Computer Network Controlled Data Orchestration System And Method For Data Aggregation, Normalization, For Presentation, Analysis And Action/Decision Making

Abstract

Embodiments disclosed include computer automated systems and methods for aggregating data from a plurality of data sources, such as proxy devices, legacy protocols, devices, applications, machines, sensors, things across locations and user types, or device clouds among devices and applications. The aggregated data is then normalized, and the normalized data is analyzed. The analyzing is based on a correlated event or events, a correlated condition or conditions, and a correlated trend or trends across the plurality of data sources. And based on the analyzed data, relevant aggregated and normalized data is combined and displayed in a display compatible format. Additionally, user needs are determined based on the analyzed aggregated, normalized data. The user need comprises a need for an item or items comprising at least one of a service, a product, and an upgrade of hardware or software components. Further a provider from a plurality of providers is determined based on the determined user need, and finally a need fulfillment transaction between the user and the provider is initiated.

We claim: 
     
         1 . A computer automated system comprising:
 a processing unit;   a memory element coupled to the processing unit;   wherein the computer automated system is configured to, in real-time:
 abstract a plurality of device classes 
 automatically aggregate device behavior data via an embedded data collection stack, from the plurality of abstracted device classes, wherein the said plurality of abstracted device classes comprise a single or plurality of proxy devices, legacy protocols, devices, applications, machines, and sensors across locations; 
 canonical-ize the aggregated device behavior data from the abstracted plurality of device classes, wherein canonicalization comprises adding meta-data and derived data to aggregated device behavior data from each of the abstracted plurality of device classes; 
 based on a correlated event or events and a correlated condition or conditions across the plurality of device classes, combine the aggregated and canonicalized device behavior data from the abstracted plurality of device classes; and 
 display the combined device behavior data in a display compatible format. 
   
     
     
         2 . The computer automated system of  claim 1  wherein the computer automated system is further configured to, in real-time:
 automatically normalize the aggregated device behavior data from the abstracted plurality of device classes; 
 wherein automatic normalization comprises normalization at the collection point or at an edge from an associated or recognized proxy, and normalization at an associated server from an unassociated or unrecognized proxy. 
 
     
     
         3 . The computer automated system of  claim 1  wherein abstracting the plurality of device classes comprises abstracting a plurality of sensors, and connected devices comprising medical CT scanners, medical Mills, printers, and UPS systems. 
     
     
         4 . The computer automated system of  claim 1  wherein the computer system is further caused to:
 via a plurality of extensible connectors, extract log file data, or proprietary or standard protocol comprising SNMP data and HTTP data from each of the plurality of abstracted device classes. 
 
     
     
         5 . The computer automated system of  claim 4  wherein the system is further configured to:
 map device behavior data-model parameters to variables in device models via a previously created plurality of templates; and 
 wherein the said plurality of extensible connectors are further caused to extract the device behavior data model parameters mapped to the said variables in the said device models. 
 
     
     
         6 . The computer automated system of  claim 1  wherein the system is configured to:
 store each device parameter as a name value pair in a no-schema database. 
 
     
     
         7 . The computer automated system of  claim 1  wherein:
 said adding meta-data and derived data to the aggregated device behavior data from each of the plurality of device classes in canonicalization of the aggregated device behavior data comprises adding the meta data and the derived device behavior data via a single or plurality of workflows; and 
 automatically analyze the canonical-ized device behavior data based on the correlated event or events, and the correlated condition or conditions, across the abstracted plurality of device classes. 
 
     
     
         8 . The computer automated system of  claim 1  wherein the system is configured to:
 display device behavior data in a plurality of different forms wherein the said plurality of different forms comprises at least one of a graph, a chart, and a table. 
 
     
     
         9 . The computer automated system of  claim 8  wherein the said graph, chart and table are configured to show device behavior data relevant to each device class. 
     
     
         10 . The computer automated system of  claim 1  wherein the computer automated system is further caused to:
 analyze the canonical-ized device behavior data based on the correlated event or events, and the correlated condition or conditions, across the abstracted plurality of device classes; and 
 the analysis comprises analysis via a single or plurality of Device Internet of Things (TOT) stacks and gateways; and 
 wherein based on the said analysis, the system is configured to implement a single or plurality of decisions, in real-time, in a return path or closed loop, on a plurality of machines, sensors, devices and applications. 
 
     
     
         11 . The computer automated system of  claim 1  further comprising a mobile device. 
     
     
         12 . In a computer automated system comprising a processing unit coupled to a memory element, an embedded data collection stack, and having instructions encoded thereon, a method comprising, in real-time:
 abstracting a plurality of device classes;   aggregating device behavior data via an embedded data collection stack from the abstracted plurality of device classes, wherein the abstracted plurality of device classes comprise a single or plurality of proxy devices, legacy protocols, devices, applications, machines, sensors and things across locations;
 canonicalizing the aggregated device behavior data from the abstracted plurality of device classes, wherein canonicalization comprises adding meta-data and derived data to aggregated device behavior data from each of the abstracted plurality of device classes; 
 automatically analyzing the canonicalized device behavior data based on a correlated event or events, a correlated condition or conditions, and a correlated trend or trends across the plurality of device classes; and 
 based on a correlated event or events, a correlated condition or conditions, and a correlated trend or trends across the plurality of device classes combining the aggregated and canonicalized device behavior data, and displaying the combined device behavior data in a display compatible format. 
   
     
     
         13 . The method of  claim 12  further comprising: automatically normalizing the aggregated device behavior data from the abstracted plurality of device classes; wherein automatic normalization comprises normalization at the collection point or at an edge from an associated or recognized proxy, and normalization at an associated server from an unassociated or unrecognized proxy. 
     
     
         14 . The method of  claim 12  wherein the abstracting the plurality of classes of the plurality of device classes comprises abstracting a plurality of sensors, and connected devices comprising medical CT scanners, medical Mills, printers, and UPS systems. 
     
     
         15 . The method of  claim 12  further comprising:
 extracting a log file data, or proprietary or standard protocol comprising at least one of an SNMP data and HTTP data from each of the abstracted plurality of device classes via a plurality of extensible connectors. 
 
     
     
         16 . The method of  claim 15  further comprising:
 mapping data-model parameters to variables in device class models via a plurality of pre-created templates; and 
 extracting the data model parameters mapped to the said variables in the said device class models via the said plurality of extensible connectors. 
 
     
     
         17 . The method of  claim 12  further comprising:
 storing each device parameter as a name value pair in a no-schema database. 
 
     
     
         18 . The method of  claim 12  wherein:
 said adding meta-data and derived data to the aggregated device behavior data from each of the plurality of device classes in canonicalization of the aggregated device behavior data comprises adding the meta data and the derived data via a single or plurality of workflows; and 
 automatically analyzing the canonicalized device behavior data based on the correlated event or events, the correlated condition or conditions, and the correlated trend or trends across the abstracted plurality of device classes. 
 
     
     
         19 . The method of  claim 12  further comprising:
 displaying device behavior data in a plurality of forms wherein the said plurality of forms comprises at least one of a graph, a chart, and a table. 
 
     
     
         20 . The method of  claim 19  wherein the said graph, chart and table are configured to show behavior data relevant to each device class. 
     
     
         21 . The method of  claim 18  wherein the analyzing of device behavior data based on the correlated event or events, and the correlated condition or conditions, across the plurality of device classes comprises:
 analyzing via a single or plurality of Device Internet of Things (TOT) stacks and gateways; and 
 based on the said analyzing, implementing a single or plurality of actions, in real-time, in a return path or closed loop, on a single or plurality of machines, sensors, devices or applications. 
 
     
     
         22 . A mobile wireless communication device comprising:
 a processing unit;   a memory element coupled to the processing unit;   an embedded data collection stack;   a plurality of sensors;   encoded instructions that configure the mobile device to, automatically in real-time:
 enable a computer automated system to: 
   aggregate behavior data of the mobile wireless communication device and a plurality of such mobile wireless communication devices over a network via the embedded data collection stack;
 abstract the plurality of mobile wireless communication devices; 
 canonical-ize the aggregated device behavior data from the abstracted plurality of mobile wireless devices, wherein canonicalization comprises adding meta-data and derived data to aggregated device behavior data from each of the plurality of devices; 
 analyze the canonicalized device behavior data based on a correlated event or events, a correlated condition or conditions, and a correlated trend or trends across the plurality of devices; and 
 based on the analyzed device behavior data, combine the aggregated and normalized device behavior data, and display the combined device behavior data in a display compatible format. 
   
     
     
         23 . The mobile wireless communication device of  claim 22  wherein the device is further configured to:
 based on the analyzed device behavior data, trigger a single or plurality of actions, in real-time, in a return path or closed loop, on a single or plurality of machines, sensors, devices or applications.