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1.发明授权公开(公告)号：US11514515B2
公开(公告)日：2022-11-29
申请号：US16037700
申请日：2018-07-17
Applicant: Adobe Inc.
Inventor： Maoqi Xu , Zhenyu Yan , Jin Xu , Abhishek Pani
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.
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公开(公告)号：US20200027102A1
公开(公告)日：2020-01-23
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.
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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.
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4.发明申请公开(公告)号：US20200027103A1
公开(公告)日：2020-01-23
申请号：US16042882
申请日：2018-07-23
Applicant: Adobe Inc.
Inventor： Jin Xu , Zhenyu Yan , Wenqing Yang , Tianyu Wang , Abhishek Pani
Abstract: Prioritization techniques and systems are described that utilize a historical purchase sequence and customer features to prioritize products and services to generate product and service recommendations. In an example, feature data describing a customer and historical purchase data for the customer is received that indicates products or services purchased by the customer. The historical purchase data further includes indicators of when the products or services were purchased by the customer. Then, probabilities of future purchases by the customer of additional products are determined by classifying the additional products using a multiclass classification. The multiclass classification is based on the historical purchase data and the feature data describing the customer. Next, a ranking of the additional products is generated based on the determined probabilities of future purchases. The ranking of the additional products is output in a user interface based on the determined probabilities.
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Patent Agency Ranking
公开(公告)号：US20250036706A1
公开(公告)日：2025-01-30
申请号：US18226079
申请日：2023-07-25
Applicant: Adobe Inc.
Inventor： Yuan Yuan , Bei Huang , Lijing Wang , Yancheng Li , Bowen Wang , Jin Xu , Zhenyu Yan , Qilong Yuan
IPC: G06F16/957 , G06F9/451 , H04L67/50
Abstract: In some embodiments, a computing system extrapolates aggregated interaction data associated with users of an online platform by applying a mixed granularity model to generate extrapolated interaction data for each user in the users. The aggregated interaction data includes a total number of occurrences of a target action performed by the users with respect to the online platform. The extrapolated data includes a series of actions leading to the target action for each user. The computing system identifies an impact of each action in the series of actions for each user on leading to the target action based, at least in part, upon the extrapolating a series of actions associated with the user. User interfaces presented on the online platform can be modified based on at least the identified impacts to improve customization of the user interfaces to the users or enhance an experience of the users.
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6.发明公开公开(公告)号：US20240311643A1
公开(公告)日：2024-09-19
申请号：US18185828
申请日：2023-03-17
Applicant: Adobe Inc.
Inventor： Bowen Wang , Yuan Yuan , Bei Huang , Lijing Wang , Yancheng Li , Jin Xu , Qilong Yuan , Zhenyu Yan
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for generating a modified analytics prediction machine learning model using an iterative transfer learning approach. For example, the disclosed systems generate an initial version of an analytics prediction machine learning model for predicting an analytics metric according to learned parameters. In some embodiments, the disclosed systems determine expected data channel contributions for the analytics metric according to prior data. Additionally, in some cases, the disclosed systems generate a modified analytics prediction machine learning model by iteratively updating model parameters such that predicted data channel contributions are within a threshold similarity of expected data channel contributions.
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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.
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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.
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9.发明申请公开(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
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.
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