Patents are a good information resource for obtaining IoT (Internet of Things) R&D status in a company. Followings are some examples of patents that provide IBM IoT R&D status: IoT sensing devices energy management, heterogeneous IoT devices management, big data analytics and smart haptic medical devices.

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IBM Internet of Things R&D Insights from Patents
Patents are a good information resource for obtaining IoT (Internet of Things) R&D status in a company.
Followings are some examples of patents that provide IBM IoT R&D status: IoT sensing devices energy
management, heterogeneous IoT devices management, big data analytics and smart haptic medical devices.
IoT Sensing Devices Energy Management
US20140331073 (System and method for efficient service-instance oriented energy management in the internet of
things; IBM) illustrates a system for the IoT sensing devices energy management. The energy management system
is implemented in an IoT energy management server. The IoT energy management server is used to control the
duty cycle of a physical sensor to keep the whole system the most energy-efficient. The IoT energy management
server is in the application infrastructure layer of the IoT application system (e.g. smart home system), which is a
distributed middleware platform to support the execution of IoT applications. The application infrastructure
platform provides the capabilities of managing physical sensors as services. For example, both of “temperature in
3rd-Floor Room 301” and “humidity in 3rd-Floor Room 301” are examples of services achieved by the application
infrastructure. With flexible combination of various services achieved by the application infrastructure, IoT
applications may form their application logic suitable for various practical scenarios. Each service may require the
involvement of physical sensors from different network domains.

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For each service, its runtime instance is defined as a service instance (SI). A SI is an instance of a service that can
be provided based on a combination of sensors. When an application actually uses (invokes) a service, the SI of the
service is generated and run in the IoT system, and the running requires corresponding sensors to work. Energy
management performed by the system is based on SIs instead of sensors. The SI oriented energy management

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system includes a prediction means configured to predict target SIs to be serviced in a subsequent time period,
based on a SI transition model; a selection means configured to select an ON-sensor set to be turned on to provide
services on which the target SIs are based, according to a critical covering set corresponding to the SIs, the use
history data of sensors in the critical covering set and energy parameters of the sensors; and a control means
configured to perform, in response to start of the time period, an ON/OFF control on the sensors in the IoT system
so that the sensors in the ON-sensor set are turned on and the sensors other than those in the ON-sensor set are
turned off.
Heterogeneous IoT Devices Management
US8671099 (Clustering devices in an internet of things (‘IoT’); IBM) illustrates a system for the heterogeneous
IoT devices management exploiting the device clustering module. The device clustering module clusters Internet
connected IoT devices in the sense that the device clustering module associates similar Internet connected IoT
devices with a particular cluster group. The device clustering module can cluster Internet connected IoT devices,
for example, through the use of a database, table, or other data structure that includes an identifier of a particular
device and an identifier of a particular cluster that the IoT device is part of. Clustering similar Internet connected
IoT devices within a particular cluster group can enable a larger system to better manage a collection of
heterogeneous devices by, for example, imposing usage rules and policies on the set of similar devices that are
included in a cluster group, providing access control and security restrictions to the set of similar devices that are

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included in a cluster group, performing device configuration operations on the set of similar devices that are
included in a cluster group, and so on.
The device clustering module is configured to receive a characteristic set for an IoT device. The characteristic set
specifies device attributes and an attribute value for each device attribute. Devices may be deemed as being similar
and therefore subject to clustering in dependence on attributes of the device such as, for example, the type of
network connection supported by the device, the communications protocols supported by the device, the
manufacturer of the device, different capabilities of the device, and so on. Such device attributes are specified in a
characteristic set that specifies device attributes and an attribute value for each device attribute. The characteristic
set may be embodied, for example, as a structured document such as an extensible markup language (`XML`)
document that includes an identifier of a particular attribute of the device as well as a value for the attribute.

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Big Data Analytics
US20140068180 (Data analysis system; IBM) illustrates a system capable of efficiently analyzing big data. The
data analysis system includes an analyst server, data storage unit, a client terminal independent of the analyst
server, and a caching device independent of the analyst server. The caching device includes a caching memory, a
data transmission interface, and a controller for obtaining a data access pattern of the client terminal with respect to
the data storage unit, performing caching operations on the data storage unit according to a caching criterion to
obtain and store cache data in the caching memory, and sending the cache data to the analyst server via the data
transmission interface, such that the analyst server analyzes the cache data to generate an analysis result, which
may be used to request a change in the caching criterion.
US20150134704 (Real Time Analysis of Big Data; IBM) illustrates a system for processing large scale
unstructured data in real time. The interconnected IoT sensing devices continuously generate massive information
at a very high speed. Thus a technology for effectively processing a huge amount of information in the form of a
data stream in real time is very important. The real time big data analysis system includes a receiver for receiving
streamed input data from live data sources, a pattern generator for deriving emergent patterns in data subsets, a
pattern identifier for identifying a repeating pattern and corresponding data subset within the emergent patterns, a
compressor for reducing the identified data subset and identified pattern to a compressed signature and a repository

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for storing the streamed input data with the compressed signature and without the identified data subset in which
the data subset can be rebuilt if necessary using the compressed signature.

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Smart Haptic Medical Devices
Smart haptic devices employing tactile feedback technology which recreates the sense of touch can be used for an
operator of a surgical instrument or diagnostics of a patient remotely. US9046926 (System and method of
dynamically generating a frequency pattern to realize the sense of touch in a computing device; IBM) illustrates a
system for providing the sense that a doctor can feel a wound without being co-located with a patient. The system
comprises an image processing module capable of extracting a plurality of features from a visual representation of
a physical object and a frequency generation module capable of generating a variable frequency pattern
corresponding to the extracted features, and sending the variable frequency pattern to a computing device in which
the computing device generates the tactile feedback in response to a stimulation applied by a user based on the
variable frequency pattern.

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