Adobe, Inc.

Eunyee Koh

Ryan A Rossi

Fan Du

Sungchul Kim

Sana Malik Lee

Tak Yeon Lee

Anup Bandigadi Rao

Shunan Guo

Nedim Lipka

Vasanthi Holtcamp

Jane Elizabeth Hoffswell

Nathan Carl Ross

Xin Qian

Atanu Ranjan Sinha

Iftikhar Ahamath Burhanuddin

Di Jin

John Anderson

Charles Li Chen

Jihyun Lee

Byungmoon Kim

Niyati Himanshu Chhaya

Georgios Theocharous

Sumit Shekhar

Branislav Kveton

Tung Mai

Hung Hai Bui

Charles Menguy

Nikhil Sheoran

Tong Yu

Deepali Jain

Margarita Savova

Nikhil Belsare

Saurabh Mahapatra

Deepali Gupta

Yeuk-Yin Chan

Victor Soares Bursztyn

Zening Qu

Md Main Uddin Rony

Chang Xiao

Qisheng Li

Carlos Riquelme Ruiz

John Boaz Tsang Lee

Shenyu Xu

Aldo Gael Carranza

Scott David Cohen

Walter Chang

Wei Zhang

William Brandon George

Russell Robert Stringham

Saayan Mitra

Chen Chen

Piyush Gupta

Gang Wu

Walter Wei-Tuh Chang

Handong Zhao

Michele Saad

Irgelkha Mejia

Sonali Surange

Ritwik Sinha

Xiang Chen

Lauren Dest

Zhicheng Liu

David Arbour

Bongwon Suh

Uttaran Bhattacharya

Thomas J Malloy

Said Kobeissi

Zhao Song

Jordan Walker

Gautam Choudhary

Kenneth George Russell

Subrahmanya Kumar Giliyaru

Prithvi Bhutani

Duy-Trung Trong Dinh

Dustin Ground

Arpit Ajay Narechania

Manas Garg

Thomas W R Jacobs

Ashley Rosie Weiling Chen

Christian Perez

Camille Harris

Andrew Thomson

Aravind Reddy Talla

Catherine Chiodo

Jinyoung Yeo

Bingjie Xu

Fayokemi Ojo

Hyeok Kim

Zhuohao Zhang

Binit Kumar Sinha

Rhema P Linder

Silky Kedawat

Gaurav Makkar

Sana Lee

Yeuk Yin Chan

Tim Gollub

Yeuk-yin Chan

Tyler Rasmussen

Samuel Nephi Grigg

The present disclosure relates to generating hierarchical queries from text queries. A query generation system is configured to encode a text query to obtain a text embedding. Then, the system may select a field of a data schema by comparing the text embedding to a field embedding corresponding to the field. Subsequently, the system may generate a hierarchical query including a value corresponding to the selected field. Some implementations of the system may further include one or more formatting models configured to format values included in the text query.

Generating hierarchical queries from natural language

In implementations of systems for generating node embeddings for multiple roles, a computing device implements an embeddings system to cluster nodes of a graph into clusters. An initial role membership vector is computed for each of the nodes based on the clusters. The embeddings system generates a first set of role embeddings for a particular node of the nodes based on the initial role membership vector for the particular node and nodes connected to the particular node in the graph. The embeddings system determines an indication of at least one of a node classification or a link prediction for the graph based on the first set of role embeddings and a second set of role embeddings for an additional node of the nodes.

Generating node embeddings for multiple roles

A visualization recommendation system generates recommendation scores for multiple visualizations that combine data attributes of a dataset with visualization configurations. The visualization recommendation system maps meta-features of the dataset to a meta-feature space and configuration attributes of the visualization configurations to a configuration space. The visualization recommendation system generates meta-feature vectors that describe the mapped meta-features, and generates configuration attribute sets that describe the attributes of the visualization configurations. The visualization recommendation system applies multiple scoring models to the meta-feature vectors and configuration attribute sets, including a wide scoring model and a deep scoring model. In some cases, the visualization recommendation system trains the multiple scoring models using the meta-feature vectors and configuration attribute sets.

Machine learning techniques for generating visualization recommendations

Methods, systems, and non-transitory computer readable media are disclosed for utilizing machine learning models to extract digital signals from low-results web queries and generate item demand deficiency predictions for digital item lists corresponding to websites. In one or more embodiments, the deficiency identification system identifies a low-results query submitted by client devices navigating a website. The deficiency identification system generates features for the low-results query and the digital item list to generate a deficiency prediction relative to demand indicated by the low-results query. In some embodiments, the deficiency identification system utilizes a deficiency prediction model to process the extracted signals and generate a deficiency confidence score corresponding to the low-results query. Based on the deficiency confidence score, the deficiency identification systemd can generate and provide demand notifications via one or more graphical user interfaces.

Utilizing machine learning models to process low-results web queries and generate web item deficiency predictions and corresponding user interfaces

Embodiments of the present invention provide systems, methods, and computer storage media for generating and recommending responsive visualizations. In an example embodiment, a design specification of a source visualization and an author's preferences are used to identify and rank compatible sets of candidate responsive transformations (e.g., using answer set programming). Each set is evaluated and ranked according to one or more cost metrics that quantify changes in information density, messaging, or popularity. Some embodiments generate a transformation specification in a declarative grammar that represent the sets of candidate responsive transformations independent of the structure of the source visualization specifications, compile each declarative transformation specification into a rendering grammar specification, and generate a responsive visualization by compiling the rendering grammar specification using a rendering grammar compiler. In some embodiments, the highest ranked responsive visualizations are presented as authoring recommendations and/or the highest ranked responsive visualization is automatically selected and applied.

Recommender for responsive visualization transformations

A method, apparatus, non-transitory computer readable medium, and system for document retrieval include obtaining a query and a document. A prompt generator generates a prompt for a reasoning model based on the query and the document. The reasoning model generates a reasoning result based on the prompt. In some cases, the reasoning result indicates that the document answers the query. A machine learning model provides the document in response to the query based on the reasoning result.

In-context and semantic-aware ensemble model for document retrieval

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.

Interactive tree representing attribute quality or consumption metrics for data ingestion and other applications

Embodiments provide systems, methods, and computer storage media for a Nonsymmetric Determinantal Point Process (NDPPs) for compatible set recommendations in a setting where data representing entities (e.g., items) arrives in a stream. A stream representing compatible sets of entities is received and used to update a latent representation of the entities and a compatibility distribution indicating likelihood of compatibility of subsets of the entities. The probability distribution is accessed in a single sequential pass to predict a compatible complete set of entities that completes an incomplete set of entities. The predicted complete compatible set is provided a recommendation for entities that complete the incomplete set of entities.

Online inference and learning for nonsymmetric determinantal point processes

Graphic visualizations, such as charts or graphs conveying data attribute values, can be generated based on natural language queries, i.e., natural language requests. To do so, a natural language request is parsed into n-grams, and from the n-grams, word embeddings are determined using a natural language model. Data attributes for the graphic visualization are discovered in the vector space from the word embeddings. The type of graphic visualization can be determined based on a request intent, which is determined using a trained intent classifier. The graphic visualization is generated to include the data attribute values of the discovered data attributes, and in accordance with the graphic visualization type.

Learning-based method for generating visualizations via natural language

Systems and methods for data analysis are described. Embodiments of the present disclosure data analysis include displaying, via a data analysis interface, a data visualization in a first region of the data analysis interface; and displaying, via the data analysis interface, an analysis thread visualization in a second region of the data analysis interface. The analysis thread visualization depicts an analysis thread graph including a first node corresponding to the data visualization and an edge corresponding to an analysis path between the first node and a second node.

Generating data insights

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.

Facilitating generation and presentation of advanced insights

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.

Graph convolutional networks with motif-based attention

A system and methods for providing human-invisible AR markers is described. One aspect of the system and methods includes identifying AR metadata associated with an object in an image; generating AR marker image data based on the AR metadata; generating a first variant of the image by adding the AR marker image data to the image; generating a second variant of the image by subtracting the AR marker image data from the image; and displaying the first variant and the second variant of the image alternately at a display frequency to produce a display of the image, wherein the AR marker image data is invisible to a human vision system in the display of the image.

System and methods for providing invisible augmented reality markers

The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.

Utilizing a graph neural network to generate visualization and attribute recommendations

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.

Systems for generating interactive reports

Growing community of inventors

San Jose, CA, United States of America

Eunyee Koh