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 system for implementing and managing network-based, variable authentication protocols receives information relating to a digital monetary transaction. Additionally, the system accesses an initial authentication protocol dataset. The system also generates a variable authentication protocol dataset. The system then communicates the variable authentication protocol dataset to the point-of-sale computer system. The system also receives, from the point-of-sale computer system, authentication tokens. Further, the system validates the authentication tokens in view of the variable authentication protocol dataset. Further still, in response to the validation of the authentication tokens, the system processes the digital monetary transaction.

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: 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: Methods, apparatus, systems, and articles of manufacture are disclosed that improve sleep state demotion with a hardware power monitor. An example apparatus includes memory, and processor circuitry to perform at least one of the operations to instantiate: detector circuitry to detect power output data of the computing device via a hardware power monitor, power analyzer circuitry to determine the power output data for sleep states of the computing device based on multiple wake intervals, identifier circuitry to identify crossover thresholds at ones of the multiple wake intervals, and controller circuitry to limit the computing device to the crossover thresholds at ones of the multiple wake intervals.

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: A computerized method comprising: receiving channel events comprising (a) user-sourced data provided by users each using a respective mobile device within a channel and (b) location data associated with the respective mobile devices of the users with the channel; generating a correlation between the location data and the user-sourced data; and generating, using a predictive engine, channel analysis data for the channel based on the channel events and the correlation between the location data and the user-sourced data. Other embodiments are disclosed herein.

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 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: Improved multi-modal machine learning networks integrate computer vision systems with language models. In certain embodiments, a computer vision system analyzes at least one image to generate a computer vision output. The language model generates an output based, at least in part, on a consideration of the computer vision output. The outputs of the language model can be generated by jointly considering textual information learned by the language model and visual content extracted by the computer vision system, thereby significantly improving the accuracy, breadth, and comprehensiveness of the outputs.

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 analyte sensor automatically or on demand 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: 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: Improved multi-modal machine learning networks integrate computer vision systems with language models. In certain embodiments, a computer vision system analyzes at least one image to generate a computer vision output. The language model generates an output based, at least in part, on a consideration of the computer vision output. The outputs of the language model can be generated by jointly considering textual information learned by the language model and visual content extracted by the computer vision system, thereby significantly improving the accuracy, breadth, and comprehensiveness of the outputs.

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: 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: 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: A system for measurement of an analyte level including an analyte sensor having an in vivo portion in contact with the interstitial fluid of a user and an ex vivo portion. The sensor further includes at least one working electrode and a reference electrode located on the in vivo portion, and a first substrate. The at least one working electrode and reference electrode sense signals associated with a measured analyte level in the interstitial fluid of a user. Further, the ex vivo portion includes a plurality of electronic components mounted thereon, and at least one of the electronic components are configured to receive the generated signals associated with the measured analyte level. The electronic components are mounted to the ex vivo portion using photonic soldering.

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 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.