Decision engine and method and applications thereof

Claim 1

A method of creating a programmable decision engine for jury selection in a computer-readable medium including a Bayesian network, comprising: Retrieving data from a client database, said client database containing demographic information about a jury pool and forming a focus database of demographic information about a jury pool, wherein the retrieving includes retrieving data from a static customer database, said customer database containing demographic information about a jury pool, and retrieving data from a data stream; Employing an expectation maximization algorithm to provide a value to valueless records in the focus database; Applying a set of initial rules to the focus database to form at least two nodes relating to variables in the demographic information; Applying a first learning process to determine a set of arcs to be applied between the at least two nodes; Applying a second learning process to determine a set of states to be applied within each node, the set of states relating to values taken by the variables; Applying a third learning process to determine a set of probabilities applicable to the states learned in the second learning process; and Applying a fourth learning process to update a structure of the at lease two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states, such that the focus database of jury pool demographic information is updated and contains updated probabilities for the states relating to each node, and further contains updated information relating to the arcs between the nodes.

Claim 2

A method of creating a programmable decision engine for jury selection in a computer-readable medium including a Bayesian network, comprising: Retrieving data from a client database, said client database containing demographic information about a jury pool and forming a focus database of demographic information about a jury pool, wherein the retrieving includes retrieving data from a static customer database, said customer database containing demographic information about a jury pool, and retrieving data from a data stream; Employing an expectation maximization algorithm to provide a value to valueless records in the focus database; Applying a set of initial rules to the focus database to form at least two nodes relating to variables in the demographic information; Applying a first learning process to determine a set of arcs to be applied between the at least two nodes; Applying a second learning process to determine a set of states to be applied within each node, the set of states relating to values taken by the variables; Applying a third learning process to determine a set of probabilities applicable to the states learned in the second learning process; and Applying a fourth learning process to update a structure of the at lease two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states, such that the focus database of jury pool demographic information is updated and contains updated probabilities for the states relating to each node, and further contains updated information relating to the arcs between the nodes.

Claim 17

A method of using a programmable decision engine for jury selection in a computer readable medium including a Bayesian network, comprising: Retrieving data from a client database, said client database containing demographic information about a jury pool and forming a focus database of demographic information about a jury pool, wherein the retrieving includes retrieving data from a static customer database, said customer database containing demographic information about a jury pool and retrieving data from a data stream; Employing an expectation maximization algorithm to provide a value to valueless records in the focus database; Applying a set of initial rules to the focus database to form at least two nodes relating to variables in the demographic information; Applying a first learning process to determine a set of arcs to be applied between the at least two nodes; Applying a second learning process to determine a set of states to be applied within each node, the set of states relating to values taken by the variables; Applying a third learning process to determine a set of probabilities applicable to the states learned in the second learning process; Applying a fourth learning process to update a structure of the at least two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states; Applying evidence to at least one of the nodes; and Updating the structure according to the applied evidence using at least one of the first, second, third, or fourth learning processes, such that the focus database of jury pool demographic information is updated and contains updated probabilities for the states relating to each node, and further contains updated information relating to the arcs between the nodes.

Claim 18

A method of using a programmable decision engine for jury selection in a computer readable medium including a Bayesian network, comprising: Retrieving data from a client database, said client database containing demographic information about a jury pool and forming a focus database of demographic information about a jury pool, wherein the retrieving includes retrieving data from a static customer database, said customer database containing demographic information about a jury pool and retrieving data from a data stream; Employing an expectation maximization algorithm to provide a value to valueless records in the focus database; Applying a set of initial rules to the focus database to form at least two nodes relating to variables in the demographic information; Applying a first learning process to determine a set of arcs to be applied between the at least two nodes; Applying a second learning process to determine a set of states to be applied within each node, the set of states relating to values taken by the variables; Applying a third learning process to determine a set of probabilities applicable to the states learned in the second learning process; Applying a fourth learning process to update a structure of the at least two nodes, the set of arcs, the set of states within each node, and the set of probabilities for the states; Applying evidence to at least one of the nodes; and Updating the structure according to the applied evidence using at least one of the first, second, third, or fourth learning processes, such that the focus database of jury pool demographic information is updated and contains updated probabilities for the states relating to each node, and further contains updated information relating to the arcs between the nodes.