Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
1997-02-14
2001-03-27
Alam, Hosain T. (Department: 2172)
Data processing: database and file management or data structures
Database design
Data structure types
C705S007380, C706S045000, C709S203000
Reexamination Certificate
active
06208989
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of Invention
The present invention relates to effectively coupling a computer user to multi-media data. More specifically, the present invention relates to a method to determine a value of selected subsets of multi-media based on a user's subjective preferences.
2. Related Materials and Definitions
This application is related to the following co-pending applications which are hereby incorporated by reference:
UNIVERSAL TAG IDENTIFIER ARCHITECTURE (application Ser. No. 07/963,885),
METHOD FOR GENERATING CONNECTIONS BETWEEN OBJECTS IN A COMPUTER NETWORK (GRINDING), Ser. No. 08/262,999, now U.S. Pat. No. 5,608,900
FACILITY FOR THE STORAGE AND MANAGEMENT OF INFORMATION OBJECTS (NOUMENA SERVER), Ser. No. 08/,263,146, now U.S. Pat. No. 5,557,790
METHOD FOR THE ASSOCIATION OF HETEROGENEOUS INFORMATION, Ser. No. 08/262,838 pending,
FACILITY FOR THE STORAGE AND MANAGEMENT OF CONNECTIONS (CONNECTION SERVER) Ser. No. 08/267,022 pending and
METHOD FOR STORING AND RETRIEVING HETEROGENEOUS CLASSIFICATION SYSTEMS Ser. No. 08/263,379 pending.
The following definitions may be helpful to the understanding of the terminology as cited throughout the above related materials. This terminology may be used throughout the background, specification and claims of the present invention:
Tags: Tags are globally unique identifiers. Tags are sequentially numbered identifiers identifying data objects (i.e. video, text, audio, observations, opinions, etc.)
Phenomena: The logical structure of the system begins with a unit of human perception, the “phenomena”. In the universe of a computer system, “Phenomena” is defined as a representation of phenomena which exist in the universe of human experience Phenomena can be ideas, written matter, video, computer data, etc. Examples include viewing a computer file using a word processor, watching a digital video clip or listening to a digital audio segment.
Connections: That which gathers (or links) Phenomena into interrelated collections. Connections are that which lead the user from one Phenomena to another Phenomena. Connections are not simply a road-map from a Phenomena to all other Phenomena. More specifically, Connections represent an observation of related Phenomena made by human or by computer observers.
Connection Attributes: In the logical structure of the system, “Connection Attributes” allow the entire network of Phenomena and Connections to become usable to each user of the system. Connection Attributes store the rationale behind each connection. In fairly generic terms, Connection Attributes describe the Who, What, Where, When and Why of a particular observation.
Noumena: Another concept in the logical structure of the system is “Noumena”. Noumena are that which lie beyond the realm of human perception. In computer-based systems, such as the instant invention, they are the computer stored data, examples are “computer files” or datasets”. When these computer files, the Noumena, are observed in their “raw” form, they do not resemble pictures, sounds, nor words. These Noumena resemble a series of bits, bytes, or numbers. These computer files must be manipulated by computer programs, “Phenominated”, to become as they appear to the observer. In the present system, Noumena are all of the generic format computer files needed to produce a representation of a Phenomena. This includes the computer data files as well as the computer program files.
Grinding: Grinding is a systematic, computer-based observation of Phenomena. This is typically done with a “narrow view”. The programs are usually looking for well defined criteria. When Phenomena are observed by the computer programs, the programs make Connections between the observed Phenomena and other Phenomena known by the programs. In effect, acting as a human observer would when viewing a Phenomena and manually Connection it to other Phenomena.
Persona: to determine the value of information based on each user's subjective preferences.
Capture: During knowledge capture, the human or computer observer Connects two Phenomena and provides the rationale for the Connection by supplying Connection Attributes. The user can also Connect a new Phenomena to previously existing Phenomena.
Retrieve: During knowledge retrieval, an observer navigates from Phenomena to Phenomena via Connections. Knowledge is delivered by experiencing the reconstituted Phenomena. Which knowledge is delivered is controlled by the Connections and the assessment of the Connection Attributes, preferably under the auspices of a Persona.
The present invention supports the overall system of co-pending application “Method for Association of Heterogeneous Information” It supports the Tag Architecture, Connection Server, Grinding, Noumena Server and the design and infrastructure of the overall system, but is not limited thereto. The term “Phenomena” could be read “object”, and the term “Connection” could be read “link” in this disclosure. The distinction between Noumena and Phenomena is made to distinguish between objects as experienced by users (Phenomena) and objects as they are actually stored (Noumena).
DISCUSSION OF PRIOR ART
Accessing computer information has become an overwhelming task. Today databases, both local and distributed, contain enormous volumes of information. No longer are databases limited to textual entries but rather a whole world of data is now available in the form of video, audio, photographs, etc.—collectively referred to as multi-media. For the average person trying to locate a particular subset of data quickly, or at all, has become near impossible.
Various prior art systems have attempted to provide various filters to limit the breadth of information or to narrow the scope of a search for particular information. Common word searching techniques are useful in locating specific textual information desired. The user is limited however by their own knowledge of the techniques of word searching and by their knowledge and understanding of the subject for which they are seeking corresponding stored data. Narrowing the search with specific key words may obtain specific references which might be useful. The immediate consequences of using specific key words is potential elimination of many excellent sources germane to the subject matter desired. The converse, broadening the search terms creates information overload with no immediate method to analyze the quantity or quality of the information. The user is forced to use additional key terms or other filters to eliminate sources of material until it approaches a manageable subset. Each filter stage eliminates potentially applicable references.
The following example might be helpful in illustrating typical word search limitations. If a user were trying to locate all computer stored information on “rock climbing”, a simple word search on a small database of data, using the term “rock climbing”, should produce a volume of manageable references which the user could then peruse If the source database was now expanded to include many computer coded sources of data (i.e. network sources), the terms “rock climbing” would produce an enormous quantity of source documents. The user would be confronted with: articles on rock climbing, video (i.e. movies or recorded television about rock climbing), audio (recorded instructional tapes, songs), photographs, etc. The shear volume of material would be overwhelming with no intelligent means to synthesize the useful from the inapplicable.
The following are examples of known prior art.
Hypertext/Hypermedia
Hypertext, and its multimedia counterpart hypermedia, are methods used by programmers to interconnect references to additional related sources. Hypertext programmers usually store maps of selected links for a particular application within the application itself. These are “closed” systems with no external API's to add links from outside their application Additional limitations of Hypertext are its static authoring linking process, rapid development of large volumes of data and its inability to crosslink easi
Dockter Michael J.
Farber Joel F.
Seppi Kevin D.
Alam Hosain T.
International Business Machines - Corporation
Lacasse Randy W.
Lacasse & Associates
Strimaitis Ray
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