IP Library Granted Patent US 11,295,216
Granted Patent B2
US 11,295,216 · App. 16/787,349 · Granted Apr 5, 2022

Structurally defining knowledge elements within a cognitive graph

Inventor: Hannah R. Lindsley (Austin, TX)
Assignee: Cognitive Scale, Inc.
G06N5/02G06F16/3329G06F16/367G06F16/84G06F16/9024G06F16/90335G06N5/022G06N5/04G06N5/043G06N5/048G06N20/00G06N5/003
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Quick Facts
Patent No.
US 11,295,216
App. No.
16/787,349
Granted
Apr 5, 2022
Kind
B2
Abstract

A computer-implementable method for managing a cognitive graph comprising: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, each knowledge element being structurally defined within the cognitive graph.

Claims (17)

1. A computer-implementable method for managing a cognitive graph of a universal knowledge repository comprising:

receiving, by an information processing system that includes a processor, data from a data source;

processing, by a cognitive inference and learning system executing on the information processing system, the data from the data source, the processing of the data from the data source identifying a plurality of knowledge elements, the plurality of knowledge elements representing quantity in a universal cognitive graph of the universal knowledge repository, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising the universal cognitive graph:

storing, by the information processing system, the knowledge elements within the universal cognitive graph as a collection of the knowledge elements, the storing universally representing knowledge obtained from the data, each knowledge element of the knowledge elements being structurally defined within the universal cognitive graph, the collection of knowledge elements being accurately and precisely modeled via a ladder entailment pattern and a diamond entailment pattern, the ladder entailment pattern comprising a pattern formed in the universal cognitive graph where rungs of the ladder entailment pattern are formed by a first set of nodes having a categorical relationship with a corresponding second set of nodes and rails of the ladder entailment pattern are formed by attributive relationships respectively associated with the first set and second set of nodes, wherein the nodes are the entities, and wherein structurally defining each knowledge element being implemented with an ontology, the ontology universally representing knowledge and comprising a representation of the entities along with properties and relations of the entities according to a system of categories, the ontology storing a knowledge element within the universal cognitive graph based upon a set of the categories of the knowledge element and a set of attributes of the knowledge element;

receiving, by the information processing system from a user via a network, a user query that includes text of the data to generate a graph query;

performing, by the information processing system based on mapper rule set, mapping operations on the user query;

parsing, by the information processing system based on using a parsing operation or a parse rule set, the user query to identify ambiguities of the text after the performing mapping operations, the parsing operation comprising a lossless parsing operation, the lossless parsing operation identifying a minimum basic known true structure for all parse variations, the using of the parse rule set resulting in generation of a set of parse trees, each parse tree of the parse trees being represented as the text being parsed;

ranking, by the information processing system based on the parse ranking rule set, the parse trees which result in determination of top-ranked parse trees to resolve ambiguities in the text of the query, the parse trees being transformed to a tree representing an interpretation of ambiguous portions of the data, the interpretation of the ambiguous portions of the data being changed based upon user Feedback, the top-ranked parse trees are stored in the universal knowledge repository;

identifying, by the information processing system, knowledge elements of the parse trees that represent ambiguous portions of the text, identified knowledge elements of the parse trees being stored within the universal cognitive graph in a configuration representing a relationship of concepts;

querying in response to the user query, by the information processing system, the universal knowledge repository and using the identified knowledge elements of the parse trees and the identified plurality of knowledge elements of the universal cognitive graph to produce a plurality of cognitive insights as recommendations that satisfy the graph query;

ranking, by the information processing system based information in insight streams, the plurality of cognitive insights that are provided in a cognitive insight summary, the information being related to a location of the user, the user feedback and a device used by the user;

publishing, by the information processing system, the ranked plurality of cognitive insights in the cognitive insight summary to the user of cognitive insight data, the ranked plurality of cognitive insights being related to a cognitive user profile.

2. The method of claim 1 , wherein: structurally defining a knowledge element comprises storing a knowledge element within the cognitive graph based upon a set of categories of the knowledge element and a set of attributes of the knowledge element.

3. The method of claim 1 , wherein: a relationship between a first knowledge element and a second knowledge element comprises only one of a categorical relationship and an attributive relationship, the categorical relationship representing inheritance of an entity and the attributive relationship representing attribution of the entity.

4. The method of claim 1 , wherein the diamond entailment pattern comprises a pattern formed in the universal cognitive graph when two parent nodes of a first node inherit from a common second node.

5. The method of claim 1 , wherein: inheritance relationships between a base node and a parent node define a particular concept within the universal cognitive graph.

6. The method of claim 1 , wherein: a plurality of knowledge elements representing different quantities are stored within the universal cognitive graph.

Assignments (4)
SECURITY INTEREST Recorded Dec 22, 2022
From: TECNOTREE TECHNOLOGIES INC.
To: TRIPLEPOINT CAPITAL LLC
Reel/Frame 062213/0388 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 14, 2022
From: COGNITIVE SCALE, INC.; COGNITIVESCALE SOFTWARE INDIA PVT. LTD.; COGNITIVE SCALE UK LTD.; COGNITIVE SCALE (CANADA) INC.
To: TECNOTREE TECHNOLOGIES, INC.
Reel/Frame 062125/0051 →
SECURITY INTEREST Recorded Oct 25, 2022
From: COGNITIVE SCALE INC.
To: TRIPLEPOINT CAPITAL LLC
Reel/Frame 061771/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 11, 2020
From: LINDSLEY, HANNAH R.
To: COGNITIVE SCALE, INC.
Reel/Frame 051782/0981 →
Continuity (3)
Continuation 15290517 · Oct 11, 2016
Provisional Application 62335970 · May 13, 2016
Related Publication 20200184347A1 · Jun 11, 2020
Cited By (1)
US 12,562,917