Knowledge Graph Object Event Prediction
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Solution Overview
Problem
Current autonomous systems, such as robots and self-driving cars, are limited in their ability to interpret and predict events in real-world environments beyond simple object recognition, as they lack the capability to contextualize objects and events within a structured knowledge graph, which is essential for advanced interpretation and anticipation of future actions.
Innovation Solution
A method and system that utilize a knowledge graph with multiple abstraction levels to associate recognized objects with possible events and attributes, enabling the prediction of future or past events by contextualizing objects within a time vicinity, and triggering actions based on these predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional detection or classification techniques with neural networks are used, then object identification in images is achieved, but the system cannot interpret context or predict events between objects
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary structure that connects detected objects with their contextual relationships and possible events. The knowledge graph serves as a mediator between simple object detection and complex event prediction, storing predefined relationships, attributes, and event probabilities that enable context interpretation without requiring the neural network to directly learn all contextual patterns
Solution Approach 2:
The system segments the complex task of event prediction into distinct components: object detection by neural network, knowledge graph lookup for relationships, and event probability calculation. This segmentation allows each component to specialize in its strength while working together to solve the overall problem of contextual interpretation
2Reliability
If a knowledge graph with multiple abstraction levels is implemented, then event prediction capability is enhanced, but computational complexity increases
Solution Approach 1:
The knowledge graph is organized into multiple abstraction levels with different granularities. Each level contains relationships and events appropriate to that level of detail, allowing the system to query only the relevant level for a given prediction task rather than searching through all possible relationships, thus managing complexity through localized organization
Solution Approach 2:
The knowledge graph is pre-populated with relationships, attributes, and event probabilities before runtime. This preliminary action allows the system to perform fast lookups during event prediction rather than computing relationships in real-time, enhancing prediction accuracy while managing computational complexity through pre-computation
3Loss of information
If only object detection is performed, then processing speed is maintained, but the system cannot anticipate future events or understand scene interpretation
Solution Approach 1:
The system performs partial contextualization by querying the knowledge graph for the most relevant relationships and events based on detected objects, rather than computing all possible relationships. This selective approach provides sufficient scene interpretation for practical purposes while avoiding the computational burden of exhaustive analysis
Solution Approach 2:
The knowledge graph acts as a pre-computed intermediary that provides scene interpretation information without requiring real-time computation of all contextual relationships. The system queries the knowledge graph for relevant events and attributes, obtaining interpretation capabilities while maintaining processing efficiency through the intermediary's pre-organized structure
Data Source
AI summary
A method for event predictions is provided. The method includes receiving input data. The method further includes identifying an object in the input data with the identified object associated with a first node in a knowledge graph. The method further includes determining a second node of a first object event with the second node related to the first node in the knowledge graph. The method further includes contextualizing the identified input object with the first object event.