# US 11,998,324 B2 · Biomarker monitoring fitness system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,998,324 B2 - Document ID: US11998324B2 - Status: Granted - Kind: B2 - Application number: 17/509,386 - Filed: 2021-10-25 - Published: 2022-02-10 - Granted: 2024-06-04 - Earliest claimed: 2019-08-30 - Assignees: TT1 Products, Inc. - Applicants: TT1 Products, Inc. - Inventors: Harold Philpott Southerland, III; Todd Furneaux; Juan Pablo Frias; Roger Steven Mazze - Web page: https://patentgrep.com/patent/US11998324B2 ## CPC - A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement} - A61B 5/0004: {characterised by the type of physiological signal transmitted} - A61B 5/0024: {for multiple sensor units attached to the patient, e.g. using a body or personal area network} - A61B 5/0205: Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition - A61B 5/1112: {Global tracking of patients, e.g. by using GPS} - A61B 5/6801: {specially adapted to be attached to or worn on the body surface} - A61B 5/7292: {Prospective gating, i.e. predicting the occurrence of a physiological event for use as a synchronisation signal} - A61B 5/742: {using visual displays (displays for heart-related electrical signals, e.g. ECG, A61B5/339)} - A61B 5/002: {Monitoring the patient using a local or closed circuit, e.g. in a room or building (A61B5/0017 takes precedence)} - A61B 5/02055: {Simultaneously evaluating both cardiovascular condition and temperature} - A61B 5/021: Measuring pressure in heart or blood vessels - A61B 5/024: Measuring pulse rate or heart rate - A61B 5/0816: {Measuring devices for examining respiratory frequency (measuring frequency of electric signals G01R23/00)} - A61B 5/1118: {Determining activity level} - A61B 5/1451: {for interstitial fluid} - A61B 5/14539: {for measuring pH} - A61B 5/14542: {for measuring blood gases (A61B5/14551 takes precedence)} - A61B 5/14546: {for measuring analytes not otherwise provided for, e.g. ions, cytochromes} - A61B 5/165: {Evaluating the state of mind, e.g. depression, anxiety} - A61B 5/4266: {sweat secretion} - A61B 5/4806: {Sleep evaluation (A61B5/4821 takes precedence; devices for inducing sleep A61M21/02)} - A61B 5/4875: {Hydration status, fluid retention of the body} - A61B 5/6803: {Head-worn items, e.g. helmets, masks, headphones or goggles} - A61B 5/681: {Wristwatch-type devices} - A61B 5/6831: {Straps, bands or harnesses} - A61B 5/6833: {Adhesive patches} - A61B 5/6867: {specially adapted to be attached or implanted in a specific body part (A61B5/6847 takes precedence)} - A61B 5/7475: {User input or interface means, e.g. keyboard, pointing device, joystick} - A61B 2503/10: Athletes - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract Disclosed herein are systems and processes for measuring, monitoring, processing, and visualizing biological data including blood glucose levels. In various embodiments, a biomarker monitoring fitness system may facilitate one or more of the monitoring of fitness data including blood glucose levels measured by continuous glucose monitoring (CGM) sensor for an individual user or group of users, the prediction of carbohydrate intake needs or insulin delivery needs during physical activity of a user and the active indication thereof to the user and/or a delivery system of the user, and the collection and management of de-identified and identifiable user fitness data including blood glucose data. ## Claims ### Claim 1 A computer-implemented method in which one or more processing devices perform operations comprising: determining a glucose exposure for a particular hour based on glucose data received from a sensor on a particular interval; determining a target glucose exposure for the particular hour by multiplying a glucose exposure limit per hour by a numerical representation of the particular hour; receiving additional biomarker data comprising one or more of cardiovascular, pulmonary, and perspiratory data; integrating the glucose exposure for the particular hour and the additional biomarker data for display; and displaying the glucose exposure for the particular hour as a proportion of the target glucose exposure for the particular hour and the additional biomarker data on a display. ### Claim 2 (depends on 1) The computer-implemented method of claim 1, wherein the glucose data comprises a glucose reading taken from a filament interacting with interstitial fluid. ### Claim 3 (depends on 1) The computer-implemented method of claim 1, the operations further comprising determining an average glucose level for the particular hour by averaging one or more glucose readings received on the particular interval during the particular hour. ### Claim 4 (depends on 3) The computer-implemented method of claim 3, wherein the particular interval is 15 minutes. ### Claim 5 (depends on 3) The computer-implemented method of claim 3, wherein the particular interval is 1 minute. ### Claim 6 (depends on 3) The computer-implemented method of claim 3, wherein the operation of determining the glucose exposure for the particular hour comprises adding the average glucose level for the particular hour to a summation of average glucose levels of the patient for hours preceding the particular hour in a 24-hour period. ### Claim 7 (depends on 6) The computer-implemented method of claim 6, wherein the 24-hour period begins at midnight. ### Claim 8 (depends on 7) The computer-implemented method of claim 7, wherein: the particular interval is 15 minutes; the particular hour is 9:00 AM and defined by 60 minutes between 8:01 AM to 9:00 AM; determining the average glucose level for 8:01 AM to 9:00 AM by averaging one or more glucose readings received every 15 minutes during the particular hour; determining the glucose exposure for 8:01 AM to 9:00 AM comprises adding the average glucose level for 8:01 AM to 9:00 AM to a summation of average glucose levels of the patient from midnight until 8:00 AM; and determining the target glucose exposure for 8:01 AM to 9:00 AM comprises multiplying the glucose exposure limit per hour by 9. ### Claim 9 (depends on 1) The computer-implemented method of claim 1, the operations further comprising: detecting movement of a patient associated with the glucose exposure; and providing recommendations for meeting the glucose exposure limit based on the glucose exposure for the particular hour and the information associated with the movement of the patient. ### Claim 10 A system comprising: a processor; and a non-transitory computer-readable medium having instructions stored thereon, the instructions executable by the processor for performing operations comprising: determining a glucose exposure for a particular hour based on glucose data received on a particular interval; determining a target glucose exposure for the particular hour by multiplying a glucose exposure limit per hour by a numerical representation of the particular hour; integrating the glucose exposure for the particular hour and additional biomarker data via the integration module for display; and displaying the glucose exposure for the particular hour as a proportion of the target glucose exposure for the particular hour and the additional biomarker data on a display. ### Claim 11 (depends on 10) The system of claim 10, wherein the operations further comprise deobfuscating obfuscated glucose data received from a sensor on the particular interval to generate the glucose data. ### Claim 12 (depends on 11) The system of claim 11, wherein the operation of deobfuscating the obfuscated glucose data comprises decrypting the obfuscated glucose data. ### Claim 13 (depends on 12) The system of claim 12, wherein the obfuscated glucose data comprises a glucose reading taken from a filament interacting with interstitial fluid. ### Claim 14 (depends on 10) The system of claim 10, wherein the operations further comprise receiving, from a sensor of a wearable computing device, the additional biomarker data comprising one or more of cardiovascular, pulmonary, and perspiratory data. ### Claim 15 (depends on 10) The system of claim 10, wherein the operations further comprise determining an average glucose level for the particular hour by averaging one or more glucose readings received on the particular interval during the particular hour. ### Claim 16 A non-transitory computer-readable medium having program code that is stored thereon, the program code executable by one or more processing devices for performing operations comprising: determining a glucose exposure for a particular hour based on glucose data received from a sensor on a particular interval; determining a target glucose exposure for the particular hour by multiplying a glucose exposure limit per hour by a numerical representation of the particular hour; integrating the glucose exposure for the particular hour and the additional biomarker data for display; and displaying the glucose exposure for the particular hour as a proportion of the target glucose exposure for the particular hour and the additional biomarker data on a display. ### Claim 17 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the operations further comprise receiving, from a sensor of a wearable computing device, the additional biomarker data comprising one or more of cardiovascular, pulmonary, and perspiratory data. ### Claim 18 (depends on 16) The non-transitory computer-readable medium of claim 16, wherein the glucose data comprises a glucose reading taken from a filament interacting with interstitial fluid. ### Claim 19 (depends on 16) The non-transitory computer-readable medium of claim 16, the operations further comprising determining an average glucose level for the particular hour by averaging one or more glucose readings received on the particular interval during the particular hour. ### Claim 20 (depends on 19) The non-transitory computer-readable medium of claim 19, wherein the operation of determining the glucose exposure for the particular hour comprises adding the average glucose level for the particular hour to a summation of average glucose levels of the patient for hours preceding the particular hour in a 24-hour period.