PATENT CLAIM ANALYSIS

Application Number: 15977168
Application Type: Utility
Filing Date: 2018-05
Publication Date: 2018-11
Patent Classification: ["706", "012000"]

Abstract:
A system and method includes receiving a tuning work request for tuning an external machine learning model; implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

Claim (Index 5):
A method for tuning an external machine learning model, the method comprising:\n at a remote machine learning tuning service, wherein the remote machine learning tuning service is hosted on a distributed networked system:\n receiving, via a network, a tuning work request for tuning an external machine learning model, wherein the tuning work request comprises tuning work data that identifies one or more hyperparameters of the external machine learning model for tuning and constraint data comprising one or more tuning operation constraints; \n implementing a plurality of distinct queue worker machines that perform various tuning operations based on the tuning work data of the tuning work request; \n implementing a plurality of distinct tuning sources that generate values for each of the one or more hyperparameters of the tuning work request; \n selecting, by one or more queue worker machines of the plurality of distinct queue worker machines, one or more tuning sources of the plurality of distinct tuning sources for tuning the one or more hyperparameters; and \n using the selected one or more tuning sources to generate one or more suggestions for the one or more hyperparameters, the one or more suggestions comprising values for the one or more hyperparameters of the tuning work request.

Metadata:
- Claim Count in Document: 10.0
- Percentile: 93.0
- Lexical Diversity: 2.97959
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15822462', '15377448', '15404052', '15829981', '15956450']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3479126711094791
- 35 USC 102 Novelty (BERT): 0.4888032224615545
- Combined Prediction Score: 0.3620017262446867
- Mean Citation Score: 165.13183999999995
- Max Citation Score: 202.97345
- Similarity Product: 137.0454928288579

Labels:
- Claim Label 101: 1
- Claim Label 102: 0
- Claim Label 103: 1
- Claim Label 112: 1
- Combined Label: 0
- Label 101 Adjusted: 1

Dataset: test