PATENT CLAIM ANALYSIS

Application Number: 16172430
Application Type: Utility
Filing Date: 2018-10
Publication Date: 2020-01
Patent Classification: ["706", "015000"]

Abstract:
A model optimizer is disclosed for managing training of models with automatic hyperparameter tuning. The model optimizer can perform a process including multiple steps. The steps can include receiving a model generation request, retrieving from a model storage a stored model and a stored hyperparameter value for the stored model, and provisioning computing resources with the stored model according to the stored hyperparameter value to generate a first trained model. The steps can further include provisioning the computing resources with the stored model according to a new hyperparameter value to generate a second trained model, determining a satisfaction of a termination condition, storing the second trained model and the new hyperparameter value in the model storage, and providing the second trained model in response to the model generation request.

Claim (Index 20):
A non-transitory computer readable medium containing instructions that, when executed by one or more processors, cause a computing system to perform operations comprising:\n receiving a model generation request comprising a data schema of a dataset and indicating at least one of a model type, a data statistic, a training dataset type, a model task, or a training dataset identifier; retrieving a stored model and a stored hyperparameter value for the stored model from a model storage,\n the retrieving being based on the model generation request, an index of stored models comprising a stored data schema, and a distance metric between the stored data schema and the received data schema, \n the index further comprising at least one of a model type, a data statistic, a training dataset type, a model task, or a training dataset identifier, \n the stored model being configured to generate synthetic data that matches the stored data schema; \n provisioning computing resources with multiple instances of the stored model according to the multiple hyperparameter values to generate first trained models, the multiple hyperparameter values including the stored hyperparameter value; provisioning the computing resources with the stored model according to a new hyperparameter value to generate a second trained model, the new hyperparameter value based on values of the performance metric associated with the first trained models; determining satisfaction of a termination condition based on a value of the performance metric associated with the second trained model; storing the second trained model and the new hyperparameter value in the model storage together with the value of the performance metric associated with the second trained model; updating the index to include the second trained model and the new hyperparameter value; and providing the second trained model in response to the model generation request.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 97.0
- Lexical Diversity: 2.55556
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16172344', '16172508', '16151385', '15822462', '16059241']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4150448869689382
- 35 USC 102 Novelty (BERT): 0.5210292753886248
- Combined Prediction Score: 0.4256433258109068
- Mean Citation Score: 302.266202
- Max Citation Score: 349.2498
- Similarity Product: 281.8200806017399

Labels:
- Claim Label 101: 1
- Claim Label 102: 1
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 1
- Label 101 Adjusted: 1

Dataset: test