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Self-Adjusting Sales Pattern Optimization

IP.com Disclosure Number: IPCOM000239093D
Publication Date: 2014-Oct-10
Document File: 2 page(s) / 44K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed is an automated, self-learning system that identifies which areas, and at which times, are the most profitable for a mobile vendor. This allows the vendor to adjust routes in order to maximize profits.

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Self-Adjusting Sales Pattern Optimization

By definition, mobile vendors move around a large area throughout the day. This is true for existing merchants such as food trucks, ice cream trucks, sharpening services, or shredding services, as well as future automatic merchants such as automated delivery services, mobile vending machines, or robotic sales carts.

A system is needed to assist mobile vendors with optimizing daily routes in order to maximize profits.

The novel contribution is an automated, self-learning system that identifies which areas, and at which times, are the most profitable for a mobile vendor.

The system links the vendor's sales register with the vehicle's Global Positioning System (GPS). Throughout the day it tracks

where the locations that have the most sales. This can be in terms of total profit, number of items sold, the types of items sold, and individual products. Weather conditions such as temperature and precipitation can also be captured .

This data is compiled into a central system/database. This can be specific to a particular vendor or amalgamated from multiple vendors all working for the same company. Data mining and processing are performed on this data to identify the most profitable route for the day.

At the start of each day, the system optimizes the vendor's path based upon the daily goals. For instance, if the vendor has overstocked on a particular product, the route for the day can be set to go to areas in which that item tends to sell well. Daily goals can be (but are not limited to):

Maximize profit...