中科微点-全球专利检索

  1. Login
  2. Sign Up

Search Results

32 records (took 0.021 seconds)

    Methods for determining targeting parameters and bids for online ad distribution
    3.
    发明授权
    Methods for determining targeting parameters and bids for online ad distribution 有权

    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.

    Distributing online ads by targeting online ad requests
    4.
    发明授权
    Distributing online ads by targeting online ad requests 有权

    公开(公告)号：US10650403B2

    公开(公告)日：2020-05-12

    申请号：US15264053

    申请日：2016-09-13

    Applicant: ADOBE INC.

    Inventor： Deepak Pai ,  Anil Kamath

    IPC: G06Q30/00 ,  G06Q30/02

    Abstract: Techniques for distributing online ads by targeting online ad requests using test data to predict performance. The techniques can target ad requests in automated online advertising systems in which ad requests are generated by an ad exchange server and bids are placed by marketer devices in real time. The techniques aggregate bid units and compare bid unit characteristics to select bid units to target in ways that address data sparsity, variance, and volume issues. Data sparsity issues are addressed by aggregating bid units to avoid using bid units having insufficient data. Data variance issues are addressed by computing stability metrics for bid units that enable discounting the effect of outliers. Data volume and processing efficiency issues are addressed by grouping similar bid units based on similar metrics (e.g., normalized ROI) and/or similar stability scores, and then ranking the bid units and selecting the top ranked bid units to target.

    Machine learning models applied to interaction data for facilitating modifications to online environments
    6.
    发明授权
    Machine learning models applied to interaction data for facilitating modifications to online environments 有权

    公开(公告)号：US11775412B2

    公开(公告)日：2023-10-03

    申请号：US17703188

    申请日：2022-03-24

    Applicant: Adobe Inc.

    Inventor： Meghanath M Y ,  Shankar Venkitachalam ,  Deepak Pai

    IPC: G06F11/34 ,  G06N20/00 ,  G06F9/451

    CPC classification number: G06F11/3438 ,  G06F9/451 ,  G06N20/00

    Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

    Prototype-based machine learning reasoning interpretation
    7.
    发明授权
    Prototype-based machine learning reasoning interpretation 有权

    公开(公告)号：US11610085B2

    公开(公告)日：2023-03-21

    申请号：US16289520

    申请日：2019-02-28

    Applicant: ADOBE INC.

    Inventor： Deepak Pai ,  Debraj Debashish Basu ,  Joshua Alan Sweetkind-Singer

    IPC: G06K9/62 ,  G06N20/00 ,  G06F11/30

    Abstract: In some examples, a prototype model that includes a representative subset of data points (e.g., inputs and output classifications) of a machine learning model is analyzed to efficiently interpret the machine learning model's behavior. Performance metrics such as a critic fraction, local explanation scores, and global explanation scores are determined. A local explanation score capture an importance of a feature of a test point to the machine learning model determining a particular class for the test point and is computed by comparing a value of a feature of a test point to values for prototypes of the prototype model. Using a similar approach, global explanation scores may be computed for features by combining local explanation scores for data points. A critic fraction may be computed to quantify a misclassification rate of the prototype model, indicating the interpretability of the model.

    UTILIZING A TIME-DEPENDENT GRAPH CONVOLUTIONAL NEURAL NETWORK FOR FRAUDULENT TRANSACTION IDENTIFICATION
    9.
    发明申请
    UTILIZING A TIME-DEPENDENT GRAPH CONVOLUTIONAL NEURAL NETWORK FOR FRAUDULENT TRANSACTION IDENTIFICATION 有权

    公开(公告)号：US20210233080A1

    公开(公告)日：2021-07-29

    申请号：US16751880

    申请日：2020-01-24

    Applicant: Adobe Inc.

    Inventor： Shubhranshu Shekhar ,  Deepak Pai ,  Sriram Ravindran

    IPC: G06Q20/40 ,  G06N3/08 ,  G06Q30/06

    Abstract: The present disclosure relates to utilizing a graph convolutional neural network to generate similarity probabilities between pairs of digital identities associated with digital transactions based on time dependencies for use in identifying fraudulent transactions. For example, the disclosed systems can generate a transaction graph that includes nodes corresponding to digital identities. The disclosed systems can utilize a time-dependent graph convolutional neural network to generate node embeddings for the nodes based on the edge connections of the transaction graph. Further, the disclosed systems can utilize the node embeddings to determine whether a digital identity is associated with a fraudulent transaction.

    User Segment Generation and Summarization
    10.
    发明申请
    User Segment Generation and Summarization 有权

    公开(公告)号：US20210142256A1

    公开(公告)日：2021-05-13

    申请号：US16681056

    申请日：2019-11-12

    Applicant: Adobe Inc.

    Inventor： Meghanath M Y ,  Deepak Pai

    IPC: G06Q10/06 ,  G06Q30/06

    Abstract: A user segmentation system is described that is configured to generate use segments and summarize user segments. In one example, the user segmentation system is configured to identify which attributes support a key performance indicator. This is used to generate rules that act as user segments of a user population. Further, the user segmentation system is configured to reduce overlap of user segments through summarization.

Patent Agency Ranking