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 10):
The method of  claim 8 , further comprising:\n obtaining second modeling information related to a second graph data model, the second modeling information comprising second templates for converting second data representations not compatible with a second graph database to graph data representations compatible with the second graph database; providing one or more templates of the second templates and the second data representations to the machine learning model; predicting, via the machine learning model, one or more additional predicted templates, the machine learning model being configured to predict the one or more additional predicted templates of the second templates without reliance on one or more additional templates of the second templates, the prediction being based on the one or more templates of the second templates and the second data representations; and providing the one or more additional templates of the second templates to the machine learning model as reference feedback for the machine learning model's prediction of the one or more additional predicted templates of the second templates to train the machine learning model.

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): 0.3752458208796411
- 35 USC 102 Novelty (BERT): 0.5139441014986695
- Combined Prediction Score: 0.3891156489415439
- Mean Citation Score: 232.638672
- Max Citation Score: 316.03442
- Similarity Product: 231.3330868382264

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