Automatic Signal Narration via Physical Graph Analysis
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Solution Overview
Problem
Current computing systems lack the ability to automatically generate a narration of events in a signal segment, which is essential for providing a semantic understanding of activities within a physical space, especially in complex environments with multiple distractions.
Innovation Solution
A system that accesses a physical graph to determine the actions of physical entities within a signal segment, using machine learning to identify interesting events and generate a narration based on criteria such as repetition, constant actions, shared content, and user instructions, while maintaining a manageable computer-navigable graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system narrates all events in a signal segment, then the narration is complete, but the narration becomes tedious and loses user interest
Solution Approach 1:
The system applies different narration strategies to different portions of the signal segment based on their characteristics. Interesting or unusual events receive detailed narration, while routine or uninteresting events are summarized or omitted. This selective approach maintains narration completeness for important events while avoiding tedium from mundane events.
Solution Approach 2:
The system dynamically adjusts narration parameters such as level of detail, narration speed, and selection criteria based on the content being narrated. When detecting uninteresting or repetitive events, the system changes the narration parameters to provide less detail or skip entirely, thereby maintaining user interest while preserving completeness for significant events.
2Measurement precision
If the system uses deep learning and machine learning for event recognition, then the recognition accuracy improves, but the processing time increases
Solution Approach 1:
The system performs preliminary processing of signal segments to identify and flag potentially interesting or unusual events before applying computationally intensive deep learning algorithms. This preliminary filtering step reduces the number of segments requiring full machine learning analysis, thereby maintaining high recognition accuracy while reducing overall processing time.
Solution Approach 2:
The system applies machine learning algorithms selectively rather than uniformly to all signal segments. Full deep learning analysis is applied only to segments that meet certain criteria for potential interest or unusualness, while other segments receive lighter processing. This partial application of intensive algorithms maintains accuracy for critical events while reducing total processing time.
Data Source
AI summary
Automatic generation of a narration of what is happening in a signal segment (live or recorded). The signal segment that is to be narrated is accessed from a physical graph. In the physical graph, the signal segment evidences state of physical entities, and thus has a semantic understanding of what is depicted in the signal segment. The system then automatically determines how the physical entities are acting within the signal segment based on that semantic understanding, and builds a narration of the activities based on the determined actions. The system may determine what is interesting for narration based on a wide variety of criteria. The system could use machine learning to determine what will be interesting to narrate.