Abstract:
Management of user-generated and context appropriate game play advice is disclosed. The present invention allows for management of context appropriate game play advice that is complete and up-to-date regardless of when a particular interactive gaming title is released. Game play advice is pervasive and easily accessible to game players in addition to being accurate and credible such that game players can trust or rely upon the rendered advice. The game play advice is displayed in environmental contexts that are appropriate to the advice being displayed.
Abstract:
A method for use in controlling a system includes tracking three-dimensional movements of a hand-held controller for the system, mapping the tracked three-dimensional movements of the handheld controller onto a two-dimensional image plane of a screen of a display for the system, and displaying one or more images on the two-dimensional image plane of the screen based on the mapping of the tracked three-dimensional movements of the handheld controller. Some embodiments may include one or more sensors configured to track three-dimensional movements of a hand-held controller for the system, and a processor configured to perform the above mentioned steps.
Abstract:
Methods and systems for interactive interfacing with a computer gaming system are provided. A method includes providing an input device, where the input device is configured to convey inertial data that is to be interpreted by the computer gaming system. The method then identifies an action to be performed by the computer gaming system, where the action is mapped to the inertial data provided by the input device. Then, the method applies a gearing between the action to be performed by the computer gaming system and the inertial data received from the input device. The gearing scales an adjustment to impact the action to be performed by the computer gaming system. The gearing can be set dynamically by the game, by the user, or can be preset by software or user configured in accordance with a gearing algorithm.
Abstract:
A gift card, debit card, or similar instrument and an associated system are capable of restricting purchase transactions to predesignated authorized merchandise or excluding from purchase transactions predesignated unauthorized merchandise. To facilitate this an account that does not permit one or more items or services to be purchased using the account may be established in an account system. The account system receives identification information for an item or service requested for purchase, and the system also receives identification information for the account requested to be used to pay for at least a portion of the item or service requested for purchase. The system determines whether or not the item or service requested for purchase is authorized for purchase using the account. If not, the system denies the use of the account to pay for any portion of the item or service requested for purchase. The scope of the items and/or services authorized for purchase with the card may be set and modified online.
Abstract:
A method for providing real-time recommendations to user in a shopping environment involves sampling a shopping environment using video cameras to generate video features related to a shopper in connection to an item, the sampling input to a machine learning model to create labels related to a state of a scenario, the scenario including the shopper handling the item. Supplemental information is provided to the shopper in connection with the item. The makeup of the supplemental information may be sourced from online service or device associated with the shopper. The supplemental information may be delivered to the shopper, or to a shopper-aware display in the store, or elsewhere. A processing entity associated with the store detects a scenario to identify the shopper as having finished shopping, and causing a charge of the item to a cashierless shopping cart associated with the shopper. In one example, passive, wireless weight sensors may be used to supplement input features for inferences made by the machine learning model. In another example, wireless, battery-free displays may be coupled to shelves to provide information related to supplemental information relating the item held by the shopper.
Abstract:
Method of identifying actions of a shopper to account for taken items by the shopper in a cashierless checkout includes sampling a shopping environment using one or more video cameras to generate video features related a shopper in connection to an item and sampling using one or more supplemental sensors to generate supplemental sensor feature data, receiving output of the sampled video and supplemental sensor features as feature inputs to a deep learning model used for making inferences related to the state of a scenario involving shopper action of taking the item into their possession or held and other actions including moving outside a zone initially associated with the item.
Abstract:
A cashierless tracking system uses a biometric sensor disposed at the entry of a store or the entry of a shelf or an entry to a section of a store is configured to capture biometric feature aspects of a user. Cashierless shopping is enabled without requiring a user to supply mobile phone or device to identify themselves. Instead, processing and classification of biometric features occur via a neural network to identify at least one label representing classified features of the user, the label being used to identify a profile for the user. A plurality of sensors in the store produces data to identify a take of an item by a single user or by a group of users tied to a single account. Included tracking embodiments involve overlapping cameras, skeletal tracking, microphone input, feature extraction and/or feature engineering. The take of the item is chargeable to an electronic shopping cart of the user. Items may be charged or accounted for in relation to user activity, store membership, subscription, entitlement, direct debit, etc.
Abstract:
A method of tracking a user in a store and tracking takes and puts in regard to items in said store. One method includes tracking a shopper in a physical store using two or more cameras that are overlapping to infer and account for shopping activity performed by the shopper. The method includes providing output of at least one of the two or more cameras to a processing entity to extract skeletal limb features of the shopper. Then, processing the skeletal limb features of the shopper to detect a take of an item from the store into possession of the shopper. The method further includes detecting, based on the tracking, that the shopper has exited the store and processing a charge to an account of shopper for the item based on the detected take.
Abstract:
Energy harvesting Internet of Things (IOT) devices are shown including configurations involving a persistent memory, microcontroller, and flat antenna for electromagnetic RF harvesting. In one embodiment, multiple cycles of energy harvesting are performed by an IOT device, where power harvested via the flat antenna is transferred to a power storage of the device such that when the power storage has an amount of power the microcontroller performs processing resulting in state data stored to persistent memory, then during an additional cycle or cycles of energy harvesting, the device completes a process resulting in a payload that is sent to an end node via wireless transmission. Additional energy harvesting may be provided via an energy harvesting input that provides device input and causes additional energy for harvesting. Configurations include ones where energy harvesting is performed by the device, based on capturing vocal energy from a human voice spoken to the device. Configurations also include IOT devices coupled to cloud processing and cloud storage, cloud IOT device state setting, cloud IOT device state getting, cloud provisioning, etc.
Abstract:
Methods and systems are described. One system is a light switch having a housing configured for a circuit for a switch and a wireless chip for wireless communication with an end-node. The light switch further includes a processor chip and memory for executing instructions and interfacing with the wireless chip and the switch. A microphone is integrated with the housing and a speaker is integrated with the housing. The instructions are processed by the processor chip in response to voice commands received by the microphone, and the processing of instructions is further configured to send data to the end node and receive data from the end node. The data received from the end node is used to provide an audible voice reply to one or more of the voice commands received by the microphone of the housing of the light switch. The voice commands are handled by the end node for artificial intelligence processing that includes accessing one or more external data sources and applying one or more learning algorithms for outputting the audible voice reply via the speaker of the light switch.