公开(公告)号：US20200334545A1

公开(公告)日：2020-10-22

申请号：US16389628

申请日：2019-04-19

Applicant: Adobe Inc.

Inventor： Atanu Sinha ,  Prakhar Gupta ,  Manoj Kilaru ,  Madhav Goel ,  Deepanshu Bansal ,  Deepali Jain ,  Aniket Raj

IPC: G06N5/02 ,  G06F16/2457 ,  G06N5/04 ,  G06Q30/02 ,  G06F16/901 ,  G06N3/04

Abstract: A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.

公开(公告)号：US11663497B2

公开(公告)日：2023-05-30

申请号：US16389628

申请日：2019-04-19

Applicant: Adobe Inc.

Inventor： Atanu Sinha ,  Prakhar Gupta ,  Manoj Kilaru ,  Madhav Goel ,  Deepanshu Bansal ,  Deepali Jain ,  Aniket Raj

IPC: G06N5/02 ,  G06F16/2457 ,  G06Q30/0204 ,  G06F16/901 ,  G06N3/049 ,  G06N5/043

CPC classification number: G06N5/02 ,  G06F16/24578 ,  G06F16/9024 ,  G06N3/049 ,  G06N5/043 ,  G06Q30/0204

Abstract: A method includes accessing a subject entity and a subject relation of a focal platform and accessing a knowledge graph representative of control performance data. Further, the method includes computing a set of ranked target entities that cause the subject entity based on the subject relation or are an effect of the subject entity based on the subject relation. Computing the set of ranked target entities is performed using relational hops from the subject entity within the knowledge graph performed using the subject relation and reward functions. The method also includes transmitting the set of ranked target entities to the focal platform. The set of ranked target entities is usable for modifying a user interface of an interactive computing environment provided by the focal platform.

公开(公告)号：US11769100B2

公开(公告)日：2023-09-26

申请号：US17329934

申请日：2021-05-25

Applicant: ADOBE INC.

Inventor： Atanu Sinha ,  Manoj Kilaru ,  Iftikhar Ahamath Burhanuddin ,  Aneesh Shetty ,  Titas Chakraborty ,  Rachit Bansal ,  Tirupati Saketh Chandra ,  Fan Du ,  Aurghya Maiti ,  Saurabh Mahapatra

IPC: G06Q10/0639 ,  G06F18/214 ,  G06F18/2321

CPC classification number: G06Q10/06393 ,  G06F18/214 ,  G06F18/2321

Abstract: Systems and methods for data analytics are described. One or more embodiments of the present disclosure receive target time series data and candidate time series data, where the candidate time series data includes data corresponding to each of a plurality of candidate metrics, train a prediction network to predict the target time series data based on the candidate time series data by setting temporal attention weights corresponding to a plurality of rolling time windows and by setting candidate attention weights corresponding to the plurality of candidate metrics, identify a leading indicator metric for the target time series data from the plurality of candidate metrics based on the temporal attention weights and the candidate attention weights, and signal the leading indicator metric for the target time series data.

公开(公告)号：US20220129498A1

公开(公告)日：2022-04-28

申请号：US17079945

申请日：2020-10-26

Applicant: Adobe Inc.

Inventor： Manoj Kilaru ,  Vishwa Vinay ,  Vidit Jain ,  Shaurya Goel ,  Ryan A. Rossi ,  Pratyush Garg ,  Nedim Lipka ,  Harkanwar Singh

IPC: G06F16/583 ,  G06F16/54 ,  G06F16/55 ,  G06F40/30 ,  G06F40/47 ,  G06K9/62 ,  G06N20/00

Abstract: In implementations of systems for generating occurrence contexts for objects in digital content collections, a computing device implements a context system to receive context request data describing an object that is depicted with additional objects in digital images of a digital content collection. The context system generates relationship embeddings for the object and each of the additional objects using a representation learning model trained to predict relationships for objects. A relationship graph is formed for the object that includes a vertex for each relationship between the object and the additional objects indicated by the relationship embeddings. The context system clusters the vertices of the relationship graph into contextual clusters that each represent an occurrence context of the object in the digital images of the digital content collection. The context system generates, for each contextual cluster, an indication of a respective occurrence context for the object for display in a user interface.

公开(公告)号：US20240143660A1

公开(公告)日：2024-05-02

申请号：US17978477

申请日：2022-11-01

Applicant: ADOBE INC.

Inventor： Vishwa Vinay ,  Manoj Kilaru ,  David Thomas Arbour

IPC: G06F16/2457

CPC classification number: G06F16/24578

Abstract: In various examples, an offline evaluation system obtains log data from a recommendation system and trains an imitation ranker using the log data. The imitation ranker generates a first result including a set of scores associated with document and rank pairs based on a query. The offline evaluation system may then determine a rank distribution indicating propensities associated with the document and rank pairs for a set of impressions which can be used to determine a value associated with the performance of the new recommendation system.

公开(公告)号：US11836187B2

公开(公告)日：2023-12-05

申请号：US17079945

申请日：2020-10-26

Applicant: Adobe Inc.

Inventor： Manoj Kilaru ,  Vishwa Vinay ,  Vidit Jain ,  Shaurya Goel ,  Ryan A. Rossi ,  Pratyush Garg ,  Nedim Lipka ,  Harkanwar Singh

IPC: G06F16/55 ,  G06N20/00 ,  G06F16/583 ,  G06F16/54 ,  G06F40/47 ,  G06F40/30 ,  G06F18/214

CPC classification number: G06F16/5854 ,  G06F16/54 ,  G06F16/55 ,  G06F18/214 ,  G06F40/30 ,  G06F40/47 ,  G06N20/00

Abstract: In implementations of systems for generating occurrence contexts for objects in digital content collections, a computing device implements a context system to receive context request data describing an object that is depicted with additional objects in digital images of a digital content collection. The context system generates relationship embeddings for the object and each of the additional objects using a representation learning model trained to predict relationships for objects. A relationship graph is formed for the object that includes a vertex for each relationship between the object and the additional objects indicated by the relationship embeddings. The context system clusters the vertices of the relationship graph into contextual clusters that each represent an occurrence context of the object in the digital images of the digital content collection. The context system generates, for each contextual cluster, an indication of a respective occurrence context for the object for display in a user interface.

公开(公告)号：US20230316124A1

公开(公告)日：2023-10-05

申请号：US17709615

申请日：2022-03-31

Applicant: Adobe Inc.

Inventor： Gautam Choudhary ,  Sk Izajur Rahaman ,  Siba Smarak Panigrahi ,  Prithvi Bhutani ,  Manoj Kilaru ,  Kanishk Singh ,  Iftikhar Ahamath Burhanuddin ,  Aditi Singhania

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: In some embodiments, techniques for identifying bot activity are provided. For example, a process may involve receiving a plurality of samples, wherein each sample is a record of click activity; classifying the plurality of samples among a first class and a second class, using a machine learning model trained by a training process, to produce a corresponding plurality of classification predictions; filtering click activity data, based on information from the plurality of classification predictions, to produce filtered click activity data; and causing a user interface of a computing environment to be modified based on information from the filtered click activity data. The training process includes training the machine learning model to classify samples among the first and second classes, using a training set of samples of the first class, a training set of samples of the second class, and values of a topological loss function calculated based on the training sets.

公开(公告)号：US11868714B2

公开(公告)日：2024-01-09

申请号：US17682911

申请日：2022-02-28

Applicant: ADOBE INC.

Inventor： Natwar Modani ,  Muskan Agarwal ,  Vishesh Kaushik ,  Aparna Garimella ,  Akhash N A ,  Garvit Bhardwaj ,  Manoj Kilaru ,  Priyanshu Agarwal

IPC: G06F17/00 ,  G06F40/186 ,  G06F40/284

CPC classification number: G06F40/186 ,  G06F40/284

Abstract: Methods and systems are provided for facilitating generation of fillable document templates. In embodiments, a document having a plurality of tokens is obtained. Using a machine learned model, a token state is identified for each token of the plurality of tokens. Each token state indicates whether a corresponding token is a static token that is to be included in a fillable document template or a dynamic token that is to be excluded in the fillable document template. Thereafter, a fillable document template corresponding with the document is generated, wherein for each dynamic token of the document, the fillable document template includes a fillable field corresponding to the respective dynamic token.

公开(公告)号：US20230274084A1

公开(公告)日：2023-08-31

申请号：US17682911

申请日：2022-02-28

Applicant: ADOBE INC.

Inventor： Natwar Modani ,  Muskan Agarwal ,  Vishesh Kaushik ,  Aparna Garimella ,  Akhash N A ,  Garvit Bhardwaj ,  Manoj Kilaru ,  Priyanshu Agarwal

IPC: G06F40/186 ,  G06F40/284

CPC classification number: G06F40/186 ,  G06F40/284

Abstract: Methods and systems are provided for facilitating generation of fillable document templates. In embodiments, a document having a plurality of tokens is obtained. Using a machine learned model, a token state is identified for each token of the plurality of tokens. Each token state indicates whether a corresponding token is a static token that is to be included in a fillable document template or a dynamic token that is to be excluded in the fillable document template. Thereafter, a fillable document template corresponding with the document is generated, wherein for each dynamic token of the document, the fillable document template includes a fillable field corresponding to the respective dynamic token.

公开(公告)号：US20230262237A1

公开(公告)日：2023-08-17

申请号：US17651076

申请日：2022-02-15

Applicant: ADOBE INC.

Inventor： Subrata Mitra ,  Aniruddha Mahapatra ,  Kuldeep Sharad Kulkarni ,  Abhishek Yadav ,  Abhijith Kuruba ,  Manoj Kilaru

IPC: H04N19/176 ,  H04N19/61 ,  H04N19/172

CPC classification number: H04N19/176 ,  H04N19/61 ,  H04N19/172 ,  H04N19/132

Abstract: Systems and methods for image processing are described. The systems and methods include receiving a plurality of frames of a video at an edge device, wherein the video depicts an action that spans the plurality of frames, compressing, using an encoder network, each of the plurality of frames to obtain compressed frame features, wherein the compressed frame features include fewer data bits than the plurality of frames of the video, classifying, using a classification network, the compressed frame features at the edge device to obtain action classification information corresponding to the action in the video, and transmitting the action classification information from the edge device to a central server.