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 14):
The system according to  claim 1 , wherein\n performing the second tuning includes:\n setting a tuning distance that defines a position of the second tuning region away from the first tuning region and the failure region, wherein the second tuning satisfies or exceeds a diversity threshold.

Metadata:
- Claim Count in Document: 50.0
- Percentile: 98.0
- Lexical Diversity: 3.19565
- Patent Class: 706.0
- Transitional Phrase Type: none
- Component Type: 0
- Foreign Priority: False
- Related Applications: ['16173737', '15977168', '15822462', '15377448', '16172344']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.4014334440102725
- 35 USC 102 Novelty (BERT): 0.5546824990830437
- Combined Prediction Score: 0.4167583495175496
- Mean Citation Score: 324.8426760000001
- Max Citation Score: 433.78482
- Similarity Product: 301.68152572157624

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