Abstract: A method for providing smart auto rental estimatics to a user includes: training a machine learning model including a plurality of training parameters; receiving an auto loss report; extracting machine learning parameters from the auto loss report; analyzing, using the machine learning model, the machine learning parameters and volume data to determine at least one of (i) a loss time to repair and (ii) a loss cost to repair, the volume data corresponding to real-time information pertaining to claim volume and/or shop volume, and the loss time to repair indicating a wait time the user is predicted to require a rental vehicle; and outputting, by the machine learning model, the at least one of (i) the loss time to repair and (ii) the loss cost to repair.

Abstract: A method for providing smart auto rental estimatics to a user includes training a machine learning model to predict repair information, receiving a report, analyzing the report to identify a coverage, determining a vendor, receiving a chosen vendor, determining a branch, receiving a chosen branch, analyzing the report and a volume data using the trained machine learning model to determine loss information, calculating out-of-pockets, and transmitting the out-of-pockets to the user.

Abstract: A method of determining an injury segment includes receiving a loss report, analyzing the loss report using a trained model to determine a severity of an injury, determining, based on the severity of the injury, an injury segment, and storing, via a processor, an indication of the injury segment. A computer system includes a processor configured to receive a loss report, analyze the loss report using a trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment. A non-transitory computer readable medium containing program instructions that when executed cause a computer to receive a loss report, analyze the loss report using a trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment.

Abstract: A method for providing smart auto rental estimatics to a user includes training a machine learning model to predict repair information, receiving a report, analyzing the report to identify a coverage, determining a vendor, receiving a chosen vendor, determining a branch, receiving a chosen branch, analyzing the report and a volume data using the trained machine learning model to determine loss information, calculating out-of-pockets, and transmitting the out-of-pockets to the user.

Abstract: A method for providing smart auto rental estimatics to a user includes training a machine learning model to predict repair information, receiving a report, analyzing the report to identify a coverage, determining a vendor, receiving a chosen vendor, determining a branch, receiving a chosen branch, analyzing the report and a volume data using the trained machine learning model to determine loss information, calculating out-of-pockets, and transmitting the out-of-pockets to the user.

Abstract: A method of determining an injury segment includes receiving a loss report, analyzing the loss report using a trained model to determine a severity of an injury, determining, based on the severity of the injury, an injury segment, and storing, via a processor, an indication of the injury segment. A computer system includes a processor configured to receive a loss report, analyze the loss report using a trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment. A non-transitory computer readable medium containing program instructions that when executed cause a computer to receive a loss report, analyze the loss report using a trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment.

Abstract: A method of determining an injury segment includes training a machine learning model, receiving a loss report, analyzing the loss report using the trained model to determine a severity of an injury, determining, based on the severity of the injury, an injury segment, and storing, via a processor, an indication of the injury segment. A computer system includes a processor configured to train a machine learning model, receive a loss report, analyze the loss report using the trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment. A non-transitory computer readable medium containing program instructions that when executed cause a computer to train a machine learning model, receive a loss report, analyze the loss report using the trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment.

Abstract: Systems and methods are disclosed with respect to providing seamless customer experience transactions, in particular, linking disparate systems to facilitate a lower friction customer experience. An exemplary embodiment includes receiving recorded data from one or more connected devices at a geographic location; analyzing the recorded data, wherein analyzing the recorded data includes determining that an collision has occurred involving one or more vehicles; generating a transaction including the data indicative of the collision based upon the analysis; and transmitting the transaction to at least one other participant in the distributed ledger network.

Abstract: A method for classifying total loss based on claim characteristics may include training a machine learning model using labeled data to classify a loss report, receiving a loss report associated with a policy, analyzing the loss report using the trained model to classify the loss report into a category, and storing an indication of total loss in association with the loss report when the category is a total loss category. A method for automating loss report taking may include receiving loss report data and telephony data from a user, correlating the user to a policy and a profile, determining a preferred language of the user and displaying a prepopulated loss report and a loss report word track in the preferred language of the user to a customer support user, wherein the prepopulated loss report and loss report word track are interpolated into predetermined locations in a loss report template.

Abstract: A method of determining an injury segment includes training a machine learning model, receiving a loss report, analyzing the loss report using the trained model to determine a severity of an injury, determining, based on the severity of the injury, an injury segment, and storing, via a processor, an indication of the injury segment. A computer system includes a processor configured to train a machine learning model, receive a loss report, analyze the loss report using the trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment. A non-transitory computer readable medium containing program instructions that when executed cause a computer to train a machine learning model, receive a loss report, analyze the loss report using the trained model to determine the severity of an injury, determine an injury segment, and store an indication of the injury segment.

Abstract: A method of assigning and/or routing an auto claim to an appropriate claim handling tier to mitigate delay may include training a machine learning model using historical claim data to determine a severity corresponding to an injury claim, receiving a loss report corresponding to an auto accident, analyzing the loss report using the trained machine learning model to determine the severity of at least one injury corresponding to the loss report, determining an injury segment, and storing the indication of the injury segment in association with the loss report.

Abstract: A disfluent-utterance tracking system includes a speech transducer; one or more targeted-disfluent-utterance records stored in a memory; a real-time speech recording mechanism operatively connected with the speech transducer for recording a real-time utterance; and an analyzer operatively coupled with the targeted-disfluent-utterance record and with the real-time speech recording mechanism, the analyzer configured to compare one or more real-time snippets of the recorded speech with the targeted-disfluent-utterance record to determine and indicate to a user a level of correlation therebetween.

Abstract: A system and method are disclosed for manufacturing an emitter structure in a complementary bipolar complementary metal oxide semiconductor (CBiCMOS) transistor manufacturing process. A protective layer is formed over an emitter layer in a transistor structure and lateral portions of the protective layer and the emitter layer are etched to form an emitter structure. An oxide layer is then deposited over the transistor structure and an etchback process is performed to remove portions of the oxide layer from the top of the protective layer. A source/drain implant process is then performed to implant an extrinsic base region of the transistor. The protective layer protects the emitter structure from the implant process. Then the protective layer is removed from the emitter structure.

Abstract: According to this process for the optical coding of an Internet address printed on a visible support, to allow computer processing thereof, of the type according to which a cue incorporated into a coded printed mark which can be read by a scanner is associated with the address, the cue may be arranged in at least one of the dots of the Internet address. This cue may contain the identity of the visible support. The process includes the reading of the cue by a scanner.