System, method, and recording medium for geolocation data discovery in streaming texts

A geolocation data discovery method, system, and non-transitory computer readable medium, include a secure text mining circuit configured to mine a textual alert message of an event from secure source data for a geolocational indicator, a temporal indicator, and a type of alert, a geolocation and temporal embedding circuit configured to embed a geolocational tag location and a temporal tag time to the event based on the geolocational indicator and the temporal indicator mined from the textual alert message by the secure text mining circuit, and an event mapping circuit configured to map the event with an icon related to the type of alert at the geolocational tag location on a navigational map at a time associated with the temporal tag time of the event.

BACKGROUND

The present invention relates generally to a geolocation data discovery system, and more particularly, but not by way of limitation, to a system using data streams of trusted sources already in existence to collect geolocation data and time to plot events.

Spatiotemporal data and correlation of that data with events are important components of fleet management systems, e.g. traffic and/or road condition avoidance, public safety alerts. Such event and alert data is conventionally sent using social media streams such as Twitter® and Nixle®. These alerts and messages often times contain location data, but in textual form for human consumption, e.g. “@NHDOT TRAFFIC ALERT: Emergency road construction between exits 7W and 6 on Southbound Everett Turnpike”. The alert message contains location data associated with the server that is distributing the alert, but not location data associated with the geolocation of the event. Thus, the conventional fleet management systems cannot be synced with navigational services (e.g., the location of the events cannot be mapped onto the navigational maps) because the locational data of the alert is at a centralized server location and not at the location of the alert.

Conventional navigational systems attempt to leverage real-time data to predict the most efficient route (e.g., estimate a state of traffic, delays, accidents, etc.). However, the conventional approaches rely on user inputs (such as a feedback or data drawn from a device) such that the system could be manipulated to output false statements because the data is being processed from untrusted sources. For example, if user inputs or a computer algorithm generating user inputs are enough to flood a system with an accident report in a particular location, the conventional navigation systems will display to users that there is traffic in the area even if there is actually no traffic. Alternatively, the system can be flooded with reports of no traffic in an area with heavy traffic to manipulate navigational systems to guide the users on this path that can potentially contain a high traffic state. This can create security concerns by enabling users to create traffic in predetermined locations.

That is, there is a technical problem in the conventional techniques in that the conventional fleet management techniques manage the system based on either event data from unsecure user inputs and feedback such that the event data can be manipulated to create a false positive of traffic, or allow users to intelligently change a route based on received messages from a central location from a secure input such as government alerts such that it requires intelligent interaction with the navigational system by a user outside of the system capabilities. In other words, there is a technical problem in the conventional techniques that unsecure sources can manipulate data of a fleet management system for a desired outcome that can potentially create risks to society (e.g., creating a traffic situation to prevent emergency services from arriving at a location, etc.).

SUMMARY

In view of the technical problem, the inventors have considered a non-abstract improvement to a computer technology via a technical solution to the technical problem in which a system can mine textual alert outputs from a secure trusted source for geolocation indicators (e.g., street intersections, mile marker, exit number on interstate, etc.) about an event (e.g., an accident, construction, road closing, etc.) and embed the alert outputs from the secure trusted source with the geolocation and a temporal tag such that the event can be mapped in a navigational service.

In an exemplary embodiment, the present invention can provide a geolocation data discovery system, including a secure text mining circuit configured to mine a textual alert message of an event from secure source data for a geolocational indicator, a temporal indicator, and a type of alert, a geolocation and temporal embedding circuit configured to embed a geolocational tag location and a temporal tag time to the event based on the geolocational indicator and the temporal indicator mined from the textual alert message by the secure text mining circuit, and an event mapping circuit configured to map the event with an icon related to the type of alert at the geolocational tag location on a navigational map at a time associated with the temporal tag time of the event.

Further, in another exemplary embodiment, the present invention can provide a geolocation data discovery method, including mining a textual alert message of an event from secure source data for a geolocational indicator, a temporal indicator, and a type of alert, embedding a geolocational tag location and a temporal tag time to the event based on the geolocational indicator and the temporal indicator mined from the textual alert message by the mining, and mapping the event with an icon related to the type of alert at the geolocational tag location on a navigational map at a time associated with the temporal tag time of the event.

Even further, in another exemplary embodiment, the present invention can provide a non-transitory computer-readable recording medium recording a geolocation data discovery program, the program causing a computer to perform: mining a textual alert message of an event from secure source data for a geolocational indicator, a temporal indicator, and a type of alert, embedding a geolocational tag location and a temporal tag time to the event based on the geolocational indicator and the temporal indicator mined from the textual alert message by the mining, and mapping the event with an icon related to the type of alert at the geolocational tag location on a navigational map at a time associated with the temporal tag time of the event.

There has thus been outlined, rather broadly, an embodiment of the invention in order that the detailed description thereof herein may be better understood, and in order that the present contribution to the art may be better appreciated. There are, of course, additional exemplary embodiments of the invention that will be described below and which will form the subject matter of the claims appended hereto.

DETAILED DESCRIPTION OF THE EXEMPLARY EMBODIMENTS

With reference now toFIG. 1, the geolocation data discovery system100includes secure text mining circuit101, a geolocation and temporal embedding circuit102, an event mapping circuit103, a feedback circuit104, a navigational route recommending circuit105, and an alert sending circuit106. The geolocation data discovery system100includes a processor180and a memory190, with the memory190storing instructions to cause the processor180to execute each circuit of the geolocation data discovery system100. The processor and memory may be physical hardware components, or a combination of hardware and software components.

Although the geolocation data discovery system100includes various circuits, it should be noted that a geolocation data discovery system can include modules in which the memory190stores instructions to cause the processor180to execute each module of the geolocation data discovery system100.

Also, each circuit can be a stand-alone device, unit, module, etc. that can be interconnected to cooperatively produce a transformation to a result.

With the use of these various circuits, the geolocation data discovery system100may act in a more sophisticated and useful fashion, and in a cognitive manner while giving the impression of mental abilities and processes related to knowledge, attention, memory, judgment and evaluation, reasoning, and advanced computation. That is, a system is said to be “cognitive” if it possesses macro-scale properties—perception, goal-oriented behavior, learning/memory and action—that characterize systems (i.e., humans) that all agree are cognitive.

Although as shown inFIGS. 3-5and as described later, the computer system/server12is exemplarily shown in cloud computing node10as a general-purpose computing circuit which may execute in a layer the geolocation data discovery system100(FIG. 5), it is noted that the present invention can be implemented outside of the cloud environment.

The secure source data130includes textual alert messages generated via Twitter®, Nixle®, e-mail, and other social media used by secure trusted sources (e.g., official public safety organizations for traffic, road condition and maintenance alerts). For example, an alert message can recite “accident at intersection of Maple Street and Kennedy Drive” or “construction on Jefferson Bridge between 12 A.M. and 5 A.M.”.

The secure text mining circuit101receives the textual alert messages for an event from the secure source data130and mines the textual alert messages for geolocational indicators and a type of alert. For example, the term “accident” would be mined by the secure text mining circuit101to indicate that there may be a traffic incident. The terms “Maple Street and Kennedy Drive” would be the geolocational indicators of the textual alert message. That is, the secure text mining circuit101using natural language processing techniques to mine the textual alert messages for the type of alert associated with different words such as a “crash”, “accident”, “collision”, “pile-up”, “traffic jam”, “back-up” etc. and then links the geolocational indicator with the type of alert.

Also, the secure text mining circuit101mines the textual alert message for a temporal element indicating a time of the event. For example, although the accident may be recorded at the time the textual alert message was sent, the “construction on Jefferson Bridge between 12 A.M. and 5 A.M.” is mined for the specific time of the event to occur.

Based on the geolocational indicator mined from the alert textual messages by the secure text mining circuit101, the geolocation and temporal embedding circuit102embeds the textual alert message with geolocation data matching the location of the alert (e.g., the global positioning coordinates of Maple Street and Kennedy Drive) and embeds a temporal element (e.g., the time that the textual element was sent or a time indicated in the textual element) with the alert message.

Based on the embedded geolocation data and the temporal data and the type of event identified, the event mapping circuit103maps the event on a navigational map160in the location corresponding to the geolocation data and at a time corresponding to the temporal data. For example, the event mapping circuit103would immediately display an accident on the map at the location of Maple Street and Kennedy Drive but may not display the construction on the Jefferson Bridge until 12 A.M. (or at a time within a threshold from the beginning of the construction). Preferably, the event mapping circuit103uses an icon related to the type of event (e.g., a construction icon, accident icon, etc.) to show the event on the navigational map160.

The feedback circuit104receives feedback data150from sensors displayed in a location that can view the event such as traffic cameras, security cameras, etc., from imaging devices on a secure trusted vehicle (e.g., such as government vehicles), from messages sent to the system from a secure trusted user (e.g., such as a user having a “.gov” e-mail), etc. Based on the feedback from the secure trusted sources, the feedback circuit104updates the navigational map160. For example, if a traffic camera detects that the accident has been cleared, the feedback circuit104can cause the event mapping circuit103to remove the event. Or, if a government vehicle is at the construction site and the construction ends at 3 A.M. instead of 5 A.M., images from the government vehicle fed into the feedback circuit104which are analyzed to determine that the construction is over and the event can be removed from the navigational map160.

Also, the feedback circuit104can receive feedback data150related to an event and determine if the event was mapped. For example, if a traffic camera detects an accident at mile marker seven on the interstate and the event mapping circuit103did not map the event, the feedback circuit104causes the secure text mining circuit101to mine the secure source data130to look for a textual alert message related to mile marker seven. If the secure text mining circuit101finds a textual alert message reciting “large pile-up at mile marker seven”, the feedback circuit104causes the secure text mining circuit101to learn that “pile-up” can mean accident and the event should have been sent to the geolocation and temporal embedding circuit102to embed the geolocation data and temporal data with the message. In this manner, the feedback circuit104can cause the natural language processing of the securing text mining circuit101to “learn” over time, thereby to increase the accuracy of the system100.

It is noted that the feedback circuit104can act in a cognitive manner such that the feedback circuit104can also learn false positives and not update the event mapping circuit103in the future based on the learning. For example, if particular secure vehicle cameras generate images at night that give an illusion of a street being clear but the secure trusted vehicle cameras cannot generate night time images and the street is actually not clear, the feedback circuit104learns this feedback and does not update the navigational map160based on this type of input in the future.

Thus, the feedback circuit104receives feedback data150from a secure trusted source to modify the navigational map160or learn new modifications based on past generated navigational map160sto optimize the system100. For example, the feedback circuit104receives the feedback data150from a secured trusted source (i.e., a government vehicle, government e-mail address, verified user, etc.) and causes the event mapping circuit103to update a status of the event (e.g., more traffic, accident over, construction finished, etc.) on the navigational map160based on the received feedback data150.

The navigational route recommending circuit105interfaces with a user device140including a navigational service140aand recommends a navigational route to the user based on avoiding the events mapped on the navigational map160by the event mapping circuit103. That is, the navigation route recommending circuit105matches the temporal data embedded in the navigational map with a current time (or predicted time to be at that location) from the current user location to determine how to avoid events.

In one embodiment, the entire geolocation data discovery system100can be installed on the user device140as an “application” which can be customized by the user. For example, the application on the user device includes access to the secure source data130(using GPS tags used in social media, for example) which the application dynamically subscribes to or unsubscribes from based on a geolocational position of the user. That is, the application can use GPS locations on the user device140to subscribe to specific GPS tagged secure source data130(e.g. Department of Transportation for a location, a local town construction service, etc.) instead of from a general provider of data (e.g., an unsecure provider), and a GPS tag may be for a large area outside of driving range (e.g. whole state, county, etc.). Text from feeds of the subscribed to secure source data130are analyzed by the secure text mining circuit101to determine events within an adjustable radius of the user location or on the programmed route on the navigational service140a. In other words, the secure text mining circuit101only mines secure source data130for the subscribed to streams, the geolocation and temporal embedding circuit102embeds the geolocation data and temporal data with the event, and the event mapping circuit103only displays the events to the user if the events are within the adjustable radius of the user or on the programmed route.

The application enables the driver to dismiss events or correct an event (e.g. it is not a traffic event and the secure text mining circuit101read the text incorrectly). A scoring algorithm on the device makes adjustments locally and also provides feedback on corrections to back end server for model updates. Analytics can also learn the driver behavior of what type of events are always (or often) dismissed to reduce event generation. None of the driver behavior is sent to a cloud computing node and instead the computations are done locally on the user device140. As vehicle travels on route, new subscriptions are generated for new feeds, others unsubscribed to, etc. to continuously update the navigational map160.

Also, the feedback circuit104can receive GPS data from a government vehicle traveling in a region near the reporting event such that the feedback circuit104can help predict a delay associated with the event mapped on the navigational map160. Therefore, the navigational route recommending circuit105can predict delay times in a trusted manner from secure sources such as the government vehicles traveling in the area of the event.

Thereby, the application can allow, for example, based on the location of the user device140, a set of social media feeds to be selected to be sent to the device for real time analysis. Users can limit the feeds so as to limit the data transferred. The textual data is analyzed and location, temporal data, and event type are determined and displayed in the application on the navigational map160. Users can mark the event as “false recognition” when received and this data can be sent back to a centralized data center so as to improve the model. As the location of the user device140changes, the social media feeds (e.g., the secure source data130) change automatically based on location. By shutting down some feeds (no longer subscribing) and only subscribing to feeds that are for specific states, counties, etc. this reduces the amount of data required to be transferred. The application can detect feeds that have become largely redundant with the same information and automatically turns off one of the subscriptions producing the redundant information (e.g. same information coming from a fire department and police department). It is noted that pushing the analytics to the user device140leverages battery power and potentially electric charge from the vehicle, rather than the electricity in a data center. The user can specify to only show traffic events on a designated route and with a likelihood that the driver will encounter the event based on their travel (e.g. how far a car is traveling when will it arrive at accident). This can also reduce the amount of data that needs to be transmitted.

The alert sending circuit106sends a text message, e-mail, alert notification, a “GeoSMS” message, etc. to the user device140of the event when the user device140is within a predetermined distance of the event. Thereby, if the user is a pedestrian or not currently traveling, the user can still receive alerts based on their location being near the location of an incident without having to view the navigational map160.

FIG. 2shows a high level flow chart for a method200of geolocation data discovery.

Step201receives a textual alert message for an event from the secure source data130and mines the textual alert message for geolocational and temporal indicators and a type of alert.

Based on the geolocational indicator mined from the alert textual message by Step201, Step202embeds the textual alert message with the geolocation data matching the location of the alert (e.g., the global positioning coordinates) and embeds a temporal element (e.g., the time that the textual element was sent or a time indicated in the textual element) with the alert message.

Step203maps the event on a navigational map160in a location corresponding to the geolocation data and at a time corresponding to the temporal data.

Step204receives feedback data150from sensors displayed in a location that can view the event such as traffic cameras, security cameras, etc., from imaging devices on secure trusted vehicle (e.g., such as government vehicles), from messages sent to the system from a secure trusted user (e.g., such as a user having a “.gov” e-mail), etc. Based on the feedback from the secure sources, Step204updates the events of the navigational map160.

Step205recommends a navigational route to the user based on avoiding the events mapped on the navigational map160by Step203via interfacing with a navigational service140aon a user device140.

Step206sends a text message, e-mail, alert notification, a “GeoSMS” message, etc. to the user device140of the event when the user device140is within a predetermined distance of the event. Thereby, if the user is a pedestrian or not currently traveling, the user can still receive alerts based on their location being near the location of an incident without having to view the navigational map160.

Therefore, by mining events from textual alert messages in secure source data130(e.g., trusted providers of data such as a government entity) and associating geolocation data and temporal data with the textual alert message, the events mapped on the navigational map160can be trusted to be accurate and secure from manipulation. In other words, the events mapped on the navigational map160are entirely generated based on secure source data130such that users cannot manipulate source data to create (or prevent) traffic or congestion.

Exemplary Hardware Aspects, Using a Cloud Computing Environment

Characteristics are as follows:

Service Models are as follows:

Deployment Models are as follows:

In cloud computing node10, there is a computer system/server12, which is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well-known computing systems, environments, and/or configurations that may be suitable for use with computer system/server12include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop circuits, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputer systems, mainframe computer systems, and distributed cloud computing environments that include any of the above systems or circuits, and the like.

Workloads layer90provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation91; software development and lifecycle management92; virtual classroom education delivery93; data analytics processing94; transaction processing95; and, more particularly relative to the present invention, the geolocation data discovery system100described herein.