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：US20200098015A1

公开(公告)日：2020-03-26

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：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.

公开(公告)号：US20200019984A1

公开(公告)日：2020-01-16

申请号：US16034232

申请日：2018-07-12

Applicant: Adobe Inc.

Inventor： Yuan Yuan ,  Zhenyu Yan ,  Yiwen Sun ,  Xiaojing Dong ,  Chen Dong ,  Abhishek Pani

IPC: G06Q30/02 ,  H04L29/08 ,  G06N7/00 ,  G06N99/00

Abstract: Dynamic Hierarchical Empirical Bayes techniques and systems are described that are implemented to control output of digital content. In one example, a system identifies splitting variables included in data. An amount of loss is then determined for each of the identified splitting variables by the system using a loss function. Based on the determined amounts of loss, the system selects at least one splitting variable from the plurality of splitting variables that are to be used to partition data in a respective node, e.g., a parent node to form a plurality of child nodes. The system, for instance, may select the splitting variable that minimizes the cost, i.e., has the lowest amount of cost. The selected splitting variable is then employed by the system to generate at least one hierarchical level of the hierarchical structure of the statistical model by partitioning data from the parent node into respective child nodes.

公开(公告)号：US11710065B2

公开(公告)日：2023-07-25

申请号：US16371460

申请日：2019-04-01

Applicant: Adobe Inc.

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

IPC: G06F40/279 ,  G06N20/00 ,  G06F18/2415 ,  G06F18/214 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F18/24155 ,  G06F40/279 ,  G06N7/01

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：US11636499B2

公开(公告)日：2023-04-25

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: G06Q30/0202 ,  G06Q30/0204

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：US11288709B2

公开(公告)日：2022-03-29

申请号：US15938449

申请日：2018-03-28

Applicant: Adobe Inc.

Inventor： Zhenyu Yan ,  Chen Dong ,  Abhishek Pani ,  Yuan Yuan

IPC: G06Q30/02 ,  G06N20/00

Abstract: The present disclosure includes systems, methods, and non-transitory computer readable media that train and utilize multi-phase learning models to predict performance during digital content campaigns and provide digital content to client devices in a real-time bidding environment. In particular, one or more embodiments leverage organizational structure of digital content campaigns to train two learning models, utilizing different data sources, to predict performance, generate bid responses, and provide digital content to client devices. For example, the disclosed systems can train a first performance learning model in an offline mode utilizing parent-level historical data. Then, in an online mode, the disclosed systems can train a second performance learning model utilizing child-level historical data and utilize the first performance learning model and the second performance learning model to generate bid responses and bid amounts in a real-time bidding environment.

公开(公告)号：US11287894B2

公开(公告)日：2022-03-29

申请号：US15917052

申请日：2018-03-09

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 ,  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：US11038976B2

公开(公告)日：2021-06-15

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 ,  G06N3/08 ,  G06N3/04 ,  G06F16/2457

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.