Information Management System and Method
A computer-implemented method, computer program product and computing system for: monitoring a device to receive data signals indicative of the device; comparing the data signals to defined signal norms to identify outliers; investigating the outliers to determine if an issue exists with the device; and adjusting outlier definition criteria to eliminate the outlier if an issue does not exist.
1 . A computer-implemented method, executed on a computing device, comprising:
monitoring a device to receive data signals indicative of the device;
comparing the data signals to defined signal norms to identify outliers;
investigating the outliers to determine if an issue exists with the device: and
adjusting outlier definition criteria to eliminate the outlier if an issue does not exist.
2 . The computer-implemented method of claim 1 wherein the outlier definition criteria includes signal thresholds.
3 . The computer-implemented method of claim 1 wherein the data signals concern one or more details of the device and/or uses of the device.
4 . The computer-implemented method of claim 1 wherein the device 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.
5 . The computer-implemented method of claim 1 wherein the defined signal norms include user-defined signal norms.
6 . The computer-implemented method of claim 1 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 investigating the outliers to determine if an issue exists with the device includes one or more of:
physically investigating the outliers; and
examining other data signals from the device.
10 . The computer-implemented method of claim 1 wherein adjusting the outlier definition criteria includes:
defining bespoke outlier definition criteria for the device.
11 . The computer-implemented method of claim 1 further comprising:
addressing the issue if the issue does exist.
12 . The computer-implemented method of claim 1 wherein the device includes one or more sub devices.
13 . 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:
monitoring a device to receive data signals indicative of the device;
comparing the data signals to defined signal norms to identify outliers;
investigating the outliers to determine if an issue exists with the device: and
adjusting outlier definition criteria to eliminate the outlier if an issue does not exist.
14 . The computer program product of claim 13 wherein the outlier definition criteria includes signal thresholds.
15 . The computer program product of claim 13 wherein the data signals concern one or more details of the device and/or uses of the device.
16 . The computer program product of claim 13 wherein the device 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.
17 . The computer program product of claim 13 wherein the defined signal norms include user-defined signal norms.
18 . The computer program product of claim 13 wherein the defined signal norms include machine-defined signal norms.
19 . The computer program product of claim 18 wherein the machine-defined signal norms are defined via massive data sets that are processed by machine learning.
20 . The computer program product of claim 18 wherein the machine-defined signal norms are compartmentalized (e.g., gender, race, age, location, device type, device class, seasonality, time of day, etc.).
21 . The computer program product of claim 13 wherein investigating the outliers to determine if an issue exists with the device includes one or more of:
physically investigating the outliers; and
examining other data signals from the device.
22 . The computer program product of claim 13 wherein adjusting the outlier definition criteria includes:
defining bespoke outlier definition criteria for the device.
23 . The computer program product of claim 13 further comprising:
addressing the issue if the issue does exist.
24 . The computer program product of claim 13 wherein the device includes one or more sub devices.
25 . A computing system including a processor and memory configured to perform operations comprising:
monitoring a device to receive data signals indicative of the device;
comparing the data signals to defined signal norms to identify outliers;
investigating the outliers to determine if an issue exists with the device: and
adjusting outlier definition criteria to eliminate the outlier if an issue does not exist.
26 . The computing system of claim 25 wherein the outlier definition criteria includes signal thresholds.
27 . The computing system of claim 25 wherein the data signals concern one or more details of the device and/or uses of the device.
28 . The computing system of claim 25 wherein the device 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.
29 . The computing system of claim 25 wherein the defined signal norms include user-defined signal norms.
30 . The computing system of claim 25 wherein the defined signal norms include machine-defined signal norms.
31 . The computing system of claim 30 wherein the machine-defined signal norms are defined via massive data sets that are processed by machine learning.
32 . The computing system of claim 30 wherein the machine-defined signal norms are compartmentalized (e.g., gender, race, age, location, device type, device class, seasonality, time of day, etc.).
33 . The computing system of claim 25 wherein investigating the outliers to determine if an issue exists with the device includes one or more of:
physically investigating the outliers; and
examining other data signals from the device.
34 . The computing system of claim 25 wherein adjusting the outlier definition criteria includes:
defining bespoke outlier definition criteria for the device.
35 . The computing system of claim 25 further comprising:
addressing the issue if the issue does exist.
36 . The computing system of claim 25 wherein the device includes one or more sub devices.