	US 12,248,412 B2
	Feature dictionary for bandwidth enhancement
Kenneth Marion Curewitz, Cameron Park, CA (US); Ameen D. Akel, Rancho Cordova, CA (US); Hongyu Wang, Folsom, CA (US); and Sean Stephen Eilert, Penryn, CA (US)
Assigned to Micron Technology, Inc., Boise, ID (US)
Filed by Micron Technology, Inc., Boise, ID (US)
Filed on Jun. 15, 2022, as Appl. No. 17/841,448.
Application 17/841,448 is a continuation of application No. 16/545,854, filed on Aug. 20, 2019, granted, now 11,392,796.
Prior Publication US 2022/0309291 A1, Sep. 29, 2022
Int. Cl. G06F 13/16 (2006.01); G06F 18/214 (2023.01); G06N 3/08 (2023.01)

CPC G06F 13/1668 (2013.01) [G06F 13/161 (2013.01); G06F 18/2148 (2023.01); G06N 3/08 (2013.01)]

18 Claims

1. A method, comprising:

hosting, by a first computing device, a master version of an artificial neural network (ANN);

hosting, by the first computing device, a master version of a feature dictionary;

receiving, by the first computing device, encoded features from a second computing device;

decoding, by the first computing device, the received encoded features according to the master version of the feature dictionary;

training, by the first computing device, the master version of the ANN based on the decoded features using machine learning;

receiving, by the first computing device, the local version of the feature dictionary from the second computing device; and

changing, by the first computing device, the master version of the feature dictionary based on the received local version of the feature dictionary.

5. A method, comprising:

hosting, by a second computing device, a local version of an artificial neural network (ANN);

hosting, by the second computing device, a local version of a feature dictionary;

extracting, by the second computing device, features from user data hosted by the second computing device;

encoding, by the second computing device, the extracted features according to the local version of the feature dictionary;

transmitting, by the second computing device, the encoded features to a first computing device that hosts a master version of the ANN;

determining, by the second computing device, whether the extracted features are included in the local version of the feature dictionary; and

in response to the local version of the feature dictionary including the extracted features, encoding the extracted features according to the local version of the feature dictionary.

10. A system, comprising:

a second computing device; and

a first computing device communicatively coupled to the second computing device, comprising:

memory configured to:

store a master version of an artificial neural network (ANN); and

store a master version of a feature dictionary;

transceiver configured to receive encoded features from the second computing device; and

a processor configured to:

decode the received encoded features according to the master version of the feature dictionary; and

train the master version of the ANN based on the decoded features using machine learning;

wherein the transceiver of the first computing device is configured to receive the local version of the feature dictionary from the second computing device, and wherein the processor of the first computing device is configured to:

change the master version of the feature dictionary based on the received local version of the feature dictionary; and

decode the received encoded features according to the changed master version of the feature dictionary.