Abstract: A method, apparatus, and system is provided for determining, in a trader device of an electronic trading system, whether a match exists between a client requirement, provided by a client device of the system, and a supplier offer stored in trader device. The supplier offer comprises feature data, indicative of a set of features of an offered product, including at least one updatable feature data element. The updatable feature data element is indicative of a variable feature of the offered product and includes address data indicative of a location from which a current specification of the variable feature can be obtained by the trader device in response to an update request from the trader device.

Abstract: A method of managing the processing of information using a first neural network, the information relating to the transmission of messages in a telecommunications network, uses the steps of:(i) monitoring the performance of the first neural network in processing the information;(ii) creating a second neural network of the same topology as the first when a predetermined performance threshold is reached, and(iii) retraining the second neural network while continuing to process the information using the first neural network. If the neural networks are implemented using objects, such retraining can be facilitated by using a persistance mechanism to enable the objects to be stored and moved. Applications in fraud detection.

Abstract: A generic anomaly detection engine is described which provides neural network technology to process information such as call detail records and detect anomalies in the information, such as mobile phone fraud. Information from call detail records is pre-processed to form signatures which represent the calling behavior of a subscriber. These signatures are formed in one of a plurality of predetermined formats. The generic anomaly detection engine is instantiated in order to create an anomaly detector which is suitable for a particular situation and during the instantiation process the topology of the neural network components is automatically adjusted to fit the required situation. The topology is adjusted to according to the format of the signatures.