PATENT CLAIM ANALYSIS

Application Number: 16003794
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2018-10
Patent Classification: ["704", "008000"]

Abstract:
Embodiments can provide a computer implemented method, in a data processing system comprising a processor and a memory comprising instructions which are executed by the processor to cause the processor to implement a mixed-language question answering supplement system, the method comprising receiving a question in a target language; determining the question cannot be answered using a target-language only corpus; applying natural language processing to parse the question into at least one focus; for each focus, determining if one or more target language verbs share direct syntactic dependency with the focus; for each of the one or more verbs sharing direct syntactic dependency, determining if one or more target language entities share direct syntactic dependency with the verb; determining one or more Abstract Universal Verbal Types associated with each verb; for each of the one or more Abstract Universal Verbal Types, determining whether a dependency between a source language entity and a source language verb is of the same type as the dependency between the target language verb and the target language entity; if the dependency is similar, returning the source language entity as a member of a set; populating the set of returned source language entities for each focus in the target language question; identifying one or more parallel passages wherein all core arguments are matched; for each parallel passage: identifying the presence or absence of oblique nominal arguments; and measuring the precision of the oblique nominal arguments in the parallel passages against those present in the target language question; and returning an answer to the target question in the target language based on a scoring of the parallel passages based on the accuracy of their respective oblique nominal arguments.

Claim (Index 10):
A computer program product for determining language independent candidate answers, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:\n ingest a corpus, wherein the corpus may comprise information in one or more languages; use a cognitive system to convert the corpus into one or more acyclic graphs, wherein nodes in the one or more acyclic graphs represent facts and connectors in the one or more acyclic graphs represent connections between two or more facts; generate an element data structure using the one or more acyclic graphs; identify clusters of elements in the element data structure based on characteristics of the elements; store the clusters in a cluster data structure; in response to receiving a question, apply deep analysis of the natural language text of the question to determine elements of the question and a knowledge domain of the question; search the cluster data structure to generate a listing of element clusters having the same elements as the question; analyze the listing of element clusters to identify which of the element clusters are compatible with the knowledge domain of the question, wherein the analysis uses rules that specify compatibility of the elements in the element data structure with different knowledge domains; rank the element clusters; and select the highest ranked element cluster for use in presenting an answer to the question.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 94.0
- Lexical Diversity: 2.71171
- Patent Class: 704.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16003772', '15297761', '15297763', '14749733', '15208134']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3186746479540184
- 35 USC 102 Novelty (BERT): 0.5535920418772043
- Combined Prediction Score: 0.3421663873463371
- Mean Citation Score: 328.050424
- Max Citation Score: 420.99826
- Similarity Product: 386.1741197334457

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

Dataset: test