QUANTUM INTELLIGENCE, INC. Patent applications |
Patent application number | Title | Published |
20120041901 | System and Method for Knowledge Pattern Search from Networked Agents - One or more systems and methods for knowledge pattern search from networked agents are disclosed in various embodiments of the invention. A system and a related method can utilizes a knowledge pattern discovery process, which involves analyzing historical data, contextualizing, conceptualizing, clustering, and modeling of data to pattern and discover information of interest. This process may involve constructing a pattern-identifying model using a computer system by applying a context-concept-cluster (CCC) data analysis method, and visualizing that information using a computer system interface. In one embodiment of the invention, once the pattern-identifying model is constructed, the real-time data can be gathered using multiple learning agent devices, and then analyzed by the pattern-identifying model to identify various patterns for gains analysis and derivation of an anomalousness score. This system can be useful for knowledge discovery applications in various industries, including business, competitive intelligence, and academic research. | 02-16-2012 |
20110295783 | Multiple Domain Anomaly Detection System and Method Using Fusion Rule and Visualization - The present invention discloses various embodiments of multiple domain anomaly detection systems and methods. In one embodiment of the invention, a multiple domain anomaly detection system uses a generic learning procedure per domain to create a “normal data profile” for each domain based on observation of data per domain, wherein the normal data profile for each domain can be used to determine and compute domain-specific anomaly data per domain. Then, domain-specific anomaly data per domain can be analyzed together in a cross-domain fusion data analysis using one or more fusion rules. The fusion rules may involve comparison of domain-specific anomaly data from multiple domains to derive a multiple-domain anomaly score meter for a particular cross-domain analysis task. The multiple domain anomaly detection system and its related method may also utilize domain-specific anomaly indicators of each domain to derive a cross-domain anomaly indicator using the fusion rules. | 12-01-2011 |
20110213788 | INFORMATION FUSION FOR MULTIPLE ANOMALY DETECTION SYSTEMS - The present invention is a method for detecting anomalies against normal profiles and for fusing and visualizing the results from multiple anomaly detection systems in a quantifying and unifying user interface. The knowledge patterns discovered from historical data serve as the normal profiles, or baselines or references (hereinafter, called “normal profiles”). The method assesses a piece of information against a collection of the normal profiles and decides how anomalous it is. The normal profiles are calculated from historical data sources, and stored in a collection of mining models. Multiple anomaly detection systems generate a collection of mining models using multiple data sources. When a piece of information is newly observed, the method measures the degree of correlation between the observed information and the normal profiles. The analysis is expressed and visualized through anomaly scores and critical event notifications that are triggered by fusion rules, thus allowing a user to see multiple levels of complexity and detail in a single view. | 09-01-2011 |
20080215576 | FUSION AND VISUALIZATION FOR MULTIPLE ANOMALY DETECTION SYSTEMS - The present invention is a method for detecting anomalies against normal profiles and for fusing and visualizing the results from multiple anomaly detection systems in a quantifying and unifying user interface. The knowledge patterns discovered from historical data serve as the normal profiles, or baselines or references (hereinafter, called “normal profiles”). The method assesses a piece of information against a collection of the normal profiles and decides how anomalous it is. The normal profiles are calculated from historical data sources, and stored in a collection of mining models. Multiple anomaly detection systems generate a collection of mining models using multiple data sources. When a piece of information is newly observed, the method measures the degree of correlation between the observed information and the normal profiles. The analysis is expressed and visualized through anomaly scores and critical event notifications that are triggered by fusion rules, thus allowing a user to see multiple levels of complexity and detail in a single view. | 09-04-2008 |