International Business Machines Corporation - Armonk NY
International Classification:
G06Q 40/00
US Classification:
705 44
Abstract:
A dynamically determined data-driven model for detecting fraudulent behavior is provided. An initial model is developed using historical data, such as demographic, psychographic, transactional, and environmental data, using data-driven discovery techniques, such as data mining, and may be validated using additional statistical techniques. The noise within the data models determine appropriate initial control points needed for the initial model. These initial control points define an ‘electronic fence,’ wherein data points within the fence represent acceptable behavior and data points outside the fence represent unacceptable behavior. Updated data is received. A fraud detection mechanism validates the updated data using data mining and statistical methods. The data model, or ‘electronic fence,’ is refined based on the newly acquired data. The process of refining and updating the data models is iterated until a set of limits is achieved.
A material is laced with Radio Frequency Identification (RFID) tags at a known concentration of RFID tags per unit of material. Subsequently, if an interrogation of the RFID tags reveals a reduced concentration of RFID tags in the material, then a conclusion is drawn that the material has been diluted.
Detection Of Unplanned Waste Stream Diversion Using Rfids
Robert L. Angell - Salt Lake City UT, US James R. Kraemer - Santa Fe NM, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
H04Q 5/22
US Classification:
340 105, 340 101, 3405721
Abstract:
Unplanned waste stream diversions are detected through the use of Radio Frequency Identification (RFID) tags. Input bulk material coming into the facility is laced with multiple RFID tags, which are suspended in a colloidal state in the input bulk material. Incoming RFID tags are counted, and then re-counted as they leave the facility, either as part of a known waste material stream or as part of a finished product. If the incoming and outgoing counts differ, then a conclusion is reached that some of the incoming RFID tags are within an unplanned waste stream diversion.
Robert L. Angell - Salt Lake City UT, US James R. Kraemer - Santa Fe NM, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
H04Q 5/22
US Classification:
340 105, 3405721, 340 101
Abstract:
An electronic device has at least one component that is coated with a material that is non-persistent if exposed to a specific environmental condition. If the coating is stripped off by the specific environmental condition, the previously coated component's function is altered, causing a functionality of the electronic device to be altered.
Tracking Genetically Modified Organisms With Rfids
Robert L. Angell - Salt Lake City UT, US James R. Kraemer - Santa Fe NM, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G08B 13/14
US Classification:
3405728, 3405721, 340 101, 340 105
Abstract:
A Genetically Modified Organism (GMO) is tracked using Radio Frequency Identification (RFID) tags. A bulk load of GMOs, which is intended for consumption by livestock only, is laced with RFID tags at or near a time of harvest. If the RFID tags appear in a product that is intended for human consumption, then the product is pulled from distribution, since it contains GMOs that are potentially harmful to humans if eaten.
Robert Lee Angell - Salt Lake City UT, US Robert R. Friedlander - Southbury CT, US James R. Kraemer - Santa Fe NM, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06N 5/00
US Classification:
706 45
Abstract:
A risk assessment method and system. The method includes receiving by an inference engine, first sensor cohort data associated with a first cohort located within a first aircraft. The inference engine receives first group technology inferences associated with the first cohort. The inference engine generates first risk cohort inferences based on the first group technology inferences and the first sensor cohort data. The inference engine receives first inference data comprising a first plurality of inferences associated with the first cohort. The inference engine generates second inference data comprising a second plurality of inferences associated with the first cohort. The second inference data is based on the first inference data and the first risk cohort inferences. The inference engine generates a first associated risk level score for the first cohort. The computing system stores the second inference data and the first associated risk level score.
Robert Lee Angell - Salt Lake City UT, US Robert R. Friedlander - Southbury CT, US James R. Kraemer - Santa Fe NM, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G06N 5/00
US Classification:
706 45
Abstract:
A risk assessment method and system. The method includes receiving by an inference engine, first sensor cohort data associated with a first cohort located within a gate area of an airport. The inference engine receives first group technology inferences associated with the first cohort. The inference engine generates first risk cohort inferences based on the first group technology inferences and the first sensor cohort data. The inference engine receives first inference data comprising a first plurality of inferences associated with the first cohort. The inference engine generates second inference data comprising a second plurality of inferences associated with the first cohort. The second inference data is based on the first inference data and the first risk cohort inferences. The inference engine generates a first associated risk level score for the first cohort. The computing system stores the second inference data and the first associated risk level score.
Qualitative/Quantitative Analysis Of A Material Using Rfids
Robert L. Angell - Salt Lake City UT, US James R. Kraemer - Santa Fe NM, US
Assignee:
International Business Machines Corporation - Armonk NY
International Classification:
G08B 21/00
US Classification:
340540, 3405721, 340 101, 340 105
Abstract:
Unpackaged bulk material is made up of components that have been laced with RFID tags. By interrogating the RFID tags, the different components are identified and quantified, thus providing a quick assay of the bulk material.
Pennsylvania Elementary School Beaumont TX 1952-1958, Guess Elementary School Beaumont TX 1954-1960, Austin Middle School Beaumont TX 1960-1963, Bowie Junior High School Beaumont TX 1960-1963
Community:
Randall Thompson, Beth Randall, Bernadette Charles, Donald Abbott