Cluster Mapping Neural Activity Personalized Travel Recommendations
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Solution Overview
Problem
Traditional travel reviews are subjective, biased, and lack objectivity, failing to provide individually relevant information due to mismatches between tourists' interests and reviewers' preferences, leading to distorted pictures of locations and missed interesting sites.
Innovation Solution
Computer-implemented methods for cluster mapping of location metadata based on neural activity and physiological data to provide personalized recommendations to tourists, using clusters of semantically similar location metadata and emotion metrics to determine recommended destinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual reviews are used, then reviewers can share their experiences, but the reviews become subjective and biased without objective verification
Solution Approach 1:
The patent replaces manual review verification with automated neural activity measurement. Sensors detect brain waves and physiological signals to objectively verify reviewer experiences, substituting human editorial verification with biological measurement systems.
Solution Approach 2:
The patent introduces neural activity data as an intermediary between the reviewer's experience and the published review. This biological data serves as objective evidence that mediates between subjective reviewer claims and factual verification.
2Adaptability or versatility
If general travel reviews are provided, then information is available to all tourists, but the information lacks individual relevance and misses interesting sites
Solution Approach 1:
The patent applies local quality by customizing travel recommendations to each user's specific neural response patterns. Instead of uniform general reviews, the system tailors suggestions to individual biological preferences, making each recommendation locally adapted to the user.
Solution Approach 2:
The patent makes recommendations dynamic by continuously measuring neural activity and adapting suggestions in real-time. The system evolves recommendations based on changing user responses during the travel experience rather than providing static pre-planned itineraries.
3Measurement precision
If reviews are written after the trip, then comprehensive evaluation is possible, but recollections change over time and emotions are not accurately reflected
Solution Approach 1:
The patent performs preliminary action by measuring neural activity during the actual travel experience rather than waiting for post-trip recollection. Emotional responses are captured in-real-time as they occur, eliminating the time delay and memory degradation problems.
Solution Approach 2:
The patent implements continuous feedback by measuring neural responses during the trip and using this data to adjust and refine travel recommendations. The system creates a closed-loop where real-time biological feedback directly influences ongoing itinerary suggestions.
Data Source
AI summary
Techniques are described for determining recommended tourist based on the real time collection and analysis of biological information regarding users. Sensors in proximity to a user may collect neural activity data (e.g., brain wave data) and physiological data (e.g., blood pressure, heart rate, blood sugar level, etc.). The biological information may be analyzed to determine, for each user, an emotion metric indicating an emotional state of the user at various times. The emotion metrics may be correlated with location data to determine the emotion metric of the user at various sites during a trip. Tag metadata describing the location(s) may be clustered through semantic analysis to generate clusters of semantically similar tags. Emotion metric scores for the clusters may be employed to predict destination(s) where the user(s) may exhibit positive emotion metrics, and the predicted destination(s) may be presented to users in advertisements or other content.