Systems and methods for LLM-based location control
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Solution Overview
Problem
Conventional comfort and security systems lack the ability to understand human behavior and adapt proactively, leading to inefficient operation and user frustration due to reactive responses to detected events.
Innovation Solution
Integration of a decision intelligence (DI)-based computerized framework leveraging large language models (LLMs) to predict user needs and behaviors, enabling adaptive and personalized control of environment and security systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional reactive control systems are used, then device complexity is reduced, but adaptability to user needs deteriorates
Solution Approach 1:
The patent introduces a decision intelligence framework as an intermediary layer between sensors and control systems. This framework includes a knowledge base, inference engine, and large language model that process sensor data and generate control decisions, thereby resolving the contradiction by adding adaptability through a mediating computational layer without requiring fundamental changes to underlying devices
Solution Approach 2:
The control system is segmented into distinct functional modules: data collection layer (sensors), decision intelligence layer (knowledge base, inference engine, LLM), and execution layer (control systems). This segmentation allows the complex adaptive functions to be concentrated in the decision intelligence layer while keeping individual components relatively simple, thus improving adaptability without proportionally increasing overall device complexity
2Ease of operation
If reactive control mechanisms are used, then system simplicity is maintained, but user experience quality deteriorates
Solution Approach 1:
The decision intelligence framework enables the system to serve itself by automatically interpreting user needs from sensor data and environmental context, making control decisions without requiring direct user input. The system self-adjusts climate, security, and other environmental parameters based on inferred user preferences and current conditions, thereby improving user experience while maintaining operational simplicity for end users
3Reliability
If predictive capabilities are added to control systems, then responsiveness to user needs is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary actions by predicting user needs before they are explicitly expressed. The decision intelligence framework continuously analyzes sensor data, user patterns, and environmental factors to anticipate required adjustments in climate, lighting, and security settings, executing control actions proactively rather than reactively, thereby improving responsiveness while managing complexity through structured prediction algorithms
4Reliability
If conventional rule-based security systems are used, then system simplicity is maintained, but security effectiveness deteriorates
Solution Approach 1:
The security system transitions from static rule-based parameters to dynamic parameters that change based on contextual analysis. The decision intelligence framework evaluates multiple variables including user behavior patterns, environmental sensors, and real-time conditions to dynamically adjust security parameters such as alert thresholds, access control rules, and monitoring intensity, thereby improving security effectiveness while managing complexity through systematic parameter adaptation
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AI summary
Disclosed are systems and methods that provide a novel framework for personalized location management and control via integrated large language model (LLM) capabilities within the location's control system(s). The framework operates to predict certain events within and/or around a location, and maximize the capabilities of an implemented control system to leverage such predictions via mechanisms to understand the current and/or future needs of a user(s) within such location. Such mechanisms can involve the implementation of Al, ML and/or LLMs, such that predicted events as well as currently detected data related to current and/or ongoing events can be fed to the disclosed framework, whereby adaptive, personalized and/or customized responses can be output. Accordingly, the disclosed framework can provide a dynamically adaptive, automated system that can leverage generative software algorithms to control how climate and/or security systems control an environment to comfort and protect a locations' resident users.