Cognitive Intercom Assistant for Secure IoT Access
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Solution Overview
Problem
There is a need for advanced security access management systems that can efficiently authenticate and grant access to trusted individuals while restricting unauthorized access, particularly in environments like homes or healthcare settings, where vulnerable individuals require secure and controlled access.
Innovation Solution
The implementation of a cognitive intercom assistant system using a processor that authenticates users through a knowledge domain of trusted users within an IoT network, collects and compares public information data, and provides dynamic trust levels and emergency access protocols, utilizing machine learning to adapt and improve security over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional access control systems are used, then device complexity is reduced, but security reliability and access control precision deteriorate
Solution Approach 1:
The access control system is segmented into multiple independent modules: biometric authentication module, public information database module, trust level evaluation module, and emergency protocol module. Each module performs a specific function, allowing the system to achieve high security reliability through comprehensive verification while managing complexity through modular design.
Solution Approach 2:
A cognitive intercom assistant acts as an intermediary between the user and the access control system. This AI-based mediator handles complex authentication processes, evaluates trust levels by comparing user information against public databases, and manages emergency protocols, thereby improving security reliability without requiring the physical access control hardware to become overly complex.
2Measurement precision
If comprehensive authentication processes are implemented, then access control precision is improved, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing public information data about individuals in a database before authentication is needed. During the authentication process, the system quickly queries this pre-prepared database rather than gathering information in real-time, thereby maintaining high access control precision through comprehensive verification while minimizing processing time.
Solution Approach 2:
The system replaces traditional mechanical authentication methods with cognitive and information-based verification. The cognitive intercom assistant uses AI to rapidly analyze and compare user-provided information against stored public information databases, achieving precise access control decisions faster than manual or traditional mechanical verification processes.
3Adaptability or versatility
If dynamic trust levels are implemented, then adaptability of access management is improved, but system complexity increases
Solution Approach 1:
The system implements dynamic trust levels that automatically adjust based on authentication results and comparisons with public information databases. Users can have different trust levels (e.g., high, medium, low) that determine the extent of access granted. This dynamic adaptation allows the system to respond flexibly to different users and situations while the underlying complexity is managed through automated evaluation algorithms.
Solution Approach 2:
The system changes the parameter of trust level as a variable that adjusts access management adaptability. By modifying this single parameter based on authentication outcomes and database comparisons, the system achieves versatile access control (granting full access, partial access, or denial) without requiring complex reconfiguration of the entire system architecture.
4Measurement precision
If public information database comparison is performed, then authentication accuracy is improved, but information processing complexity increases
Solution Approach 1:
The system extracts only the essential and relevant features from public information databases for comparison purposes, rather than processing entire databases or all available information. The cognitive intercom assistant identifies and compares key authentication elements (such as name, date of birth, unique identifiers) from user input against corresponding extracted fields in the database, thereby achieving high authentication accuracy while minimizing information processing complexity.
Data Source
AI summary
Embodiments for intelligent premise security access management by a processor. Identification information of a user requesting access to enter a premise via a premise entry may be authenticated using a knowledge domain of trusted users in an Internet of Things (IoT) computing network. Entry access to the premise may be granted via the premise entry upon authenticating the identification information.