Data extraction and analysis system and tool

Patent metadata

Field	Value
Publication number	US-2017004415-A1
Application number	US-201514791167-A
Country	US
Kind code	A1
Filing date	Jul 2, 2015
Priority date	Jul 2, 2015
Publication date	Jan 5, 2017
Grant date	—

Abstract

A data extraction and analysis system and tool is disclosed herein. The data extraction and analysis system and tool can include memory containing a comparison database, a factor database, and a model database that can include a multilevel model. The data extraction and analysis system and tool can include a content management server. The content management server can receive a request identifying a species and a variable and can retrieve data to generate a statistical model. Based on the statistical model, the content management server can identify and recommend an option to the requestor.

First claim

What is claimed is:

1 . A data extraction system comprising:
a database server comprising:
a comparison database comprising data ranking options based on a comparison of statistical models;
a factor database; and
a model database, wherein the model database comprises a multilevel model; and
a content management server configured to:
receive a request for a recommendation from a user device, wherein the request for a recommendation comprises data identifying a category and a variable;
identify a plurality of species data sources, wherein the species data sources comprise at least a first website hosted by a first content server and a second website hosted by a second content server;
retrieve species content from the species data sources;
identify a plurality of potential qualitative data sources, wherein the potential qualitative data sources comprise at least a third website hosted on a third content server;
retrieve the potential qualitative data from the potential qualitative data sources;
generate a statistical model based on the species content and the potential qualitative data; and
provide a recommendation based on the statistical model.

2 . The data extraction system of claim 1 , wherein the statistical model comprises a multilevel model.

3 . The data extraction system of claim 2 , wherein retrieving the species content comprises:
receiving format information, wherein the format information identifies the format in which the species content is contained in the species data source; and identifying at least one relevant field in the species data of the species data source.

4 . The data extraction system of claim 3 , wherein retrieving the species data further comprises scraping the species content from the at least one identified relevant field in the species data.

5 . The data extraction system of claim 4 , wherein scraping the species content comprises web scraping.

6 . The data extraction system of claim 5 , wherein retrieving the potential qualitative data comprises:
receiving format information, wherein the format information identifies the format in which the potential qualitative data is contained in the potential qualitative data source; identifying at least one relevant field of the potential qualitative data source; and scraping the potential qualitative data from the at least one identified relevant field of the potential qualitative data source.

7 . The data extraction system of claim 6 , wherein the content management server is further configured to extract qualitative data information from the retrieved potential qualitative data.

8 . The data extraction system of claim 7 , wherein extracting qualitative data information from the retrieved potential qualitative data comprises:
identifying ranking variables, wherein the ranking variables are stored within the model database; identifying the qualitative data; generating ranking data corresponding to the ranking variables based on the qualitative data; and storing the ranking data.

9 . The data extraction system of claim 8 , wherein generating the statistical model comprises:
identifying levels within the base content and the qualitative data; estimating level variance, wherein the level variances comprises the percentage of variation in an aggregate of the base content and the qualitative data attributable to each of the levels; generating a simplified statistical model, wherein the simplified statistical model is based on less than all of the identified levels; and calculating an error.

10 . The data extraction system of claim 9 , wherein generating the statistical model comprises:
generating a second statistical model, wherein the second statistical model is based on at least one more of the identified levels than the simplified statistical model; comparing the simplified statistical model to the second statistical model; and selecting the most accurate of the simplified statistical model and the second statistical model.

11 . The data extraction system of claim 10 , wherein the level variance is calculated as one of the variance partition coefficient (VPC) and the intraclass correlation coefficient (ICC).

12 . The data extraction system of claim 11 , wherein the most accurate of the simplified statistical model and the second statistical model is selected according to at least one of a Chi-squared likelihood ratio test, an Akaike information criterion (AIC), and a Bayesian information criterion (BIC).

13 . A method of data extraction and analysis comprising:
receiving a request for a recommendation from a user device, wherein the request for a recommendation comprises data identifying a category and a variable; identifying a plurality of species data sources, wherein the species data sources comprise at least a first website hosted by a first content server and a second website hosted by a second content server, and wherein the identified plurality of species data sources have a category matching the category of the received request; retrieving species content from the species data sources; identifying a plurality of potential qualitative data sources, wherein the potential qualitative data sources comprise at least a third website hosted on a third content server; retrieving the potential qualitative data from the potential qualitative data sources; generating a statistical model based on the species content and the potential qualitative data; and providing a recommendation based on the statistical model, wherein the recommendation identifies an action in regards to the variable of the received request.

14 . The method of claim 13 , wherein the statistical model comprises a multilevel model.

15 . The method of claim 14 , wherein retrieving the species content comprises:
receiving format information, wherein the format information identifies the format in which the species content is contained in the species data source; identifying at least one relevant field in the species data of the species data source; and scraping the species content from the at least one identified relevant field in the species data.

16 . The method of claim 15 , wherein retrieving the potential qualitative data comprises:
receiving format information, wherein the format information identifies the format in which the potential qualitative data is contained in the potential qualitative data source; identifying at least one relevant field of the potential qualitative data source; and scraping the potential qualitative data from the at least one identified relevant field of the potential qualitative data source.

17 . The method of claim 16 , further comprising extracting qualitative data information from the retrieved potential qualitative data.

18 . The method of claim 16 , wherein extracting qualitative data information from the retrieved potential qualitative data comprises:
identifying ranking variables, wherein the ranking variables are stored within the model database; identifying the qualitative data; generating ranking data corresponding to the ranking variables based on the qualitative data; storing the ranking data; identifying levels within the base content and the qualitative data; estimating level variance, wherein the level variances comprises the percentage of variation in an aggregate of the base content and the qualitative data attributable to each of the levels; generating a simplified statistical model, wherein the simplified statistical model is based on less than all of the identified

Assignees

• Pearson Education Inc

Inventors

• Moretti Antonio
• Mcknight Kathy
• González Brenes José Pablo

Classifications

• G09B5/00 Primary

  Electrically-operated educational appliances (working with questions and answers G09B7/00; simulators G09B9/00; advertising or displaying in general G09F) · CPC title

• G06Q50/20

  Education · CPC title

• G09B7/00

  Electrically-operated teaching apparatus or devices working with questions and answers (mechanically operated G09B3/00; computing arrangements G06F) · CPC title

• G06F17/3053

  Physics · mapped topic

• G06N99/005 Primary

  Physics · mapped topic