Configuration of electronic device
Technical Field
The present invention provides a method for
interpreting and using big data.
Background
The field of machine learning and data analytics is
concerned with extracting relevant information from large
amounts of data. The extracted information can then be
leveraged to solve practical problems or to positively
affect concrete situations, such as by optimizing the
operation of an electronic device. The challenges of data
analytics have to do with the process of data collection,
and the hurdle of processing large amounts of data to
extract the relevant information. It is often the case that
the big data comprises multiple individual data sets about a
single interesting phenomenon or individual. These data sets
are distinct, produced by different systems, and more often
than not maintained using different standards.
In the prior art, the prevalent approach is to collect
data actively for the purpose of determining a
user's location and/or activity: users carry one or more
electronic devices that generate a continuous stream of
readings about different aspects of the user's behavior.
However, actively collecting data creates an additional
burden: sometimes more devices must be carried, more
software needs to be run on an existing device, or more data
needs to be generated at the device about the user's
activity. In particular, extra memory is used, extra network
bandwidth is used, and extra energy is used (thereby
compromising available battery time) .
It is an object of the present invention to optimize
operation of an electronic device based on big data by
adopting a passive approach.
Summary
To this end, the invention provides a method for
configuring an electronic device, the method comprising the
steps of:
- accessing at least two distinct data sets each comprising
past user activity data;
- extracting time and location information from each of the
data sets;
- executing an algorithm to obtain, from the extracted time
and location information, time-location correlations to
recognize a location pattern over time;
- converting said location pattern over time to the present
and future to obtain a time-location expectation; and
- configuring the electronic device based on said
expectation.
According to the present invention, time and location
information is gathered from multiple data sets. Because
multiple data sets are used to extract information, the
invention cannot be interpreted as covering creation of a
specific data set for the purpose of the configuration of
the electronic device. Therefore, the use of two distinct
data sets each comprising past user activity data indicates
that ,big data' is used to extract information from. From
the big data, time and location information is extracted.
This time and location information is interpreted via the
execution of an algorithm to find correlations and recognize
location patterns over time. Thereby, insight is gained in
the behavior of the user. Particularly, the locations which
are regularly visited by a user can be recognized, and the
time of visit can be obtained. From this information, a
location pattern over time can be recognized. For example,
when a user has a nine-to-five ob during weekdays, the
information will show that during weekdays, the user is
often at a working location. Outside the working hours or in
weekends, the user activity data shows that the user is not
at the working location. Thereby, a location pattern can be
recognized over time, where a user is at a working location
between nine and five o'clock on a midweek day. This
location pattern can be converted to the present and future,
thereby obtaining time-location expectations. Based on the
extracted time and location information, which is extracted
from past user activity data, present and future user
locations can usually be predicted. In the given example, it
can be reasonably expected that a user is at the working
location between nine and five o'clock on a midweek day.
Based on this knowledge, the electronic device can be
configured. Thereby, configuration can be based on the
expectation of proximity of the user, or expected moment of
arrival or moment of leaving the location. Thereby, for
example, an electronic device can be performance optimized
when the user is expected to be in a close proximity of the
electronic device and the electronic device can be energy
optimized when the user is not expected to be in a closed
proximity of the electronic device. Thereby, the invention
provides in a method which uses big data in a concrete
situation. The distinct data sets comprising past user
activity data can comprise data that is broadcasted via
twitter (often containing location information) , foursquare,
google plus, facebook, or other social networking
applications, or that originates from telephone Call Data
Records (CDR) , or records of electronic payment
transactions. Alternatively, the data sets comprise internet
reviews where a user reviews a visit to a restaurant or
hotel. In all of these data sets, past user activity data is
stored where a location and a time is saved in relation to a
user. Alternatively, the datasets comprise internet reviews
where a user reviews a visit to a restaurant or hotel.
Preferably the algorithm uses multiple predetermined
repetitively occurring time periods, said executing
comprises associating said time and location information to
the predetermined time period and thereby recognizing
location clusters in each of the time periods. Many user
activities have a repetitive character, meaning that they
regularly occur during a predetermined time period. The
reason is that users have obligations and habits. A working
obligation requires a user to be at a prescribed workplace
at a certain time. The moment of being there can vary over
time but will, more often than not, show a pattern when
considered over a longer period of time (for example over
several months) . Users can have other habits such as
regularly attending a sports club, music lessons, or other
cultural or social activities. An appropriate algorithm can
correlate and extract repeated occurrences of time-location
patterns for the multiple user datasets. The location
information will be assigned to the respective time period,
thereby creating compilations of location information in
each time period. Particularly when the compilations show a
high density (of one location) in a time period, a user
habit can be determined.
Preferably the converting comprises assigning the
repetitively occurring time period to the present and future
and obtaining expectations based on the location cluster. A
repetitively occurring time period can, because of its
repetitive character, be extrapolated into the present and
future. When location clusters have been recognized in
certain time periods, the user can be reasonably expected to
be in the respective location in the present and future as
well. For example when past user activity data shows that a
user is at work on a Monday between twelve o'clock and one
o'clock, the user can be expected to be at work next Monday
between twelve o'clock and one o'clock. In this manner,
repetitively occurring time periods can be applies to the
present and the future so that location clusters recognized
in past user activity data provide a degree of expectation
regarding future user location.
Preferably the electronic device is configurable into a
first state wherein the devices is energy-optimized and into
a second state wherein the device is performance-optimized.
Many electronic devices, particularly electronic home
devices, can be energy-optimized or performance-optimized.
For example a home heating system can be performanceoptimized
whereby a comfort temperature is selected, or can
be energy-optimized whereby a decreased temperature is
selected. Another example is a WIFI-router which in a
performance-optimized state regularly broadcasts with high
energy to find new WIFI clients, and in a energy-optimized
state sense less regularly with a low energy broadcast
signals. Other examples comprise computers, televisions, and
related devices, which have a switched off state and a
standby state. The standby state is the performanceoptimized
state since a start-up from standby is very fast.
However in the switched off state, the device consumes
considerably less energy, and consequently this is the
energy-optimized state. Based on an expectation of user
location, the electronic devices can be switched to an
energy optimized state, when a user is expected not to be in
the proximity of the electronic device, or to a performance
optimized state, when a user is expected to be in proximity
of an electronic device. In this manner, the method
according to the invention provides in a mechanism to
dynamically adjust configuration of the electronic device
based on big data, being data that is already available.
Preferably the device is located at a predetermined
location and is configures into a second state when the user
is expected to be close to the predetermined location.
Thereby, energy consumption is optimized based on
expectations regarding user location. Thereby, the
electronic device is located at a certain location, the
latter being compared to the expected location of the user.
Preferably, the user is registered as one of a set of
main users of the electronic device. One or more users can
be registered as main users of the electronic device such as
a heating system of a house. When a user is registered as
one of the main users of the device, and the user is
expected to be close to the location of the electronic
device, electronic device can be switched to performanceoptimized
state thereby providing a maximal comfort to the
user while energy optimizing by switching the electronic
device to an energy saving state when the user is expected
to be away from the electronic device.
Preferably the two distinct data sets comprise log data
of the user. Several applications are known which log user
activity data, examples of application being twitter,
facebook, foursquare, google plus, telephone call
applications, electronic payment applications, ... .
Preferably, the two distinct data sets originated from
different applications are not related to the electronic
device: in the given examples, it is clear that a home
heating system is not related to the Twitter account of the
user living in the home. The advantage of using distinct
data sets that originate from different application not
related to the electronic device is that no energy is
consumed for active data collection purposes in the creation
of the data sets. Namely, the data sets are created for a
different purpose (for example social networking) and would
be created anyway. The collection of the data and processing
of the data is separate from the creation of the data,
because there is no active data creation. The collection and
processing can be optimized and do not need to be executed
on a wireless device carried by the user.
Preferably the electronic device is located in a
predetermined location and comprises a memory where
information relating to this predetermined location is
stored. Thereby, the location information which is stored in
the memory indicating the location of the electronic device
can be used as a base to configure the device. This location
can be compared with the expected location of the user to
determine whether the user is close to, or away from the
electronic device.
The invention further relates to a steering device
comprising an accessing module accessing at least two
distinct data sets each comprising past user activity data,
an extracting module for extracting time and location
information for each of the data sets, a processing module
for executing an algorithm to obtain, from the extracted
time and location information, time-location correlations to
recognize a location pattern over time, a converting module
for converting the location pattern over time to the present
and future to obtain a time-location expectation, and, a
configuring module operationally connected to an electronic
device to configure said electronic device based on said
expectation. Thereby, a steering device and its components
are provided to execute the above described method.
Preferably the configuration module is operationally
connected to a plurality of electronic devices and wherein
the steering device comprises a memory for storing user
information relating to each of the electronic devices. With
this steering device, multiple electronic devices can be
configured. Thereby, the configuration function for
configuring the electronic devices is implemented in an
external, steering device. This allows placing a steering
device in an existing situation and allows the steering
device to control electronic devices.
Preferably the memory further comprises location
information indicating the location of each of the
electronic devices, wherein the configuration module is
provided to configure the plurality of electronic devices
based on the expectation and based on the proximity of
adjacent electronic devices. Adjacent electronic devices can
interfere with one another. For example, WIFI routers, which
are provided to operate on a limited number of channels, can
interfere with one another when they are configured to
operate on the same channel. The steering device can
configure multiple electronic devices taking into account
such interference because the locations of the electronic
devices are known to the steering device. Thereby, the
steering device is given the "bigger picture", to thereby
optimally configure a situation based on user location
expectations and interference expectations.
Brief description of the figures
Some embodiments of apparatus and/or methods in
accordance with embodiments of the present invention are now
described, by way of example only, and with reference to the
accompanying drawings, in which:
figure 1 shows an example of an environment where
the invention is applicable;
figure 2 shows an example of a sequence of steps of
the invention.
Detailed description of embodiments
In the context of this disclosure, the term
location is to be interpreted more broadly than as mere
''geographical location' , and also covers every indication
that a user is close to, or away from a certain venue or
geographical location. Therefore the information ,calling
from a cell in another town' is considered location
information although it does not clearly points out the
geographical location of the user. It does give a relative
indication of the location. Also the information ,paying a
restaurant bill' is considered as location information since
it implies the presence of the user at a restaurant (and
thus not at home) . It is in first instance not relevant
whether this restaurant is identified.
In the context of the present disclosure, the term
dataset is defined as a collection of individual records
comprising at least one of the following, preferably all of
the following: timestamps, location information, user
identification information and optionally further metadata.
The present disclosure describes a method for
extracting meaningful features and trends from Big Data,
particularly from traces of geo-located user communications.
The disclosure furthermore describes application of these
features and trends by configuring an electronic device, for
example commanding user-facing devices or commanding
communication infrastructure elements. The problem being
solved is generating predictions or expectations of a user's
future behavior without actively monitoring the user's
position, e.g. by using a GPS tracker or other intrusive
forms of mobile sensing. In the present disclosure, a method
is proposed that combines multiple communication trails
(multiple datasets forming part of the big data) of one or
more users and produces a list of predictions about a user's
behavior as intermediate output. The list thus generated can
be used in a number of scenarios to configure programmable
devices for various purposes, e.g. reduction of power
consumption and decreasing of radio interference.
The study of human mobility patterns has a long
history. While often modeled as a purely random process,
human mobility is heavily determined by environmental
factors and has been shown to be indeed rather predictable.
A large number of methods have been devised based on
continuous monitoring of user mobility. Although accurate,
these efforts require sources of location data that provide
a high spatial and temporal sampling resolution, limiting
the applicability of their method to applicative contexts
where no constraints concerning battery life or
communication cost exist. Participatory sensing approaches,
such as the one described in this disclosure, rely on
information that is disclosed by the users as part of their
normal interaction with the system, thus eliminating the
footprint of an independent continuous monitoring activity.
Additionally, the privacy implications of collecting highresolution
information about a user' s whereabouts could be
unacceptable to users, and the very existence of these data
would have negative implications, endangering civil
liberties under repressive and/or corrupt administrations.
Other active localization techniques based on Wi-Fi
beacon fingerprinting, GSM/UMTS antenna beam triangulation,
etc., all suffer from similar drawbacks (about cost / energy
and privacy) as explained above. In contrast, the present
invention covers a set of passive methods that act on
combined sources of opportunistically collected data (e.g.
from locality-based social networks, GPS-tagged messaging
activity, cellular call records, micro- and pico-cell logs)
and as such they do not incur in any significant data
collection overhead nor additional privacy risk. Despite the
high irregularity in the temporal sampling of users'
mobility patterns, opportunistically collected communication
traces comprise quite well the regular features of a user's
daily routine that matter for the use of the infrastructure
itself. For users with as little as few hundreds of events
recorded over the course of a year, tests have shown that is
is possible to recognize and identify significant locations
and activity that emerge from the user's combined
communication history.
The problem relating to cellular radio interference
is explained hereunder. The demand for larger bandwidth from
mobile radio access network is steadily growing due to the
rising popularity of Internet-enabled mobile devices and
their multimedia applications. The radio access part is
increasingly considered as the bottleneck of the achievable
data rate for the mobile terminals because wireless
connectivity is less reliable and more error-prone than
fixed access.
The throughput of wireless communication system is
limited by several factors, such as the radio propagation
conditions, the quality of network planning, and the
physical layer transmission techniques employed. The
conventional radio resource planning for cellular networks
is both static and passive. "Static" refers to the fact that
the cellular network infrastructure is fixed after planning
the deployment of base stations at the edge and of the core
processing equipment at the central and regional offices.
"Passive" refers to the fact that each base station
allocates radio channel resources for mobile devices only
once they are localized in the coverage range of the base
station and engaged in active communication. Base station
location planning is an optimization problem: minimize the
number of deployed base stations and maximize the radio
coverage range under a reasonable radio quality, which
determines achievable throughput.
At each base station, the transmitting and
receiving power can be adjusted according to the channel
quality and interference situation. In UMTS system, uplink
power control consists of open and closed loop components
and controls. Open loop power control is used for user
equipment (UE) to set its output power to a specific value.
Closed loop power control is used by UE transmitter to
adjust its power according to the transmit power control
commands received from the downlink. The similar strategy is
also adopted in the LTE system. Moreover in the LTE system,
the sub-frequency bands are orthogonal - hence different
users are assigned to different sub-frequency bands to avoid
intra-cell interference. Inter-cell interference among
adjacent cells can be avoided by detecting potential
interfering sources and an overload indicator is exchanged
over the X2 interface among base stations for inter-cell
power control.
To cope with the fast growth of wireless access
bandwidth, mobile operators are transitioning towards the
next generation radio access networks and are considering
multi-tier cell deployments to supplement their macro-cell
infrastructure. According to the coverage range, cellular
networks can be defined in three categories: macrocells (< 3
km) , micro-cells (< 1 km) and pico-cells (< 100 m ) . Picocells
were introduced to support end-users within a limited
range, typically in an indoor environment, in order to both
extend coverage to zones not reached by a sufficient macrocell
service, and to offload mobile traffic from core
networks by exploiting a fixed broadband Internet access as
their uplink. The multi-tier coexistence among macro/micro
and pico cells is a feasible and promising deployment model
for future mobile access infrastructure but it also presents
some drawbacks, for instance, co-channel interference
coordination .
An example of the invention provides a method that
exploits out-of-band sources of user-generated information
to affect the configuration of base stations (especially -
but not limited to - micro and pico cells) by guiding the
choice among a set of pre-determined power and frequency
allocation profiles in order to limit the amount of radio
resources allocated to the expected needs of the target user
population. This method can coexist with the established
forms of transmission power control based on feedback
outlined above.
The invention furthermore discloses a family of
methods for collecting and processing geo-located
communication traces, which associate time information and
location information of a communication event driven a
user's communication activity, and exploiting them for
dynamically configuring programmable electronic devices that
are owned, managed or otherwise chiefly employed by the
users .
Such communication traces include, but are not
limited to: geo-tagged social network messages (e.g.
Twitter), location-based social check-in information (e.g.
Foursquare) , cellular phone activity traces that include
cell / micro-cell identifiers, wi-fi association /
authentication trail with a set of access points, debit card
/ credit card / electronic currency payment records that
include geographical venue information.
Target configurable electronic devices include, but
are not limited to: GSM - UMTS - LTE cells and micro-cell
access devices (ref: ALU base-station router patents), IEEE
802.11 (wi-fi or other wireless technology) access points,
home appliances and devices (such as: space heaters, A/C
system, on-demand video players, power meters for smart
grids) .
Configuration actions include, but are not limited
to: turning on and off the main function of the device in
order to save power, modify the transmission power and
allocated frequency spectrum to improve overall frequency
reuse and reduce interference, pre-fetch and store large
amounts of data at off-peak times intended for delayed
consumption, trigger a specified action when a condition is
met based on the processed trace data.
An aspect of the invention is the use of
information which is generated by the ordinary communication
activity of a user, which renders the method we propose
passive and, as such, applicable to contexts where active
sensing and collection of user-related data are not possible
due to constraints about (among others) : privacy reasons,
limited power consumption (e.g. to preserve battery life),
spotty connectivity, lack of access rights to user devices.
In the invention, big data is used to extract expectations
regarding user location in the present and future.
Another aspect of the invention is the ability to
leverage the combination of multiple different sources of
communication trails: from traditional sources (such as
cellular communication systems) to new sources of usergenerated
data (such as locality-based social networks and
micro-blogging) . The technique proposed can be extended by
combining the passive sources of data with active locality
tracking techniques found in the literature (such as wi-fi
fingerprinting or cellular antenna triangulation) as
additional sources of data, in contexts where these become
available.
A third aspect is the ability of the method to run
thanks to the passive nature of the generation of the
communication data trails - either a ) on a programmable
device whose configuration will be computed based on the
predictions locally elaborated from the collected data, or
b ) on hardware hosted in any other location (such as Cloud
computing platforms or network operator's managed hardware
pools) where the sources of communication data trails can be
accessed. The first scenario is suitable for preserving
privacy, e.g. by never releasing the locally collected
communication trail of a user to other parties, while the
second is suitable where the communication trails cannot be
made public, e.g. due to the proprietary nature of the
trails or national security reasons.
The system is composed of the following four
logical subsystems:
• Data Gatherer (s) (DG) - obtains and parses the user's
communication trace (s)
• Data Processor (DP) - aggregates and processes the
parsed trace (s) to create a schematic model of user
activity
• Rule Engine (RE) - defines the bindings between
expected user state and desired action to be performed
• Device Controller (DC) - configures the device's
functionality based on user activity model and set of
rules
The logical subsystems can be deployed in a number
of practical configurations, which we refer to as Scenarios.
In a local embodiment scenario, a device locally performs
the three logical steps of the method of the invention. Two
following cases illustrate this scenario:
• Power management of wi-fi base station based on
communication traces
• Spectrum management of small cells based on
communication traces
In a cloud embodiment scenario, a device receives a
pre-computed model of user activity that is generated by a
service provider based on communication traces that may not
be directly accessible to the user. Two following cases
illustrate this scenario:
• Pre-fetching of movies for deferred viewing based on
communication traces
· Frequency allocation planning in a cellular network
with micro/macro cells based on multiple user
communication traces
The Data Gatherer DG components take as input a
trace of localized user communication activity or locationbased
social network check-ins and extracts from it a series
of events. The DG functionality may be localized at the
device itself or on another networked machine, operating on
local data (e.g. device log files), remote data (e.g. traces
collected by a crawler), or both.
The Data Processor DP processes event series from
DG(s) and extracts their stable and recurrent features with
appropriate algorithms (k-means clustering, DBSCAN, etc.).
The output of the DP is a list of predictions that
constitutes a model for user activity associating locations
/ activities with time ranges. The DP functionality may be
localized at the device itself or on another networked
machine .
The Rule Engine RE associates locations or
activities appearing in a prediction with prescribed actions
that one or more devices need to implement in a given time
range. The RE functionality may be localized at the device
itself or on another networked machine.
The Device Controller DC implements the rules from
the RE based on the user model generated by the DP. It is
always hosted on the device itself.
Combined use of communication traces of various
origins, as detailed above, is an unobtrusive passive method
for user data collection that does not require continuous
active monitoring of a user's position. The expected user
position can be modeled based on a sufficiently long and/or
dense record of previous geo-located user communication
events, without a requirement for access or installation
rights on the user's mobile communication device.
Communication events, collected from assorted sources, can
reveal recurrent patterns in a user's daily schedule that
can then be leveraged as triggers of appropriate actions by
software running on devices, e.g. home appliances and
communication infrastructure.
The advantages of the method and device according
to the invention include:
• Lack of negative impacts on the battery life of the
mobile devices that generate the sensor data about the
user
• Control of the degree of disclosure of sensed data used
to produce the predictions about future user location
• Providing a flexible user-driven way to configure
devices without explicit user intervention: the use
cases presented above show examples where the method is
used to control power consumption and improve the
transmission rate of wireless networks, by toggling
power-saving modes and by reducing the transmission
power of unused cells and access points that would
cause needless interference.
A user routine can be defined as the repeated
occurrence of user actions that share a common feature and
happen at the same approximate time of day on a significant
number of days.
Figure 1 shows a principle scheme of a situation
where the invention is applicable. The figure shows a user
14 which, via a mobile phone 5 , is connected to the internet
3 . Thereby, the user 14 broadcasts messages comprising time
information and location information. These messages are
stored in databases 1 and 2 forming datasets comprising past
user activity data. Examples of such messages are given
above. Thereby, the datasets are distinct as they originate
from different applications. The datasets thereby implement
the functionality of the above-described data gatherer DG.
These datasets 1 and 2 can be accessed by a data
processor 4 . The data processor 4 extracts time and location
information from the datasets 1 and 2 , via an internet 3
connection, and executes an algorithm to obtain a location
pattern over time, and converts the location pattern to the
present and future to thereby obtain a time-location
expectation. Thereby, the data processor 4 implements the
functionality of the above-described data processor DP. The
physical location of the data processor 4 can vary from in
the electronic device to in a dedicated device (a steering
device) that is operationally connected to the electronic
device to a cloud computer where the functionality is spread
over different physical locations. The functions executed by
the data processor 4 will be further explained hereunder
with reference to figure 2 .
The figure 1 further shows a rule engine RE 7 ,
which is operationally connected to the steering device 4 .
The rule engine 7 comprises the logical rules for
configuring the electronic device in relation to the timelocation
expectation of the user. For example, the rule
engine will comprise a ,switch to non-active mode' in
relation with a ,user is away' expectation. This example is
a simple logical rule, however more complex rules can be
comprised by the rule engine 7 for example for configuring
multiple wireless devices in a building, based on user
expectations and to avoid interference. The rule engine can
physically be integrated in the electronic device or in the
steering device or in a cloud computer.
The figure further shows a device controller 10
which is provided to configure the one or multiple
electronic devices based on the location expectations of
users and based on the set of rules comprised by the rule
engine. The device controller can physically be integrated
in the electronic device or in the steering device or in a
cloud computer.
The electronic device is preferably a stationary
device, illustrated by the house 6 in figure 1 , meaning that
the electronic device is non-moving and has a predetermined
location. Preferably the predetermined location is saved in
a memory of the rule engine or the steering device so that
the location expectation of the user can be compared to the
predetermined location of the electronic device to decide
whether the user is expected to be in a close proximity of
the electronic device. Examples of electronic devices are
given above and comprise further heating systems, wireless
devices, backup systems and other.
Preferably, the data processor 4 , the rule engine 7
and the device controller 10 are integrated into a steering
device which is operationally connected to the one or more
electronic devices. Thereby, the steering device is adapted
to control the electronic devices based on the location
expectation of the user.
Figure 2 shows the steps of the method according to
the invention. The figure shows the datasets 1 and 2 , which
in a first step SI are accessed to thereby have past user
activity data from two distinct datasets.
In a next step S2, time and location information is
extracted from the accessed datasets.
In a next step S3, an algorithm is executed to
obtain, from the extracted information, time-location
correlations to recognize location patterns over time. These
location patterns thereby represent user habits. In this
step, the algorithm preferably uses a repetitively occurring
time periods 8 . An example of such repetitively occurring
time periods 8 is 'week day 09h - 12h' . The algorithm is
provided to organize the locations in the matching time
periods 8 . As a result, location clusters will arise in a
time period 8 when the user is often at that location during
that period. In the given example, a location cluster might
be visible showing that the user is at 'work location' on a
week day between 9h and 12h. From this cluster, a pattern
can be recognized.
In a next step S4, the location pattern recognized
in step S3 is converted to the present and future to obtain
a time-location expectation. Time-location expectation is
defined as an expectation of a user being at a location on a
certain moment of time (or during a certain time period) .
Such conversion can be obtained by assigning the repetitive
time periods onto a present and future timeline 9 . Thereby,
when a location cluster is recognized in step S3 in a time
period, a user is expected to be at that location in the
present and future corresponding time period as well.
Thereby, based on the conversion of step S4, location
expectations of a user can be obtained.
In step S5, the electronic device or electronic
devices 6A, 6B and 6C are configured based on the location
expectation of the user obtained in step S4. This step S5 is
preferably based on a set of rules defining electronic
device configuration states in relation to different
location expectations of a user. Such set of rules can be
used as a lookup table where the state is retrieved for a
corresponding location expectation of a user. Examples of
states of the electronic device are given above and further
comprise energy-optimized and performance-optimized states.
An example of a rule is that if a user is expected to be
close to the electronic device, the device is configured
into the performance-optimized state. Thereby, preferably a
user is registered as main user, or one of multiple main
users, of the electronic device. Such registration can be
saved in a memory of the electronic device or of the
steering device.
A person of skill in the art would readily
recognize that steps of various above-described methods can
be performed by programmed computers. Herein, some
embodiments are also intended to cover program storage
devices, e.g., digital data storage media, which are machine
or computer readable and encode machine-executable or
computer-executable programs of instructions, wherein said
instructions perform some or all of the steps of said abovedescribed
methods. The program storage devices may be, e.g.,
digital memories, magnetic storage media such as a magnetic
disks and magnetic tapes, hard drives, or optically readable
digital data storage media. The embodiments are also
intended to cover computers programmed to perform said steps
of the above-described methods.
The description and drawings merely illustrate the
principles of the invention. It will thus be appreciated
that those skilled in the art will be able to devise various
arrangements that, although not explicitly described or
shown herein, embody the principles of the invention and are
included within its spirit and scope. Furthermore, all
examples recited herein are principally intended expressly
to be only for pedagogical purposes to aid the reader in
understanding the principles of the invention and the
concepts contributed by the inventor (s) to furthering the
art, and are to be construed as being without limitation to
such specifically recited examples and conditions. Moreover,
all statements herein reciting principles, aspects, and
embodiments of the invention, as well as specific examples
thereof, are intended to encompass equivalents thereof.
The functions of the various elements shown in the
FIGs., including any functional blocks labeled as
"processors", may be provided through the use of dedicated
hardware as well as hardware capable of executing software
in association with appropriate software. When provided by a
processor, the functions may be provided by a single
dedicated processor, by a single shared processor, or by a
plurality of individual processors, some of which may be
shared. Moreover, explicit use of the term "processor" or
"controller" should not be construed to refer exclusively to
hardware capable of executing software, and may implicitly
include, without limitation, digital signal processor (DSP)
hardware, network processor, application specific integrated
circuit (ASIC) , field programmable gate array (FPGA) , read
only memory (ROM) for storing software, random access memory
(RAM), and non volatile storage. Other hardware,
conventional and/or custom, may also be included. Similarly,
any switches shown in the FIGS, are conceptual only. Their
function may be carried out through the operation of program
logic, through dedicated logic, through the interaction of
program control and dedicated logic, or even manually, the
particular technique being selectable by the implementer as
more specifically understood from the context.
It should be appreciated by those skilled in the
art that any block diagrams herein represent conceptual
views of illustrative circuitry embodying the principles of
the invention. Similarly, it will be appreciated that any
flow charts, flow diagrams, state transition diagrams,
pseudo code, and the like represent various processes which
may be substantially represented in computer readable medium
and so executed by a computer or processor, whether or not
such computer or processor is explicitly shown.

CLAIMS
1 . Method for configuring an electronic device, the
method comprising the steps of:
- Accessing at least two distinct datasets each
comprising past user activity data;
- Extracting time and location information from each of
the datasets;
- Executing an algorithm to obtain, from the extracted
time and location information, time-location
correlations to recognize a location pattern over time;
- Converting said location pattern over time to the
present and future to obtain a time-location
expectation; and
- Configuring the electronic device based on said
expectation .
2 . Method according to claim 1 , wherein said
algorithm uses multiple predetermined repetively occuring
time periods, said executing comprising associating said
time and location information to the predetermined time
periods and thereby recognizing location clusters in each of
the time periods.
3 . Method according to claim 2 , wherein said
converting comprises assigning said repetively occuring time
periods to the present and future, and obtaining
expectations based on the location clusters.
. Method according to any one of the previous
claims, wherein said electronic device is configurable into
a first state wherein the device is energy-optimized and
into a second state wherein the device is performanceoptimized.
5 . Method according to claim 4 , wherein the device
is located at a predetermined location and is configured
into the second state when the user is expected to be close
to said predetermined location.
6 . Method according to any one of the previous
claims, wherein the user is registered as one of a set of
main users of said electronic device.
7 . Method according to claim 6 , wherein said two
distinct datasets comprise log data of said user.
8 . Method according to claim 7 , wherein said two
distinct datasets originate from different applications not
related to the electronic device.
9 . Method according to any one of the previous
claims, wherein said electronic device is located in a
predetermined location and comprises a memory wherein
information relating to this predetermined location is
stored .
10. Steering device comprising an accessing
module for accessing at least two distinct datasets each
comprising past user activity data, an extracting module for
extracting time and location information from each of the
datasets, a processing module for executing an algorithm to
obtain, from the extracted time and location information,
time-location correlations to recognize a location pattern
over time, a converting module for converting the location
pattern over time to the present and future to obtain a
time-location expectation, and, a configuring module
operationally connected to an electronic device for
configuring said electronic device based on said
expectation .
11. Steering device according to claim 10,
wherein the configuring module is operationally connected to
a plurality of electronic devices, and wherein the steering
device comprises a memory for storing user information
relating to each of the electronic devices.
12. Steering device according to claim 11,
wherein said memory further comprises location information
indicating the location of each of the electronic devices,
wherein the configuration module is provided to configure
the plurality of electronic devices based on said
expectation and based on a proximity of adjacent electronic
devices