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 19):
The computer readable medium of  claim 18 , wherein the operations further comprise:\n determining that the natural language platform fails to satisfy the at least one performance criterion based on the computed performance metric; in response to said determining:\n identifying a topical node among the two or more topical nodes of the hierarchal data structure that the natural language model fails to accurately categorize documents into; \n selecting a second plurality of documents to be annotated, the second plurality comprising documents associated with said topical node that the natural language model failed to accurately categorize documents into; \n generating a second set of at least one annotation prompt for each document among the second plurality of documents to be annotated, said annotation prompt among the second set configured to elicit an annotation about said document to improve the natural language model in accurately categorizing documents into said topical node; \n causing display of the second set of the at least one annotation prompt for each document among the second plurality of documents to be annotated; \n receiving, by the natural language platform, for each document among the second plurality of documents to be annotated, a second set of annotations in response to the second set of displayed annotation prompts; and \n generating a refined natural language model using the adaptive machine learning process and based on the hierarchical data structure, the training data and the second set of annotations.

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): 0.2798461686524838
- 35 USC 102 Novelty (BERT): 0.6110724422300693
- Combined Prediction Score: 0.3129687960102423
- Mean Citation Score: 323.3262519999999
- Max Citation Score: 558.064
- Similarity Product: 512.4465725250243

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