Computer graphics processing and selective visual display system – Computer graphics processing – Animation
Reexamination Certificate
1999-12-21
2002-06-11
Zimmerman, Mark (Department: 2671)
Computer graphics processing and selective visual display system
Computer graphics processing
Animation
C434S350000
Reexamination Certificate
active
06404438
ABSTRACT:
TECHNICAL FIELD
The present invention relates generally to the field of telecommunication, more particularly to the field of telecommunications in which graphical user icons are used for communication in which behavior is learned.
RELATED APPLICATIONS
This application is a continuation-in-part of co-pending application Ser. No. 09/415,769 filed Oct. 8, 1999, assigned to the assignee of the present application and which is hereby incorporated by reference.
BACKGROUND OF THE INVENTION
In visual representation-based environments in which a user controls a virtual representation to interact with other users, the visual representation personality can be initially selected by the user to reflect generally the user's own behavior and personality. For example, as described in co-pending application Ser. No. 09/415,769 (hereinafter “parent application”) a user can select from a variety of personality types to represent the user, including ‘hip-hop,’ ‘upper-class,’ ‘rocker’, or the like. The embodiments described in the parent application also allowed a user to further specify the behavior of his or her visual representation by setting mood intensity values. The mood intensity values modify the behavior of the visual representation (or visual representation) to allow the visual representation to appear, on a sliding scale, positive, indifferent, or aggressive. However, in these embodiments, the user defines the visual representation's behavior explicitly through personality and mood selections and explicit gesture commands. Because the settings are generically defined, there is a limitation on how the personalities can be tailored to match the personality of the individual user. Additionally, to modify the visual representation's behavior, the user adjusts the different settings that control the visual representation's behavior. This interface provides a limitation on the virtual representation experience, as the user is constantly aware that the user is explicitly in control of the visual representation's behavior.
Accordingly, a new system is needed in which the visual representation's behavior is modified through the natural course of using the visual representation, without requiring explicit directives provided by the user. Additionally, a system is needed in which the visual representation's behavior is modified to match more closely the behavior of the user.
SUMMARY OF THE INVENTION
A system, method, and apparatus are disclosed in which utterances entered by a user are processed to identify patterns of behavioral movements executed by the user's visual representation. Once identified, the patterns are used to generate behavioral movements responsive to new utterances received from the user, without requiring the user to explicitly alter the behavioral characteristics selected by the user. Thus, the behavior of the user's visual representation is modified without requiring explicit user directives, and the modified behavior is generated responsive to the user actual communication activity, thus ensuring that the visual representation's behavior is more closely related to the user's own personality.
More specifically, in one embodiment an application module parses an utterance generated by a user to determine the presence of gesture commands. An utterance is an input text string and optional gesture commands entered by a user through a keyboard or voice commands and processed by the application module of the present invention to generate visual representation behavioral movements. Gesture commands are explicit commands made by a user to have the visual representation act out or animate a behavioral movement or series of movements to convey a particular idea or emotion. For example, a gesture command could have the visual representation bow, wave, skip around, raise an eyebrow, or the like. The user issues a gesture command by providing a gesture command identifier along with the text portion of the utterance. Thus, an exemplary utterance is: ‘Hello (wave) how are you?’, where ‘(wave)’ is a gesture command provided by the user to have the user's visual representation animate a hand wave. In a preferred embodiment, each utterance is parsed to determine the presence of gesture commands. If a gesture command is found in an utterance, the utterance is stored for behavioral learning processing.
A stored utterance is analyzed in context of the existing stored utterances to determine if the stored utterances provide the basis for creating a new behavioral rule. A behavioral rule generates behavioral movements based on natural language processing, as described in the parent application. A behavioral rule comprises a context, a weighting, and a behavioral movement. A context is a gesticulatory trigger and optionally associated word or words. A gesticulatory trigger is a linguistic category designed to organize word types into common groupings. A weighting defines the probability that the specific behavioral movement will be used; in one embodiment, the scale is 1 to 10, where 10 is a 100% probability. An exemplary behavioral rule is: {10 * Negative* shake head}. Thus, the weight value 10 specifies that the behavioral movement ‘shake head’ will be performed when a word classified as a Negative gesticulatory trigger is identified in an utterance.
In one embodiment, newly stored utterances are analyzed to generate different contexts associated with the behavioral movement, using the Specific gesticulatory trigger for the contexts. Gesticulatory triggers can include classes such as Prepositions, which includes any preposition, Referents, which includes words that refer to other objects such as ‘this’ or ‘that,’ Negatives, which includes negative words such as ‘no’ or ‘not,’ or Specifics, which are simply any specific word designated by quotation marks, such as ‘Hi’ or ‘Hello’. In one embodiment, pre-assigned words are provided with each personality type in a lexicon. Each word in the lexicon has an associated behavioral movement and/or a gesticulatory trigger class. Thus, when a specific word such as ‘no’ is encountered in an utterance, the lexicon is checked to determine whether the word ‘no’ is present in the lexicon, and if it is present, to which behavioral movement it is associated, and/or to which gesticulatory trigger class it belongs.
For example, for the utterance ‘Hello (wave) how are you?’, generated contexts using the Specific gesticulatory trigger include ‘Hello (wave)’, ‘Hello (wave) how’, ‘Hello (wave) how are’, ‘Hello (wave) how are you’, ‘(wave) how’, (wave) how are’, etc. The different words are different Specific gesticulatory triggers. To determine if any of the contexts should be used as a basis for a new behavioral rule in an embodiment in which contexts are stored, the contexts of the existing utterances in the log are compared with the new contexts. If a context appears in the log at a frequency above a threshold, then the context is used as the basis for a new behavioral rule. For example, if the threshold is 5 appearances, and the context ‘Hello (wave)’ appears for the fifth time in the new logged utterance, then a new behavioral rule based on ‘Hello (wave)’ is generated.
In one embodiment, the new behavioral rule is generated by using the Specific gesticulatory trigger as the gesticulatory trigger for the behavioral rule, assigning an arbitrary weighting, and applying the associated gesture as the behavioral movement for the rule. Thus, in the ‘Hello (wave)’ example, the new behavioral rule is defined as {10, ‘Hello’, wave} the arbitrary weighting of 10 ensuring that a wave is animated each time the word ‘hello’ is recognized in an utterance transmitted by the user. However, in a preferred embodiment, the new behavioral rule is then used to modify existing rules, or create more generally applicable rules. Accordingly, the application module of the present invention examines current rules to determine if more general rules can be created from the existing rules. For example, if
Harrison William D.
Hatlelid Kris E.
Kavanagh Ken G.
Electronic Arts Inc.
Gray Gerald T.
Sealey Lance W.
Townsend and Townsend / and Crew LLP
Zimmerman Mark
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