The document discusses potential applications of personal mobile devices (PMDs) in 2025. It predicts that by 2025, PMDs will have vastly increased computational power and will be able to perform sophisticated natural language processing and emotion detection using data from various sensors. One application discussed is using a PMD to customize a shared car like a Zoomcar before a user arrives by adjusting settings based in the user's stored preferences. Another application proposes using sensors in PMDs to detect a user's blood alcohol level and prevent intoxicated driving by calling a ride or using autonomous vehicles. The document discusses the progress of technologies like touch and infrared sensors that can accurately detect blood alcohol levels.

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Team 5
D’Shai Hendricks, Chris Johnson, Daniel Levick, Chinna O’Suji, Jamal Rashad-Patterson
MS&E 130
Bambos
Personal Digital Device: Giving Life More Personality
1 Setting the Stage: The Personal Mobile Device (PMD) of 2025
Based on current technological trends, we can make several predictions about what the
personal mobile device will look like in 2025. The safest prediction we can make is that this
device will be orders of magnitude more powerful than current devices. In the past ten years,
smart phones have increased clock speed by five times and memory by two orders of magnitude.
While limitations of battery technology may impede further exponential growth in computational
power (Schlacter 2013), the trend towards Mobile Cloud Computing promises to offload much of
the heavier computation to the cloud (Shiraz 2013).
One application for faster computation is Natural Language Processing (NLP), which is
likely to be a substantial part of the PMD user interface in 2025. In the past, NLP has been
limited to rule-based algorithms because of a lack of training data for statistical techniques
(Bellegarda 2013), but with NLP-based services becoming more commonly accepted by the
public (eg Apple’s Siri), more and more data is becoming available. The combination of
bountiful training data and increased computation power, whether on-board or in the cloud, will
enable more sophisticated algorithms to run in real-time, resulting in more accurate and intuitive
NLP interfaces. Moreover, we predict that by 2025 the PMD will gather data from an individual
user and use that data to tune and adapt the NLP algorithms to that specific user, resulting in an
interface that “gets to know you,” much like a human personal assistant would.

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This idea of “getting to know you” will likely extend to the user’s emotional patterns.
Emotion (ie “affect”) detection from facial expression and voice is already fairly reliable (Calvo
et al 2010). These techniques will be augmented by processing and mining a wealth of data from
wearable sensors, which will communicate with the PMD via short-range wireless. These
sensors will provide the PMD with ever-increasing contextual awareness from biometrics like
galvanic skin conductance, heart rate, and eye movement. Combined with contextual awareness
provided by sensors already inside smart-phones, these additional biometrics will provide
enough data for a PMD to learn to understand a user’s emotional patterns. While currently affect
detection is being targeted at increasing the effectiveness of advertising (see Affectiva’s Affdex:
www.affdex.com), it could also be used in a variety of services that are designed to alter or
maintain emotional states (music services, for instance).
Finally, it is impossible to say what form or shape the PMD will take in 10 years.
Flexible OLED displays, which consume 40% less power than LED displays, are expected to be
commonplace by 2017 (Yoon et al 2014). This means that PMDs could be incorporated into
clothing, accessories, or any number of other forms, as well as rolled up or folded. Additionally,
using short-range wireless communication the PMD could use displays incorporated into the
users environment, such as displays on the inside of a car windshield. Perhaps this will even
remove the necessity for a dedicated screen on the PMD itself.
1.1 Example Application 1: Personalized Human-Computer
Interaction (PHCI)

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Commonly held views of future devices include sophisticated personal assistant functions
(e.g. as depicted in the movie “Her”). A patent from Voicebox Technologies describes an NLP
system that incorporates “context, prior information, domain knowledge, and user specific
profile data to achieve a natural environment for one or more users making queries or commands
in multiple domains” (Kennewick 2011). The patent suggests that personalized information is
key to providing natural interactions between people and machines. Personalized algorithms
have also been shown to improve Affect Detection (Chu 2013). We predict that personalized
algorithms will be commonplace in PMD software in 2025.
The PMD is uniquely suited to gather data for personalization. For a large proportion of
the population, the PMD is already the device with which they interact most. Therefore the
PMD is capable of collecting a rich interaction history with which to train algorithms.
Additionally, its nearly constant proximity to the user’s body allows the PMD to collect, via a
comprehensive array of wearable or integrated sensors, vast quantities of biometric and
contextual information.
Finally, the advent of the Internet of Things implies that a large proportion of electronic
devices will be capable of wireless communication by 2025 (Atzori 2010). Users will expect to
interact with these devices via speech, much like they would interact with their personal assistant
PMD.
Based on these predictions, we suggest a service that would update the NLP and Affect
Detection algorithms of every device the user comes into contact to. The first time a user came
into contact with an object, a friend’s entertainment system for example, the PMD would have to
transmit a relatively large amount of data (high power consumption). However, each subsequent
update would require relatively small amounts of data transmission (low power consumption).

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This would require each device to have a large storage capacity capable of storing profiles for
many different users, which should be possible due to the rapid increase in available memory
storage. It would also be favorable to communicate via short-range wireless (versus storing data
to the cloud) to minimize latency and avoid network congestion. Short-range wireless is a
natural choice because the user will only interact with devices within a visual/vocal range.
Ultimately this would allow a user to seamlessly interact with the entirety of the internet of
things without having to go through a “getting to know you” phase with every new device.
1.2 Example Application 2: the Zoomcar
The proliferation of car-sharing programs (eg Zipcars) is likely to continue (Fournier
2014). People naturally want to feel like a space is theirs, and don’t want to have to take the time
to adjust the seat, mirrors, radio presets, climate control, etc. every time they get in a car.
We propose a system, as depicted in Figure 1, that would allow a shared car to pre-
customize itself as a user approaches. A typical use case might look like this:
1. Bob, a new Zoomcar customer, downloads the Zoomcar app onto his PMD.
2. The first time Bob uses a Zoomcar, he naturally has to adjust the seat position, mirrors,
radio presets, and climate control preferences, much like he would if he had just bought a
new car.
3. Bob’s presets are generalized (through some Zoomcar proprietary algorithm) to a
dataset that can work across car models (ie maps seat position to distance to pedals,
height above wheel, or whatever universal characteristics that could be ported to different
vehicles) and transmitted via short-range wireless to Bob’s PMD via the Zoomcar app.

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4. The next time Bob requests a Zoomcar, as soon as he comes into visual range, his
PMD transmits Bob’s unique biometric signature (see http://bionym.com/ for one
possible example) to the Zoomcar server. The server then checks his signature to make
sure Bob has access to the car before sending his PMD the car’s bluetooth access code
(this prevents malicious access to the car’s onboard computer, which could be extremely
dangerous).
5. Bob’s PMD then transmits his Zoomcar preset profile to the car over Bluetooth. The
car customizes itself so that by the time Bob gets to the door, he doesn’t have to change a
thing. He can even see the car customizing itself as he approaches, and when he opens
the door, Bob’s personal theme song is playing.
While near-term applications would likely be limited to seats, mirrors, and radio presets, longer-
term applications might include functions similar to those described in the previous section. In
addition to preset information, the PMD would update the Zoomcar’s NLP and Affect Detection
algorithms to improve Bob’s interaction with the car’s interface.

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Figure 1: Zoomcar Application System Diagram
2 The PMD: Implementations
A. The Responsible Driver
The creation of the PMD can be used for much more than serving as your personal
assistant and adjusting seats in your car. It can be used to save lives. The U.S. Department of
Transportation along with the Driver Alcohol Detection System for Safety have been spending
the last few years trying to create technology preventing drunk drivers from getting on the road
through means other than breathalyzers. These two organizations in particular have been
advocates of the current ignition interlock technology that uses breathalyzers to prevent engines
from starting up when the driver is intoxicated. By 2025, technological advancements will ensure
the feasibility of tests through both the skin and/or breath to serve as a means of determining
blood alcohol level. According to sources, the technology will be available by 2021 to be used in

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our automobiles (Dillow). The American Beverage Institute suggests that there are research and
development efforts totaling upwards of $5M to develop these interlock and alcohol detection
technologies (American Beverage Institute). Even though these technologies will most likely not
be mandated by 2025, our Personal Mobile Device will be just the technology needed to have
these prevention techniques a bit more universal.
Delving a little deeper into the technology currently being developed to test BAL through
means other than the average, intrusive breathalyzer, we find alcohol sniffers. This use of offset
spectrometry doesn’t require skin contact and can operate at a distance (Ibid). This is a huge step
up from today’s breathalyzer where you have to blow into a tube in order for your blood alcohol
level to be determined. According to the American Beverage Institute, alcohol can be detected in
the air of a car, even when the windows are half-down and the air conditioning is on (Ibid).
These sensors having the ability to grab respiratory samples from the air in the car is
revolutionary, and having your PMD embedded with these sensors will add to the accuracy and
drunk driving prevention our group is trying to ensure. The fact that this technology is already
being prototyped today means that by 2025, it will only be even more improved.
Other innovative blood alcohol-testing technologies currently in the works are touch
sensors. Being that alcohol is present in one’s skin and sweat, ignition interlock technology
seems to be moving towards touch sensors as its primary concentration. Tissue spectrometry
determines BAC through the skin by measuring how much light is absorbed at a particular
wavelength from a beam of Near-Infrared reflected from the subject’s skin (Ibid), and
transdermal sensors continuously monitor drivers’ BAC levels through their sweat.
As evidenced by the many technologies currently being developed, the ease and precision
at which we will be able to determine a driver’s blood alcohol level will drastically improve by

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the year 2025. The accuracy in measuring blood alcohol levels from respiratory samples in the
air of the car is nearly as accurate as a blood test, and it is only 2014 (Ibid). Companies like
Lumidigm and Toyota are already trying to translate the large machine currently used for blood
alcohol detection through tissue spectrometry to a finger/hand scan for the future (Ibid), which
would be extremely appropriate for use in our personal mobile device. Sweat sensors, currently
body worn, are being conceptualized for vehicles to be strategically placed on gearshifts, radio,
steering wheels, car locks, and more (Ibid). Companies are working tirelessly to bring this
technology from big and intrusive to small enough to be placed on the start button of a car, from
two to three seconds to determine BAL to 200 milliseconds, from only operating at room
temperature to being accurate between -40 to 85 degrees (Meyer). With the advancement of this
technology and our personal mobile device, the ignition interlock will be seamless for drivers on
the road.
Assuming that your PMD will be able to detect blood alcohol level, one has to weigh
certain options and possibilities such as calling emergency contacts that are pre-registered in
your PMD (ex. mom, dad, friends), calling AAA to tow your car, calling a cab service to pick
you up, or, by 2025, having the car drive autonomously. Having pre-stored numbers in your
personal mobile device is a positive because it could allow intoxicated individuals to avoid the
payment of AAA or cabs and receive rescue service from friends and family. Having your PMD
call AAA, a cab, or public transportation is also positive. Although you would be with
individuals you don’t have a personal relationship with, you would be in professional care as far
as transportation. Being that these services would be paid, I don’t believe they would mind
transporting drunken persons home safely. Drunk driving transportation could be an additional
charge in AAA’s original purchase plan when customers want to purchase AAA assistance.

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However, cabs may not be in favor of this transportation system because of the high risk
associated with drunken individuals.
The successful implementation of the ignition interlock program causes consumers to
then ponder the solution of autonomous cars as well. According to news source, CNN,
“Informed conversations about self-driving cars no longer are about feasibility. New key talking
points are ‘When?’ and ‘Which automakers first?’ and ‘Who will be responsible when an
accident happens?’” (Levin), and we believe this to be true. The successes of many companies
currently in the autonomous car space constantly reinforce that the technical feasibility of a self-
driving car is no longer in question. Nissan has publicly stated that it will sell its first driverless
car by 2020 (Ibid). In just one year, Audi decreased the size of its computer systems from a trunk
completely full of equipment to a glove compartment sized box in the corner of the vehicle
(Kelly). And Google has been the most publicly visible and successful as its Prius’ have driven
in city traffic, busy highways, and mountainous roads with barely any human intervention and
zero accidents to show for it (Guizzo). This technology has advanced so much in the past decade
that companies have even started actively pursuing how to give vehicles “intuition” and
“common sense” to make humanlike decisions in moments of driving disaster (Hirsch).
The technology for autonomous and self-driving cars is improving so rapidly that, quite
frankly, legal regulation is the only thing slowing it down. As of now, self driving cars still need
an ”active” pilot in the driver’s seat in case something goes wrong or the computer is indecisive
in its decision making. So, having an intoxicated individual behind the wheel of an autonomous
car is still many years away from being conceivable. The main obstacle preventing autonomous
driving features from being commercially available is not the public’s comfort with the idea and

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not automotive companies being skeptical of the demand but the regulations and laws that need
to be implemented in order to sustain this technology in our society.
The predicted large support base for this particular implementation of the PMD is a result
of the protective nature it would serve in the lives of its users. Support for this application of the
PDD would appeal to the parental natural instinct to protect their children, the government’s
need and desire to protect it’s citizens as well as defend it’s rules and regulations. Additionally,
support for this would come from those who desire results without work, a common theme
among consumers in the United States today. These users would be looking for a chance to be
safe without the need to actively make responsible decisions for themselves, but rather have fun
and be irresponsible while the PMD makes responsible decisions for consumers of alcohol. As a
result, these supporters would come from providers of alcohol who would want to encourage the
increased consumption of alcohol that would result from the ability to be responsible while
driving without limiting their drinking. Under this vein of thought, consumer support would
derive from a younger age demographic whose desire to continue being irresponsible outweighs
their responsibilities and demands of their adult life—thus an age demographic of those
approximately 18-34.
Focusing on men and women who are around the age of those in the height of their
alcohol also focuses on an age demographic that does not necessarily want to embrace the
responsibilities faced by older generations. By having the ability to have the car determine if the
driver or its passengers are sober enough to drive, both objectively with regard to the law and
subjectively with regard to their tolerance, the PDD makes responsible decisions for these young
drivers. Additionally, this same demographic is the same as those who would still be amenable to
the idea of allowing another object or technology to do work for them. Following the generations

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of those who previously adjusted to the new age of computers and wireless communication,
these young adults would be willing to allow a car to truly be autonomous, especially when they
as the driver would be unable to operate the car. Granted, a small percentage of this demographic
may remain uncomfortable with truly autonomous cars that might not yet be fiscally accessible
to the entire population, but the option of calling a safe ride home for the driver and potentially
inebriated passengers also presents an opportunity for the PDD to once again assume
responsibility for its users. In this way, the PDD allows its users to “Drink Responsibly” as well
as drive responsibly.
B. Life’s Musical Playlist
One significant application of the PDD will be the ability to create a fully personalized
experience with music. The premise is the application will use advanced machine learning to
determine what type of music and artists that a user likes given the current time, their current
location, and mood. The onboarding process for when the user first starts using their PDD will
consist of the user first uploading music to their PDD, but will also have integration with
common streaming sites like Spotify and Pandora and gather information about the user’s music
preferences. What gives Life’s Musical Playlist a comparative advantage over these existing
services is that Life’s Musical Playlist will use traditional algorithms currently being used by
Spotify and Pandora in order to determine someone’s musical taste to provide them with relevant
content, but this service will revolutionize the industry by factoring in information that the other
services do not such as the time of day, where the user is, who the user is with, and what the user
is feeling in order to offer an unprecedented level of accuracy and immersiveness within the

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music recommendation service. The PDD will begin to analyze patterns in terms of how the user
decides to play music and automatically start playing music based on those perceived
preferences. The owner may start out in the morning asking the PDD to play “smooth jazz” and
soon the PDD will be able to realize that the user likes to listen to that type of music when he or
she first wakes up and will associate that time of day and the user’s mood with that type of music.
After a while the PDD will then extrapolate the notion that the user likes to listen to mellow
music in the morning and start playing music in the morning that is similar to smooth jazz and
see if the user responds positively to the PDD’s suggestions. In contrast, the user may listen to
more upbeat music that keeps them stimulated while at work to ensure productivity and the
owner’s PDD will take note of this trend and play similar music accordingly. The PDD will also
be able to recognize the owner’s location. Say GPS indicates that the user is currently at the gym,
then the PDD will either play preset music according to these preferences, or if the user has it set
so, it will play the user similar style music that the user has never heard before based on the
preferences already established. In order to prevent the user from getting annoyed, there will be a
feature that allows the user to decide whether or not he or she wants the PDD to start playing
music automatically when he or she enters the gym or whether or not the user wants to be
prompted prior to playing music. In addition, the PDD will have state of the art sensors built into
the casing of the device that allow the PDD to pick up on the owner’s current mood so that it can
provide the user with relevant music choices that way. For instance, say the user is relaxing at
the park on a Saturday afternoon just lying down on a blanket reading a novel. The PDD will
have the ability to tell based on the user’s heart rate, sweat secretion, and stress level that the user
is currently relaxing and will default to playing calm and tranquil music. However, say forty-five
minutes into his or her reading session the user gets asked to play a pickup game of basketball,

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odds are his or her heart rate and excitement level will increase and the PDD will be able to pick
up on that shift in the user’s mood and start playing songs that are no longer peaceful and
tranquil, but instead are a bit more upbeat and exciting to begin to get the user pumped up. It is
important to note that the PDD doesn’t need to know the exact situation that the user is currently
in order to start playing relevant music--only information that it can determine itself such as time
of day, location, who the user is with, and mood. Since nearly all technology in your life will be
integrated together at this point the information the PDD will be able to gather about you and
your surroundings will all work in a beautifully coherent way. For example, consider if your
PDD notices that you instantly switch from calm and relaxed to extremely tense, stressed, and
angry all within the span of a fraction of a second. The PDD will begin to look for context clues
surrounding the shift. The PDD will know you’re currently driving on your way to work (and
through communication with your car) that you slammed on the breaks a fraction of a second
after that detected mood change. The PDD will understand in this context it is likely that the user
was just cut off in traffic and should not switch to playing more intense and angry songs, if
anything, the PDD should play songs that will calm the user down after his or her near accident
to prevent road rage.
When in a group setting, such as a car ride, the PDDs that currently have music
preferences listed and are willing to share those preferences, will be taken into account and the
car will compile a joint playlist of songs that match the group’s taste as a whole. The car will
serve as the host for collecting this data about the passenger’s music preferences, and if the
owner of the individual PDD allows it, the car will build and save a profile for that user for much
easier access in the future. The PDD would also take into account the relationship dynamic
between the people within the car. That is to say if person A has person B listed in his or her

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phone as “mother” the car wouldn’t start playing sensual love songs even if both people had such
songs listed as preferences. However, if the user gets into his car with his girlfriend of eight
months on a Friday night at 9pm the car may not refrain from playing such a song. Just as the
information collected about the time of day, where the user is, who the user is with, and what the
user is feeling is integrated together to play the most appropriate music possible, the different
pieces of information can also act as a great system of checks and balances. Consider the
situation that you are driving your boss around. Even if you and your boss have a very similar
taste in music your PDD will refrain from playing certain songs with excessive expletives due to
the relationship dynamic between the two of you as elaborated in previous examples. However,
if you consider the situation of you driving around your boss at 1am on a Friday night after
coming from a bar and restaurant and both of you are very relaxed the PDD may give a little less
weight to the relationship dynamic and be a little more liberal with the song selection because it
is clear you aren’t on the clock.
Another key feature of the group music experience would be for example at a party.
Assuming stereo systems have progressed to the point where they would be able to pull
information wirelessly from peoples’ PDDs, the stereo system would serve as the host for
collecting and analyzing everyone at the partys music preferences in order to play music that
catered to the audience. Given our target audience of people aged 20-30, the two most common
styles of party music are rap and electronic dance music. Ideally, the stereo would compile a
playlist of songs that matched the overall group’s taste, but would not keep switching to and
from genres or styles of music as to promote continuity. The stereo system might periodically,
say every thirty minutes, regather information about people at the party’s music preferences so
that if a majority of the people who like EDM leave the party, the stereo will not continue to play

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EDM for the rest of the night. If the host of the party insisted on being the DJ, instead of creating
a playlist matching everyone’s music preferences and playing those songs automatically, the
PDD would compile a list of songs and allow the DJ to still choose what songs to play, but now
he would have a good idea of the music preferences of the people at the party but there would
still room for the DJ’s own creativity. This application could then be extrapolated to music artists
who were playing a live show. Album sales are at an all time low and people are even beginning
to shy away from buying digital copies of songs when they can get them for free off youtube,
their friends, or music sharing sites. More and more artists are finding that as sales of their songs
drop they need to make up for that income by putting on spectacular tours and shows. If an artist
walks onto stage knowing which of his or her songs are most popular with the crowd, this puts
the artist in great position to put on a fantastic show that the attendees will love. Furthermore,
having this data about their fans’ music preferences also gives the artist key insights as to who
they might consider collaborating with on a song or going on tour with because the fans love
both artists.
We want the user to have as much control as possible when using the life’s musical
playlist feature. Because of this, the application will have a variety of features that the user will
be able to control themselves and personalize as they wish. For some users, they will get tired of
giving the application feedback on song selection within the first week of the app--for others, this
period may extend into months. The user will have the ability to choose whether or not the app
requests feedback on songs and the frequency with which it requests that information. How
effective the app is at providing the user with good music selections will obviously directly tie
into how often the user gives feedback. Even if a user has his or her PDD set so that it will never
prompt the user directly for feedback, if the user particularly likes or dislikes a song they will be

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able to press a hotkey--such as the home button twice that will automatically pull up the
feedback screen so they can input their feedback on the most recent songs that have played.
Furthermore, we want the user to have as much control over what information their PDD is
sharing. An individual PDD owner would have the ability to specify which (if any) users they
would like to share their music preferences with. For example, they would have the ability to
share their music preferences with only family, friends, emergency contacts, contacts, a uniquely
created list, or no one at all.
Assuming the technical capabilities of our personal mobile device in 2025, much of the
other technical aspects of the aforementioned music playlist are surprisingly already available
and successful today. Companies like Apple already use metrics to determine one’s daily routine
and capitalize on that data in several ways. For example, your iPhone has a very strong idea of
where your job is located, what school you go to, your favorite restaurants, music choices, your
home address, what times you wake up in the morning and go to sleep at night, and even what
times you normally use certain applications. Apple uses this information to do things like start
the weather app on your phone even before you open it because it knows you usually check the
weather when you wake up in the morning. It also utilizes its GPS technology to guess where
“home” is and where “work” is. So, the time based, location based, and company based music
playlist technology described above already exists and will only become exceedingly better and
more accurate by 2025. The most interesting technological advancement we’re beginning to see
gain some prominence, though, is the mood based platform suggested above. A company called
Neurowear has produced headphones that play certain songs by determining one’s mood through
his/her brainwaves. The headphones utilize an electroencephalograph sensor on the user’s
forehead to interpret your mood and a custom music app that searches though a music library to

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play a song that’ll match your current state of mind (Chua). Currently, the headphones are not
marketable because technologists are very skeptical of the current technology being able to
accurately predict a human’s mood and the slightest disruptions like fast walking or a stray hair
can throw off the sensor (Isaacson). But, the fact that this technology has had even the slightest
breakthrough in today’s world gives many people hope that by 2025, mood based music
selection may be the next big thing.
Music is something that has been an ever-present focal point in societies for hundreds of
years. In the near future, music will continue to be consistently present at the forefront of peoples’
daily lives. Additionally, with regards to the focus of the population, the rapidly growing focus
on dramatized television versions of people’s “real” lives, shown alongside it’s own playlist on
reality television channels has and will continue to create a demand for each person’s life to
seem dramatized as well. Helping this growing notion that each person’s life deserves to be
worthy of television, is the lifestyle playlist implementation of the PMD.
The majority of the population that will be focused on the dramatics surrounding their
busy lives will be those drawn to the entertainment world as well as those with higher education
in the beginning steps of their professional careers—most likely those ranging from age 20-34.
This sector of society coincides with those who are young enough to not only appreciate
different and constantly changing music genres, but also will appreciate the integration of this
playlist into their everyday lives. In regards to marketing this playlist to this age demographic,
the focus would be on emphasizing that these busy people will now have the chance to focus on
more important things than making a playlist for every part of their life—their PDD can help
them do that more quickly before ultimately making the playlists for them completely.
Additionally, this implementation of the PDD could allow them to feel like they are living a

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celebrity lifestyle with the soundtrack compiled by their very own PMD.

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