Utility for determining competitive restaurants

• US 8,595,050 B2
• Filed: 12/27/2011
• Issued: 11/26/2013
• Est. Priority Date: 12/27/2011
• Status: Active Grant

• Current Assignee: GrubHub Inc.
• Original Assignee: GrubHub Inc.
• Inventors: Scotto, Stefano Alexander, Zivin, Rhiannon Sterling
• Primary Examiner(s): Singh, Gurkanwaljit
• Assistant Examiner(s): Koneru, Sujay
• Application Number:
  US13/337,347
• Publication Number:
  US 20130166347A1
• Time in Patent Office: 700 Days
• Field of Search: 705 711- 742, 705/15
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 50/12 Hotels or restaurants

Abstract

A utility for determining the competitors of a subject restaurant accesses a database that contains information about other, nearby restaurants. The utility assigns a competitor score to each restaurant within the maximum radius based on the distance from the subject restaurant, as well as the cuisine types, menus, and order histories of the subject and competitor restaurants.

Claims

1. A method of determining competitors for a first restaurant within a database of restaurants, wherein each restaurant in the database has a location, an order history, a cuisine type and a menu comprising a plurality of menu items, the method operating within a computer and comprising the steps of:

i) retrieving a plurality of restaurants from the database over a network interface wherein the network interface is coupled to a processor of the computer;

ii) filtering, by the processor, the plurality of restaurants to produce a first set of restaurants within a radius of the location of the first restaurant;

iii) filtering, by the processor, the first set of restaurants to produce a second set of restaurants with a similar cuisine as the first restaurant;

iv) associating, by the processor, each menu item of each restaurant with a true item, wherein the true item is classified by a common nomenclature;

v) assigning, by the processor, a match score for the similarity to each pair of true items;

vi) comparing, by the processor, the true items of the first restaurant to the true items of each of the restaurants within the second set of restaurants to produce a set of menu scores for the second set of restaurants;

vii) comparing, by the processor, an order history of the first restaurant with an order history of each of the restaurants within the second set of restaurants to produce a set of order history scores for the second set of restaurants, wherein each order history includes a plurality of orders wherein each order in the plurality of orders records at least a cost of the order;

viii) computing a set of weighted average scores of the set of menu scores and the set of order history scores; and

ix) based on the set of weighted average scores, filtering, by the processor, the second set of restaurants to produce a first set of competitor restaurants.

2. A method of determining competitors for a first restaurant within a database of restaurants, wherein each restaurant in the database has a location, an order history, a cuisine type and a menu comprising a plurality of menu items, the method operating within a computer and comprising the steps of:

i) retrieving a plurality of restaurants from the database over a network interface wherein the network interface is coupled to a processor of the computer;

ii) filtering, by the processor, the plurality of restaurants to produce a first set of restaurants within a radius of the location of the first restaurant;

iii) filtering, by the processor, the first set of restaurants to produce a second set of restaurants with a similar cuisine as the first restaurant;

iv) associating, by the processor, each menu item of each restaurant with a set of true items to form a menu item association, wherein the true items are classified by a common nomenclature;

v) assigning, by the processor, a similarity to each pair of true items;

vi) comparing, by the processor, the sets of true items of the first restaurant to the sets of true items of each of the restaurants within the second set of restaurants to produce a set of menu scores for the second set of restaurants;

vii) comparing, by the processor, an order history of the first restaurant with an order history of each of the restaurants within the second set of restaurants to produce a set of order history scores for the second set of restaurants, wherein each order history includes a plurality of orders wherein each order in the plurality of orders records at least a cost of the order;

viii) computing a set of weighted average scores of the set of menu scores and the set of order history scores; and

ix) based on the set of weighted average scores, filtering, by the processor, the second set of restaurants to produce a first set of competitor restaurants.

3. The method of claim 2 wherein the comparing the sets of true items comprises deriving menu item association based match scores, wherein the set of menu scores are derived from the menu item association based match scores.

4. The method of claim 3 wherein the menu item association based match score between any two sets of true items is a sum of multiplication products of similarities corresponding to true items of the two sets of true items.