Detecting device component defects and generating corresponding recommendations using artificial intelligence techniques

Inventors: Ravi Shukla (Bengaluru, IN); Jeffrey Scott Vah (Round Rock, TX); Wilson Tetsuia Kitsunai (Sorocaba, BR); Aaron Sanchez (Austin, TX)

Assignee: Dell Products L.P.

G06T7/0008 G06T7/0004 G06T11/60 G06V10/25 G06V10/764 G09G3/006 G06F16/90 G06T2207/20081 G06T2207/20084 G06T2207/30121

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Quick Facts

Patent No.: US 12664758
App. No.: 18/422,797
Granted: Jun 23, 2026
Kind: B2

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Abstract

Methods, apparatus, and processor-readable storage media for detecting device component defects and generating corresponding recommendations using artificial intelligence techniques are provided herein. An example computer-implemented method includes obtaining image data of one or more device components and user input pertaining to at least a portion of the device component(s); predicting at least one defect associated with the device component(s) by processing at least a portion of the image data and the user input using a first set of artificial intelligence techniques; determining, using a second set of artificial intelligence techniques, that the at least one predicted defect is repairable; generating recommendation(s) for repairing the defect(s) by processing, using the second set artificial intelligence techniques, the at least a portion of the image data, the at least a portion of the user input, and information pertaining to the defect(s); and performing automated actions based on the recommendation(s).

Claims (38)

1 . A computer-implemented method comprising:

obtaining image data of one or more device components and user input pertaining to at least a portion of the one or more device components;

predicting at least one defect associated with the one or more device components by processing at least a portion of the image data and at least a portion of the user input using a first set of one or more artificial intelligence techniques;

determining, using a second set of one or more artificial intelligence techniques, that the at least one predicted defect is repairable;

generating one or more recommendations for repairing the at least one predicted defect by processing, using the second set of one or more artificial intelligence techniques, the at least a portion of the image data, the at least a portion of the user input, and information pertaining to the at least one predicted defect; and

performing one or more automated actions based at least in part on the one or more generated recommendations, wherein performing the one or more automated actions comprises one or more of (i) automatically training at least a portion of the first set of one or more artificial intelligence techniques using feedback related to the one or more generated recommendations, and (ii) automatically training at least a portion of the second set of one or more artificial intelligence techniques using the feedback related to the one or more generated recommendations;

wherein the method is performed by at least one processing device comprising a processor coupled to a memory.

2 . The computer-implemented method of claim 1 , wherein predicting the at least one defect associated with the one or more device components comprises processing the at least a portion of the image data and the at least a portion of the user input using one or more deep learning-based image classification techniques.

3 . The computer-implemented method of claim 2 , wherein processing the at least a portion of the image data and the at least a portion of the user input using one or more deep learning-based image classification techniques comprises processing the at least a portion of the image data and the at least a portion of the user input using at least one pretrained convolutional neural network relevant to the at least a portion of the one or more device components.

4 . The computer-implemented method of claim 2 , wherein processing the at least a portion of the image data and the at least a portion of the user input using one or more deep learning-based image classification techniques comprises identifying one or more items of historical image data comprising at least a predetermined level of similarity to one or more portions of the obtained image data.

5 . The computer-implemented method of claim 4 , wherein predicting the at least one defect associated with the one or more device components comprises determining and obtaining one or more items of historical user input corresponding to at least a portion of the one or more items of identified historical image data.

6 . The computer-implemented method of claim 1 , wherein predicting the at least one defect associated with the one or more device components comprises processing the at least a portion of the image data and the at least a portion of the user input using at least one object detection model.

7 . The computer-implemented method of claim 6 , wherein processing the at least a portion of the image data and the at least a portion of the user input using at least one object detection model comprises generating a modified version of the at least a portion of the image data comprising at least one bounding box represented in connection with the at least one predicted defect.

8 . The computer-implemented method of claim 1 , wherein generating the one or more recommendations for repairing the at least one predicted defect using the second set of one or more artificial intelligence techniques comprises processing the at least a portion of the image data, the at least a portion of the user input, and the information pertaining to the at least one predicted defect using at least one multimodal large language model (LLM).

9 . The computer-implemented method of claim 1 , wherein generating the one or more recommendations for repairing the at least one predicted defect comprises generating a sequentially ordered set of instructions for repairing the at least one predicted defect.

10 . The computer-implemented method of claim 1 , wherein performing the one or more automated actions comprises outputting the one or more generated recommendations to one or more of at least one user and at least one automated system associated with the one or more device components.

11 . The computer-implemented method of claim 1 , wherein obtaining the user input pertaining to at least a portion of the one or more device components comprises obtaining at least one user-provided description of at least one issue associated with the one or more device components.

12 . A non-transitory processor-readable storage medium having stored therein program code of one or more software programs, wherein the program code when executed by at least one processing device causes the at least one processing device:

to obtain image data of one or more device components and user input pertaining to at least a portion of the one or more device components;

to predict at least one defect associated with the one or more device components by processing at least a portion of the image data and at least a portion of the user input using a first set of one or more artificial intelligence techniques;

to determine, using a second set of one or more artificial intelligence techniques, that the at least one predicted defect is repairable;

to generate one or more recommendations for repairing the at least one predicted defect by processing, using the second set of one or more artificial intelligence techniques, the at least a portion of the image data, the at least a portion of the user input, and information pertaining to the at least one predicted defect; and

to perform one or more automated actions based at least in part on the one or more generated recommendations, wherein performing the one or more automated actions comprises one or more of (i) automatically training at least a portion of the first set of one or more artificial intelligence techniques using feedback related to the one or more generated recommendations, and (ii) automatically training at least a portion of the second set of one or more artificial intelligence techniques using the feedback related to the one or more generated recommendations.

13 . The non-transitory processor-readable storage medium of claim 12 , wherein predicting the at least one defect associated with the one or more device components comprises processing the at least a portion of the image data and the at least a portion of the user input using one or more deep learning-based image classification techniques.

14 . The non-transitory processor-readable storage medium of claim 12 , wherein predicting the at least one defect associated with the one or more device components comprises processing the at least a portion of the image data and the at least a portion of the user input using at least one object detection model.

15 . The non-transitory processor-readable storage medium of claim 12 , wherein generating the one or more recommendations for repairing the at least one predicted defect using the second set of one or more artificial intelligence techniques comprises processing the at least a portion of the image data, the at least a portion of the user input, and the information pertaining to the at least one predicted defect using at least one multimodal LLM.

16 . An apparatus comprising:

at least one processing device comprising a processor coupled to a memory;

the at least one processing device being configured:

to obtain image data of one or more device components and user input pertaining to at least a portion of the one or more device components;

to determine, using a second set of one or more artificial intelligence techniques, that the at least one predicted defect is repairable;

17 . The apparatus of claim 16 , wherein predicting the at least one defect associated with the one or more device components comprises processing the at least a portion of the image data and the at least a portion of the user input using at least one of one or more deep learning-based image classification techniques and at least one object detection model.

18 . The apparatus of claim 16 , wherein generating the one or more recommendations for repairing the at least one predicted defect using the second set of one or more artificial intelligence techniques comprises processing the at least a portion of the image data, the at least a portion of the user input, and the information pertaining to the at least one predicted defect using at least one multimodal LLM.

19 . The apparatus of claim 16 , wherein generating the one or more recommendations for repairing the at least one predicted defect comprises generating a sequentially ordered set of instructions for repairing the at least one predicted defect.

20 . The apparatus of claim 16 , wherein performing the one or more automated actions comprises outputting the one or more generated recommendations to one or more of at least one user and at least one automated system associated with the one or more device components.