Abstract: An underwriting estimator predictive machine learning model receives as inputs a limited number of details about an applicant, and outputs an immediate underwriting estimate of risk class. A preliminary pre-screening review redirects applicants with one or more screening impairments to a human-in-the-loop quick quote process. Model inputs include estimator inputs data that are pre-selected from the dataset of impairments data after excluding the screening impairments from the dataset of impairments. The underwriting estimator model may incorporate alternative pathways that output individualized underwriting estimates for some applicants and cohort-level marginal distributions for other applicants. Model outputs also include explanation files providing interpretability of underwriting estimates. The explanation files may include additive feature attribution data and rule based natural language explanations.

Abstract: A handheld work apparatus includes a motor, a transmission shaft, a tool, a guide tube, a housing, and a first anti-vibration element. The motor via the transmission shaft drives the tool. The transmission shaft is received in the guide tube. The motor is disposed in the housing. The tool is disposed on a front longitudinal end of the guide tube. The housing is disposed on the rear longitudinal end of the guide tube. The housing at least partially encompasses the guide tube, wherein the guide tube projects from the housing. The first anti-vibration element is disposed such that it acts in a vibration-decoupling manner between the housing and the guide tube. The first anti-vibration element is disposed on that side of the motor facing the tool. The handheld work apparatus includes a second anti-vibration element connecting the motor to the housing in an at least indirectly vibration-decoupled manner.

Abstract: The present disclosure relates to a method for operating a lighting device for a motor vehicle, wherein the lighting device has at least an exterior lamp and a control device. The exterior lamp has an illuminating area having at least two independently controllable segments each comprising at least one light source, wherein, in at least one dynamic operating mode of the control device, for at least one dynamic group of the segments comprising at least two segments a pseudo-randomized or randomized, temporally variable lighting operation of the segments of the dynamic group takes place.

Abstract: Disclosed herein are embodiments of systems, methods, and products comprises an analytic server, which determines user health attributes by tracking the user’s behaviors and activities within a predetermined space. The analytic server receives tracking data from a set of sensors installed in the predetermined space. The sensors track a beacon worn by the user. The analytic server determines micro-locations and user behaviors based on the tracking data. The analytic server determines the coordinates of the sensors based on the sensor identifiers and maps the coordinates to regions by referring to a floor plan map. The analytic server determines the user behaviors and activities by aggregating the micro-locations and regions the user visited at different time. The analytic server determines the user’s health score based on the micro-locations and user behaviors by executing an artificial intelligence model. The analytic server determines a recommendation of premium based on the health score.

Abstract: A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. Fluidless models are trained by application of a random forest ensemble including survival, regression, and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer.

Abstract: A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer.

Abstract: Embodiments disclosed herein disclose a back-end computer to generate a risk score and a front-end visualization engine to hierarchically display the generated risk core. The back-end computer users a machine learning model for a stepwise perturbation from a digital reference profile until a user profile to be score is reached. The computer may calculate intermediate risk score for each perturbation and calculate the final risk score after all the perturbations are completed. The front-end visualization engine generates an interactive hierarchical display showing information associated with the risk score calculation. More specifically, the visualization engine may show a filtered list of users sharing one or more attributes with the user profile, a visual rendering of the top factors contributing to the risk score, and individual input values within a factor; and juxtapose the scores and attributes of the user profile in the graphical information display of the associated population.

Abstract: The invention concerns a device for quantifying the dexterity of the fingers of a hand, comprising a main body, several systems for measuring the movement and/or the force applied by a finger in a pressure direction, each system for measuring comprising a sensor for deformation, the sensor for deformation comprising a deformable body, characterized in that each system for measuring also comprises a guide bearing, integral with the main body, a shaft having a bearing surface, the bearing surface being in contact with the deformable body, a tube, adapted to slide in translation in the guide bearing and around the shaft in the pressure direction, having a head adapted to fix the finger to the tube, a first spring connecting the bearing to the tube, a second spring connecting the shaft to the tube, the first spring and the second spring being pre-tensioned, so as to pre-tension the deformable body.

Abstract: Embodiments disclosed herein disclose a back-end computer to generate a risk score and a front-end visualization engine to hierarchically display the generated risk core. The back-end computer users a machine learning model for a stepwise perturbation from a digital reference profile until a user profile to be score is reached. The computer may calculate intermediate risk score for each perturbation and calculate the final risk score after all the perturbations are completed. The front-end visualization engine generates an interactive hierarchical display showing information associated with the risk score calculation. More specifically, the visualization engine may show a filtered list of users sharing one or more attributes with the user profile, a visual rendering of the top factors contributing to the risk score, and individual input values within a factor; and juxtapose the scores and attributes of the user profile in the graphical information display of the associated population.

Abstract: The present invention relates to a new method for quantifying key components of manual dexterity. The present invention also provides methods for diagnosing impaired upper limb and/or hand function in patients based on how these components are affected.