Note: Descriptions are shown in the official language in which they were submitted.
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User interface for creating a playlist
FIELD OF THE INVENTION
The present invention relates to the creation of
playlists comprising data elements of any kind. More
specifically, the invention relates to creating playlists using
a graphical user interface.
BACKGROUND
Driven by the rapid expansion of the Internet, a
variety of data is becoming available more and more. Examples of
such data are media content, e.g. digital video, digital audio
and digital photos, devotional content, educational content,
sports content, gaming content, e-reading content and fashion
related content. From the enormous amount of data it is becoming
difficult to find favorite data. E.g. with respect to digital
audio, it is to be expected that at some point in time
substantially all music will be available online, e.g. through
music portal websites. With potentially billions of music tracks
from new and existing artists being added to the worldwide
online available music collection on a monthly time scale, it is
becoming very difficult to find favorite music tracks or new
music tracks to ones liking from the vast collection of music.
Analogous growth in available content is seen for other types of
data.
Single items of data, such as e.g. a music track, a
video, a photo, a news item or informative item, a game, an e-
book or an e-cartoon are examples of data elements. To enable
searching for and/or selection of particular data element, data
elements are typically provided with characterizing information
in the form of meta-data. E.g. for music tracks the meta-data
typically includes characteristics like artist, title, producer,
genre, style, composer, year of release and/or any other
characterizing information. Analogously, other types of data can
be enriched with meta-data.
A playlist is a collection of data elements grouped
together under a particular logic. The playlist can be created
such to e.g. reflect a particular mood, accompany a particular
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activity (e.g. work, romance, sports), serve as background
music, or to explore novel songs for music discoveries.
Playlist may be generated either automatically or
manually. Known online music portals, such as Spotify
(www.spotify.com) offer tools to make, share, and listen to
playlists in the form of sequences of music tracks selected
manually. Individual tracks in the playlist are selectable from
an online library of music. Automatically created playlists
typically contain data elements with matching or similar meta-
data. Manually selection of a data element is typically driven
by watching or listening to a particular data element, a
recommendation of a data element or a preset playlist. It is
possible that a user provides a manually selected data element,
e.g. a music track, or particular meta-data, e.g. a music
artist, as a query and that a playlist is generated
automatically in response to this query. Such a service is often
offered by online music radios such as Pandora
(www.pandora.com), and Last.fm (www.last.fm).
A graphical user interface can support a user to select
data elements to be included in a playlist. The graphical user
interface is typically part of a web page, an online
application, a software application for use on e.g. a PC,
notebook, smart phone or tablet PC, or an application plug-in.
Known graphical user interfaces for creating playlists are found
to be either difficult to use, difficult to learn or requiring a
lot of user interaction.
A visual playlist creation method is known from VAN
GULIK R ET AL: "Visual Playlist Generation on the Artists Map",
PROCEEDINGS OF THE 6TH INTERNATIONAL CONFERENCE ON MUSIC
INFORMATION RETRIEVAL, ISMIR 2005, 11 September 2005, pages 520-
523, XP002613950, London. The method involves the drawing of a
path using a mouse and/or clicking individual points of interest
on the map and interconnecting these points to form the path.
The drawn path is used as a basis for creating a playlist. A
disadvantage of this method is that the user will have to spend
lot of time to produce the desired shape of the path requiring
lot of attempts to produce the playlist path. For example, using
a mouse movement method (recording of the mouse movements within
the visualization area while the left button is pressed) a user
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is able to decide on the starting point and the end point of the
playlist path, but the playlist path drawn connecting the
desired start point and the end point may not have the desired
intermediate points. Thus the user will have to redraw the
playlist path again to get the desired path of the intermediate
points whilst remembering the start point and the end point of
the previously attempted playlist path. The other method of
clicking individual points of interest on the map is also not
very user friendly since this method requires lot of time and
effort to affix the points and then further connecting these
points in a series of steps.
Moreover, the method disclosed by VAN GULIK R ET AL
will be difficult to use when the desired playlist path is
complex to draw when there are singular or multiple points of
desired intersection(s) of the start/end/intermediate point(s)
with the start/end/intermediate point(s) defining the playlist
path, or when the desired play listing points (start point, end
point, intermediate point(s)) are falling on the exact/near-
exact/near to the outer boundary of the visual map. Every new
attempt to produce the desired playlist path requires the user
to erase the playlist path and redraw the playlist path. The
user will have to repeat the steps until the user is able to
produce a desired playlist path.
There is a need for a play listing method using a
graphical user interface that addresses the above mentioned
difficulties and offers a logical method which is easy to use
and reproduces the desired playlist path. The playlisting path
can be used for creating a playlist for different types of data
from a vast and growing amount of available data elements.
SUMMARY OF THE INVENTION
According to an aspect of the invention a computer-
implemented method is proposed for creating a playlist. The
playlist comprises a plurality of data elements. Each data
element is associated with meta-data representing one or more
characteristics of the data element. The method comprises
displaying a graphical object wherein points representing
predefined characteristics are located at predefined coordinates
in a Cartesian coordinate system. Each predefined characteristic
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is unique. The predefined characteristics form a set of
characteristics. The one or more characteristics of each data
element is a subset of the set of characteristics. The method
further comprises displaying a graphical representation of an
initial trajectory in the graphical object. The initial
trajectory is formed by a mathematical spline function
connecting a first point at first coordinates in the graphical
object and representing a first characteristic, a second point
at second coordinates in the graphical object and representing a
second characteristic, and a predefined number of intermediate
points between the first point and the second point, wherein
each intermediate point represents to an intermediate
characteristic. The first point and the second point are control
points in the mathematical spline function. The method further
comprises displaying a first steering point in or near the
graphical object. The first steering point is a first control
point in the mathematical spline function. The method further
comprises displaying a second steering point in or near the
graphical object. The second steering point is a second control
point in the mathematical spline function. The method further
comprises receiving a first user input selecting one of the
first point and the second point as a starting point and
defining the other one of the first point and the second point
as an end point. The method further comprises receiving a second
user input moving at least one of the first steering point and
the second steering point and using the mathematical spline
function to calculate an updated trajectory and to recalculate
the coordinates of the intermediate points such that the
intermediate points are located substantially on the updated
trajectory. The method further comprises selecting the data
elements by searching for data elements with meta-data that best
match the first characteristic represented by the first point on
the updated trajectory, the one or more intermediate
characteristics represented by the one or more intermediate
points on the updated trajectory and the second characteristic
represented by the second point on the updated trajectory. The
method further comprises creating the playlist. In the playlist
the data elements are ordered a first data element corresponding
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to the starting point to a last data element corresponding to an
end point.
AccordThg to another aspect of the invention a
graphical user interface is proposed for creating a playlist.
The playlist comprises a plurality of data elements wherein each
data element is associated with meta-data representing one or
more characteristics of the data element. The graphical user
interface comprises a graphical object wherein points
representing predefined characteristics are located at
predefined coordinates in a Cartesian coordinate system. Each
predefined characteristic is unique. The predefined
characteristics form a set of characteristics. The one or more
characteristics of each data element is a subset_ of the set qf
characteristics. The graphical user interface further comprises
a graphical representation of an initial trajectory displayed in
the graphical object. The initial trajectory is formed by a
mathematical spline function connecting a first point at first
coordinates in the graphical object and representing a first
characteristic, a second point at second coordinates in the
graphical object and representing a second characteristic, and a
predefined number of intermediate points between the first point
and the second point, wherein each intermediate point represents
an intermediate characteristic. The first point and the second
point are control points in the mathematical spline function.
The graphical user interface further comprises a first steering
point displayed in or near the graphical object. The first
steering point is a first control point in the mathematical
spline function. The graphical user interface further comprises
a second steering point displayed in or near the graphical
object. The second steering point is a second control point in
the mathematical spline function. A first user input is
receivable for selecLing one of the first point and the second
point as a starting point and defining the other one of the
first point and the second point as an end point. A second user
input is receivable for moving at least one of the first
steering point and the second steering point and resulting in
the mathematical spline function calculating an updated
trajectory and recalculating the coordinates of the intermediate
points such that the intermediate points are located
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substantially on the updated trajectory. The graphical user
interface enables the data elements to be selected by searching
for data elements with meta-data that best match the first
characteristic represented by the first point on the updated
trajectory, the one or more intermediate characteristics
represented by the one or more intermediate points on the
updated trajectory and the second characteristic represented by
the second point on the updated trajectory. The use of the
graphical user interface results in the playlist wherein the
20 data elements are ordered from a first data element
corresponding to the starting point to a last data element
corresponding to an end point.
Thus, a user friendly method and graphical user
interface implementing the method are provided for creating a
playlist for different types of data from a vast and growing
amount of available data elements. For a type of data, the
graphical oloecL has different characteristics at different
coordinates in the graphical object. The characteristics
represented by the points on the trajectory are used to find
data elements having characteristics that best match the
characteristics at the points in the trajectory. Typically the
graphical object contains textual or graphical labels indicated
where in the graphical object specific characteristics are
located. The trajectory thus gives an indication of what data
elements can be expected in the playlist. Changing the
trajectory resulting in different data elements to be included
in the playlist is very user friendly. In essence only the
steering points, possibly only one of the steering points, have
to be moved using the graphical user interface.
The first user input for selecting the starting point
can be a direct selection of the first or second point, e.g. by
clicking on one of these points, or an indirect selection by
selecting one of the steering points of the first or second
point.
The moving of the steering poinLs and the updated
trajectory replacing the initial trajectory can be displayed in
real time. The trajectory may be updated in the graphical user
interface while moving the steering points.
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It is possible that there are more than two steering
points that are used by the spline function to shape the
trajectory.
It is possible that for the purpose of play listing a
spline function is produced on the visual map by clicking on the
visual map in a series of steps. In this case the first click as
a first user input will always corresponds to the starting
point, the second click as a second user input will correspond
to the end point of the playlist path; a third click as a third
user input will correspond to the end point of the playlist and
in this case the second click will become the first steering
point. If there is a fourth click as a fourth user input then it
will correspond to the end point of the playlist path and third
point will become the second steering point, the second clicked
point will become the first steering point. It may be programmed
to restrict the number of clicks in this method. For example,
three allowed clicks on a visual map shall result in a playlist
path that has a start point (first click), an end point (third
click) and a steering point (second click).
User inputs can be received via keyboard, mouse, touch
screen and/or any other user input device such as vocal or
gesture recognition.
The embodiment of claim 2 advantageously enables a
predefined or stored trajectory to be used as initial
trajectory. Predefined trajectories may be selectable by the end
user. The predefined points may be chosen randomly to form the
initial trajectory.
The embodiment of claim 3 advantageously enables the
end-user to draw an initial trajectory or a part of the initial
trajectory.
The embodiments of claims 4 and 13 advantageously
enable the first and second points to be moved to change the
starting point and end point of the trajectory and thus change
the start and the end of the playlist.
The embodiments of claims 5 and 14 advantageously
enable the graphical user interface to become even more user
friendly. In these embodiments the end user can manipulate the
first line and the second line by moving a single steering
point. Moreover the end user can fine tune the trajectory by
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moving an intermediate steering point that is connected to one
or more of the intermediate points. The steering mechanism for
the intermediate steering point is similar to that of the other
steering points, i.e. the line between the intermediate steering
point and the corresponding intermediate point and the
trajectory at the intermediate point is minimized.
The embodiments of claims 6 and 15 advantageously
enable the location of the intermediate points to be restricted
to within the area of the graphical object. This avoids the
situation wherein intermediate points are located at coordinates
unrelated to any characteristics and thus cannot be used to
search in the meta-data and select data elements.
The embodiment of claim 7 advantageously enables a
better visualization of the relationship between the first
steering point and the first point and between the second
steering point and the second points. This may avoid the
situation wherein it is not entirely clear which steering point
is related to which point. It is possible that the steering
points and/or the first line and the second line are not
displayed all the time, but are displayed e.g. only during user
interaction with the graphical user interface.
The embodiment of claim 8 advantageously enables a
better visualization of the steering points. It is possible that
the steering points and/or the further line are/is not displayed
all the time, but displayed e.g. only during user interaction
with the graphical user interface.
The embodiment of claim 9 advantageously enables the
graphical object to be either 2D or 3D. In case of 2D the points
on the trajectory typically have an x and y coordinate. In case
of 3D the points on the trajectory typically have an x, y and z
coordinate. Moreover, the embodiment of claim 9 advantageously
enables the trajectory to have a one-dimensional, two-
dimensional or three-dimensional meaning in the 2D of 3D
graphical object, resulting in a selection of songs based on one
variable, two variables or three variables, respectively.
The embodiment of claim 10 advantageously enables the
selection of the data elements in the playlist to be limited by
the received restrictions. E.g. interactive objects or selection
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boxes can be part of the graphical user interface for receiving
the fourth user input.
The embodiment of claim 11 advantageously enables
location related data to be used to restrict the selection of
data elements. The location related information is typically
obtained from an external server or an application or module
running on the device where the graphical user interface is
used. A module for obtaining the current location may acquire
the current location using a GPS signal, cell information from a
mobile network or any other known technical means. It is
possible that the end user provides a location by user input.
Location related information may be geographical information or
information related to a geographical location such as local
weather conditions in the real time or foreseeable weather
conditions that can be expected during the duration of the
playlist. This invention may be utilized to restrict or adapt
the selection of data elements with events in the real time. For
example, a goal or a victory event in the soccer game.
According to an aspect of the invention a computer
program product is proposed, which, when being executed by a
processor, is adapted to carry out one or more of the above
described method steps.
Hereinafter, embodiments of the invention will be
described in further detail. It should be appreciated, however,
that these embodiments may not be construed as limiting the
scope of protection for the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS
Aspects of the invention will be explained in greater
detail by reference to exemplary embodiments shown in the
drawings, in which:
Fig.1 shows a graphical object of an exemplary
embodiment of the invention;
Fig.2 shows elements of a graphical user interface of
an exemplary embodiment of the invention;
Fig.3 shows elements of a graphical user interface of
another exemplary embodiment of the invention;
Fig.4 shows elements of a graphical user interface of
another exemplary embodiment of the invention;
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Fig.5 shows elements of a graphical user interface of
another exemplary embodiment of the invention;
Fig.6 shows a graphical representation of a
mathematical algorithm for selecting data elements of an
5 exemplary embodiment of the invention;
Fig.7 shows steps of a method for creating a playlist
according to an exemplary embodiment of the invention;
Fig.8 shows a Nurbs curve with weighted control points;
and
10 Figs.9-14 each shows a graphical object of other
exemplary embodiments of the invention.
DETAILED DESCRIPTION
A graphical user interface is used to create a playlist
of data elements of any type. A graphical object is displayed
representing a content based logical map arranged in two or
three dimensions and defining characteristics at predefined
coordinates. The characteristics are relevant to the selection
of data elements for the playlist, as will be explained.
Examples of content based logical maps, which are
typically related to a particular type of data elements, are
logical maps with e.g. mood, arousal-valence or tempo-decade
characteristics to explore media items such as music, photos or
videos. Other examples of content based logical maps are logical
maps for devotional content, educational content, sports
content, games, cartoons, books and fashion items. It is to be
understood that the invention is not limited to these logical
maps and their related data elements.
The outer shape of the graphical object can be any 2D
shape such as square, circle or rectangle, but preferably a
circle. The outer shape can alternatively be any 3D shape.
In the example of Fig.1 a circular logical map is shown
that may be used for the selection of music tracks based on mood
characteristics. In Fig.1 a model for classification of emotions
is used, known as an emotional wheel 10. The emotion wheel is
founded on psychology results and views of scientists like
Plutchik in 1980, Russell in 1980, Thayer in 1989 and Russell
and Feldman Barrett in 1999. The emotional wheel captures a wide
range of significant variations in emotions in a two dimensional
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space. Emotions can be located in the two dimensional Cartesian
system along the various intensities of emotions and degree of
activation. The x-axis defines the level of valence. The y-axis
defines the level of arousal. Each emotional state can be
understood as a linear combination of these two dimensions. The
four quadrants of the emotional wheel identify the primary
emotions joy, anger, sadness and neutral. Secondary emotions,
providing a more detailed classification, are indicated in
italics and include the emotions pleased, happy, interested,
exited, alarmed, annoyed, nervous, afraid, angry, furious,
terrified, sad, depressed, bored, sleepy, calm, relaxed, content
and serene. It is possible to define other and/or more primary
and/or secondary emotions.
Figs.9-14 show other examples of logical maps that can
be used for selecting data elements of various kinds.
In Fig.9 an example of an existence map is shown based
on the four states of existence defined by Alek Vilo in the year
2005. Intensity increases from slow to fast when going from the
center of the circle to the outer circle. The four quadrants
define the states "awareness", "awakening", "darkness" and
"celebration" (counter clockwise starting from top right).
In Fig.10 an example of a natural science map is shown.
The four quadrants in this example define the fields of
oceanography, physics, biology and chemistry (counter clockwise
starting from top right). Each of the quadrants can be
configured to be changeable into e.g. one of astronomy, material
science and atmospheric science. The level of difficulty of the
selectable data elements, such as teaching material, varies from
beginner level to advanced level when going from the center of
the circle to the outer circle.
In Fig.11 an example of a social science map is shown.
The four quadrants in this example define the fields of history,
economics, political sciences and law (counter clockwise
starting from top right). Each of the quadrants can be
configured to be changeable into e.g. one of anthropology,
education, human geography, linguistics, psychology and
sociology. The level of difficulty of the selectable data
elements, such as teaching material, varies from beginner level
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to advanced level when going from the center of the circle to
the outer circle.
In FIG. 12 an example of a spiritual map is shown, .
wherein different sections correspond to different spiritual
believes. Data elements selectable using this map are e.g.
encyclopedic information about the spiritual believes or music
items from the spiritual believes.
In FIG. 13 an example of an artist map is shown. The
eight sections in this example define eight pop artists: Radio
Head, Prince, Britney Spears, Madonna, Lady Gaga, Mariah Carey,
Bob Marley and Michael Jackson. The selected artists as well as
the number of artists can be different. The tempo of selectable
music by the artists varies from slow tempo to fast tempo when
going from the center of the circle to the outer circle. In FIG.
13 a trajectory is shown interconnecting points corresponding to
potentially selected songs.
Tempo variation of the artist map of FIG. 13 can be
replaced with e.g. the mood variation of the tracks that can
vary from sad mood to cheerful mood to excited mood when going
from the center of the circle to the outer circle.
In FIG. 14 an example of a decade-versus-tempo map is
shown. The y-axis defines the tempo from very slow at the
bottom, via slow, medium and fast to very fast at the top. The
x-axis defines decades in time from <1960 at the left, via 1960-
1970, 1970-1980, 1980-1990 and 1990-2000 to 2000-2010 at the
right. It will be understood that the axes can be scaled
differently. In FIG. 14 a trajectory is shown along which songs
can be selected that match the decade-tempo combination at '
points along the trajectory.
FIG. 1 and FIGS. 9-14 show tha-= the logical map can
have a one-dimensional meaning or a two-dimensional meaning. In
the examples of FIG. 1, FIGS. 9-11 and FIGS. 13-14 the two axes
represent different variables, resulting in a two-dimensional
(i.e. based on two variables) selection of songs along a
trajectory. In the example of FIG. 12 the logical map merely
defines areas from which data elements can be chosen. In this
example the two axes (not shown) have no meaning and the
selection of data elements is one-dimensional (i.e. based on one
variable). Although not shown, it is possible to use logical
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maps with a three-dimensional meaning. Such logical map
typically shows a three-dimensional object wherein three axes
represent three different variables, resulting in a three-
dimensional (i.e. based on three variables) selecsions of media
items along a trajectory.
The invention enables a user to draw a srajectory on
the graphical object, such as the representation of the emotion
wheel, as a basis for the generation of a playliss. In the
example of the emotional wheel, a user will be given the option
to specify an initial mood and a destination mood. The
trajectory between the initial mood and the destination mood may
be steered through the emotions falling in-between. Thus the
obtained trajectory is then populated by music tracks to form a
playlist.
FIG. 2 shows the emotional wheel as shown in FIG. 1.
For roadability purposes the labels indicating the axis, primary
emotions and secondary emotions as shown in FIG. 1 are not shown
in FIG. 2. A user may select the starting point 1 as starting
point for a playlist. The starting point typically corresponds
to a current emotion of the user. Furthermore the user may
select the end point 2 as the end point for the playlist. A
mathematical spline function connects the starting point 1 and
the end point 2 to form the trajectory 11, 12 or 13 depending on
the location of intermediate points.
A graphical user interface showing the emotional wheel
may be presented, through which the user first affixes a
starting point by using a mouse pointer to move the starting
point and affixing it on the spot on the emotional wheel that
resembles a perceived prevailing mood or emotion. The end-point
resembling a desired future mood or emotion is then also affixed
in a similar fashion by moving the second point. Next the
trajectory is calculated between the two points and one or more
intermediate points using the mathematical spline function. The
trajectory may be altered to form a desired path Through desired
moods or emotions by moving the starting point 1 or end point 2
or by moving steering points for the starting point I and the
end point 2, as will be explained.
It is to be understood that the user interface can be
programmed to accept other mouse gestures to achieve a similar
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effect. Alternatively the user interface may use a touch screen
interface, or any other man-machine interaction means.
As shown in Fig.3, it is possible that a trajectory 14
between a starting point 1 and an end point 2 self intersects at
one or more points 3, thus resulting in one or more recurring
emotions along the trajectory.
It is possible to have the user interface display one
or more predefined initial trajectories obtainable from a
backend database. Examples of predefined trajectories related to
moods are "from sad to happy" and "from excited to calm". The
selected predefined mood trajectory will be displayed on the
emotional wheel as initial trajectory and may be altered.
Fig.4 shows a graphical user interface of an exemplary
embodiment of the invention, wherein a graphical object 100 is
displayed. Typically the graphical object 100 includes text
labels or images to visualize where in the graphical object 100
what characteristics are located, such as e.g. shown in Fig.l.
For readability purposes this is not shown in Fig.4. It is to be
understood that the circular shape of the graphical object 100
is merely an example and not limiting the invention, as
described above.
The graphical user interface shows a trajectory 200
connecting a first point 210 with a second point 220 via
intermediate points 230. The first time the trajectory is shown
it is called the initial trajectory. The initial trajectory 200
is formed by a mathematical spline function connecting the first
point 210 at first coordinates in the graphical object 100 and
corresponding to a first characteristic, the second point 220 at
second coordinates in the graphical object 100 and corresponding
to a second characteristic, and the intermediate points 230
between the first point 210 and the second point 220, wherein
each intermediate point 230 corresponds to an intermediate
characteristic.
The start point could be any of the two points 210 and
220. The point that is selected by the user, e.g. by clicking on
it, becomes the start point. The end point if not selected by
the user then automatically becomes the end-point.
There are two steering points 211 and 221 tagged along
the trajectory 200. These steering points 211,221 are used to
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maneuver the intermediate points 230. The first steering point
211 is displayed in the graphical object 100 and defines a first
line between the first steering point 211 and the first point
210. The second steering point 221 is displayed near the
5 graphical object 100 and defines a second line between the
second steering point 221 and the second point 220.
It is possible to use a single steering point (not
shown) preferably in the middle of the trajectory for both the
first point 210 and the second point 220.
10 The end user can optionally fine tune the trajectory by
moving an intermediate steering point that is connected to one
or more of the intermediate points. The steering mechanism for
the intermediate steering point is similar to that of the other
steering points, i.e. the line between the intermediate steering
15 point and the corresponding intermediate point and the
trajectory at the intermediate point is minimized.
The line connecting the steering point to the
respective start or end point can be displayed. This line also
moves with the movement of steering point 211,221. A third line
connecting the steering points 211,221 may be displayed as well.
In principle the first point 210 and the second point
220 of the trajectory 200 will never go outside the logical map.
However the steering point(s) 211,221 may go outside the logical
map. This allows for density variation along the trajectory 200,
indicating more exploration on the denser points.
The two steering points 211,221 can be used to tweak
the desired trajectory 200 which changes the trajectory per the
user's wish-list offering him/her an instant gratification on
the way of selection on the map-based content exploration.
Hereto at least one of the first steering point 211 and the
second steering point 221 is moved and the mathematical spline
function is used to recalculate the coordinates of the
intermediate points 230. The updated trajectory 200 is
displayed, possibly in real time.
With reference to Fig.5, the graphical object 100
typically comprises an edge 101. The coordinates of the first
point 210 and the coordinates of the second point 220 are
typically restricted to coordinates within or on the edge 101.
For each intermediate point beyond the edge 101, e.g. caused by
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the steering points 211,221 being located as shown in FIG. 5,,
coordinates of corrected intermediate points 231 are calculated
on the edge 101 using a perpendicular projection of the
intermediate point on the edge 101. In the trajectory 200 the
intermediate points beyond the edge 101 are replaced with the
corrected intermediate points 231.
With reference to FIG. 4 and FIG. 5, when the end user
finishes maneuvering the trajectory, the two steering points
211,221 and the lines connecting them with the first and second
points 210,220 may be hidden.
Once the trajectory is finished a backend engine,
which is implemented as a software module or a set of software
modules, uses a mathematical algorithm to populate the playlist
with data elements along the trajectory. The backend engine may
reside at a server, such as an online music portal server for
music tracks, or be partially or entirely running on the client
device where the user interface is active.
Data elements in the playlist may be pre-mapped
streaming from a saved database and/or live streaming.
Alternatively, hybrid content with hybrid quadrants may be used.
An example of a mathematical algorithm for selecting
the data elements by searching in the meta-data for
characteristics that best match the first characteristic at the
first point 210 in the updated trajectory 200, the one or more
intermediate characteristics at the one or more intermediate
points 230 in the updated trajectory 200 and the second
characteristic at the second point 220 in the updated trajectory
200 is shown in FIG. 6. The selected data elements are
subsequently used to create the playlist wherein the data
elements are ordered from starting point to end point.
Virtual circular sections are created along the
trajectory as visualized in FIG. 6. The virtual circular sections
are typically not shown in the graphical user interface. The
circular sections are used to stretch the trajectory 15, which may
be the trajectory 200 of FIG. 4 or FIG. 5, and define an area
wherein the characteristics for the "to be selected" data elements
are to be found. A first series of circular sections 21 are
calculated having their centre points lying along the trajectory
15. A second series of circular section 22 are calculated having
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their center points at the points of intersection of the first
series circular sections. Data elements with characteristics
residing within the area of the first and second series of
circular sections cover the data tracks being selectable for
insertion in the playlist.
Instead of calculating circular sections as shown in
Fig.6, the area around the trajectory 15 may be calculated by a
probabilistic distribution function virtually forming a regular
or irregular shaped area following the trajectory 15.
Another example of a mathematical algorithm uses a
distance function to virtually create one or more additional
trajectories following the shape of the trajectory 15 may be
calculated at a predefined distances to a trajectory 15. The
mathematical algorithm is then applied to the trajectory and the
one or more additional trajectories. The thus obtained areas
along the different trajectories together form a larger area.
Data elements with characteristics residing within the larger
area cover the data elements being selectable for insertion in
the playlist.
On a mood map as depicted in Fig.1, data elements can
e.g. be selected as follows. The X-axis and Y-axis may be scaled
between (-1,1). The user affixed and by the spline function
generated trajectory on the 2D mood map is used to calculate
points on or near the trajectory. The calculation of these
points may be performed in the front-end application at the end-
user or alternatively at a backend system where the backend
engine resides. In case the front-end application calculates the
points, the front-end send the coordinates of the points (i.e.
of the start point, end point and the intermediate points) to
the backend system. The searching algorithm at the backend
system looks up the songs that have pre-mapped mood
characteristics corresponding to the x and y coordinates of the
trajectory points as received from the front-end application or
calculated in the backend system and returns those songs for the
purpose of playlisting. It will be understood that the number of
intermediate points and the grid resolution defining the number
of possible points on or near the trajectory may vary.
The processing logic for selecting data element can
vary based on the type of the logical map. For maps other than
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the mood map as depicted in Fig.1, the processing logic
typically looks up characteristics other than mood
characteristics. E.g. in the exemplary artist map as depicted in
Fig.13 the playlisting is based on tempo of the tracks and the
artists.
The playlist may be refined in various ways.
Refinements may have a real-time impact on the playlist.
Through the user interface a user may be given an
option to restrict the selectable data elements, such as music
tracks using available meta-data. The meta-data is e.g. used to
select music tracks from a single genre or a combination of two
or more genres, an artist, overlapping of two or more artists, a
year of release or a time frame for release years, or any other
meta-data or combinations of meta-data.
The user interface may be used to display the total
time-length of the data elements, such as music tracks, and/or
the total number of data elements, such as music tracks,
sequenced to be played in the playlist. An option may be
displayed to make the playlist shorter using input parameters
such as the total number of songs and/or the total time-length
of the playlist.
The user interface may display an option to partially
play or display the data elements or a selection of the data
elements in the playlist. An option is e.g. displayed to show
e.g. 30%, 50% or 100% of the total time-length of each date
element. This way the total playlist can be played with short-
play. Additionally there may be an automatic suggestive option
to affect only selective data elements in the playlist. Through
this option e.g. the most liked music tracks will be played for
a longer duration while music tracks with a predefined low
rating will be short-played.
Location related data may be used to restrict the
selection of data elements. The location related information is
typically obtained from an external server or from an
application or module running on the device where the graphical
user interface is used. A module for obtaining a current
location may acquire the current location using a GPS signal,
cell information from a mobile network or any other known
technical means. It is possible that the end user provides a
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location by user input. Location related information may be
geographical information or information related to a
geographical location such as local weather conditions. The
location related data may e.g. be used to adapt the playlist
length to the expected duration of a journey or adapt the data
elements in the playlist to current and/or predictable weather
conditions that are valid or may alter subsequently in steps of
every song playing during the duration of the playlist.
In Fig.7 steps of a method are shown for creating a
playlist according to an exemplary embodiment of the invention.
In step 1001 the graphical object 100 is displayed wherein
predefined characteristics are located at predefined
coordinates. In step 1002 the graphical representation of the
initial trajectory 200 is displayed in the graphical object 100.
In step 1003 the first steering point 211 is displayed in or
near the graphical object 100. In step 1004 the second steering
point 221 is displayed in or near the graphical object 100. In
step 1005 first user input is received for selecting one of the
first point 210 and the second point 220 as the starting point
of the trajectory and defining the other one of the first point
210 and the second point 220 as the end point. In step 1006
second user input is received moving at least one of the first
steering point 211 and the second steering point 221 and
resulting in using the mathematical spline function to
recalculate the coordinates of the intermediate points 230 and
resulting in an updated trajectory 200. In step 1007 the data
elements are selected. In step 1008 the playlist is created from
the selected data elements, wherein the data elements are
ordered from starting point to end point.
It is to be understood that the order of the method
steps is not fixed to the order shown in Fig.7.
In the examples above, two steering points are shown
that are associated with the starting point 1 and the end point
2 on the trajectory, respectively. The invention is not limited
to the use of these two steering points. It is e.g. possible to
have more than two steering points that can be associated with
intermediate points on the trajectory or have no associated
point on the trajectory and merely shape the trajectory using
the spline function.
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The mathematical spline function connects the first
point 210 to the second point 220 to form the trajectory 200,
which shape is determined by the position of the steering points
211, 221. The trajectory 200 is the spline that is defined by
5 the spline function and the first point 210, the second point
220 and the steering points 211,221, wherein the first point 210
and the second point 220 are points on the spline and the
steering points 211,221 are points that are to be approached as
close as possible by the spline.
10 In their most general form, splines can be considered a
mathematical model that associate a continuous representation of
a curve or surface with a discrete set of points in a given
space. Spline fitting is a form of piecewise approximation using
various forms of polynomials of degree n, or more general
15 functions, on an interval in which they are fitted to the
function at specified points, known as control points, nodes,
knots or knot points. The first point 210, second point 220 and
steering points 211,221 are such knot points. The polynomial
used can change, but the derivatives of the polynomials are
20 required to match up to degree n-1 at each side of the knots, or
to meet related interpolatory conditions. Boundary conditions
are also imposed on the end points of the intervals, i.e. the
first point 210 and the second point 220, namely that these
points are to be on the spline.
Examples of mathematical spline functions are B-
splines, Bezier splines, non-uniform rational basis splines
(also known as Nurbs) and Ferguson splines. To illustrate how
the mathematical spline function is used in the generation of
the trajectory 200 the Nurbs function will be explained in more
detail. The invention is not limited to the use of Nurbs
functions, any other mathematical spline function may be used.
A Nurbs curve is defined by its order, a set of
(possibly weighted) control points and a knot vector. Nurbs
curves and surfaces are generalizations of both B-splines and
Bezier curves and surfaces, the primary difference being the
possibility of weighting of the control points which makes Nurbs
curves rational (non-rational B-splines are a special case of
rational B-splines).
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By evaluating a Bezier or a Nurbs curve at various
values of the parameter, the curve can be represented in
Cartesian two- or three-dimensional space. Likewise, by
evaluating a Nurbs surface at various values of the two
parameters, the surface can be represented in Cartesian space.
Nurbs curves and surfaces are useful for a number of
reasons: they are invariant under affine as well as perspective
transformations (operations like rotations and translations can
be applied to Nurbs curves and surfaces by applying them to
their control points); they offer one common mathematical form
for both standard analytical shapes (e.g. conics) and free-form
shapes; they provide the flexibility to design a large variety
of shapes; they reduce the memory consumption when storing
shapes (compared to simpler methods); they can be evaluated
reasonably quickly by numerically stable and accurate
algorithms.
The control points determine the shape of the curve.
Typically, each point of the curve is computed by taking a
weighted sum of a number of control points. The weight of each
point varies according to the governing parameter. For a curve
of degree d, the weight of any control point is only nonzero in
d+1 intervals of the parameter space. Within those intervals,
the weight changes according to a polynomial function (basis
functions) of degree d. At the boundaries of the intervals, the
basis functions go smoothly to zero, the smoothness being
determined by the degree of the polynomial.
Nurbs can be calculated for non-uniform knot vectors
that have uneven spacing between knot points and/or multiple
internal knot values with fixed end points. This makes the Nurbs
function highly suitable for defining the trajectory 200.
Nurbs curves are not only controlled by control points,
such as the steering points 211,221, but they can also be
controlled by weights (at each knot point if required).
A Nurbs curve is a vector-valued piecewise rational
polynomial function of the form:
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71
E wiPiNi,k(u)
C (U) = ________________________
wifYi,k(u)
i.0
where wi are the weights, Pi are the knot points and 1\71,k are the
normalized basis-spline basis function of degree k. The basis
splines are defined recursively as:
U ¨ ti ti k4_1 ¨
Ni,k(u) 4 __ Nt k-1(u) ________________ IV i+1 A-1(u)
t,i+k t ¨ ti 1
and
11 if t- < U< t-
_
N2,-,a(u) {
0 else
where ti are the knots:
(.7 Ito,ti,t2, .... tm
The knot vector uniquely determines the basis-splines
as is clear from the above equations. The relation between the
number of knots (m+1), the degree (k) of Ni,k and the number of
control points (n+1) is given by m=n+k+1. The sequence of knots
in the knot vector U, is assumed to be non-decreasing, such
that. ti<=ti+l, so each successive pair of knots represents an
interval [t11t1+1) for the parameter values to calculate a segment
of a shape. The relative parametric intervals (or knot
intervals) for Nurbs need not be the same for every shape
segment, so that the knot spacing can be non-uniform, leading to
a non-periodic knot vector of the form:
U = fa, ...a, tk+i ...tm_k_l, b,
where a and b can be repeated knot values with a multiplicity
equal to k+1. The multiplicity of a knot affects the parametric
continuity at this knot. Nonperiodic basis-splines, like Nurbs,
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are infinitely continuously differentiable in the interior of a
knot span and k-m-1 times continuously differentiable at a knot,
where M is the multiplicity of the knot. This is in contrast to
a periodic knot vector U=i ,1,...,n1, which is everywhere k-1
times continuously differentiable. Considering the knot vector
for Nurbs, the end knot points (tk,tn+.0 with multiplicity k+1
coincide with the end control points Po,Pn.
Given that knot spacing can be non-uniform, the basis-
splines are therefore no longer the same for each interval
[tirti+/) and the degree of the basis-spline can also vary. Over
the whole range of parameter values represented by the knot
vector, the different basis-splines build up continuous
(overlapping) blending functions Ari,km, as previously defined,
over this range. These blending functions have the following
properties:
= Ni,k(u) >=0, for all i, k, u;
= N1,k(u)=0, if u not in [ti,ti k+/), meaning local support of k+1
knot spans, where Nidco; is nonzero;
= if u in [ti,ti,/), the non-vanishing blending functions are:
ATi-k,k (u) r = = =
7-"` -
3,k(u) = E Ari,k(u) =
= in case of multiple knots, 0/0 is deemed to be zero.
Taken together, the result into the convex hull, the
control points build up for a shape show that k+1 successive
control points a shape segment is defined and a control point is
involved in k+1 neighboring shape segments. Therefore, a change
to a control point or weight influences only k+1 shape segments,
defined over the given interval.
The previous definition of a Nurbs-curve can be
rewritten using rational basis functions:
Wi--1\ri,k(u)
Ri,k(u) = n
E
j=0
into:
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C(14.)¨ Fiti
¨ s ,k (u)
i=0
When using weights to construct a curve, the weight wi
of a knot point P, affects only the range [ti,ti+k+/), as can be
seen in the Nurbs example of Fig.8. In the example of Fig.8 the
following points are defined:
B = C(u;wi=0)
N = C(u;wi=1)
Bi = C(u; wi0{ 0, 1})
N and Bi can also be expressed as:
N = (1-a)B+aPi
B, = (1-b)B+bP1
where
a = Ri,k(u;w1=1)
b = Ri,k(u)
The following identity is obtained from the expression
of a and b:
(1-a)/a : (1-b)/b = PiN/BN : PiBi/BB, =
which is called the cross- or double ratio of the four points Pi,
B, N, B.
From these expressions, the effect of shape
modification can be easily seen as follows: Bi sweeps out on a
straight line segment, if w1=0, then Pi has no effect on shape;
if w, increases, so b and the curve is pulled toward Pi and
pushed away from Pi, for j0i; if wi decreases, so b and the curve
are pushed away from Pi and pulled toward Pi, for jOi; if
then b,1 and if u in [tõt,,,ki) . The geometric impact and
meaning of using weights can be clearly seen in Fig.8.
The mathematical spline function thus defining the
trajectory 200 is used to calculate the coordinates of the
intermediate points 230 and for drawing the trajectory 200 in
the graphical user interface.
One embodiment of the invention may be implemented as a
program product for use with a computer system. The program(s)
of the program product define functions of the embodiments
(including the methods described herein) and can be contained on
a variety of computer-readable storage media. Illustrative
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computer-readable storage media include, but are not limited to:
(i) non-writable storage media (e.g., read-only memory devices
within a computer such as CD-ROM disks readable by a CD-ROM
drive, ROM chips or any type of solid-state non-volatile
5 semiconductor memory) on which information is permanently
stored; and (ii) writable storage media (e.g., floppy disks
within a diskette drive or hard-disk drive or any type of solid-
state random-access semiconductor memory or flash memory) on
which alterable information is stored. Moreover, the invention
10 is not limited to the embodiments described above, which may be
varied within the scope of the accompanying claims.
