PATENT CLAIM ANALYSIS

Application Number: 15864257
Application Type: Utility
Filing Date: 2018-01
Publication Date: 2019-07
Patent Classification: ["706", "012000"]

Abstract:
A device receives source data, target data, external data, and a target task, and generates features of and differentiators between the source data and the target data. The device identifies a set of mappings between the source data and the target data based on the features and the differentiators, and determines different clusters of the source data based on the external data, the features, and the differentiators. The device generates, based on the external data, a set of artificial intelligence (AI) models as candidates to perform the target task, and generates a performance measure for the set of AI models based on the features, the differentiators, and the external data. The device refines the set of mappings, and identifies an AI model, from the set of AI models, to perform the target task based on the different clusters of the source data and based on the performance measure.

Claim (Index 21):
The device of  claim 1 , where the one or more processors, when generating the features of and the differentiators between the source data and the target data, are to:\n utilize a Bayesian or multivariate Gaussian mixture model to generate the features of and the differentiators between the source data and the target data.

Metadata:
- Claim Count in Document: 7.0
- Percentile: 86.0
- Lexical Diversity: 3.84091
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['15864610', '15862219', '15448283', '15294044', '15836100']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3749958925970518
- 35 USC 102 Novelty (BERT): 0.4986621181788391
- Combined Prediction Score: 0.3873625151552305
- Mean Citation Score: 152.692968
- Max Citation Score: 167.4086
- Similarity Product: 95.56603994839192

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