Micro-location Personalized User Experience in Telecommunications
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Solution Overview
Problem
Existing technologies face challenges in customizing user experiences in physical locations using micro-location and generative artificial intelligence, particularly in minimizing user annoyance while providing functional information and predicting optimal information presentation.
Innovation Solution
The use of active or passive triggers, such as QR codes or beacons, to generate unique content based on historical user interactions, allowing for personalized experiences and tailored interactions in retail environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If location-based services provide personalized information to users, then user satisfaction improves, but user annoyance increases due to excessive information presentation
Solution Approach 1:
The system divides the physical location into micro-locations and provides different information content tailored to each specific micro-location. Instead of uniform information presentation, the system applies local quality by customizing information based on the user's precise position within the retail environment, thereby improving satisfaction while avoiding excessive information in any single location.
Solution Approach 2:
The system uses historical user interaction data to pre-process and predict what information the user will find useful before the user actually arrives at a micro-location. This preliminary action allows the system to prepare personalized content in advance, reducing on-the-spot information processing and minimizing user annoyance from excessive presentations.
2Loss of information
If the system presents more information to users, then functional information coverage improves, but the timing and relevance of information delivery deteriorates
Solution Approach 1:
The system dynamically adjusts information delivery based on real-time user location, movement patterns, and interaction history. Instead of static information presentation, the system continuously adapts what information is presented, when it is presented, and how it is presented, ensuring both comprehensive coverage and optimal timing.
Solution Approach 2:
The system incorporates user interaction feedback to refine information delivery timing. By monitoring user responses to previously presented information, the system learns optimal timing patterns and adjusts future information delivery accordingly, ensuring that comprehensive information is provided at moments when users are most receptive.
3Productivity
If the system uses micro-location tracking to personalize experiences, then service efficiency improves, but system complexity increases
Solution Approach 1:
The system uses a multi-functional approach where the micro-location tracking infrastructure serves multiple purposes: personalization, information delivery optimization, user behavior analysis, and service efficiency improvement. By making the tracking system universal rather than dedicated to a single function, the patent reduces overall system complexity while maintaining high service efficiency.
Data Source
AI summary
A system can personalize a user-subscriber experience for a subscriber of a telecommunications network. The system can detect presence of a wireless mobile device in the indoor environment, proximate to a smart device (e.g., a smartphone offered for sale). The system can retrieve subscriber data based on a unique identifier of the subscriber. The subscriber data is indicative of activity of the subscriber on the telecommunications network. The system can input the subscriber data into a large language model (LLM) that outputs a personalized offer agreement to present on the wireless mobile device or the smart device. The personalized offer agreement is tailored for the subscriber and the smart device. In one example, the offer agreement includes contextual pricing for the smart device based on the subscriber data.