Data processing: presentation processing of document – operator i – Presentation processing of document – Layout
Reexamination Certificate
1999-09-10
2004-02-17
Feild, Joseph H. (Department: 2178)
Data processing: presentation processing of document, operator i
Presentation processing of document
Layout
C715S252000, C715S252000, C709S241000, C717S108000, C717S165000
Reexamination Certificate
active
06694482
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to architectural frameworks for development of multimedia applications, and more specifically to architectural frameworks for developing adaptive, personalized, interactive multimedia applications and services.
2. Background and Material Information
In general, designing and implementing interactive systems is a complex and lengthy task. If one adds multimedia to the development equation, the level of complexity, the content variability and the required management support immediately soars and can overwhelm the development process. On the other hand, there presently exists a very dynamic and rich environment that potentially offers a business opportunity allowing one to build a family of applications that can be strongly differentiated by leveraging the same rich and complex content. Thus, a double edged sword exists.
If one examines the requirements, present and future, of information, more specifically multimedia information, one discovers that in general these requirements are a response to the “dynamics of information”. These dynamics can be characterized by: constantly changing information; broad user population; and heterogenous landscape of delivery devices. If one grafts onto this picture the dynamics of collaboration or computer-supported work in synchronous or asynchronous mode, and potentially the technical problems are further compounded by the opportunity for differentiated and value-added services increases, i.e., the double-edged sword once again.
The best way to understand a system is to have an abstraction that describes a simpler picture of the structure and the machinery. A metaphoric vehicle is useful in that it allows framing of a problem and likewise offers a solution that supports and promotes flexibility, expressiveness, and scalability in information design and display. One can say that a multi-media presentation is like “telling a story”. The presentation author is attempting to convey a communicative intent and more than likely it was constructed with a particular audience in mind, as well as a specific context and medium.
The computational narrative model, as disclosed in Brooks, K. M., “Do Agent Stories Use Rocking Chairs: The Theory and Implementation of One Model for Computational Narrative”, Processings of the Fourth ACM International Multimedia Conference on Intelligent User Interfaces, ACM Press 1996 and Murtaugh, M. “The Automatist Storytelling System: Putting the Editor's Knowledge in Software”, MIT MS Thesis, 1996, offers a metaphor for creating tools that are capable of going beyond traditional storytelling by enhancing the editorial through the leveraging of the computer's ability to support rapid decision making. According to Brooks, narrative represents the universe of story elements for a given story, i.e., the collection of possibility, and narration as a specific navigation through that universe.
As shown in
FIG. 1
, the process of computational storytelling involves the author supplying the elements of the story and the structure to organize the story elements. The agent takes the elements of the story and the structure and generates a story, more precisely, a narrative, and presents the “story” to an audience. The audience reacts and generates feedback to the agent. The agent acting as proxy for the author can react to the feedback by modifying the presentation.
Some current conceptual views regarding the techniques or technical strategies that are related to developing a framework for creating and delivering interactive multimedia applications include: dynamic presentation, behavior-based artificial intelligence, memory-based learning, and user modeling.
Regarding dynamic presentation, Maybury, M. “Intelligent Multimedia Interfaces”, AAAI/MIT Press, Cambridge, Mass., 1993, discloses that automatic multimedia presentation involves the stages of content selection (i.e., what to say), media allocation (i.e., what media to present it in), and media realization (i.e., how to say it). The focus is the media allocation and realization phase. More specifically, how to create presentations without knowing all “facts” during design time. The basic objective is to enable the creation of user interfaces that are sufficiently flexible and adaptive to “re-invent” themselves at run-time. To support this flexibility and adaptability, an interface needs to be developed not to a final fixed form, but to some protean form that can be reshaped at run time, time after time, to meet the requirements of any situation that invalidates its current form.
Szekely P., “Retrospective and Challenges for Model-Based Interface Development”, USC Information Sciences Institute, Marina del Rey, Calif., 1996, proposes one architecture. Szekely discloses that a model-based user interface calls for a model of the interface that is organized as three levels of abstraction: task and domain model for the application, an abstract user interface specification, and a concrete user interface specification. The task model represents the task that the user will undertake to perform with the application. The domain model represents the data and the operations that are part of an application.
The second level, according to Szekely, is the abstract user interface specification. At this level, an interface is defined in terms of abstract interaction units, information elements, and presentation units. The abstract interaction units are low-level interactions such as showing a presentation unit. Information elements represent data such as attributes extracted from the domain model. Presentation units are abstractions of windows and specify collections of abstract presentation units and information elements that are to be treated as a unit. Basically, the abstract user interface specification abstractly specifies the way information will be presented in the interface and form for interaction with the information.
The third level, according to Szekely, is the concrete user interface specification that specifies rendering styles for the presentation units, i.e., widgets. Different model-based user interface (UI) frameworks differ in what models they provide. Szekely discloses that some frameworks have one model but not the other two, while in other cases, only one model is defined.
FIG. 2
is a flowchart showing a generic model-based presentation system as disclosed in Szekely.
An alternative reasoning framework has emerged in Artificial Intelligence circles called Behavior-Based AI (BBAI) as disclosed in Maes, P. “Behavior-Based Artificial Intelligence”, Proceedings of Second Animat Conference on Adaptive Behavior, 1992. This new approach represents more of a different way of thinking about a problem domain than an alternative reasoning technique. The knowledge-based approach involves capturing the rules to solve a domain. In contrast, the BBAI approach relies on a set of lower level competencies which are each experts at solving one part of the larger problem domain as disclosed in Brooks.
Additionally, the BBAI approach tends to emphasize the system behavior as opposed to the system knowledge. Furthermore, BBAI stresses that the system should be situated in its environment and have direct (or as close as possible) access to the problem domain. This framework enables a system to bring together different classes of reasoning techniques, heuristic, statistical, etc., and incorporate each application of a technique into a lower-level competency module or “expert”. In effect, these modules come together to form a multi-agent system.
Another learning technique, as disclosed in Stanfield, C. et al., “Toward Memory-Based Reasoning”, Communications of the ACM, 20(12), ACM Press, 1986, is memory-based learning. Basically, memory-based learning entails comparing a new situation against each of the situations which have occurred before. Given a new situation, a memory-based learning agent looks at the actions taken in N of the “closest” situations or “nearest neighbors” to predict the action for a new sit
Arellano Javier B.
Divine Abha S.
Dobes Zuzana K.
Liu Guangtian
Feild Joseph H.
Greenblum & Bernstein P.L.C.
Ludwig Matthew
SBC Technology Resources Inc.
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