IoT Interaction System Using Fine-Grained Location Detection
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Solution Overview
Problem
Existing systems for interacting with embedded devices in buildings often present users with lengthy lists of devices, leading to omission of relevant devices due to binary detectors and coarse-grain location techniques, which are not precise enough to facilitate efficient user interactions.
Innovation Solution
A method and system that utilize fine-grained location techniques and interaction data to identify recommended embedded devices based on user device location, generating heat maps of interaction frequencies and providing tailored recommendations to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If passive infrared room occupancy detectors are used to detect user presence, then the system can identify which room the user is in, but the location detection precision is coarse-grained and leads to omission of relevant embedded devices
Solution Approach 1:
The patent segments the building into multiple floors and rooms, creating a hierarchical location structure. Instead of treating the entire building as one detection zone, the system divides it into granular spatial units (floors, rooms, zones) that can be independently analyzed. This segmentation allows the system to precisely identify which specific room or zone the user is in, thereby preventing omission of relevant embedded devices located in those specific areas.
Solution Approach 2:
The patent introduces multiple dimensions for location detection beyond simple room occupancy. It incorporates vertical dimension (floor level), horizontal dimension (room/zone within floor), and temporal dimension (time-based interaction patterns). By adding these additional dimensions, the system achieves fine-grained location precision that enables accurate identification of context-relevant embedded devices across multiple spatial and temporal scales.
2Ease of operation
If a list of all embedded devices is presented to the user, then all devices are visible, but the list becomes excessively long and difficult to navigate
Solution Approach 1:
The patent extracts and displays only the subset of embedded devices that are relevant to the user's current context (location, time, interaction history). Instead of presenting the complete list of all devices in the building, the system filters and extracts only those devices located in or near the user's current room/zone, or devices that have been previously interacted with. This extraction principle dramatically reduces the visible device list while ensuring all relevant devices are prominently displayed.
Solution Approach 2:
The patent applies local quality by making the device list dynamically adaptive to the user's specific context. Different locations, times, and user profiles receive different customized device lists tailored to their local needs. For example, a user in the kitchen sees kitchen appliances, while a user in the bedroom sees bedroom devices. This local customization ensures each user sees only the devices relevant to their current situation, improving ease of operation without losing any potentially relevant devices.
3Ease of manufacture
If binary detectors are used for location detection, then the system implementation is simple, but the detection precision is insufficient for efficient device interaction
Solution Approach 1:
The patent implements a nested detection architecture where multiple levels of location detection are combined. It nests simple binary occupancy detectors within a broader framework that includes floor-level detection, room-level detection, and zone-level detection. Each detection layer builds upon the previous one, creating a hierarchical structure that maintains implementation simplicity while achieving high precision. The nested approach allows the system to use multiple inexpensive sensors working together to achieve precision that would be difficult with a single complex sensor.
Data Source
AI summary
Systems and methods for facilitating interactions with embedded devices are provided. In one embodiment, a method can include obtaining a first set of data indicative of at least a plurality of interactions between a user device and a plurality of embedded devices associated with a building, and one or more locations of the user device associated with each respective interaction. The method can include generating a second set of data for each embedded device based, at least in part, on the first set of data. Each second set of data can be indicative of at least a number of interactions between the user device and the respective embedded device for each location. The method can include determining a particular location of the user device. The method can include identifying one or more recommended embedded devices and providing information about at least one recommended embedded device to the user device.