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公开(公告)号：US11200592B2
公开(公告)日：2021-12-14
申请号：US16514576
申请日：2019-07-17
Applicant: Adobe Inc.
Inventor： Meghanath Macha Yadagiri , Ritwik Sinha , Shiv Kumar Saini
Abstract: This disclosure involves allocating content-delivery resources to electronic content-delivery channels based on attribution models accuracy. For instance, a simulation is executed that involves simulating user exposures, times between user exposures, and user responses. The simulation is performed based on parameters associated with simulating user exposures to electronic content-delivery channels and user responses to the user exposures. An accuracy of a channel attribution model when estimating an attribution of an electronic content-delivery channel to a user response is evaluated based on the simulation. A channel attribution model is selected based on the evaluation. An attribution of the electronic content-delivery channel is determined by applying the selected channel attribution model to actual user exposures and actual user responses. This attribution can be used to allocate content-delivery resources to the electronic content-delivery channel in accordance with the selected channel attribution model, and thereby provide interactive content via the electronic content-delivery channel.
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公开(公告)号：US20190340641A1
公开(公告)日：2019-11-07
申请号：US16514576
申请日：2019-07-17
Applicant: Adobe Inc.
Inventor： Meghanath Macha Yadagiri , Ritwik Sinha , Shiv Kumar Saini
Abstract: This disclosure involves allocating content-delivery resources to electronic content-delivery channels based on attribution models accuracy. For instance, a simulation is executed that involves simulating user exposures, times between user exposures, and user responses. The simulation is performed based on parameters associated with simulating user exposures to electronic content-delivery channels and user responses to the user exposures. An accuracy of a channel attribution model when estimating an attribution of an electronic content-delivery channel to a user response is evaluated based on the simulation. A channel attribution model is selected based on the evaluation. An attribution of the electronic content-delivery channel is determined by applying the selected channel attribution model to actual user exposures and actual user responses. This attribution can be used to allocate content-delivery resources to the electronic content-delivery channel in accordance with the selected channel attribution model, and thereby provide interactive content via the electronic content-delivery channel.
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公开(公告)号：US10657559B2
公开(公告)日：2020-05-19
申请号：US15164698
申请日：2016-05-25
Applicant: Adobe Inc.
Inventor： Moumita Sinha , Meghanath Macha Yadagiri , Kokil Jaidka , Niyati Chhaya
IPC: G06Q30/02
Abstract: Methods and systems for providing targeted marketing include using consumer-centric indices to identify users who are most conversant with marketing communications. In particular, one or more embodiments generate a model that indicates a probability of user interactions based on dynamic data. The dynamic data indicates a time to action for each user interaction with a marketing communication within an observation window. The model fits the dynamic data to a distribution and determines the parameters of the distribution. Using the parameters of the distribution, one or more embodiments calculate interest scores for users who have received marketing communications. One or more embodiments select a set of users as a target audience based on the interest scores and provide marketing communications to target audience.
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Patent Agency Ranking
4.发明公开公开(公告)号：US20230393960A1
公开(公告)日：2023-12-07
申请号：US17805377
申请日：2022-06-03
Applicant: Adobe Inc.
Inventor： Meghanath Macha Yadagiri , Anish Narang , Deepak Pai , Sriram Ravindran , Vijay Srivastava
CPC classification number: G06F11/3452 , G06K9/6267 , G06K9/6263 , G06N20/00
Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that control bias in machine learning models by utilizing a fairness deviation constraint to learn a decision matrix that modifies machine learning model predictions. In one or more embodiments, the disclosed systems generate, utilizing a machine learning model, predicted classification probabilities from a plurality of samples comprising a plurality of values for a data attribute. Moreover, the disclosed systems determine utilizing a decision matrix and the predicted classification probabilities, that the machine learning model fails to satisfy a fairness deviation constraint with respect to a value of the data attribute. In addition, the disclosed systems generate a modified decision matrix for the machine learning model to satisfy the fairness deviation constraint by selecting a modified decision threshold for the value of the data attribute.
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公开(公告)号：US10387909B2
公开(公告)日：2019-08-20
申请号：US15005205
申请日：2016-01-25
Applicant: Adobe Inc.
Inventor： Meghanath Macha Yadagiri , Ritwik Sinha , Shiv Kumar Saini
Abstract: Techniques for managing a marketing campaign of a marketer are described. In an example, the marketing campaign uses multiple marketing channels. Attribution of each marketing channel to a user conversion is estimated. Usage of a marketing channel within the marketing campaign is set according to the respective attribution. A marketing channel attribution model is selected from candidate marketing channel attribution models and is applied to estimate the attributions. The selection is based on the accuracy of each of the models associated with estimating the attributions given a set of parameters. To evaluate the accuracy, user journeys are simulated given the set of parameters. True attributions of each marketing channel are determined from the simulation. Each of the marketing channel attribution models is also applied to the simulation to generate estimated attributions. The true and estimated attributions are compared to derive the accuracies.
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公开(公告)号：US11093957B2
公开(公告)日：2021-08-17
申请号：US15819808
申请日：2017-11-21
Applicant: Adobe Inc.
Inventor： Atanu R. Sinha , Meghanath Macha Yadagiri , Pranav Ravindra Maneriker , Sopan Khosla , Avani Samdariya , Navjot Singh
Abstract: Modifications to the DiD technique are disclosed which provide an estimate of the effectiveness of a site-wide action where no control group exists within the data subsequent to implementation of the site-wide action. In some examples, a method may include identifying a treatment group based on a modified treatment period, selecting a control group from a control period prior to the modified treatment period, and performing a modified difference-in-differences (DiD) estimation for a metric based on the modified treatment period, the treatment group, the control period, and the control group. The modified treatment period may encompass an intervention of a site-wide action, and include a pre-intervention time period and a post-intervention time period.
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公开(公告)号：US10395272B2
公开(公告)日：2019-08-27
申请号：US14942109
申请日：2015-11-16
Applicant: Adobe Inc.
Inventor： Meghanath Macha Yadagiri , Shiv Kumar Saini , Ritwik Sinha
Abstract: Techniques for analyzing marketing channels are described. Users are exposed to the marketing channels. User responses (e.g., purchases and no-purchases) to the exposures are tracked. Upon a request from a marketer to analyze an attribution of a marketing channel, the user responses are analyzed. The attribution represents the credit that the marketing channel should get for influencing the users exposed thereto into exhibiting a particular user response (e.g., a purchase). The analysis involves multiple steps. In a first step, a non-parametric estimation is used to generate a value function at a user-level. In a second step, a coalitional game approach is used to estimate the attribution based on the value function. A response is provided to the marketer with data about the attribution.
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公开(公告)号：US20190156359A1
公开(公告)日：2019-05-23
申请号：US15819808
申请日：2017-11-21
Applicant: Adobe Inc.
Inventor： Atanu R. Sinha , Meghanath Macha Yadagiri , Pranav Ravindra Maneriker , Sopan Khosla , Avani Samdariya , Navjot Singh
Abstract: Modifications to the DiD technique are disclosed which provide an estimate of the effectiveness of a site-wide action where no control group exists within the data subsequent to implementation of the site-wide action. In some examples, a method may include identifying a treatment group based on a modified treatment period, selecting a control group from a control period prior to the modified treatment period, and performing a modified difference-in-differences (DiD) estimation for a metric based on the modified treatment period, the treatment group, the control period, and the control group. The modified treatment period may encompass an intervention of a site-wide action, and include a pre-intervention time period and a post-intervention time period.
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