Systems and methods for assessing needs

    公开(公告)号:US11790432B1

    公开(公告)日:2023-10-17

    申请号:US17516609

    申请日:2021-11-01

    CPC classification number: G06Q30/0631 G06Q10/067 G06Q40/08

    Abstract: Systems and methods for assessing the needs of customers using predictive modeling techniques are disclosed. The method receives customer data from a first database and provided by the user. The method generates an instruction to a second database and receives additional customer information received from external databases to generate a basic profile for data based on the customer. The system further analyzes data provided by the user. The method generates a customer profile based on the basic customer data and additional data. The method determines missing data from the customer profile associated and a set of attributes of the user. The method identifies a profile with the customer similar set of attributes and estimates the missing data using predictive modeling techniques to generate estimated customer information. The system further pre-populates one or more missing fields of the full profile associated with the customer based on said estimated customer information. The system. The method additionally analyzes the full updated customer profile associated with the customer to generate one or more insurance recommendations for the customer that will allow customers to fulfill one or more proposed future financial goals while ensuring the financial stability of the customer. The systems and methods disclosed allow the level of data-entry efforts required from the user to be significantly reduced.

    Systems and methods for managing dynamic transportation networks using simulated future scenarios

    公开(公告)号:US11790289B2

    公开(公告)日:2023-10-17

    申请号:US17580994

    申请日:2022-01-21

    Applicant: Lyft, Inc.

    Inventor: Chinmoy Dutta

    CPC classification number: G06Q10/047 G01C21/3438 G06Q10/067

    Abstract: The disclosed computer-implemented method may include (i) receiving a first transport request and a second transport request, (ii) evaluating a fitness of matching the first and second transport requests to be fulfilled by a transport provider, based at least partly on a transportation overlap between the first and second transport requests, (iii) generating a simulated future transport request, (iv) evaluating a fitness of matching the first transport request with the simulated future transport request, based at least in part on a transportation overlap between the first transport request and the simulated future transport request, and (v) matching the first and second transport requests based at least in part on the fitness of matching the first and second transport requests and based at least in part on the fitness of matching the first transport request with the simulated future transport request. Various other methods, systems, and computer-readable media are disclosed.

    METHODS AND SYSTEMS FOR DIGITAL PLACEMENT AND ALLOCATION

    公开(公告)号:US20230325762A1

    公开(公告)日:2023-10-12

    申请号:US17715733

    申请日:2022-04-07

    CPC classification number: G06Q10/087 G06Q30/0202 G06Q10/067

    Abstract: Methods, systems, and platforms are described for digital placement and allocation planning. An unconstrained distribution of items in a retail supply chain may be determined from digital demand forecasts by aggregating the digital demand forecasts based on location identifiers. An item allocation ratio between shipping locations may be determined using a model based on items being ordered together and the speed of items being ordered. An unconstrained DPA plan may be generated, with the distribution and the ratio, for placing and allocating a projected total quantity. Constraints relating to the supply chain may be identified. In response to the constraints, a constrained distribution may be generated from an unconstrained distribution. A constrained plan may be generated in response to the constrained distribution. The unconstrained or constrained DPA plan may be sent to a plan executor for initiating movements of items according to the plan within the supply chain.

    COMPUTER-IMPLEMENTED METHOD AND SYSTEM FOR TESTING A MODEL

    公开(公告)号:US20230325757A1

    公开(公告)日:2023-10-12

    申请号:US18042968

    申请日:2021-07-15

    CPC classification number: G06Q10/067 G06Q20/202

    Abstract: A computer-implemented method for testing a model indicating a parametric relationship between a plurality of channels of multivariate data, the method comprising: obtaining a real dataset of observed multivariate data comprising the plurality of channels; generating a control dataset of multivariate data comprising the plurality of channels, the control dataset being generated based on the model; for each of a plurality of sample subsets from the real dataset and the control dataset, calculating a p-value for the model; and determining whether (1) a distribution difference characteristic of a distribution of the obtained p-values for sample subsets of the real dataset and a distribution of the obtained p-values for sample subsets of the control dataset falls within a second predetermined significance range, and, if condition (1) is met, determining that the model is accurate.

    Finance management platform and method

    公开(公告)号:US11783431B2

    公开(公告)日:2023-10-10

    申请号:US15818105

    申请日:2017-11-20

    CPC classification number: G06Q40/12 G06F16/93 G06Q10/067

    Abstract: A finance management platform and method are disclosed. A model element data repository encodes a plurality of model elements representing invoicing outcomes, each model element including a score value. A processor is configured to execute computer program code for providing a finance management platform for a plurality of clients, including receiving invoicing data from a client data repository remote from the finance management platform, translating the received invoicing data into a common format and store the invoicing data in the common format in an invoice data repository, accessing the model element data repository and determine one or more of the model elements applicable to the invoicing data, calculating an overall score for the invoicing data from the applicable model element's scores and triggering a communication to a payment processor and an update to the invoice data repository upon the overall score exceeding a predetermined threshold.

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