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 15):
The system according to  claim 1 , wherein\n the machine learning-based tuning service implements an intelligent hyperparameter tuning system comprising:\n a cluster of distinct machine learning tuning sources that perform distinct tuning operations of the two or more hyperparameters of third-party machine learning model; \n a plurality of queue worker machines that selectively operate one or more of the cluster of distinct tuning sources based on a receipt of the tuning work request, wherein the plurality of queue worker machines includes a plurality of distinct queue worker machines that operate asynchronously to perform disparate tuning operations using one or more of the cluster of distinct machine learning tuning sources; \n a shared work queue that is accessible by each of the plurality of distinct queue worker machines, wherein the shared work queue comprises an asynchronous queue that enable asynchronous tuning operations by the plurality of queue worker machines; and \n a platform database comprising a central repository that collects tuning data generated during tuning sessions of the two or more hyperparameters of the third-party machine learning model.

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): 0.3679006893614366
- 35 USC 102 Novelty (BERT): 0.5630681955127947
- Combined Prediction Score: 0.3874174399765724
- Mean Citation Score: 324.8426760000001
- Max Citation Score: 433.78482
- Similarity Product: 399.15808293006415

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