Commonality Detection System for IoT User Interaction
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Solution Overview
Problem
Existing systems fail to effectively facilitate meaningful conversations between individuals by identifying common interests and experiences, making social interactions challenging, especially in networking events or meetings with new people.
Innovation Solution
A computer-implemented method using IoT sensors to collect and categorize data on users' locations, objects interacted with, and communication styles, comparing this data with nearby users to suggest conversation topics based on shared interests and experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If a system collects and analyzes multiple types of user data (locations, objects, communication styles) to suggest conversation topics, then the quality and meaningfulness of suggested conversation topics improves, but the complexity of the system increases
Solution Approach 1:
The patent segments user data into distinct categories: location data, object interaction data, and communication style data. Each category is processed and analyzed separately by dedicated modules, then the results are integrated to generate conversation topic suggestions. This segmentation reduces system complexity by breaking down the complex task of commonality detection into manageable, independent components.
Solution Approach 2:
The patent introduces an intermediary processing layer that collects raw sensor data from multiple sources, categorizes and structures the information, and then feeds it to the conversation suggestion engine. This intermediary layer acts as a buffer that organizes complex multi-source data into a standardized format, reducing the complexity burden on both data collection and analysis components.
2Measurement precision
If the system processes and compares detailed user data from multiple sources, then the precision of conversation topic suggestions improves, but the time required for processing increases
Solution Approach 1:
The patent performs preliminary categorization and structuring of user data as it is collected, organizing location data, object data, and communication style data into predefined categories before comparison is needed. This preliminary organization reduces the computational burden during real-time comparison operations, enabling precise commonality detection without excessive processing delays.
Solution Approach 2:
The patent applies different processing strategies to different types of data based on their characteristics. Location data is geospatially categorized, object data is semantically categorized, and communication style data is behaviorally categorized. Each data type receives targeted processing appropriate to its nature, improving overall detection precision while optimizing processing efficiency for each data category.
Data Source
AI summary
A computer-implemented method includes: receiving first sensor data related to a first user; determining locations visited by the first user based on the first sensor data; determining objects with which the first user has interacted based on the first sensor data; determining communication style information of the first user based on the first sensor data; categorizing the determined locations, the determined objects, and the determined communication style information; comparing the categorized determined locations to categorized locations of a second user; comparing the categorized determined objects to categorized objects with which the second user has interacted; comparing, by the computer device, the categorized determined communication style information to categorized communication style information of the second user, the comparing resulting from the second user being within the predetermined distance from the first user; and suggesting to the first user a topic of conversation based on the comparing.