Align Technology, Inc.

Tableau Software, Inc.

Andrew C Beers

Muhammad Chishti

Huafeng Wen

Phillips Alexander Benton

Ross J Miller

Brian Freyburger

Kelsey D Wirth

Timothy N Jones

Muhammad Ziaullah Khan Chishti

Patrick M Hanrahan

Sean Curry

Sheldon Baumrind

Jonathan E Taylor

Matthew W Eldridge

Loc X Phan

Jae H Anh

Amir Abolfathi

Scott A Sherman

Jeffrey Jon Weir

Jun Ho Kim

Justin Talbot

Bora Beran

Ryan Andrew Atallah

Ginger J Gloystein

Sean Boon

Eric Roy Brochu

David John Mosimann

Eleanor Page Darby Fields

Rachel Stern Kalmar

Hon To Ming

Leiling Tao

Lu Yu

Kelsey Wirth

Embodiments are directed to managing visualizations of data. A provided data model may include a tree specification that declares parent-child relationships between objects in the data model. In response to a query associated with objects in the data model: employing the parent-child relationships to determine a tree that includes parent objects and child objects from the objects based on the parent-child relationships; determining a root object based on the query and the tree; traversing the tree from the root object to visit the child objects in the tree; determining partial results based on characteristics of the visited child objects such that the partial results are stored in an intermediate table; and providing a response to the query that includes values based on the intermediate table and the partial results.

Data processing for visualizing hierarchical data

Embodiments are directed to managing visualizations of data. Visualizations to monitor may be determined based on metrics associated with the monitored visualizations. Mark values may be sampled from each monitored visualization such that the sampled mark values may be stored with a timestamp. If an amount of the sampled mark values for a monitored visualization exceeds a threshold value mark models may be trained to classify a portion of the sampled mark values associated with the monitored visualization such that the mark models may predict ranges of values for the classified mark values associated with the monitored visualization. If the portion of the classified mark values associated with the monitored visualization has a value outside of the predicted range values, indicating that the monitored visualization may be associated with an anomalous mark value.

Detecting anomalies in visualizations

Embodiments are directed to generating metrics based on visualizations. A dashboard that may be associated with source visualizations display a current value of metrics from source visualization models. A classifier may automatically use characteristics from the source visualizations to determine metrics for the source visualization. The source visualization models may be sample to provide values of the metrics across time, at a sampling rate determined by a metric profile. The sampled values may be stored with time values in a metric data store such that the time values may correspond to when the values sampled from the visualization. Metric visualizations may be generated based on the values and the time values such that the metric visualizations display previously sampled values of the metrics.

Providing and surfacing metrics for visualizations

Embodiments are directed to a display engine that provides a graphical user interface (GUI) that includes a graph panel and a visualization panel may be generated. The graph panel may receive graph related inputs or interactions. Graph information based on the graph related inputs or interactions and a data source may be generated. A graph model based on the graph information and the data source may be generated. Elements that correspond to objects in the graph model or relationships in the graph model may be displayed in the graph panel. The graph information and the graph model may be employed to execute a query that provides a data model based on results of the query. The data model may be employed to provide a visualization that is displayed in the display panel.

Interactive data visualization interface for data and graph models

Embodiments are directed to visualizing data. A graphical user interface (GUI) that includes a shelf panel and a display panel may be generated. Column fields may be generated and displayed in the shelf panel based on a data source or a table specification. The table specification may be employed to generate a table view that may be displayed in the display panel such that the table view includes columns that correspond to each column field. Data items may be determined based on the data source and the column fields such that each column field corresponds to a portion of the data items. Each portion of the data items may be displayed in a column of the table view that corresponds to the column field that corresponds to the portion of the data items.

Flexible table based visualizations

A method displays a first data visualization according to user placement of data fields in shelves of a user interface. Each shelf specifies a property of the data visualization and the data visualization includes visual data marks corresponding to data values for data fields in a dataset. A user selects a subset of the visual data marks. A popup summary is displayed that includes data value distributions for several data fields. In the popup summary, a second user input is detected corresponding to a first data field whose data value distribution is displayed in the popup summary. In response, the method displays a moveable icon corresponding to the first data field. The method detects a third user input to place the interactive moveable icon in a shelf in the user interface. In response, a second data visualization is displayed according to placement of data fields, including the first data field.

Data visualization user interface with summary popup that includes interactive objects

An apparatus and method define a fit of a set of upper and lower teeth of a patient by generating a computer representation of the teeth; and determining an occlusion from the computer representation of the teeth using one or more keys.

System for determining final position of teeth

A method of generating a statistical model for a dataset operates at a computer system having one or more processors and memory. The memory stores one or more programs configured for execution by the one or more processors. The process receives a visual specification. The visual specification defines a graphical representation of a portion of the dataset. The visual specification includes a first field and a second field of the dataset. The method determines a set of data properties for each of the first and second fields. The process then generates a statistical model of a mathematical relationship between the first and second fields based on the data properties of the first and second fields and data values associated with the first and second fields in the dataset. The process displays the graphical representation and the statistical model superimposed on the graphical representation.

Systems and methods for generating models of a dataset for a data visualization

Methods and apparatus fit a set of upper and lower teeth in a masticatory system by generating a computer representation of the masticatory system and computing an occlusion based on interactions in the computer representation of the masticatory system.

System and method for positioning teeth

A computer is used to create a plan for repositioning an orthodontic patient's teeth. The computer receives an initial digital data set representing the patient's teeth at their initial positions and a final digital data set representing the teeth at their final positions. The computer then uses the data sets to generate treatment paths along which the teeth will move from the initial positions to the final positions.

Computer automated development of an orthodontic treatment plan and appliance

A method of automatically generating models from a dataset includes multiple steps. First, a description of a view of a dataset is provided. The description includes multiple fields associated with the dataset. Next, a set of properties is determined for each of the multiple fields. Finally, the description is automatically translated into one or more models based on the respective properties of the multiple fields and a set of predefined heuristics.

Computer systems and methods for automatic generation of models for a dataset

A computer is used to create a plan for repositioning an orthodontic patient's teeth. The computer receives an initial digital data set representing the patient's teeth at their initial positions and a final digital data set representing the teeth at their final positions. The computer then uses the data sets to generate treatment paths along which teeth will move from the initial positions to the final positions.

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Redwood City, CA, United States of America

Andrew C Beers