公开(公告)号：US11341516B2

公开(公告)日：2022-05-24

申请号：US16877385

申请日：2020-05-18

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Suofei Wu ,  Chang Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Shengyun Peng

IPC: G06Q30/02 ,  H04L51/56 ,  G06Q10/10

Abstract: Introduced here are approaches for identifying the optimal send time for messages by accounting for hidden confounders, such as the content of those messages, delivery channel, etc. These approaches use a causal inference framework to discover and then remove the impact of hidden confounders. These approaches may be employed by a marketing and analytics platform (or simply “marketing platform”) that may be used to design, implement, or review digital marketing campaigns. The marketing platform can consider the send time as a treatment and then employ machine learning (ML) models that consider the send time, features of the recipient, and hidden confounders to produce a ranked series of send times with the effect of the hidden confounders marginalized. Approaches to performing offline evaluations that mimic A/B tests using data related to existing field experiments are also introduced here.

公开(公告)号：US11227226B2

公开(公告)日：2022-01-18

申请号：US15783223

申请日：2017-10-13

Applicant: Adobe Inc.

Inventor： Eugene Chen ,  Zhenyu Yan ,  Xiaojing Dong

IPC: G06N7/00 ,  G06N5/04

Abstract: Methods, systems, and computer readable storage media are disclosed for generating joint-probabilistic ensemble forecasts for future events based on a plurality of different prediction models for the future events. For example, in one or more embodiments the disclosed system determines error values for various predictions from a plurality of different prediction models (i.e., “forecasters”) for previous events. Moreover, in one or more embodiments the system generates an error probability density function by mapping the error values to an error space and applying a kernel density estimation. Furthermore, the system can apply the error probability density function(s) to a plurality of predictions from the forecasters for a future event to generate a likelihood function and a new prediction for the future event.

公开(公告)号：US20210075875A1

公开(公告)日：2021-03-11

申请号：US16564768

申请日：2019-09-09

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Abhishek Pani

IPC: H04L29/08 ,  G06F16/2457 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times for distributing digital content to client devices utilizing a recommendation system approach. For example, the disclosed systems can utilize a recommendation system model such as a matrix factorization model, a factorization machine model, and/or a neural network to implement collaborative filtering to generate predicted response rates for particular candidate send times. Based on the predicted response rates indicating likelihoods of receiving responses for particular send times, the disclosed system can generate a distribution schedule to provide electronic communications at one or more of the send times.

公开(公告)号：US20200311487A1

公开(公告)日：2020-10-01

申请号：US16371460

申请日：2019-04-01

Applicant: Adobe Inc.

Inventor： Jun He ,  Zhenyu Yan ,  Yi-Hong Kuo ,  Wuyang Dai ,  Shiyuan Gu ,  Abhishek Pani

IPC: G06K9/62 ,  G06F17/27 ,  G06N7/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations. In addition, the disclosed systems can update the response rate prediction models and/or the Bayes upper-confidence-bound send time model based on providing additional electronic communications and receiving additional responses to modify model weights.

公开(公告)号：US20200065713A1

公开(公告)日：2020-02-27

申请号：US16112546

申请日：2018-08-24

Applicant: Adobe Inc.

Inventor： Xiang Wu ,  Zhenyu Yan ,  Yi-Hong Kuo ,  Wuyang Dai ,  Julia Viladomat Comerma ,  Abhishek Pani

IPC: G06N99/00

Abstract: Techniques and systems are described that employ survival analysis and classification to predict occurrence of future events by a digital analytics system. Survival analysis involves modeling time to event data. Survival analysis is used by digital analytics systems to analyze an expected duration of time until an event happens. In the techniques described herein, survival analysis is employed as part of a classification technique by a digital analytics system. In one example, a digital analytics system generates training data from a dataset in accordance with a survival analysis technique such that, after generated, the training data is usable to train a classification model.

公开(公告)号：US20240168751A1

公开(公告)日：2024-05-23

申请号：US17989362

申请日：2022-11-17

Applicant: Adobe Inc.

Inventor： Luwan Zhang ,  Zhenyu Yan ,  Jun He ,  Hsiang-yu Yang ,  Cheng Zhong

IPC: G06F8/656 ,  G06N20/00

CPC classification number: G06F8/656 ,  G06N20/00

Abstract: In implementations of systems for estimating temporal occurrence of a binary state change, a computing device implements an occurrence system to compute a posterior probability distribution for temporal occurrences of binary state changes associated with client computing devices included in a group of client computing devices. The occurrence system determines probabilities of a binary state change associated with a client computing device included in the group of client computing devices based on the posterior probability distribution, and the probabilities correspond to future periods of time. A future period of time is identified based on a probability of the binary state change associated with the client computing device. The occurrence system generates a communication based on a communications protocol for transmission to the client computing device via a network at a period of time that correspond to the future period of time.

公开(公告)号：US11816272B2

公开(公告)日：2023-11-14

申请号：US17656782

申请日：2022-03-28

Applicant: Adobe Inc.

Inventor： Zhenyu Yan ,  Fnu Arava Venkata Kesava Sai Kumar ,  Chen Dong ,  Abhishek Pani ,  Ning Li

IPC: G06Q30/00 ,  G06F3/01 ,  G06N3/08 ,  G06F3/0484 ,  G06F3/0481 ,  H04L67/50

CPC classification number: G06F3/017 ,  G06F3/0481 ,  G06F3/0484 ,  G06N3/08 ,  H04L67/535

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.

公开(公告)号：US11651383B2

公开(公告)日：2023-05-16

申请号：US16191289

申请日：2018-11-14

Applicant: ADOBE INC.

Inventor： Xiang Wu ,  Zhenyu Yan ,  Yi-Hong Kuo ,  Wuyang Dai ,  Polina Bartik ,  Abhishek Pani

IPC: G06Q30/02 ,  G06Q10/06 ,  G06F17/16 ,  G06N20/00 ,  G06Q30/0204 ,  G06Q10/0639 ,  G06Q10/067

CPC classification number: G06Q30/0204 ,  G06F17/16 ,  G06Q10/067 ,  G06Q10/06393 ,  G06N20/00

Abstract: An improved analytics system generates actionable KPI-based customer segments. The analytics system determines predicted outcomes for a key performance indicator (KPI) of interest and a contribution value for each variable indicating an extent to which each variable contributes to predicted outcomes. Topics are generated by applying a topic model to the contribution values for the variables. Each topic comprises a group of variables with a contribution level for each variable that indicates the importance of each variable to the topic. User segments are generated by assigning each user to a topic based on attribution levels output by the topic model.

公开(公告)号：US20220221939A1

公开(公告)日：2022-07-14

申请号：US17656782

申请日：2022-03-28

Applicant: Adobe Inc.

Inventor： Zhenyu Yan ,  Fnu Arava Venkata Kesava Sai Kumar ,  Chen Dong ,  Abhishek Pani ,  Ning Li

IPC: G06F3/01 ,  G06N3/08 ,  G06F3/0484 ,  H04L67/50 ,  G06F3/0481

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.

公开(公告)号：US20200027157A1

公开(公告)日：2020-01-23

申请号：US16037700

申请日：2018-07-17

Applicant: Adobe Inc.

Inventor： Maoqi Xu ,  Zhenyu Yan ,  Jin Xu ,  Abhishek Pani

IPC: G06Q40/02 ,  G06N5/04 ,  G06Q30/02 ,  G06N99/00

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for using reject inference to generate synthetic data for modifying lead scoring models. For example, the disclosed system identifies an original dataset corresponding to an output of a lead scoring model that generates scores for a plurality of prospects to indicate a likelihood of success of prospects of the plurality of prospects. In one or more embodiments, the disclosed system selects a reject inference model by performing simulations on historical prospect data associated with the original dataset. Additionally, the disclosed system uses the selected reject inference model to generate an imputed dataset by generating synthetic outcome data representing simulated outcomes of rejected prospects in the original dataset. The disclosed system then uses the imputed dataset to modify the lead scoring model by modifying at least one parameter of the lead scoring model using the synthetic outcome data.