Patent Document ID: 7567946
Application ID: 11137565
Patent Flag: 1

Claim One:
1. A computer-implemented method for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs to generate clusters of users with similar behavior for each class of service in a network of shared devices offering different classes of service, comprising: recording usage job data of observed users and devices carrying out the jobs; defining a probability model with an observed user variable, an observed device variable, a latent job cluster variable, a latent job service class variable, and the dependencies among the observed user variable, the observed device variable, the latent job cluster variable, and the latent job service class variable including a dependency of the latent job service class variable to the observed device variable constrained by knowledge of the service classes supported by devices; wherein the distribution of the devices is constrained by the knowledge of the service classes they support, and wherein defining a probability model according to generating the job cluster index, which has no dependencies, generating the user id, which is dependent on job clusters, generating the job service class, which is dependent on the user, generating the device choice, which is dependent on the job clusters and the job service classes; determining a range of service classes associated with the shared devices; selecting an initial number of job clusters; learning parameters of the probability model using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters, wherein the latent job cluster variable and the latent job service class variable are learned together; and applying the learned parameters of the probability model to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.