Information Management System and Method
A computer-implemented method, computer program product and computing system for: defining an incident as the occurrence of a plurality of required alarms; monitoring a plurality of devices to detect the occurrence of alarms, thus defining a plurality of detected alarms; and predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred.
1 . A computer-implemented method, executed on a computing device, comprising:
defining an incident as the occurrence of a plurality of required alarms;
monitoring a plurality of devices to detect the occurrence of alarms, thus defining a plurality of detected alarms; and
predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred.
2 . The computer-implemented method of claim 1 wherein defining an incident as the occurrence of a plurality of required alarms includes:
defining an incident as the occurrence of a plurality of required alarms within a defined period of time.
3 . The computer-implemented method of claim 1 wherein monitoring a plurality of devices to detect the occurrence of alarms includes:
monitoring the plurality of devices to receive data signals indicative of the plurality of devices; and
comparing the data signals to defined signal norms to identify one or more of the plurality of detected alarms.
4 . The computer-implemented method of claim 3 wherein the data signals concern one or more details of the plurality of devices and/or one or more uses of the plurality of devices.
5 . The computer-implemented method of claim 3 wherein the defined signal norms include user-defined signal norms.
6 . The computer-implemented method of claim 3 wherein the defined signal norms include machine-defined signal norms.
7 . The computer-implemented method of claim 6 wherein the machine-defined signal norms are defined via massive data sets that are processed by machine learning.
8 . The computer-implemented method of claim 6 wherein the machine-defined signal norms are compartmentalized (e.g., gender, race, age, location, device type, device class, seasonality, time of day, etc.).
9 . The computer-implemented method of claim 1 wherein the plurality of devices includes one or more of: a medical device, a process control device, a networking device, a computing device, a manufacturing device, an agricultural device, an energy/refining device, an aerospace device, a forestry device, and a defense device.
10 . The computer-implemented method of claim 1 wherein the plurality of devices are geographically dispersed.
11 . The computer-implemented method of claim 1 wherein the defined portion of the plurality of required alarms is defined via massive data sets that are processed by machine learning.
12 . The computer-implemented method of claim 1 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred in a defined sequence.
13 . The computer-implemented method of claim 1 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred within a defined period of time.
14 . A computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by a processor, cause the processor to perform operations comprising:
defining an incident as the occurrence of a plurality of required alarms;
monitoring a plurality of devices to detect the occurrence of alarms, thus defining a plurality of detected alarms; and
predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred.
15 . The computer program product of claim 14 wherein defining an incident as the occurrence of a plurality of required alarms includes:
defining an incident as the occurrence of a plurality of required alarms within a defined period of time.
16 . The computer program product of claim 14 wherein monitoring a plurality of devices to detect the occurrence of alarms includes:
monitoring the plurality of devices to receive data signals indicative of the plurality of devices; and
comparing the data signals to defined signal norms to identify one or more of the plurality of detected alarms.
17 . The computer program product of claim 16 wherein the data signals concern one or more details of the plurality of devices and/or one or more uses of the plurality of devices.
18 . The computer program product of claim 16 wherein the defined signal norms include user-defined signal norms.
19 . The computer program product of claim 16 wherein the defined signal norms include machine-defined signal norms.
20 . The computer program product of claim 19 wherein the machine-defined signal norms are defined via massive data sets that are processed by machine learning.
21 . The computer program product of claim 19 wherein the machine-defined signal norms are compartmentalized (e.g., gender, race, age, location, device type, device class, seasonality, time of day, etc.).
22 . The computer program product of claim 14 wherein the plurality of devices includes one or more of: a medical device, a process control device, a networking device, a computing device, a manufacturing device, an agricultural device, an energy/refining device, an aerospace device, a forestry device, and a defense device.
23 . The computer program product of claim 14 wherein the plurality of devices are geographically dispersed.
24 . The computer program product of claim 14 wherein the defined portion of the plurality of required alarms is defined via massive data sets that are processed by machine learning.
25 . The computer program product of claim 14 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred in a defined sequence.
26 . The computer program product of claim 14 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred within a defined period of time.
27 . A computing system including a processor and memory configured to perform operations comprising:
defining an incident as the occurrence of a plurality of required alarms;
monitoring a plurality of devices to detect the occurrence of alarms, thus defining a plurality of detected alarms; and
predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred.
28 . The computing system of claim 27 wherein defining an incident as the occurrence of a plurality of required alarms includes:
defining an incident as the occurrence of a plurality of required alarms within a defined period of time.
29 . The computing system of claim 27 wherein monitoring a plurality of devices to detect the occurrence of alarms includes:
monitoring the plurality of devices to receive data signals indicative of the plurality of devices; and
comparing the data signals to defined signal norms to identify one or more of the plurality of detected alarms.
30 . The computing system of claim 29 wherein the data signals concern one or more details of the plurality of devices and/or one or more uses of the plurality of devices.
31 . The computing system of claim 29 wherein the defined signal norms include user-defined signal norms.
32 . The computing system of claim 29 wherein the defined signal norms include machine-defined signal norms.
33 . The computing system of claim 32 wherein the machine-defined signal norms are defined via massive data sets that are processed by machine learning.
34 . The computing system of claim 32 wherein the machine-defined signal norms are compartmentalized (e.g., gender, race, age, location, device type, device class, seasonality, time of day, etc.).
35 . The computing system of claim 27 wherein the plurality of devices includes one or more of: a medical device, a process control device, a networking device, a computing device, a manufacturing device, an agricultural device, an energy/refining device, an aerospace device, a forestry device, and a defense device.
36 . The computing system of claim 27 wherein the plurality of devices are geographically dispersed.
37 . The computing system of claim 27 wherein the defined portion of the plurality of required alarms is defined via massive data sets that are processed by machine learning.
38 . The computing system of claim 27 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred in a defined sequence.
39 . The computing system of claim 27 wherein predicting the occurrence of the incident if a defined portion of the plurality of required alarms has occurred includes:
requiring that the defined portion of the plurality of required alarms have occurred within a defined period of time.