公开(公告)号：US20220414468A1

公开(公告)日：2022-12-29

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 ,  G06N7/00 ,  G06N20/10 ,  G06N5/02 ,  G06N3/04

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：US20250124235A1

公开(公告)日：2025-04-17

申请号：US18485204

申请日：2023-10-11

Applicant: ADOBE INC.

Inventor： Victor Soares BURSZTYN ,  Xiang CHEN ,  Vaishnavi MUPPALA ,  Uttaran BHATTACHARYA ,  Tong YU ,  Saayan MITRA ,  Ryan ROSSI ,  Manas GARG ,  Kenneth George RUSSELL ,  Eunyee KOH ,  Alexandru Ionut HODOROGEA

IPC: G06F40/40 ,  G06F40/279

Abstract: Methods and systems are provided for using generative artificial intelligence to evaluate fine-tuned language models. In embodiments described herein, natural language text snippets are generated via a generative language model based on corresponding data. A language model is fine-tuned into a fine-tuned language model via a language model fine-tuning component using the natural language text snippets and the corresponding data as training data. Independent natural language text snippets are generated via the generative language model based on the corresponding data. Each independent natural language text snippet is different than each corresponding natural language text snippet. An evaluation metric of the fine-tuned language model is generated via an evaluation component based on the independent natural language text snippets and the corresponding data.

公开(公告)号：US20240095440A1

公开(公告)日：2024-03-21

申请号：US18484674

申请日：2023-10-11

Applicant: Adobe Inc.

Inventor： Md Main Uddin RONY ,  Fan DU ,  Iftikhar Ahamath BURHANUDDIN ,  Ryan ROSSI ,  Niyati Himanshu CHHAYA ,  Eunyee KOH

IPC: G06F40/106 ,  G06F40/40

CPC classification number: G06F40/106 ,  G06F40/40

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating generation and presentation of insights. In one implementation, a set of data is used to generate a data visualization. A candidate insight associated with the data visualization is generated, the candidate insight being generated in text form based on a text template and comprising a descriptive insight, a predictive insight, an investigative, or a prescriptive insight. A set of natural language insights is generated, via a machine learning model. The natural language insights represent the candidate insight in a text style that is different from the text template. A natural language insight having the text style corresponding with a desired text style is selected for presenting the candidate insight and, thereafter, the selected natural language insight and data visualization are providing for display via a graphical user interface.

公开(公告)号：US20230169140A1

公开(公告)日：2023-06-01

申请号：US18061697

申请日：2022-12-05

Applicant: Adobe Inc.

Inventor： John Boaz Tsang LEE ,  Ryan ROSSI ,  Sungchul KIM ,  Eunyee KOH ,  Anup RAO

IPC: G06F17/10 ,  G06F16/901 ,  G06N3/08 ,  G06F17/16 ,  G06V10/426 ,  G06F18/21 ,  G06F18/24 ,  G06N3/047 ,  G06V10/82

CPC classification number: G06F17/10 ,  G06F16/9024 ,  G06N3/08 ,  G06F17/16 ,  G06V10/426 ,  G06F18/21 ,  G06F18/24 ,  G06N3/047 ,  G06V10/82

Abstract: Various embodiments describe techniques for making inferences from graph-structured data using graph convolutional networks (GCNs). The GCNs use various pre-defined motifs to filter and select adjacent nodes for graph convolution at individual nodes, rather than merely using edge-defined immediate-neighbor adjacency for information integration at each node. In certain embodiments, the graph convolutional networks use attention mechanisms to select a motif from multiple motifs and select a step size for each respective node in a graph, in order to capture information from the most relevant neighborhood of the respective node.

公开(公告)号：US20190155863A1

公开(公告)日：2019-05-23

申请号：US16254125

申请日：2019-01-22

Applicant: ADOBE INC.

Inventor： Sungchul KIM ,  Nedim LIPKA ,  Eunyee KOH

IPC: G06F16/9535 ,  G06F16/335

Abstract: A method for clustering geolocations using geo-point density includes receiving a user log of geolocation data extracted from user interactions with at least one electronic device. A density is determined relative to other geo-points for each geo-point in a set of geo-points extracted from the user log. Lower density geo-points in the set are merged into higher density geo-points in the set to result in a merged set of geo-points, and clusters of geo-points are identified from the merged set. Merging the geo-points tends to preserve frequently occurring geo-points while reducing those that constitute noise, which improves the reliability of identifying the clusters. Core geo-points of the user log are selected from the clusters. The core geo-points of the user log can be compared to core geo-points of other use logs to identify associations between the user logs.