Dynamic Environmental Control via Anonymized Biological Data
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Solution Overview
Problem
Current technologies are unable to effectively control or optimize a surrounding environment for a group of individuals based on the collection of biological data from multiple people within a location, failing to provide comprehensive modifications to enhance the comfort and well-being of all individuals present.
Innovation Solution
A computer-implemented method that collects and anonymizes biological, location, and time data from multiple devices within a physical environment, using IoT networks to analyze this data and modify environmental settings such as lighting, temperature, and air conditioning to create a more comfortable atmosphere for the group.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If biological data is collected from multiple individuals to optimize environmental control, then environmental comfort for the group is improved, but individual privacy and data security deteriorate
Solution Approach 1:
The patent extracts personally identifiable information (PII) from collected biological data before processing and storage. By removing names, addresses, and other identifying markers, the system maintains the utility of aggregate environmental optimization while eliminating privacy risks associated with individual data exposure.
Solution Approach 2:
The patent introduces anonymization as an intermediary process between data collection and environmental control execution. This intermediary layer transforms raw biological data into aggregated, anonymized metrics that can be used for group environmental optimization without exposing individual identities, thus bridging the gap between privacy protection and effective environmental control.
2Manufacturing precision
If detailed biological data is collected and processed, then environmental control precision is improved, but system complexity and processing requirements worsen
Solution Approach 1:
The patent extracts only the essential biological parameters needed for environmental control (temperature preferences, humidity levels, air quality sensitivity) while discarding redundant detailed information. This extraction approach maintains sufficient precision for effective environmental optimization while reducing computational burden and system complexity.
Solution Approach 2:
The patent segments the environmental control system into distinct functional modules: data collection, anonymization, aggregation, analysis, and execution. Each module handles specific tasks independently, which simplifies the overall system architecture and makes complex processing more manageable while maintaining precision in each functional area.
3Ease of operation
If real-time environmental adjustments are made based on collected data, then individual comfort is improved, but energy consumption and processing load worsen
Solution Approach 1:
The patent implements periodic environmental adjustments rather than continuous real-time modifications. The system collects biological data over time periods, aggregates this information, and makes environmental adjustments at scheduled intervals. This periodic approach maintains comfort benefits while significantly reducing the energy consumption and processing load associated with constant real-time control.
Solution Approach 2:
The patent applies partial environmental adjustments based on aggregated trends rather than responding to every individual data point. By making selective adjustments that address the most significant comfort issues identified from the data, the system achieves satisfactory comfort levels with reduced energy expenditure compared to exhaustive real-time optimization.
Data Source
AI summary
Computer-implemented methods, systems and computer program products leveraging collection and analysis of anonymized biological data, location data, individual IDs and time data from groups of individuals within a surrounding environment. The anonymized data can be combined with sources of map data and available historical data to help provide context about the surrounding environment of the users and stored for analysis and decision-making that physically impacts and alters the surrounding environment. At periodic or sporadic intervals, the collected data is extracted and analyzed. Based on the analysis of the anonymized data, physical changes are dynamically implemented within the physical environment, including remotely altering the physical environment by instructing changes to surrounding environment over a computer network such as modifying one or more settings of IoT devices positioned within the surrounding environment analyzed.