# US 12,119,119 B2 · Methods and systems for real-time sensor measurement simulation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,119,119 B2 - Document ID: US12119119B2 - Status: Granted - Kind: B2 - Application number: 16/848,695 - Filed: 2020-04-14 - Published: 2021-06-10 - Granted: 2024-10-15 - Earliest claimed: 2019-12-09 - Assignees: Medtronic MiniMed, Inc. - Applicants: Medtronic Minimed, Inc. - Inventors: Elaine Gee; Peter Ajemba; Bahman Engheta; Jeffrey Nishida; Andrea Varsavsky; Keith Nogueira - Web page: https://patentgrep.com/patent/US12119119B2 ## CPC - 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 5/1451: {for interstitial fluid} - A61B 5/14532: {for measuring glucose, e.g. by tissue impedance measurement} - A61B 5/7253: {characterised by using transforms} - A61B 5/7267: {involving training the classification device} - A61B 5/7275: {Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor} - G06F 18/214: Generating training patterns; Bootstrap methods, e.g. bagging or boosting - G06N 3/045: Combinations of networks - G06N 3/0475: Generative networks - G06N 3/0499: Feedforward networks - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 3/09: Supervised learning - G06N 3/094: Adversarial learning - 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 40/67: for remote operation - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 50/30: for calculating health indices; for individual health risk assessment - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16H 70/60: relating to pathologies - A61B 5/1495: Calibrating or testing of in-vivo probes - A61B 5/746: {Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms} - A61B 5/7475: {User input or interface means, e.g. keyboard, pointing device, joystick} - A61B 2560/0223: of calibration, e.g. protocols for calibrating sensors - A61B 2560/0228: using calibration standards - A61M 5/1723: {using feedback of body parameters, e.g. blood sugar or pressure} - A61M 2205/3303: Using a biosensor - A61M 2205/50: with microprocessors or computers - 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 2230/201: Glucose concentration - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - 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)} ## Abstract Medical devices and related systems and methods are provided. A method of estimating a physiological condition using a first sensing arrangement involves obtaining a sensor translation model associated with a relationship between the first sensing arrangement and a second sensing arrangement, wherein the second sensing arrangement is different from the first sensing arrangement, obtaining one or more measurements from a sensing element coupled to the processing system of the first sensing arrangement, determining simulated measurement data for the second sensing arrangement by applying the sensor translation model to the one or more measurements from the sensing element of the first sensing arrangement, and determining an estimated value for the physiological condition by applying an estimation model associated with the second sensing arrangement to the simulated measurement data. ## Claims ### Claim 1 A method of estimating a physiological condition using data associated with different sensing arrangements, the method comprising: obtaining, by a processing system, a sensor translation model associated with a relationship between a first sensing arrangement and a second sensing arrangement, wherein: the first sensing arrangement comprises a first sensing element configured to measure the physiological condition, the second sensing arrangement comprises a second sensing element configured to measure the same physiological condition, and the sensor translation model is based on measurements from the first sensing element and corresponding measurements from the second sensing element; obtaining, by the processing system, one or more measurements from the first sensing element in a first instance of the first sensing arrangement; determining, by the processing system, simulated measurement data for the second sensing arrangement by applying the sensor translation model to the one or more measurements from the first sensing element in the first instance of the first sensing arrangement, wherein the simulated measurement data represents one or more measurements that the second sensing element of the second sensing arrangement would output in response to a value of the physiological condition that resulted in the one or more measurements from the first sensing element in the first instance of the first sensing arrangement; and determining, by the processing system, an estimated value of the physiological condition by applying an estimation model associated with the second sensing arrangement to the simulated measurement data, wherein the estimation model is different from the sensor translation model. ### Claim 2 (depends on 1) The method of claim 1, wherein obtaining the sensor translation model comprises: obtaining first measurement data from different instances of the first sensing arrangement; obtaining second measurement data from different instances of the second sensing arrangement concurrent to the first measurement data; and determining the sensor translation model based on relationships between the first measurement data and the second measurement data. ### Claim 3 (depends on 2) The method of claim 2, wherein determining the sensor translation model comprises training the sensor translation model using the first measurement data and the second measurement data as inputs to the sensor translation model. ### Claim 4 (depends on 2) The method of claim 2, wherein determining the sensor translation model comprises determining the sensor translation model based on relationships between the first measurement data and the second measurement data, using a series of invertible, memory-less transforms to shift sensing signals between a first domain associated with the first sensing arrangement and a second domain associated with the second sensing arrangement. ### Claim 5 (depends on 2) The method of claim 2, wherein determining the sensor translation model comprises determining the sensor translation model based on relationships between the first measurement data and the second measurement data using a concatenative modeling technique. ### Claim 6 (depends on 1) The method of claim 1, further comprising downloading, by the processing system, the estimation model associated with the second sensing arrangement from a remote server via a network. ### Claim 7 (depends on 6) The method of claim 6, wherein obtaining the sensor translation model comprises downloading, by the processing system, the sensor translation model from the remote server via the network. ### Claim 8 (depends on 1) The method of claim 1, further comprising outputting, by the processing system, the estimated value of the physiological condition. ### Claim 9 (depends on 1) The method of claim 1, wherein the first sensing arrangement comprises a new sensing arrangement influenced by the physiological condition and the second sensing arrangement comprises a legacy sensing arrangement influenced by the physiological condition. ### Claim 10 (depends on 9) The method of claim 9, wherein: the physiological condition comprises a glucose level; the new sensing arrangement comprises a new glucose sensor; the legacy sensing arrangement comprises a legacy glucose sensor; and the estimation model comprises a sensor glucose estimation model associated with the legacy glucose sensor. ### Claim 11 (depends on 1) The method of claim 1, wherein the first sensing arrangement comprises a template sensing device influenced by the physiological condition and the second sensing arrangement comprises a target sensing device influenced by the physiological condition, and wherein the method further comprises: obtaining first measurement data from different instances of the template sensing device; obtaining second measurement data from different instances of the target sensing device concurrent to the first measurement data, wherein respective sets of the first measurement data and the second measurement data are concurrently obtained from a respective common patient using respective instances of the template sensing device and the target sensing device; and determining the sensor translation model based at least in part on relationships between the first measurement data and the second measurement data. ### Claim 12 (depends on 11) The method of claim 11, further comprising: obtaining reference values for the physiological condition together with third measurement data associated with the reference values, wherein the third measurement data comprises historical measurements from additional instances of the target sensing device; and training the estimation model using the third measurement data as an input to the estimation model and the reference values as an output of the estimation model. ### Claim 13 (depends on 11) The method of claim 11, wherein determining the sensor translation model comprises training the sensor translation model using the first measurement data as an input to the sensor translation model and using the second measurement data as an output of the sensor translation model. ### Claim 14 (depends on 11) The method of claim 11, wherein determining the sensor translation model comprises determining the sensor translation model based on relationships between the first measurement data and the second measurement data, using a series of invertible, memory-less transforms to shift sensing signals from a first domain associated with the first sensing arrangement to a second domain associated with the second sensing arrangement. ### Claim 15 (depends on 11) The method of claim 11, wherein determining the sensor translation model comprises determining the sensor translation model based on relationships between the first measurement data and the second measurement data using a concatenative modeling technique. ### Claim 16 (depends on 1) The method of claim 1, wherein the first sensing arrangement comprises a legacy sensing arrangement influenced by the physiological condition and the second sensing arrangement comprises a new sensing arrangement influenced by the physiological condition. ### Claim 17 (depends on 16) The method of claim 16, wherein: the physiological condition comprises a glucose level; the new sensing arrangement comprises a new glucose sensor; the legacy sensing arrangement comprises a legacy glucose sensor; and the estimation model comprises a sensor glucose estimation model associated with the new glucose sensor. ### Claim 18 (depends on 1) The method of claim 1, further comprising determining a delivery command for operating an actuation arrangement of an infusion device based at least in part on the estimated value of the physiological condition. ### Claim 19 (depends on 1) The method of claim 1, wherein the one or more measurements from the first sensing element in the first instance of the first sensing arrangement comprise at least one: current values, voltage values, or electrochemical impedance values, and wherein the simulated measurement data for the second sensing arrangement comprises at least one of: current values, voltage values, or electrochemical impedance values. ### Claim 20 (depends on 1) The method of claim 1, wherein the estimation model has been pre-trained using measurements from one or more instances of the second sensing arrangement. ### Claim 21 (depends on 1) The method of claim 1, wherein: the first sensing element and the second sensing element are glucose sensing elements, the one or more measurements from the first sensing element in the first instance of the first sensing arrangement comprise a value of a signal produced by the first sensing element in response to a particular glucose level, and the simulated measurement data comprises a value of a signal that the second sensing element would output in response to the particular glucose level.