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：US20220138781A1

公开(公告)日：2022-05-05

申请号：US17577818

申请日：2022-01-18

Applicant: Adobe Inc.

Inventor： Jin Xu ,  Zhenyu Yan ,  Wenqing Yang ,  Tianyu Wang ,  Abhishek Pani

IPC: G06Q30/02

Abstract: Quantitative rating systems and techniques are described that prioritize customers by propensity to buy and buy size to generate customer ratings. In one example, a propensity model is used to determine a likelihood of a potential customer to purchase a product, and a projected timeframe buy size for the potential customer is determined. An expected value for the potential customer is generated by combining the likelihood of the potential customer to purchase the product and the projected timeframe buy size. In another example, a ratio model of annualized recurring revenue (ARR) is used to determine a timeframe buy size for an existing customer in consecutive time frames. An upsell opportunity for the existing customer is determined based on the timeframe buy size less an ARR for a current time frame for the existing customer. A rating of the potential or existing customer is output in a user interface.

公开(公告)号：US11263649B2

公开(公告)日：2022-03-01

申请号：US16042770

申请日：2018-07-23

Applicant: Adobe Inc.

Inventor： Jin Xu ,  Zhenyu Yan ,  Wenqing Yang ,  Tianyu Wang ,  Abhishek Pani

IPC: G06Q30/02

Abstract: Quantitative rating systems and techniques are described that prioritize customers by propensity to buy and buy size to generate customer ratings. In one example, a propensity model is used to determine a likelihood of a potential customer to purchase a product, and a projected timeframe buy size for the potential customer is determined. An expected value for the potential customer is generated by combining the likelihood of the potential customer to purchase the product and the projected timeframe buy size. In another example, a ratio model of annualized recurring revenue (ARR) is used to determine a timeframe buy size for an existing customer in consecutive time frames. An upsell opportunity for the existing customer is determined based on the timeframe buy size less an ARR for a current time frame for the existing customer. A rating of the potential or existing customer is output in a user interface.

公开(公告)号：US11222268B2

公开(公告)日：2022-01-11

申请号：US15454799

申请日：2017-03-09

Applicant: Adobe Inc.

Inventor： Zhenyu Yan ,  Yang Wang ,  Arava Sai Kumar ,  Abhishek Pani

IPC: G06N5/02 ,  G06N20/00 ,  H04L29/08

Abstract: The present disclosure relates to a media attribution system that improves multi-channel media attribution by employing discrete-time survival modeling. In particular, the media attribution system uses event data (e.g., interactions and conversions) to generate positive and negative conversion paths, which the media attribution system uses to train an algorithmic attribution model. The media attribution system also uses the trained algorithmic attribution model to determine attribution scores for each interaction used in the conversion paths. Generally, the attribution score for an interaction indicates the effect the interaction has in influencing a user toward conversion.

公开(公告)号：US10997634B2

公开(公告)日：2021-05-04

申请号：US16695562

申请日：2019-11-26

Applicant: Adobe Inc.

Inventor： Deepak Pai ,  Trung Nguyen ,  Sy Bor Wang ,  Jose Mathew ,  Abhishek Pani ,  Neha Gupta

IPC: G06Q30/02

Abstract: Systems and methods are disclosed herein for distributing online ads with electronic content according to online ad request targeting parameters. One embodiment of this technique involves placing online test ads across multiple online ad request dimensions and tracking a performance metric for the online test ads. The performance of the online ad request dimensions is estimated based on the tracking of the performance metric for the online test ads and online ad request targeting parameters are established for spending a budget of a campaign to place online ads in response to online ad requests having particular online ad request dimensions. Online ads are then distributed based on using the online ad request targeting parameters to select online ad requests.

公开(公告)号：US20200327419A1

公开(公告)日：2020-10-15

申请号：US16384558

申请日：2019-04-15

Applicant: Adobe Inc.

Inventor： Lei Zhang ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Abhishek Pani

IPC: G06N3/08 ,  G06F17/18 ,  G06N3/10

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a target distribution schedule for providing electronic communications based on predicted behavior rates by utilizing a genetic algorithm and one or more objective functions. For example, the disclosed systems can generate predicted behavior rates by training and utilizing one or more behavior prediction models. Based on the predicted behavior rates, the disclosed systems can further utilize a genetic algorithm to apply objective functions to generate one or more candidate distribution schedules. In accordance with the genetic algorithm, the disclosed systems can select a target distribution schedule for a particular user/client device. The disclosed systems can thus provide one or more electronic communications to individual users based on respective target distribution schedules.

公开(公告)号：US20200151746A1

公开(公告)日：2020-05-14

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

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：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.