Data processing: structural design – modeling – simulation – and em – Simulating electronic device or electrical system
Reexamination Certificate
1998-06-03
2002-06-11
Teska, Kevin J. (Department: 2123)
Data processing: structural design, modeling, simulation, and em
Simulating electronic device or electrical system
C705S014270, C706S046000, C345S215000, C345S215000
Reexamination Certificate
active
06405159
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
This invention relates to modeling system users, and more specifically to modeling system users to aid in the design of user interfaces.
2. Description of the Related Art
A user model is a representation of the set of behaviors that a user actually exhibits while performing a set of tasks. The purpose of user modeling is to build a model of the behaviors used when a user interacts with a system. For example, if the system the user interacts with is a computer system, then the interaction occurs primarily with the computer interface (i.e. keyboard, monitor, mouse, and sound).
An interface design team may be assembled to gather information on users in order to design a user interface. If the interface design team is emphasizing performance, the behaviors and characteristics that emerge are items related to the expert user. The expert users usually can effectively articulate their suggestions and are normally interested in achieving performance. Therefore, interviewers from the interface design team pay close attention to the comments and suggestions of these expert users. Another aspect for giving credence to the expert user is that experts are usually the people whom get promoted and are likely to be chosen as members on the design team. The problem is, of course, that other types of users do not have the same behaviors and capabilities as these experts and, thus, their needs are not represented in the requirements gathering phase of the interface design. Expert users are typically a smaller percentage of the user population. If the interface is designed for the expert user, this leaves a high percentage of users where the interface is unsuitable or less than optimum.
In some design projects, ease of learning, training, or novice aspects are emphasized to a great extent. This is particularly true when a trainer is in a lead position on the design team or when management places a high priority on reducing the costs of training. However, having the novices' needs be dominant in the interface design phase is no better than permitting the experts' needs to be dominant. One group is still being used for the design to the exclusion of the other group's needs. Novices generally also comprise a very small percentage of the user population. Therefore, designing an interface just for the novice user may improve their performance, but may jeopardize overall performance of other users.
If behaviors of users were condensed into a single set of behaviors, the set definition would be so wide and variable that it would have a limited contribution to the interface designers. That is, the characterization of the users would be so broad, that the designers could not determine what interface options would make a difference in the users' performance.
If there is no overwhelming performance issue or training issue that directs the team, then anecdotal behavioral information is obtained for a variety of users. User requirements information is usually gathered by more than one person from the design team. Thus, a great deal of discussion ensues following the information gathering on users because each gatherer may have interviewed a different user who probably had different capabilities and a different view of the system and a different set of needs. Therefore, the resulting set of user requirements is a composite or average view of the user needs. In this situation, many of the needs of users do indeed surface but they are not organized in a manner that is intuitively obvious. Also, interface designs to meet these needs are not necessarily optimally beneficial to any one group of users. This method of designing an interface for the composite or average user thus presents a substantial risk that very few users will be fully accommodated by the interface.
Another current practice is that if users are categorized, they are done so on an informal basis, based primarily on the opinion and judgment of the local operating management. Even though these individual users may be identified, their needs are mixed in with the needs of other users without regard as to the group they represent. Also, with current practice, the descriptions of user behavior are done anecdotally, not statistically. Quantitative performance results are not incorporated into the behavioral descriptions. User models are generally not constructed primarily because there is only one user representation and all of the design team members think they know the needs of the single user.
The user modeling goal should thus characterize the users in such a way that the designers can incorporate the users' behaviors into the interface design so that performance is maximized (while acknowledging and compensating for the human element). The expectation is that the user models would also allow for the prediction of performance after the newly designed interface is operational. The style and type of user interface can significantly impact the resulting performance.
Therefore, a method is needed to model system users that produces information that can be used in the design of an interface that maximizes the performance of the users, and also allows for the prediction of performance after the newly designed interface is operational.
SUMMARY OF THE INVENTION
Accordingly, the present invention is directed to a method for categorizing, describing, and modeling system users that substantially obviates one or more of the problems arising from the limitations and disadvantages of the related art.
It is an object of the present invention to provide a method that accurately categorizes, describes, and models a user's behavior while interacting with a system.
It is a further object of the present invention to provide a method for modeling system users that provides qualitative and quantitative models.
It is also an object of the present invention to provide a method for modeling types of system users that allows for the prediction of performance after the new user interface is operational.
Another object of the present invention is to provide a method for modeling system users that aids in designing an interface more familiar and comfortable to users because particular components of the interface will be better suited for their particular style.
The foregoing objects are achieved by the present invention that preferably comprises a method for modeling types of system users. Behaviors of a variety of types of users are categorized into two or more groups. Descriptions of the behaviors of each user group are created based on behaviors of selected users from each user group. Models are generated for the described behaviors of each user group. A user interface can then be designed using information from these models. The performance of the variety of types of users is improved when the interface is used by these users.
The behaviors may include navigation behaviors, parallel processing behaviors, and customer sales behaviors. Categorizing may comprise charting the behaviors on a chart having two, three, four, or more dimensions. The dimensions may include performance measures, cognitive workload measures, behavioral measures, or user characteristic measures.
The descriptions of the behaviors of each user group may be related to the similarities within each group or the differences between each group. The descriptions of the behaviors of each user group may comprise listing the tasks by frequency and importance and selecting from the most important tasks for detailed task analysis. The detailed task analysis may comprise capturing the perceptual, cognitive, and motor stages of human behavior, and quantifying each stage as to processing speed and cognitive load. The detailed task analysis may be accomplished by using a modified GOMS methodology.
The models may include qualitative models which may include how the users within a specific group behave in certain situations, or how the users within a specific group perform certain functions. The models may include quantitative models whic
Bushey Robert R.
Mauney Jennifer M.
Greenblum & Bernstein P.L.C.
Phan T.
SBC Technology Resources Inc.
Teska Kevin J.
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