PATENT CLAIM ANALYSIS

Application Number: 15897190
Application Type: Utility
Filing Date: 2018-02
Publication Date: 2018-11
Patent Classification: ["706", "049000"]

Abstract:
Embodiments of the present invention provide systems and methods for increasing the quality of interactions between two or more entities. These entities are either individuals (e.g., human beings using a computer device) or artificial intelligence (AI) agents. The interactions between all of the entities within a computing environment are mapped and analyzed. Based on the mapped interactions, a relationship model is generated in order to run multiple applications within a computing environment.

Claim (Index 1):
A method for generating solutions with an environment containing a plurality of individuals and a plurality of artificial intelligence (AI) agents, comprising:\n observing, by one or more processors, interactions between entities contained within an environment; creating, by one or more processors, a cognitive profile for each respective entity within the environment, wherein creating the cognitive profile includes incorporating biometric data for a plurality of individuals and special testing data for the plurality of AI agents; mapping, by one or more processors, a set of interactions to each cognitive profile for each respective entity based on the observed interactions between the entities contained within the environment, wherein mapping the set of interactions, comprises:\n utilizing data objects to represent each of the entities, \n utilizing data links to represent connections between two data objects, \n analyzing, by one or more processors, the mapped set of interactions to gauge a level of positive sentiment and a level of negative sentiment of each interaction within the mapped set of interactions, \n creating, by one or more processors, a plurality of relationship models based on the analyzed mapped set of interactions, wherein each relationship model is associated with a single interaction within the analyzed mapped set of interactions, and \n utilizing, by one or more processors, the plurality of relationship models to implement solutions for each respective entity within the environment based on the created cognitive profile and the mapped set of interactions for each respective entity. \n wherein the mapped set of interactions contain:\n data objects that represent each of the entities, and \n data links that represent connections between two data objects; \n responsive to mapping the set of interactions, generating, by one or more processors, a plurality of relationship models based on the analyzed mapped set of interactions, wherein each relationship model is associated with a single interaction within the analyzed mapped set of interactions; identifying, by one or more processors, a level positive sentiment and a level of negative sentiment of each interaction within the mapped set of interactions generating, by one or more processors, the solutions for each respective entity within the environment based on the created cognitive profile and the mapped set of interactions for each respective entity; and implementing, by one or more processors, the solutions for each respective entity within the environment based on the created cognitive profile and the mapped set of interactions for each respective entity using the plurality of relationship models.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 88.0
- Lexical Diversity: 1.46429
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15604908', '12011982', '13409514', '15426959', '11548909']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3430275954951145
- 35 USC 102 Novelty (BERT): 0.4813156785072598
- Combined Prediction Score: 0.356856403796329
- Mean Citation Score: 140.293301
- Max Citation Score: 242.27533
- Similarity Product: 206.20009237761136

Labels:
- Claim Label 101: 1
- Claim Label 102: 0
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 1

Dataset: test