Recommendation Network Using Machine Learning

    公开(公告)号:US20240161166A1

    公开(公告)日:2024-05-16

    申请号:US18407739

    申请日:2024-01-09

    CPC classification number: G06Q30/0631 G06N3/04 G06Q30/0625 H04L51/52

    Abstract: Methods, systems, and apparatuses are described herein for providing purchase recommendations by analyzing social networks using machine learning. A machine learning model may be trained to select one or more of the first plurality of users. Purchase intention data that indicates an intention of a first user to acquire a type of asset may be received. Social networking data that comprises a plurality of associations between a second plurality of users may be received. Purchase history data indicating one or more purchases, of one or more assets associated with the type of asset, made by the second plurality of users may be received. The trained machine learning model may be provided the data. In return, the trained machine learning model may provide an indication of a second user. A notification may be sent to the second user.

    AUTOMATIC ANNOTATION FOR VEHICLE DAMAGE

    公开(公告)号:US20230102293A1

    公开(公告)日:2023-03-30

    申请号:US18059656

    申请日:2022-11-29

    Abstract: Aspects described herein may allow an automated generation of an interactive multimedia content with annotations showing vehicle damage. In one method, a server may receive vehicle-specific identifying information of a vehicle. Image sensors may capture multimedia content showing aspects associated with exterior regions of the vehicle, and may send the multimedia content to the server. For each of the exterior regions of the vehicle, the server may determine, using a trained classification model, instances of damage. Furthermore, the server may generate an interactive multimedia content that shows images with annotations indicating instances of damage. The interactive multimedia content may be displayed via a user interface.

    Systems and Methods for Automated Trade-In With Limited Human Interaction

    公开(公告)号:US20230065825A1

    公开(公告)日:2023-03-02

    申请号:US17974104

    申请日:2022-10-26

    Abstract: Aspects described herein may facilitate an automated trade-in of a vehicle with limited human interaction. A server may receive a request to begin a value determination of a vehicle associated with the user. The server may receive first data comprising: vehicle-specific identifying information, and multimedia content showing a first aspect of the vehicle. The user may be directed to place the vehicle within a predetermined staging area. The server may receive, from one or more image sensors associated with the staging area, second data comprising multimedia content showing a second aspect of the vehicle. The server may create a feature vector comprising the first data and the second data. The feature vector may be inputted into a machine learning algorithm corresponding to the vehicle-specific identifying information of the vehicle. Based on the machine learning algorithm, the server may determine a value of the vehicle.

    System and method for reducing client-server network traffic for internet database queries

    公开(公告)号:US11436239B1

    公开(公告)日:2022-09-06

    申请号:US17360229

    申请日:2021-06-28

    Abstract: Methods and systems are described herein for improvements for reducing client-server network traffic. For example, methods and systems allow for the reduction of client-server network traffic by altering search filters based on their respective rank and/or frequencies of being searched with other search filters included in a search query. In this way, users do not have to resubmit queries that are illogical or would return a null set of search results. For example, a first query may be received. A first search may be performed based on the first query. A first search filter may be selected based on a hierarchical tree structure of search filters. A second query may be generated comprising the first search filter and one or more other search filters, wherein the second query comprises fewer search filters than the first query, and a second search may be performed based on the second query.

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