Abstract: A method of updating a first neural network is disclosed. The method includes providing a computer system with a computer-readable memory that stores specific computer-executable instructions for the first neural network and a second neural network separate from the first neural network. The method also includes providing one or more processors in communication with the computer-readable memory. The one or more processors are programmed by the computer-executable instructions to at least process a first data with the first neural network, process a second data with the second neural network, update a weight in a node of the second neural network by a delta amount as a function of the processing of the second data with the second neural network, and update a weight in a node of the first neural network as a function of the delta amount. A computer system for updating a first neural network is also disclosed. Other features of the preferred embodiments are also disclosed.

Abstract: A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text.

Abstract: A method of updating a first neural network is disclosed. The method includes providing a computer system with a computer-readable memory that stores specific computer-executable instructions for the first neural network and a second neural network separate from the first neural network. The method also includes providing one or more processors in communication with the computer-readable memory. The one or more processors are programmed by the computer-executable instructions to at least process a first data with the first neural network, process a second data with the second neural network, update a weight in a node of the second neural network by a delta amount as a function of the processing of the second data with the second neural network, and update a weight in a node of the first neural network as a function of the delta amount. A computer system for updating a first neural network is also disclosed. Other features of the preferred embodiments are also disclosed.

Abstract: A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text.

Abstract: A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text.

Abstract: A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text.

Abstract: A method of providing an auto-encoder for anonymizing data associated with a population of entities is disclosed. The method includes providing a computer system with a memory storing specific computer-executable instructions for a neural network. The neural network includes an input layer of nodes; three or more layers of nodes; and an output layer of nodes to provide an encoded output vector. The second layer of nodes has more nodes than the first and third layers of nodes. The method also includes identifying a plurality of characteristics associated with the entities and preparing a plurality of input vectors that include a characteristic. The characteristics appear in the input vector as transformed numeric information from human recognizable text.

Abstract: A method of updating a first neural network is disclosed. The method includes providing a computer system with a computer-readable memory that stores specific computer-executable instructions for the first neural network and a second neural network separate from the first neural network. The method also includes providing one or more processors in communication with the computer-readable memory. The one or more processors are programmed by the computer-executable instructions to at least process a first data with the first neural network, process a second data with the second neural network, update a weight in a node of the second neural network by a delta amount as a function of the processing of the second data with the second neural network, and update a weight in a node of the first neural network as a function of the delta amount. A computer system for updating a first neural network is also disclosed. Other features of the preferred embodiments are also disclosed.