Amazon Technologies, Inc.

Cisco Technology, Inc.

Nikhil Sharma

Andrew Paul Breen

Arent Van Korlaar

Elena Sergeevna Sokolova

Panagiotis Agis Oikonomou Filandras

Bartosz Perz

Tiago Etiene Queiroz

Ioannis Douratsos

Jonas Felix Ananda Rohnke

Antonio Bonafonte

Prashant Anand Srivastava

Yongjun Wu

Sanjay Kumar Hooda

Rajeev Kumar

David Allen Fotland

Shyamsundar Nandkishor Maniyar

Muninder Singh Sambi

Robert Franklin Ebert

Roger Robert Webster

Thomas Edward Merritt

Sitaraman Ganapathy

Vasanthakumar Soundararajan

Chang Young Kim

Kayoko Yanagisawa

Kedar Karmarkar

Daniel Korzekwa

Himanshu Mehra

James Charles Zamiska

Mohit Deshpande

Kamil Pokora

Piotr Tadeusz Bilinski

Aarthi Raveendran

Abdelhamid Ezzerg

Raghu Ram Sreepada Srinivas

Rachel Liao

Gaurav Guruprasad Manur

Tushar Agarwal

Sebastian Dariusz Cygert

Systems and techniques for generation, storage, and updating of common floor plans across shared autonomous mobile devices. The systems and techniques include extraction of semantically meaningful data from one or more occupancy maps to provide a floorplan to a user that aligns with the user's understanding of the space using heuristics and machine learning techniques. The techniques also enable repair of damaged or inconsistent floor plan geometries through the use of polygon triangulation and graph cuts.

Floor plan repair system for autonomous mobile devices

Techniques for generating customized synthetic voices personalized to a user, based on user-provided feedback, are described. A system may determine embedding data representing a user-provided description of a desired synthetic voice and profile data associated with the user, and generate synthetic voice embedding data using synthetic voice embedding data corresponding a profile associated with a user determined to be similar to the current user. Based on user-provided feedback with respect to a customized synthetic voice, generated using synthetic voice characteristics corresponding to the synthetic voice embedding data and presented to the user, and the synthetic voice embedding data, the system may generate new synthetic voice embedding data, corresponding to a new customized synthetic voice. The system may be configured to assign the customized synthetic voice to the user, such that a subsequent user may not be presented with the same customized synthetic voice.

User-customized synthetic voice

Systems and techniques for updating floor plans for use by autonomous mobile devices. The techniques include accessing a first floor plan with associated spatial metadata. An updated occupancy map is received with additional geographic updates over the first floor plan. A transformation of the first floor plan is determined to match the new occupancy map. The spatial data and the first floor plan are transformed and the transformed spatial metadata is associated with the new occupancy map to form a second floor plan.

Floor plan update system for autonomous mobile devices

A system for utilizing media content reference point information to perform media content encoding, and supplemental content stitching and/or insertion. Media content can be encoded and packaged based on boundaries of the media content. The boundaries can be received from a third-party and/or generated via an automated process. Target boundaries can be selected based on accuracy levels associated with the received and/or generated boundaries. Supplemental content can be stitched and/or inserted into packaged media content based on audio and video content of the packaged media content being aligned.

Media content boundary-aware supplemental content management

A speech-processing system receives both text data and natural-understanding data (e.g., a domain, intent, and/or entity) related to a command represented in the text data. The system uses the natural-understanding data to vary vocal characteristics in determining spectrogram data corresponding to the text data based on the natural-understanding data.

Synthetic speech processing

An autonomous mobile device (AMD) uses sensors to explore a physical space and determine the locations of obstacles. Simultaneous localization and mapping (SLAM) techniques are used by the AMD to designate as keyframes some images and their associated descriptors of features in the space. Each keyframe indicates a location and orientation of the AMD relative to those features. Anchors are specified relative to keyframes. A marker is specified relative to one or more anchors. Because markers are associated with features in the physical space, they maintain their association with the physical space through various processes such as SLAM loop closures. Markers may specify locations in the physical space, such as navigation waypoints, navigation destinations such as a goal pose for exploring an unexplored area, as an observation target to facilitate exploration, and so forth. Markers may also be used to specify block listed locations to be avoided during exploration.

System for automated exploration by an autonomous mobile device using markers based on image features

Techniques for automated configuration are provided. A first device detects a new device connected by one or more new links in a network, and the first device transmits, to a dynamic host configuration protocol (DHCP) server, a request for a first new subnet. The first device then assigns a first address of the first new subnet to a first new interface of the first device. The first device additionally transmits a second address of the first new subnet to the new device, where the new device uses the second address to establish connectivity to the network.

Secure onboarding of network devices

Growing community of inventors

Fremont, CA, United States of America

Nikhil Sharma