The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Jan. 20, 2026

Filed:

Sep. 28, 2023
Applicant:

Robert Bosch Gmbh, Stuttgart, DE;

Inventors:

Chen Qiu, Pittsburgh, PA (US);

Clement Fung, Pittsburgh, PA (US);

Maja Rudolph, Madison, WI (US);

Assignee:

Robert Bosch GmbH, Stuttgart, DE;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 40/40 (2022.01); H04N 19/46 (2014.01);
U.S. Cl.
CPC ...
G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 40/40 (2022.01); H04N 19/46 (2014.11);
Abstract

A computer-implemented system and method relate to anomaly detection. Latent code of a source image is obtained. The latent code is designated as a target image. Source embedding data is generated form the source image. Text data, which is of a different domain than that of the source image, is obtained. Text embedding data is generated from the text data. Additional embedding data is generated using the source embedding data and the text embedding data. The additional embedding data provides guidance for modifying the source image. A modified image is generated via an iterative process that includes at least one iteration, where each iteration includes at least (i) encoding the target image to generate target embedding data, (ii) generating updated embedding data by combining the target embedding data and the additional embedding data, (iii) decoding the updated embedding data to generate a new image, and (iv) assigning the new image as the target image and the modified image. A non-anomalous label is generated for the source image and an anomalous label is generated for the modified image. A machine learning model is trained or fine-tuned using a dataset, which includes at least the source image with the non-anomalous label and the modified image with the anomalous label.


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