DataPath CaseUsing Personal Behavioral

Data to Improve Smartphone Usability

Green Team 2Aditi BhaleraoJesse DelprioraMelody HuangKanwal Jaggi

George Tattersall

Improving Mobile Personal AssistantsIntroduction

Smartphones have come a long way since Apple released the first iPhone back in 2007. Manufacturers today are producing smartphones that are not only superior in terms of computational power and processing but also in terms of sensor availability. Due to this, there is an abundance of data that is produced on a daily basis as smartphones become indispensable to people’s everyday lives. A study conducted by Nielsen shows smartphone adoption around the world.

Not only is smartphone adoption growing substantially across the world but also its importance in people’s lives is growing. ExactTarget conducted a study to discover how mobile devices fit into the day-to-day lives of people and found that 85% of respondents felt that their smartphone was central to their everyday life. As a consequence of this growing trend, researchers now have access to data streams that were not available before. Through these data streams, we can gain insights into device owner’s routines, habits and daily activities. With the increase in processing capabilities, “powerful machine learning algorithms for statistical inferences from sensor data can be designed to run on commodity phones” (1). Automated Personal Assistants within smartphones such as Siri and Cortana are also gaining in popularity as they aim to improve the interaction experience between people and their devices. A major function of such assistants today is using data from other apps installed on one’s smartphone. Our idea aims to improve automated personalized assistants to not only help integration with social aspects of one’s life but also streamline their interaction with their smartphones.

Idea Formulation

Our team wanted to make automated personal assistants more personalized for their users. The current situation with such assistants is that they are limited in their functionality. As compared to the data that is available today as well as the computational power of modern devices, it is only a matter of time before automated personal assistants help simplify our interactions further.

We propose to create an application, which learns a person’s daily routine and automates and simplifies the tasks the user typically performs on their smartphone. We will accomplish this by predicting what activities a user will perform in any given context during the day. For example, Joe usually leaves for work at around 9 am. On his commute to work, Joe almost always listens to music. Once Joe has left his house and is sitting in the train, car or bus that is on the move, a notification is sent asking Joe whether he would like to listen to music. With one simple click, Joe can now enjoy his music without having to unlock his phone, find the music app and play a song. Essentially, our application aims to reduce the amount of time you spend doing unnecessary tasks. Another example of the functionality our application would provide: It’s 12am on a weekday and Joe has forgotten to set his alarm to wake him up for work tomorrow. The application realizes this after analyzing certain data available that is available as well as from what it has learnt and sets an alarm for 7:30 am in order to wake up Joe for work.

We hope to truly understand a user’s behavior pattern on their smartphones. In addition to personal assistants helping users by partnering with external apps such as Yelp, Rotten Tomatoes and being able to use the internet to answer questions, we want to analyze and understand the context in which users perform certain activities on their phones. Another feature of our application might include dynamic homepages, which display the applications that the personal assistant thinks you are most likely to use during the course of your day. It is all about finding patterns in the consumer's usage of their phones. This application will of great use to people who are in a set routine such as working professionals.

ExactTarget Study (2)

Sources and Kinds of Data

There are several sources of data in a smartphone. The illustration below gives us a good indication of the sources of passive data. A majority of modern smartphones has these capabilities.

Sources of Passive Data (3)

For our application specifically, the following sources of data will be most useful (Mobile Analytics).

1. Motion sensors:a. Accelerometer – It measures the device linear accelerationb. Gyroscope - It measures the angular rate of change (i.e. rotation velocity)c. Magnetometer or compass - It measures magnetic field strength

2. Location sensors:a. GPS which is commonly used in outdoor settingsb. Network-based location services which uses cellular network and Wi-Fi to

determine the location (i.e. via triangulation)3. Ambient sensors:

a. Light sensorb. Microphone,c. Proximity sensor – This detects nearby objects and can indicate when the

phone is near the user’s ear

We will also need data to put activities into context. For this we will simply use the time and date feature. We will develop algorithms that will analyze what the user does before and after every action i.e. it will try and develop a context for the action of the user. For example, in the use-case presented earlier, the smartphone will not just set an alarm for user every night at 12am if the user has not set an alarm. By analyzing the current data, it will be able to determine whether the user is at home and not anywhere else, that it is a weekday, when was the last time the user interacted with the phone and so on. In a sense,

our personal assistant application or software will be using gathered historical data to make its decisions.

Capturing location data will be extremely important for this software. While GPS is the most commonly used and known sensor, network-based triangulation with cellular network and Wi-Fi location can also be used to sufficiently determine location. An advantage of the latter is that it is battery-friendly and also provides good accuracy in dense urban areas and for locations that are frequently visited (e.g. work, school and grocery store) (ExactTarget). Network-based location services are available via many providers who normally have a large database containing numerous geocoded Wi-Fi networks and cellular towers. This information is later used to determine the smartphone location. A pre-requisite to using network-based location services to determine one’s location is an Internet connection. Our software will reduce the dependency on Internet by maintaining a small database of “landmarks” such as cell id and Wi-Fi network for frequently visited locations (Mobile Data Collection). This functionality will allow us to store information about the cell id and Wi-Fi networks available along with GPS coordinates and detect landmarks as well as identify the location without an Internet connection. Furthermore, by analyzing Wi-Fi networks, the application might be able to indicate the arrival and departure time from known locations. Detecting a known signal can indicate the arrival while disconnecting from one might indicate departure. Some public places such as restaurants and cafes might have the business name of that place as their Wi-Fi network name, which could be extremely useful. For example, if a user walks into a Dunkin Donuts and the phone picks up the SSID “Dunkin Donuts” our app could search the coupons.com database for coupons for Dunkin Donuts in our user’s location and then alert the user of any found coupons. Unlocking the phone would bring up a webpage with the coupon and barcode loaded to present to the cashier.

Using sensors, one can easily determine whether the user is on the go or not. Sensors can be used as an alternative to GPS location, which can also be used to determine where the user is. Interestingly, sensors can also be used to determine what kind of transportation mode the user is using. This is based on the assumption that each mode of transportation has a unique motion pattern. This attests to the fact that these sensors are reliable and need to be integrated into personal assistants in order to severely improve their core functions. Here’s a list of applications that current personal assistants use as data sources.

Executing the idea

We will execute our idea in 2 stages. There will be a planning phase and a deployment phase. The planning stage will involve doing thorough research and developing use-cases for our application. Through this research we will finalize the functionality of our automated personal assistant as well define some preliminary scope boundaries. Furthermore, our research will incorporate searching for applications that currently do similar things involving using sensor data. The aim of the planning stage will be to have a software blueprint, which identifies what the application can and cannot do. The blueprint should be primarily composed of specific use cases we want to achieve in version 1.0. It should also define common contexts that can be reliably inferred such as: being at home, commuting to/from work, at work/school, or travelling (in a foreign location for the first time). We also need to do research in regard to gaining access to this data. This would involve approaching 3rd party applications that might have access to such data or going directly mobile phone manufacturers. During this stage we will also develop a design prototype of our application.

During the deployment stage, we will do the actual coding of the application as well as develop algorithms, which will help us mine data for patterns. For this task we would require two computer science students or programmers. It would be beneficial to also have someone with a background in Business Intelligence Analytics in order to help find patterns in the data as well as develop models, which will predict user behavior. Below is a projected timeline of our project, which we estimate will take 6 months to complete.

During the deployment stage we will also begin tracking down a smaller, established mobile app development firm to potentially partner with. Since our application requires time to learn how a user interacts with their device it would be great to go to market with this data preloaded. We could accomplish this by installing our backend data tracking

software on another application such as a 3rd party Facebook application or video game. Demonstrating that our backend can be put beneath another application will be extremely valuable if we decide to partner with a larger firm as described in more in the monetization section. Similarly, a substantial amount of this idea’s value comes from the ability to understand the way a user interacts with their phone. After this stage, we might even find that partnering with another application is easier than trying to make this app customer facing by automating their tasks.

Research

Planning & Design

Hire Interns

Development

Testing

3/1/14 5/11/14 7/21/14 9/30/14

Monetization

There are a plethora of exciting options when considering how to monetize an idea utilizing such useful data. Developing an application will provide a platform for licensing, in-app purchases or downloading fees among many others. Determining which of the different options is best will ultimately be decided once the target market has been identified and the product is ready to be brought to market.

We believe that an application on iOS would be the best way to successfully bring the product to market. This would target Apple users whether it is through their iPhone or iPad Apple is one of the most well marketed companies in the world. It would also take advantage of Apple’s fan base of early adopters who love to experiment with new features. Therefore, targeting this operating system would prove success and provide a platform of huge growth into other operating system markets.

Once this decision is made it is important to consider the options to truly turn the product into a profit. We have identified some of the options and analyzed the opportunities presented below.

Licensing: Licensing the code is a great option if the idea can eventually become proprietary in nature. This will provide the opportunity to sell the application to different operating systems, developing companies or smartphone producers

In-app Purchases: This is a prevalent space to be in within the application market. This will provide users to add capability in terms of processing speed or learning speed. Also, advertising other applications or other products that have previously been developed can lead to cross-selling opportunities.

Downloading Fee: This is a quite straightforward option. The average application makes approximately ten cents per download. If we chose to mark the price at $1 and eventually sold to multiple operating systems the monetary opportunities are endless.

Partnering with a larger firm: This strategy seeks a symbiotic relationship with an established mobile development firm such as Zynga or Rovio. We would be able to leverage data gathered by our application to provide actionable insights about how customers use their firm’s apps in order to improve them. It also has the added benefit of positioning ourselves for an easy sellout by demonstrating value to a target firm.

Ultimately, deciding to attack the iOS market is the best plan going forward. Doing so by originally selling the code to Apple to use on their devices will provide the necessary publicity and customer base to move forward to a larger market in Android. Find below some statistics on the potential revenue our app could generate.

Appendix

Please find below specific examples of the kind of data that will be generated from sensors. The following example takes the Samsung S3 as a test product. The measurement description and data columns are very useful (Mitchell).

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