A method of generating feedback for a container load process includes: during a load process for a container at a load bay, controlling a sensor assembly disposed at the load bay to capture sensor data depicting an interior of the container; at a computing device connected to the sensor assembly, detecting, based on the sensor data, a wall of items placed in the container interior; determining, at the computing device from the sensor data: (i) a boundary attribute of the detected wall, and (ii) a composition attribute of the detected wall; comparing, at the computing device, (i) the boundary attribute to a boundary criterion, and (ii) the composition attribute to a composition criterion; and responsive to at least one of the boundary attribute not meeting the boundary criterion, or the composition attribute not meeting the composition attribute, generating an alert for transmission to a client computing device.

A vacation rental locator system is provided. The system operates to provide interaction regarding vacation rentals allowing users to obtain information regarding a property that is geo-based for that property and engage is immediate rental of the property or future rental of the property through standard messaging applications.

A vacation rental locator system is provided. The system operates to provide interaction regarding vacation rentals allowing users to obtain information regarding a property that is geo-based for that property and engage is immediate rental of the property or future rental of the property through standard messaging applications.

A system for determining customer interest in goods includes one or more memory devices storing instructions and one or more processors configured to execute the instructions. The processors are configured to receive customer location data from a smart device associated with a customer indicating the customer is within a retail venue of a retailer and to monitor, based on the customer location data, a current location of the customer within the retail venue. The processors are further configured to receive goods location data indicating locations of goods for sale within the retail venue and determine that the customer is interested in a particular good for sale within the retail venue based on the current customer location remaining in proximity to the location of the particular good for a predetermined period of time. The processors also conduct a search of pricing of the particular good at one or more other retailers and send a price comparison to the customer.

A system for determining customer interest in goods includes one or more memory devices storing instructions and one or more processors configured to execute the instructions. The processors are configured to receive customer location data from a smart device associated with a customer indicating the customer is within a retail venue of a retailer and to monitor, based on the customer location data, a current location of the customer within the retail venue. The processors are further configured to receive goods location data indicating locations of goods for sale within the retail venue and determine that the customer is interested in a particular good for sale within the retail venue based on the current customer location remaining in proximity to the location of the particular good for a predetermined period of time. The processors also conduct a search of pricing of the particular good at one or more other retailers and send a price comparison to the customer.

Asset tracking systems and methods include one or more tracking devices that pair with one or more hub devices to transmit sensor data from a tracking device only to a paired hub device with a hub device state quality rating, and from the paired hub device to a network server, thereby reducing redundant communications and communication network usage.

Asset tracking systems and methods include one or more tracking devices that pair with one or more hub devices to transmit sensor data from a tracking device only to a paired hub device with a hub device state quality rating, and from the paired hub device to a network server, thereby reducing redundant communications and communication network usage.

The disclosure relates to method and system for performing real-time monitoring of a container containing a non-gaseous fluid substance. The method includes receiving, from a sensing device installed in the container and by a monitoring device, a dataset corresponding to the container and to a non-gaseous fluid substance in the container. The dataset may be acquired and transmitted by the sensing device at a preconfigured time-interval and may include level data corresponding to a level of the non-gaseous fluid substance in the container, temperature data corresponding to a temperature inside the container, and movement data corresponding to movement of the container or a lid of the container. The method further includes performing, by the monitoring device, a time-series analysis on the dataset for determining an anomaly; and generating, by the monitoring device, a graded alert based on the anomaly.

A goods sensing system includes: a sample collector that collects a plurality of sets of image samples, where each set of the image samples comprise sample images of a type of goods at multiple angles, where a set of the image samples of a same type of goods are provided with a same group identification, and the group identification is the type of the goods corresponding to the set of image samples; a model trainer that trains a convolutional neural network model according to each sample image and a group identification of the sample image to obtain a goods identification model; a real-time image collector that continuously acquires at least one real-time image of space in front of a shelf, each real-time image including part or all of images of goods; and a goods category deriver that obtains a type and quantity of the goods displayed in the real-time.

A goods sensing system includes: a sample collector that collects a plurality of sets of image samples, where each set of the image samples comprise sample images of a type of goods at multiple angles, where a set of the image samples of a same type of goods are provided with a same group identification, and the group identification is the type of the goods corresponding to the set of image samples; a model trainer that trains a convolutional neural network model according to each sample image and a group identification of the sample image to obtain a goods identification model; a real-time image collector that continuously acquires at least one real-time image of space in front of a shelf, each real-time image including part or all of images of goods; and a goods category deriver that obtains a type and quantity of the goods displayed in the real-time.