PATENT CLAIM ANALYSIS

Application Number: 16194192
Application Type: Utility
Filing Date: 2018-11
Publication Date: 2019-05
Patent Classification: ["706", "012000"]

Abstract:
Systems and methods include receiving a tuning work request for tuning hyperparameters of a third-party model or system; performing, by a machine learning-based tuning service, a first tuning of the hyperparameters in a first tuning region; identifying tuned hyperparameter values for each of the hyperparameters based on results of the first tuning; setting a failure region based on the tuned hyperparameter values of the first tuning; performing, by the machine learning-based tuning service, a second tuning of the hyperparameters in a second tuning region that excludes the failure region; identifying additional tuned hyperparameter values for each of the hyperparameters based on results of the second tuning; and returning the tuned hyperparameter values and the additional hyperparameter values for implementing the third-party model or system with one of the tuned hyperparameter values and the additional hyperparameter values.

Claim (Index 1):
A system for tuning hyperparameters for improving an effectiveness including accuracy and computational performances of a machine learning model, the system comprising:\n a machine learning-based tuning service that is hosted on a distributed networked system that:\n receives a tuning work request for tuning two or more hyperparameters of a third-party machine learning model; \n performs, by the machine learning-based tuning service, a first tuning of the two or more hyperparameters in a first tuning region; \n identifies tuned hyperparameter values for each of the two or more hyperparameters based on results of the first tuning; \n sets a failure region based on the tuned hyperparameter values of the first tuning; \n performs, by the machine learning-based tuning service, a second tuning of the two or more hyperparameters in a second tuning region that excludes the failure region; \n identifies additional tuned hyperparameter values for each of the two or more hyperparameters based on results of the second tuning; and \n returns the tuned hyperparameter values and the additional hyperparameter values for implementing the third-party machine learning model with one of the tuned hyperparameter values and the additional hyperparameter values.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 98.0
- Lexical Diversity: 3.19565
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['16173737', '15977168', '15822462', '15377448', '16172344']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3707883360933838
- 35 USC 102 Novelty (BERT): 0.5572939184206035
- Combined Prediction Score: 0.3894388943261058
- Mean Citation Score: 324.8426760000001
- Max Citation Score: 433.78482
- Similarity Product: 346.283349454043

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