IP Library › Granted Patent US 12,556,727
Granted Patent B2
US 12,556,727 · App. 17/907,643 · Granted Feb 17, 2026

Content generating device, content distribution system, content generating method, and content generating program

Inventors: Takashi Kojima (Tokyo, JP); Kazuhiko Kusano (Tokyo, JP); Hajime Kato (Tokyo, JP)
Assignee: Dwango Co., Ltd.
H04N19/423H04N19/136H04N19/184H04N19/85
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,556,727
App. No.
17/907,643
Granted
Feb 17, 2026
Kind
B2
Abstract

According to one or more embodiments, a content generating device is provided. The content generating device comprises a first generator, a second generator, and a first transmitter. The first generator generates low-bit-rate encoded data that is original data having been encoded to a low-bit-rate. The second generator generates machine-learned model data for generating improved data obtained by improving human perceptually the low-bit-rate encoded data, based on a machine-learned model. The first transmitter transmits the low-bit-rate encoded data and the model data to outside.

Claims (64)

1 . A content generating device, comprising at least one processor configured to:

obtain original data including category information;

encode the original data to a low-bit-rate and generate low-bit-rate encoded data;

generate, based on a machine-learned model, machine-learned model data corresponding to the category information of the original data by using teacher data corresponding to the category information, the machine-learned model data to be used for generating improved data that is human perceptually improved from the low-bit-rate encoded data; and

transmit the low-bit-rate encoded data and the model data to outside the device.

2 . The content generating device of claim 1 , wherein the at least one processor is further configured to:

store the model data generated in the past; and

transmit, to the outside of the device, the model data generated in the past together with the low-bit-rate encoded data.

3 . The content generating device of claim 2 , wherein

the original data includes the category information for classifying content according to characteristics thereof, and

the at least one processor records the model data classified based on the category information or an initial value of the model data.

4 . The content generating device of claim 1 , wherein

the original data is image data, and

the low-bit-rate encoded data includes meta-information.

5 . The content generating device of claim 4 , wherein the meta-information of the low-bit-rate encoded data is at least one of: a coding block quantization parameter (QP), a prediction error coefficient, prediction mode information, and motion vector information in an image coding technique.

6 . A content distribution system, comprising:

a content generating device including at least one processor configured to:

obtain original data including category information;

encode the original data to a low-bit-rate and generate low-bit-rate encoded data;

generate, based on a machine-learned model, machine-learned model data corresponding to the category information of the original data by using teacher data corresponding to the category information, the machine-learned model data to be used for generating improved data that is human perceptually improved from the low-bit-rate encoded data; and

transmit the low-bit-rate encoded data and the model data to outside the content generating device; and

a content distribution device including at least one processor configured to:

receive the low-bit-rate encoded data and the model data transmitted from the content generating device;

generate the improved data based on the low-bit-rate encoded data, from the low-bit-rate encoded data and the model data received; and

distribute the improved data as content data.

7 . The content distribution system of claim 6 , wherein the at least one processor of the content generating device is further configured to:

store the model data generated in the past; and

transmit, to the outside the content generating device, the model data generated in the past together with the low-bit-rate encoded data.

8 . The content distribution system of claim 7 , wherein

the original data includes the category information for classifying content according to characteristics thereof, and

the at least one processor of the content generating device records the model data classified based on the category information or an initial value of the model data.

9 . The content distribution system of claim 6 , wherein

the original data is image data, and

the low-bit-rate encoded data includes meta-information.

10 . The content distribution system of claim 9 ,

wherein the meta-information of the low-bit-rate encoded data is at least one of: a coding block quantization parameter (QP), a prediction error coefficient, prediction mode information, and motion vector information in an image coding technique.

11 . A content generating method, comprising:

obtaining original data including category information;

encoding the original data to a low-bito rate and generating low-bit-rate encoded data;

generating, based on a machine-learned model, machine-learned model data corresponding to the category information of the original data by using teacher data corresponding to the category information, the machine-learned model data to be used for generating improved data that is human perceptually improved from the low-bit-rate encoded data; and

transmitting the low-bit-rate encoded data and the model data to outside a device.

12 . The content generating method of claim 11 , further comprising:

receiving the low-bit-rate encoded data and the model data; and

generating the improved data based on the low-bit-rate encoded data, from the low-bit-rate encoded data and the model data received.

13 . The content generating method of claim 11 , further comprising:

storing the model data generated in the past; and

transmitting the model data generated in the past together with the low-bit-rate encoded data to the outside the device.

14 . The content generating method of claim 13 , wherein

the original data includes the category information for classifying content according to characteristics thereof, and

the method further comprises recording the model data classified based on the category information or an initial value of the model data.

15 . The content generating method of claim 11 , wherein

the original data is image data, and

the low-bit-rate encoded data includes meta-information.

16 . The content generating method of claim 15 , wherein the meta-information of the low-bit-rate encoded data is at least one of: a coding block quantization parameter (QP), a prediction error coefficient, prediction mode information, and motion vector information in an image coding technique.

17 . A non-transitory computer-readable medium storing a program that, when executed, causes a computer to execute the content generating method of claim 11 .

18 . The non-transitory computer-readable medium of claim 17 , wherein the method further comprises:

receiving the low-bit-rate encoded data and the model data; and

generating the improved data based on the low-bit-rate encoded data, from the low-bit-rate encoded data and the model data received.

19 . The non-transitory computer-readable medium of claim 17 , wherein the method further comprises:

storing the model data generated in the past; and

transmitting the model data generated in the past together with the low-bit-rate encoded data to the outside the device.

20 . The non-transitory computer-readable medium of claim 17 , wherein

the original data is image data, and

the low-bit-rate encoded data includes meta-information.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 28, 2022
From: KOJIMA, TAKASHI; KUSANO, KAZUHIKO; KATO, HAJIME
To: DWANGO CO., LTD.
Reel/Frame 061247/0060 →
Priority Claims (1)
JP 2020-080551 · Apr 30, 2020 · national
Continuity (1)
Related Publication 20230142432A1 · May 11, 2023
References Cited (37)
US 5841904A · Sugiura · 1998 [cited by applicant]
US 11800185B2 · Kojima et al. · 2023 [cited by applicant]
US 20070113242A1 · Fetkovich · 2007 [cited by examiner]
US 20080043853A1 · Kawa · 2008 [cited by examiner]
US 20130170746A1 · Zhang et al. · 2013 [cited by applicant]
US 20180139458A1 · Wang et al. · 2018 [cited by applicant]
US 20180302455A1 · Bordoloi · 2018 [cited by examiner]
US 20200175362A1 · Zhang · 2020 [cited by examiner]
US 20210092493A1 · Codenie · 2021 [cited by examiner]
US 20210150243A1 · Wang · 2021 [cited by examiner]
US 20210275908A1 · Amer · 2021 [cited by examiner]
US 20220070527A1 · Kojima · 2022 [cited by examiner]
CN 106791927A · 2017 [cited by applicant]
CN 107945108A · 2018 [cited by applicant]
CN 110754093A · 2020 [cited by applicant]
GB 2548749A · 2017 [cited by applicant]
JP H04302272A · 1992 [cited by applicant]
JP H05191796A · 1993 [cited by applicant]
JP 2015201819A · 2015 [cited by applicant]
JP 5956761B2 · 2016 [cited by applicant]
JP 2016534654A · 2016 [cited by applicant]
JP 2017049686A · 2017 [cited by applicant]
JP 2017123649A · 2017 [cited by applicant]
JP 2017158067A · 2017 [cited by applicant]
JP 2017195429A · 2017 [cited by applicant]
JP 2019129328A · 2019 [cited by applicant]
JP 2020524418A · 2020 [cited by applicant]
WO 2017164297A1 · 2017 [cited by applicant]
WO 2019225793A1 · 2019 [cited by applicant]
WO 2020137050A1 · 2020 [cited by applicant]
WO 2021221046A1 · 2021 [cited by applicant]
Decision of Rejection for Chinese Patent Application No. 201980077647.5 dated Jul. 20, 2023, pp. all. [cited by applicant]
[English Translation] First Office Action for Chinese Patent Application No. 201980077647.5, dated Dec. 20, 2022, pp. all. [cited by applicant]
Notice of First Review Opinion for Chinese Patent Application No. 202180014290.3 dated May 18, 2023, pp. all. [cited by applicant]
International Search Report and Written Opinion for PCT/JP2019/037580, mailed Dec. 24, 2019. [cited by applicant]
International Search Report and Written Opinion for PCT/JP2021/016747, mailed on Jun. 22, 2021. [cited by applicant]
Dong, Chao , et al., “Image Super-Resolution Using Deep Convolutional Networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, No. 2,, Jan. 6, 2015, pp. 295-307. [cited by applicant]