Update optimization using feedback on probability of change for regions of data

Patent metadata

Field	Value
Publication number	US-11262927-B2
Application number	US-201916526502-A
Country	US
Kind code	B2
Filing date	Jul 30, 2019
Priority date	Jul 30, 2019
Publication date	Mar 1, 2022
Grant date	Mar 1, 2022

Abstract

A method, system and non-transitory computer readable instructions for update optimization comprising, receiving application metadata wherein the application metadata includes a likelihood of future data change metric for one or more regions of application data. Determining from the application metadata which regions of the application data have a high likelihood of data change and generating variable data chunk boundaries based on the regions of the application data that have the high likelihood of data change.

First claim

What is claimed is:

1. A method for update optimization comprising:
a) determining from application metadata which regions of application data have a high likelihood of data change, wherein the application metadata includes a likelihood of future data change metric for one or more regions of the application data;
b) generating variable data chunk boundaries based on the regions of the application data that have the high likelihood of data change;
c) dividing the application data into variable sized data chunks based on the variable data chunk boundaries;
d) receiving patch data having nonreferable data areas
e) merging the non-referable data areas with a variable data chunk that has regions with a high likelihood of data change; and
f) compressing each of the variable sized data chunks.

2. The method of claim 1 , wherein generating variable data chunk boundaries includes modifying existing variable data chunk boundaries.

3. The method of claim 1 , wherein generating variable data chunk boundaries includes creating new variable data chunk boundaries.

4. The method of claim 1 wherein the application metadata includes one or more labels for the one or more regions of application data.

5. The method of claim 4 wherein the one or more labels for the one or more regions of application data includes a table of contents region label.

6. The method of claim 1 wherein generating variable data chunk boundaries includes fitting variable data chunk boundaries around application data regions indicated to have a low likelihood of change.

7. A system for update optimization comprising:
a processor;
a memory coupled to the processor;
non-transitory instruction embedded in memory that when executed cause the processor to carry out the method comprising:
a) determining from application metadata which regions of application data have a high likelihood of data change, wherein the application metadata includes a likelihood of future data change metric for one or more regions of the application data;
b generating variable data chunk boundaries based on the regions of the application data that have the high likelihood of data change
c) dividing the application data into variable sized data chunks based on the variable data chunk boundaries;
d) receiving patch data having nonreferable data areas
e) merging the non-referable data areas with a variable data chunk that has regions with a high likelihood of data change; and
f) compressing each of the variable sized data chunks.

8. The system of claim 7 , wherein generating variable data chunk boundaries includes modifying existing variable data chunk boundaries.

9. The system of claim 7 , wherein generating variable data chunk boundaries includes creating new variable data chunk boundaries.

10. The system of claim 7 wherein the application metadata includes one or more labels for the one or more regions of application data.

11. The system of claim 10 wherein the one or more labels for the one or more regions of application data includes a table of contents region label.

12. The system of claim 7 wherein generating variable data chunk boundaries includes fitting variable data chunk boundaries around application data regions indicated to have a low likelihood of change.

13. Non-transitory computer readable medium having instructions embedded thereon that when executed cause a computer to carry out the method for update optimization comprising;
a) determining from application metadata which regions of application data have a high likelihood of data change, wherein the application metadata includes a likelihood of future data change metric for one or more regions of the application data;
b) generating variable data chunk boundaries based on the regions of the application data that have the high likelihood of data change
c) dividing the application data into variable sized data chunks based on the variable data chunk boundaries;
d) receiving patch data having nonreferable data areas
e) merging the non-referable data areas with a variable data chunk that has regions with a high likelihood of data change; and
f) compressing each of the variable sized data chunks.

14. The non-transitory computer readable medium of claim 13 , wherein generating variable data chunk boundaries includes modifying existing variable data chunk boundaries.

15. The non-transitory computer readable medium of claim 13 , wherein generating variable data chunk boundaries includes creating new variable data chunk boundaries.

16. The non-transitory computer readable medium of claim 13 , wherein the application metadata includes one or more labels for the one or more regions of application data.

17. The non-transitory computer readable medium of claim 16 , wherein the one or more labels for the one or more regions of application data includes a table of contents region label.

18. The non-transitory computer readable medium of claim 13 , wherein generating variable data chunk boundaries includes fitting variable data chunk boundaries around application data regions indicated to have a low likelihood of change.

Assignees

• Sony Interactive Entertainment LLC

Inventors

• Cerny Mark Evan
• Whittaker Simon

Classifications

• G06N5/01

  Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound · CPC title

• G06N7/01

  Probabilistic graphical models, e.g. probabilistic networks · CPC title

• G06F2212/7207

  management of metadata or control data · CPC title

• G06F12/0246

  in block erasable memory, e.g. flash memory · CPC title

• G06F12/04

  Addressing variable-length words or parts of words · CPC title