GENERATING EVENT DEFINITIONS BASED ON SPATIAL AND RELATIONAL RELATIONSHIPS
First Claim
1. A dynamic event detection architecture configured as one or more hypernodes and comprising:
- (a) a workflow engine that runs one or more workflows employed for processing input data by facilitating logic and state manipulation of the input data based on requirements of a specified task;
(b) a semantic database defined by an ontological model that is related to the specified task and which includes a meaning, rules, and data elements based on the ontological model;
(c) a hyperfragmenter system that processes the input data in real-time, in a workflow, to produce fragments of input data that are self-contained and discrete;
(d) a hyperasset file system joined with the semantic database such that both operate automatically, the hyperasset file system enabling the fragments of input data to be stored and retrieved based on specified criteria;
(e) a plurality of distributed experts that use an application program interface to facilitate review of the fragments of input data by the distributed experts at any point in the workflow and to provide additional information to the fragments of input data;
(f) a plurality of event definitions used by a first rule-based language engine to define events based on the fragments of input data in the workflow or based on relationships defined in the semantic database;
(g) a plurality of situation definitions used by a second rule-based engine to define complex situations to create situational rules; and
(h) a filtration system that applies the situational rules to the workflow to determines if an output of the workflow requires further analysis.
3 Assignments
0 Petitions
Accused Products
Abstract
Data from one or more sensors is input to a workflow and fragmented to produce HyperFragments. The HyperFragments of input data are processed by a plurality of Distributed Experts, who make decisions about what is included in the HyperFragments or add details relating to elements included therein, producing tagged HyperFragments, which are maintained as tuples in a Semantic Database. Algorithms are applied to process the HyperFragments to create an event definition corresponding to a specific activity. Based on related activity included in historical data and on ground truth data, the event definition is refined to produce a more accurate event definition. The resulting refined event definition can then be used with the current input data to more accurately detect when the specific activity is being carried out.
-
Citations
31 Claims
-
1. A dynamic event detection architecture configured as one or more hypernodes and comprising:
-
(a) a workflow engine that runs one or more workflows employed for processing input data by facilitating logic and state manipulation of the input data based on requirements of a specified task; (b) a semantic database defined by an ontological model that is related to the specified task and which includes a meaning, rules, and data elements based on the ontological model; (c) a hyperfragmenter system that processes the input data in real-time, in a workflow, to produce fragments of input data that are self-contained and discrete; (d) a hyperasset file system joined with the semantic database such that both operate automatically, the hyperasset file system enabling the fragments of input data to be stored and retrieved based on specified criteria; (e) a plurality of distributed experts that use an application program interface to facilitate review of the fragments of input data by the distributed experts at any point in the workflow and to provide additional information to the fragments of input data; (f) a plurality of event definitions used by a first rule-based language engine to define events based on the fragments of input data in the workflow or based on relationships defined in the semantic database; (g) a plurality of situation definitions used by a second rule-based engine to define complex situations to create situational rules; and (h) a filtration system that applies the situational rules to the workflow to determines if an output of the workflow requires further analysis. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
-
-
14. A method for processing input data in a managed workflow to identify events corresponding to a specified activity, comprising:
-
(a) fragmenting the input data to produce fragments of input data that are self-contained and discrete; (b) processing the fragments of input data at one or more nodes, using a plurality of distributed experts, the plurality of distributed experts working at the one or more nodes making determinations about the fragments of input data or adding details to the fragments of input data to produce tagged fragments of input data; (c) reviewing the tagged fragments of input data to create definitions for the events evident in the tagged fragments of input data; and (d) determining if the events evident in the tagged fragments of input data likely correspond to the specified activity. - View Dependent Claims (15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28)
-
-
29. A method for processing data used for carrying out a task, comprising:
-
(a) specifying one or more tagonomies for breaking down the task to facilitate processing of the data at a plurality of workflow nodes; (b) employing at least one of the one or more tagonomies to provide a logic tree with defined paths and properties associated with the task and thereby linking the one or more tagonomies to an ontology for the task; (c) associating a plurality of distributed experts with the plurality of workflow nodes, so that at least one of the plurality of distributed experts is assigned to process data at each of the plurality of workflow nodes in accord with the tagonomy specified for that workflow node, where the at least one tagonomy is employed for the task in the processing of data carried out at each workflow node; and (d) collecting the results from the processing of the data by each of the plurality of distributed experts to complete the task. - View Dependent Claims (30, 31)
-
Specification