Method and system for automated bid advice for auctions

• US 20030093357A1
• Filed: 09/10/2001
• Published: 05/15/2003
• Est. Priority Date: 09/10/2001
• Status: Active Grant

• Current Assignee: Micro Focus LLC (Open Text Corporation)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Guler, Kemal, Tang, Hsiu-Khuern, Liu, Tongwei
• Granted Patent: US 7,536,338 B2
• Time in Patent Office: Days
• US Class Current: 705/37
• CPC Class Codes: G06Q 30/08 Auctions
• CPC Class Codes: G06Q 40/04 Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Abstract

The present invention provides an automated estimation and optimization solution for selecting the optimal bid for an item in an auction. The characteristics of the auction are selected (e.g., auction format, reserve price). A relevant bidding model, based on the characteristics of the auction, is selected. The structure of the auction is estimated based on the relevant bidding model. A bid function is determined based on the auction structure and user inputs regarding the item being bid on and the characteristics of the rival bidders. An optimal bid is determined based on the bid function and user-defined evaluation criterion. An embodiment of the present invention provides a method and system that determines the latent elements of the auction environment taking into account the strategic and information conditions with minimal assumptions on the distributions of unobserved random elements. The present invention allows a bidder to estimate the unobservable private signals of rival bidders and to determine the optimal bid the bidder can employ to optimize their evaluation criterion.

Claims

1. A method for determining an optimal bid for an item in a market, said method comprising:

a) selecting characteristics of said market;

b) selecting a bidding model;

c) estimating a structure of said market;

d) determining a bid function; and

e) determining said optimal bid.

2. The method as recited in claim 1, wherein said step a) comprises:

receiving a first user input, wherein said first user input comprises information identifying an item to be bid on;

accessing a database;

retrieving historical bids data from said database;

retrieving auction characteristics data from said database, wherein said auction characteristics data comprise information relating to historical auctions of items similar to said item to be bid on;

outputting said historical bids data; and

outputting said auction characteristics data.

3. The method as recited in claim 1, wherein said step b) comprises:

receiving auction characteristics data;

accessing a database;

retrieving from said database said bidding model, wherein said bidding model is selected based on a corresponding relevance of said auction characteristics data; and

outputting said bidding model.

4. The method as recited in claim 1, wherein said step c) comprises:

receiving said bidding model;

receiving historical bids data;

expressing unobservable variables in terms of observable bids, wherein said unobservable variables are expressed in terms of observable bids by inverting said bidding model;

transforming said historical bids data to a sample of inverted bids, wherein said historical bids data are transformed by inverting said bidding model;

estimating a structure of said market, wherein said sample of inverted bids receives application of statistical density estimation techniques to obtain said structure; and

outputting said structure.

5. The method as recited in claim 1, wherein said step d) comprises:

receiving a second user input,

receiving a structure;

generating a bid function, wherein said bid function is based on said structure and said second user input; and

outputting said bid function.

6. The method as recited in claim 5, wherein said second user input comprises:

an auction format;

a valuation of said item; and

an expected number of rival bidders.

7. The method as recited in claim 1, wherein said step e) comprises:

receiving a third user input, wherein said third user input comprises an evaluation criteria;

receiving said bid function;

calculating said optimal bid based on said third user input and said bid function; and

outputting said optimal bid.

8. A computer system comprising:

a bus;

a memory interconnected with said bus; and

a processor interconnected with said bus, wherein said processor executes a method for determining an optimal bid for an item in a market, said method comprising;

a) selecting characteristics of said market;

b) selecting a bidding model;

c) estimating a structure of said market;

d) determining a bid function; and

e) determining said optimal bid.

9. The computer system as recited in claim 8, wherein said step a) comprises:

receiving a first user input, wherein said first user input comprises information identifying an item to be bid on;

accessing a database;

retrieving historical bids data from said database;

retrieving auction characteristics data from said database, wherein said auction characteristics data comprise information relating to historical auctions of items similar to said item to be bid on;

outputting said historical bids data; and

outputting said auction characteristics data.

10. The computer system as recited in claim 8, wherein said step b) comprises:

receiving auction characteristics data;

accessing a database;

retrieving from said database said bidding model, wherein said bidding model is selected based on a corresponding relevance of said auction characteristics data; and

outputting said bidding model.

11. The computer system as recited in claim 8, wherein said step c) comprises:

receiving said bidding model;

receiving historical bids data;

expressing unobservable variables in terms of observable bids, wherein said unobservable variables are expressed in terms of observable bids by inverting said bidding model;

transforming said historical bids data to a sample of inverted bids, wherein said historical bids data are transformed by inverting said bidding model;

estimating a structure of said market, wherein said sample of inverted bids receives application of statistical density estimation techniques to obtain said structure; and

outputting said structure.

12. The computer system as recited in claim 8, wherein said step d) comprises:

receiving a second user input;

receiving a structure;

generating a bid function, wherein said bid function is based on said structure and said second user input; and

outputting said bid function.

13. The method as recited in claim 12, wherein said second user input comprises:

an auction format;

a valuation of said item; and

an expected number of rival bidders.

14. The computer system as recited in claim 8, wherein said step e) comprises:

receiving a third user input, wherein said third user input comprises an evaluation criteria;

receiving said bid function;

calculating said optimal bid based on said third user input and said bid function; and

outputting said optimal bid

15. A computer readable medium for causing a computer system to execute the steps in a method for determining an optimal bid for an item in a market, said method comprising:

a) selecting characteristics of said market;

b) selecting a bidding model;

c) estimating a structure of said market;

d) determining a bid function; and

e) determining said optimal bid.

16. The computer readable medium as recited in claim 15, wherein said step a) comprises:

receiving a first user input, wherein said first user input comprises information identifying an item to be bid on;

accessing a database;

retrieving historical bids data from said database;

retrieving auction characteristics data from said database, wherein said auction characteristics data comprise information relating to historical auctions of items similar to said item to be bid on;

outputting said historical bids data; and

outputting said auction characteristics data.

17. The computer readable medium as recited in claim 15, wherein said step b) comprises:

receiving auction characteristics data;

accessing a database;

retrieving from said database said bidding model, wherein said bidding model is selected based on a corresponding relevance of said auction characteristics data; and

outputting said bidding model.

18. The computer readable medium as recited in claim 15, wherein said step c) comprises:

receiving said bidding model;

receiving historical bids data;

expressing unobservable variables in terms of observable bids, wherein said unobservable variables are expressed in terms of observable bids by inverting said bidding model;

transforming said historical bids data to a sample of inverted bids, wherein said historical bids data are transformed by inverting said bidding model;

estimating a structure of said market, wherein said sample of inverted bids receives application of statistical density estimation techniques to obtain said structure; and

outputting said structure.

19. The computer readable medium as recited in claim 15, wherein said step d) comprises:

receiving a second user input;

receiving a structure;

generating a bid function, wherein said bid function is based on said structure and said second user input; and

outputting said bid function.

20. The method as recited in claim 19, wherein said second user input comprises:

an auction format;

a valuation of said item; and

an expected number of rival bidders.

21. The computer readable medium as recited in claim 15, wherein said step e) comprises:

receiving a third user input, wherein said third user input comprises an evaluation criteria;

receiving said bid function;

calculating said optimal bid based on said third user input and said bid function; and

outputting said optimal bid.