T-mobile Usa, Inc.

Michael Mitchell

Peter P Myron

Justin Johnson

A suitability analysis platform may receive one or more environmental parameter values of one or more environmental parameters collected for a plurality of sites by multiple sensor devices. The platform may aggregate the one or more environmental parameter values collected for each site of the plurality of sites by each site. Subsequently, the platform may receive queries for suitable sites for performing activities from user devices, in which each of the queries includes one or more environmental parameter value criteria for at least one corresponding environmental parameter of a suitable site for performing a specific activity. The platform may provide responses to the queries in which each response includes information on one or more sites of the plurality of sites with one or more environmental parameter values that match or are closest to the one or more environmental parameter value criteria of the corresponding query.

Suitability metrics based on environmental sensor data

The disclosed technology includes a system that optimizes computing resources for dashboards. The system includes a computing device and memory that caches snapshots of dashboard outputs. A dashboard output captures a snapshot (e.g., instance in time) of dashboard data that can be presented on display devices without needing to maintain continuous access to the underlying dashboard data. The snapshot has a quick response (QR) code overlayed at a position for a viewer to capture the QR code with a camera on a smartphone. The smartphone can read the QR code to access dashboard data through a backend communications link. The QR code can embed data that is used to track and store information about what data is being accessed by users and the frequency with which the data is being accessed.

Caching system for presenting dashboard snapshots with quick response access to live dashboard data

Systems and methods for generating encoded text representations of spoken utterances are disclosed. Audio data is received for a spoken utterance and analyzed to identify a nonverbal characteristic, such as a sentiment, a speaking rate, or a volume. An encoded text representation of the spoken utterance is generated, comprising a text transcription and a visual representation of the nonverbal characteristic. The visual representation comprises a geometric element, such as a graph or shape, or a variation in a text attribute, such as font, font size, or color. Analysis of the audio data and/or generation of the encoded text representation can be performed using machine learning.

Generating encoded text based on spoken utterances using machine learning systems and methods

This disclosure describes techniques that enable a Meeting Expense and Verification (MEV) controller to infer the meeting expense of a proposed meeting. The MEV controller may analyze a meeting request to identify meeting attendees, and in doing so, determine the meeting expense. In response to the meeting expense being greater than a predetermined meeting expense threshold, the MEV controller may suspend the meeting request.

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North Bend, WA, United States of America

Michael Mitchell