Glucose prediction using machine learning and time series glucose measurements
US12205718B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 4, 2020 |
| Grant date | Jan 21, 2025 |
| Priority date | — |
| Expiry date | Jan 23, 2041 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L67/12
- WIPO fieldMedical technology
- WIPO sectorInstruments
Abstract
Glucose prediction using machine learning (ML) and time series glucose measurements is described. Given the number of people that wear glucose monitoring devices and because some wearable glucose monitoring devices can produce measurements continuously, a platform providing such devices may have an enormous amount of data. This amount of data is practically, if not actually, impossible for humans to process and covers a robust number of state spaces unlikely to be covered without the enormous amount of data. In implementations, a glucose monitoring platform includes an ML model trained using historical time series glucose measurements of a user population. The ML model predicts upcoming glucose measurements for a particular user by receiving a time series of glucose measurements up to a time and determining the upcoming glucose measurements of the particular user for an interval subsequent to the time based on patterns learned from the historical time series glucose measurements.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.