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 8):
A non-transitory computer-readable medium storing instructions, the instructions comprising:\n one or more instructions that, when executed by one or more processors, cause the one or more processors to:\n receive source data, target data, external data, and a target task associated with the source data and the target data,\n the source data being associated with a source domain, \n the target data being associated with a target domain that is different than the source domain, and \n the external data being associated with the source data and the target data; \n \n generate features of the source data and the target data; \n generate differentiators between the source data and the target data based on the features of the source data and the target data; \n identify a set of mappings between the source data and the target data based on the features of the source data and the target data and the differentiators between the source data and the target data,\n each mapping being dependent upon a level of misalignment between the source data and the target data, \n the set of mappings being used to embed or transfer data from the source data to the target data; \n \n determine different clusters of the source data based on the external data, the features of the source data and the target data, and the differentiators between the source data and the target data; \n generate, based on the external data, a set of artificial intelligence models as candidates to perform the target task; \n generate a performance measure for the set of artificial intelligence models based on the features of the source data and the target data, the differentiators between the source data and the target data, the set of mappings, and the different clusters of the source data; \n refine the set of mappings based on the different clusters of the source data and based on the performance measure to generate enhanced target data; \n identify an artificial intelligence model, from the set of artificial intelligence models, to perform the target task based on the set of mappings, the different clusters of the source data, and the performance measure,\n the enhanced target data being used to train the identified artificial intelligence model; \n \n receive a request to perform the target task; and \n utilize the trained identified artificial intelligence model to perform the target task based on the request.

Metadata:
- Claim Count in Document: 7.0
- Percentile: 86.0
- Lexical Diversity: 3.84091
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15864610', '15862219', '15448283', '15294044', '15836100']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3525349476781469
- 35 USC 102 Novelty (BERT): 0.4756275195255193
- Combined Prediction Score: 0.3648442048628841
- Mean Citation Score: 152.692968
- Max Citation Score: 167.4086
- Similarity Product: 117.43642419908048

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