Enhanced smart refrigerator systems and methods

Patent metadata

Field	Value
Publication number	US-10592963-B2
Application number	US-201615222533-A
Country	US
Kind code	B2
Filing date	Jul 28, 2016
Priority date	Jul 28, 2015
Publication date	Mar 17, 2020
Grant date	Mar 17, 2020

Abstract

An enhanced smart refrigerator (ESR) for automatically populating a virtual shopping cart is provided. The ESR stores a purchase log including a purchase history of a target product. The ESR determines a current interval between a most recent delivery date and a proposed next delivery date based on the purchase history of the target product, and calculates a purchase propensity for the target product based on the current interval and the purchase history of the target product, and automatically adds the product to the virtual shopping cart for submission to a party for purchase of the target product if the purchase propensity meets a first criteria.

First claim

The invention claimed is:

1. An enhanced smart refrigerator (ESR) for automatically populating a virtual shopping cart, said ESR comprising:
a plurality of walls enclosing a refrigerated section;
the virtual shopping cart;
a user interface;
a processor; and
a memory in communication with said processor, wherein said processor is programmed to:
receive a plurality of purchase inputs through said user interface, the plurality of purchase inputs initiating a plurality of purchases of a plurality of products;
store a purchase log within the memory, the purchase log including a purchase history including the plurality of purchases of the plurality of products initiated using the user interface and a date of each purchase of the plurality of purchases;
store a product catalog in the memory, wherein the product catalog includes the plurality of products and a respective assigned default half-life therefor, wherein the default half-life represents a default length of time from an initial purchase of a product by which a typical consumer consumes half of the product;
for each of the products, determine a current interval between a most recent delivery date of the product and a proposed next delivery date of the product based on the purchase history of the product;
for each of the products, access the purchase log stored within the memory to determine, from the purchase log, a plurality of purchase intervals between sequential past purchases of the product;
for each of the products, calculate a purchase propensity for the product based on the current interval, the plurality of purchase intervals, and the default half-life of the product;
display the virtual shopping cart including a plurality of display regions, the plurality of display regions including a first region including products for which the purchase propensity exceeds a first criteria indicating a high likelihood of purchase, and a second region including products for which the purchase propensity does not exceed the first criteria but exceeds a second criteria, wherein the products in the first region are pre-selected for purchase and the products in the second region are un-selected for purchase;
enable, via the user interface, the user to de-select each of the products in the first region and select each of the products in the second region; and
submit the selected products in the virtual shopping cart to a merchant computing device for purchase.

2. The ESR of claim 1 , said processor further programmed to:
for each of the products, calculate a historical need for the product during each of the plurality of purchase intervals; and
calculate the purchase propensity further based on the calculated historical need.

3. The ESR of claim 1 , wherein said processor is further programmed to:
for each of the products, calculate a current need for the product for the current interval; and
calculate the purchase propensity further based on the calculated current need.

4. The ESR of claim 1 , wherein the purchase propensity is a value between 0 and 1.

5. The ESR of claim 1 , wherein the first criteria is a first threshold purchase propensity of 0.5.

6. The ESR of claim 1 , wherein said processor is further programmed to determine at least one of an average need of n prior purchases and a median need of n prior purchases.

7. The ESR of claim 6 , wherein n is one of 3, 5, and 10.

8. The ESR of claim 1 , wherein said processor is further programmed to:
input a representation of user interaction with the first and second regions into a usage pattern component to update the calculated purchase propensity for each product that is manually selected or de-selected by the user.

9. The ESR of claim 1 , said processor further programmed to display the products in the first region as highlighted.

10. The ESR of claim 1 , said processor further programmed to calculate the purchase propensity for each of a second plurality of products based on a purchase history of other users.

11. The ESR of claim 10 , said processor further programmed to display in the second region, based on the purchase propensity for the second plurality of products, at least one product of the second plurality of products which has never previously been added to the virtual shopping cart of the user.

12. The ESR of claim 10 , said processor further programmed to display in a third region of the plurality of regions, based on the purchase propensity for the second plurality of products, at least one product of the second plurality of products which has never previously been added to the virtual shopping cart of the user.

13. A computer-implemented method for automatically populating a virtual shopping cart, said method implemented using an enhanced smart refrigerator (ESR) including a plurality of walls enclosing a refrigerated section, a processor, the virtual shopping cart, a memory in communication with the processor, and a user interface, said method comprising:
receiving, by the ESR, a plurality of purchase inputs through the user interface, the plurality of purchase inputs initiating a plurality of purchases of a plurality of products;
storing, by the ESR, a purchase log within the memory, the purchase log including a purchase history including the plurality of purchases of the plurality of products initiated using the user interface and a date of each purchase of the plurality of purchases;
storing a product catalog in the memory, wherein the product catalog includes the plurality of products and a respective assigned default half-life therefor, wherein the default half-life represents a default length of time from an initial purchase of a product by which a typical consumer consumes half of the product;
for each of the products, determining a current interval between a most recent delivery date of the product and a proposed next delivery date of the product based on the purchase history of the product;
for each of the products, accessing the purchase log stored within the memory to determine, from the purchase log, a plurality of purchase intervals between sequential past purchases of the product;
for each of the products, calculating a purchase propensity for the product based on the current interval, the plurality of purchase intervals, and the default half-life of the product;
displaying the virtual shopping cart including a plurality of display regions, the plurality of display regions including a first region including products for which the purchase propensity exceeds a first criteria indicating a high likelihood of purchase, and a second region including products for which the purchase propensity does not exceed the first criteria but exceeds a second criteria, wherein the products in the first region are pre-selected for purchase and the products in the second region are un-selected for purchase;
enabling, via the user interface, the user to de-select each of the products in the first region and select each of the products in the second region; and
submitting the selected products in the virtual shopping cart to a merchant computing device for purchase.

14. The computer-implemented method of claim 13 , further comprising:
for each of the products, calculating a historical need for the product during each of the plurality of purchase intervals,
wherein calculating a purchase propensity for the product comprises calculating the purchase propensity further based on the calculated historical need.

15. The computer-implemented method of claim 13 further comprising calculating, for each of the products, a current need for the product for the current interval, wherein cal

Assignees

• Mastercard International Inc

Inventors

• Merz Christopher John
• Humphreys Derek
• Cummins Oran
• Rizzini Andrea
• Elder Stephen
• Toner Stephen

Classifications

• G06Q30/0641

  utilising user interfaces specially adapted for shopping · CPC title

• G06Q30/0603

  Catalogue creation or management · CPC title

• G06Q30/0633 Primary

  Managing shopping lists, e.g. compiling or processing purchase lists (shipping orders G06Q10/083; order filling G06Q10/087) · CPC title

• G06Q30/0631

  Recommending goods or services · CPC title

• G06Q30/0202

  Market predictions or forecasting for commercial activities · CPC title