Notification of human safety reputation of a place based on historical events, profile data, and dynamic factors
View Patent ↗In an example, one or more computing devices operate to provide a context-aware reputation of a place, such as in relation to a human user. Context may include the user's identity and purpose, as well as environmental factors such as time of day, weather, and political drivers. The device may communicate with a server to receive globalized safety intelligence. When the user enters a zone, the device may determine a context-sensitive reputation, such as “Green,” “Yellow,” or “Red.” Depending on the reputation, the device may then take an appropriate action, such as warning the user or providing additional information.
1. A reputation server comprising:
a user data interface;
a location data interface;
an incident data interface; and
one or more logic elements, including at least a hardware computing platform, comprising a reputation engine operable for:
receiving via the user data interface context data for a user, the context data comprising dynamic factors about the user, and profile data for the user, the profile comprising factors about the user that are relatively static with respect to the context data;
receiving location data identifying a target location over the location data interface;
receiving local incident data via the incident data interface, the local incident data comprising one or more human-driven historical incidents local to the target location;
calculating a context-aware human safety reputation for the location for the user based on a combination of the context data, the profile data, and the incident data; and
notifying the user of the human safety reputation.
2. The reputation server of claim 1 , wherein calculating the context-aware human safety reputation for the target location relative to the user comprises a computation of the form S i =I(.)*G S (.)*G T (.), where G S is a spatial influence factor, and G T is a temporal influence factor.
3. The reputation server of claim 2 , wherein a score for each type of incident comprises a computation of the form S C i =Σ j=1 #C i I C i *G S C i (.)*G T C i (.).
4. The reputation server of claim 3 , wherein G s is a monotonically decreasing function of a distance between the target location and an incident location.
5. The reputation server of claim 4 , wherein G s is computed according to
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6. The reputation server of claim 3 , wherein G T is a monotonically decreasing function of time since an incident.
7. The reputation server of claim 6 , wherein G T is computed according to
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8. The reputation server of claim 1 , wherein calculating the context-aware reputation for the location relative to the user comprises extracting one or more incidents from the incident data, and classifying each incident according to an incident severity and a subject profile.
9. The reputation server of claim 1 , wherein the context-aware reputation is a scalar.
10. The reputation server of claim 1 , wherein the context-aware reputation is a multi-dimensional vector.
11. The reputation server of claim 1 , wherein the user data comprises a profile factor selected from the group consisting of size, height, weight, age, race, ethnicity, gender, sexual orientation, gender identity status, religious affiliation, party affiliation, distinctive markings, skin color, dignitary status, fiscal worth, and training.
12. The reputation server of claim 1 , wherein the user data comprises a context factor selected from the group consisting of mode of transportation, equipment, armament, protection, physiological condition, mood, heart rate, stress indicators, body temperature, job function, group size, distance from an operational base, communication capability, time of day, and weather.
13. The reputation server of claim 1 , wherein the user data comprises a purpose factor.
14. One or more computer-readable mediums having stored thereon instructions for providing a reputation engine operable for:
receiving context data for a user, the context data comprising dynamic factors about the user, and profile data for the user, the profile comprising factors about the user that are relatively static with respect to the context data;
receiving location data identifying a target location;
receiving local incident data, the local incident data comprising one or more human-driven historical incidents local to the target location;
calculating a context-aware human safety reputation for the location for the user based on a combination of the context data, the profile data, and the incident data; and
notifying the user of the human safety reputation.
15. The one or more computer-readable mediums of claim 14 , wherein calculating the context-aware human safety reputation for the target location relative to the user comprises a computation of the form S i =I(.)*G S (.)*G T (.), where G S is a spatial influence factor, and G T is a temporal influence factor.
16. The one or more computer-readable mediums of claim 15 , wherein a score for each type of incident comprises a computation of the form S C i =Σ j=1 #C i I C i *G S C i (.)*G T C i (.).
17. The one or more computer-readable mediums of claim 16 , wherein G s is a monotonically decreasing function of a distance between the target location and an incident location.
18. The one or more computer-readable mediums of claim 17 , wherein G s is computed according to
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σ
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=
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19. The one or more computer-readable mediums of claim 16 , wherein G T is a monotonically decreasing function of time since an incident.
20. The one or more computer-readable mediums of claim 19 , wherein G T is computed according to
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21. The one or more computer-readable mediums of claim 14 , wherein calculating the context-aware reputation for the location relative to the user comprises extracting one or more incidents from the incident data, and classifying each incident according to an incident severity and a subject profile.
22. The one or more computer-readable mediums of claim 14 , wherein the context-aware reputation is a scalar.
23. The one or more computer-readable mediums of claim 14 , wherein the context-aware reputation is a multi-dimensional vector.
24. A mobile device comprising:
a position locator;
a user data engine; and
a reputation engine client operable for:
receiving a location from the position locator;
receiving a user profile, intent, and context data for a user, the context data comprising dynamic factors about the user, and the profile comprising factors about the user that are relatively static with respect to the context data from the user data engine; and
determining a reputation for the location, wherein the reputation is based at least in part on the user profile, intent, and context.
25. The mobile device of claim 24 , wherein the reputation engine client is further operable for providing a user warning based on the reputation for the location.