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 15):
A method for updating a natural language model, the method comprising:\n utilizing the natural language model to identify topical content of untested data and to classify said untested data into at least two topical nodes of a hierarchical data structure according to the identified topical content of the untrained data, the hierarchical data structure comprising the at least two topical nodes, wherein the at least two topical nodes represent partitions organized by two or more topical themes among the topical content of the untested data within which the untested data is to be subdivided into; determining that the natural language model classifies at least a subset of the untested data into the at least two topical nodes with a low degree of certainty; and modifying the natural language model with updated data, the updated data comprising a subset of the untested data that the natural language model has classified with a low degree of certainty.

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): 0.2840799002291456
- 35 USC 102 Novelty (BERT): 0.5983374008723903
- Combined Prediction Score: 0.31550565029347
- Mean Citation Score: 323.3262519999999
- Max Citation Score: 558.064
- Similarity Product: 461.0477336578368

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