Location-based services (LBS) are a general class of computer program-level services that provide access to location and time data as control features in computer programs. LBS are typically utilized to provide you with information specifically tailored to your current location. Google for example has the ability to provide search (and advertizing) that is tailored to your current location. For example, if you type “movie theaters” into Google what appears at the top of the search engine are the movie theater locations that are closest to your current location and not the millions of movie theaters across the country. (Note: even if you do not give Google direct access to your location, it can still locate you through LBS associated with your IP address.)
LBS are becoming increasingly popular in everyday devices and in particular within mobile devices due to their increasing power, memory, and capabilities. What once required a computer the size of a house to do is now available on someone's mobile phone.
LBS are now being incorporated into all mobile smart phones. For example, for both the iPhone and iPad one of the newest application associated with the iOS 5 operating is Reminders. Reminders is a location-based organization tool that aims to offer an improved to-do lists and one of its features is that it can provide location-based alerts, such as reminding a user to buy milk when they arrive at the grocery store.
The use of LBS by mobile apps is still in its infancy and have yet to be fully embraced by most mobile customers. For the apps that have been created so far, most of the current apps have either a social or a marketing component. The table below highlights some representative apps that use LBS.
NameDescriptionBabblevilleBabbleville is a web and phone-based service that allows you tocreate location-based message boards and messages. Whenother users approach the location, it will automatically show up ontheir screen, allowing users to communicate with other usersnearby in an anonymous and secure way.BanjoBanjo connects with your Facebook and Twitter accounts andsends you a push notification when your friends are close by.ChatSquareChatSquare is a service that lets you chat with other people in thesame location. For example, if you're at a night club, you can chatonline with others visitors, the bartender, or even the bouncer.DroppDropp functions as a location-based reminder app that will triggerwhen you enter an area. It also has secondary use as apersonalized messaging service that allows you to drop text orpicture messages in an area that will be picked up by friends orfamily automatically.foursquarefoursquare is an app that allows you to store information aboutplaces that you have visited that can later be picked up by yourfriends and other like minded individuals.GeoNotesGeoNotes is a location-based reminder system allowing you toreceive personal or public pop-up reminders as you leave or arriveat any location. Unique to the system is the ability to specify“layers”, which are essentially public messages groups that youcan belong to.LoKastLoKast, which is actually short for “local-casting,” connects peoplein its network based on proximity. Once you set up a profile with allof your photos, selected contacts, videos, web links and music onyour mobile phone.MessagePartyMessageParty is a location-based chat room app. When you openthe app, you'll see a list of chat rooms that have been createdwithin 1000 feet of you, so you choose one to your liking to join in.PlacePunchPlacePunch provides location-based marketing solutions thatmake it easy for you to incorporate marketing campaigns thatintegrate with Foursquare, Facebook and Twitter.RepudoWith Repudo you can drop all kinds of multimedia like a text, aphoto, a video or an audio message at any location you like. Oncethe message is picked up it is gone from the map. It is now on yourphone and only you can decide what to do with it.YobongoYobongo is an app that serendipitously connects nearby strangersin chatroom-like environments. When you open the application,you are automatically dropped in a chat room—based on yourGPS location—where you can start chatting with others.
The way these apps work in very similar to the method specified in U.S. Pat. No. 8,005,489 (inventor: Frank E. Fransioli). In this patent, the current location information is repeatedly determined within the portable wireless device and then sent to a server for processing. Once received by the server, the location information is then tracked and processed to determine if a specified criteria has been met, which in this particular patent can be based both location and direction of travel criteria. If the criteria has been met then a message is sent back to the portable wireless device.
There are two deficiencies of this type of system. The first is privacy and the second is cost.
With respect to privacy, the user's current position is repeatedly being sent to the server and is being monitored and processed elsewhere. While this might not be a big deal for someone who wants to be reminded about buying milk when they arrive at the grocery store, this is not true of a registered sexual predator that may want to, as a reminder to stay away, be notified whenever he/she is within 500 feet of a particular elementary school. Such an individual might be terrified of using such a system if they knew their position information was constantly being monitored on a server.
The lack of privacy would potentially be an issue for people with all sorts of medical and mental health issues, particularly those associated with addictions and healthy lifestyles that often have destructive behavior associated with a particular location. For example, a substance abuser that wants to stay away from a particular location that he previously purchased illegal substances at might not want the location to be located somewhere else. Or, someone who is pre-diabetic and might be interested in making healthier eating choices when they arrive at a particular restaurant might be concerned about being a member of a public group since this is health related information.
The other big issue is cost. While the apps themselves may be free, the processing of the location information is done on the server side and the mobile device must constantly be sending data to the server, which is not free. While there is definitely a financial cost associated with having to repeatedly send data back and forth to a server, there is also significant cost in battery power consumption, which can be even more important to users than the financial aspect. Additionally, by having the processing done on the server there is a processing delay due to communication time and a big cost in processing resources, since communication is traditionally a very resource intensive process.
In order to overcome some of the cost issues associated with a system where all of the processing and position tracking is done at the server, in U.S. Pat. No. 8,099,105 (inventor Drew Morin) a hybrid system was developed. In this system, as the mobile device moves around it produces specific events (such as coming in contact with a new network tower), which causes all of the points of interest information within a specified region to be downloaded to the mobile device. The mobile device then takes over and is responsible for tracking proximity to the newly down loaded points of interest and determining when to display information related to a specific point. While this is certainly an improvement in terms of the associated costs, it does nothing to address the privacy issues, since all of the information is still stored on the server. Additionally, the process still exchanges data more often than is typically necessary or desirable. As someone is driving down the street, heading towards a destination, they don't need to know that they are passing a sale at Barney's Department Store, even though an advertiser certainly would want them to know that fact.
Constantly getting updates of all the potential sites around you is an advertiser dream but is not something that most people need. Additionally, distracted driving is becoming a national crisis and being bombarded by information that someone needs to take their eyes off the road to view is certainly not desirable, nor is it typically valuable, since most people are on their way to a particular destination and don't have the time to be distracted.
At the other end of the spectrum is the personal location-based “Reminders” app previously mentioned. In this system, the position is tracked within the mobile device itself, using the LBS provided as part of the notification system in iOS 5. This app overcomes many of the privacy and cost related issues of the system above but is limited in that it only has access to the information that is stored within it. Imagine being a user that was addicted to the lottery having to input every single location in the state in which lottery tickets are sold. While they might reasonably input the locations closest to them, they could be traveling a couple of towns away from home and still need a reminder not to buy that lottery ticket when they enter that deli to buy lunch.
An interesting application of location-based services is part of a system currently being developed by scientists at NorthWestern University (IL) called, “Mobilyze!”. Mobilyze! is a mobile phone application and supporting architecture, in which machine learning models are used to try and predict a patients' mood, emotions, cognitive/motivational state. It reportedly uses at least 38 concurrent sensor values including global positioning system, ambient light, and recent calls to try and predict when a patient is feeling depressed. When it senses a pattern that might be indicative of a depressed state, the intention is that the system will act as a virtual therapist and offer suggestions such as, “I notice that you haven't been out of the house for a couple of days, why don't you go for a walk in the park or call your sister.”
While Mobilyze! is a unique therapeutic tool, it is more of a reactive system more suited to mental health conditions such as major depressive disorder. However, for many other medical and mental health issues, particularly those associated with addictions and healthy lifestyles, the trigger is often purely based upon a location, such as a compulsive gambler coming within 25 miles of Atlantic City, N.J. Or, it might be based upon location and a particular time of day, such as a compulsive overeater arriving at White Castle after 10 PM at night. Or, it might be based upon a location and environmental factors such as the presence of particular people (e.g. a fellow substance abusers being present at your sister's house) and situations (such as a family event where everybody is toasting one another). In the cases above, the triggers of undesirable behavior are known and there is no need to resort to predictive modeling. In this type of situation, what the individual often simply needs is a timely reminder of their intervention strategies in order to make healthier/better choices.
Therefore, there is a need for a location-based message system where the information is kept private (locally on the individuals mobile device) that also combines the ability to query for public messages in a manner that is low cost.