Modeling a user's emotion and personality in a computer user int

Details Modeling a user's emotion and personality in a computer user int Modeling a user's emotion and personality in a computer user int

1998-06-30

1999-11-16

Hudspeth, David R.

Data processing: speech signal processing, linguistics, language

Speech signal processing

Application

704275, G10L 300

Patent

active

059874155

ABSTRACT:
The invention is embodied in a computer user interface including an observer capable of observing user behavior, an agent capable of conveying emotion and personality by exhibiting corresponding behavior to a user, and a network linking user behavior observed by said observer and emotion and personality conveyed by said agent. The network can include an observing network facilitating inferencing user emotional and personality states from the behavior observed by the observer as well as an agent network facilitating inferencing of agent behavior from emotion and personality states to be conveyed by the agent. In addition, a policy module can dictate to the agent network desired emotion and personality states to be conveyed by the agent based upon user emotion and personality states inferred by the observing network. Typically, each network is a stochastic model. Each stochastic model is preferably a Bayesian network, so that the observing network is a first Bayesian network while the agent network is a second Bayesian network. Generally, the first and second Bayesian networks are similar copies of one another. Each of the two Bayesian networks include a first layer of multi-state nodes representing respective emotional and personality variables, and a second layer of multi-state nodes representing respective behavioral variables. Each one of the nodes includes probabilities linking each state in the one node with states of others of the nodes. More specifically, each one of the nodes in the first layer includes probabilities linking the states of the one first layer node to the states of nodes in the second layer. Similarly, each one of the nodes in the second layer include probabilities linking the states of the one second layer node to states of nodes in the first layer.

REFERENCES:
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