公开(公告)号：US11010772B2

公开(公告)日：2021-05-18

申请号：US15161810

申请日：2016-05-23

Applicant: Adobe Inc.

Inventor： Jinyoung Yeo ,  Nedim Lipka ,  Eunyee Koh

IPC: G06Q30/00 ,  G06Q30/02 ,  H04L29/08

Abstract: Sales Forecasting using Browsing Ratios and Browsing Durations is described. In one or more implementations, browsing ratios representative of how much users visited webpages associated with a product or service, and browsing durations representing how much time users spent visiting the webpages associated with the product or service are determined. Based on the determined browsing ratios and browsing durations, a sales forecast of the product or service can be accurately determined.

公开(公告)号：US10728104B2

公开(公告)日：2020-07-28

申请号：US16192313

申请日：2018-11-15

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Sungchul Kim ,  Eunyee Koh

IPC: H04L12/24 ,  H04L12/26 ,  H04L12/751 ,  H04L29/08

Abstract: In implementations of time-dependent network embedding, a computing device maintains time-dependent interconnected data in the form of a time-based graph that includes nodes and node associations that each represent an edge between two of the nodes in the time-based graph based at least in part on a temporal value that indicates when the two nodes were associated. The computing device includes a network embedding module that is implemented to traverse one or more of the nodes in the time-based graph along the node associations, where the traversal is performed with respect to the temporal value of each of the edges that associate the nodes. The network embedding module is also implemented to determine a time-dependent embedding for each of the nodes traversed in the time-based graph, the time-dependent embedding for each of the respective nodes being representative of feature values that describe the respective node.

公开(公告)号：US10699204B2

公开(公告)日：2020-06-30

申请号：US15586387

申请日：2017-05-04

Applicant: Adobe Inc.

Inventor： Carlos Riquelme Ruiz ,  Eunyee Koh

IPC: G06N5/04 ,  G06N7/00 ,  G06N3/08 ,  G06N5/02 ,  H04L12/24 ,  H04L12/26 ,  H04L29/08

Abstract: Techniques are disclosed herein for making predictions with respect to how content consumers will interact with a digital asset. For example, in the context of website visitors browsing digital assets provided via a website, web traffic data can be collected and modeled using a belief network. The belief network may represent a probability distribution for a set of variables that define the web traffic data. Examples of such variables include browser type, browsing session duration, geographic location, visitor demographic characteristics, and a browsing outcome. Certain of the embodiments disclosed herein can be used to extract knowledge from the belief network, thereby allowing statistical inferences to be drawn with respect to how certain classes of website visitors will interact with the website. The influence of one or more first variables (for example, geographic location) can be quantified with respect to one or more second variables (for example, the successful result indicator).

公开(公告)号：US10558852B2

公开(公告)日：2020-02-11

申请号：US15814979

申请日：2017-11-16

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：US20250036858A1

公开(公告)日：2025-01-30

申请号：US18225906

申请日：2023-07-25

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Ryan Aponte ,  Shunan Guo ,  Nedim Lipka ,  Jane Hoffswell ,  Chang Xiao ,  Eunyee Koh ,  Yeuk-yin Chan

IPC: G06F40/154 ,  G06F40/117 ,  G06F40/143

Abstract: Techniques discussed herein generally relate to applying machine-learning techniques to design documents to determine relationships among the different style elements within the document. In one example, hypergraph model is trained on a corpus of hypertext markup language (HTML) documents. The trained model is utilized to identifying one or more candidate style elements for a candidate fragment and/or a candidate fragment. Each of the candidates are scored, and at least a portion of the scored candidates are presented as design options for generating a new document.

公开(公告)号：US20240311403A1

公开(公告)日：2024-09-19

申请号：US18183997

申请日：2023-03-15

Applicant: ADOBE INC.

Inventor： Victor Soares Bursztyn ,  Eunyee Koh ,  Jane Elizabeth Hoffswell ,  Shunan Guo

IPC: G06F16/332

CPC classification number: G06F16/3329 ,  G06F16/3325

Abstract: Systems and methods for natural language processing are described. Embodiments of the present disclosure obtain a chart and a query via a user interface. An answer model generates an answer to the query based on the chart, wherein the answer model comprises a machine learning model trained based on chart data for the chart. A description model generates a visual description based on the answer and the chart, wherein the description model comprises a machine learning model trained based on a chart specification for the chart. A response component transmits a response to the query based on the answer and the visual description.

公开(公告)号：US12020195B2

公开(公告)日：2024-06-25

申请号：US17474188

申请日：2021-09-14

Applicant: Adobe Inc.

Inventor： Sana Malik Lee ,  Zhuohao Zhang ,  Zhicheng Liu ,  Tak Yeon Lee ,  Shunan Guo ,  Ryan A. Rossi ,  Fan Du ,  Eunyee Koh

IPC: G06T11/60 ,  G06N20/00 ,  G06Q10/0639 ,  G06T11/20

CPC classification number: G06Q10/0639 ,  G06N20/00 ,  G06T11/206 ,  G06T11/60

Abstract: In implementations of systems for generating interactive reports, a computing device implements a report system to receive input data describing a dataset and an analytics report for the dataset that depicts a result of performing analytics on the dataset. The report system generates a declarative specification that describes the analytics report in a language that encodes data as properties of graphic objects. Editing data is received describing a user input specifying a modification to the analytics report. The report system modifies the declarative specification using the language that encodes data as properties of graphic objects based on the user input and the dataset. An interactive report is generated based on the modified declarative specification that includes the analytics report having the modification.

公开(公告)号：US11995547B2

公开(公告)日：2024-05-28

申请号：US17823390

申请日：2022-08-30

Applicant: Adobe Inc.

Inventor： Fan Du ,  Sungchul Kim ,  Shunan Guo ,  Sana Lee ,  Eunyee Koh

IPC: G06N3/08 ,  G06N3/04 ,  G06N3/045 ,  G06N5/02 ,  G06N7/01 ,  G06N20/10

CPC classification number: G06N3/08 ,  G06N3/045 ,  G06N5/02 ,  G06N7/01 ,  G06N20/10

Abstract: Disclosed systems and methods predict and visualize outcomes based on past events. For example, an analysis application encodes a sequence of events into a feature vector that includes, for each event, a numerical representation of a respective category and a respective timestamp. The application applies a time-aware recurrent neural network to the feature vector, resulting in one or more of (i) a set of future events in which each event is associated with a probability and a predicted duration and (ii) a sequence embedding that contains information about predicted outcomes and temporal patterns observed in the sequence of events. The application applies a support vector model classifier to the sequence embedding. The support vector model classifier computes a likelihood of a categorical outcome for each of the events in the probability distribution. The application modifies interactive content according to the categorical outcomes and probability distribution.

公开(公告)号：US11946753B2

公开(公告)日：2024-04-02

申请号：US17364480

申请日：2021-06-30

Applicant: Adobe Inc.

Inventor： Fan Du ,  Sana Malik Lee ,  Georgios Theocharous ,  Eunyee Koh

IPC: H04W4/024 ,  G01C21/34 ,  G06Q10/047 ,  H04W4/021

CPC classification number: G01C21/343 ,  G01C21/3476 ,  G01C21/3484 ,  G06Q10/047 ,  H04W4/021 ,  H04W4/024

Abstract: The present disclosure relates to generating and modifying recommended event sequences utilizing a dynamic user preference interface. For example, in one or more embodiments, the system generates a recommended event sequence using a recommendation model trained based on a plurality of historical event sequences. The system then provides, for display via a client device, the recommendation, a plurality of interactive elements for entry of user preferences, and a visual representation of historical event sequences. Upon detecting input of user preferences, the system can modify a reward function of the recommendation model and provide a modified recommended event sequence together with the plurality of interactive elements. In one or more embodiments, as a user enters user preferences, the system additionally modifies the visual representation to display subsets of the plurality of historical event sequences corresponding to the preferences.

公开(公告)号：US11775756B2

公开(公告)日：2023-10-03

申请号：US17094435

申请日：2020-11-10

Applicant: Adobe Inc.

Inventor： Eunyee Koh ,  Xin Qian ,  Tak Yeon Lee ,  Sana Malik Lee ,  Ryan Anthony Rossi ,  Fan Du ,  Duy-Trung Trong Dinh

IPC: G06F40/216 ,  G06F40/58 ,  G06F40/169 ,  G06F40/56 ,  G06F40/295

CPC classification number: G06F40/216 ,  G06F40/169 ,  G06F40/295 ,  G06F40/56 ,  G06F40/58

Abstract: A dataset captioning system is described that generates captions of text to describe insights identified from a dataset, automatically and without user intervention. To do so, given an input of a dataset the dataset captioning system determines which data insights are likely to support potential visualizations of the dataset, generates text based on these insights, orders the text, processes the ordered text for readability, and then outputs the text as a caption. These techniques also include adjustments made to the complexity of the text, globalization of the text, inclusion of links to outside sources of information, translation of the text, and so on as part of generating the caption.