Hewlett-packard Development Company, L.p.

Nokia Corporation

Other

Nokia Technologies Oy

Rafael A Ballagas

Hiroshi Horii

Mithra Vankipuram

Hayes Solos Raffle

Ian N Robinson

Mary G Baker

Koichi Mori

Lihua Zhao

William James Allen

Kristopher J Erickson

Kevin Smathers

Jarrid Wittkopf

David George

Sarthak Ghosh

Mirjana Spasojevic

Alexander Thayer

Eric Faggin

Ning Ge

Craig Peter Sayers

Sunil Bharitkar

Jishang Wei

Srikanth Kuthuru

Ji Won Jun

Jose Dirceu Grundler Ramos

Alexandre Santos Da Silva, Jr

Michael Sutherland

Gabriel Lando

Glenda Revelle

Junrui Yang

Alexandra Ju

Rafael Dal Zotto

Erika Siegel

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 system includes an analysis engine to detect a first set of significant portions of a target. The system includes an event engine to detect a set of potential events in a physiological signal and identify a second set of significant portions of the target based on the set of potential events. The system includes a comparison engine to identify a difference between the first set of significant portions and the second set of significant portions.

Identifying differences between physiologically-identified significant portions of a target and machine-identified significant portions

In example implementations, an apparatus is provided. The apparatus includes a physiological sensor, a memory, and a processor. The physiological sensor is to measure a physiological parameter. The memory is to store an extended reality application and a baseline level for the physiological parameter. The processor is in communication with the physiological sensor and the memory. The processor is to execute the extended reality application. In response to the physiological parameter exceeding the baseline level by a difference threshold, the processor is to adjust the extended reality application.

Extended reality adjustments based on physiological measurements

Examples of methods for notification delivery are described herein. In some examples, a method may include determining a flow state of a user based on biometric data from a biometric sensor in an extended reality device. In some examples, the method may include determining a modality for notification delivery based on the flow state. In some examples, the method may include delivering the notification using the modality.

Notification delivery in a virtual space based on a user being in a flow state

In one example in accordance with the present disclosure, a system is described. The system includes a reader to read an identifier from a storage element associated with a part. An extractor of the system extracts, based on the identifier, lifecycle conditions specific to the part. A controller of the system alters manufacturing operations based on extracted lifecycle conditions for the part.

Lifecycle condition-based manufacturing alteration

In one example in accordance with the present disclosure, a system is described. The system includes a reader to 1) read an identifier from a three-dimensional (3D) System printed object that includes a storage element and 2) read a location of the 3D printed object within a build material bed. An extractor of the system extracts, based on the identifier, a post processing operation to execute on the 3D printed object. A controller of the system controls a post processing operation based on extracted post processing operation information and the location.

Automated handling based on part identifier and location

In one example in accordance with the present disclosure, a system is described. The system includes a reader to read an identifier associated with a part. An extractor of the system extracts, based on the identifier, sensor output data for the part. A transmitter of the system transmits a re-order request for the part based on the sensor output data.

Part re-order based on part-specific sensor data

An example non-transitory computer-readable storage medium comprises instructions that, when executed by a processing resource of a computing device, cause the processing resource to, responsive to identifying a spatial area of attention of a gaze of a user, enhance audio associated with the spatial area of attention. The instructions further cause the processing resource to modify the enhancing based at least in part on a physiological observation of the user while hearing the audio.

Modify audio based on physiological observations

An example system includes an identification engine to determine production data associated with a three-dimensionally printed object. The system also includes an alignment engine to determine a placement of the three-dimensionally printed object relative to a display device. The system includes a rendering engine to generate an image including a visual representation of the production data. The rendering engine is to position the visual representation of the production data so that representations of data points in the production data will overlay corresponding portions of the three-dimensionally printed object upon displaying by the display device.

Overlaying production data on rendered 3D printed object

A mixed reality presentation system may include a headwear to present virtual images to a person wearing the headwear and a main unit. The main unit may include sensors to sense a surrounding physical environment and position and orientation of the headwear, a virtual image generator and a transmitter. The virtual image generator is to generate the virtual images based on the sensed surrounding physical environment and the sensed headwear position and orientation. The transmitter is to transmit the generated virtual images to the headwear for presentation.

Mixed reality presentation

A method is described in which biometric input is associated with at least one selectable option on a user controller prior to receipt of a user selection for the at least one selectable option; a past input by a user of a previous selectable option on the user controller is identified; and the at least one selectable option is preemptively executed according to the biometric input and the past input of the previous selectable option, prior to receipt of the user selection for the at least one selectable option.

Biometrics for predictive execution

An example non-transitory machine-readable medium includes instructions executable by a processor to capture a biometric signal from a biometric sensor at a headset that includes a stereoscopic display, capture a facial motion signal from a facial motion sensor associated with the headset, determine facial movement of a wearer of the headset based on the facial motion signal, apply a weight to the biometric signal based on the facial movement to obtain a weighted biometric signal, and use the weighted biometric signal to determine an emotional state of the wearer.

Headset signals to determine emotional states

In example implementations, an apparatus is provided. The apparatus includes a body portion and a plurality of legs movably coupled to the body portion. The body portion is to rest on a backside of a hand of a user. Each one of the plurality of legs include a curved portion to fit between fingers of a user. Respective ends of the plurality of legs are to contact a palm of the user.

Extended reality grasp controller

An actuation device includes a controller that interfaces with a sensing module to cause the sensing module to be non-responsive to events of a class that exceed the threshold level of detectability. The controller may be triggered by a corresponding sensor interface to switch the sensing module to be responsive to events of the class that exceed the threshold level of detectability.

Actuation module to control when a sensing module is responsive to events

An example system includes a microphone sensor for an electronic device and a chamber coupled to the microphone sensor. The chamber is to be selectively filled with a fluid or having a vacuum therein. When the chamber is filled with the fluid, sound waves are allowed to travel through the chamber to the microphone sensor, and fluid pressure in the chamber causes an indicator to be in a first position. When the chamber has a vacuum therein, sound waves are prevented from traveling through the chamber to the microphone sensor and the vacuum in the chamber causes the indicator to be in a second position different from the first position.

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Palo Alto, CA, United States of America

Rafael A Ballagas