PATENT CLAIM ANALYSIS

Application Number: 16444616
Application Type: Utility
Filing Date: 2019-06
Publication Date: 2019-10
Patent Classification: ["704", "009000"]

Abstract:
Systems and methods for determining knowledge-guided information for a recurrent neural networks (RNN) to guide the RNN in semantic tagging of an input phrase are presented. A knowledge encoding module of a Knowledge-Guided Structural Attention Process (K-SAP) receives an input phrase and, in conjunction with additional sub-components or cooperative components generates a knowledge-guided vector that is provided with the input phrase to the RNN for linguistic semantic tagging. Generating the knowledge-guided vector comprises at least parsing the input phrase and generating a corresponding hierarchical linguistic structure comprising one or more discrete sub-structures. The sub-structures may be encoded into vectors along with attention weighting identifying those sub-structures that have greater importance in determining the semantic meaning of the input phrase.

Claim (Index 14):
The method of  claim 12 , wherein the root node indicates a general purpose of the input phrase.

Metadata:
- Claim Count in Document: 20.0
- Percentile: 100.0
- Lexical Diversity: 1.6
- Patent Class: 704.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15258639', '15901722', '15817161', '11258248', '15817153']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2744114047434579
- 35 USC 102 Novelty (BERT): 0.5578940794640574
- Combined Prediction Score: 0.3027596722155178
- Mean Citation Score: 238.425034
- Max Citation Score: 476.38187
- Similarity Product: 305.92772014397025

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

Dataset: test