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74
results.
Updated on
03/04/2026 [07:57:00]
Absstract of: US20260091175A1
The invention discloses a drug delivery system with graphical user interface comprises a detection device, for detecting the blood glucose level of the user; an infusion device, for infusing a drug into the user's body; and a control device, wirelessly communicated with the detection device and the infusion device, the control device controlling the drug delivery of the infusion device, and the control device provided with a graphical user interface, the graphical user interface comprising a main screen, the main screen comprising an insulin information display area and a blood glucose information display area, wherein the insulin information display area comprising insulin infusion status information, the graphical user interface displays the insulin infusion status information in a graphical and/or textual manner, to facilitate the user to intuitively know the insulin infusion status information and blood glucose information at that time.
Absstract of: AU2026202005A1
Methods, devices, and kits are provided for determining a recommended insulin dose to be administered to user based upon analyte data determined by an analyte sensor. ar a r
Absstract of: AU2026201926A1
22514182_1 (GHMatters) P118105.AU.1 Embodiments relate to an adaptive glycemia monitoring and forecasting system that includes an event monitor configured to receive blood glucose levels of an individual or information about an activity performed by the individual, and generate an event output. The system includes a control module configured to pull observation data, predictor variables, and population estimated vector of covariate weightings coefficients from a database, and generate updated estimated vector of covariate weightings coefficients for the individual user based on the event output. The updated estimated vector of covariate weightings coefficients are determined by a cross-entropy loss objective function. The updated estimated vector of covariate weightings coefficients are used to predict at least one or more of a predicted hypoglycemia state, a predicted normal glycemia state, or a predicted hyperglycemia state for the individual user. the individual user. ar a r
Absstract of: AU2026201994A1
22510489_1 (GHMatters) P117930.AU.1 Provided are a system and method for an artificial pancreas having multi-stage model predictive control to minimize and/or prevent occurrence of hypoglycemia associated with Type 1 diabetes. The control implements predictive modeling of a probability of glucose uptake associated with exercise based on at least one exercise 5 profile for a subject with Type 1 diabetes. Based on the probability, the control implements an automatic adjustment of basal insulin infusion to counteract a risk of exercise-induced hypoglycemia in advance of the subject engaging in the exercise. The control further implements adjustment of such infusion based on real-time signaling of exercise likely to induce hypoglycemia. Prior to consumption of a meal, the control 10 further implements adjustment of a meal-time bolus so as to account for the effect of delay in glucose uptake resulting from exercise previously engaged in by the subject. Consequently, the control acts to minimize and/or prevent hypoglycemia from occurring both during and immediately after the subject engages in exercise. ar a r
Absstract of: AU2026201970A1
MARKED-UP COPY MARKED-UP COPY Disclosed are systems and methods for generating graphical displays of analyte data and/or health information. In some implementations, the graphical displays are generating based on a self-referential dataset that are modifiable based on identified portions of the data. The modified graphical displays can indicate features in the analyte data of a host. ar a r
Absstract of: AU2026201971A1
Systems and methods are provided to provide guidance to a user regarding management of a physiologic condition such as diabetes. The determination may be based upon a patient glucose concentration level. The glucose concentration level may be provided to a stored model to determine a state. The guidance may be determined based at least in part on the determined state. ar a r
Absstract of: EP4717171A2
According to the present disclosure, an applicator for a continuous blood glucose measurement device, the applicator being operated by attaching a body attachment unit to the body of a user, the body attachment unit including a sensor member which is inserted into the body of the user in order to measure the blood glucose, comprises: a main case; a plunger to which the body attachment unit is detachably coupled and which is installed in the main case to be movable from a first position to a second position so that the body attachment unit can be discharged to the outer direction of the main case; a needle which is detachably coupled to the body attachment unit so as to be inserted into the body of the user along with the sensor member; and a needle separating unit which separates the needle from the body of the user by moving the needle in the direction opposite to the discharge direction of the plunger, wherein the needle separating unit includes a locking unit which can be assembled with the plunger in the manner of being engaged with one side of the plunger.
Absstract of: AU2024280120A1
Blood glucose level measurement includes a light source configured to irradiate light to a subject; a monochrome part configured to separate wavelength components of the light that is reflected and scattered from the subject; a light receiver configured to receive the light transmitted via the monochrome part and to generate electrical signals based on the received light; and a processor configured to extract information on the blood glucose level of the subject based on a frequency shift of the light due to the Raman effect.
Absstract of: US20260083357A1
Systems and methods for determining a glucose value for a user are disclosed herein. The method includes receiving a plurality of data inputs associated with biometric data of the user, the plurality of data inputs including at least one data input representative of a past estimated glucose value of the user and processing the plurality of data inputs with a multi-headed temporal convolutional neural network to generate a blood glucose value for the user. The method also includes providing a notification to the user based at least in part on the blood glucose value.
Absstract of: US20260089162A1
Systems, devices, and methods are disclosed for wireless communication of analyte data. In embodiments, a method of using a diabetes management partner interface to configure an analyte sensor system for wireless communication with a plurality of partner devices is provided. The method includes the analyte sensor system receiving authorization to provide one of the partner devices with access to a set of configuration parameters via the diabetes management partner interface. The set of configuration parameters is stored in a memory of the analyte sensor system. The method also includes, responsive to input received from the one partner device via the diabetes management partner interface, the analyte sensor system setting or causing a modification to the set of configuration parameters, according to a system requirement of the one partner device.
Absstract of: US20260088171A1
An ambulatory glucose profile (AGP) intelligent interpretation and insulin adjustment method based on an expert system includes: establishing a knowledge base in an inference mechanism; constructing an interpretation and decision support expert system with a simplified expert system architecture based on the knowledge base; constructing a patient problem analysis tree in three dimensions of hypoglycemia, blood glucose fluctuation, and hyperglycemia of patients; expanding each rule with expert AGP interpretation and empirical data; constructing a basal insulin dosage adjustment rule and a mealtime insulin dosage adjustment rule based on an interval type-2 fuzzy expert system; and adjusting a node of the patient problem analysis tree based on the interpretation and decision support expert system and a group of the patient, and providing a decision suggestion in combination with the basal insulin dosage adjustment rule and the mealtime insulin dosage adjustment rule.
Absstract of: US20260083911A1
Enclosed herein are methods and systems for establishing communication protocols between wireless devices in infusion pump systems. Infusion pump systems can include a number of components capable of wireless communication with one or more other components including an infusion pump, a continuous glucose monitoring (CGM) system, and a smartphone or other multi-purpose consumer electronic device (i.e., remote control device). Communications among these devices can be coordinated to ensure reliable and consistent transmission of medical data.
Absstract of: US20260088150A1
Techniques disclosed herein relate to operating a fluid delivery device in a personalized manner based at least in part on historical data of a patient. In some embodiments, the techniques involve obtaining historical meal data for the patient associated with historical meal events for the patient; determining a meal content based at least in part on the historical meal data; obtaining nutritional information associated with the meal content and historical meal events for the patient; determining, by a control system, a dosage of insulin based at least in part on the nutritional information and the historical meal events; and operating, by the control system, an actuation arrangement of the infusion device to deliver the dosage of the insulin to the patient.
Absstract of: WO2025046158A1
The invention relates to a method for controlling glucose in a flexible-structure bihormonal artificial pancreas that manages optional meal and/or exercise alerts by means of coordinated control actions, comprising: measuring a plasma glucose signal; calculating an incremental plasma glucose measurement (y); defining a model for incremental plasma glucose; defining a carbohydrate ingestion as dependent on a carbohydrate content estimated by the patient; defining an expected postprandial incremental plasma glucose y*(s) according to an insulin bolus that has been administered; defining a corrected incremental plasma glucose y̅(s), a corrected insulin infusion ū(s) and a corrected carbohydrate ingestion d̅(s); defining a virtual control action μ(s), divided between regulatory actions μ r and counterregulatory actions μ cr ; and calculating the control actions using a 2-DOF feedback controller with a prefilter having a nominal value F r (s).
Absstract of: AU2024351716A1
A method of non-invasive determination of the blood glucose concentration in the patient's tissue based on a radio noise signal received using an antenna brought close to the patient's skin according to the invention involves use of the radio noise signal is measured using a passive radiometer and additionally the method includes the following steps: a step of obtaining transformation coefficients, a step of measuring the temperature of the tissue surface, a step of measuring the temperatures of the active elements of the receiving chain of the radiometer, a step of measuring the currents consumed by the active elements of the receiving chain of the radiometer, a step of measuring the power of the radio noise signal originating from the tissue, and a step of determining the blood glucose concentration based on the aforementioned values. The invention also relates to a computer program, a radiometer, and a device for determining glucose concentration.
Absstract of: WO2026064554A1
Provided herein are methods of treating various eye disorders, including, for example, wet age-related macular degeneration (wAMD) or diabetic retinopathy (DR), using an anti-VEGF therapy, such as an anti-VEGF antibody conjugate.
Absstract of: WO2026062700A1
The present disclosure relates to a system and method to predict the post- prandial glucose response in the individuals type 2 diabetes mellitus (T2DM) in India by taking sample of patients with T2DM from various locations across India and fitting them with continuous glucose monitors (CGM) and calculating the postprandial glucose response based on the different meal intakes by the patients. The present disclosure further relates to the utilization of k-means clustering models to classify food types based on nutritional information and classification of patients. The present disclosure also relates to utilizing XGBoost to predict postprandial blood glucose responses based on patient phenotypes and food categories and providing personalized recommendations taking into account meal type, preferences, and regional influences which would allow patients with T2DM to eat and drink foods to maintain their blood sugar within prescribed limits and prevent disease progression.
Absstract of: US20260083358A1
In implementations of systems for determining a similarity of sequences of glucose values, a computing device implements a similarity system to receive input data describing a sequence of user glucose values measured by a continuous glucose monitoring (CGM) system. The similarity system computes similarity scores for a plurality of sequences of glucose values by comparing each glucose values included in the sequence of user glucose values with ever glucose value included in each sequence of the plurality of sequences. A particular sequence of glucose values that is associated with a highest similarity score is identified. The similarity system determines an externality associated with the particular sequence. The similarity system generates an indication of the externality for display in a user interface.
Absstract of: US20260083363A1
This document describes medical systems for detecting biological analytes. For example, this document describes sensors for the continuous monitoring of biological analytes, such as glucose and/or lactate, in aqueous solutions and body fluids (e.g., blood) based on a readout of fluorescence or luminescence signals.WO
Absstract of: US20260083910A1
Methods of insulin delivery may include selecting a basal insulin delivery rate responsive to a projected blood glucose level that approximates a target blood glucose level. Methods of insulin delivery may further include generating insulin delivery instructions for an insulin delivery device, the insulin delivery instructions corresponding to the basal insulin delivery rate and for a variable time duration relative to an intended time duration.
Absstract of: US20260083909A1
A reinforcement learning process with self attention is used for insulin dosing decisions in an automated medical system. The State-Action-Reward-Next State (SARS) sequence is used. The state represents the current condition, including recent continuous glucose monitoring readings, insulin doses, meal information, and potentially other relevant factors like time of day or physical activity levels. Based on this state, the agent takes an action by deciding on an insulin dose. It then receives a reward, a numerical value quantifying the quality of the action, based on resulting glucose levels and their proximity to the target range. This leads to a new state, and the process repeats. Through this iterative process, the algorithm updates the neural network weights, allowing the agent to learn which actions lead to better outcomes in different states.
Absstract of: US20260083912A1
Exemplary embodiments account for differing needs of a user over the menstrual cycle of the user to better control the blood glucose concentration of the user. The exemplary embodiments may be realized in control systems for medicament delivery devices that deliver medicaments, such as medicaments that regulate blood glucose concentration levels. Examples of such medicaments that regulate blood glucose concentration levels include insulin, glucagon, and glucagon peptide-1 (GLP-1) agonists. The exemplary embodiments are able to better tailor the dosages of the medicament delivered to the user with the medicament delivery device to reduce the risk of hyperglycemia and hypoglycemia and help reduce blood glucose concentration excursions.
Absstract of: EP4671882A2
The present disclosure relates to a system for closed loop control of glycemia. In one arrangement, the system comprises: an insulin delivery device; a user interface for inputting patient data, the patient data including a basal insulin profile, an insulin-to-carbohydrate ratio, and meal data; and a controller in communication with the user interface and the insulin delivery device and configured to receive glucose data. The controller is further configured to execute: estimating an amount of active insulin in the patient, the active insulin not including the basal insulin profile, determining a meal carbohydrate value from the meal data, estimating a physiological glucose for the patient and a rate of change of physiological glucose based in part on the glucose data, determining an attenuation factor based on the physiological glucose and the rate of change of the physiological glucose, determining a meal bolus based on meal data, the insulin-to-carbohydrate ratio, and the determined attenuation factor, modifying the determined meal bolus based on the estimated amount of active insulin in the patient, and transmitting a request to deliver the modified meal bolus to the insulin delivery device.
Absstract of: EP4715554A2
One or more embodiments of the present disclosure may include an insulin delivery system that includes an insulin delivery device, a user interface that includes multiple user-selectable icons or buttons each representing different meal characteristics, memory to store one or more user-specific dosage parameter, and a processor in communication with the memory and adapted to receive blood glucose data. The processor may also be adapted to determine initial meal characteristics associated with each of the user-selectable icons or buttons based on at least one of the user-specific dosage parameters. The processor may also be adapted to update the meal characteristics associated with each of the user-selectable icons or buttons based upon the blood glucose data.
Nº publicación: EP4715836A1 25/03/2026
Applicant:
I SENS INC [KR]
i-Sens, Inc
Absstract of: EP4715836A1
A meal monitoring apparatus according to an embodiment of the present invention includes a data receiving unit configured to receive measurement data of parameters related to an ear of a target object and blood glucose data of the target object; a first estimated time calculating unit configured to calculate a first estimated time estimated as a food intake activity time of the target object based on the measurement data; a second estimated time calculating unit configured to calculate a second estimated time estimated as a glucose absorption time of the target object based on the blood glucose data; and a meal time calculating unit configured to calculate a meal time of the target object based on the first estimated time and the second estimated time.
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