In-vehicle Entertainment Personalization via Predictive Profiles
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Solution Overview
Problem
Current commercial passenger vehicle entertainment systems are inflexible and costly to customize, leading to delayed deployment and reduced scalability, which hampers innovation and increases costs due to lengthy development processes and rapid technological changes.
Innovation Solution
Implementing a machine learning/neural network-based system that uses traveler profiles to provide personalized entertainment options on portable devices, allowing for real-time updates and upselling/cross-selling opportunities, thereby enhancing passenger interaction and revenue generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional customization methods are used for entertainment systems, then personalized content can be provided, but deployment time increases and scalability decreases
Solution Approach 1:
The patent applies universality by creating a single platform that serves multiple functions: it handles content delivery, personalization, and updates across different devices (set-top boxes, mobile devices, tablets) without requiring separate customization processes for each device type. This universal approach enables personalized content delivery while maintaining fast deployment and scalability.
Solution Approach 2:
The system implements dynamics through real-time content updates and adaptive personalization. Content can be dynamically updated on user devices without requiring system reconfiguration, and the personalization adapts based on user behavior patterns detected through machine learning algorithms, allowing the system to evolve rapidly without lengthy deployment cycles.
2Adaptability or versatility
If traditional customization methods are used for entertainment systems, then personalized content can be provided, but system complexity and costs increase
Solution Approach 1:
The patent extracts the personalization logic from the device hardware and relocates it to a centralized server platform. The server handles all complex personalization algorithms, content selection, and user profile management, while user devices only need to display content and transmit basic interaction data. This extraction dramatically reduces device complexity while maintaining full personalization capability.
Solution Approach 2:
The system introduces a server as an intermediary between the content library and user devices. This intermediary handles all complex operations including content recommendation, personalization, and device management, simplifying the architecture by centralizing complexity in one location rather than distributing it across multiple devices.
3Productivity
If rapid technological changes occur, then innovation is enhanced, but deployment delays reduce scalability
Solution Approach 1:
The system performs preliminary actions by pre-processing and pre-packaging content on the server before delivery to user devices. Content is prepared, personalized, and validated in advance, allowing rapid deployment when new content or features are introduced. This preliminary processing eliminates time-consuming on-device configuration and enables quick scalability.
4Ease of operation
If personalized interaction opportunities are increased, then passenger experience improves, but system complexity and costs increase
Solution Approach 1:
The system implements self-service through automated machine learning algorithms that continuously analyze user behavior patterns and automatically generate personalized content recommendations without human intervention. The system serves itself by autonomously adapting to user preferences, managing content delivery, and optimizing personalization strategies, thereby improving passenger experience while avoiding the complexity and costs associated with manual personalization management.
Data Source
AI summary
Devices, systems and methods for providing customized entertainment or productivity options to passengers on commercial passenger vehicles are disclosed. An exemplary method implemented by a computer on a commercial passenger vehicle includes receiving and storing from a server system communication on a memory and a display screen of a portable device of a passenger, prior to a start of a current travel segment of the commercial passenger vehicle, data for predictive preference selection during the current travel segment; determining, during the current travel segment, for the passenger, a personalized display menu of the passenger for one or more items or services from a plurality of items and services; and providing, during the travel segment, an interactive session having the personalized display menu based on the traveler profile and displayed on the display screen for the passenger on the portable device based on the determining.