In-Vehicle Content Personalization Using Conversation and Video Cues
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Solution Overview
Problem
Conventional navigation and telematics services in vehicles cannot provide customized advertising content based on passengers' interests or video information displayed in the vehicle.
Innovation Solution
A method and apparatus that generate customized advertising content by analyzing the content of conversations of passengers and video information displayed in a vehicle, matching this information with point-of-interest (POI) data, and providing relevant content to passengers based on their interests and location.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional navigation or telematics services provide advertising content, then advertising information is delivered to passengers, but the content is not customized based on passengers' interests or video information
Solution Approach 1:
The system performs preliminary analysis of passenger conversations and video content before generating advertising content. Voice recognition converts speech to text, and NLP extracts keywords and topics in advance. Video analysis pre-identifies content themes and objects. This preliminary processing enables rapid customization of advertising content without adding significant complexity during real-time delivery.
Solution Approach 2:
The patent introduces an intermediary content generation system that bridges the gap between raw passenger data (conversations and videos) and advertising content delivery. This intermediary layer processes voice and video inputs, extracts meaningful information through NLP and computer vision, matches results with POI databases, and generates customized advertising content. This mediator architecture manages system complexity by modularizing the customization process.
2Measurement precision
If the system analyzes conversations and video information to generate customized content, then content relevance to passengers is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by selectively analyzing only the most relevant portions of passenger conversations and video content. NLP extracts key keywords and topics rather than processing entire transcripts. Video analysis focuses on identifying main objects and themes rather than every detail. This selective processing maintains high content matching accuracy while significantly reducing processing time and computational resources.
Solution Approach 2:
The patent implements skipping mechanisms by using pre-trained NLP models and computer vision algorithms that rapidly process voice and video inputs. The system skips unnecessary processing steps by directly extracting meaningful features and matching them with POI data. This enables fast generation of customized advertising content without sacrificing accuracy, reducing the time loss associated with comprehensive analysis.
3Ease of operation
If the system provides only set-time advertisements, then system operation is simple, but passenger engagement and relevance are limited
Solution Approach 1:
The patent transforms the static, fixed-time advertisement system into a dynamic one that adapts to passenger interests and context in real-time. The system continuously monitors passenger conversations and video consumption, dynamically adjusting advertising content based on extracted keywords, topics, and preferences. This dynamic approach maintains operational simplicity through automated processes while dramatically improving advertising effectiveness and passenger engagement.
Solution Approach 2:
The system changes key parameters of advertising delivery by shifting from fixed time-based scheduling to interest-based content selection. It modifies content relevance parameters by matching advertising with passenger preferences derived from conversation analysis and video viewing habits. It also changes delivery timing parameters by providing advertisements at optimal moments based on passenger engagement levels. These parameter changes enhance productivity without significantly complicating system operation through automated decision-making algorithms.
Data Source
AI summary
An in-vehicle content provision method and apparatus are provided. The in-vehicle content provision method includes generating first information from content of conversations of passengers of a vehicle. The method also includes generating second information from a video displayed in the vehicle. The method additionally includes generating interest information on the basis of the first information and the second information. The method further includes receiving point-of-interest (POI) information from a server. The method also includes generating, on the basis of the interest information and the POI information, content to be provided to the passengers of the vehicle and providing the generated content to the passengers of the vehicle.