Granted Patent
B2
US 12,322,492 · App. 18/749,277 · Granted Jun 3, 2025
Personalized event detection
Inventors:
Sinu Bessy Abraham (Georgetown, TX); Chantal M. McMahon (Atlanta, GA); Pratik J. Agrawal (Stevenson Ranch, CA); Boyi Jiang (Pasadena, CA); Yuxiang Zhong (Arcadia, CA); Michael P. Stone (Long Beach, CA); Huzefa F. Neemuchwala (Simi Valley, CA)
Assignee:
MEDTRONIC MINIMED, INC.
G16H20/60A61B5/4839A61B5/7225A61B5/7275A61B5/743A61M5/1723G06N7/01G16H20/17G16H50/20A61B5/14532A61M2205/3584A61M2205/50A61M2205/502
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Abstract
Techniques for detection of occurrence of a meal are provided. In some embodiments, the techniques may involve obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval. The techniques may involve determining one or more glucose measurement statistics based on the plurality of glucose measurements. The techniques may involve determining a likelihood of the patient having consumed a meal during the analysis interval based on the one or more glucose measurement statistics that are correlative to an occurrence of a meal for the patient. The techniques may involve detecting an occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval.
Claims (38)
1. A processor-implemented method comprising:
obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval;
determining one or more glucose measurement statistics based on the plurality of glucose measurements that are correlative to an occurrence of a meal for the patient;
determining a likelihood of the patient having consumed a meal during the analysis interval based on the one or more glucose measurement statistics; and
detecting the occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval.
2. The method of claim 1 , wherein determining the likelihood of the patient having consumed the meal during the analysis interval is based on historical event data associated with the patient.
3. The method of claim 2 , wherein the historical event data comprises meal bolus data associated with the patient, and wherein determining the likelihood is based on the meal bolus data associated with the patient.
4. The method of claim 1 , further comprising:
determining respective correlation coefficient values for the one or more glucose measurement statistics using a patient-specific meal detection model,
wherein determining the likelihood of the patient having consumed the meal during the analysis interval is based on the values for the one or more glucose measurement statistics and the respective correlation coefficient values for the one or more glucose measurements statistics.
5. The method of claim 1 , further comprising determining whether a meal bolus was identified and/or delivered during the analysis interval.
6. The method of claim 1 , further comprising providing an indication of the occurrence of the meal during the analysis interval.
7. The method of claim 1 , wherein the detected occurrence of the meal corresponds to detection of an unannounced meal.
8. The method of claim 1 , further comprising updating historical event data associated with the patient to indicate the occurrence of the meal associated with the analysis interval.
9. The method of claim 1 , wherein the one or more glucose measurement statistics are particular to the patient and have been identified as correlative of meal consumption for the patient.
10. The method of claim 1 , wherein the one or more glucose measurement statistics comprise at least a mean or average rate of change of the plurality of glucose measurements over at least a portion of the analysis interval.
11. A system comprising:
one or more processors; and
one or more processor-readable storage media storing instructions which, when executed by the one or more processors, cause performance of:
obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval;
determining one or more glucose measurement statistics based on the plurality of glucose measurements that are correlative to an occurrence of a meal for the patient;
determining a likelihood of the patient having consumed a meal during the analysis interval based on the one or more glucose measurement statistics; and
detecting the occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval.
12. The system of claim 11 , wherein determining the likelihood of the patient having consumed the meal during the analysis interval is based on historical event data associated with the patient.
13. The system of claim 12 , wherein the historical event data comprises meal bolus data associated with the patient, and wherein determining the likelihood is based on the meal bolus data associated with the patient.
14. The system of claim 11 , wherein the instructions further cause performance of:
determining respective correlation coefficient values for the one or more glucose measurement statistics using a patient-specific meal detection model,
wherein determining the likelihood of the patient having consumed the meal during the analysis interval is based on the values for the one or more glucose measurement statistics and the respective correlation coefficient values for the one or more glucose measurements statistics.
15. The system of claim 11 , wherein the instructions further cause performance of determining whether a meal bolus was identified and/or delivered during the analysis interval.
16. The system of claim 11 , wherein the instructions further cause performance of providing an indication of the occurrence of the meal during the analysis interval.
17. The system of claim 11 , wherein the detected occurrence of the meal corresponds to detection of an unannounced meal.
18. The system of claim 11 , wherein the instructions further cause performance of updating historical event data associated with the patient to indicate the occurrence of the meal associated with the analysis interval.
19. A processor-implemented method comprising:
obtaining a plurality of glucose measurements indicative of a glucose level in a body of a patient during an analysis interval;
determining one or more glucose measurement statistics based on the plurality of glucose measurements;
determining a likelihood of the patient having consumed a meal during the analysis interval using a patient-specific meal detection model and the one or more glucose measurement statistics; and
detecting an occurrence of the meal during the analysis interval based on the likelihood of the patient having consumed the meal during the analysis interval.
20. The method of claim 19 , wherein the patient-specific meal detection model is generated using time segments of historical data classified as corresponding to meal consumption or no meal consumption and corresponding glucose statistics for the time segments.
Assignments (2)
SECURITY INTEREST
Recorded Jan 16, 2026
From: MEDTRONIC MINIMED, INC.; COMPANION MEDICAL, INC.
To: CITIBANK, N.A.
Reel/Frame 074394/0237 →
ASSIGNMENT OF ASSIGNOR'S INTEREST
Recorded Jun 21, 2024
From: ABRAHAM, SINU BESSY; MCMAHON, CHANTAL M.; AGRAWAL, PRATIK; JIANG, BOYI; ZHONG, YUXIANG; STONE, MICHAEL P.; NEEMUCHWALA, HUZEFA F.
To: MEDTRONIC MINIMED, INC.
Reel/Frame 067802/0784 →
Continuity (12)
Provisional Application
62329021
· Apr 28, 2016
Provisional Application
62304618
· Mar 7, 2016
Provisional Application
62304615
· Mar 7, 2016
Provisional Application
62304609
· Mar 7, 2016
Provisional Application
62304605
· Mar 7, 2016
Provisional Application
62286828
· Jan 25, 2016
Provisional Application
62266820
· Dec 14, 2015
Provisional Application
62208479
· Aug 21, 2015
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