PATENT CLAIM ANALYSIS

Application Number: 15946331
Application Type: Utility
Filing Date: 2018-04
Publication Date: 2018-10
Patent Classification: ["706", "025000"]

Abstract:
A computing system provides distributed training of a neural network model. Explore phase options, exploit phase options, a subset of a training dataset, and a validation dataset are distributed to a plurality of computing devices. (a) Execution of the model by the computing devices is requested using the subset stored at each computing device. (b) A first result of the execution is received from a computing device. (c) Next configuration data for the neural network model is selected based on the first result and distributed to the computing device. (a) to (c) is repeated until an exploration phase is complete. (d) Execution of the neural network model is requested. (e) A second result is received. (f) Next configuration data is computed based on the second result and distributed to the computing device. (d) to (f) is repeated until an exploitation phase is complete. The next configuration data defines the model.

Claim (Index 23):
A method of providing distributed training of a neural network model, the method comprising:\n distributing, by a computing device, explore phase options to each computing device of a plurality of computing devices; distributing, by the computing device, a subset of a training dataset to each computing device of a plurality of computing devices; distributing, by the computing device, a validation dataset to each computing device of the plurality of computing devices; requesting, by the computing device, initialization of a neural network model by the plurality of computing devices; (a) requesting, by the computing device, execution of the initialized neural network model by the plurality of computing devices using the subset of the training dataset stored at each computing device of the plurality of computing devices; (b) receiving, by the computing device, a first result of the execution from a computing device of the plurality of computing devices; (c) selecting, by the computing device, next configuration data for the neural network model based on the received first result; (d) distributing, by the computing device, the selected next configuration data to the computing device; repeating, by the computing device, (a) to (d) until an exploration phase is complete based on the explore phase options; distributing, by the computing device, exploit phase options to each computing device of the plurality of computing devices; (e) requesting, by the computing device, execution of the neural network model by the plurality of computing devices using the subset of the training dataset stored at each computing device of the plurality of computing devices; (f) receiving, by the computing device, a second result of the execution from the computing device of the plurality of computing devices; (g) computing, by the computing device, next configuration data for the neural network model based on the received second result; (h) distributing, by the computing device, the computed next configuration data to the computing device; repeating, by the computing device, (e) to (h) until an exploitation phase is complete based on the exploit phase options; and outputting, by the computing device, at least a portion of the computed next configuration data to define a trained neural network model.

Metadata:
- Claim Count in Document: 2.0
- Percentile: 91.0
- Lexical Diversity: 2.83077
- Patent Class: 706.0
- Transitional Phrase Type: open
- Component Type: 1
- Foreign Priority: False
- Related Applications: ['15822462', '15433662', '15928363', '15658566', '14490189']

Analysis Scores:
- 35 USC 101 Eligibility (BERT): 0.3354964135641706
- 35 USC 102 Novelty (BERT): 0.4983709516714243
- Combined Prediction Score: 0.351783867374896
- Mean Citation Score: 220.110194
- Max Citation Score: 251.74866
- Similarity Product: 193.6363729405582

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

Dataset: test