ML-Based Tenant Selection in Multi-Tenant Vehicles
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Solution Overview
Problem
In multi-tenancy environments, existing systems face challenges in dynamically and accurately selecting the active tenant to provide differentiated services and content to end-users, often relying on manual switching or inefficient data analysis methods.
Innovation Solution
A content control component utilizing machine learning and AI to analyze structured and unstructured data, such as sensor information and schedules, to automatically select the appropriate tenant based on predictive models and user feedback, ensuring real-time and context-aware content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual switching or inefficient data analysis methods are used to select the active tenant, then the system operation is simple to implement, but the tenant selection accuracy and responsiveness are poor
Solution Approach 1:
The patent replaces manual switching and traditional data analysis methods with machine learning algorithms and AI models. The content control component uses trained machine learning models to automatically analyze sensor data, schedule information, and operational parameters to identify the active tenant, substituting human-operated mechanical switching with intelligent automated decision-making systems.
Solution Approach 2:
The system enables self-service by allowing the multi-tenancy environment to automatically identify and switch between tenants without manual intervention. The machine learning models continuously analyze incoming data streams and autonomously determine which tenant is currently active based on patterns in sensor information, schedules, and operational context, making the system self-identifying and self-managing.
2Productivity
If machine learning techniques are used to automatically select the active tenant, then the tenant selection accuracy and real-time performance are improved, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models offline with historical tenant data, sensor information, and operational patterns. This pre-training phase prepares the models in advance so that during runtime, the content control component can rapidly infer active tenant identity from incoming data streams without performing complex training computations, thus achieving fast real-time selection while managing computational complexity through prior preparation.
Solution Approach 2:
The system introduces an intermediary layer between raw data collection and tenant selection decision-making. The machine learning models act as intermediaries that process and interpret sensor data, schedule information, and operational parameters, transforming raw inputs into meaningful tenant identification results. This intermediary processing layer manages computational complexity by breaking down the decision-making process into manageable analytical stages.
3Adaptability or versatility
If differentiated content delivery is implemented for different tenants, then the user experience and service customization are improved, but the system complexity and content management overhead increase
Solution Approach 1:
The patent implements universality by creating a single content control component that serves multiple tenants through automated machine learning-based tenant identification. Instead of requiring separate content management systems for each tenant, the unified component dynamically adapts its behavior based on the identified active tenant, delivering differentiated content and services through a single multi-functional platform that reduces overall system complexity.
Solution Approach 2:
The system applies dynamics by making content delivery adaptive and dynamic rather than static. The content control component continuously monitors sensor data and operational context to dynamically adjust which tenant's content and services are delivered at any given moment. This dynamic adaptation allows the system to provide customized experiences for different tenants without requiring manual reconfiguration, automatically transitioning between service modes based on real-time conditions.
Data Source
AI summary
A system described herein may allow for the intelligent, dynamic selection of an active tenant for a standalone, multi-tenant environment, such as a multi-operator bus or other vehicle. Intelligent selection may be performed using machine learning and/or other suitable techniques, which may be based on similarity to previous usage by a registered tenant, and may further include analyzing structured and/or unstructured data regarding the environment. In addition, the system may allow different profiles, content, or content templates to be associated with different tenants, thus granting a high level of dynamic flexibility in tailoring the content and/or services provided to the users of the environment based on the selected tenant.