Context-Aware IoT Recommendation Engine Using Segmented Data Processing
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Solution Overview
Problem
Conventional personal recommendation engines lack the ability to provide contextually relevant recommendations due to their reliance on limited online purchase histories and user correlations, failing to account for real-time information from Internet of Things (IoT) environments, which limits their effectiveness in suggesting relevant products or services.
Innovation Solution
A real-time context-aware recommendation engine that monitors, aggregates, and processes information from IoT devices, including device profiles, states, usage patterns, and user interactions, to generate personalized recommendations based on ranked associations and contextual relevance within the user's IoT environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation engines use limited online purchase histories and user correlations, then the system complexity is reduced, but the recommendation accuracy and contextual relevance deteriorate
Solution Approach 1:
The patent segments the data collection process by dividing information gathering into multiple sources: IoT device data (usage patterns, device states), online purchase histories, and user correlations. Each segment is processed independently through specialized modules before being integrated, allowing the system to manage complexity while improving recommendation accuracy through comprehensive data analysis
Solution Approach 2:
The patent introduces an intermediary recommendation engine that acts as a mediator between raw data from multiple sources and the final recommendations. This intermediary layer processes, filters, and integrates data from IoT devices, purchase histories, and user correlations, managing system complexity while enhancing recommendation accuracy through sophisticated data synthesis
2Loss of information
If the recommendation engine monitors and processes information from multiple IoT devices, then the contextual relevance of recommendations is improved, but the information processing complexity increases
Solution Approach 1:
The patent segments information processing by creating specialized modules for different data types: IoT device monitoring module, usage pattern analysis module, device state tracking module, and recommendation generation module. Each module handles specific aspects of information processing independently, reducing overall processing complexity while maintaining complete contextual information
Solution Approach 2:
The patent performs preliminary actions by pre-processing IoT device data, usage patterns, and device states before recommendation generation. Data is collected, filtered, and organized in advance through dedicated monitoring and analysis modules, reducing the complexity of real-time processing while ensuring complete information is available for accurate recommendations
3Measurement precision
If the recommendation engine uses real-time IoT data, then the recommendation relevance is improved, but the data processing time increases
Solution Approach 1:
The patent performs preliminary data collection and processing actions by continuously monitoring IoT devices, collecting usage patterns, and tracking device states in real-time before recommendation requests. This pre-processing ensures that when recommendations are needed, the data is already organized and ready for rapid analysis, reducing actual processing time while maintaining high recommendation relevance
Solution Approach 2:
The patent maintains continuous data collection and processing actions through persistent monitoring of IoT devices and continuous analysis of usage patterns. This ongoing process ensures real-time data is always available, enabling rapid recommendation generation with high relevance without the need for time-consuming data collection at the moment of recommendation requests
Data Source
AI summary
The disclosure relates to a recommendation engine that may monitor, aggregate, filter, and otherwise process relevant information associated with a user Internet of Things (IoT) environment to provide personal and context-aware recommendations based on relevant real-time knowledge about various IoT devices and other items in the IoT environment. For example, the recommendations may be generated based on ranked associations between the user and the various items in the IoT environment, which may be determined from profiles, states, usage patterns, proximities, and other contextually relevant information about the IoT environment. Furthermore, the recommendations may be uploaded to a recommendation data server, shared with friends, or otherwise used to provide similar recommendations to other users, and in a similar respect, the recommendations may be based on information stored on the recommendation data server and/or recommendations provided to friends or other users having similar profiles to the user.