Abstract: A computerized learning multi-modal fraud prevention system and method for generating a data signature of a user, such as one engaged in electronic commerce, to prevent fraudulent activities by machines and persons imitating the user. Steps comprise: fetching a signal of a user's signature stored in memory; generating at least one challenge sequence based on the signal to create a second signature; presenting the generated challenge sequence to the user; collecting the user's challenge response to the generated challenge sequence; computing a quality factor between the user's challenge response and the generated challenge sequence; computing a transaction quality factor and content quality factor and reporting an impostor or re-challenging if the quality factor is below a threshold. Lastly, generating a new signature based on any portion of a user's challenge response and/or any portion of the previously generated signature and/or any portion of collectable information from the user's device memory.

Abstract: A computerized learning multi-modal fraud prevention system and method for generating a data signature of a user, such as one engaged in electronic commerce, to prevent fraudulent activities by machines and persons imitating the user. Steps comprise: fetching a signal of a user's signature stored in memory; generating at least one challenge sequence based on the signal to create a second signature; presenting the generated challenge sequence to the user; collecting the user's challenge response to the generated challenge sequence; computing a quality factor between the user's challenge response and the generated challenge sequence; computing a transaction quality factor and content quality factor and reporting an impostor or re-challenging if the quality factor is below a threshold. Lastly, generating a new signature based on any portion of a user's challenge response and/or any portion of the previously generated signature and/or any portion of collectable information from the user's device memory.

Abstract: A computerized learning fraud prevention system and method for generating a voice signature of a user, such as one engaged in electronic commerce, to prevent fraudulent activities by machines and persons imitating the user. Steps comprise: fetching a signal of a user's signature stored in memory; generating at least one challenge sequence based on the signal to create a second signature; presenting the generated challenge sequence to the user; collecting the user's challenge voice response to the generated challenge sequence; computing a quality factor between the user's challenge response and the generated challenge sequence; computing a transaction quality factor and content quality factor and reporting an impostor or re-challenging if the quality factor is below a threshold. Lastly, generating new signature based on any portion of user's challenge voice response and/or any portion of the previously generated signature and/or any portion of collectable information from user's device memory.

Abstract: A system and method for providing content and advertisements to a user. Embodiments of the invention may enable an advertiser to provide preferred content and advertisements and to avoid providing undesirable content and advertisements by storing content selection parameters on a customer computer or an advertisement selection computer. The selection parameters may be used to select content and advertisements to be provided to a user. A user identification parameter may be used to locate associated selection parameters. Other embodiments are described and claimed.

Abstract: A method of and apparatus for packet data streaming handover between disparate wireless and wired end-user agents may address changes in circumstances surrounding packet communications between end-user agents connected over various communications networks. The end-user agents may be coupled to at least one handover controller. One or more handover controllers may be used to implement handovers, which may be based on quality signals received from end-user agents.