PROCESSING TECHNIQUES AND SYSTEM ARCHITECTURES FOR AUTOMATED CORRESPONDENCE MANAGEMENT

    公开(公告)号:US20230222601A1

    公开(公告)日:2023-07-13

    申请号:US18174921

    申请日:2023-02-27

    CPC classification number: G06F40/186 H04L51/046

    Abstract: In a computer-implemented method for automated management of correspondence related to an insurance claim, an evaluation trigger (e.g., occurrence of a pre-scheduled time, a triggering event associated with the claim, and/or reception of an evaluation request from an application) may be detected and, in response, an evaluation may be performed. Performing the evaluation may include retrieving one or more data elements from a claims database, and/or generating evaluation output data by processing the retrieved data element(s) according to a first set of rules implemented by a rules engine. Based upon the evaluation output data, at least one action may be selected from among a set of potential actions. The set of potential actions may include automatically generating correspondence (or selected portions thereof) associated with the claim and scheduling a future evaluation. The selected action may then be performed using a second set of rules implemented by the rules engine.

    Systems and methods for augmented reality for disaster simulation

    公开(公告)号:US11699005B2

    公开(公告)日:2023-07-11

    申请号:US17476382

    申请日:2021-09-15

    CPC classification number: G06F30/20 G06T19/006 G06Q40/08

    Abstract: An augmented reality (AR) system for generating and displaying a pre-disaster Enhanced Situation Visualization (ESV) is provided. The AR system may include an ESV computing device, a user computing device operated by a user and a reference database. The user computing device may transmit a reference request message to the ESV device, the reference request message including an image and/or GPS location of a property. The ESV computing device may determine the subject of the image is the property, retrieve reference information including peril maps associated with the property from the reference database, and determine situation information specific to the subject. The reference and situation information including a loss estimate may be displayed on the user computing device to provide an ESV of the property. The ESV may be used for insurance-related activities, such as handling, adjusting, and/or generating an insurance policy, premium, and/or discount, and/or generating insurance-related recommendations.

    OPTIMIZING INTEREST ACCRUAL BETWEEN A USER'S FINANCIAL ACCOUNTS

    公开(公告)号:US20230206316A1

    公开(公告)日:2023-06-29

    申请号:US16783979

    申请日:2020-02-06

    CPC classification number: G06Q40/02 G06Q30/0205

    Abstract: Techniques are disclosed utilizing cognitive computing to improve banking experiences. A user's financial account(s) may be monitored to predict when a surplus of funds is unnecessarily present and for how long this will likely be the case. Once this is determined, techniques include automatically drafting funds from the account to another account having a higher interest rate where the funds may accrue more interest. The techniques also include predicting when an overdraft may occur and taking appropriate action when such a prediction is made. Predictions may be based upon different weighted inputs used in accordance with a predictive modeling system, which may attempt to predict for a particular user, location, and retailer, whether the user will spend an anticipated amount in excess of the user's current balance. If so, passive (e.g., notifications) and active (e.g., transferring cover funds) actions may be performed.

    Detecting and Mitigating Local Individual Driver Anomalous Behavior

    公开(公告)号:US20230196855A1

    公开(公告)日:2023-06-22

    申请号:US18111488

    申请日:2023-02-17

    Inventor: Michael Bernico

    CPC classification number: G07C5/0841 G06Q40/08 G07C5/0816 G06N20/00

    Abstract: Systems and methods for identifying anomalous driving behavior for a vehicle based on past driving behavior are disclosed herein. The method may include receiving a set of time-series driving data for the vehicle, wherein the set of time-series driving data is indicative of a set of operating conditions for the vehicle. Performing machine learning operations on the set of time-series driving data. Identifying a set of anomalous conditions in the time-series driving data based on a result set produced by the machine learning operations, wherein the set of anomalous conditions are indicative of an anomalous vehicle behavior. Comparing the set of anomalous conditions to a set of historical time-series driving data for the vehicle. Generating a vehicle feedback based on the time-series driving data and the comparison of the set of anomalous conditions to the set of historical time-series driving data.

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