# US 2023/0069375 A1 · Personalized parameter modeling methods and related devices and systems > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2023/0069375 A1 - Document ID: US20230069375A1 - Status: Published application - Kind: A1 - Application number: 18/045,034 - Filed: 2022-10-07 - Published: 2023-03-02 - Earliest claimed: 2015-08-21 - Assignees: Medtronic MiniMed, Inc. - Applicants: Medtronic MiniMed, Inc. - Inventors: Andrea Varsavsky; Yunfeng Lu; Keith Nogueira; Jeffrey Nishida - Web page: https://patentgrep.com/patent/US20230069375A1 ## CPC - A61M 5/1723: {using feedback of body parameters, e.g. blood sugar or pressure} - A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement} - A61B 5/4839: {combined with drug delivery} - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 20/17: delivered via infusion or injection - G16H 40/40: for the management of medical equipment or devices, e.g. scheduling maintenance or upgrades - G16H 40/63: for local operation - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/50: for simulation or modelling of medical disorders - A61B 5/0022: {Monitoring a patient using a global network, e.g. telephone networks, internet} - A61B 5/02438: {with portable devices, e.g. worn by the patient} - A61B 5/7239: {using differentiation including higher order derivatives} - A61B 5/7242: {using integration} - A61B 2560/0242: adapted to measure environmental factors, e.g. temperature, pollution - A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches - A61M 5/14244: {adapted to be carried by the patient, e.g. portable on the body} - A61M 2205/3569: sublocal, e.g. between console and disposable - A61M 2205/502: User interfaces, e.g. screens or keyboards - A61M 2205/52: with memories providing a history of measured variating parameters of apparatus or patient - A61M 2205/70: with testing or calibration facilities - A61M 2230/005: Parameter used as control input for the apparatus - A61M 2230/06: Heartbeat rate only - A61M 2230/201: Glucose concentration - G16H 15/00: ICT specially adapted for medical reports, e.g. generation or transmission thereof - A61M: DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY (introducing media into or onto the bodies of animals A61D7/00; means for inserting tampons A61F13/26; devices for administering food or medicines orally A61J; containers for collecting, storing or administering blood or medical fluids A61J1/05); DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY (surgery A61B; chemical aspects of surgical articles A61L); DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR {(Electrotherapy, e.g. producing anaesthesia by the use of alternating or intermittent currents A61N1/36021)} - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract A processor-implemented method comprises obtaining current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition. ## Claims ### Claim 1 A processor-implemented method comprising: obtaining current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition. ### Claim 2 (depends on 1) The method of claim 1, wherein outputting the calibrated measurement value comprises transmitting the calibrated measurement value to an infusion device, wherein the infusion device is autonomously operated to deliver fluid to the body of the patient based on a difference between the calibrated measurement value and a reference value. ### Claim 3 (depends on 1) The method of claim 1, further comprising: obtaining historical operational context information associated with preceding operation of the sensing device; obtaining historical values for a calibration factor associated with the preceding operation of the sensing device; and determining the expected calibration factor model associated with the patient based on the historical operational context information and the historical values. ### Claim 4 (depends on 3) The method of claim 3, wherein determining the expected calibration factor model comprises: identifying a subset of predictive variables from among the historical operational context information based on a correlation between one or more of the predictive variables and the historical values for the calibration factor; determining the expected calibration factor model as a function of the subset of predictive variables based on historical values for the subset of predictive variables and the historical values for the calibration factor, wherein the current operational context information includes current values for the subset of predictive variables, and wherein calculating the expected calibration factor value comprises calculating the expected calibration factor value as the function of the current values for the subset of predictive variables according to the expected calibration factor parameter model. ### Claim 5 (depends on 1) The method of claim 1, further comprising: obtaining a measurement offset model associated with the patient; calculating an expected offset value based on the current operational context information using the measurement offset model; and subtracting the expected offset value from the one or more electrical signals prior to converting the one or more electrical signals into the calibrated measurement value. ### Claim 6 (depends on 5) The method of claim 5, wherein converting the one or more electrical signals into the calibrated measurement value comprises multiplying one or more results of subtracting the expected offset value from the one or more electrical signals by the expected calibration factor value. ### Claim 7 (depends on 6) The method of claim 6, further comprising: obtaining historical operational context information associated with preceding operation of the sensing device; obtaining historical values for a calibration factor associated with the preceding operation of the sensing device; obtaining historical values for a measurement offset associated with the preceding operation of the sensing device; determining the expected calibration factor model associated with the patient based on the historical operational context information and the historical values for the calibration factor; and determining the measurement offset model associated with the patient based on the historical operational context information and the historical values for the measurement offset. ### Claim 8 (depends on 1) The method of claim 1, further comprising generating a user notification based at least in part on the expected calibration factor value. ### Claim 9 (depends on 8) The method of claim 8, further comprising determining a remaining usage life associated with the sensing device based at least in part on the expected calibration factor value, wherein generating the user notification comprises providing an indication of the remaining usage life. ### Claim 10 (depends on 8) The method of claim 8, further comprising determining an optimal calibration time based at least in part on the expected calibration factor value, wherein generating the user notification comprises providing an indication of the optimal calibration time. ### Claim 11 (depends on 8) The method of claim 8, further comprising determining a recommended sensor site location based at least in part on the expected calibration factor value, wherein generating the user notification comprises providing an indication of the recommended sensor site location. ### Claim 12 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 current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition. ### Claim 13 (depends on 12) The system of claim 12, further comprising an infusion device, wherein outputting the calibrated measurement value comprises transmitting the calibrated measurement value to the infusion device, wherein the infusion device is autonomously operated to deliver fluid to the body of the patient based on a difference between the calibrated measurement value and a reference value. ### Claim 14 (depends on 12) The system of claim 12, wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of: obtaining historical operational context information associated with preceding operation of the sensing device; obtaining historical values for a calibration factor associated with the preceding operation of the sensing device; and determining the expected calibration factor model associated with the patient based on the historical operational context information and the historical values. ### Claim 15 (depends on 14) The system of claim 14, wherein determining the expected calibration factor model comprises: identifying a subset of predictive variables from among the historical operational context information based on a correlation between one or more of the predictive variables and the historical values for the calibration factor; determining the expected calibration factor model as a function of the subset of predictive variables based on historical values for the subset of predictive variables and the historical values for the calibration factor, wherein the current operational context information includes current values for the subset of predictive variables, and wherein calculating the expected calibration factor value comprises calculating the expected calibration factor value as the function of the current values for the subset of predictive variables according to the expected calibration factor parameter model. ### Claim 16 (depends on 12) The system of claim 12, wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of: obtaining a measurement offset model associated with the patient; calculating an expected offset value based on the current operational context information using the measurement offset model; and subtracting the expected offset value from the one or more electrical signals prior to converting the one or more electrical signals into the calibrated measurement value. ### Claim 17 (depends on 16) The system of claim 16, wherein converting the one or more electrical signals into the calibrated measurement value comprises multiplying one or more results of subtracting the expected offset value from the one or more electrical signals by the expected calibration factor value, and wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of: obtaining historical operational context information associated with preceding operation of the sensing device; obtaining historical values for a calibration factor associated with the preceding operation of the sensing device; obtaining historical values for a measurement offset associated with the preceding operation of the sensing device; determining the expected calibration factor model associated with the patient based on the historical operational context information and the historical values for the calibration factor; and determining the measurement offset model associated with the patient based on the historical operational context information and the historical values for the measurement offset. ### Claim 18 (depends on 12) The system of claim 12, wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of generating a user notification based at least in part on the expected calibration factor value. ### Claim 19 (depends on 18) The system of claim 18, wherein the one or more processor-readable storage media further store instructions which, when executed by the one or more processors, cause performance of, at least one of: determining a remaining usage life associated with the sensing device based at least in part on the expected calibration factor value; determining an optimal calibration time based at least in part on the expected calibration factor value; or determining a recommended sensor site location based at least in part on the expected calibration factor value, wherein generating the user notification comprises at least one of: providing an indication of the remaining usage life; providing an indication of the optimal calibration time; or providing an indication of the recommended sensor site location. ### Claim 20 One or more non-transitory processor-readable storage media storing instructions which, when executed by one or more processors, cause performance of: obtaining current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition.