Reinforcement Learning for Precision Hygiene Control
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Solution Overview
Problem
Current hygiene methods, either subtractive or additive, fail to effectively control microbes on surfaces both quantitatively and qualitatively, and do not adequately consider bacterial resistance or human microbiota, leading to incomplete prevention of microbial colonization.
Innovation Solution
The implementation of a hybrid hygiene approach that iteratively combines antimicrobial techniques with bacterial products, using reinforcement learning to optimize the application of bacterial quantities and products based on temporal and spatial variability, facilitated by precision hygiene strategies and automated systems like drones for variable rate applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If subtractive hygiene (antimicrobial techniques) is used, then the number and type of microbes can be reduced, but bacterial resistance to biocides develops and re-colonization occurs between applications
Solution Approach 1:
The patent combines subtractive hygiene (antimicrobial techniques) and additive hygiene (bacterial products) into a hybrid approach. The system integrates both methods in a coordinated manner where antimicrobial techniques reduce existing microbial loads while bacterial products prevent re-colonization, thereby achieving sustained microbe control without promoting resistance.
Solution Approach 2:
The system implements periodic alternating applications of subtractive and additive hygiene methods. Reinforcement learning determines optimal timing for switching between antimicrobial techniques and bacterial product applications, creating a periodic action pattern that prevents resistance development while maintaining effective microbe control.
2Quantity of substance
If additive hygiene (bacterial products) is used, then permanent prevention of targeted bacteria colonization is achieved, but the type of reduced bacteria cannot be influenced and human microbiota is not considered
Solution Approach 1:
The system dynamically adjusts the type and quantity of bacterial products applied based on real-time sensor data and reinforcement learning decisions. This dynamic adaptation allows the system to select specific bacterial strains for different locations and times, influencing which types of bacteria are controlled while preserving beneficial human microbiota.
Solution Approach 2:
The system applies different bacterial products to different locations based on spatial variability in microbial contamination patterns. Sensors detect local microbial conditions and the reinforcement learning system prescribes location-specific bacterial applications, ensuring that the right bacterial type is applied to the right location to control specific pathogens while preserving beneficial flora.
3Ease of operation
If uniform hygiene application is used, then simple implementation is achieved, but temporal and spatial variability of bacterial exposure is not addressed
Solution Approach 1:
The system changes application parameters (type of bacterial product, quantity, timing, and location) based on sensor data and reinforcement learning decisions. This parameter adaptation allows precise control of hygiene application to match temporal and spatial variability in microbial contamination, moving from uniform to targeted applications.
Solution Approach 2:
The system uses autonomous sensors and reinforcement learning algorithms to automatically detect microbial conditions and prescribe appropriate bacterial applications without human intervention. This self-service capability enables complex variable-rate applications while maintaining ease of operation, as the system autonomously adapts to changing conditions.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reinforcement learning in the field of hygiene. Specifically, the features described relate to selecting actions in a context to be performed by an agent that interacts with an environment by receiving observations, in response, performing actions from a set of actions, wherein the context comprises a bacterial product to be prescribed, wherein the observations comprise data from the mapping of applied bacteria, and wherein the actions comprise data for the prescription of the bacterial product.