Source: http://www.google.fr/patents/USRE42870?hl=fr
Timestamp: 2013-05-26 09:43:23
Document Index: 496372158

Matched Legal Cases: ['art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41', 'art 41']

Brevet USRE42870 - Text mining system for web-based business intelligence applied to web site ... - Google�BrevetsRecherche Images Maps Play YouTube Actualit�s Gmail Drive Plus » Recherche avanc�e dans les brevets | Historique Web | Connexion Recherche avanc�e dans les brevets BrevetsA text mining system for collecting business intelligence about a client, as well as for identifying prospective customers of the client, for use in a lead generation system accessible by the client via the Internet. The text mining system has various components, including a data acquisition process...http://www.google.fr/patents/USRE42870?utm_source=gb-gplus-shareBrevet USRE42870 - Text mining system for web-based business intelligence applied to web site server logs Num�ro de publicationUSRE42870 E1Type de publicationOctroi Num�ro de demande12/325,881 Date de publication25 oct. 2011 Date de d�p�t1 d�c. 2008 Date de priorit�4 oct. 2000Autre r�f�rence de publicationUS7330850 InventeursYu FengRobert L. FosterJohn C. Seibel Cessionnaire d'origineDafineais Protocol Data B.V., Llc Classification aux �tats-Unis707/769707/805707/776 Classification internationaleG06F7/00G06F17/30 Classification coop�rativeG06F17/3089G06F17/30616 Classification europ�enneG06F17/30T1EG06F17/30W7R�f�rencesCitations de brevets (99)Citations hors brevets (78) R�f�renc� par (2)Liens externesUSPTO Cession USPTO EspacenetText mining system for web-based business intelligence applied to web site server logsUS RE42870 E1 R�sum� A text mining system for collecting business intelligence about a client, as well as for identifying prospective customers of the client, for use in a lead generation system accessible by the client via the Internet. The text mining system has various components, including a data acquisition process that extracts textual data from Internet web sites, including their logs, content, processes, and transactions. The system compares log data to content and process data, and relates the results of the comparison to transaction data. This permits the system to provide aggregate cluster data representing statistics useful for customer lead generation.
The input data 42 can be the client's sales data, customer-contact data, customer purchase data and account data etc. Various data sources for customer data can be contact management software packages such as ACT, MarketForce, Goldmine, and Remedy. Various data sources for accounting data are Great Plains, Solomon and other accounting packages typically found in small and medium-sized businesses. If the client has ERP (enterprise resource planning) systems (such as JD Edwards, PeopleSoft and SAP) installed, the data sources for customer and accounting data will be extracted from ERP customer and accounting modules. This data is typically structured and stored in flat files or relational databases. System 41 is typically an OLAP (On-line analytic processing) type server-based system. It has five major components. A data acquisition component 41a collects and extracts data from different data sources, applying appropriate transformation, aggregation and cleansing to the data collected. This component consists of predefined data conversions to accomplish most commonly used data transformations, for as many different types of data sources as possible. For data sources not covered by these predefined conversions, custom conversions need to be developed. The tools for data acquisition may be commercially available tools, such as Data Junction, ETI*EXTRACT, or equivalents. Open standards and APIs will permit employing the tool that affords the most efficient data acquisition and migration based on the organizational architecture.
Data mart 41b captures and stores an enterprise's sales information. The sales data collected from data acquisition component 41a are �sliced and diced� into multidimensional tables by time dimension, region dimension, product dimension and customer dimension, etc. The general design of the data mart follows data warehouse/data mart Star-Schema methodology. The total number of dimension tables and fact tables will vary from customer to customer, but data mart 41b is designed to accommodate the data collected from the majority of commonly used software packages such as PeopleSoft or Great Plains.
Various commercially available software packages, such as Cognos, Brio, Informatica, may be used to design and deploy data mart 41b. The Data Mart can reside in DB2, Oracle, Sybase, MS SQL server, P.SQL or similar database application. Data mart 41b stores sales and accounting fact and dimension tables that will accommodate the data extracted from the majority of industry accounting and customer contact software packages.
A Predefined Query Repository Component 41c is the central storage for predefined queries. These predefined queries are parameterized macros/business rules that extract information from fact tables or dimension tables in the data mart 41b. The results of these queries are delivered as business charts (such as bar charts or pie charts) in a web browser environment to the end users. Charts in the same category are bounded with the same predefined query using different parameters. (i.e. quarterly revenue charts are all associated with the same predefined quarterly revenue query, the parameters passed are the specific region, the specific year and the specific quarter). These queries are stored in either flat file format or as a text field in a relational database.
A Business Intelligence Charts Repository Component 41d serves two purposes in the database server system 41. A first purpose is to improve the performance of chart retrieval process. The chart repository 41d captures and stores the most frequently visited charts in a central location. When an end user requests a chart, system 41 first queries the chart repository 41d to see if there is an existing chart. If there is a preexisting chart, server 41e pulls that chart directly from the repository. If there is no preexisting chart, server 41e runs the corresponding predefined query from the query repository 41c in order to extract data from data mart 41b and subsequently feed the data to the requested chart. A second purpose is to allow chart sharing, collaboration and distribution among the end users. Because charts are treated as objects in the chart repository, users can bookmark a chart just like bookmarking a regular URL in a web browser. They can also send and receive charts as an email attachment. In addition, users may logon to system 41 to collaboratively make decisions from different physical locations. These users can also place the comments on an existing chart for collaboration.
Another component of system 41 is the Web Server component 41e, which has a number of subcomponents. A web server subcomponent (such as Microsoft IIS or Apache server or any other commercially available web servers) serves HTTP requests. A database server subcomponent (such as Tango, Cold Fusion or PHP) provides database drill-down functionality. An application server subcomponent routes different information requests to different other servers. For example, sales revenue chart requests will be routed to the database system 41; customer profile requests will be routed to a Data Mining server, and competition information requests will be routed to a Text Mining server. The latter two systems are discussed below. Another subcomponent of server 41e is the chart server, which receives requests from the application server. It either runs queries against data mart 41b, using query repository 41c, or retrieves charts from chart repository 41c.
As an example of operation of system 41, gross revenue analysis of worldwide sales may be contained in predefined queries that are stored in the query repository 41c. Gross revenue queries accept region and/or time period as parameters and extract data from the Data Mart 41b and send them to the web server 41e. Web server 41e transforms the raw data into charts and publishes them on the web.
Data sources 62 for system 61 are the Data Mart 41b, e.g., data from the tables that reside in Data Mart 41b, as well as data collected from marketing campaigns or sales promotions.
For data coming from the Data Mart 41b, data acquisition process 61a between Mining Base 61b and Data Mart 41b extract/transfer and format/transform data from tables in the Data Mart 41b into Data Mining base 61b. For data collected from sales and marketing events, data acquisition process 61a may be used to extract and transform this kind of data and store it in the Data Mining base 61b.
Data Mining base 61b is the central data store for the data for data mining system 61. The data it stores is specifically prepared and formatted for data mining purposes. The Data Mining base 61b is a separate data repository from the Data Mart 41b, even though some of the data it stores is extracted from Data Mart's tables. The Data Mining base 61b can reside in DB2, Oracle, Sybase, MS SQL server, P.SQL or similar database application.
Chart repository 61d contains data mining outputs. The most frequently used decision tree charts are stored in the chart repository 61d for rapid retrieval.
Customer purchasing behavior analysis is accomplished by using predefined Data Mining models that are stored in a model repository 61e. Unlike the predefined queries of system 41, these predefined models are industry-specific and business-specific models that address a particular business problem. Third party data mining tools such as IBM Intelligent Miner and Clementine, and various integrated development environments (IDEs) may be used to explore and develop these data mining models until the results are satisfactory. Then the models are exported from the IDE into standalone modules (in C or C++) and integrated into model repository 61e by using data mining APIs.
Data mining server 61c supplies data for the models, using data from database 61c. FIG. 7 illustrates the data paths and functions associated with server 61c. Various tools and applications that may be used to implement server 61c include VDI, EspressChart, and a data mining GUI.
The outputs of server 61e may include various options, such as decision trees, Rule Sets, and charts.
By default, all the outputs have drill-down capability to allow users to interactively navigate and explore information in either a vertical or horizontal direction. Views may also be varied, such as by influencing factor. For example, in bar charts, bars may represent factors that influence customer purchasing (decision-making) or purchasing behavior. The height of the bars may represent the impact on the actual customer purchase amount, so that the higher the bar is the more important the influencing factor is on customers, purchasing behavior. Decision trees offer a unique way to deliver business intelligence on customers' purchasing behavior. A decision tree consists of tree nodes, paths and node notations. Each individual node in a decision tree represents an influencing. A path is the route from root node (upper most level) to any other node in the tree. Each path represents a unique purchasing behavior that leads to a particular group of customers with an average purchase amount. This provides a quick and easy way for on-line users to identify where the valued customers are and what the most important factors are when customer are making purchase decisions. This also facilitates tailored marketing campaigns and delivery of sales presentations that focus on the product features or functions that matter most to a particular customer group. Rules Sets are plain-English descriptions of the decision tree. A single rule in the RuleSet is associated with a particular path in the decision tree. Rules that lead to the same destination node are grouped into a RuleSet. RuleSet views allow users to look at the same information presented in a decision tree from a different angle. When users drill down deep enough on any chart, they will reach the last drill-down level that is data view. A data view is a table view of the underlying data that supports the data mining results. Data Views are dynamically linked with Data Mining base 61b and Data Mart 41b through web server 61f.
Web server 61f, which may be the same as database server 41e, provides Internet access to the output of mining server 61c. Existing outputs may be directly accessed from storage in charts repository 61d. Or requests may be directed to models repository 61e. Consistent with the application service architecture of lead generation system 10, access by the client to web server 61f is via the Internet and the client's web browser.
For data acquisition 81a from web sites, user-interactive web crawlers are used to collect textual information. Users can specify the URLs, the depth and the frequency of web crawling. The information gathered by the web crawlers is stored in a central repository, the text archive 81b. For data acquisition from newsgroups, a news collector contacts the news server to download and transform news articles in an html format and deposit them in text archive 81b. Users can specify the newsgroups names, the frequency of downloads and the display format of the news articles to news collector. For data acquisition from Internet mailing lists, a mailing list collector automatically receives, sorts and formats email messages from the subscribed mailing lists and deposit them into text archive 81b. Users can specify the mailing list names and address and the display format of the mail messages. For data acquisition from client text files, internal documents are sorted, collected and stored in the Text Archive 81b. The files stored in Text Archive 81b can be either physical copies or dynamic pointers to the original files.
The Text Archive 81b is the central data store for all the textual information for mining. The textual information it stores is specially formatted and indexed for text mining purpose. The Text Archive 81b supports a wide variety of file formats, such plain text, html, MS Word and Acrobat.
Text Mining Server 81c operates on the Text Archive 81b. Tools and applications used by server 81c may include ThemeScape and a Text Mining GUI 81c. A repository 81d stores text mining outputs. Web server 81e is the front end interface to the client system 13, permitting the client to access database 81b, using an on-line search executed by server 81c or server 81e.
The outputs of system 81 may include various options. Map views and simple query views may be delivered over the Internet or Intranet. By default, all the outputs have drill-down capability to allow users to reach the original documents. HTML links will be retained to permit further lateral or horizontal navigation. Keywords will be highlighted or otherwise pointed to in order to facilitate rapid location of the relevant areas of text when a document is located through a keyword search. For example, Map Views are the outputs produced by ThemeScape. Textual information is presented on a topological map on which similar �themes� are grouped together to form �mountains.� On-line users can search or drill down on the map to get the original files. Simple query views are similar to the interfaces of most of the Internet search engines offered (such as Yahoo, Excite and HotBot). It allows on-line users to query the Text Archive 81b for keywords or key phrases or search on different groups of textual information collected over time.
A typical user session using text-mining system 81 might follow the following steps. It is assumed that the user is connected to server 81e via the Internet and a web browser, as illustrated in FIG. 1. In the example of this description, server 81e is in communication with server 81c, which is implemented using ThemeScape software.
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