Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
1999-07-07
2003-02-18
Jung, David (Department: 2175)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C707S793000, C707S793000, C707S793000
Reexamination Certificate
active
06523022
ABSTRACT:
NOTICE OF COPYRIGHT
A portion of the disclosure of this patent document contains material that is subject to copyright protection and to which a claim of copyright protection is made. The owner has no objection to the facsimile reproduction by anyone of the patent disclosure, as it appears in the Patent and Trademark Office patent files or records, but otherwise reserves all copyright rights whatsoever.
FIELD OF THE INVENTION
The present invention relates to information retrieval, and the application and deployment architecture for such information retrieval. Specifically, the present invention concerns a multi-tier client/server model for record retrieval wherein optimum record retrieval from a database is achieved based on embedded expert judgments linked to words, phrases, sentences and paragraphs of text; or numbers; or maps, charts, and tables; or still pictures and/or graphics; or moving pictures and/or graphics; or audio elements (hereinafter sometimes collectively referred to as the “links” or “Linked Terms,” or when any one of the aforementioned elements are used singly, as the “link” or “Linked Term”), contained in documents on a network resource, such as a web site and incorporating an intuitive graphical user interface (GUI) to correlate through a plurality of frames or inline frames, dynamic framesets, layers or adding to the display a plurality of fixed or floating pop-up windows, or any combination of the foregoing the retrieved records with records from one remote database or a large collection of remote databases maintained by one company, called a Data Warehouse, plus means to select various databases or Data Warehouses and a comprehensive selectable index of the linked embedded expert judgments.
BACKGROUND OF THE INVENTION
“Pull” Technology
A conventional information retrieval system includes a database of records, a processor for executing searches on the records, and application software that controls how the retrieval system, such as a database management system (DBMS), accepts the search queries, manages the search, and handles the search results. Generally, the database includes records such as text documents, financial or court records, medical files, personnel records, graphical data, technical information, audio and video files or various combinations of such data. Typically, a user enters a password and client billing information, and then initiates the search by finding the appropriate database or groups of databases to search and formulating a proper query that is sent to the DBMS. This process is known as searching by pull technology. To effectively search and retrieve records from the database, the DBMS typically offers a limited variety of search operations, or query models, specifically designed to operate on the underlying records in the database. The query models are coordinated and executed by an application generally referred to as a search engine. For example, a document database, such as a database of court opinions, may be organized with each court opinion as a record with fields for the title of the case, jurisdiction, court and body text. A simple search engine may support a full text searching query model for all the text fields, individual field searching, such as searching by court or jurisdiction, and various Boolean search operations such as and, or, and not. More sophisticated search engines may support the following query models:
1. nested Boolean or natural language searches;
2. grammatical connectors that search for terms in a grammatical relationship such as within the same sentence or paragraph (e.g., “/s”, “/p”, etc.);
3. proximity connectors that require search terms to appear within a specified number of terms of each other (e.g., “w/5”);
4. exclusion terms (“BUTNOT”);
5. weighted keyword terms;
6. wildcards;
7. specification of the order in which the database processes the search request (e.g., grouping words in parenthetical expressions);
8. restriction of the search to certain fields, and formulation of a restricted search such as by date, subject, jurisdiction, title, etc.; and
9. combination of the fields of search.
In addition, large commercial database providers, such as BLOOMBERG, DIALOG, LEXIS/NEXIS and WESTLAW typically have thousands of individual databases. These large commercial database providers are Data Warehouses, which comprise an architecture and process where data are extracted from external information providers, then formatted, aggregated, and integrated into a read only database that is optimized for decision making. Users subscribe to the Data Warehouses by monthly or yearly subscription, and then typically pay stratified levels of hourly charges for access to certain databases, or groups of databases. More recently, Data Warehouses have been selling their content over the Internet by individually pricing each article or document. This is known as “by the drink” pricing since the user does not subscribe to a service over time, but buys only the article s/he wants at one time.
Drawbacks of Pull Technology
One limitation of existing information retrieval systems, especially among the commercial Data Warehouses, is the burden on the user to first enter client and billing information and passwords to gain access and initiate the search, and then formulate the search query. Typically, the subscription based commercial database services provide password administration and extensive catalogues, both in print and on-line, describing the content and scope of the databases offered, and in some cases, live assistance by telephone by reference librarians who assist the user to find the proper databases. However, the user must remember the password, and spend time finding the proper database by catalogue, on-line access, or phone, or else incur more expensive hourly charges searching through single databases or groups of databases for the appropriate database content and scope.
A second limitation of pull technology is the formulation of the search query. To use the more powerful commercial Data Warehouses effectively, a user must be trained to use all of the aforementioned query models, and have sufficient knowledge of the topic to choose the appropriate keywords or natural language terms. The complexity of the search process compels the commercial Data Warehouses to offer training and keyword help to their subscribers by multiple publications that describe search tips; interactive software based training modules; account representatives who visit the user and train him or her; and customer service and reference librarians available by phone.
A third limitation of pull technology concerns how it is employed on the World Wide Web area of the Internet (“WWW”) by such search engines as THE ELECTRIC LIBRARY, EXCITE!, FOUR ONE ONE (411), HOTBOT, INFOSEEK, LINKSTAR, LYCOS, MAGELLAN, ALTA VISTA, OPEN TEXT INDEX, WEB CRAWLER, WWWWORM, and YAHOO!, just to name a few. These search engines' query models are beginning to approach the sophistication and complexity of those of the commercial database companies, but unlike the commercial databases, they offer minimal customer support. Another drawback of the Internet search engines, well documented in the computer business and popular press, is that their search engine algorithms cause multiple irrelevant responses to a query. Other drawbacks of Internet search engines employing pull technology include:
1. The great majority of the Internet search engines have no control over the records in their database. Unlike the commercial Data Warehouses who have an ongoing relationship with the content provider (usually by a license agreement), and who carefully screen, cleanse and format the information provided by their information providers, many Internet search engines sweep through the WWW periodically and automatically, and catalogue web sites as records in their databases. They also permit any web publisher to submit his or her web site as a record entry with little or no prior screening.
2. As a result of little or no screening, and absolutely no contact with
Coudert Brothers
Jung David
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