Granted Patent B2

US 12,248,412 · App. 17/841,448 · Granted Mar 11, 2025

Inventors: Kenneth Marion Curewitz (Cameron Park, CA); Ameen D. Akel (Rancho Cordova, CA); Hongyu Wang (Folsom, CA); Sean Stephen Eilert (Penryn, CA)

Assignee: Micron Technology, Inc.

G06F13/1668G06F13/161G06F18/2148G06N3/08

Abstract

Claims (63)

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.

2. The method of claim 1 , comprising transmitting, by the first computing device, the trained master version of the ANN to the second computing device.

3. The method of claim 1 , wherein the decoding comprises decoding the encoded features according to the changed master version of the feature dictionary.

4. The method of claim 1 , comprising transmitting, by the first computing device, the changed master version of the feature dictionary to the second computing device.

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.

6. The method of claim 5 , comprising:

in response to the local version of the feature dictionary not including the extracted features, changing the local version of the feature dictionary based on the extracted features and then encoding the extracted features according to the changed local version of the feature dictionary.

7. The method of claim 6 , comprising:

transmitting the changed local version of the feature dictionary to the first computing device so that the master version of the feature dictionary is changed according to the local version of the feature dictionary.

8. The method of claim 7 , comprising:

receiving a changed master version of the feature dictionary that was changed according to the local version of the feature dictionary; and

changing the local version of the feature dictionary based on the changed master version of the feature dictionary.

9. The method of claim 5 , comprising:

receiving the trained master version of the ANN; and

changing the local version of the ANN based on the trained master version of the ANN.

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.

11. The system of claim 10 , wherein the transceiver of the first computing device is configured to transmit the trained master version of the ANN to the second computing device.

12. The system of claim 10 , 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.

13. The system of claim 12 , wherein the transceiver of the first computing device is configured to transmit the changed master version of the feature dictionary to the second computing device.

14. The system of claim 10 , wherein the second computing device comprises:

memory configured to:

store user data;

store a local version of the ANN; and

store the local version of the feature dictionary;

a processor configured to:

extract features from the stored user data; and

encode the extracted features according to the local version of the feature dictionary; and

a transceiver configured to transmit the encoded features to the first computing device so that the encoded features are decoded by the master version of the feature dictionary and then used as input to train the master version of the ANN using the machine learning.

15. The system of claim 14 , wherein the processor of the second computing device is further configured to:

determine whether the extracted features are included in the local version of the feature dictionary;

in response to the local version of the feature dictionary including the extracted features, encode the extracted features according to the local version of the feature dictionary; and

in response to the local version of the feature dictionary not including the extracted features, change the local version of the feature dictionary based on the extracted features and then encode the extracted features according to the changed local version of the feature dictionary.

16. The system of claim 15 , wherein the transceiver of the second computing device is configured to transmit the changed local version of the feature dictionary to the first computing device so that the master version of the feature dictionary is changed according to the local version of the feature dictionary.

17. The system of claim 16 , wherein the transceiver of the second computing device is configured to receive the changed master version of the feature dictionary, and wherein the processor of the second computing device is configured to change the local version of the feature dictionary based on the changed master version of the feature dictionary.

18. The system of claim 14 , wherein the transceiver of the second computing device is configured to receive the trained master version of the ANN, and wherein the processor of the second computing device is configured to change the local version of the ANN based on the trained master version of the ANN.

Assignments (1)

Continuity (2)

References Cited (224)