Abstract: A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded biosignals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient.

Abstract: A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded biosignals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient.

Abstract: A Digital Twin clinical trial simulator identifies an intervention parameter in a treatment recommendation. The treatment recommendation comprises instructions for adjusting the intervention parameter to cause a target improvement. The Digital Twin clinical trial simulator generates a cohort of patients sensitive to the intervention. The Digital Twin clinical trial simulator separates the cohort of patients into a control cohort comprising a first subset of patients and a test cohort comprising a second subset of patients. The Digital Twin clinical trial simulator determines an effect of the treatment recommendation on the cohort of patients by inputting the instructions of the treatment recommendation to a patient-specific metabolic model for each patient of the test cohort and comparing the effect predicted by the patient-specific metabolic of each patient in the test cohort to representations of metabolic states of patients in the control cohort.

Abstract: A Digital Twin clinical trial simulator generates a plurality of candidate treatments for causing a target improvement in metabolic health. Each candidate treatment comprises one or more intervention parameters and instructions for adjusting the one or more intervention parameters of the one or more intervention parameters to cause the target improvement. For each candidate treatment, the Digital Twin clinical trial simulator identifies a cohort of patients sensitive to adjustments to the one or more intervention parameters. The Digital Twin clinical trial simulator inputs the candidate treatment to patient-specific metabolic models, which predict the effectiveness of the candidate treatment for each patient of the cohort. The Digital Twin clinical trial simulator identifies a shortlist of effective treatments based on the predictions of the patient-specific metabolic models and generates instructions for performing physical experiments to validate the effectiveness of each shortlisted candidate treatment.

Abstract: A Digital Twin clinical trial simulator determines one or more trial parameters for a physical experiment to validate a candidate treatment recommendation. For each of one or more variations of the physical experiment, the Digital Twin clinical trial simulator determines an effectiveness of the variation in validating the candidate treatment recommendation. The effectiveness describes a likelihood that the physical experiment will provide insight regarding the effect of the candidate treatment recommendation on a metabolic state. For a variation that satisfies a threshold effectiveness, the Digital Twin clinical trial simulator determines one or more metabolic features shared among a cohort of patients sensitive to the candidate treatment recommendation.

Abstract: A patient health management platform determines of a metabolic state for a first time period. The platform generates a patient-specific treatment recommendation for a second time period following the first time period that identifies objectives for the patient to complete to improve the metabolic state determined for the first time period. Periodically during the second time period, the platform receives recordings of patient data indicating foods consumed by the patient, medication taken by the patient, and/or symptoms experienced by the patient. The platform compares the received recordings of patient data from the second time period to the generated patient-specific treatment recommendation to determine a number of objectives completed by the patient and updates a score representing n adherence of the patient to the patient-specific treatment recommendation based the number of completed objectives.

Abstract: A patient health management platform implements a machine-learned metabolic model to generate a prediction of a patient's glucose level. The platform implements a short-term prediction model to generate a daily prediction of the patient's glucose level based on nutrition data reported by the patient and sensor data and lab test data collected for the patient. The platform implements a long-term prediction model generate a prediction of the patient's glucose level during an extended time period based on sensor data and lab test data collected for the patient. Using the short-term prediction model, the long-term prediction model, or both, the patient health management platform generates predictions of the patient's glucose level and updates a digital twin of the patient's metabolic profile.

Abstract: A patient health management platform implements a machine-learned metabolic model to generate a prediction of a patient's blood pressure. The platform implements a short-term prediction model to generate a daily prediction of the patient's blood pressure based on nutrition data reported by the patient and sensor data and lab test data collected for the patient. The platform implements a long-term prediction model generate a prediction of the patient's blood pressure during an extended time period based on sensor data and lab test data collected for the patient. Using the short-term prediction model, the long-term prediction model, or both, the patient health management platform generates predictions of the patient's diastolic and systolic blood pressure and updates a digital twin of the patient's metabolic profile.

Abstract: A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded biosignals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient.

Abstract: A patient health management platform determines of a metabolic state for a first time period. The platform generates a patient-specific treatment recommendation for a second time period following the first time period that identifies objectives for the patient to complete to improve the metabolic state determined for the first time period. Periodically during the second time period, the platform receives recordings of patient data indicating foods consumed by the patient, medication taken by the patient, and/or symptoms experienced by the patient. The platform compares the received recordings of patient data from the second time period to the generated patient-specific treatment recommendation to determine a number of objectives completed by the patient and updates a score representing n adherence of the patient to the patient-specific treatment recommendation based the number of completed objectives.

Abstract: A Core Service Platform (CSP) system is integrated with an operator network and IT system to provide services to subscribers and operators. Based on information collected from the operator network and IT system, the CSP system delivers alerts to a subscriber's device and provides offers to resolve the condition causing the alerts. The CSP system provides customized contextual offers to the subscriber's device based on contextual assessments of a subscriber's current context, such as time in contract, loyalty status, data and voice usage, value of customer, time, location and purchase history. The CSP system also provides an operator a suite of tools for the operator to manage its pricing, offers, campaigns and other subscriber-related issues.

Abstract: A patient health management platform accesses a metabolic profile for a patient and biosignals recorded for the patient during a current time period comprising sensor data and/or lab test data collected for the patient. The platform receives patient data recorded during the current time period comprising food items consumed, medications taken, and symptoms experienced by the patient. The platform implements a machine-learned metabolic model to determine a metabolic state of the patient at a conclusion of the current time period by comparing a true representation of the metabolic state and a prediction of the metabolic state. The true representation and the prediction are determined based on the recorded biosignals and the recorded patient data, respectively. The platform generates a patient-specific treatment recommendation outlining instructions for the patient to improve their metabolic state and provides the patient-specific treatment recommendation to the patient device for display to the patient.

Abstract: A patient health management platform determines of a metabolic state for a first time period. The platform generates a patient-specific treatment recommendation for a second time period following the first time period that identifies objectives for the patient to complete to improve the metabolic state determined for the first time period. Periodically during the second time period, the platform receives recordings of patient data indicating foods consumed by the patient, medication taken by the patient, and/or symptoms experienced by the patient. The platform compares the received recordings of patient data from the second time period to the generated patient-specific treatment recommendation to determine a number of objectives completed by the patient and updates a score representing n adherence of the patient to the patient-specific treatment recommendation based the number of completed objectives.

Abstract: A Core Service Platform (CSP) system is integrated with an operator network and IT system to provide services to subscribers and operators. Based on information collected from the operator network and IT system, the CSP system delivers alerts to a subscriber's device and provides offers to resolve the condition causing the alerts. The CSP system provides customized contextual offers to the subscriber's device based on contextual assessments of a subscriber's current context, such as time in contract, loyalty status, data and voice usage, value of customer, time, location and purchase history. The CSP system also provides an operator a suite of tools for the operator to manage its pricing, offers, campaigns and other subscriber-related issues.

Abstract: A Core Service Platform (CSP) system is integrated with an operator network and IT system to provide services to subscribers and operators. Based on information collected from the operator network and IT system, the CSP system delivers alerts to a subscriber's device and provides offers to resolve the condition causing the alerts. The CSP system provides customized contextual offers to the subscriber's device based on contextual assessments of a subscriber's current context, such as time in contract, loyalty status, data and voice usage, value of customer, time, location and purchase history. The CSP system also provides an operator a suite of tools for the operator to manage its pricing, offers, campaigns and other subscriber-related issues.

Abstract: A mobile wireless device which operates in a wireless network is capable of: displaying a request for a user specified subscriber identification code on a GUI, receiving a single entry of the user specified subscriber identification code from the GUI, appending a code specifying a request for a type of activation and a request for data representing customer specific services to the subscriber identification code to form an activation code, transmitting the activation code to a control center wherein the transmission of the activation code results from the single entry of the user specified subscriber identification code into the GUI, receiving data representing customer specific services from the control center in response to the transmission of the activation code, and storing the received data representing the customer specific services in the memory.

Abstract: Disclosed is method comprising: storing in a home location register (HLR) at least one subscription record of a mobile device of the plurality of mobile devices, the mobile device having a subscriber identity module (SIM) identified by a currently activated first international mobile subscriber identity (IMSI), the currently activated first IMSI belonging to a set of IMSIs allocated to the system, wherein the provisioning server is operative to: receive a notification that the mobile devices has moved into a first one of the wireless networks; confirm that an allocation rule is satisfied; add and activate a second one of the IMSIs in the set of IMSIs to the HLR and remove the currently activated first IMSI from the HLR; and send the second IMSI to the mobile device to enable the mobile device to communicate wirelessly in the first wireless network as a local device.

Abstract: A cellular network system comprises a device identifier comparator and a connection enable indicator. A device identifier comparator for comparing a received device identifier with one of a plurality of stored device identifiers, wherein the one of the stored plurality of stored device identifiers is associated with a stored subscriber identifier. A connection enable indicator for indicating whether a connection from a cellular device associated with the received device identifier to a data network associated with the cellular network system should be enabled.

Abstract: Disclosed is system comprising: a server platform operative to communicate with a wireless communication network; and data storage coupled to the server platform to store a plurality of business rules, wherein the server platform is operative to: generate a first provision instruction to provision a Policy and Charging Rules Function (PCRF) with a first rule set associated with a first set of business rules for wireless usage incurred by a mobile terminal operating in the wireless communication network; generate a first billing instruction based on the first set of business rules and a first data type determined by a packet gateway that inspects packet data communicated to or from the mobile terminal.

Abstract: A system and method for managing wireless devices in a wireless network that performs operation comprising: receiving a diagnostic request to analyze a problem associated with a wireless device operating in the wireless network; retrieving contextual information associated with the wireless device from a database of the wireless network; determining at least one solution for the problem associated with the wireless device based on the contextual information; transmitting the at least one solution; and receiving a confirmation that the problem has been resolved.