RF Beacon Content Discovery Using Sensor Data
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Solution Overview
Problem
Existing location-based services struggle to provide users with relevant content in real-time, especially in indoor environments, as they lack efficient methods to determine user interest and capability to receive recommendations, and often require pre-downloaded content.
Innovation Solution
RF beacons broadcast signals that, combined with client device sensor data, recommend locally relevant content by determining user interest and capability, allowing for automatic download and installation of content, even if the user is not actively interacting with the device, using a beacon management service to commission and manage beacons for targeted advertising and content delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If location-based services use pre-downloaded content, then content availability is ensured, but user device storage and bandwidth are consumed in advance
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing content metadata, beacon identifiers, and recommendation rules on the server side. When a user enters a beacon zone, the system quickly matches the beacon UUID with pre-prepared content recommendations, enabling instant content delivery without requiring pre-download of actual content files to the user device.
Solution Approach 2:
The patent introduces a content recommendation server as an intermediary between the beacon system and user devices. This mediator handles content storage, processing, and delivery, allowing the user device to remain lightweight while ensuring content availability through server-side infrastructure.
2Measurement precision
If the system continuously monitors sensor data to determine user interest, then recommendation accuracy improves, but energy consumption increases
Solution Approach 1:
The system implements periodic monitoring of sensor data rather than continuous monitoring. The client device checks sensor data at intervals triggered by beacon detection events or user interaction states, reducing energy consumption while maintaining adequate precision for determining user interest and capability to receive recommendations.
Solution Approach 2:
The system applies partial monitoring by selectively evaluating only the sensor data most relevant to recommendation delivery (e.g., display visibility, device orientation, motion state) rather than continuously analyzing all sensor inputs, thereby reducing computational overhead and energy usage while maintaining recommendation accuracy.
3Productivity
If the system provides real-time content recommendations, then user engagement increases, but system complexity increases
Solution Approach 1:
The patent segments the content recommendation system into distinct functional modules: beacon detection module, sensor data analysis module, recommendation generation module, and content delivery module. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while enabling real-time recommendations through coordinated module operations.
Solution Approach 2:
The client device leverages existing multi-functional components (GPS, accelerometer, gyroscope, display, haptic feedback) already present in modern smartphones and tablets, rather than requiring specialized hardware. This universality reduces system complexity by reusing established components for multiple functions including location tracking, user state detection, and feedback delivery.
4Reliability
If feedback is provided through multiple channels (visual, audio, haptic), then user notification reliability improves, but device power consumption increases
Solution Approach 1:
The system implements self-service feedback by automatically selecting the most appropriate notification channel based on current device state and user context. For example, if the device is in a pocket (detected via accelerometer), the system automatically uses haptic feedback or audio instead of visual display, eliminating the need for manual user configuration while maintaining notification reliability with reduced energy consumption.
Data Source
AI summary
A radio frequency (RF) beacon deployed in an indoor or outdoor environment broadcasts an RF signal that can be received by a client device operating in the environment. Based on information provided by the beacon and client device sensor data, content (e.g., a software application) locally relevant to the environment is recommended to a user of the client device. The recommended content can be manually or automatically downloaded and installed on the client device so that it can be used in the environment. In some implementations, sensor data is used to indicate whether the user is interested in receiving the recommendation and is capable of receiving the recommendation.