# US 2018/0082192 A1 · Cognitive modeling apparatus including multiple knowledge node and supervisory node devices

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- Number: US 2018/0082192 A1
- Document ID: US20180082192A1
- Status: Published application
- Kind: A1
- Application number: 15/707,830
- 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/US20180082192A1

## 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 series of interconnected compute servers including a supervisory hardware node and a plurality of knowledge hardware nodes, wherein the series of interconnected compute servers are configured to categorize and scale performance of multiple disjoint algorithms across a seemingly infinite actor population, wherein the series of interconnected compute servers are configured to normalize data using a common taxonomy, distribute normalized data relatively evenly across the plurality of knowledge hardware nodes, supervise algorithm execution across knowledge hardware nodes, and collate and present results of analysis of the seemingly infinite actor population.

## Claims

### Claim 1

A system comprising:
a series of interconnected compute servers comprising a supervisory hardware node and a plurality of knowledge hardware nodes, wherein the series of interconnected compute servers are configured to categorize and scale performance of multiple disjoint algorithms across a seemingly infinite actor population, wherein the series of interconnected compute servers are configured to:
normalize data using a common taxonomy;
distribute normalized data relatively evenly across the plurality of knowledge hardware nodes;
supervise algorithm execution across knowledge hardware nodes; and
collate and present results of analysis of the seemingly infinite actor population.

### Claim 2 (depends on 1)

The system of claim 1, wherein at least one knowledge hardware node comprises a relevancy engine comprising:
a rule package; and
a series of processors organized in a tree arrangement and configured to perform functions according to a rule set contained in the rule package; wherein the topmost processor in the tree structure provides results of analysis using fuzzy logic;
wherein the rule package comprises set comprises a series of rule sets, each rule set corresponding to a different one of the series of processors.

### Claim 3 (depends on 1)

The system of claim 1, wherein the plurality of knowledge hardware nodes comprise processor nodes and aggregator nodes, and a first set of the knowledge hardware nodes performs computations and provides results of the computations to one aggregator node.

### Claim 4 (depends on 1)

The system of claim 1, wherein the rule package comprises is a hardware container including a collection of rules organized logically or thematically.

### Claim 5 (depends on 1)

The system of claim 1, wherein each rule set comprises rules from a rules storage device and an event described in a corresponding data model.

### Claim 6 (depends on 5)

The system of claim 5, wherein the corresponding data model defines a type of event, event organization, and characteristics of each data field in the event.

### Claim 7 (depends on 1)

The system of claim 1, wherein the rule set comprises a relevancy rule that generates a compatibility index measuring degree to which an associated premise is true.

### Claim 8 (depends on 3)

The system of claim 3, wherein each processor node comprises an effect assessment contribution set by collection of rules in an associated rule set comprising an average of compatibility index values for at least one rule.

### Claim 9 (depends on 3)

The system of claim 3, wherein each processor node has a relevant weighting representing a weight the node is to be accorded by its aggregator node.

### Claim 10

A system comprising:
a knowledge base system comprising a rule package comprising a plurality of rule sets; and
a relevancy processor arrangement comprising a series of processors organized in a tree arrangement and configured to perform functions according to at least one of the plurality of rule sets contained in the rule package, wherein a topmost processor in the tree structure provides results of analysis using fuzzy logic, wherein the relevancy processor arrangement comprises a series of interconnected compute servers comprising aggregator hardware nodes and processor hardware nodes, wherein the series of interconnected compute servers are configured to categorize and scale performance of multiple disjoint algorithms across a seemingly infinite actor population;
wherein the series of interconnected compute servers are configured to normalize data using a common taxonomy.

### Claim 11 (depends on 10)

The system of claim 10, wherein the series of interconnected compute servers are further configured to distribute normalized data relatively evenly across the plurality of knowledge hardware nodes.

### Claim 12 (depends on 11)

The system of claim 11, wherein the series of interconnected compute servers are further configured to supervise algorithm execution across knowledge hardware nodes.

### Claim 13 (depends on 12)

The system of claim 12, wherein the series of interconnected compute servers are further configured to collate and present results of analysis of the seemingly infinite actor population.

### Claim 14 (depends on 10)

The system of claim 10, wherein a first set of the knowledge hardware nodes performs computations and provides results of the computations to one aggregator node.

### Claim 15 (depends on 10)

The system of claim 10, wherein the rule package comprises is a hardware container including a collection of rules organized logically or thematically.

### Claim 16 (depends on 10)

The system of claim 10, wherein each rule set comprises rules from a rules storage device and an event described in a corresponding data model.

### Claim 17 (depends on 16)

The system of claim 16, wherein the corresponding data model defines a type of event, event organization, and characteristics of each data field in the event.

### Claim 18 (depends on 10)

The system of claim 10, wherein the rule set comprises a relevancy rule that generates a compatibility index measuring degree to which an associated premise is true.

### Claim 19

A system comprising:
a knowledge base hardware system comprising a rule package comprising a plurality of rule sets configured to receive rules from a rule device and events from an events device; and
a relevancy processor arrangement comprising a series of processors organized in a tree arrangement and configured to perform functions according to at least one of the plurality of rule sets contained in the rule package using fuzzy logic, wherein the relevancy processor arrangement comprises a series of interconnected compute servers comprising aggregator hardware nodes and processor hardware nodes, wherein the series of interconnected compute servers are configured to categorize and scale performance of multiple disjoint algorithms across a seemingly infinite actor population.

### Claim 20 (depends on 19)

The system of claim 19, wherein the series of interconnected compute servers are configured to normalize data using a common taxonomy, distribute normalized data relatively evenly across the plurality of knowledge hardware nodes, supervise algorithm execution across knowledge hardware nodes, and collate and present results of analysis of the seemingly infinite actor population.