IP Library › Granted Patent US 12,620,200
Granted Patent B2
US 12,620,200 · App. 18/340,264 · Granted May 5, 2026

Method for operating a technical system and technical system

Inventors: Jiaojiao Zhao (Amsterdam, NL); Sadaf Gulshad (Amsterdam, NL); Jan Hendrik Metzen (Boeblingen, DE); Smeulders Arnold (Amsterdam, NL)
Assignee: ROBERT BOSCH GMBH
G06V10/764G06V10/44G06V10/7715G06V10/774G06V10/82G06V20/582
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Quick Facts
Patent No.
US 12,620,200
App. No.
18/340,264
Granted
May 5, 2026
Kind
B2
Abstract

A method for operating a technical system and a technical system. The method includes providing for at least one class at least one class attribute comprising a description for members of the class, providing features characterizing a digital image, determining a class of the at least one class that classifies the digital image depending on the features, and determining at least one first attribute depending on the at least one class attribute provided for the class that classifies the digital image. The at least one first attribute includes an explanation for classifying the digital image with the class that classifies the digital image. The method further includes operating the technical system depending on the class that classifies the digital image and/or depending on the at least one first attribute.

Claims (79)

1 . A method for operating a technical system, the method comprising the following steps:

providing, for at least one class, at least one class attribute including a description for members of the class;

providing features characterizing a digital image;

determining a class of the at least one class that classifies the digital image depending on the features;

determining at least one first attribute depending on the at least one class attribute provided for the class that classifies the digital image, wherein the at least one first attribute includes an explanation for classifying the digital image with the class that classifies the digital image; and

operating the technical system depending on the class that classifies the digital image and/or depending on the at least one first attribute,

wherein the determining of the class that classifies the digital image includes:

providing at least one attribute query;

mapping the features and the at least one attribute query with a decoder to projected features;

mapping the projected features with a first layer of at least one neural network to the at least one first attribute; and

mapping the features with a second layer of the at least one neural network to the class that classifies the digital image.

2 . The method according to claim 1 , further comprising:

training a neural network to determine the class that classifies the digital image and the at least one first attribute, wherein the training includes minimizing a mean square error between the at least one class attribute and the at least one first attribute and/or minimizing a cross-entropy loss that depends on the class that classifies the digital image.

3 . The method according to claim 1 , further comprising:

receiving the digital image; and

outputting the class that classifies the digital image and the at least one first attribute.

4 . The method according to claim 1 , further comprising:

capturing the digital image with a camera and/or a radar sensor and/or a LiDAR sensor and/or an ultrasonic sensor and/or a motion sensor and/or a thermal image sensor; and/or

the operating of the technical system includes outputting the class that classifies the digital image and/or the at least one first attribute characterizing a traffic sign and/or a road surface and/or a pedestrian and/or a vehicle.

5 . A method for operating a technical system, the method comprising the following steps:

providing, for at least one class, at least one class attribute including a description for members of the class;

providing features characterizing a digital image;

determining a class of the at least one class that classifies the digital image depending on the features;

determining at least one first attribute depending on the at least one class attribute provided for the class that classifies the digital image, wherein the at least one first attribute includes an explanation for classifying the digital image with the class that classifies the digital image; and

operating the technical system depending on the class that classifies the digital image and/or depending on the at least one first attribute,

wherein the determining of the class that classifies the digital image includes:

providing at least one attribute query;

mapping the features and the at least one attribute query with a decoder to projected features;

mapping the projected features with a first layer of at least one neural network to the at least one first attribute; and

determining the class that classifies the digital image depending on a dot product between the at least one first attribute and the at least one class attribute.

6 . A method for operating a technical system, the method comprising the following steps:

providing, for at least one class, at least one class attribute including a description for members of the class;

providing features characterizing a digital image;

determining a class of the at least one class that classifies the digital image depending on the features;

determining at least one first attribute depending on the at least one class attribute provided for the class that classifies the digital image, wherein the at least one first attribute includes an explanation for classifying the digital image with the class that classifies the digital image; and

operating the technical system depending on the class that classifies the digital image and/or depending on the at least one first attribute,

wherein the determining of the class that classifies the digital image includes:

providing at least one attribute query;

mapping the features and the at least one attribute query with a decoder to projected features;

mapping the projected features with a first layer of at least one neural network to the at least one first attribute;

mapping the projected features with a second layer of the at least one neural network to at least one second attribute; and

determining the class that classifies the digital image depending on a dot product between the at least one first attribute and the at least one second attribute.

7 . A method for operating a technical system, the method comprising the following steps:

providing, for at least one class, at least one class attribute including a description for members of the class;

providing features characterizing a digital image;

determining a class of the at least one class that classifies the digital image depending on the features;

determining at least one first attribute depending on the at least one class attribute provided for the class that classifies the digital image, wherein the at least one first attribute includes an explanation for classifying the digital image with the class that classifies the digital image; and

operating the technical system depending on the class that classifies the digital image and/or depending on the at least one first attribute,

wherein the providing of the features includes:

providing the digital image;

mapping the digital image with a neural network to at least one token characterizing a local structure in the digital image, or at least one edge or at least one line in the digital image;

determining a fixed length output, wherein the determining of the fixed length output includes reshaping the at least one token in a spatial dimension, and/or splitting the at least one token with overlapping and/or padding and a stride, determining the features with an encoder depending on the output.

8 . A technical system for classifying digital images, the technical system comprising:

at least one processor; and

at least one memory, wherein the at least one memory is configured to store computer-readable instructions that, when executed by the at least one processor, cause the technical system to perform the following steps:

providing, for at least one class, at least one class attribute including a description for members of the class;

providing features characterizing a digital image;

determining a class of the at least one class that classifies the digital image depending on the features;

determining at least one first attribute depending on the at least one class attribute provided for the class that classifies the digital image, wherein the at least one first attribute includes an explanation for classifying the digital image with the class that classifies the digital image; and

operating the technical system depending on the class that classifies the digital image and/or depending on the at least one first attribute,

wherein the determining of the class that classifies the digital image includes:

providing at least one attribute query;

mapping the features and the at least one attribute query with a decoder to projected features;

mapping the projected features with a first layer of at least one neural network to the at least one first attribute; and

mapping the features with a second layer of the at least one neural network to the class that classifies the digital image.

9 . The technical system according to claim 8 , further comprising:

a sensor configured to capture the digital image, the sensor including a camera sensor and/or a radar sensor and/or a LiDAR sensor and/or an ultrasonic sensor and/or a motion sensor and/or a thermal image sensor; and/or

an output configured to output the class that classifies the digital image and/or the at least one first attribute characterizing a traffic sign and/or a road surface and/or a pedestrian and/or a vehicle.

10 . A non-transitory computer-readable medium on which is stored a computer program including computer-readable instructions for operating a technical system, the instructions, when executed by computer, causing the computer to perform the following steps:

providing, for at least one class, at least one class attribute including a description for members of the class;

providing features characterizing a digital image;

determining a class of the at least one class that classifies the digital image depending on the features;

determining at least one first attribute depending on the at least one class attribute provided for the class that classifies the digital image, wherein the at least one first attribute includes an explanation for classifying the digital image with the class that classifies the digital image; and

operating the technical system depending on the class that classifies the digital image and/or depending on the at least one first attribute,

wherein the determining of the class that classifies the digital image includes:

providing at least one attribute query;

mapping the features and the at least one attribute query with a decoder to projected features;

mapping the projected features with a first layer of at least one neural network to the at least one first attribute; and

mapping the features with a second layer of the at least one neural network to the class that classifies the digital image.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 17, 2023
From: ZHAO, JIAOJIAO; GULSHAD, SADAF; METZEN, JAN HENDRIK; ARNOLD, SMEULDERS
To: ROBERT BOSCH GMBH
Reel/Frame 064624/0326 →
Priority Claims (1)
DE 10 2022 206 722.3 · Jun 30, 2022 · national
Continuity (1)
Related Publication 20240005634A1 · Jan 4, 2024
References Cited (6)
Metwaly, K., Kim, A., Branson, E., & Monga, V. (2022). Glidenet: Global, local and intrinsic based dense embedding network for multi-category attributes prediction. In Proceedings of the IEEE/CVF conference on computer … [cited by examiner]
Mao et al., “Towards Robust Vision Transformer,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021, pp. 1-12. [cited by applicant]
Zhang et al., “Auto-FSL: Searching the Attribute Consistent Network for Few-Shot Learning,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, No. 3, 2022, pp. 1213-1223. [cited by applicant]
Li et al., “TokenPose: Learning Keypoint Tokens for Human Pose Estimation,” 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 11313-11322. [cited by applicant]
Xu et al., “Where is the Model Looking at?—Concentrate and Explain the Network Attention.,” IEEE Journal of Selected Topics in Signal Processing, vol. 14, No. 3, 2020, pp. 1-11. [cited by applicant]
Yuan et al., “Tokens-to-Token VIT: Training Vision Transformers From Scratch on Imagenet,” Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2021, pp. 1-10. [cited by applicant]