GPS technology is great at getting you from Point A to Point B. What if you had a system that alerted you to risk of crime, weather, points of interest, and cost savings tips along the way? Microsoft seems headed this way in light of its newly-awarded patent that ties GPS location to useful information for pedestrians. Here is a description:
“As a pedestrian travels, various difficulties can be encountered, such as traveling through an unsafe neighborhood or being in an open area that is subject to harsh temperatures. A route can be developed for a person taking into account factors that specifically affect a pedestrian. Moreover, the route can alter as a situation of a user changes; for instance, if a user wants to add a stop along a route.”
This is a classic example of the Attribute Dependency Technique, one of five in Systematic Inventive Thinking. It creates a correlation (dependency) between a person’s location and the type of information that is sent to the device. Microsoft’s new concept gathers data, analyzes the data and user requirements, then generates suggested routes. It considers the user’s preferences such as avoiding neighborhoods that exceed a certain threshold of violent crime statistics. The system might direct you to “take the subway” rather than walk if bad weather looms. It even considers cost factors such as parking, extra traffic, and other situations that might make you vary your path.
For this month’s LAB, let’s see if we can extend Microsoft’s concept by a systematic use of the Attribute Dependency Technique. Attribute Dependency differs from the other templates in that it uses attributes (variables) of the situation rather than components. Start with an attribute list, then construct a matrix of these, pairing each against the others. Each cell represents a potential dependency (or potential break in an existing dependency) that forms a Virtual Product. Using Function Follows Form, we work backwards and envision a potential benefit or problem that this hypothetical solution solves.
Here are new ideas in the same vein as the Microsoft patent using Attribute Dependency:
1. Type of Insurance vs. Route: The GPS unit recommends a travel route based on the type of insurance the person has. Perhaps the insurance company stores preferred routes based on risk of loss. Taking this further, perhaps the person using the GPS and complying with these recommended routes earns a “safe pedestrian” discount.
2. Health Status vs. Route: The GPS unit calculates a forecast of calories to be burned along a route. Depending on the person’s health status and exercise habits, the unit makes recommendations based on difficulty and degree of exercise (light, medium, and hard exertion).
3. Social Status vs. Route: The GPS unit pulls in information from the person’s social networks (Facebook, Twitter, LinkedIn, and so on) and makes route recommendations based on various factors. Such factors could include location of friends, recommended restaurants by friends, places of work, etc. Any information conveyed by a member of your social networks is integrated to your route of travel to give you richer context about it. A site called Dopplr is approaching this now.
4. Time of Day vs. Type of Output: When using Attribute Dependency, it is almost always a good idea to include “time” as an external variable. Many aspects of our lives are time-dependent, and the tool can yield valuable innovations to account for time. In this example, imagine the output of the GPS unit varies by time of day. Perhaps it switches between the LCD on the unit and other display options such as a smartphone or TV. Perhaps it tracks the users calendar and relays time of arrival via SMS text. The essence of this idea is to match the way information is delivered to the user in context of what else is happening.