公开(公告)号：US20230030341A1

公开(公告)日：2023-02-02

申请号：US17383114

申请日：2021-07-22

Applicant: Adobe Inc.

Inventor： Eunyee Koh ,  Tak Yeon Lee ,  Andrew Thomson ,  Vasanthi Holtcamp ,  Ryan Rossi ,  Fan Du ,  Caroline Kim ,  Tong Yu ,  Shunan Guo ,  Nedim Lipka ,  Shriram Venkatesh Shet Revankar ,  Nikhil Belsare

IPC: G06N3/08 ,  H04L12/26 ,  G06F40/186 ,  G06N3/04

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a dynamic user interface and machine learning tools to generate data-driven digital content and multivariate testing recommendations for distributing digital content across computer networks. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to generate digital recommendations at multiple development stages of digital communications that are targeted on particular performance metrics. For example, the disclosed systems utilize historical information and recipient profile data to generate recommendations for digital communication templates, fragment variants of content fragments, and content variants of digital content items. Ultimately, the disclosed systems generate multivariate testing recommendations incorporating selected fragment variants to intelligently narrow multivariate testing candidates and generate more meaningful and statistically significant multivariate testing results.

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：US20230289696A1

公开(公告)日：2023-09-14

申请号：US17693778

申请日：2022-03-14

Applicant: ADOBE INC.

Inventor： Arpit Ajay Narechania ,  Fan Du ,  Atanu R. Sinha ,  Ryan A. Rossi ,  Jane Elizabeth Hoffswell ,  Shunan Guo ,  Eunyee Koh ,  John Anderson ,  Sonali Surange ,  Saurabh Mahapatra ,  Vasanthi Holtcamp

IPC: G06Q10/06

CPC classification number: G06Q10/06393 ,  G06F3/0482

Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality”—the condition of data (e.g., presence of incorrect or incomplete values), its “consumption”—the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility”—a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle. For example, an interactive tree view may visually represent a nested attribute schema and attribute quality or consumption metrics to facilitate discovery of bad data before ingesting into a data lake.

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：US20230306033A1

公开(公告)日：2023-09-28

申请号：US17693811

申请日：2022-03-14

Applicant: ADOBE INC.

Inventor： Arpit Ajay Narechania ,  Fan Du ,  Atanu R. Sinha ,  Ryan A. Rossi ,  Jane Elizabeth Hoffswell ,  Shunan Guo ,  Eunyee Koh ,  John Anderson ,  Sonali Surange ,  Saurabh Mahapatra ,  Vasanthi Holtcamp

IPC: G06F16/2457 ,  G06F16/25 ,  G06F16/215

CPC classification number: G06F16/24575 ,  G06F16/215 ,  G06F16/254

Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality” - the condition of data (e.g., presence of incorrect or incomplete values), its “consumption” - the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility” - a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle. For example, current and historical data metrics may be periodically aggregated, persisted, and/or monitored to facilitate discovery and removal of less effective data from a data lake.

公开(公告)号：US20230289839A1

公开(公告)日：2023-09-14

申请号：US17693799

申请日：2022-03-14

Applicant: ADOBE INC.

Inventor： Arpit Ajay Narechania ,  Fan Du ,  Atanu R. Sinha ,  Ryan A. Rossi ,  Jane Elizabeth Hoffswell ,  Shunan Guo ,  Eunyee Koh ,  John Anderson ,  Sonali Surange ,  Saurabh Mahapatra ,  Vasanthi Holtcamp

IPC: G06Q30/02

CPC classification number: G06Q30/0204

Abstract: Embodiments provide systems, methods, and computer storage media for management, assessment, navigation, and/or discovery of data based on data quality, consumption, and/or utility metrics. Data may be assessed using attribute-level and/or record-level metrics that quantify data: “quality”—the condition of data (e.g., presence of incorrect or incomplete values), its “consumption”—the tracked usage of data in downstream applications (e.g., utilization of attributes in dashboard widgets or customer segmentation rules), and/or its “utility”—a quantifiable impact resulting from the consumption of data (e.g., revenue or number of visits resulting from marketing campaigns that use particular datasets, storage costs of data). This data assessment may be performed at different stages of a data intake, preparation, and/or modeling lifecycle. For example, a data selection interface may filter based on consumption and/or quality metrics to facilitate discovery of more effective data for machine learning model training, data visualization, or marketing campaigns.

公开(公告)号：US20220147540A1

公开(公告)日：2022-05-12

申请号：US17091941

申请日：2020-11-06

Applicant: ADOBE INC.

Inventor： RYAN Rossi ,  Vasanthi Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Sana Lee ,  Nathan Ross ,  John Anderson ,  Fan Du ,  Eunyee Koh ,  Xin Qian

IPC: G06F16/26 ,  G06F11/30 ,  G06F11/34 ,  G06F3/0482

Abstract: Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.

公开(公告)号：US20250124023A1

公开(公告)日：2025-04-17

申请号：US18486603

申请日：2023-10-13

Applicant: ADOBE INC.

Inventor： Yeuk-Yin Chan ,  Victor S. Bursztyn ,  Eunyee Koh ,  Nathan Ross ,  Vasanthi Holtcamp

IPC: G06F16/242

Abstract: Systems and methods for generating hierarchical queries from text queries are described. Embodiments are configured to encode a text query to obtain a text embedding. Then, embodiments select a field of a data schema by comparing the text embedding to a field embedding corresponding to the field. Subsequently, embodiments generate a hierarchical query including a value corresponding to the selected field. Some embodiments further include one or more formatting models configured to format values included in the text query.

公开(公告)号：US11995048B2

公开(公告)日：2024-05-28

申请号：US17036453

申请日：2020-09-29

Applicant: ADOBE INC.

Inventor： Handong Zhao ,  Yikun Xian ,  Sungchul Kim ,  Tak Yeon Lee ,  Nikhil Belsare ,  Shashi Kant Rai ,  Vasanthi Holtcamp ,  Thomas Jacobs ,  Duy-Trung T Dinh ,  Caroline Jiwon Kim

IPC: G06F16/00 ,  G06F16/21 ,  G06F18/2115 ,  G06F18/214 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/262

CPC classification number: G06F16/213 ,  G06F18/2115 ,  G06F18/2148 ,  G06F18/2431 ,  G06N3/08 ,  G06V30/274

Abstract: Systems and methods for lifelong schema matching are described. The systems and methods include receiving data comprising a plurality of information categories, classifying each information category according to a schema comprising a plurality of classes, wherein the classification is performed by a neural network classifier trained based on a lifelong learning technique using a plurality of exemplar training sets, wherein each of the exemplar training sets includes a plurality of examples corresponding to one of the classes, and wherein the examples are selected based on a metric indicating how well each of the examples represents the corresponding class, and adding the data to a database based on the classification, wherein the database is organized according to the schema.

公开(公告)号：US11960517B2

公开(公告)日：2024-04-16

申请号：US17383051

申请日：2021-07-22

Applicant: Adobe Inc.

Inventor： Piyush Gupta ,  Binit Kumar Sinha ,  Eunyee Koh ,  Fan Du ,  Gaurav Makkar ,  Silky Kedawat ,  Subrahmanya Kumar Giliyaru ,  Vasanthi Holtcamp ,  Nikhil Belsare

IPC: G06F16/33 ,  G06F16/338 ,  G06F40/40

CPC classification number: G06F16/3344 ,  G06F16/3346 ,  G06F16/338 ,  G06F40/40

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that generate a dynamic cross-platform ask interface and utilize a cross-platform language processing model to provide platform-specific, contextually based responses to natural language digital text queries. In particular, in one or more embodiments, the disclosed systems utilize machine learning models to extract registered intents from digital text queries to identify platform-specific configurations associated with the registered intents. Utilizing the platform-specific configurations, the disclosed systems can generate tailored platform-specific requests for information, as well as customized end-user search results that cause client devices to efficiently, accurately, and flexibly render platform-specific search results.