Building Security Recommendation Generation via ML
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Solution Overview
Problem
Current building management and security systems face challenges in generating precise and timely data for responding to equipment issues and security events, often resulting in incorrect or imprecise recommendations due to limitations in existing machine learning models, such as language models, which struggle with agility in novel queries and lack transparency in output explanations.
Innovation Solution
Implementing a system that utilizes machine learning models, including neural networks and generative AI, to process sensor data and generate accurate recommendations for building management and security operations, incorporating feedback loops for model improvement and integrating diverse data sources to enhance the accuracy and quality of outputs, while providing real-time messaging and conversational interfaces for users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models and language models are used for building security recommendations, then the system can process sensor data and generate responses, but the response accuracy and precision deteriorate due to inability to handle novel queries and lack of transparency
Solution Approach 1:
The patent introduces an intermediary reasoning layer that bridges sensor data and recommendations. This layer includes a situation classifier that categorizes sensor events into predefined situations, and a recommendation generator that selects appropriate responses based on the classified situation. This intermediary structure improves reliability by ensuring consistent, transparent decision-making while maintaining adaptability through the flexibility of the classification framework.
Solution Approach 2:
The patent segments the recommendation generation process into distinct modular components: sensor data reception, situation classification, recommendation generation, and output presentation. This segmentation allows each component to be optimized independently - the situation classifier handles novel queries through structured categorization, while the recommendation generator ensures accurate, transparent responses based on classified situations.
2Measurement precision
If complex machine learning models are implemented to improve recommendation precision, then the measurement precision of sensor data interpretation improves, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary reasoning layer that bridges sensor data and recommendations. This layer includes a situation classifier that categorizes sensor events into predefined situations, and a recommendation generator that selects appropriate responses based on the classified situation. This intermediary structure improves reliability by ensuring consistent, transparent decision-making while maintaining adaptability through the flexibility of the classification framework.
Solution Approach 2:
The patent segments the recommendation generation process into distinct modular components: sensor data reception, situation classification, recommendation generation, and output presentation. This segmentation allows each component to be optimized independently - the situation classifier handles novel queries through structured categorization, while the recommendation generator ensures accurate, transparent responses based on classified situations.
Data Source
AI summary
Systems and methods are disclosed relating to autonomous building security recommendation generation. For example, a method can include receiving, by one or more processors, sensor data from one or more sensors associated with a building system. The method can further include determining, by the one or more processors using a machine learning model and the sensor data, a recommended action for an operator to perform, the machine learning model trained using training data comprising data retrieved from one or more data sources maintained by at least one of a first entity associated with the building system or a second entity associated with the one or more sensors. The method can further include presenting, by the one or more processors using at least one of a display device or an audio output device, a notification corresponding to the recommended action.