Method and system for selecting optimal commodities based upon business profile and preferences

• US 7,707,090 B2
• Filed: 08/21/2008
• Issued: 04/27/2010
• Est. Priority Date: 02/04/2000
• Status: Expired due to Fees

• Current Assignee: Globys Inc (Constellation Software Incorporated)
• Original Assignee: Globys Inc (Constellation Software Incorporated)
• Inventors: Snyder, Scott Andrew
• Primary Examiner(s): Subramanian; Narayanswamy
• Application Number:
  US12/195,752
• Publication Number:
  US 20090030796A1
• Time in Patent Office: 614 Days
• Field of Search: 705 35- 37, 705/1, 705/26
• US Class Current: 705/35
• CPC Class Codes: G06Q 30/0207 Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 30/0601 Electronic shopping [e-shopping]
• CPC Class Codes: G06Q 30/0603 Catalogue ordering
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
• CPC Class Codes: G06Q 40/06 Asset management; Financial planning or analysis

Abstract

A system and method for assisting a customer in choosing a combination of commodities based on preferences of the customer. A combination is a set of related commodities, wherein bundling discounts may be applied to particular bundles of related commodities. Combination options are created by optimizing the categories within the combination in parallel, and then selecting the best value options from each category into a grouping. The effective cost of a grouping is calculated as a total of the effective costs of each option within the grouping. The effective costs consider weighted values of performance features in addition to the actual cost of a commodity. The groupings are ranked and presented to the user, so that the user may select a grouping as combination of commodities for purchase.

Claims

1. A computer-implemented method for assisting a customer in choosing a combination of commodities, wherein a combination is composed of one commodity selected from each of N categories, and wherein N is an integer greater than one, the method comprising:

(a) ranking the commodities within each category based, in part, on at least one optimization parameter;

(b) creating a first combination for a first category by;

(i) selecting a highest ranked commodity from the first category;

(ii) automatically selecting any commodities in other categories that are linked to the commodity selected in step(b)(i);

(iii) selecting a valid commodity for each of the categories from which a commodity has not already been selected for the first combination;

(c) repeating (b) for the other N−

1 categories to create N−

1 other combinations;

(d) calculating a total cost of each combination; and

(e) presenting the combinations to the customer, wherein steps (a) through (e), inclusive, are executed on a computer device.

2. The method of claim 1, further comprising:

for the first category, using the computer device creating another combination by;

(iv) selecting a next ranked commodity from the first category;

(v) automatically selecting any commodities in other categories that are linked to the commodity selected in step (iv); and

(vi) selecting a valid commodity for each of the categories from which a commodity has not been selected for the another combination.

3. The method of claim 2, wherein (iv), (v) and (vi) are repeated for a plurality of ranks.

4. The method of claim 1, wherein (e) comprises presenting the combinations of commodity options ranked by the total cost.

5. The method of claim 1, wherein (a) comprises calculating an effective cost for each commodity and ranking the options within each category by effective cost.

6. The method of claim 5, wherein the effective cost calculations include bundling discounts.

7. The method of claim 5, wherein (d) comprises calculating a total effective cost for each of the combinations by adding the effective costs of the selected commodities in a combination.

8. The method of claim 5, wherein (a) comprises, for each category:

(i) identifying at least one first parameter associated with a commodity;

(ii) associating at least one value to the at least one first parameter;

(iii) calculating an estimated cost of the commodity based on features of the category that are desired by the customer;

(iv) obtaining from the customer a preference weighting on at least one second parameter;

(v) calculating an effective cost of the commodity by adjusting the estimated cost based on the preference weighting and the at least one value assigned to the parameters; and

(vi) ranking the commodities within each category by effective cost.

9. The method of claim 8, wherein the parameter is a feature, an attribute, or a performance characteristic associated with a category.

10. The method of claim 8, wherein (a)(ii) includes the steps of:

setting a range for the at least one first parameter;

sampling a random set of customers over the range; and

determining a best fit utility function using regression analysis on data received as a result of sampling.

11. The method of claim 10, wherein the utility function is evaluated to obtain the at least one value, wherein the value represents a cost or benefit of the parameter to the customer.

12. The method of claim 10, wherein the regression analysis employs at least one constant to determine the utility function.

13. The method of claim 1, wherein (e) comprises presenting the combinations to the customer, whereby the customer selects a portion of a combination of commodities for purchase.

14. A computer-readable medium encoded with a plurality of instructions for controlling a computing system to perform an operation for assisting a customer in choosing a combination of commodities, wherein a combination is composed of one commodity selected from each ofN categories, and wherein N is an integer greater than one, the operation comprising:

(a) ranking the commodities within each category based, in part, on at least one optimization parameter;

(b) creating a first combination for a first category by;

(i) selecting a highest ranked commodity from the first category;

(ii) automatically selecting any commodities in other categories that are linked to the commodity selected in step(b)(i);

(iii) selecting a valid commodity for each of the categories from which a commodity has not already been selected for the first combination;

(c) repeating (b) for the other N−

1 categories to create N−

1 other combinations;

(d) calculating a total cost of each combination; and

(e) presenting the combinations to the customer.

15. The computer-readable medium of claim 14, wherein the operation further comprises:

for the first category, creating another combination by;

(iv) selecting a next ranked commodity from the first category;

(v) automatically selecting any commodities in other categories that are linked to the commodity selected in step (iv); and

(vi) selecting a valid commodity for each of the categories from which a commodity has not been selected for the another combination.

16. The computer-readable medium of claim 14, wherein (a) comprises calculating an effective cost for each commodity and ranking the options within each category by effective cost.

17. The computer-readable medium of claim 16, wherein (d) comprises calculating a total effective cost for each of the combinations by adding the effective costs of the selected commodities in a combination.

18. The computer-readable medium of claim 14, wherein (a) comprises, for each category:

(i) identifying at least one first parameter associated with a commodity;

(ii) associating at least one value to the at least one first parameter;

(iii) calculating an estimated cost of the commodity based on features of the category that are desired by the customer;

(iv) obtaining from the customer a preference weighting on at least one second parameter;

(v) calculating an effective cost of the commodity by adjusting the estimated cost based on the preference weighting and the at least one value assigned to the parameters; and

(vi) ranking the commodities within each category by effective cost.

19. The computer-readable medium of claim 18, wherein the associating at least one value includes:

setting a range for the at least one first parameter;

sampling a random set of customers over the range; and

determining a best fit utility function using regression analysis on data received as a result of sampling.

20. The computer-readable medium of claim 19, wherein (e) comprises providing to the customer means to select a portion of a combination of commodity options for purchase.