Authors:
Paul Scrutton, Wendy Darby, Patricia Hogan
Abstract:
This Disclosure describes a new device that utilizes a combination of sensor technologies (radar, vision, electronic nose, humidity, weight) built into one station that can be used in a store to improve the purchasing power of the customer in terms of selecting an item that is perfect for them at that time, and could drive product pricing based on age of product. This could be a differentiator for improving product sales through improved information to the customer and advantages to the Retailer by selling food at reduced prices before it spoils and has to be discarded.
Background:
In today's Retail environment a customer uses their own senses to select fruit and produce. These are usually either weighed on scales throughout the store or at checkout. This Disclosure describes a modern replacement for the set of scales commonly found in a grocery store which the customer could use to evaluate the weight and thereby the cost of produce goods. The Produce Quality Expert Station would be a digital replacement for a common set of scales, employing several sensors to recognize produce types, and to detect the produce state and quality. The Expert Station could give the purchaser some feedback on the length of useful time that the purchaser has to use up the produce that is based on actual data of when the item is scanned by the customer.
Description:
The Quality Expert Station would have sensors on it as follows:
- vision - for AI driven product recognition
- radar to sense movement for live goods (such as Frogs, Lobsters)
- electronic nose to sniff and determine if fish is OK or about to go bad
- moisture / humidity detector to detect the denseness and moisture content in a product. These are commonly available in products such as dehumidifiers. - temperature - via infrared detector - weighing scale
Detection of Quality of Item:
Vision sensor and artificial intelligence (AI )could be employed within the unit for product recognition. Qualities of a product type could be mapped (moisture/humidity/temperature) to build a bell-curve of reasonable values and detect items outside of the normal ranges.
For time-sensitive items like avocados, software could be designed to sniff and detect the moistness and tell you how ripe the item is and how many days until it spoils. This would be a good use of technology as customers buy fruit for when they want to eat it, so could select a non-ripe product if planning to consume it a few days later. With some testing / tuning, and using the inputs from the vision, humidity detector, electronic nose, the station could produce reliable estimates for when some food will be at peak ripeness.
If food is past peak-ripeness, price of item could be reduced to improve sales and reduce food wastage. If customer[1]identification occurred at produce scale (via scan of shoppers card), email reminder could be sent out to encourage item to be used prior to going bad.
Label-printing: Device could print out a label with a price and barcode that could be scanned by the POS.
Usages: Enhanced scale giving useful information to customer on quality of produce.
Claims:
- Makes the task of produce selection by the customer a little easier.
- Customer could get information on the useful lifetime of the produce they plan to purchase.
Enabling Technology:
- Camera
- AI engine for product recognition
- radar to sense movement for live goods
- electronic nose
- moisture / humidity detector
TGCS Reference 3117
