Method for Automatic Detection of Pair-Wise Interaction Effects Among Large Number of Variables

    公开(公告)号:US20240160696A1

    公开(公告)日:2024-05-16

    申请号:US18241713

    申请日:2023-09-01

    CPC classification number: G06F18/2113 G06F17/18 G06F18/27

    Abstract: Techniques for automatically detecting pair-wise interaction effects among a large number of variables are provided. An example method includes obtaining a data set including data related to a target variable and each of a plurality of variables upon which the target variable depends; grouping the data related to each variable, of the plurality of variables, into a pre-determined number of groups of grouped variable values; analyzing the grouped variable values related to each variable as compared to the grouped variable values related to each other variable, of the plurality of variables, in order to determine a grouped variable interaction score for each pair of variables, of the plurality of variables; and identifying a pre-determined number of pairs of variables having the highest interaction scores, based on the grouped variable interaction score for each pair of variables.

    SYSTEMS AND METHODS FOR AUTONOMOUSLY GENERATING AND MAINTAINING NON-FUNGIBLE TOKENS FOR REAL-TIME SUBJECT ASSESSMENT

    公开(公告)号:US20240146532A1

    公开(公告)日:2024-05-02

    申请号:US18496158

    申请日:2023-10-27

    CPC classification number: H04L9/3213 H04L9/50

    Abstract: A computer system for generating and maintaining non-fungible tokens for real-time subject assessment is described herein. The computer system includes at least one processor in communication with at least one memory device, the at least one processor programmed to: (i) receive a plurality of data associated with a subject history of a subject; (ii) generate a container file for the subject history to include the plurality of data; (iii) generate a non-fungible token (NFT) for the subject based upon the container file; (iv) store the NFT and the container file for the subject; (v) retrieve the NFT to access the plurality of data; and (vi) input the plurality of data to a trained model to receive an initial subject assessment as output from the trained model.

    Systems and Methods for Detecting and Preventing Damage to Pipes

    公开(公告)号:US20240142330A1

    公开(公告)日:2024-05-02

    申请号:US18095675

    申请日:2023-01-11

    CPC classification number: G01M3/007 G01M3/243

    Abstract: Systems and methods are described for detecting a leak based upon home telematics data. The method may include: (1) receiving home telematics data from one or more sensors associated with one or more pipes (or piping systems) in a structure, wherein the home telematics data is indicative of the frequency with which the one or more pipes are being used; (2) determining, using a trained machine learning algorithm, pipe activity associated with the one or more pipes is occurring at an irregular frequency; (3) determining, based upon at least the determination that the pipe activity associated with the one or more pipes is occurring at the irregular frequency, that the one or more pipes are leaking; and (4) transmitting an indication to a user associated with the home that the one or more pipes are leaking.

    Systems and methods for identifying ancillary home costs

    公开(公告)号:US11972499B2

    公开(公告)日:2024-04-30

    申请号:US18309387

    申请日:2023-04-28

    CPC classification number: G06Q50/16 G06F3/048 G06Q30/0283 G06Q40/03 G06Q40/08

    Abstract: A home cost analysis server is configured to train an image processing program to identify features of homes, receive user input including a prospective home, and access a first database storing metadata and images associated with homes, including the prospective home, available for purchase. The server is also configured to input images of the prospective home to the machine-learned image processing program, which outputs a feature of the prospective home, access a second database storing historical ancillary costs, and perform a lookup in the second database to retrieve comparable historical ancillary costs associated homes having a comparable feature to the outputted feature. The server is further configured to analyze the metadata associated with the prospective home, the outputted feature, and the comparable historical ancillary costs to determine ancillary home costs associated with the prospective home, and display the ancillary home costs and an overall monthly cost for the prospective home.

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