公开(公告)号：US20200226675A1

公开(公告)日：2020-07-16

申请号：US16248287

申请日：2019-01-15

Applicant: Adobe Inc.

Inventor： Saayan Mitra ,  Aritra Ghosh ,  Somdeb Sarkhel ,  Jiatong Xie

IPC: G06Q30/08 ,  G06N20/00

Abstract: The present disclosure relates to generating digital bids for providing digital content to remote client devices based on parametric bid distributions generated using a machine learning model (e.g., a mixture density network). For example, in response to identifying a digital bid request in a real-time bidding environment, the disclosed systems can utilize a trained parametric censored machine learning model to generate a parametric bid distribution. To illustrate, the disclosed systems can utilize a parametric censored, mixture density machine learning model to analyze bid request characteristics and generate a parametric, multi-modal distribution reflecting a plurality of parametric means, parametric variances, and combination weights. The disclosed systems can then utilize the parametric, multi-modal distribution to generate digital bids in response to the digital bid request in real-time (e.g., while a client device accesses digital assets corresponding to the bid request).

公开(公告)号：US12131350B2

公开(公告)日：2024-10-29

申请号：US17403702

申请日：2021-08-16

Applicant: Adobe Inc.

Inventor： Somdeb Sarkhel ,  Saayan Mitra ,  Jiatong Xie ,  Alok Kothari

IPC: G06Q30/0273 ,  G06N20/00 ,  G06Q30/0241

CPC classification number: G06Q30/0275 ,  G06N20/00 ,  G06Q30/0277

Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.

公开(公告)号：US20210374809A1

公开(公告)日：2021-12-02

申请号：US17403702

申请日：2021-08-16

Applicant: Adobe Inc.

Inventor： Somdeb Sarkhel ,  Saayan Mitra ,  Jiatong Xie ,  Alok Kothari

IPC: G06Q30/02 ,  G06N20/00

Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.

公开(公告)号：US11127050B2

公开(公告)日：2021-09-21

申请号：US16687082

申请日：2019-11-18

Applicant: Adobe Inc.

Inventor： Somdeb Sarkhel ,  Saayan Mitra ,  Jiatong Xie ,  Alok Kothari

IPC: G06Q30/02 ,  G06N20/00

Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.

公开(公告)号：US20210150585A1

公开(公告)日：2021-05-20

申请号：US16687082

申请日：2019-11-18

Applicant: Adobe Inc.

Inventor： Somdeb Sarkhel ,  Saayan Mitra ,  Jiatong Xie ,  Alok Kothari

IPC: G06Q30/02 ,  G06N20/00

Abstract: Techniques are disclosed for real-time bidding (e.g., for dynamic online content placement) using an optimized final bid. The final bid is determined based on a predicted clearing price and an initial bid. The initial bid represents a value to a prospective content provider, and may be computed based on campaign information. The predicted clearing price is a predicted amount paid, and may be predicted using a model trained using historical winning bids data. The clearing price may be predicted using a quantile regression model, where the quantile can be selected to control bid aggressiveness. In some cases, the quantile is determined based on pacing in an overall campaign. Once the initial bid and the predicted clearing price are calculated, the final bid is computed based on the initial bid and the predicted clearing price.