Abstract: A computerized method comprising: tracking user behavior data of users each using a respective mobile device within a channel; detecting a demand surge in the channel based on channel analysis data for the channel, wherein the channel analysis data for the channel are based channel events for the channel; and generating one or more first incentives for one or more first users of the users based on the demand surge and incentive metrics, wherein the one or more first incentives are configured to change user behavior of the one or more first users. Other embodiments are disclosed herein.

Abstract: This disclosure relates to improved techniques for personalizing vehicle routes and operator sessions using pre-trained machine learning language models. In certain embodiments, a language model is trained on operator interaction data to learn operator route preferences for vehicle operators. These learned operator route preferences can be leveraged to optimize and personalize vehicle routes and operator sessions in various ways. Other embodiments are disclosed herein as well.

Abstract: A computerized method comprising: tracking user behavior data of users each using a respective mobile device within a channel; detecting a demand surge in the channel based on channel analysis data for the channel, wherein the channel analysis data for the channel are based channel events for the channel; and generating one or more first incentives for one or more first users of the users based on the demand surge and incentive metrics, wherein the one or more first incentives are configured to change user behavior of the one or more first users. Other embodiments are disclosed herein.

Abstract: Methods and devices to monitor an analyte in body fluid are provided. Embodiments include continuous or discrete acquisition of analyte related data from a transcutaneously positioned in vivo analyte sensor automatically or upon request from a user.

Abstract: This disclosure relates to improved techniques for personalizing vehicle routes and operator sessions using pre-trained machine learning language models. In certain embodiments, a language model is trained on operator interaction data to learn operator route preferences for vehicle operators. These learned operator route preferences can be leveraged to optimize and personalize vehicle routes and operator sessions in various ways. Other embodiments are disclosed herein as well.

Abstract: Embodiments described herein relate to an analyte monitoring device having a user interface with a display and a plurality of actuators. The display is configured to render a plurality of display screens, including a home screen and an alert screen. The home screen is divided into a plurality of simultaneously displayed panels, with a first panel displays a rate of change of continuously monitored analyte levels in interstitial fluid, a second panel simultaneously displays a current analyte level and an analyte trend indicator, and a third panel displays status information of a plurality of components of the device. When an alarm condition is detected, the display renders the alert screen in place of the home screen, the alert screen displaying information corresponding to the detected alarm condition. Furthermore, the actuators are configured to affect further output of the analyte monitoring device corresponding to the detected condition.

Abstract: Embodiments described herein relate to an analyte monitoring device having a user interface with a display and a plurality of actuators. The display is configured to render a plurality of display screens, including a home screen and an alert screen. The home screen is divided into a plurality of simultaneously displayed panels, with a first panel displays a rate of change of continuously monitored analyte levels in interstitial fluid, a second panel simultaneously displays a current analyte level and an analyte trend indicator, and a third panel displays status information of a plurality of components of the device. When an alarm condition is detected, the display renders the alert screen in place of the home screen, the alert screen displaying information corresponding to the detected alarm condition. Furthermore, the actuators are configured to affect further output of the analyte monitoring device corresponding to the detected condition.

Abstract: This disclosure relates to artificial intelligence (AI) and machine learning networks for predicting or determining demand metrics across multiple channels. An analytics platform can receive channel events from multiple channels corresponding to geographic areas, and channel features related to demand conditions in the channels can be extracted from the channel events. During a training phase, the channel features can be accumulated into one or more training datasets for training one or more demand prediction models. The one or more demand prediction models can be trained to predict or determine demand metrics for each of the channels. The demand metrics can indicate or predict demand conditions based on the current conditions in the channels and/or based on future, predicted conditions in the channels. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to decentralized computing networks, architectures and techniques for collecting, analyzing, and processing data over multiple channels. A decentralized computing network comprises a plurality of computing nodes, each of which is dedicated to analyzing and processing events for a particular channel corresponding to a geographic region. Each node of the decentralized computing network can operate independently to process channel analysis data for a corresponding channel. The decentralized configuration of the nodes enables efficient processing of data collected over large geographic areas, increases the reliability of the system, and facilitates easy scaling of the system. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to artificial intelligence (AI) and machine learning networks for predicting or determining demand metrics across multiple channels. An analytics platform can receive channel events from multiple channels corresponding to geographic areas, and channel features related to demand conditions in the channels can be extracted from the channel events. During a training phase, the channel features can be accumulated into one or more training datasets for training one or more demand prediction models. The one or more demand prediction models can be trained to predict or determine demand metrics for each of the channels. The demand metrics can indicate or predict demand conditions based on the current conditions in the channels and/or based on future, predicted conditions in the channels. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to decentralized computing networks, architectures and techniques for collecting, analyzing, and processing data over multiple channels. A decentralized computing network comprises a plurality of computing nodes, each of which is dedicated to analyzing and processing events for a particular channel corresponding to a geographic region. Each node of the decentralized computing network can operate independently to process channel analysis data for a corresponding channel. The decentralized configuration of the nodes enables efficient processing of data collected over large geographic areas, increases the reliability of the system, and facilitates easy scaling of the system. Other embodiments are disclosed herein as well.

Abstract: Embodiments described herein relate to an analyte monitoring device having a user interface with a display and a plurality of actuators. The display is configured to render a plurality of display screens, including a home screen and an alert screen. The home screen is divided into a plurality of simultaneously displayed panels, with a first panel displays a rate of change of continuously monitored analyte levels in interstitial fluid, a second panel simultaneously displays a current analyte level and an analyte trend indicator, and a third panel displays status information of a plurality of components of the device. When an alarm condition is detected, the display renders the alert screen in place of the home screen, the alert screen displaying information corresponding to the detected alarm condition. Furthermore, the actuators are configured to affect further output of the analyte monitoring device corresponding to the detected condition.

Abstract: Method, device and system for providing consistent and reliable glucose response information to physiological changes and/or activities is provided to improve glycemic control and health management.

Abstract: Methods and devices to monitor an analyte in body fluid are provided. Embodiments include continuous or discrete acquisition of analyte related data from a transcutaneously positioned in vivo analyte sensor automatically or upon request from a user. The in vivo analyte sensor is coupled to an electronics unit holding a memory with instruction to cause processing circuitry to initiate a predetermined time period that is longer than a predetermined life of the sensor, during the predetermined time period, convert signals from the sensor related to glucose to respective corresponding glucose levels, without relying on any post-manufacture independent analyte measurements from a reference device, and at the expiration of the predetermined time period, disable, deactivate, or cease use of one or more feature.

Abstract: Methods and devices and systems include generating analyte data based on signals from an analyte sensor transcutaneously positioned in contact with interstitial fluid under a skin layer of a user and to generate signals corresponding to an analyte level of a target analyte. The analyte data corresponds to the analyte level of the target analyte over a wear period. Methods include storing the analyte data over the wear period. Methods include determining one or more indications based on the analyte data. Methods include determining a reimbursement insurance code based at least in part on stored sensor information associated with the analyte sensor. Methods include communicating at least a portion of the analyte data and the reimbursement insurance code to an electronic record associated with the user and stored by a remote electronic medical record system. Methods include providing the one or more indications on a user interface unit.

Abstract: This disclosure relates to improved techniques for monitoring activities across a plurality of channels and predicting conditions in the channels. In certain embodiments, channel events corresponding to the channels are collected and monitored by an analytics dashboard of a geolocation platform. A channel analysis function analyzes utilizes the channel events to generate demand predictions and/or other metrics. In some cases, the channel analysis function can execute an anomaly detection model, a time series forecasting model, and/or other learning model to generate the predictions and metrics. The analytics dashboard generate various displays to visualizing the demand predictions and/or other metrics. Other embodiments are disclosed herein as well.

Abstract: Methods and devices to monitor an analyte in body fluid are provided. Embodiments include continuous or discrete acquisition of analyte related data from a transcutaneously positioned in vivo analyte sensor automatically or upon request from a user. The in vivo analyte sensor is coupled to an electronics unit holding a memory with instruction to cause processing circuitry to initiate a predetermined time period that is longer than a predetermined life of the sensor, during the predetermined time period, convert signals from the sensor related to glucose to respective corresponding glucose levels, without relying on any post-manufacture independent analyte measurements from a reference device, and at the expiration of the predetermined time period, disable, deactivate, or cease use of one or more feature.

Abstract: This disclosure relates to improved techniques for personalizing vehicle routes and operator sessions using pre-trained machine learning language models. In certain embodiments, a language model is trained on operator interaction data to learn operator route preferences for vehicle operators. These learned operator route preferences can be leveraged to optimize and personalize vehicle routes and operator sessions in various ways. Other embodiments are disclosed herein as well.

Abstract: This disclosure relates to improved techniques for accessing and presenting service offerings and/or services options from multiple service provider platforms. In some embodiments, a front-end of the user application includes a client interface that enables an end-user to communicate with a pre-trained language model. The language model can serve as an intermediary between the end-user and a plurality of service provider platforms. The language model can communicate with each of the of service provider platforms to identify and present service options to the end-user via the client interface. Other embodiments are disclosed herein as well.

Abstract: Method, device and system for providing consistent and reliable glucose response information to physiological changes and/or activities is provided to improve glycemic control and health management.