Artificial Intelligence (AI) is essential to
provide value added Internet of Things (IoT) services by finding the patterns,
correlations and anomalies in user behaviors for autonomous context-aware
actions of the IoT system surrounding the user. Patents can provide insights
regarding the state of the art and technical details of the AI innovation for
the IoT applications. For example, US20150039105 (Home intelligence system and
method; Wen-Sung Lee) illustrates the smart home intelligence system to fulfill
the special needs of each family member exploiting AI.
The smart home intelligence system includes
an identification sensor, a real-time locating system and an AI device. The
identification sensor is configured to detect presence of a home member and
identify ID information of the home member. The real-time locating system is
provided to automatically identify and track location of the home member in real
time within the house. The AI device includes a data logger, a controller and a
learning module. The data logger is provided to record the ID information of
the home member from the identification sensor and further records location
information of the home member from the real-time locating system. The
controller is configured to detect control parameters of appliances switched or
adjusted by the home member and transmits the detected control parameters to
the data logger. The learning module is configured to correlate at least the ID
information and the location information with the detected control parameters
to customize for the home member a set of preferred control parameters for
operating the appliances. Accordingly, upon the home member is detected with
the ID information and the location information, the controller retrieves from
the learning module the set of preferred control parameters based on the ID
information and the location information and directs the appliances to operate
with the set of preferred control parameters for the detected home member.
For example, a family member (A) enters in
the first floor room, and therefore the identification sensor detects the
presence of the family member (A) and identifies by the profile of the family
member that the family member (A) is a father with ID information (father), the
real-time locating system automatically identifies and tracks location of the
father in real time within the house. The identification sensor, the real-time
locating system and the time clock then transmit the ID information, the
location information and the time information to the data logger. The data
logger receives and records the father's ID information from the identification
sensor, the location information from the real-time locating system and the
time information from the time clock. Father then utilizes his smartphone to
adjust the temperature of the HVAC system 61 to be 22° C., the lighting system to
provide an illuminance of 50 lux, and the stereo system to be off. At this
time, the controller detects the control parameters of appliances adjusted by
the father and transmit the detected control parameters to the data logger. The
learning module then correlates the ID information (father), the location
information (1st floor room), and the time information (7:00 am) with the
detected control parameters to customize for the home member a set of preferred
control parameters for operating the appliances. In this way, next time the
father comes in the first floor room again, the father will be detected by the
home intelligence system and the system will automatically directs the HVAC
system to work on 22° C., the lighting system 62 to provide an illuminance of
50 lux, and the stereo system 63 to be off. In this case, the learning module selects,
among different previously used control parameters, a set of most recently used
control parameters as the set of preferred control parameters for the home
member. The learning module may select a set of his most frequently used
control parameters as the set of preferred control parameters for the home
member.
AI can also be exploited in the smart
healthcare applications. For example, wearable devices track your health condition,
e.g. heart rate, and send a notification if a potential problem is detected based
on AI analysis of the real-time streams of health data. For example, US20140073486
(SYSTEMS, DEVICES AND METHODS FOR CONTINUOUS HEART RATE MONITORING AND
INTERPRETATION: BOBO ANALYTICS) illustrates a heart rate monitoring system by
providing the best type of sensor to use at a given time determined by AI based
on the level of motion (e.g., via an accelerometer) and whether the user is
asleep (e.g., based on movement input, skin temperature and heart rate).
Common symptoms of being asleep are periods
of no movement or small bursts of movement (such as shifting in bed), lower
skin temperature (although it is not a dramatic drop from normal), and heart
rate that is below the typical resting heart rate when the user is awake. These
variables depend on the physiology of a person and thus a machine learning
algorithm is trained with user-specific input to determine when he/she is
awake/asleep and determine from that the exact parameters that cause the
algorithm to deem someone asleep.
US20140108307 (METHODS AND SYSTEMS FOR
PROVIDING PERSONALIZED AND CONTEXT-AWARE SUGGESTIONS; Wipro) illustrates AI
exploitation in the connected car applications: Base on the profile information
and/or contextual information, AI system provides suggestions to the driver.
For instance, the AI system obtains contextual information indicating that a
contact of the driver is only a few minutes in driving time from the driver’s route.
Further, based on social communications and social media information stored in
the driver profile, the AI system may also determine that the contact is a
friend of the user. Based on that determination, the AI system informs the
driver, "Your friend Peter is at a few minutes away from your route; would
you like meet?"
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