PATENT CLAIM ANALYSIS

Application Number: 16181102
Application Type: Utility
Filing Date: 2018-11
Publication Date: 2019-10
Patent Classification: ["704", "009000"]

Abstract:
Methods are presented for generating a natural language model. The method may comprise: ingesting training data representative of documents to be analyzed by the natural language model, generating a hierarchical data structure comprising at least two topical nodes within which the training data is to be subdivided into by the natural language model, selecting a plurality of documents among the training data to be annotated, generating an annotation prompt for each document configured to elicit an annotation about said document indicating which node among the at least two topical nodes said document is to be classified into, receiving the annotation based on the annotation prompt; and generating the natural language model using an adaptive machine learning process configured to determine patterns among the annotations for how the documents in the training data are to be subdivided according to the at least two topical nodes of the hierarchical data structure.

Claim (Index 8):
The method of  claim 1 , further comprising accessing one or more rules configured to instruct the natural language model how to categorize one or more documents into the two or more nodes of the hierarchical data structure.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 98.0
- Lexical Diversity: 2.18056
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14964517', '14964512', '14964511', '14964526', '14143011']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2780391976582341
- 35 USC 102 Novelty (BERT): 0.616498394766993
- Combined Prediction Score: 0.3118851173691099
- Mean Citation Score: 323.3262519999999
- Max Citation Score: 558.064
- Similarity Product: 534.5279529895781

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