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 1):
A system for providing neural-network-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 graph modeling information related to a graph data model set, the graph modeling information related to each graph data model of the graph data model set comprising templates for converting non-graph data representations in a non-graph database to graph data representations compatible with a graph database; \n for each graph data model of the graph data model set and the non-graph data representations that the graph data model is configured to convert:\n provide one or more templates of the graph data model's templates and the non-graph data representations to a neural network; \n predict, via the neural network, one or more additional predicted templates for the graph data model, the neural network being configured to perform the prediction of the one or more additional predicted templates without reliance on one or more additional templates of the graph data model's templates, the prediction being based on the one or more templates of the graph data model's templates and the non-graph data representations; and \n provide the one or more additional templates of the graph data model's templates to the neural network as reference feedback for the neural network's prediction of the one or more additional predicted templates to train the neural network; and \n \n provide a collection of non-graph data representations from a given non-graph database 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.

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): 0.3753710935912164
- 35 USC 102 Novelty (BERT): 0.5125846437916559
- Combined Prediction Score: 0.3890924486112604
- Mean Citation Score: 232.638672
- Max Citation Score: 316.03442
- Similarity Product: 241.2368907028961

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