Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Google Inc.

Joseph Edwin Johnson, Jr

Emmanouil Koukoumidis

Dustin Isaac Abramson

Hailong Mu

Nathan Eugene Masters

Shiblee Imtiaz Hasan

Ying Wang

Donald Brinkman

Benjamin Aldouby Schwartz

Kati Amanda London

Matthew W Schuerman

Di Li

Hudong Wang

Steven G Zittrower

Michael Jarrett

Zhan Chen

Vishal Kapoor

Oussama Elachqar

Kelli A Stuckart

Pengfei Hao

Michael Brian Stoops

Ronald L Thompson

Don Edward Diekneite

Kevin Michael Munoz

Brandon Scott Jones

Maria A Rangel

David C Lee

Charles Andrew Turner

Donald Frank Brinkman, Jr

Jason J Joyce

Ajith Kumar

Wen-Sheng Chu

Maria Alexandropoulou

Daniel Vann

Nitin Sood

Kuntal Sengupta

Saurangshu Pandey

Zhiying Zhang

Manjeet Chayel Mohinder Singh

Chris Hluchan

Cem Kemal Hamami

Piotr Kulaga

Zachary Iqbal

Dan Vann

William Pfeifer

Youssef Hammad

Derrick Fu

Mohamed Mostafa Ibrahim Elshenawy

James Bigbee Garver

JaeHa Oh

A method includes receiving data indicative of an image of a face of an unknown user of the computing device while the computing device is in a reduced access mode locked state. The method also includes determining whether the unknown user is the known user by at least comparing the image of the face of the unknown user to one or more images of a plurality of images of a face of a known user of the computing device. The method further includes setting the computing device to an increased access mode in response to determining that the unknown user is the known user.

User authentication using pose-based facial recognition

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

Techniques of wireless charging involve a charging base configured to predict when the user is about to charge a device based on a state of the device. For example, the state of the device may be defined by inertial measurement units (IMUs). The charging base then activates in response to predicting that the user intends to charge the device. In some implementations, the prediction is based on a machine learning (ML) engine trained to classify a movement of a device by a user as being about to charge or not being about to charge. If a movement of a device is classified as being about to charge, the charging base activates before the device is detected by the charging base.

Predictive wireless charging of electronic devices

To provide navigation directions for a scenic route, a machine learning model is trained using (i) characteristics of road segments that have been assigned a scenic metric and (ii) the scenic metrics for the road segments. In response to a request for navigation directions, a set of candidate routes for navigating from the starting location to the destination location is identified. Then for each road segment within each candidate route, characteristics of the road segment are applied to the machine learning model to generate the scenic metric for the road segment. A route is then selected from the set of candidate routes based at least in part on the scenic metrics of the road segments within the route. A set of navigation directions is provided for presentation on a client device for navigating from the starting location to the destination location via the selected route.

Scenic route-based navigation directions

Among other things, embodiments of the present disclosure may be used to help train speech recognizers for improving generalized voice experience quality in a chat bot system. In some embodiments, the system provides users with games to play to increase user engagement with the chat bot system.

Voice synthesized participatory rhyming chat bot

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 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

Examples of the present disclosure describe systems and methods of creating a conversational chat bot of a specific person. In aspects, social data (e.g., images, voice data, social media posts, electronic messages, written letters, etc.) about the specific person may be accessed. The social data may be used to create or modify a special index in the theme of the specific person's personality. The special index may be used to train a chat bot to converse in the personality of the specific person. During such conversations, one or more conversational data stores and/or APIs may be used to reply to user dialogue and/or questions for which the social data does not provide data. In some aspects, a 2D or 3D model of a specific person may be generated using images, depth information, and/or video data associated with the specific person.

Creating a conversational chat bot of a specific person

Systems and methods for automated mathematical chatting. The systems and methods convert any identified non-numerical inputs into vectors and then perform the mathematical equation utilizing the vectors instead of the nonnumeric inputs along with any other identified numeric inputs to obtain a numerical vector result. The systems and methods decode the numerical vector result into a result feature and then search one or more databases for output based on the result feature. The systems and methods provide the selected output from the one or more databases in response to the mathematical query.

Systems and methods for a mathematical chat bot

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

Examples of the present disclosure describe systems and methods relating to conversational system user behavior identification. A user of the conversational system may be evaluated based on one or more factors. The one or more factors may be compared to an aggregated measure for a larger group of conversational system users, such that 'anomalous' behavior (e.g., behavior that deviates from a normal behavior) may be identified. When a user is identified as exhibiting anomalous behavior, the conversational system may adapt its interactions with the user in order to encourage, discourage, or further observe the identified behavior. As a result, the conversational system may be able to verify a user's anomalous behavior, discourage the anomalous behavior, or take other action while interacting with the user.

Conversational system user behavior identification

Representative embodiments disclose mechanisms for discovering, synthesizing, presenting and interacting with multiple chat bots. A broker system can interact with a user and receive a query which is passed to a search engine. The search results are evaluated for bots provided by domains. The query can be submitted to the discovered bots. For domains that do not provide a bot, a bot can be synthesized if desired and the query submitted to the synthesized bots. Answers from the bots are retrieved. Answer are directly presented for bots that are directly surfaced to the user in the search results page. Answers can be fused for bots that are not directly surfaced to the user and presented via a broker bot. Answers from the one bot can be fed to other bots so that the bots can participate in a group chat between bots and the user.

Conversation bot discovery and response fusion

Methods, systems, and computer programs are presented for providing a user interface (UI) for monitoring and debugging an Artificial Intelligence (AI) chatting hot. One method includes operations for receiving a selection on the UI to replay an electronic conversation between a first and a second party, selecting conversation data associated with the electronic conversation from a data log having conversation data from several electronic conversations, and analyzing the conversation data to identify conversation parameters. The conversation parameters include text in each entry of the electronic conversation, timing of the entries, and debugging parameters for each entry. The method further includes an operation for causing presentation of the electronic conversation on the UI, which includes presenting the text of each entry, the audio corresponding to speech associated with each entry timed according to the timing of the entry, and the debugging parameters embedded within the presented text.

Conversational log replay with voice and debugging information

Examples of the present disclosure describe systems and methods relating to conversational system user experience. In an example, a conversational system may use one or more sensors of a user device to affect the topic or direction of a conversation or to identify a new conversation topic. The conversational system may also receive input from a user, wherein a GUI may enable the user to specify or alter semantic information used during the conversation. The GUI may comprise one or more skeuomorphic elements designed to provide a familiar or intuitive way for the user to interact with the conversational system. The GUI may also be used to disambiguate messages or convey emotion or sentiment to the user. In another example, haptic or audio feedback may be provided alongside a message to convey emotion to the user during the conversation.

Conversational system user experience

Growing community of inventors

Seattle, WA, United States of America

Joseph Edwin Johnson, Jr