Intel Corporation

Intel Coproration

Cagri Tanriover

Asli Arslan Esme

Nese Alyuz Civitci

Rahul C Shah

Chieh-Yih Wan

Hassnaa Moustafa

Paulo Lopez Meyer

John W Sherry

Richard T Beckwith

Ke-Yu Chen

Sinem Aslan

Dave A Cavalcanti

Meghashree Dattatri Kedalagudde

Daniel Pohl

Hector A Cordourier Maruri

Cliodhna Ni Scanaill

Robert J Firby

Vishakha Gupta

Maik Sven Fox

Ece Oktay

Eda Okur

Enno Luebbert Egge Luebbers

Selin Sargut

Hector Cordourier

Ergin Genc

Eda Okur Kavil

Techniques are disclosed for performing RFID motion tracking in an intelligent manner that facilitates the generation of accurate and useful metrics for marketing and other applications. The techniques function to reduce problematic false positive rates on RFID tags attached to items to improve the accuracy of the motion presence of a small subset of browsed items among a much larger set of tagged items in the same space. This accurate motion inference enables the calculation of metrics such as customer-item interaction duration, pauses in interactions (potentially indicating close examining), and the extraction of patterns of motion that can indicate interest leading to realized sales, as well as concurrent motion detection of multiple items indicating which related items shall be placed in close proximity to increase sales of matching items.

Probabilistic contextual inference using RFID tag-interactions

Various systems and methods for engagement dissemination. A face detector detects a face in video data. A context filterer determines a student is on-platform and a section type. An appearance monitor selects an emotional and a behavioral classifiers. Emotional and behavioral components are classified based on the detected face. A context-performance monitor selects an emotional and a behavioral classifiers specific to the section type, Emotional and behavioral components are classified based on the log data. A fuser combines the emotional components into an emotional state of the student based on confidence values of the emotional components. The fuser combines the behavioral components a behavioral state of the student based on confidence values of the behavioral components. The user determine an engagement level of the student based on the emotional state and the behavioral state of the student.

Engagement level determination and dissemination

In one embodiment, an apparatus comprises processing circuitry to: receive wireless signal data corresponding to an RFID tag, wherein the wireless signal data comprises signal strength data and signal phase data corresponding to wireless signals transmitted by the RFID tag and received by an RFID reader; generate decomposed signal strength data based on a seasonal decomposition of the signal strength data; generate a frequency-phase curve based on the signal phase data; extract a set of signal strength features based on the decomposed signal strength data; extract a set of signal phase features based on the frequency-phase curve; and detect a motion state of the RFID tag using a machine learning classifier, wherein the machine learning classifier is trained to detect the motion state based on the set of signal strength features and the set of signal phase features.

RFID motion detection for dense RFID tag environments

Apparatus, method and computer readable medium associated with autonomous/semi-autonomous driving are disclosed herein. In embodiments, an apparatus for autonomous/semi-autonomous driving may comprise a management system to be disposed in an autonomous/semi-autonomous vehicle. The management system may include a reservation subsystem to receive, from a cloud server, a reservation of the autonomous or semi-autonomous vehicle for a passenger or a driver, and an access control subsystem to control access to the autonomous or semi-autonomous vehicle that includes a trust function to gain trust of the passenger or driver with respect to the passenger or driver's data privacy requirements will be met, when the passenger or driver attempts to exercise the reservation. Other embodiments may be disclosed or claimed.

Autonomous/semi-autonomous driving method and apparatus with trusted data collection, retention and/or sharing

A vehicle relocator may include one or more processors, which are configured to receive first sensor data representing a current or predicted weather condition; receive second sensor data representing at least position or a vicinity of a vehicle; determine from at least the first sensor data and the second sensor data a risk of damage to the vehicle; and if the determined risk is outside of a target range, send an instruction for the vehicle to travel to an alternate location.

Inclement weather condition avoidance

Herein is disclosed a virtual embodiment display system comprising one or more image sensors, configured to receive one or more images of a vehicle occupant; one or more processors, configured to determine a gaze direction of the vehicle occupant from the one or more images; select a display location corresponding to the determined gaze direction; and control an image display device to display a virtual embodiment of an intelligent agent at the display location; the image display device, configured to display the virtual embodiment of the intelligent agent at the selected display location according to the one or more processors.

Intelligent vehicle point of focus communication

Systems, apparatus, and computer-readable media for managing data storage for vehicle-embedded computer devices (VECDs) are disclosed. Embodiments include a data hierarchy, which classifies data based on the data source, data destination, the intended use of the data or a target application, data processing requirements of the data, and/or delivery time requirements of the data. A VECD may classified obtained data according to the hierarchy and may store the data in different storage devices based on the classification of data. Other embodiments are described and/or claimed.

Technologies for data management in vehicle-based computing platforms

Safety systems for unmanned vehicles are disclosed. An example vehicle includes a housing and a propulsion system supported by the housing. The propulsion system to generate lift. An anti-crash module is coupled to the housing. The anti-crash module has a compressible foam that is to deploy to protect the propulsion system from an impact.

Safety systems for unmanned vehicles

Methods and apparatuses for identifying food chewed or beverage drank are disclosed herein. In embodiments, a device may comprise one or more sensors to provide vibration signal data representative of vibrations sensed from a nasal bridge of a user wearing the device, a chewing analyzer to extract from the vibration signal data a first plurality of features associated with chewing activities, and/or a drinking analyzer is to extract from the vibration signal data a second plurality of features associated with drinking activities. The extracted first and/or second plurality of features, in turn, may be used to determine a category of food the user was chewing, or a category of beverage the user was drinking. In embodiments, the methods and apparatuses may use personalized models.

Methods and apparatus for identifying food chewed and/or beverage drank

Technologies for emotion prediction based on breathing patterns include a wearable device. The wearable device includes a breathing sensor to generate breathing data, one or more processors, and one or more memory devices having stored therein a plurality of instructions that, when executed, cause the wearable device to calibrate a personalized emotion predictive model associated with the user, collect breathing data of the user of the wearable device, analyze the breathing data to determine a breathing pattern, predict, in response to an analysis of the breathing data, the emotional state of the user using the personalized emotion predictive model, and output the emotional state of the user.

Technologies for emotion prediction based on breathing patterns

Methods and apparatus are described for considering a remote driver state. Embodiments decode, from a message received from a remote vehicle, a remote driver state of a driver of the remote vehicle and a level of the remote driver state. A change in a driving characteristic of a vehicle is determined based on the remote driver state and the level of the remote driver state. The change in the driving characteristic is then made.

Remote driver state consideration for autonomous vehicles

Behavior detection using RFID in environments with high RFID tag density

Technologies for a fabric acoustic sensor are disclosed. The fabric acoustic sensor includes a conductive thread and a non-conductive thread, which form a diaphragm that vibrates in response to a sound wave. As a result of the vibration, the conductive thread stretches, and a resistance of the conductive thread varies. The change in resistance is measured by a compute device, and the compute device may determine the sound wave based on the change in resistance. In some embodiments, the fabric acoustic sensor may be used to monitor a heart rate, locate an object, and/or provide an input for noise cancellation.

Technologies for a fabric acoustic sensor

Apparatus, systems, and/or methods may provide a mental state determination. For example, a data collector may collect image data for a side of a face of a user from an image capture device on the user (e.g., a wearable device). The image data may include two or more perspectives of a feature on the side of the face of the user. In addition, a state determiner may determine a mental state of the user based on the image data. In one example, fields of view may be combined to determine a total region and/or a total overlap region. Changing the position that one or more image capture devices point may modulate the total region and/or the total overlap region. In addition, one or more sensors may be utilized to further improve mental data determinations.

Growing community of inventors

Bethany, OR, United States of America

Cagri Tanriover