First Orion Corp.

Acxiom Corporation

Charles Donald Morgan

Samuel Kenton Welch

Daniel Francis Kliebhan

Mark Hamilton Botner

Collin Michael Turney

Jamelle Adnan Brown

Chee-Fung Choy

Nysia Inet George

Andrew Collin Shaddox

Robert F Piscopo, Jr

John R Talburt

Robert Francis Piscopo

Terry Talley

James E Mooney

Walter Anderson

Mike Dobrovich

Ali Kooshesh

G Leigh McLaughlin

Marvin G Fogata

Joy L Baker

Joy E Cook

David B Roland

Jeannie K Ratliff

Melissa K Ward

Wally Anderson

Kari Johnston

Russell Baxter

Ray Hashemi

Richard Farmer

Charles Bussell

C Stuart White

Walter E Anderson

Shawn G Harvey

Shawn Harvey

One example method of operation may include collecting call metric data over a predefined period of time for identified calls, querying the call metric data to identify whether one or more call filtering criteria parameters require changes, determining one or more call filtering criteria parameters require changes based on a deviation from one or more expected call metric data values included in the call metric data, modifying one or more of the call filtering criteria parameters, and updating an active call scam model stored on a call processing server based on the one or more call filtering parameters.

Call screening service for communication devices

One example method of operation may include identifying a call from a caller and destined for a callee, receiving a data message associated with the call, forwarding the data message to a call processing server, processing the data message to identify one or more call parameters, comparing the one or more call parameters to an active call scam model applied by the call processing server, determining a scam score for the call based on the comparing of the one or more call parameters to the active call scam model applied by the call processing server, and determining whether to notify the callee that the call is a scam based on the scam score.

One example method of operation may include receiving one or more data messages, identifying calls and corresponding call data from the one or more messages, identifying call parameters from the call data, applying a call activity filter criteria to the call parameters to identify a suspect sub-set of the call parameters which indicate an elevated likelihood of call scam, forwarding the call parameters and the suspect sub-set of call parameters to one or more call data tables, and assigning one or more scam designation threshold scores to the suspect sub-set of the call parameters in the one or more call data tables.

One example method of operation may include identifying call data associated with a received call, identifying call parameters from the call data, and the call parameters include one or more call routing parameters associated with call routing of the call and one or more call session parameters associated with a call session of the call, assigning weights to one or more of the call routing parameters and the call session parameters, determining a scam score for the call based on a sum of the weights applied to the call routing parameters and the call session parameters, and blocking the call when the scam score is greater than or equal to a predetermined threshold scam score.

A method for linking data using persistent links is disclosed. The links are created in a central repository, which maintains an identification class for each entity. The identification class contains all available information concerning the entity. By matching links instead of names and addresses, potential ambiguities and erroneous duplicates are eliminated. The links are encoded before external distribution with a client-specific key; the link encoding discourages clients from collaborating to use personal information without authorization.

Data linking system and method using encoded links

A method for linking data using permanent tokens is disclosed. The tokens may be used to link data pertaining to a consumer, a business, an address, an occupancy, or a household. The tokens are created in a central repository, which maintains an identification class for each entity. The identification class contains all available information concerning the entity. The tokens may be applied to a data storage system to allow real-time construction of a total customer view. The tokens may also be used to link the data storage system to a repository, such that the total customer view contains all available information concerning the customer. The total customer view may be used to formulate a response to customer input, such as a purchase or access to an Internet web page maintained by the data owner. By matching tokens instead of names and addresses, potential ambiguities and erroneous duplicates are eliminated. Data updates may be performed incrementally, and may be pushed from the repository to the data owner as new information is received.

Data linking system and method using tokens

A method and system for the creation, enhancement and update of data on a customer database using data from a central database and persistent keys that are linked to each data structure is disclosed. The persistent keys allow the matching of equivalent data structures between the remote and central databases so that enhancement data from the central database can be overlaid onto the customer database. The persistent keys also make the update of data on the customer database more efficient, since key matching allows the transfer of only the data that is needed to update the customer database. The central database may physically contain all of the enhancement data, or may use a central database manager to transparently link a group of physically remote databases using the persistent keys. Data may be transferred from the central database to the customer database either in batch mode or in a near real-time mode through a direct table join. The linking feature of the persistent keys further allows the linkage of the customer database and the central database to create a multidimensional database.

Method and system for the creation, enhancement and update of remote

Growing community of inventors

Little Rock, AR, United States of America

Charles Donald Morgan