Patent pending - Cleaning Price of a Room Using the Messiness Quotient Determined By an ML Algorithm
Objective pricing = Happy customer
Using the messiness quotient of a room to determine the actual price of the cleaning as determined by a Machine learning algorithm.
Amenify is revolutionizing the way cleaning services are priced with our latest innovation. Last summer, our team's innovative work has led to the filing of a patent for a method and system that utilizes Machine learning to objectively determine the messiness quotient of a room and calculate a fair cleaning price accordingly. Here's a closer look at this patent pending technology.
The Genesis of Innovation
The cleaning industry, integral to hospitality, residential, and commercial sectors, has long grappled with the challenge of pricing services fairly and consistently. Traditional methods rely heavily on subjective assessments by cleaning personnel, leading to variability in pricing that can result in customer dissatisfaction and operational inefficiencies. Recognizing this gap, Amenify set out to devise a method that introduces objectivity and precision into the pricing process.
A Closer Look at the Method and System
Our patented system utilizes advanced machine learning algorithms to analyze photographs of rooms, identifying various degrees of messiness through object detection, clutter analysis, and dirt recognition. This analysis yields a messiness quotient—a numerical value representing the room's cleanliness level. This quotient then informs the cleaning price, aligning cost with the actual cleaning needs as determined by objective criteria.
Key Components of the System:Image Data Acquisition: Using the Amenify’s Resident App camera view to capture comprehensive views of rooms, ensuring a detailed assessment of the space.
Preprocessing and Feature Extraction: Enhancing image quality and extracting relevant visual features to prepare for analysis.
Machine Learning Algorithm: A neural network-based framework trained on a dataset of room photos with expert-annotated messiness levels, learning to accurately predict a room’s cleanliness from visual cues.
Messiness Quotient Calculation: The algorithm assesses room photos to output a messiness quotient, serving as the basis for pricing.
Dynamic Pricing Algorithm: Utilizes the messiness quotient to calculate a fair cleaning price, incorporating factors such as room size, cleaning complexity, and demand dynamics.
Advantages of The Approach
The benefits of this method are manifold. It provides an objective, data-driven approach to determining cleaning prices, thereby reducing subjectivity and potential disputes. It also offers a scalable solution that can be adapted to different room types and cleaning scenarios, enhancing efficiency and customer satisfaction. This method stands to benefit the industry and customers alike by:
Enhancing Fairness and Transparency: Providing a clear, objective basis for cleaning costs.
Improving Efficiency: Streamlining the estimation process, allowing for quicker, more accurate pricing.
Fostering Consistency: Reducing variability in pricing, leading to improved customer trust and satisfaction.
Enabling Customization: Allowing for pricing adjustments based on specific room conditions, complexity, and customer requirements.
Implementation and Future Directions
The system is designed with scalability in mind, capable of adapting to a wide array of environments and cleaning requirements. This patent signifies the beginning of our journey towards integrating more AI and machine learning technologies into real estate services, aiming to enhance operational efficiencies and customer experiences across the board.
Conclusion
Amenify's latest patent is our commitment to excellence and customer satisfaction. We look forward to the positive changes this Technology will bring to our clients.
