Abstract: A computer-implemented method for adjusting one or more electronic medical bills for a claimant injured in an accident comprises generating a user interface to be presented to a claims adjuster; receiving a first user input identifying a claimant; responsive to the first user input, retrieving and aggregating multiple electronic medical bills each having at least one line; generating one or more findings and multiple scenarios by providing the aggregated electronic medical bills as inference input to a trained machine learning model, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding findings and scenarios, wherein responsive to the inference input, the trained machine learning model outputs the one or more findings and the multiple scenarios, wherein the one or more findings represent rationales for approving, denying, or repricing, and wherein the multiple scenarios include cost estimates based on the one or more findings.

Abstract: A computer-implemented method comprises providing images and attributes of a damaged vehicle that has been damaged in a collision event to a trained computer vision machine learning model, which in response provides a predicted severity class indicating a severity of the physical damage sustained by the damaged vehicle during the collision event; providing the predicted severity class, damaged vehicle physical damage claim data related to the damaged vehicle and the collision event, and bodily injury claim data related to an occupant of the damaged vehicle during the collision event to a trained regression machine learning model, which in response provides a predicted frequency and/or duration of medical treatments to treat bodily injury sustained by the occupant during the collision event; and providing the predicted frequency and/or duration of medical treatments to an analyst for use in evaluating a bodily injury claim related to the occupant.

Abstract: A computer-implemented method comprises providing images and attributes of a damaged vehicle that has been damaged in a collision event to a trained computer vision machine learning model, which in response provides indicators of physical damage sustained by the damaged vehicle during the collision event; providing the indicators to a trained classifier machine learning model, which in response provides a predicted class of bodily injury sustained by an occupant of the damaged vehicle during the collision event and a confidence indicator representing a level of confidence that the predicted class of bodily injury is correct; and providing the predicted class of bodily injury and the confidence indicator to an analyst for use in evaluating a bodily injury claim related to the occupant of the damaged vehicle and the collision event.

Abstract: A computer-implemented method comprises providing images and attributes of a damaged vehicle that has been damaged in a collision event to a trained computer vision machine learning model, which in response provides indicators of physical damage sustained by the damaged vehicle during the collision event; providing the indicators of physical damage to a trained classifier machine learning model, which in response provides a predicted class of bodily injury sustained by an occupant of the damaged vehicle during the collision event and a confidence indicator representing a level of confidence that the predicted class of bodily injury is correct; determining a likelihood of attorney representation of the occupant concerning the bodily injury based on the predicted class of bodily injury; and providing the likelihood of attorney representation of the occupant to an analyst for use in evaluating a bodily injury claim related to the occupant.

Abstract: A computer-implemented method comprises providing images and attributes of a damaged vehicle that has been damaged in a collision event to a trained computer vision machine learning model, wherein responsive to the first inference input, which in response provides indicators of physical damage sustained by the damaged vehicle during the collision event; providing the indicators to a trained classifier machine learning model, which in response provides predicted standard medical diagnostic codes related to bodily injury sustained by an occupant of the damaged vehicle during the collision event and confidence indicators representing levels of confidence that the one or more predicted standard medical diagnostic codes are correct; and providing the predicted standard medical diagnostic codes and the confidence indicators to an analyst for use in evaluating a bodily injury claim related to the occupant of the damaged vehicle and the collision event.

Abstract: A computer-implemented method comprises providing images and attributes of a damaged vehicle that has been damaged in a collision event to a trained computer vision machine learning model, which in response indicators of physical damage sustained by the damaged vehicle during the collision event; providing the indicators, damage claim data related to the collision event, and bodily injury claim data related to an occupant of the damaged vehicle during the collision event to a trained regression machine learning model, which in response provides a projected cost to repair the damaged vehicle and a projected cost to treat bodily injury sustained by the occupant during the collision event, and providing the projected costs to an analyst for use in evaluating a bodily injury claim related to the occupant of the damaged vehicle and the collision event and a damaged vehicle physical damage claim related to the collision event.

Abstract: A computer-implemented method comprises providing images and attributes of a damaged vehicle that has been damaged in a collision event to a trained computer vision machine learning model, which in response provides indicators of physical damage sustained by the damaged vehicle during the collision event; providing the indicators to a trained classifier machine learning model, which in response provides a predicted likelihood that bodily injury was sustained by an occupant of the damaged vehicle during the collision event and a confidence indicator representing a level of confidence that the predicted likelihood that bodily injury was sustained is correct; and providing the predicted likelihood of bodily injury and the confidence indicator to an analyst for use in evaluating a bodily injury claim related to the occupant of the damaged vehicle and the collision event.

Abstract: A computer-implemented method comprises providing images and attributes of a damaged vehicle that has been damaged in a collision event to a trained computer vision machine learning model, which in response provides indicators of physical damage sustained by the damaged vehicle during the collision event; providing the indicators to a trained classifier machine learning model, which in response provides a predicted likelihood that bodily injury was sustained by an occupant of the damaged vehicle during the collision event and a confidence indicator representing a level of confidence that the predicted likelihood that bodily injury was sustained is correct; and providing the predicted likelihood of bodily injury and the confidence indicator to an analyst for use in evaluating a bodily injury claim related to the occupant of the damaged vehicle and the collision event.

Abstract: Disclosed herein is a computer-implemented method of providing an authenticated certificate to be modified in the generation of certified user data. Also disclosed is a computer-implemented method of enabling the characterization of an unknown user from certified user data, and a method of characterizing the unknown user from the certified user data. The generation of the certified user data avoids the risk of personal information being leaked into the digital domain, except in circumstances where an unknown user is required to be characterized or identified with the assistance of a trustee system.

Abstract: A computer-implemented method of and a system for securely establishing control of a resource in a distributed ledger is disclosed. The distributed ledger comprises a first shard instantiated on one or more first processor(s) of a first system. The first shard is configured to respond to an instruction originating from a first party. The distributed ledger further comprises a second shard instantiated on one or more second processor(s) and configured to respond to an instruction originating from a second party. The method comprises creating, by means of the publishing of a record by the first system, the resource, wherein the publishing of the record occurs in response to an instruction originating from the first party, wherein the publishing of the record is to the first shard of the ledger, and wherein the publishing of the record establishes that control of the resource resides with the second party.

Abstract: A method for automatically determining injury treatment relation to a motor vehicle accident comprises obtaining a plurality of images of a damaged motor vehicle, vehicle data describing the motor vehicle, occupant data describing an occupant who occupied the motor vehicle during the motor vehicle accident, and injury data from an electronic insurance claim associated with the motor vehicle accident, the injury data specifying an injury to the occupant; determining a delta velocity value for the damaged motor vehicle by applying a first machine learning model to the set of images of the damaged motor vehicle and the vehicle data; determining an injury severity score by applying a second machine learning model to the delta velocity value for the damaged motor vehicle, the vehicle data, and the occupant data; and automatically adjudicating the electronic insurance claim.

Abstract: A computer-implemented method comprises: receiving, from an insurer system, an electronic medical bill; generating a decision representing a workflow selected from a plurality of the workflows based on at least one of a plurality of decisioning factors describing the electronic medical bill by providing the electronic medical bill as an inference input to a trained machine learning model, wherein responsive to the inference input the trained machine learning model generates the decision representing a workflow, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding historical decisions, the workflows including a provider network workflow and a provider negotiations workflow, and routing the electronic medical bill accordingly.

Abstract: A computer-implemented method for adjusting one or more electronic medical bills for a claimant injured in an accident comprises generating a user interface to be presented to a claims adjuster; receiving a first user input identifying a claimant; responsive to the first user input, retrieving and aggregating multiple electronic medical bills each having at least one line; generating one or more findings and multiple scenarios by providing the aggregated electronic medical bills as inference input to a trained machine learning model, wherein the trained machine learning model has been trained with historical electronic medical bills and corresponding findings and scenarios, wherein responsive to the inference input, the trained machine learning model outputs the one or more findings and the multiple scenarios, wherein the one or more findings represent rationales for approving, denying, or repricing, and wherein the multiple scenarios include cost estimates based on the one or more findings.

Abstract: A computer-implemented method of and a system for securely establishing control of a resource in a distributed ledger is disclosed. The distributed ledger comprises a first shard instantiated on one or more first processor(s) of a first system. The first shard is configured to respond to an instruction originating from a first party. The distributed ledger further comprises a second shard instantiated on one or more second processor(s) and configured to respond to an instruction originating from a second party. The method comprises creating, by means of the publishing of a record by the first system, the resource, wherein the publishing of the record occurs in response to an instruction originating from the first party, wherein the publishing of the record is to the first shard of the ledger, and wherein the publishing of the record establishes that control of the resource resides with the second party.

Abstract: A method for automatically determining injury treatment relation to a motor vehicle accident comprises obtaining a plurality of images of a damaged motor vehicle, vehicle data describing the motor vehicle, occupant data describing an occupant who occupied the motor vehicle during the motor vehicle accident, and injury data from an electronic insurance claim associated with the motor vehicle accident, the injury data specifying an injury to the occupant; determining a delta velocity value for the damaged motor vehicle by applying a first machine learning model to the set of images of the damaged motor vehicle and the vehicle data; determining an injury severity score by applying a second machine learning model to the delta velocity value for the damaged motor vehicle, the vehicle data, and the occupant data; and automatically adjudicating the electronic insurance claim.

Abstract: A method for analyzing existing digital files to recognize sensitive data in the textual content. The method includes extracting features describing the environmental context in which a file was created and the file content itself and modeling and analyzing pairwise relations between text that exist within a given file; the text itself; and characteristics that exist about the text in relation to the entire file. The method takes the extracted features, including the data itself and its context, and analyzes this data with artificial intelligence (AI) algorithms such as decision trees and neural networks to predict whether a document includes sensitive data. Leveraging AI algorithms rather than discrete algorithms carries with it the advantage of being able to handle massive volumes of data, as well as the ever increasing varieties of data.

Abstract: A portable item carrier and belt feed system is provided along with related methods. One illustrative embodiment includes a carrier and feed structure comprising a semi-rigid material with a divider support extending perpendicularly from a center floor section of the carrier. A belt of items is folded over the divider to reduce jams or misfeeds as the belt of items is pulled through a feed chute near an upper side section of the carrier and feed structure at a predetermined range of feed angles to the divider support. A latch disposed on a lid of the feed structure is configured for quick and easy access to the belt of items. A flexible outer container encloses the carrier and feed structure and can be coupled to a harness section configured to couple with a mobile structure. Additional embodiments include variants suitable for field manufacturing as well as portable manufacturing systems and methods.

Abstract: Methods, non-transitory computer readable media, and insurance claim analysis devices are disclosed that generate an injury severity score based on a delta velocity value for a damaged motor vehicle and occupant data for an occupant of the damaged motor vehicle or motor vehicle data associated with the damaged motor vehicle. A first set of condition indications are identified based on a correlation of the injury severity score with a stored mapping of condition indications to injury severity scores. A determination is made when one or more of the first set of condition indications correspond to one or more of a second set of condition indications in injury data for an electronic insurance claim. The electronic insurance claim is automatically adjudicated based on a likelihood value generated based on the determination. The likelihood value is indicative of whether a reported injury of the occupant resulted from an associated motor vehicle accident.

Abstract: Various systems and methods are provided for increasing situational awareness of an operator carrying, integrating, and using a combination of equipment items in a variety of operating environments in a compact configuration enabling rapid viewing and access or stowage with minimized eye and hand movement. In particular, embodiments include apparatuses and methods associated with a ruggedized hands-free body mounted portable display device holder. In particular, various embodiments are provided which enable an operator to use a combination equipment together in a body mounted system which increase situational awareness and ability to operate using a variety of body mounted equipment as well as carried equipment that reduce mass or streamline the operator's equipment loadout that is carried on their body e.g., body armor with equipment mounting loops or sewn strips.

Abstract: Various systems and methods are provided for increasing situational awareness of an operator carrying, integrating, and using a combination of equipment items in a variety of operating environments in a compact configuration enabling rapid viewing and access or stowage with minimized eye and hand movement. In particular, embodiments include apparatuses and methods associated with a ruggedized hands-free body mounted portable display device holder. In particular, various embodiments are provided which enable an operator to use a combination equipment together in a body mounted system which increase situational awareness and ability to operate using a variety of body mounted equipment as well as carried equipment that reduce mass or streamline the operator's equipment loadout that is carried on their body e.g., body armor with equipment mounting loops or sewn strips.