Hourglass Software LLC

Amazon Technologies, Inc.

Symbol Technologies, LLC

Faisal Ahmad Qureshi

Anatoly Kotlarsky

David P Goren

Vladimir P Gurevich

Anatoly A Kotlarsky

The described technology generally relates to modifying network routing and bandwidth balancing for lower latency and high quality fair traffic. For instance, based on network routing data comprising network quality data representative of a network quality applicable to a wide area network and using machine learning applied to historical network routing data, other than the network routing data, available routes are classified, the available routes being between a source node on the wide area network and a destination node on the wide area network. Further, based on the network routing data and using a result of the machine learning applied to the historical network routing data, bandwidth allocations applicable to the classified available routes can be determined. Then, based on the classified available routes and the predicted bandwidth allocations, a route is set for data transmitted from the source node to the destination node.

Modifying network routing and bandwidth balancing for lower latency and high quality fair traffic

Machine learning models can be trained to vend profiles with a high likelihood of matching with a user comprises: training machine learning models with feature vectors representing potential profile matches identified as input to a user match selection process, updating the trained models with feature vectors identified as further input to the selection process, in response to the updating, swapping the trained model out of and a further trained machine learning model into a foreground execution space, resulting in the untrained model being in the foreground and the trained model being in a processing background, after satisfying a defined criterion, injecting an outlier potential match entity as supplemental input to the further training with a supplemental feature vector as further input to the selection process, determining respective confidence values corresponding to candidate profiles accessible to the selection process, and rendering profile images of candidate profiles for the selection process.

High confidence predicted profiles for online dating matching using continuously learning machine learning models trained for each user with physical and facial attributes

Systems, methods, and computer-readable media are disclosed for systems and methods for simultaneously sending a single signal comprising distinct instructions for two separate devices. Example methods may include receiving, by a control device instructions to adjust the operation of a first device and a second device, generating a single signal corresponding to the received instructions for two separate devices, and simultaneously sending the single signal to the two separate devices causing the devices to perform the distinct instructions intended for each respective device.

Simultaneous multi-IR code protocol using phase modulation

Various acoustic manipulation and microphone gain adjustment techniques are enabled. For instance, a method comprises receiving, by a voice-enabled device comprising a processor and from a microphone, an input comprising environmental audio, determining, by the voice-enabled device, a level of the environmental audio, determining, by the voice-enabled device, a gain-adjust value based on the environmental audio, and adjusting, by the voice-enabled device, a gain of the microphone by the gain-adjust value.

Dynamic and self-adjusting microphone gain for voice-enabled devices

An imaging reader has an illuminating light assembly for illuminating a target with illumination light, and a solid-state image sensor with a rolling shutter for sequentially exposing an array of pixels to capture an image from the illuminated target. Target illumination is controlled by controlling an exposure time and a gain of the sensor, and by controlling an output power of the illumination light. The output power is maintained at a maximum level when the exposure time and/or the gain exceed a minimum threshold, and is lowered when the exposure time and/or the gain does not exceed the minimum threshold, while concomitantly maintaining the exposure time and/or the gain constant at the minimum threshold.

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Seattle, WA, United States of America

Faisal Ahmad Qureshi