Abstract: 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.

Abstract: The present disclosure relates to a mobile agent in an RFID system with reduced power consumption and increased localization capabilities. In one aspect, a relay circuit attached to a mobility mechanism such as a drone or robot is in communication with an RFID reader and a backend host computer. The mobile agent moves around a warehouse, for instance, in which landmark RFID tags are arranged. The mobile agent can transmit with an adjustable power to identify a single landmark tag and associate it with object tags of one or more objects within the range of the mobile agent. The host computer can communicate with the mobile agent to instruct it to adjust its power when multiple landmark tags are detected.

Abstract: 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.

Abstract: The present disclosure provides techniques for automatically changing device display orientation. A computing device includes an inertial measurement unit (IMU), a camera to detect user eye position on a continuing basis, a display, memory, and at least one processor to execute machine-readable instructions to cause the at least one processor to: access data from the inertial measurement unit IMU; analyze IMU data for changes in an orientation of the electronic device; determine the user eye position in relation to the display; and orient the display in accordance with the orientation of the electronic device.

Abstract: 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.

Abstract: A positioning device and positioning method in which a first wireless signal is transmitted along a first signal path having a first signal path angle that changes relative to time; second wireless signal data representing a response of a wireless station to the first wireless signal is received; a third wireless signal is transmitted along a second signal path; and an assumption that an obstruction is between the wireless communication device and the wireless station is generated if the wireless communication device receives a response from the wireless station to the first wireless signal but does not receive a response from the wireless station to the third wireless signal; wherein the second signal path is a linear path.

Abstract: 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.

Abstract: Systems, devices, and techniques are provided for occupancy assessment of a vehicle. For one or more occupants of the vehicle, the occupancy assessment establishes position and/or identity for some or all of the occupant(s).

Abstract: A positioning device and positioning method in which a first wireless signal is transmitted along a first signal path having a first signal path angle that changes relative to time; second wireless signal data representing a response of a wireless station to the first wireless signal is received; a third wireless signal is transmitted along a second signal path; and an assumption that an obstruction is between the wireless communication device and the wireless station is generated if the wireless communication device receives a response from the wireless station to the first wireless signal but does not receive a response from the wireless station to the third wireless signal; wherein the second signal path is a linear path.

Abstract: Systems and methods for autonomously generating temporally limited targeted offers and/or directed advertisements based on detected interaction between customers and products are provided. A regional monitoring system may detect the movement of a customer in the establishment. A customer that moves within a limited area of the establishment may be identified by targeted offer distribution circuitry as interested in one or more products in the area. A customer interest monitoring device detects interaction between the customer and the product. Using information obtained from the customer interest monitoring device, the targeted offer distribution circuitry autonomously generates a temporally limited targeted offer or directed advertisement. The targeted offer distribution circuitry delivers the targeted offer or directed advertisement to an output device proximate the customer or to a processor-based device or media carried or possessed by the customer.

Abstract: 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.

Abstract: In one embodiment, an apparatus may comprise a sensor to detect a plurality of radio signals from one or more transmitters. The apparatus may further comprise a processor to: identify the plurality of radio signals detected by the sensor; detect a proximity of one or more assets based on the plurality of radio signals, wherein the one or more assets are associated with the one or more transmitters; identify the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset; identify a plurality of signal characteristics associated with the plurality of radio signals; detect a proximity of a human based on the plurality of signal characteristics; and detect one or more human-asset interactions based on the plurality of signal characteristics.

Abstract: Technologies for automated context-aware media curation include a computing device that captures context data associated with media objects. The context data may include location data, proximity data, behavior data of the user, and social activity data. The computing device generates inferred context data using one or more cognitive or machine learning algorithms. The inferred context data may include semantic time or location data, activity data, or sentiment data. The computing device updates a user context model and an expanded media object graph based on the context data and the inferred context data. The computing device selects one or more target media objects using the user context model and the expanded media object graph. The computing device may present context-aware media experiences to the user with the target media objects. Context-aware media experiences may include contextual semantic search and contextual media browsing. Other embodiments are described and claimed.

Abstract: In some embodiments, the disclosed subject matter involves identifying environmental factors and user context that affect sleep quality. Embodiments use information about the static sleep environment, as well as dynamic environmental factors, such as sound, light, movement, correlated with user context, such as physical and emotional state, as well, as recent behavior to classify sleep data. The correlated and classified sleep data may be used to provide change recommendations, where implementing the recommended change is believed to improve the user's sleep quality. Other embodiments are described and claimed.

Abstract: Systems, devices, and techniques are provided for occupancy assessment of a vehicle. For one or more occupants of the vehicle, the occupancy assessment establishes position and/or identity for some or all of the occupant(s).

Abstract: Apparatuses, methods, and computer-readable media for a proximity-based connection facilitation device (“PBC”) may be described which may facilitate discovery and connection to computing devices. The PBC may present visual elements representing devices based on their locations, such as their proximity to the PBC. The PBC may present visual elements for a limited set of devices. The PBC may present computing devices in a visual manner that may depict their proximity to the PBC. The PBC may also provide for facilitated connection to devices that are not as proximate to the PBC. The PBC may facilitate identification of devices that are proximate to an identified device. By facilitating a user in selecting a first device and then visualizing devices proximate to the first device, the PBC may facilitate a user in chaining from a first device to devices that are less easily identified. Other embodiments may be described and/or claimed.

Abstract: In one embodiment, an apparatus may comprise a sensor to detect a plurality of radio signals from one or more transmitters. The apparatus may further comprise a processor to: identify the plurality of radio signals detected by the sensor; detect a proximity of one or more assets based on the plurality of radio signals, wherein the one or more assets are associated with the one or more transmitters; identify the one or more assets based on an identity of the one or more transmitters, wherein each transmitter is associated with a particular asset; identify a plurality of signal characteristics associated with the plurality of radio signals; detect a proximity of a human based on the plurality of signal characteristics; and detect one or more human-asset interactions based on the plurality of signal characteristics.

Abstract: A portable electronic device may generate a (RF) radio frequency fingerprint that includes information representative of at least a portion of RF signals received at a given physical location. The RF fingerprint may include, for example, a unique identifier and a signal strength that are both logically associated with at least a portion of the received RF signals. The portable electronic device may also receive data representative of a number of environmental parameters about the portable electronic device. These environmental parameters may be measured using sensors carried by the portable electronic device. Considered in combination, these environmental parameters provide an environmental signature for a given location. When combined into a data cluster, the RF fingerprint and the environmental signature may provide an indication of the physical subdivision where the portable electronic device is located.

Abstract: Systems, devices, and techniques are provided for occupancy assessment of a vehicle. For one or more occupants of the vehicle, the occupancy assessment establishes position and/or identity for some or all of the occupant(s).

Abstract: In some embodiments, the disclosed subject matter involves identifying environmental factors and user context that affect sleep quality. Embodiments use information about the static sleep environment, as well as dynamic environmental factors, such as sound, light, movement, correlated with user context, such as physical and emotional state, as well, as recent behavior to classify sleep data. The correlated and classified sleep data may be used to provide change recommendations, where implementing the recommended change is believed to improve the user's sleep quality. Other embodiments are described and claimed.