TL;DR:
- Apple has filed a patent application for an advanced machine learning system that utilizes microlocation data to enhance user experiences within their homes.
- The system aims to determine the user’s position within the home and identify the most relevant application for that specific location.
- By leveraging sensor measurements and clusters, the system can recommend and automate actions based on historical usage patterns.
- The technology allows for seamless control of accessory devices throughout the home, such as kitchen appliances, lighting, and smart locks.
- The system generates tagged samples based on user actions in specific microlocations, enabling accurate predictions and proactive recommendations.
- Apple’s patent covers various aspects, including sensor measurements, predicting user interactions, semi-supervised machine learning, and application-specific models.
Main AI News:
Apple, the tech giant known for its groundbreaking inventions, has recently revealed an exciting patent application focused on enhancing the user experience within their homes. The patent, published by the US Patent & Trademark Office, introduces an advanced machine learning system that harnesses microlocations’ tagged data to determine the user’s position and identify the most relevant application for that specific room. This groundbreaking technology aims to streamline everyday tasks, such as turning on the TV or controlling home appliances, by providing seamless integration and automation.
In the modern era, mobile devices have become an indispensable part of our lives, accommodating a multitude of applications. With the increasing number of applications stored on these devices, users often face challenges when attempting to locate and utilize a desired application among the vast array available. Apple recognizes this hurdle and aims to address it with its latest invention.
The patented system offers significant improvements in determining the user’s precise position within their home and subsequently identifying the application that best suits that location. For instance, imagine entering the family room, and your Apple TV app instantly activates, ready to provide an immersive entertainment experience. Alternatively, upon entering the garage, a garage door opener app seamlessly springs into action. These are just a few examples of how Apple’s innovation can revolutionize daily interactions within our living spaces.
The home application, residing on a mobile device, serves as the control hub for various accessory devices scattered throughout the home, including kitchen appliances, lighting systems, thermostats, smart locks, window shades, and more. Whether the user is physically present in the same room as the controlled device or located elsewhere, the home application ensures effortless management. For instance, while in the kitchen, users can easily close the garage door by utilizing the home application on their mobile devices.
To establish the connection between the mobile device and the accessory devices, the home application employs the concept of “accessory devices,” which are devices located in or near a specific environment, such as a home, apartment, or office. Examples of accessory devices include garage doors, door locks, fans, lamps, thermometers, windows, kitchen appliances, and any other devices that can be controlled through a dedicated application.
Determining the association between an accessory device and a home is facilitated by the home application itself. This can be achieved through various methods, such as automatic scanning of the environment by the mobile device, which detects accessory devices, or manual input of accessory device information by the user through the home application.
Users often find themselves repeatedly performing specific actions with accessory devices while in particular locations. For example, every time a user arrives home, they may close the garage door while in the kitchen. Similarly, when it’s dark outside, turning on a living room lamp or adjusting the thermostat in the living room may become routine. However, these regular and repetitive tasks can be time-consuming and tedious.
Apple’s invention addresses this challenge by leveraging historical usage data of the application at identifiable locations, referred to as microlocations. By measuring sensor values using mobile device sensors (e.g., antennas and associated circuitry), which capture wireless signals emitted by stationary signal sources like routers or network-enabled appliances, the system generates reproducible sensor values. These sensor values serve as proxies for a physical position, forming what is known as a “sensor position” in sensor space.
A cluster, a group of sensor positions within close proximity, plays a pivotal role in this technology. Clusters consist of measurements taken at various sensor positions that are determined to be within a threshold distance of each other or from a cluster’s centroid. In other words, clusters represent groups of sensor positions that appear close to one another when viewed in sensor space. Such clusters are often found in specific areas of a house, like rooms or particular regions, such as hallways or front door areas.
Each cluster corresponds to a microlocation within a house or building, representing a distinct area or region. A microlocation can refer to a backyard, front door area, hallway, or any other designated space within the user’s home. Apple’s inventive system takes advantage of this microlocation concept to facilitate the automatic generation of tagged samples, providing valuable data for machine learning.
To illustrate, let’s consider a scenario where a user opens the front door using the home application on their mobile device while standing in the driveway. If the front door is equipped with a smart lock, the home application can automatically generate a tagged sample by capturing signal values at that location and labeling it as “front door.” Once the machine learning model is trained, the home application can recommend “opening the front door” on the user interface or even automate the process when it predicts that the user is in the driveway, based on similarities to the data points associated with that location.
Another compelling example involves a wireless streaming application employing a semi-supervised machine learning model to predict target devices for projecting video or audio. Once the model is trained, the application can recommend the living room TV when it predicts the user’s presence in that specific area.
Apple’s patent encompasses a wide range of topics, including sensor measurements and clusters, predicting user interaction with a device, learning and generating clusters, performing proactive actions based on measured sensor position and clusters, events triggering prediction, detecting events, user interaction events, device connection events, determining triggering events, identifying applications, performing associated actions, contextual information, predictor modules for recommendation determination, updating controllers, possible issues with unsupervised machine learning for microlocation, predicting targets based on microlocation using unsupervised machine learning, possible issues with microlocation using unsupervised machine learning, semi-supervised machine learning for microlocation, using both tagged and untagged samples, self-training approach of semi-supervised machine learning, labeling tagged samples, generating tagged samples, application-specific models, application-specific models with samples tagged with target areas, application-specific models with samples tagged with actions, object prediction system for multiple applications, fusion of different sensors, methods of predicting target objects for a mobile device, location-based model and prediction model, and action-based model.
Conclusion:
Apple’s advanced machine learning system for microlocation-based home apps represents a significant development in the market. By leveraging sensor data and historical usage patterns, Apple aims to provide users with seamless control and automation of their home devices and applications. This technology has the potential to enhance user experiences, simplify daily routines, and further integrate smart home functionalities.
As the market for smart homes continues to grow, Apple’s innovative approach positions them at the forefront of delivering intuitive and personalized solutions for homeowners. Businesses operating in the smart home ecosystem should closely monitor these advancements and explore opportunities to leverage similar technologies to enhance their own products and services.
