18140517. SYSTEM AND TECHNIQUES FOR IMPROVING IN-ROOM PERSON DETECTION simplified abstract (APPLE INC.)
SYSTEM AND TECHNIQUES FOR IMPROVING IN-ROOM PERSON DETECTION
Organization Name
Inventor(s)
Yoav Feinmesser of Herzliya (IL)
Naftali Sommer of Herzliya (IL)
SYSTEM AND TECHNIQUES FOR IMPROVING IN-ROOM PERSON DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18140517 titled 'SYSTEM AND TECHNIQUES FOR IMPROVING IN-ROOM PERSON DETECTION
Simplified Explanation
The abstract describes a technique for determining the presence of a person in a room using an electronic device that transmits an electromagnetic wireless signal. Here is a simplified explanation of the abstract:
- An electronic device sends out an electromagnetic wireless signal using a first sensor.
- The device receives an electromagnetic return signal from the transmitted signal.
- Based on the return signal, the device detects a potential target (person) in the room.
- A second sensor is used to confirm that the potential target is indeed in the room.
- If the target is confirmed, the device saves a training signature of the return signal for future use.
- This process can be repeated to obtain a set of training signatures for different potential targets.
- The device then uses machine learning to train a model using the set of training signatures.
- The trained model can detect when a target is in the room using the first sensor.
Potential Applications
This technology has potential applications in various fields, including:
- Home security systems: The technique can be used to detect the presence of intruders in a room.
- Occupancy detection: It can be used to determine if a room is occupied or vacant, useful for energy-saving purposes.
- Elderly care: The technique can be employed to monitor the presence and movement of elderly individuals in their homes.
- Retail analytics: It can be used to track customer movement and behavior within a store for marketing and operational purposes.
Problems Solved
The technique addresses the following problems:
- Non-intrusive presence detection: It allows for the detection of a person's presence without the need for physical contact or invasive methods.
- Accurate target identification: By using multiple sensors and machine learning, the technique aims to accurately identify and confirm the presence of a person in a room.
- Training efficiency: The process of saving training signatures and using machine learning helps improve the accuracy and efficiency of target detection.
Benefits
The use of this technique offers several benefits, including:
- Enhanced security: It provides a reliable method for detecting the presence of potential intruders in a room.
- Energy efficiency: By accurately determining occupancy, it enables energy-saving measures such as automatic lighting and HVAC control.
- Improved care and monitoring: The technique can assist in monitoring the movement and presence of individuals, particularly in healthcare and elderly care settings.
- Data-driven insights: By analyzing the collected data, valuable insights can be gained for various applications, such as retail analytics and space utilization optimization.
Original Abstract Submitted
A technique for determining a presence of a person in a room may include an electronic device transmitting an electromagnetic wireless signal of a first sensor. The technique may include receiving an electromagnetic return signal from the electromagnetic wireless signal. The technique may include detecting a potential target in the room based on the electromagnetic return signal. The technique may include determining that the potential target is in the room using a second sensor. Responsive to determining the potential target is in the room, the technique may include saving a training signature of the electromagnetic return signal for training a machine learning model. This technique can be repeated to obtain a set of training signatures corresponding to potential targets. The technique may include training, using the set of training signatures, the machine learning model to detect when a target is in the room using the first sensor.
