Extensible Object Models for Probabilistic IoT Relationship Modeling

The patent segments the relational model into distinct components: nodes representing devices/entities and edges representing relationships with associated probabilities. This segmentation allows complex relationships to be broken down into manageable probabilistic connections between individual nodes, enabling accurate representation while maintaining structural clarity through the graph-based organization.

The patent introduces a probabilistic dimension to the relational model by assigning probability values to edges connecting nodes. This adds a new dimension (probability space) to the traditional binary relationship model, enabling the representation of uncertain and complex relationships without significantly increasing structural complexity, as the probability values are scalar attributes on existing edges.

The patent applies local quality by calculating and storing probabilities only for locally relevant node pairs that have direct or indirect connections in the graph, rather than computing all possible pairwise relationships. This localized approach maintains accuracy for relevant relationships while reducing computational complexity by focusing resources on locally significant interactions rather than global computations.

The patent performs preliminary actions by pre-calculating and storing probability values for relationships that are likely to be queried, using event data and templates to establish probable connections before they are needed. This preliminary computation reduces real-time computational complexity by having probabilities readily available when nodes are added or relationships are queried.

The patent implements dynamics by making the relational model adaptive and updatable in real-time as new event data arrives. The graph structure allows dynamic addition of nodes and edges, and probability values can be updated incrementally without recalculating the entire model. This dynamic approach maintains high model accuracy through continuous data integration while preserving processing efficiency through localized updates rather than full model recomputation.

The patent incorporates feedback mechanisms where event data and observational information are continuously fed back into the model to update relationship probabilities. This feedback loop improves model accuracy by incorporating real-world observations, while the efficient graph-based structure allows feedback processing to occur incrementally, minimizing the impact on overall system productivity.

The patent applies universality by designing a graph-based relational model that serves multiple functions: representing device relationships, storing probabilistic connections, enabling query operations, and supporting visualization. This universal graph structure provides comprehensive enterprise-wide views through a single unified model, reducing implementation complexity compared to maintaining separate specialized systems for each function.

The patent uses copying by creating digital twin representations of physical systems within the graph model. These digital twins are simplified copies that replicate the essential relationships and properties of physical devices, providing comprehensive information about enterprise operations without requiring direct access to or complexity of the actual physical systems, thus reducing implementation difficulty while maintaining information completeness.