Simulation augmented reinforcement learning for real-time content selection

    公开(公告)号:US11847670B1

    公开(公告)日:2023-12-19

    申请号:US17955783

    申请日:2022-09-29

    CPC classification number: G06Q30/0255 G06Q30/0275 G06Q30/0277

    Abstract: Systems, devices, and methods are described herein for improving inventory management. As used herein, “inventory” refers to digital space at an inventory providers webpage at which content can be delivered. The disclosed techniques utilize reinforced machine learning and an offline training process to train various models with which a content request corresponding to the inventory can be classified according to historical requests and a selection process identified for the request (e.g., a direct or an indirect selection process). If an indirect selection process is chosen, the content request may be optimized for that process utilizing additional machine learning models trained using reinforced machine learning and the offline training process. The disclosed techniques enable the inventory provider to optimize content selections according to a preferred objective. The training operations are performed offline, in a training system configured to simulate the run time environment.

    ENHANCED ON-TARGET RATE OPTIMIZATION FOR VIDEO USING MACHINE LEARNING

    公开(公告)号:US20230239524A1

    公开(公告)日:2023-07-27

    申请号:US17584009

    申请日:2022-01-25

    CPC classification number: H04N21/252 H04N21/25883 H04N21/812 H04N21/26241

    Abstract: Devices, systems, and methods are provided for on-target rate optimization for video. A method may include receiving streaming video advertisement impression data; receiving user activity data indicative of day-parts when viewers watch content; generating, based on the streaming video advertisement impression data and the survey data, using a machine learning model, a demographic probability vector, wherein each entry of the demographic probability vector is indicative of a probability that a viewer is in a respective age range of the non-overlapping demographic groups; generating, using the machine learning model, an audience recognition model with the demographic probability vector; generating a synthetic audience model predicting future advertisement viewing behavior; generating an assignment of an advertisement bid to a respective demographic group of the non-overlapping demographic groups; and generating, based on the assignment, a list of target demographic groups of the non-overlapping demographic groups for a bid request associated with the advertisement bid.

    Enhanced recognition of content audiences

    公开(公告)号:US11671640B1

    公开(公告)日:2023-06-06

    申请号:US17457024

    申请日:2021-11-30

    CPC classification number: H04N21/26241 H04N21/25883 H04N21/44218 H04N21/812

    Abstract: Devices, systems, and methods are provided for audience recognition. A method may include receiving over-the-top (OTT) advertisement impression data comprising metadata and content of advertisement bid requests, the metadata indicative of scheduled OTT media presentation; receiving user activity data indicative of day-part times when viewers watch content absent from the OTT advertisement impression data; generating, based on the OTT advertisement impression data, a first demographic probability vector; generating, based on the user activity data, a second demographic probability vector; generating, based on a combination of the first demographic probability vector and the second demographic probability vector, a third demographic probability vector, each entry of the third demographic probability vector indicative of a third probability that a viewer is in a respective age range; and generating an indication of the third demographic probability vector.

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