发明名称 |
MULTIDIMENSIONAL RECURSIVE LEARNING PROCESS AND SYSTEM USED TO DISCOVER COMPLEX DYADIC OR MULTIPLE COUNTERPARTY RELATIONSHIPS |
摘要 |
A multidimensional recursive and self-perfecting process used to discover dyatic or multi-counterparty relationships between parties, the process comprising: (a) collecting information from a plurality of data sources; (b) discovering dyatic or multi-counterparty relationships between the parties from the collected information; (c) clustering the parties to infer the dyatic or multi-counterparty relationships between the parties based on common or partially intersecting attributes between the parties, thereby forming clustered parties; (d) evaluating the clustered parties for business linkage potential by integrating the collected information and contextually assessing indicia from the data sources to detect and measure consistency and inconsistency for a given party or dyatic or multi-counterparty relationship; (e) positing and evaluating relationship type and role said party plays in each relationship; and (f) assessing the confidence level regarding the likelihood that the dyatic or multi-counterparty relationship exists between the parties. |
申请公布号 |
US2015186807(A1) |
申请公布日期 |
2015.07.02 |
申请号 |
US201414581258 |
申请日期 |
2014.12.23 |
申请人 |
THE DUN & BRADSTREET CORPORATION |
发明人 |
SCRIFFIGNANO Anthony J.;SPINGARN David A.;RIZZOLO Barry;DAVIES Robin;YOUNG Michael R.;SHIMER Laurie;NICODEMO John Mark |
分类号 |
G06Q10/06 |
主分类号 |
G06Q10/06 |
代理机构 |
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代理人 |
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主权项 |
1. A multidimensional recursive process used to discover dyatic or multi-counterparty relationships between parties, said process comprising:
a. collecting information from a plurality of data sources; b. discovering dyatic or multi-counterparty relationships between said parties from said collected information; c. clustering said parties to infer said dyatic or multi-counterparty relationships between said parties based on common or partially intersecting attributes between said parties, thereby forming clustered parties; d. evaluating said clustered parties for business linkage potential by integrating said collected information and contextually assessing indicia from said data sources to detect and measure consistency and inconsistency for a given party or dyatic or multi-counterparty relationship; e. positing and evaluating relationship type and role said party plays in each relationship; and f. assessing the confidence level regarding the likelihood that said dyatic or multi-counterparty relationship exists between said parties. |
地址 |
Short Hills NJ US |