Abramson

Kati Amanda London

Hudong Wang

Steven G Zittrower

Kelli A Stuckart

Qi Yao

Donald Brinkman

Zhan Chen

Matthew W Schuerman

Oussama Elachqar

Maria Alexandropoulou

Maria A Rangel

Pengfei Hao

Xianchao Wu

Donald Frank Brinkman, Jr

Daniel Massey

Scott A Schwarz

Konstantinos Aisopos

Adam Edward Shirey

Derrick Fu

James Bigbee Garver

Zhiying Zhang

Francis Robert Burianek

Tanasak Huangyutitham

Izi Behar

Sergei Tuterov

The present disclosure provides method and apparatus for providing a response to a user in a session. At least one message associated with a first object may be received in the session, the session being between the user and an electronic conversational agent. An image representation of the first object may be obtained. Emotion information of the first object may be determined based at least on the image representation. A response may be generated based at least on the at least one message and the emotion information. The response may be provided to the user.

Providing a response in a session

Aspects of the present disclosure provide systems and methods for detecting memorable events of a user, storing those memorable events in a user profile, and leveraging those events to the user. In so doing, memorable events with positive emotional weight may be recalled to encourage delightful experiences. Alternatively or additionally, memorable events with negative emotional weight may be recalled to increase user engagement for conversational systems.

Memorable event detection, recording, and exploitation

Systems and methods may be used to process and output information related to a non-speech vocalization, for example from a user attempting to mimic a non-speech sound. A method may include determine a mimic quality value associated with an audio file by comparing a non-speech vocalization to a prerecorded audio file. For example, the method may include determining an edit distance between the non-speech vocalization and the prerecorded audio file. The method may include assigning a mimic quality value to the audio file based on the edit distance. The method may include outputting the mimic quality value.

Audio processing for voice simulated noise effects

Systems and methods for receiving various input data, processing said input data, and utilizing artificial emotional intelligence to analyze said input data to return calculated response stimuli are provided. Various electronic devices may be utilized to acquire input data related to a specific user, a group of users, or environments. This input data, which may comprise tone of voice, facial expressions, social media profiles, and surrounding environmental data, may be compared with past data related to a certain user, group of users, or environment. The systems and methods herein may employ artificial emotional intelligence to evaluate the aggregated data and provide a response stimulus to a user or group of users. The response stimulus may be in the form of uplifting/encouraging music, quotes, pictures, jokes, suggestions, etc. The purpose of providing the response stimuli is to significantly increase the productivity of meetings, conversations, and other interactions across electronic devices.

EQ-digital conversation assistant

Systems and methods are disclosed for conversationally-relevant content requests. In one implementation, a first communication is processed to identify a first content element within the communication. The first content element is stored within a first content repository. A content request is generated based on the first content repository. The content request is provided to the user. A second communication is received from the user in response to the content request. An action is initiated based on the second communication.

Conversationally-relevant content requests

A system provides emotionally and intellectually relevant responses to user queries received by a chatbot instantiated by a computing device. The system also receives user data associated with the user query. The user query is communicated to a first supervised machine learning model to obtain a first plurality of ranked responses. The system also communicates the received user data to a second supervised machine learning model to obtain a first plurality of emotions associated with the user data. The system then determines determine a second plurality of emotions for the first plurality of responses and re-ranks the first plurality of responses based on a comparison of the first plurality of emotions and the second plurality of emotions. The system then selects a response to the user query from the re-ranked responses and outputs the selected response using the chatbot instantiated by the computing device.

Emotional intelligence for a conversational chatbot

Systems and methods are disclosed for response retrieval using communication session vectors. In one implementation, a first communication session is received. The first communication session includes a first communication. The first communication session is encoded as a first vector. A second vector is identified within a defined proximity of the first vector. The second vector represents a second communication session that includes a second communication. The second communication is provided within the first communication session in response to the first communication.

Response retrieval using communication session vectors

Systems and methods for personality consistent automated chatting are provided. The systems and methods track the emotional state of the chat bot, track entity related dispositions of the chat bot and/or track the closeness of the relationship between the chat bot and a user. Accordingly, the systems and methods are able to provide consistent personality and/or points of view by generating replies based on the tracked chat bot emotional state, chat bot entity dispositions, and/or the relationship closeness between the chat bot and the user.

Systems and methods for a personality consistent chat bot

A system implemented on a computer network for delegating tasks within a project to achieve collaboration among resources in a team environment. Components of the computer network permit communication among a project manager and resources working on the project. Task delegations can be performed vertically from the project manager to resources and horizontally among resources. The project manager can monitor the delegations or can allow the resources to control the delegations. Resources can also further define and subdivide tasks. Ownership of tasks and other information about the status of the tasks can be stored and assembled in various useful formats for the project manager.

System and method for delegation in a project management context

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Bellevue, WA, United States of America

Ying Wang