Surveillance Event Rules From Natural Language Operator Input
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Solution Overview
Problem
Existing surveillance systems face complexity, high computational costs, and legal compliance burdens due to the need for fine-tuning and reinforcement learning, which are inadequate for capturing specific surveillance scenarios and require significant resource allocation.
Innovation Solution
A computer-implemented method that uses a natural language input from operators to generate rules based on contextual knowledge, reducing the need for fine-tuning and reinforcement learning by employing a machine learning model to adapt the surveillance system to specific environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fine-tuning techniques are used to adapt models to specific tasks, then model performance is improved, but computational cost and system complexity increase
Solution Approach 1:
The patent extracts the model adaptation process from the surveillance system by using a separate prompt generator that creates natural language prompts based on operator feedback. This separates the fine-tuning process from the core surveillance system, reducing system complexity while maintaining model performance through prompt-based adaptation rather than traditional fine-tuning.
Solution Approach 2:
The patent introduces a prompt generator as an intermediary between operator feedback and the machine learning model. This intermediary translates operator feedback into natural language prompts that guide model behavior without requiring complex fine-tuning processes, thereby reducing computational cost and system complexity while maintaining reliability.
2Adaptability or versatility
If fine-tuning is performed within the installation, then model adaptation to specific scenarios is improved, but resource requirements and operational disruption increase
Solution Approach 1:
The patent creates a virtual copy of the adaptation process by using prompt generation instead of actual model fine-tuning. The prompt generator creates natural language prompts that replicate the effect of fine-tuning without requiring the computational resources or operational disruption of actual retraining within the installation.
Solution Approach 2:
The patent replaces the mechanical fine-tuning process (which requires significant computational resources and may suspend services) with a software-based prompt generation system. This substitution uses natural language processing to achieve model adaptation with minimal resource requirements and no operational disruption.
3Adaptability or versatility
If reinforcement learning is used to optimize system outcomes, then system adaptability is improved, but resource requirements and complexity increase
Solution Approach 1:
The patent implements a simplified feedback mechanism where operator feedback on generated events is translated into natural language prompts. This feedback loop provides system adaptability without the complexity of reinforcement learning, as the prompts directly guide model behavior based on operator preferences rather than requiring complex reward function design and training processes.
4Ease of operation
If rules-based solutions are implemented to determine analysis results, then system control is improved, but operator time and system rigidity increase
Solution Approach 1:
The patent transforms static rules-based solutions into a dynamic system where the prompt generator automatically adapts prompts based on operator feedback. This dynamic approach maintains system control through natural language guidance while eliminating the need for operators to manually define and update rigid rules, thereby reducing operator time investment.
Solution Approach 2:
The system performs self-service by automatically generating and updating prompts based on operator feedback without requiring operators to manually create or maintain rules. The prompt generator autonomously adapts the system behavior through natural language processing, reducing both operator time and system rigidity while maintaining ease of control.
Data Source
AI summary
A computer-implemented method for augmenting a surveillance system configured to receive a data stream from a detection device, analyse the data stream, generate content metadata about the content of the data stream based on the analysis of the data stream, and determine an event for display to a surveillance system operator based on the content metadata, the method including receiving a surveillance system operator input comprising a natural language text input; accessing contextual knowledge of the surveillance system from a contextual knowledge source; determining an input to a machine learning model based on the received surveillance system operator input and the contextual knowledge of the surveillance system from the contextual knowledge source; generating one or more rules by the machine learning model based on the determined input; and applying the one or more generated rules for modifying the event displayed to the operator.