PATENT CLAIM ANALYSIS

Application Number: 16007911
Application Type: Utility
Filing Date: 2018-06
Publication Date: 2019-12
Patent Classification: ["706", "012000"]

Abstract:
In some embodiments, templates related to each graph data model of a graph data model set for converting non-graph data representations in a non-graph database to graph data representations compatible with a graph database may be obtained. One or more templates and the non-graph data representations may be provided to a neural network for the neural network to predict additional templates. The additional templates may be provided to the neural network as reference feedback for the neural network's prediction of the additional templates to train the neural network. A collection of non-graph data representations from a given non-graph database may be provided to the neural network for the neural network to generate one or more templates for a given graph data model for converting non-graph data representations in the given non-graph database into graph data representations compatible with a given graph database.

Claim (Index 18):
A system for providing machine-learning-model-based generation of a graph data model for converting non-graph data representations to compatible graph data representations, the system comprising:\n a computer system that comprises one or more processors programmed with computer program instructions that, when executed, cause the computer system to:\n obtain first modeling information related to a first graph data model, the first modeling information comprising first templates for converting first data representations not compatible with a first graph database to graph data representations compatible with the first graph database; \n provide one or more templates of the first templates and the first data representations to a machine learning model; \n predict, via the machine learning model, one or more additional predicted templates, the machine learning model being configured to perform the prediction of the one or more predicted additional templates without reliance on one or more additional templates of the first templates, the prediction being based on the one or more templates of the first templates and the first data representations; \n provide the one or more additional templates of the first templates to the machine learning model as reference feedback for the machine learning model's prediction of the one or more additional predicted templates to train the machine learning model; and \n provide a collection of data representations from a given database to the machine learning model for the machine learning model to generate one or more templates for a given graph data model for converting the given database's data representations into graph data representations for a given graph database.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 94.0
- Lexical Diversity: 2.96
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16007850', '16007639', '15488433', '15147222', '14828150']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3752511664858483
- 35 USC 102 Novelty (BERT): 0.5138858093386882
- Combined Prediction Score: 0.3891146307711323
- Mean Citation Score: 232.638672
- Max Citation Score: 316.03442
- Similarity Product: 231.75776969376687

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