The present invention provides systems and methods for processing return transactions over a network. An embodiment of the invention discloses an online return application that generates an electronic return shipping label that can be delivered to a browser of a customer that wishes to make a return. Also, disclosed is the creation and transmission of label delivery links, which provide for dynamic generation and delivery of shipping labels.

The present invention provides systems and methods for processing return transactions over a network. An embodiment of the invention discloses an online return application that generates an electronic return shipping label that can be delivered to a browser of a customer that wishes to make a return. Also, disclosed is the creation and transmission of label delivery links, which provide for dynamic generation and delivery of shipping labels.

Initial sales cluster is divided by the control circuit into a plurality of velocity buckets. Subsequently, each velocity bucket is divided into a plurality of micro-clusters. The micro-clusters are defined according to demographic information or store characteristic information. A importance score and a performance score for each of the micro-clusters is determined. An optimal sales cluster and a corresponding optimal planogram for each retail store in each micro-cluster are determined based upon the importance score and the performance score.

Theft increases the average product cost to consumers. A mentoring system is presented that can help to reduce or prevent the inventory from lost or theft. Theft is a serious concern in the consumer market place. Industry loses billions per year on theft of merchandise. According to a Reuters report, last year, thefts by employees of U.S. retail merchandise accounted for $15.9 billion, or 44 percent of theft losses at stores, more than shoplifting and vendor fraud combined. Thus, the total thief by the customers and store employees during the year 2008 amounted to $36 billion. Several embodiments of ways to control or reduce the thefts in the market place are presented.

Theft increases the average product cost to consumers. A mentoring system is presented that can help to reduce or prevent the inventory from lost or theft. Theft is a serious concern in the consumer market place. Industry loses billions per year on theft of merchandise. According to a Reuters report, last year, thefts by employees of U.S. retail merchandise accounted for $15.9 billion, or 44 percent of theft losses at stores, more than shoplifting and vendor fraud combined. Thus, the total thief by the customers and store employees during the year 2008 amounted to $36 billion. Several embodiments of ways to control or reduce the thefts in the market place are presented.

A system and method for real-time inventory and food portion management. The system is a cloud-based network containing a predictive inventory management engine, an inventory optimization engine, a food logger, mobile and compute devices, staff and vendors, gateways for vendors and staff to interface with financial institutions and other 3.sup.rd party businesses, enterprise database to store and retrieve including financial data, staffing data, and inventory data. The system can optimize organizational operations by predicting and optimizing in real-time key operational decisions using artificial intelligence or other computerized methods around inventory and food portion management based upon a multitude of variables associated with the patron and enterprise.

Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Systems and methods that employ an autonomous robotic vehicle (ARV) alone or in combination with a remote computing device during the installation of electronic shelf labels (ESLs) in a facility are discussed. The ARV may detect pre-existing product information from paper labels located on modular units prior to their removal and then detect the location of electronic shelf labels (ESLs) after installation. Pre-existing product information gleaned from the paper labels is associated with the corresponding ESLs. The ARV may also determine compliance or non-compliance of modular units to which an ESL is affixed with a planogram of the facility.

A receptacle for detecting delivery and retrieval events has a door sensor, an item sensor, a scanner, and a control unit for operating the sensors, and transmitting the sensor information for processing and analysis. A system can determine whether an event was a delivery or retrieval event based on sensor information and item tracking information. A system can further include an item configured to interact with a computing device.

The technology relates to cargo vehicles. National, regional and/or local regulations set requirements for operating cargo vehicles, including how to distribute and secure cargo, and how often the cargo should be inspected during a trip. However, such regulations have been focused on traditional human-driven vehicles. Aspects of the technology address various issues involved with securement and inspection of cargo before a trip, as well as monitoring during the trip so that corrective action may be taken as warranted. For instance, imagery and other sensor information may be used to enable proper securement of cargo before starting a trip. Onboard sensors along the vehicle monitor the cargo and securement devices/systems during the trip to identify issues as they arise. Such information is used by the onboard autonomous driving system (or a human driver) to take corrective action depending on the nature of the issue.