Event Pattern Guided Content Services for Wearables
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Solution Overview
Problem
Current systems for creating and delivering content for wearable devices and augmented/virtual reality applications face challenges in providing real-time, personalized, and efficient content services, especially in handling user-specific issues and dynamic environments, due to the complexity of manual content design and the limitations of pre-made tutorials and search-based solutions.
Innovation Solution
A method and system for event pattern-guided content services that automatically select and deliver content based on real-time sensor data, using a service entity to identify and match content creators with user behavior patterns, enabling dynamic content creation and consumption with minimal manual intervention, and rating content effectiveness for continuous improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual content design and development processes are used, then content quality can be maintained, but the complexity and time required for content creation increases significantly
Solution Approach 1:
The system enables content to be automatically generated and delivered based on event patterns detected from sensor data. The service entity autonomously identifies content needs, selects appropriate content creators, and delivers content without manual intervention, allowing the system to serve itself in content creation and delivery.
Solution Approach 2:
The system pre-identifies content creator candidates and their associated content based on event patterns before actual content delivery is needed. By maintaining a database of content creators and their content in advance, the system can quickly match and deliver appropriate content when events occur, reducing real-time complexity.
2Ease of operation
If pre-made tutorials and search-based solutions are used, then content delivery can be simplified, but the ability to provide real-time personalized content decreases
Solution Approach 1:
The system dynamically selects content and content creators based on real-time event patterns detected from sensor data. Instead of static pre-made tutorials, the system adapts content delivery to the specific context and needs of each user situation, enabling both simplicity through automation and personalization through real-time adaptation.
Solution Approach 2:
The system monitors user progress and content effectiveness, using this feedback to continuously improve content selection and delivery. By tracking whether delivered content achieves desired results, the system can refine its matching algorithms and provide increasingly personalized and effective content over time.
3Productivity
If automated content selection based on sensor events is implemented, then real-time personalized content delivery is enabled, but the system complexity increases
Solution Approach 1:
The system segments the content delivery process into distinct functional modules: event pattern detection, content creator identification, content selection, and content delivery. Each module handles a specific aspect of the process, making the overall complex system manageable through modular design and allowing independent optimization of each component.
4Reliability
If continuous monitoring of user progress is performed, then content effectiveness can be improved, but the amount of data processing and system resources required increases
Solution Approach 1:
The system extracts only the essential progress indicators and effectiveness metrics needed to evaluate content delivery, rather than processing all available sensor data continuously. By focusing on key performance indicators relevant to content effectiveness, the system maintains high reliability while reducing overall data processing requirements and energy consumption.
Data Source
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AI summary
A method and system for event pattern (EP) guided content services are disclosed. A service entity may identify content creator candidates based on a pattern of events which correspond to sensor events received from a requesting entity, and the service entity may select a content creator among the content creator candidates based on a best match to the pattern of events. The service entity may transmit a request for content to the selected content creator, such that the request for content is automatically generated in response to the pattern of events. The service entity may deliver the content to the requesting entity and may monitor progress of the requesting entity based on a playback of the content by the requesting entity.