公开(公告)号：US20230077515A1

公开(公告)日：2023-03-16

申请号：US17989483

申请日：2022-11-17

Applicant: Adobe Inc.

Inventor： Somak Aditya ,  Atanu Sinha

IPC: G06F40/30 ,  G06N5/04 ,  G06N5/02 ,  G06F40/295 ,  G06F40/253 ,  G06N20/00

Abstract: In some embodiments, a knowledge graph generation system extracts noun-phrases from sentences of a knowledge corpora and determines the relations between the noun-phrases based on a relation classifier that is configured to predict a relation between a pair of entities without restricting the entities to a set of named entities. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the noun-phrases and the determined relations. Nodes or entities of the sub-graph represent the non-phrases in the sentence and edges represent the relations between the noun-phrases connected by the respective edges. The knowledge graph generation system merges the sub-graphs to generate the knowledge graph for the knowledge corpora.

公开(公告)号：US20210311751A1

公开(公告)日：2021-10-07

申请号：US17350889

申请日：2021-06-17

Applicant: Adobe Inc.

Inventor： Nikhil Sheoran ,  Nayan Raju Vysyaraju ,  Varun Srivastava ,  Nisheeth Golakiya ,  Dhruv Singal ,  Deepali Jain ,  Atanu Sinha

IPC: G06F9/451 ,  G06K9/62 ,  G06F11/34 ,  G06N20/00 ,  G06F3/048

Abstract: A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.

公开(公告)号：US20240311406A1

公开(公告)日：2024-09-19

申请号：US18482754

申请日：2023-10-06

Applicant: ADOBE INC.

Inventor： Arpit Narechania ,  Fan Du ,  Atanu Sinha ,  Nedim Lipka ,  Alexa F. Siu ,  Jane Elizabeth Hoffswell ,  Eunyee Koh ,  Vasanthi Holtcamp

IPC: G06F16/332 ,  G06F16/338 ,  G06F40/205

CPC classification number: G06F16/3329 ,  G06F16/338 ,  G06F40/205

Abstract: Aspects of a method, apparatus, non-transitory computer readable medium, and system include obtaining a document and a query. A plurality of data elements are identified from the document by locating a plurality of corresponding flexible anchor elements. Then, the data elements are extracted based on the plurality of flexible anchor elements. Content including an analysis of the extracted data elements based on the query is generated.

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：US20210117509A1

公开(公告)日：2021-04-22

申请号：US16656163

申请日：2019-10-17

Applicant: Adobe Inc.

Inventor： Somak Aditya ,  Atanu Sinha

IPC: G06F17/27 ,  G06N5/02 ,  G06N5/04 ,  G06N20/00

Abstract: In some embodiments, a knowledge graph generation system extracts noun-phrases from sentences of a knowledge corpora and determines the relations between the noun-phrases based on a relation classifier that is configured to predict a relation between a pair of entities without restricting the entities to a set of named entities. The knowledge graph generation system further generates a sub-graph for each of the sentences based on the noun-phrases and the determined relations. Nodes or entities of the sub-graph represent the non-phrases in the sentence and edges represent the relations between the noun-phrases connected by the respective edges. The knowledge graph generation system merges the sub-graphs to generate the knowledge graph for the knowledge corpora.

公开(公告)号：US20240311581A1

公开(公告)日：2024-09-19

申请号：US18185547

申请日：2023-03-17

Applicant: ADOBE INC.

Inventor： Arpit Narechania ,  Fan Du ,  Atanu Sinha ,  Nedim Lipka ,  Alexa F. Siu ,  Jane Elizabeth Hoffswell ,  Eunyee Koh ,  Vasanthi Holtcamp

IPC: G06F40/40 ,  G06F40/279 ,  G06F40/30 ,  G06V30/19 ,  G06V30/412

CPC classification number: G06F40/40 ,  G06F40/279 ,  G06F40/30 ,  G06V30/19147 ,  G06V30/412

Abstract: Aspects of the method, apparatus, non-transitory computer readable medium, and system include obtaining a document and an information element. The aspects further include identifying, from the document, an anchor element that has an anchor type and a relationship type, wherein the anchor type describes a structure of a set of anchor elements, and the relationship type describes a relationship between the anchor element and the information element. The aspects further include extracting information corresponding to the information element based on the anchor element, the anchor type, and the relationship type, and displaying the extracted information to a user.

公开(公告)号：US11687352B2

公开(公告)日：2023-06-27

申请号：US17350889

申请日：2021-06-17

Applicant: Adobe Inc.

Inventor： Nikhil Sheoran ,  Nayan Raju Vysyaraju ,  Varun Srivastava ,  Nisheeth Golakiya ,  Dhruv Singal ,  Deepali Jain ,  Atanu Sinha

IPC: G06F9/451 ,  G06F11/34 ,  G06N20/00 ,  G06F3/048 ,  G06F18/23

CPC classification number: G06F9/451 ,  G06F3/048 ,  G06F11/3438 ,  G06F18/23 ,  G06N20/00

Abstract: A method includes identifying interaction data associated with user interactions with a user interface of an interactive computing environment. The method also includes computing goal clusters of the interaction data based on sequences of the user interactions and performing inverse reinforcement learning on the goal clusters to return rewards and policies. Further, the method includes computing likelihood values of additional sequences of user interactions falling within the goal clusters based on the policies corresponding to each of the goal clusters and assigning the additional sequences to the goal clusters with greatest likelihood values. Furthermore, the method includes computing interface experience metrics of the additional sequences using the rewards and the policies corresponding to the goal clusters of the additional sequences and transmitting the interface experience metrics to the online platform. The interface experience metrics are usable for changing arrangements of interface elements to improve the interface experience metrics.

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：US10783361B2

公开(公告)日：2020-09-22

申请号：US16723619

申请日：2019-12-20

Applicant: ADOBE INC.

Inventor： Sungchul Kim ,  Deepali Jain ,  Deepali Gupta ,  Eunyee Koh ,  Branislav Kveton ,  Nikhil Sheoran ,  Atanu Sinha ,  Hung Hai Bui ,  Charles Li Chen

IPC: G06K9/00 ,  G06N3/04 ,  G06N3/08 ,  G06F16/954 ,  G06K9/62

Abstract: Systems and methods provide for generating predictive models that are useful in predicting next-user-actions. User-specific navigation sequences are obtained, the navigation sequences representing temporally-related series of actions performed by users during navigation sessions. To each navigation sequence, a Recurrent Neural Network (RNN) is applied to encode the navigation sequences into user embeddings that reflect time-based, sequential navigation patterns for the user. Once a set of navigation sequences is encoded to a set of user embeddings, a variety of classifiers (prediction models) may be applied to the user embeddings to predict what a probable next-user-action may be and/or the likelihood that the next-user-action will be a desired target action.

公开(公告)号：US20190213476A1

公开(公告)日：2019-07-11

申请号：US15867169

申请日：2018-01-10

Applicant: Adobe Inc.

Inventor： Harvineet Singh ,  Sahil Garg ,  Neha Banerjee ,  Moumita Sinha ,  Atanu Sinha

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Methods, systems, and non-transitory computer readable storage media are disclosed for determining and applying digital content transmission times using machine-learning. For example, in one or more embodiments, the disclosed system trains a recurrent neural network based on past electronic messages for a user that have been partitioned into a plurality of time bins. Additionally, in one or more embodiments, the system utilizes the trained recurrent neural network to generate predictions of engagement metrics (e.g., a hazard metric based on survival analysis or interaction probability metric) for sending a new electronic message within the plurality of time bins. The system then executes the digital content campaign by selecting a time bin based on the predicted engagement metrics and then sending the new electronic message at a send time corresponding to the selected time bin.