Computing device, method, and system for detecting whether target object is abnormal

Patent metadata

Field	Value
Publication number	US-2025053788-A1
Application number	US-202318448354-A
Country	US
Kind code	A1
Filing date	Aug 11, 2023
Priority date	Aug 11, 2023
Publication date	Feb 13, 2025
Grant date	—

Abstract

A method comprises obtaining original data representing one or more characteristics of a target object; selecting an artificial intelligence generation model from among a plurality of artificial intelligence generation models based on the original data; generating reconstructed data based on the original data using the selected artificial intelligence generation model; calculating an anomaly score by comparing the original data and the reconstructed data; and outputting detection result data indicating whether the target object is abnormal based on the anomaly score.

First claim

Please enter the following amendments to the claims:

1 . A method comprising:
obtaining original data representing one or more characteristics of a target object; selecting an artificial intelligence generation model from among a plurality of artificial intelligence generation models based on the original data; generating reconstructed data based on the original data using the artificial intelligence generation model; calculating an anomaly score by comparing the original data and the reconstructed data; and outputting detection result data indicating whether the target object is abnormal based on the anomaly score.

2 . The method of claim 1 , wherein the obtaining of the original data comprises:
receiving a data stream in real time and dividing the data stream into a plurality of batches, wherein the original data comprises a batch of the plurality of batches; and wherein the method further comprises: calculating a plurality of anomaly scores corresponding to the plurality of batches and generating distribution data including a distribution of the plurality of anomaly scores.

3 . The method of claim 1 , wherein the calculating of the anomaly score comprises:
encoding the original data using an encoder neural network of the artificial intelligence generation model to obtain encoded data; decoding the encoded data using a decoder neural network of the artificial intelligence generation model to obtain reconstructed data; and computing a difference between the reconstructed data and the original data.

4 . The method of claim 1 , wherein the selecting of the artificial intelligence generation model comprises:
calculating a first anomaly score based on previously received data; calculating a second anomaly score based on the original data using the artificial intelligence generation model; and calculating a first reliability value representing a compatibility between the original data and the artificial intelligence generation model based on the first anomaly score and the second anomaly score, wherein the artificial intelligence generation model is selected based at least in part on the first reliability value.

5 . The method of claim 4 , wherein the calculating of the first reliability value comprises:
comparing the first anomaly score and the second anomaly score.

6 . The method of claim 4 , wherein the selecting of the artificial intelligence generation model further comprises:
determining that the first reliability value is greater than a first reference value.

7 . The method of claim 4 , wherein the selecting of the artificial intelligence generation model further comprises:
calculating a third anomaly score based on the original data using another artificial intelligence generation model; and calculating a second reliability value representing a compatibility between the original data and the another artificial intelligence generation model based on the first anomaly score and the third anomaly score, wherein the artificial intelligence generation model is selected based at least in part on the second reliability value.

8 . The method of claim 1 , wherein the selecting of the artificial intelligence generation model comprises:
calculating a first anomaly score based on previously received data using each of the plurality of artificial intelligence generation models; calculating a second anomaly score based on the original data using each of the plurality of artificial intelligence generation models; calculating a reliability value corresponding to a compatibility between the original data and each of the plurality of artificial intelligence generation models, respectively, based on the first anomaly score and the second anomaly score; and updating a model pool including the plurality of artificial intelligence generation models based on the reliability value.

9 . The method of claim 1 , further comprising:
training a new artificial intelligence generation model using the original data; generating a first latent vector using the new artificial intelligence generation model; generating a second latent vector using each of the plurality of the artificial intelligence generation models; comparing the first latent vector to the second latent vector for each of the plurality of artificial intelligence generation models, respectively; selecting a similar artificial intelligence generation model from the plurality of artificial intelligence generation models based on the comparison; and merging the new artificial intelligence generation model with the similar artificial intelligence generation model.

10 . The method of claim 9 , wherein the comparing of the first latent vector to the second latent vector comprises:
calculating a difference between the first latent vector and the second latent vector; and calculating a similarity value based on the difference, wherein the similar artificial intelligence generation model is selected based on the similarity value.

11 . A computing device comprising:
a memory storing model pool data corresponding to a plurality of artificial intelligence generation models; and a processor configured to:
obtain original data representing one or more characteristics of a target object;
select an artificial intelligence generation model from among the plurality of artificial intelligence generation models based on the original data;
generate reconstructed data based on the original data using the artificial intelligence generation model;
calculate an anomaly score by comparing the original data and the reconstructed data; and
output detection result data indicating whether the target object is abnormal based on the anomaly score.

12 . The computing device of claim 11 , wherein the obtaining of the original data comprises:
receiving a data stream in real time and dividing the data stream into a plurality of batches, wherein the original data comprises a batch of the plurality of batches; and wherein the processor is configured to further calculate a plurality of anomaly scores corresponding to the plurality of batches and generating distribution data including a distribution of the plurality of anomaly scores.

13 . The computing device of claim 11 , wherein the calculating of the anomaly score comprises:
encoding the original data using an encoder neural network of the artificial intelligence generation model to obtain encoded data; decoding the encoded data using a decoder neural network of the artificial intelligence generation model to obtain reconstructed data; and computing a difference between the reconstructed data and the original data.

14 . The computing device of claim 11 , wherein the selecting of the artificial intelligence generation model comprises:
calculating a first anomaly score based on previously received data; calculating a second anomaly score based on the original data using the artificial intelligence generation model; and calculating a first reliability value representing a compatibility between the original data and the artificial intelligence generation model based on the first anomaly score and the second anomaly score, wherein the artificial intelligence generation model is selected based at least in part on the first reliability value.

15 . The computing device of claim 14 , wherein the calculating of the first reliability value comprises:
comparing the first anomaly score and the second anomaly score.

16 . The computing device of claim 14 , wherein the selecting of the artificial intelligence generation model further comprises:
determini

Assignees

• Samsung Electronics Co Ltd
• Korea Advanced Inst Sci & Tech

Inventors

• Lee Jae-Gil
• Yoon Susik
• Lee Byung Suk
• Lee Youngjun

Classifications

• G06N3/088

  Non-supervised learning, e.g. competitive learning · CPC title

• G06N3/047

  Probabilistic or stochastic networks · CPC title

• G06N3/045

  Combinations of networks · CPC title

• G06N3/0455 Primary

  Auto-encoder networks; Encoder-decoder networks · CPC title