The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Mar. 03, 2020

Filed:

Mar. 02, 2016
Applicant:

Roche Diabetes Care, Inc., Indianapolis, IN (US);

Inventors:

David L. Duke, Fishers, IN (US);

Bernd Steiger, Roemerberg, DE;

Chinmay Uday Manohar, Fishers, IN (US);

Assignee:

Roche Diabetes Care, Inc., Indianapolis, IN (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
A61B 5/145 (2006.01); A61B 5/00 (2006.01); G06F 19/00 (2018.01); G06F 17/16 (2006.01); G06K 9/62 (2006.01); G06K 9/00 (2006.01);
U.S. Cl.
CPC ...
A61B 5/7271 (2013.01); A61B 5/14503 (2013.01); A61B 5/14532 (2013.01); A61B 5/4842 (2013.01); A61B 5/7246 (2013.01); A61B 5/742 (2013.01); G06F 17/16 (2013.01); G06F 19/3468 (2013.01); G06K 9/00496 (2013.01); G06K 9/6218 (2013.01);
Abstract

A patient diabetes monitoring system with an efficient unsupervised daily monitoring profile clustering algorithm, a method, and a computer product thereof are disclosed. The system may include a physiological data input device or sensor which receives a plurality of physiological measurements to generate a dataset, a memory which stores a clustering algorithm, and a processor. The clustering algorithm when executed by the processor, causes the processor to automatically pre-process the dataset to control an amount of bias/aggressiveness from the collected unsupervised daily monitoring profiles, thereby generating a pre-processed dataset, build a similarity matrix from the pre-processed dataset, and output an optimum number of similarity clusters found by the processor from the similarity matrix.


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