IP Library › Granted Patent US 11,676,070
Granted Patent B1
US 11,676,070 · App. 17/111,477 · Granted Jun 13, 2023

Multidimensional machine learning data and user interface segment tagging engine apparatuses, methods and systems

Inventors: Jason Alan Snyder (Ridgewood, NJ); Manuel De Araujo Pedreira Neto (New York, NY); Elena Klau Silverman (New York, NY); Stephen Michael Gorman (Hastings on Hudson, NY); Michael Clark (Saint Charles, MO)
Assignee: Momentum NA, Inc.
G06N20/00G06F16/2433G06F16/835
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Quick Facts
Patent No.
US 11,676,070
App. No.
17/111,477
Granted
Jun 13, 2023
Kind
B1
Abstract

The Multidimensional Machine Learning Data and User Interface Segment Tagging Engine Apparatuses, Methods and Systems (“MLUI”) transforms ambient condition data, sales data, user interface selections, cognitive intelligence question input inputs via MLUI components into project projections, campaigns, user interface visualizations, cognitive intelligence question output outputs. A cognitive intelligence (CI) datapoint identifier cache datastructure is generated, the CI datapoint identifier cache datastructure configured to comprise a category identifier and an entity segment identifier. A CI datapoint value cache datastructure is generated, the CI datapoint value cache datastructure configured to comprise a set of module datastructures, each module datastructure corresponding to a module identifier associated with the category identifier, each module datastructure comprising a set of metric datastructures, each metric datastructure corresponding to a set of calculated metrics. The generated CI datapoint identifier cache datastructure and the generated CI datapoint value cache datastructure are stored as a key-value pair.

Claims (39)

1. A cache datastructure generating apparatus, comprising:

a memory;

a component collection in the memory;

a processor disposed in communication with the memory and configured to issue a plurality of processor-executable instructions from the component collection, the processor-executable instructions configured to:

generate, via at least one processor, a cognitive intelligence (CI) datapoint identifier cache datastructure, the CI datapoint identifier cache datastructure configured to comprise a category identifier and an entity segment identifier;

generate, via at least one processor, a CI datapoint value cache datastructure, the CI datapoint value cache datastructure configured to comprise a set of module datastructures, each module datastructure corresponding to a module identifier associated with the category identifier specified in the CI datapoint identifier cache datastructure, each module datastructure comprising a set of metric datastructures, each metric datastructure corresponding to a set of calculated metrics, for an allowable response question identifier associated with the respective module datastructure's module identifier, for the entity segment identifier specified in the CI datapoint identifier cache datastructure; and

store, via at least one processor, the generated CI datapoint identifier cache datastructure and the generated CI datapoint value cache datastructure as a key-value pair.

2. The apparatus of claim 1 , further, comprising:

the category identifier is configured to identify a user interface section.

3. The apparatus of claim 1 , further, comprising:

the entity segment identifier is configured to identify an entity comprising any of: person, item of manufacture, service, asset, brand, ad, category of manufacture, category of service, category of person, demographic, sentiment.

4. The apparatus of claim 1 , further, comprising:

each calculated metric is configured to be calculated using survey data associated with the entity segment identifier specified in the CI datapoint identifier cache datastructure.

5. The apparatus of claim 4 , further, comprising:

the survey data is configured to comprise any of: ambient social, consumer interaction, point of sale, third party, internet of things, and internal survey data.

6. The apparatus of claim 1 , further, comprising:

each metric datastructure is configured to comprise an allowable response question identifier associated with the respective metric datastructure.

7. The apparatus of claim 6 , further, comprising:

each metric datastructure is configured to comprise a display name corresponding to the allowable response question identifier associated with the respective metric datastructure.

8. The apparatus of claim 1 , further, comprising:

the generated CI datapoint identifier cache datastructure and the generated CI datapoint value cache datastructure are configured to be stored in a key-value database.

9. The apparatus of claim 8 , further, comprising:

the key-value database is configured as a NoSQL database.

10. The apparatus of claim 1 , further, comprising:

the generated CI datapoint identifier cache datastructure and the generated CI datapoint value cache datastructure are configured to be stored in JSON format.

11. A cache datastructure generating processor-readable, non-transient medium, comprising processor-executable instructions configured to:

generate, via at least one processor, a cognitive intelligence (CI) datapoint identifier cache datastructure, the CI datapoint identifier cache datastructure configured to comprise a category identifier and an entity segment identifier;

generate, via at least one processor, a CI datapoint value cache datastructure, the CI datapoint value cache datastructure configured to comprise a set of module datastructures, each module datastructure corresponding to a module identifier associated with the category identifier specified in the CI datapoint identifier cache datastructure, each module datastructure comprising a set of metric datastructures, each metric datastructure corresponding to a set of calculated metrics, for an allowable response question identifier associated with the respective module datastructure's module identifier, for the entity segment identifier specified in the CI datapoint identifier cache datastructure; and

store, via at least one processor, the generated CI datapoint identifier cache datastructure and the generated CI datapoint value cache datastructure as a key-value pair.

12. A cache datastructure generating processor-implemented system, comprising:

means to process processor-executable instructions;

means to issue processor-issuable instructions from a processor-executable component collection via the means to process processor-executable instructions, the processor-issuable instructions configured to:

generate, via at least one processor, a cognitive intelligence (CI) datapoint identifier cache datastructure, the CI datapoint identifier cache datastructure configured to comprise a category identifier and an entity segment identifier;

generate, via at least one processor, a CI datapoint value cache datastructure, the CI datapoint value cache datastructure configured to comprise a set of module datastructures, each module datastructure corresponding to a module identifier associated with the category identifier specified in the CI datapoint identifier cache datastructure, each module datastructure comprising a set of metric datastructures, each metric datastructure corresponding to a set of calculated metrics, for an allowable response question identifier associated with the respective module datastructure's module identifier, for the entity segment identifier specified in the CI datapoint identifier cache datastructure; and

store, via at least one processor, the generated CI datapoint identifier cache datastructure and the generated CI datapoint value cache datastructure as a key-value pair.

13. A cache datastructure generating processor-implemented process, comprising executing processor-executable instructions to:

generate, via at least one processor, a cognitive intelligence (CI) datapoint identifier cache datastructure, the CI datapoint identifier cache datastructure configured to comprise a category identifier and an entity segment identifier;

generate, via at least one processor, a CI datapoint value cache datastructure, the CI datapoint value cache datastructure configured to comprise a set of module datastructures, each module datastructure corresponding to a module identifier associated with the category identifier specified in the CI datapoint identifier cache datastructure, each module datastructure comprising a set of metric datastructures, each metric datastructure corresponding to a set of calculated metrics, for an allowable response question identifier associated with the respective module datastructure's module identifier, for the entity segment identifier specified in the CI datapoint identifier cache datastructure; and

store, via at least one processor, the generated CI datapoint identifier cache datastructure and the generated CI datapoint value cache datastructure as a key-value pair.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 2, 2023
From: SNYDER, JASON ALAN; PEDREIRA NETO, MANUEL DE ARAUJO; SILVERMAN, ELENA KLAU; GORMAN, STEPHEN MICHAEL; CLARK, MICHAEL PATRICK GEORGE
To: MOMENTUM NA, INC.
Reel/Frame 063499/0473 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 24, 2023
From: SNYDER, JASON ALAN; NETO, MANUEL DE ARAUJO PEDREIRA; SILVERMAN, ELENA KLAU; GORMAN, STEPHEN MICHAEL
To: MOMENTUM NA, INC.
Reel/Frame 063420/0736 →
Continuity (3)
Continuation In Part 16221437 · Dec 14, 2018
Provisional Application 62598847 · Dec 14, 2017
Provisional Application 63051873 · Jul 14, 2020
Cited By (5)
US 1,070,880 US 1,102,442 US 1,108,447 US 12,205,099 US 12,271,387