公开(公告)号：US10296546B2

公开(公告)日：2019-05-21

申请号：US14551365

申请日：2014-11-24

Applicant: Adobe Inc.

Inventor： Niyati Chhaya ,  Deepak Pai ,  Dhwanit Agarwal ,  Nikaash Puri ,  Paridhi Jain ,  Ponnurangam Kumaraguru

IPC: G06F16/00 ,  G06F16/9535 ,  H04L29/08 ,  G06F16/435 ,  G06F16/335

Abstract: Techniques are disclosed for identifying the same online user across different communication networks, and further creating a unified profile for that user. The unified profile is an aggregation of publicly available user profile attributes across the different networks. In an embodiment, the techniques are implemented as a computer implemented methodology, including: (1) feature space analysis to identify relevant user features that allows for clusterization of the given target network(s), (2) unsupervised candidate selection to identify one or more candidate user profiles from each target network and that are likely belonging to a target user or so-called queried user, and (3) supervised user identification to identify a likely matching user profile for that target user from each target network. A unified user profile can then be built from data taken from all matched user profiles, and effectively allows a marketer to better understand that user and hence execute more informed targeting.

公开(公告)号：US20240420009A1

公开(公告)日：2024-12-19

申请号：US18210756

申请日：2023-06-16

Applicant: Adobe Inc.

Inventor： Nimish Srivastav ,  Vijay Srivastava ,  Deepak Pai

IPC: G06N20/00

Abstract: Multi-factor metric drift evaluation and attribution techniques are described. A drift attribution model is trained to compute, for a segment of input data that defines an observed value for a metric and observed values for each of a plurality of factors that influence the value of the metric, a contribution by each of the plurality of factors to the observed metric value. Drift observations output by the trained drift attribution model are further processed using a Shapely explainer to represent contributions of each of the metric factors, and their associated values, relative to one or more observed values of a metric during the time segment. The respective magnitude by which each metric factor affects an observed value of the metric is described in a metric drift report, which objectively quantifies respective impacts of a factor, relative to other factors that affect a metric.

公开(公告)号：US20240362821A1

公开(公告)日：2024-10-31

申请号：US18140543

申请日：2023-04-27

Applicant: Adobe Inc.

Inventor： Nimish Srivastav ,  Shankar Venkitachalam ,  Satya Deep Maheshwari ,  Mihir Naware ,  Deepak Pai

IPC: G06T7/90 ,  G06T5/40 ,  G06V10/25 ,  G06V10/56

CPC classification number: G06T7/90 ,  G06T5/40 ,  G06V10/25 ,  G06V10/56 ,  G06T2207/10024

Abstract: In implementations of systems for generating image metadata using a compact color space, a computing device implements a color system to receive input data describing pixels of a digital image and corresponding RGB values of the pixels. The color system assigns a color of a compact color space to each of the pixels based on the corresponding RGB values of the pixels. The compact color space includes a subset of colors included in an RGB color space. The color system computes a histogram of colors of the compact color space and determines a particular color of the compact color space based on the histogram. The color system generates color metadata for the digital image describing a natural language name of the particular color of the compact color space.

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

IPC: G06F11/34 ,  G06K9/62 ,  G06N20/00

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.

公开(公告)号：US20230282018A1

公开(公告)日：2023-09-07

申请号：US17653414

申请日：2022-03-03

Applicant: Adobe Inc.

Inventor： Debraj Debashish Basu ,  Shankar Venkitachalam ,  Vinh Khuc ,  Deepak Pai

IPC: G06V30/416 ,  G06V30/19 ,  G06F40/295 ,  G06N20/00

CPC classification number: G06V30/416 ,  G06V30/19127 ,  G06V30/19113 ,  G06F40/295 ,  G06N20/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize intelligent contextual bias weights for informing keyphrase relevance models to extract keyphrases. For example, the disclosed systems generate a graph from a digital document by mapping words from the digital document to nodes of the graph. In addition, the disclosed systems determine named entity bias weights for the nodes of the graph utilizing frequencies with which the words corresponding to the nodes appear within named entities identified from the digital document. Moreover, the disclosed systems generate a keyphrase summary for the digital document utilizing the graph and a machine learning model biased according to the named entity bias weights for the nodes of the graph.

公开(公告)号：US11449712B2

公开(公告)日：2022-09-20

申请号：US16220333

申请日：2018-12-14

Applicant: ADOBE INC.

Inventor： Deepak Pai ,  Vijay Srivastava ,  Joshua Sweetkind-Singer ,  Shankar Venkitachalam

IPC: G06K9/62 ,  G06F16/904 ,  G06N20/00 ,  G06N5/00

Abstract: In various embodiments of the present disclosure, output data generated by a deployed machine learning model may be received. An input data anomaly may be detected based at least in part on analyzing input data of the deployed machine learning model. An output data anomaly may further be detected based at least in part on analyzing the output data of the deployed machine learning model. A determination may be made that the input data anomaly contributed to the output data anomaly based at least in part on comparing the input data anomaly to the output data anomaly. A report may be generated that is indicative of the input data anomaly and the output data anomaly, and the report may be transmitted to a client device.

公开(公告)号：US20200279140A1

公开(公告)日：2020-09-03

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 ,  G06N5/04 ,  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.

公开(公告)号：US20200234158A1

公开(公告)日：2020-07-23

申请号：US16253892

申请日：2019-01-22

Applicant: Adobe Inc.

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

IPC: G06N5/04 ,  G06N20/00

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for analyzing feature impact of a machine-learning model using prototypes across analytical spaces. For example, the disclosed system can identify features of data points used to generate outputs via a machine-learning model and then map the features to a feature space and the outputs to a label space. The disclosed system can then utilize an iterative process to determine prototypes from the data points based on distances between the data points in the feature space and the label space. Furthermore, the disclosed system can then use the prototypes to determine the impact of the features within the machine-learning model based on locally sensitive directions; region variability; or mean, range, and variance of features of the prototypes.

公开(公告)号：US10521828B2

公开(公告)日：2019-12-31

申请号：US15176760

申请日：2016-06-08

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：US20220300557A1

公开(公告)日：2022-09-22

申请号：US17203300

申请日：2021-03-16

Applicant: ADOBE INC.

Inventor： Debraj Debashish Basu ,  Ganesh Satish Mallya ,  Shankar Venkitachalam ,  Deepak Pai

IPC: G06F16/906 ,  G06F16/9035 ,  G06F16/901

Abstract: Enhanced methods for improving the performance of classifiers are described. A ground-truth labeled dataset is accessed. A classifier predicts a predicted label for datapoints of the dataset. A confusion matrix for the dataset and classifier is generated. A credibility interval is determined for a performance metric for each label. A first labels with a sufficiently large credibility interval is identified. A second label is identified, where the classifier is likely to confuse, in its predictions, the first label with the second label. The identification of the second label is based on instances of incorrect label predictions of the classifier for the first and/or the second labels. The classifier is updated based on a new third label that includes an aggregation of the first label and the second label. The updated classifier model predicts the third label for any datapoint that the classifier previously predicted the first or second labels.