IP Library Granted Patent US 12,541,997
Granted Patent B2
US 12,541,997 · App. 17/906,761 · Granted Feb 3, 2026

Training system and data collection device

Inventors: Andrew Shin (Tokyo, JP); Yoshiyuki Kobayashi (Tokyo, JP); Kenji Suzuki (Tokyo, JP)
Assignee: SONY GROUP CORPORATION
G06V40/176G06F3/015G06T7/10G06F2203/011G06T2207/20081G06T2207/20084
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 12,541,997
App. No.
17/906,761
Granted
Feb 3, 2026
Kind
B2
Abstract

Provided is a training system that performs training of a machine learning model that generates an impressing content or estimates a camera operation for capturing an impressing content. The training system includes a data collection device that collects data, and a training device that performs training of a machine learning model by using the data collected by the data collection device, in which the training device performs re-training of the machine learning model by using learning data that affects the training of the machine learning model to a predetermined degree or more, insufficient learning data, or data similar thereto, collected on the basis of a result of analyzing learning data that affects the training of the machine learning model.

Claims (53)

1 . A training system, comprising:

a data collection device configured to collect learning data; and

a training device configured to:

execute a training operation of a machine learning model, based on the collected learning data;

analyse the collected learning data that affects the training operation of the machine learning model; and

execute a re-training operation of the machine learning model, based on specific learning data, wherein

the specific learning data includes one of

learning data that affects the training operation of the machine learning model to more than a specific degree, or

insufficient learning data, and

the specific learning data is collected based on a result of the analysis of the collected learning data that affects the training operation of the machine learning model.

2 . The training system according to claim 1 , wherein the machine learning model:

generates impressing content, or

estimates a camera operation for capture of the impressing content.

3 . The training system according to claim 1 , wherein the analysis is based on one of

an explainable artificial intelligence (XAI),

a confidence score calculation,

an influence function, or

a Bayesian deep neural network (DNN).

4 . The training system according to claim 1 , wherein

the training device is further configured to

transmit, to the data collection device, a request signal to request for the one of

the learning data that affects the training operation of the machine learning model to more than the specific degree, or

the insufficient learning data,

the data collection device is further configured to transmit, based on the request signal, collected data to the training device, and

the training device is further configured to execute the re-training operation of the machine learning model based on the collected data transmitted from the data collection device.

5 . The training system according to claim 1 , wherein

the data collection device is one of a camera or an imager,

the data collection device is configured to:

capture an image, and

transmit, to the training device, image data of the image captured by change in one of a resolution, a frame rate, a luminance, a color, an angle of view, a viewpoint position, or a line-of-sight direction of the one of the camera or the imager, wherein

the one of the resolution, the frame rate, the luminance, the color, the angle of view, the viewpoint position, or the line-of-sight direction is changed based on a degree of influence on the training operation of the machine learning model.

6 . The training system according to claim 1 , wherein

the training device is further configured to transmit a request signal to request for the specific learning data of the machine learning model to the data collection device,

the data collection device is further configured to transmit, to the training device, the one of the learning data that affects the training operation of the machine learning model to more than the specific degree, or the insufficient learning data, and

the training device is further configured to execute the re-training operation the machine learning model based on the transmitted the one of the learning data that affects the training operation of the machine learning model to more than the specific degree, or the insufficient learning data.

7 . The training system according to claim 6 , wherein the training device is further configured to transmit, based on the request signal, information necessary for the analysis to the data collection device.

8 . A data collection device, comprising:

a processor configured to:

receive, from a training device, a first request signal, wherein

the first request signal requests for learning data of a machine learning model, and

the training device executes a training operation of the machine learning model;

analyse the learning data that affects the training operation of the machine learning model;

collect specific learning data, based on a result of the analysis of the learning data that affects the training operation of the machine learning model, wherein

the specific learning data includes one of

learning data that affects the training operation of the machine learning model to more than a specific degree, or

insufficient learning data; and

control transmission of the collected specific learning data to the training device, wherein the training device executes a re-training operation of the machine learning model based on the specific learning data.

9 . The data collection device according to claim 8 , wherein the processor is further configured to:

receive, from the training device, a second request signal, wherein the second request signal requests for the specific learning data; and

collect the specific learning data based on the received second request signal.

10 . The data collection device according to claim 8 , wherein

the processor is further configured to collect image data captured by change in one of a resolution, a frame rate, a luminance, a color, an angle of view, a viewpoint position, or a line-of-sight direction of one of a camera or an imager that captures an image, and

the image is captured based on a degree of influence on the training operation of the machine learning model.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 21, 2022
From: SHIN, ANDREW; KOBAYASHI, YOSHIYUKI; SUZUKI, KENJI
To: SONY GROUP CORPORATION
Reel/Frame 062165/0598 →
Priority Claims (2)
JP 2020-065069 · Mar 31, 2020 · national
JP 2020-120049 · Jul 13, 2020 · national
Continuity (1)
Related Publication 20230360437A1 · Nov 9, 2023
References Cited (22)
US 6092017A · Ishida · 2000 [cited by examiner]
US 20180329994A1 · Xie · 2018 [cited by examiner]
US 20190174109A1 · Yoshikawa · 2019 [cited by examiner]
US 20200074156A1 · Janumpally · 2020 [cited by examiner]
US 20200327709A1 · Liu · 2020 [cited by examiner]
US 20210226939A1 · Patel · 2021 [cited by examiner]
JP 11085719A · 1999 [cited by applicant]
JP 2008269065A · 2008 [cited by applicant]
JP 2009111938A · 2009 [cited by applicant]
JP 2010093584A · 2010 [cited by applicant]
JP 2012160082A · 2012 [cited by applicant]
JP 2018149669A · 2018 [cited by applicant]
WO 2018030206A1 · 2018 [cited by applicant]
WO WO2019215778A1 · 2019 [cited by applicant]
International Search Report and Written Opinion of PCT Application No. PCT/JP2021/012368, issued on Jun. 29, 2021, 13 pages of ISRWO. [cited by applicant]
Gu, et al., “Using Brain Data for Sentiment Analysis”, Journal for Language Technology and Computational Linguistics (JLCL) 2014—Band 29 (1), Aug. 2014, pp. 79-94. [cited by applicant]
Yang, et al., “Music Emotion Classification: A Fuzzy Approach”, Proceedings of the 14th ACM International Conference on Multimedia, Oct. 23-27, 2006, pp. 81-84. [cited by applicant]
Li, et al., “Visual Social Relationship Recognition”, Computer Vision and Pattern Recognition, Dec. 13, 2018, 15 pages. [cited by applicant]
Koh, et al., “Understanding Black-box Predictions via Influence Functions”, Proceedings of the 34th International Conference on Machine Learning, Australia, Dec. 29, 2020, 12 pages. [cited by applicant]
Kendall, et al., “What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?”, 31st Conference on Neural Information Processing Systems (NIPS 2017), USA, Oct. 5, 2017, 12 pages. [cited by applicant]
Yokoi, et al., “A Study of Business Interpretation Technique for AI Predictions”, Institute of Electronics, Information and Communication Engineers (IEICE) Technical Report, vol. 118, No. 513, Mar. 10, 2019, pp. 61-66. [cited by applicant]
Light; “High-speed image recognition solutions using deep learning techniques”, Monthly Automatic Recognition, Mar. 2020, vol. 33, No. 3, pp. 33-38, ISSN: 0915-1060. [cited by applicant]