Systems and methods for calculating actual dollar costs for entities

• US 9,922,315 B2
• Filed: 01/08/2015
• Issued: 03/20/2018
• Est. Priority Date: 01/08/2015
• Status: Active Grant

• Current Assignee: Outseeker Corp.
• Original Assignee: Outseeker Corp.
• Inventors: Feldschuh, Jonathan
• Primary Examiner(s): ADE, OGER GARCIA
• Application Number:
  US14/592,449
• Publication Number:
  US 20170024718A1
• Time in Patent Office: 1,167 Days
• Field of Search: 705 15
• CPC Class Codes: G06Q 20/201 Price look-up processing, e.g. updating
• CPC Class Codes: G06Q 50/12 Hotels or restaurants

Abstract

Systems, methods, and computer programs for performing calculations of cost of entities are described. According to one embodiment, a method is described wherein one or more data sources are identified; information is gathered from those data sources, which is then used to calculate an adjusted cost of the entities. According to another embodiment, the information gathered from the data sources relates to dollar costs and other information pertaining to the entities, which is used to statistically model cost-related figures pertaining to the entities.

Claims

1. A system for executing computer software to generate adjusted dollar cost;

the system comprising;

a processor;

computer-readable memory coupled to the processor;

a network interface coupled to the processor;

a data processing system programmed to;

navigate to a plurality of third party data sources;

search for information associated with one or more entities on the plurality of third party data sources;

identify a subset of third party data sources from the plurality of third party data sources including the searched information associated with one or more entities;

identify predefined types of information on each of the subset of third party data sources, wherein the predefined types of information include one or more dollar costs and one or more predictor variables;

extract the one or more dollar costs and the one or more predictor variables, from the subset of third party data sources, wherein at least one of the one or more predictor variables includes a longitude and latitude data associated with the location of the one or more entities;

automatically store the extracted dollar costs and the predictor variables directly from the subset of third party data sources in a database, using an application programming interface API;

encode the longitude and latitude data;

determine a dollar cost from one or more of the dollar costs for the entities with one or more dollar costs;

identify one or more groups;

determine an order of the one or more groups for use in predicting dollar cost estimates;

predict dollar cost estimates by joint modeling the dollar costs or adjusted dollar costs as a first independent variable and using the one or more of the groups as a second independent variable;

determine adjusted dollar costs for one or more of the entities that have a dollar cost estimate in their record from at least in part the dollar cost estimates for those entities;

construct one or more of individual models for each of the predictor variables with the dollar cost as the dependent variable for only the entities with a calculated dollar cost in their record;

measure a goodness-of-fit for each of the individual models;

select, from the set of individual models for each of the predictor variables, the individual model with the best goodness-of-fit measurement; and

discard from further calculations the predictor variables with the individual models with the best goodness-of-fit measurement below a goodness-of-fit threshold value.

2. The system of claim 1 wherein the data processing system is further programmed to send the adjusted dollar costs for one or more entities to users.

3. The system of claim 1, wherein the entities are restaurants.

4. The system of claim 1, wherein the entities are hotels.

5. The system of claim 1 wherein the data processing system is further programmed to select from the set of the individual models for each of the predictor variables the individual model with the best goodness-of-fit measurement, wherein the predictors associated with the individual model with the best goodness-of-fit are used in the groups.

6. The system of claim 1 wherein the data processing system is further programmed to select from the set of the individual models for each of the predictor variables the individual model with the best goodness-of-fit measurement, wherein the predictions of the individual models with the best goodness-of-fit measurements are used in the groups.

7. The system of claim 1, wherein the groups are determined by at least one of source uniformity, row uniformity, joint source uniformity, active source uniformity, or pairwise presence.

8. A computer implemented method of calculating adjusted dollar costs for entities, comprising:

navigating to the one or more third party data sources;

searching for associated with one or more entities on the plurality of third party data sources;

identifying a subset of third party data sources from the plurality of third party data sources including the searched information associated with one or more entities;

identifying predefined types of information on each of the subset of third party data sources, wherein the predefined types of information include one or more dollar costs and one or more predictor variables;

extracting the one or more dollar costs and the one or more predictor variables, from the subset of third party data sources, wherein at least one of the one or more predictor variables includes a longitude and latitude data associated with the location of the one or more entities;

automatically storing the extracted dollar costs and the predictor variables directly from the subset of third party data sources in a database, using an API;

encoding the longitude and latitude data;

determining a dollar costs from one or more of the dollar costs available for the entities that have a dollar cost value in their record;

identifying one or more groups;

determining an order of the one or more groups for use in predicting dollar cost estimates;

predicting dollar cost estimates by joint modeling the dollar costs or adjusted dollar costs as a first independent variable and using the one or more of the groups as a second independent variable;

determining the adjusted dollar costs for one or more of only the entities that have a dollar cost estimate in their record from at least in part the dollar cost estimates for those entities;

constructing one or more of individual models for each of the predictor variables with the dollar cost as the dependent variable for only the entities with a calculated dollar cost in their record;

measuring a goodness-of-fit for each of the individual models;

selecting, from the set of individual models for each of the predictor variables, the individual model with the best goodness-of-fit measurement; and

discarding from further calculations the predictor variables with the individual models with the best goodness-of-fit measurement below a goodness-of-fit threshold value.

9. The method of claim 8, wherein the entities are restaurants.

10. The method of claim 8, wherein the entities are hotels.

11. The method of claim 8, further comprising:

providing the adjusted dollar costs for one or more of the entities to users.

12. The method of claim 8, wherein only objective predictor variables or the predictions from objective predictor variables'"'"' individual models form the groups.

13. The method of claim 8, further comprising:

retaining only the predictor variables with their individual models from their one or more models having the best goodness-of-fit measurement to form the groups.

14. The method of claim 8, further comprising:

retaining only the predictions of the individual models of the predictor variables with the individual models from the predictor variable'"'"'s one or more models having the best goodness of fit measurement to form the groups.

15. The method of claim 8, wherein the groups are determined by at least one of source uniformity, row uniformity, joint source uniformity, active source uniformity, or pairwise presence.

16. The method of claim 15, wherein the predicting dollar cost estimates step and the calculating adjusted dollar costs step include the steps of:

predicting the dollar cost estimates for a first group by joint modeling the calculated dollar costs as the dependent variable and the first group as the independent variables for all entities having at least one predictor variable value or the value of a prediction of a predictor variable'"'"'s individual model in its record;

calculating adjusted dollar costs from the dollar cost estimates predicted from the first group and the calculated dollar costs for all entities with a calculated dollar cost in their record;

setting adjusted dollar costs equal to the dollar cost estimates predicted from the first group for all entities without a calculated dollar cost in their record;

predicting iteratively new dollar cost estimates for each new group by joint modeling dollar cost estimates using the adjusted dollar costs from the previous iteration as the dependent variable and the new group as the independent variables;

calculating new adjusted dollar costs from the new dollar cost estimates and the adjusted dollar costs from the previous iteration for all entities with an adjusted dollar cost in their record; and

setting new adjusted dollar costs equal to the new dollar cost estimates for all entities without an adjusted dollar cost in their record.

17. The method of claim 13, further comprising:

using NA level coding for all categorical and logical predictor variables.

18. A non-transitory computer readable medium including instructions that, when executed by a processor of a central server, cause the central server to:

navigate to the one or more third party data sources;

search for associated with one or more entities on the plurality of third party data sources;

identify a subset of third party data sources from the plurality of third party data sources including the searched information associated with one or more entities;

identify predefined types of information on each of the subset of third party data sources, wherein the predefined types of information include one or more dollar costs and one or more predictor variables;

extract the one or more dollar costs and the one or more predictor variables, from the subset of third party data sources wherein at least one of the one or more predictor variables includes a longitude and latitude data associated with the location of the one or more entities;

automatically store the extracted dollar costs and the predictor variables directly from the subset of third party data sources in a database, using an API;

encode the longitude and latitude data;

determine a dollar costs from one or more of the dollar costs available for the entities that have a dollar cost value in their record;

identifying one or more groups;

determine an order of the one or more groups for use in predicting dollar cost estimates;

predict dollar cost estimates by joint modeling the dollar costs or adjusted dollar costs as a first independent variable and using the one or more of the groups as a second independent variable;

determine the adjusted dollar costs for one or more of only the entities that have a dollar cost estimate in their record from at least in part the dollar cost estimates for those entities;

construct one or more of individual models for each of the predictor variables with the dollar cost as the dependent variable for only the entities with a calculated dollar cost in their record;

measure a goodness-of-fit for each of the individual models;

select, from the set of individual models for each of the predictor variables, the individual model with the best goodness-of-fit measurement; and

discard from further calculations the predictor variables with the individual models with the best goodness-of-fit measurement below a goodness-of-fit threshold value.