# US 2018/0082207 A1 · Cognitive modeling system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0082207 A1 - Document ID: US20180082207A1 - Status: Published application - Kind: A1 - Application number: 15/707,773 - Filed: 2017-09-18 - Published: 2018-03-22 - Earliest claimed: 2016-09-21 - Assignees: Scianta Analytics, LLC - Applicants: Scianta Analytics, LLC - Inventors: Michael E. Cormier; Earl D. Cox; William E. Thackrey; Joseph McGlynn; Harry Gardner - Web page: https://patentgrep.com/patent/US20180082207A1 ## CPC - G06N 20/00: Machine learning - G06F 11/0754: {by exceeding limits} - G06F 11/3447: {Performance evaluation by modeling} - G06F 16/2365: {Ensuring data consistency and integrity} - G06F 16/27: Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor - G06N 3/043: based on fuzzy logic, fuzzy membership or fuzzy inference, e.g. adaptive neuro-fuzzy inference systems [ANFIS] - G06N 5/02: Knowledge representation; Symbolic representation - G06N 5/022: Knowledge engineering; Knowledge acquisition - G06N 5/025: Extracting rules from data - G06N 5/043: Distributed expert systems; Blackboards - G06N 5/048: Fuzzy inferencing - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 7/02: using fuzzy logic (computing arrangements based on biological models G06N3/00; computing arrangements using knowledge-based models G06N5/00) - G06N 7/046: {Implementation by means of a neural network (neural networks using fuzzy logic G06N3/043)} - G05B 17/02: electric - G06F 17/15: Correlation function computation {including computation of convolution operations (arithmetic circuits for sum of products per se, e.g. multiply-accumulators G06F7/5443; digital filters, e.g. FIR, IIR, adaptive filters H03H17/00)} - G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06F 18/2411: based on the proximity to a decision surface, e.g. support vector machines - G06F 18/2433: Single-class perspective, e.g. one-against-all classification; Novelty detection; Outlier detection - G06F 2201/81: Threshold - G06N 3/126: Evolutionary algorithms, e.g. genetic algorithms or genetic programming - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) ## Abstract The present design is directed to a cognitive system including a receiver configured to receive a set of actors and associated actor information and receive assets and their associated asset information, a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries for a time period, and a computing apparatus configured to compute trust of the cognitive model as a fuzzy number and activate the cognitive model if trust of the cognitive model is above a cognitive model trust threshold. When the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model. ## Claims ### Claim 1 A cognitive system comprising: a receiver configured to receive a set of actors and associated actor information and receive assets and their associated asset information; a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries for a time period; and a computing apparatus configured to compute trust of the cognitive model as a fuzzy number and activate the cognitive model if trust of the cognitive model is above a cognitive model trust threshold; wherein when the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model; wherein, for selected data dictionary entries, the cognitive modeling system is configured to normalize associated actor actions by converting at least one event to data dictionary format, insert at least one normalized terrain entry into the cognitive model, and update the cognitive model. ### Claim 2 (depends on 1) The cognitive modeling system of claim 1, wherein the computing apparatus comprises a series of node devices, comprising a series of processor nodes configured to provide data to a series of aggregator nodes. ### Claim 3 (depends on 1) The cognitive modeling system of claim 1, wherein the anomaly analysis determines a severity and degree of inconsistency from normal behavior. ### Claim 4 (depends on 1) The cognitive modeling system of claim 1, wherein the cognitive modeling system is further configured to perform a threat detection comprising an importance value assigned to a resource. ### Claim 5 (depends on 1) The cognitive modeling system of claim 1, wherein the computing apparatus normalizes, migrates, supervises and analyzes data to identify issues between actors in the taxonomy. ### Claim 6 (depends on 2) The cognitive modeling system of claim 2, wherein the taxonomy comprises a common language understood by the series of node devices such that data can be shared between the series of node devices. ### Claim 7 A cognitive modeling system comprising: a receiver configured to receive a set of actors, associated actor information, assets, and associated asset information; a creation apparatus configured to create data dictionary entries for a taxonomy based on the set of actors and the assets and create a cognitive model using the data dictionary entries applicable to a time period; and a computing apparatus configured to compute trust of the cognitive model as a fuzzy number and activate the cognitive model if trust of the cognitive model is above a cognitive model trust threshold. ### Claim 8 (depends on 6) The cognitive modeling system of claim 6, wherein when the cognitive model is activated, the cognitive modeling system is configured to schedule a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model. ### Claim 9 (depends on 6) The cognitive modeling system of claim 6, wherein, for selected data dictionary entries, the cognitive modeling system is configured to normalize associated actor actions by converting at least one event to data dictionary format. ### Claim 10 (depends on 6) The cognitive modeling system of claim 6, wherein, for selected data dictionary entries, the cognitive modeling system is configured to insert at least one normalized terrain entry into the cognitive model ### Claim 11 (depends on 6) The cognitive modeling system of claim 6, wherein, for selected data dictionary entries, the cognitive modeling system is configured to update the cognitive model. ### Claim 12 (depends on 6) The cognitive modeling system of claim 6, wherein the computing apparatus comprises a series of node devices, comprising a series of processor nodes configured to provide data to a series of aggregator nodes. ### Claim 13 (depends on 6) The cognitive modeling system of claim 6, wherein the anomaly analysis determines a severity and degree of inconsistency from normal behavior. ### Claim 14 (depends on 6) The cognitive modeling system of claim 6, wherein the cognitive modeling system is further configured to perform a threat detection comprising an importance value assigned to a resource. ### Claim 15 (depends on 6) The cognitive modeling system of claim 6, wherein the computing apparatus normalizes, migrates, supervises and analyzes data to identify issues between actors in the taxonomy. ### Claim 16 A cognitive modeling method comprising: receiving at a hardware computing arrangement, comprising a number of processor nodes and a number of aggregator nodes, a set of actors, associated actor information, assets, and associated asset information; creating data dictionary entries for a taxonomy based on the set of actors and the assets; creating a cognitive model using the data dictionary entries for a time period; computing trust of the cognitive model as a fuzzy number; activating the cognitive model if trust of the cognitive model is above a cognitive model trust threshold; when the cognitive model is activated, scheduling a collection of tasks to run that perform regular extraction of actions from an original data source and perform at least one anomaly analysis associated with the cognitive model; and for selected data dictionary entries, normalizing associated actor actions by: converting at least one event to data dictionary format; inserting at least one normalized terrain entry into the cognitive model; and updating the cognitive model. ### Claim 17 (depends on 16) The cognitive modeling method of claim 16, wherein anomaly analysis determines a severity and degree of inconsistency from normal behavior. ### Claim 18 (depends on 16) The cognitive modeling method of claim 16, further comprising performing a threat detection comprising an importance value assigned to a resource. ### Claim 19 (depends on 16) The cognitive modeling method of claim 16, wherein the taxonomy comprises a common language understood by the series of node devices such that data can be shared between the series of node devices.