Technical overview

The goal of Mode’s dosing platform is to allow users to vaporize a precise amount of oil concentrate during a puff without the need for proprietary cartridges. Achieving this goal presents a significant technical challenge, as the amount of vapor delivered over the course of a puff depends on a number of complex variables. To overcome this challenge, we have taken a data-driven approach, building machine learning models that take the above variables (“features”) as input and output a predicted dose. 

 

On-Device Measurements

Measuring relevant signals using sensors that are directly integrated into the device

 

Ground-Truth Dosing Measurements

Collecting ground truth dosing measurements to generate a dataset for machine learning

 

Dosing Simulator 

Improving feature coverage of our statistical models with a dosing simulator

 

 

 

Model optimization

Optimizing statistical models with a high-throughput machine learning pipeline

 

Concentrate-agnostic models

Improving puff simulations with data collected from human users

 

Continuously Improving

We strive for precision accuracy and consistently improving the user experience. We will continue to run tests to optimize our dosing algorithms,with improvements being  sent over-the-air through the mode mobile apps.