The assignee for this patent application is
Reporters obtained the following quote from the background information supplied by the inventors: "The dynamic nature of information often makes it difficult and costly to maintain, and typically due to constant change, the data intrinsically lacks complete accuracy. Most systems require absolute data, which is often difficult to obtain and misleading because it lacks absolute accuracy. In addition, the various systems of an organization typically contain. 'application firewalls' between the systems. Each of these disparate systems is geared toward different user constituencies or business functions, and although much of the data originates from the same source, the disparate systems effectively contribute to data corruption. Furthermore, systems are usually architected to limit the data from self-learning. Fixed relationships pre-define context, and such relationships do not respond well to divergent user needs. Such data compartmentalization often undermines organizational performance by, for example, limiting insight into the impacts of decision-making across planes of significance (e.g., cost, revenue, customer outcome) and contributing little to the understanding of how the downstream impact of such decisions greatly affects the bottom line.
"Therefore, a need exists for a system and method for processing information based upon probabilistic significance that varies with a variety of factors such as time, user, industry, external events and the value of the data itself. In order to promote sophisticated understanding of information and its impact on a given organization, a need exists for a system that combines the use of sophisticated object data models, probabilistic modeling, statistical analysis and artificial intelligence techniques to create intelligent, self-learning data objects, and to derive relevant and insightful results for a multitude of end-user constituencies."
In addition to obtaining background information on this patent application, VerticalNews editors also obtained the inventors' summary information for this patent application: "The system includes an Understanding Generator ('UG') software application which comprises, in one embodiment a data rationalizer, an intelligent business object creator, a context profiler, a memory creator, a context overlay generator, a relationship generator, a what-if analyzer, a trend analyzer, a corrective action analyzer, a results formatter and an object database. The UG creates business objects that are self-aware and self-learning, and that enable the system to generate relevant results, without the need to be prompted by a user request. UG yields useful result sets by contextualizing answers via relevant significance, which are then, in one embodiment, prioritized by the user against other result sets obtained across all dimensions and all interested parties.
"UG may model user perception as an energy field that is experienced over time. UG sequences and groups these perceptions into knowledge, and such knowledge is discerned by context. Experience of the context can yield significance and such significance is expressed as a probability. Business objects are therefore selected for a result set because of the probability of the business objects' significance to the business problem as perceived by the user. Such selection contributes to the creation of understanding expressed as the perception of significant context. Hence, in UG, understanding is the practical application of knowledge.
"More particularly, the invention uses artificial intelligence to understand user context and the contextual significance of data in order to generate accurate, tailored and predictive result sets, in one embodiment, the invention includes: i) an artificial intelligence context profiler that is configured to create a proxy of the user in the system and interpret job function, personality profile and preference to create a user context and encapsulate it in a user context intelligent context object; ii) an artificial intelligence data rationalizer configured to processes and interrelates input data into self-learning data objects including intelligent physical object that are configured to store static attributes of a data element, intelligent energy objects that are configured to store the dynamic attributes of the data element and intelligent context objects that are configured to store data element context; and iii) a probability of fitness function that uses artificial intelligence, probabilistic and forecasting techniques to discern the significance of data within a given context.
BRIEF DESCRIPTION OF THE DRAWINGS
"A more complete understanding of the present system and method may be derived by referring to the detailed description and claims when considered in connection with the Figures, wherein like reference numbers refer to similar elements throughout the Figures, and:
"FIG. 1 is an overview of a representative system for providing a data analysis and relationships in the user's unique context, in accordance with one embodiment of the present invention.
"FIG. 2 is a conceptual overview of the major functions of the system, in accordance with one embodiment of the present invention.
"FIG. 3 is a diagram of broad process flow overview, in accordance with one embodiment of the present invention.
"FIG. 4 is a representative process flow diagram for instantiating an object, in accordance with one embodiment of the present invention.
"FIG. 5 is a representative process flow showing object interaction within the system to dynamically create hierarchies and relationships, in accordance with one embodiment of the present invention.
"FIG. 6 is a data awareness ladder illustrating how data evolves, in accordance with one embodiment of the present invention."
For more information, see this patent application: Oaten, Philip J.;
Keywords for this news article include: Machine Learning, Emerging Technologies, Artificial Intelligence,
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