Abstract: A system in accordance with present embodiments includes a device comprising a sensor configured to detect sensed data for an individual. The sensed data comprises data related to health of the individual. The system also includes a health analysis system communicatively coupled to the device. The health analysis system comprises a memory and a processor. The memory is configured to store instructions that cause the processor to receive the sensed data from the device, analyze the sensed data to determine health trend data for the individual, retrieve group health trend data associated with the health trend data for the individual, generate a predicted health trend for the individual based at least in part on a comparison between the health trend data for the individual and the group health trend data, and adjust offered products, premiums, or both, based on predicted health trend for the individual.

Abstract: Machine learning technology can analyze in real-time the data from a call between a person and a customer service representative. Based on this analysis, a server can determine a sentiment score that describes a sentiment expressed by the person or the customer service representative. If the server determines that the sentiment score is less than or equal to a pre-determined value, the server can inform the customer service representative's manager so that the manager can take further action to help the person and/or the customer service representative.

Abstract: A system may include sensors that may acquire health data related to an individual. The system may also include a processor that may receive trip data associated with a current location of the individual and a destination location for the individual, traffic condition data associated with routes between the current location and the destination location, and weather related data associated with the routes. The processor may also receive the health data from the sensors and determine whether the individual is associated with a driver assessment score above a threshold based on the trip data, the traffic condition data, the weather data, the health data, or any combination thereof. The processor may then send an activation signal to a vehicle control system in response to the driver assessment score being above the threshold, such that the activation signal may enable the vehicle control system to control operations of a vehicle.

Abstract: Machine learning technology can analyze in real-time the data from a call between a person and a customer service representative. Based on this analysis, a server can determine a sentiment score that describes a sentiment expressed by the person or the customer service representative. If the server determines that the sentiment score is less than or equal to a pre-determined value, the server can inform the customer service representative's manager so that the manager can take further action to help the person and/or the customer service representative.

Abstract: Machine learning technology can analyze in real-time the data from a call between a person and a customer service representative. Based on this analysis, a server can determine a sentiment score that describes a sentiment expressed by the person or the customer service representative. If the server determines that the sentiment score is less than or equal to a pre-determined value, the server can inform the customer service representative's manager so that the manager can take further action to help the person and/or the customer service representative.

Abstract: A system may include sensors that may acquire health data related to an individual. The system may also include a processor that may receive trip data associated with a current location of the individual and a destination location for the individual, traffic condition data associated with routes between the current location and the destination location, and weather related data associated with the routes. The processor may also receive the health data from the sensors and determine whether the individual is associated with a driver assessment score above a threshold based on the trip data, the traffic condition data, the weather data, the health data, or any combination thereof. The processor may then send an activation signal to a vehicle control system in response to the driver assessment score being above the threshold, such that the activation signal may enable the vehicle control system to control operations of a vehicle.

Abstract: The AUTOMATIC REMOTE DEPOSIT IMAGE PREPARATION APPARATUSES, METHODS AND SYSTEMS (hereinafter “MultiCrop”) transforms uploaded check images and check deposit information inputs via MultiCrop components image into deposit confirmation outputs. For example, in one embodiment, a user may employ a personal computer connected to a web camera, and/or a smartphone with a built-in camera to initiate the remote deposit by holding a check in front of the camera, and the MultiCrop may capture images of the check and send them to a financial institution for deposit processing.

Abstract: Machine learning technology can analyze in real-time the data from a call between a person and a customer service representative. Based on this analysis, a server can determine a sentiment score that describes a sentiment expressed by the person or the customer service representative. If the server determines that the sentiment score is less than or equal to a pre-determined value, the server can inform the customer service representative's manager so that the manager can take further action to help the person and/or the customer service representative.

Abstract: The AUTOMATIC REMOTE DEPOSIT IMAGE PREPARATION APPARATUSES, METHODS AND SYSTEMS (hereinafter “MultiCrop”) transforms uploaded check images and check deposit information inputs via MultiCrop components image into deposit confirmation outputs. For example, in one embodiment, a user may employ a personal computer connected to a web camera, and/or a smartphone with a built-in camera to initiate the remote deposit by holding a check in front of the camera, and the MultiCrop may capture images of the check and send them to a financial institution for deposit processing.

Abstract: Methods and systems for remote deposit of financial instruments are disclosed. The method may include using a mobile device such as a tablet computer or smart phone to automatically review live video data to determine a location of a check in the video image, and to obtain a still photo of the check image in response to determining that the mobile device is being held at a desired steadiness. The method may include providing a user with visual feedback that the mobile device is actively tracking the check. The method may further include cropping the captured check image and sending only a portion of the image to a remote server to verify a check MICR line has been captured. The method may further include a virtual endorsement module executable by the mobile device to apply endorsement information to a check image. A mobile device with a processor and memory containing instructions to execute the method is also disclosed.

Abstract: The AUTOMATIC REMOTE DEPOSIT IMAGE PREPARATION APPARATUSES, METHODS AND SYSTEMS (hereinafter “MultiCrop”) transforms uploaded check images and check deposit information inputs via MultiCrop components image into deposit confirmation outputs. For example, in one embodiment, a user may employ a personal computer connected to a web camera, and/or a smartphone with a built-in camera to initiate the remote deposit by holding a check in front of the camera, and the MultiCrop may capture images of the check and send them to a financial institution for deposit processing.

Abstract: The present disclosure provides apparatus, systems, and methods for computing system monitoring. The apparatus, systems, and methods provide a map that indicates a location of a system problem within the computing system.