Hewlett-packard Development Company, L.P.

Entit Software LLC

Jishang Wei

Nelson L Chang

Gregory William Cook

Mithra Vankipuram

Hiroshi Horii

Kevin Smathers

Chih Pin Hsiao

Ming C Hao

Ian N Robinson

Daniel Keim

Rafael A Ballagas

Michael Hund

Alexander Thayer

Wei-Nchih Lee

Srikanth Kuthuru

Ji Won Jun

Jose Dirceu Grundler Ramos

Arun Kumar Paruchuri

Alexandre Santos Da Silva, Jr

Gabriel Lando

Rafael Dal Zotto

Erika Siegel

Jinrong Xie

Chih-Pin Hsiao

Examples are described herein for determining aggregate psychological states of multiple individuals. In various examples, sensor data indicative of affects of multiple individuals may be analyzed. Based on the analyzing, the multiple individuals may be partitioned into multiple distinct clusters of individuals. An aggregate psychological state of the individuals of a given cluster of the multiple distinct clusters of individuals may be determined. Data indicative of the aggregate psychological state may be transmitted to cause a computing device to render output that conveys the aggregate psychological state of the individuals of the cluster.

Conveying aggregate psychological states of multiple individuals

An example method is provided in according with one implementation of the present disclosure. The method includes identifying an intention of a user of a system in relation to a three-dimensional (3D) virtual object and selecting a 3D navigation mode from a plurality of 3D navigation modes based on the identified user intention. The plurality of 3D navigation modes includes at least a model navigation mode, a simple navigation mode, a driving navigation mode, a reaching navigation mode, and a multi-touch navigation mode. The method further includes transitioning the system to the selected 3D navigation mode.

3D navigation mode

In one example of the disclosure, presence data indicative of a presence factor for a remote subject is received from a server. The presence data is analyzed and a display representative of the presence factor is caused at a 3D object. The 3D object includes a 3D element representative of the remote subject.

Displays representative of remote subjects

Visualization of a cohort for high-dimensional categorical data is disclosed. One example is a system including a display module to identify real-time selection of a query data element in an interactive visual representation of high-dimensional categorical data elements comprising a plurality of categorical components. A matrix generator generates a binary distance matrix with columns representing categorical components, and entries in a row indicative of a degree of similarity of respective categorical components of the selected query data element to a data element represented by the row, and determines a category weighting matrix by associating a weight with entries in each column of the binary distance matrix. An evaluator evaluates a weighted similarity score for a data element represented by a row of the category weighting matrix based on entries of the row. A selector iteratively and interactively selects, based on weighted similarity scores, a cohort of categorical data elements.

Iterative visualization of a cohort for weighted high-dimensional categorical data

Interactive analysis of data based on progressive visualizations is disclosed. One example is a system including a data module, a visualization module, an interaction module, and an analytics module. The data module processes a plurality of data elements. The visualization module progressively displays, via an interactive graphical user interface, an overview visualization of a sub-plurality of the plurality of data elements, the overview visualization based on at least one of data resolution and display resolution, and a detailed visualization based on an interaction with the overview visualization. The interaction module processes the interaction with the overview visualization. The analytics module analyzes the interaction to generate a deployment rule.

Interactive analysis of data based on progressive visualizations

An example collaboration system is provided in according with one implementation of the present disclosure. The system includes a 3D display a 3D data visualization, at least two hand avatars of two different users, and a view field avatar. The system also includes a plurality of auxiliary computing devices and a behavior analysis engine to perform a behavior analysis of a user. The behavior analysis engine is to: determine an attention engagement level of the user, and determine a pose of the user in relation to the auxiliary computing device. The system further includes an intention analysis engine to determine an intention of the user in relation to the 3D visualization based on the user's attention engagement level and the user's pose, and a collaboration engine to implement an action with the 3D data visualization by using a hand avatar based on the user's intention and an identified gesture.

Collaboration with 3D data visualizations

An example system is provided in according with one implementation of the present disclosure. The system includes a 3D display displaying at least one three-dimensional (3D) visualization, an auxiliary computing device including a multi-touch display and a plurality of sensors, and a behavior analysis engine to perform a behavior analysis of a user by using data from the plurality of sensors. The behavior analysis engine is to: determine an attention engagement level of the user, and determine a pose of the user in relation to the auxiliary computing device. The system further includes an intention analysis engine to determine an intention of the user in relation to the at least one 3D visualization based on the user's attention engagement level and the user's pose, and an interaction mode engine to automatically adjust the system to an interaction mode based on the identified user intention.

3D visualization

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Palo Alto, CA, United States of America

Jishang Wei