PATENT CLAIM ANALYSIS

Application Number: 15917022
Application Type: Utility
Filing Date: 2018-03
Publication Date: 2018-09
Patent Classification: ["704", "009000"]

Abstract:
Computerized methods are disclosed for automated question generation from source documents through natural language processing, for applications including training and testing. Interleaved selection and transformation phases employ combined semantic-syntactic analysis to progressively refine natural input text into a high density of text fragments having high content value. Non-local semantic content and attributes such as emphasis attributes can be attached to the text fragments. The text fragments are reverse parsed by matching against a precomputed library of combined semantic-syntactic patterns. Once the patterns of each fragment are determined, transformation of fragments into question-answer pairs is performed using question selectors and answer selectors tailored to each pattern. Methods for constructing distractors, both internal and external, are also disclosed. The ecosystem of machine learning components, ontology resources, and process improvement are also described.

Claim (Index 1):
A computer-implemented method for generating questions from a source document, comprising:\n selecting passages of text from the source document based on a first criterion; transforming the selected text passages based on coreference analysis; selecting fragments of text in the transformed text passages based on matching combined semantic-syntactic patterns from a pattern library; and automatically generating the questions by transforming the selected text fragments.

Metadata:
- Claim Count in Document: 1.0
- Percentile: 90.0
- Lexical Diversity: 1.55435
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['14577642', '12723449', '12200962', '14900758', '13012514']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.2635377444335194
- 35 USC 102 Novelty (BERT): 0.4863312179262387
- Combined Prediction Score: 0.2858170917827913
- Mean Citation Score: 210.59594
- Max Citation Score: 226.16975
- Similarity Product: 169.95562311431766

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

Dataset: test