Systems and methods for automatic generation of information

• US 20070112618A1
• Filed: 11/08/2006
• Published: 05/17/2007
• Est. Priority Date: 11/09/2005
• Status: Abandoned Application

• Current Assignee: Generation 5 Mathematical Technologies, Inc.
• Original Assignee: Generation 5 Mathematical Technologies, Inc.
• Inventors: Krneta, Milorad
• Application Number:
  US11/594,147
• Publication Number:
  US 20070112618A1
• Time in Patent Office: Days
• US Class Current: 705/10
• 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 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 20/201 Price look-up processing, e.g. updating
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

Methods and systems consistent with the principles of some embodiments of the present invention provide for determining a set of variables that together have strong predictive power relative to some target variable(s); identifying a subset of variables that together describe the majority of the information in the database; statistically segmenting the database; predicting unknown values in the target variables; combining prediction and VIVa; filling gaps in databases for further analysis or database completion; predicting probabilities; and outputting information based on the processed data. The outputs may relate to optimizing price to maximize return, producing a database based on postal code, identifying a trade area for sale of goods; and/or optimizing the distribution of marketing funds across various marketing channels.

Claims

1. A method for optimizing price of a good to maximize returns from sales, comprising:

receiving user specifications and automatically generating a file containing descriptions of a plurality of scenarios covering at least one price supplied by the user;

for each scenario and price, estimating sales on the basis of patterns in observed cases;

searching for an optimal price by one of

     1) inspections of all scenarios,

     2) by a random search on a sample of scenarios,

     3) by numerical optimization of a price function; and

     4) a combination of a random preliminary search followed by numerical optimization; and

providing the optimal price based on the search.

2. The method of claim 1, wherein the estimate of expected sales for at least one price is determined by averaging predicted sales values at a sample of general market conditions.

3. The method of claim 2, wherein confidence bands for prediction may be computed by re-sampling.

4. An apparatus for optimizing price of a good to maximize returns from sales, comprising:

a memory storing a set of instructions; and

a processor executing the stored set of instructions to perform a method including;

receiving user specifications and automatically generating a file containing descriptions of a plurality of scenarios covering at least one price supplied by the user;

for each scenario and price, estimating sales on the basis of patterns in observed cases;

searching for an optimal price by one of

     1)inspections of all scenarios,

     2) by a random search on a sample of scenarios,

     3) by numerical optimization of a price function; and

     4) a combination of a random preliminary search followed by numerical optimization; and

providing the optimal price based on the search.

5. The apparatus of claim 4, wherein the estimate of expected sales for at least one price is determined by averaging predicted sales values at a sample of general market conditions.

6. The apparatus of claim 5, wherein confidence bands for prediction may be computed by re-sampling.

7. A method for determining consumer trade areas comprising:

receiving information related to an acceptable percentage of relative expenditures;

determining a plurality of zip codes for a store and order the plurality of zip codes by distance;

determining total consumption for the store;

calculating relative sums of expenditures for each of the plurality of zip codes;

generating a convex hull including the relative sums of expenditures based on the received information relating to the acceptable percentage of relative expenditures; and

designating a consumer trade area based on the generated convex hull.

8. An apparatus for determining consumer trade areas comprising:

a memory storing a set of instructions; and

a processor executing the stored set of instructions to perform a method including;

receiving information related to an acceptable percentage of relative expenditures;

determining a plurality of zip codes for a store and order the plurality of zip codes by distance;

determining total consumption for the store;

calculating relative sums of expenditures for each of the plurality of zip codes;

generating a convex hull including the relative sums of expenditures based on the received information relating to the acceptable percentage of relative expenditures; and

designating a consumer trade area based on the generated convex hull.

9. A method for optimizing the distribution of marketing funds across various marketing channels, comprising:

accessing a dataset including information related to at least one of product or category sales, general predictors, and marketing mix variables;

receiving analytical options relating to at least one of total marketing budget constraints, total or incremental return on investment constraints, and marketing mix variables to be tested;

generating sales predictions for every marketing mix; and

reporting the generated sales predictions.

10. An apparatus for optimizing the distribution of marketing funds across various marketing channels, comprising:

a memory storing a set of instructions; and

a processor executing the stored set of instructions to perform a method including;

accessing a dataset including information related to at least one of product or category sales, general predictors, and marketing mix variables;

receiving analytical options relating to at least one of total marketing budget constraints, total or incremental return on investment constraints, and marketing mix variables to be tested;

generating sales predictions for every marketing mix; and

reporting the generated sales predictions.

11. A method for load balancing a plurality of queries, comprising:

receiving a query for processing at a load balancing module;

identifying one of a plurality of servers capable of processing the received query by analyzing a queue of pending queries each of the plurality of servers;

sending the received query to the identified server for processing;

determining that the received query was processed; and

reporting the results of the processed query.

12. The method of claim 11, wherein if all of the plurality of servers have a full queue of pending queries, the load balancing module stores the received query until the pending queue of one of the plurality of servers is capable of receiving the query.

13. An apparatus for load balancing a plurality of queries, comprising:

a memory storing a set of instructions; and

a processor executing the stored set of instructions to perform a method including;

receiving a query for processing;

identifying one of a plurality of servers capable of processing the received query by analyzing a queue of pending queries each of the plurality of servers;

sending the received query to the identified server for processing;

determining that the received query was processed; and

reporting the results of the processed query.

14. The apparatus of claim 13, wherein if all of the plurality of servers have a full queue of pending queries, the load balancing module stores the received query until the pending queue of one of the plurality of servers is capable of receiving the query.

15. A method for producing a database based on postal code, comprising:

creating a geographical linkage representing a connection between granular level units and aggregated level units;

creating a historical cases dataset and anchor variables on target cases; and

producing a database by using the geographical linkage and historical cases dataset to predict a target dataset.