METHOD AND APPARATUS FOR UPDATING PREDICTIVE MODEL PREDICTING PRODUCT FAILURE

Various embodiments of the present disclosure disclose a method and apparatus, and comprise: a communication module comprising communication circuitry; a memory; and at least one processor comprising processing circuitry operatively connected to the communication module and/or the memory, wherein at least one processor is configured to: generate an AI predictive model based on component input data; acquire fail result data according to the AI predictive model; acquire pass result data according to the fail result data; and update the AI predictive model based on at least one of the component input data, the fail result data, and the pass result data.

BACKGROUND

Field

The disclosure relates to a method and an apparatus for updating a predictive model for predicting a product failure.

Description of Related Art

With the advancement of digital technology, various types of electronic devices, such as a mobile communication terminal, a personal digital assistant (PDA), an electronic organizer, a smartphone, a tablet personal computer (PC), or a wearable device, are widely used. In order to support and improve the functions of electronic devices, hardware and/or software of electronic devices is continuously being developed.

Electronic devices may include a plurality of components (or electronic components) (e.g., a processor, a camera, and an antenna) to provide various functions. Once an electronic device is manufactured, the electronic device may be tested to identify whether there is an abnormality. Since it is impossible to test every electronic device produced, a subset of electronic devices may be tested out of all produced electronic devices. Conventionally, electronic devices are tested using predictive models.

A conventional performance test of a complete product (e.g., an electronic devices) may be performed using data about a component (or an electronic component) (e.g., a processor and a camera). Conventionally, since performance (e.g., pass/fail) of a complete product is predicted using only a result of testing a component instead of testing performance of the complete product, the same prediction result may always be provided. Further, a result of testing a complete product may vary due to changing external factors even though the same component is used. Since a conventional predictive model is not updated once produced, accuracy of the predictive model predicting good or defective products may decrease over time.

SUMMARY

Embodiments of the disclosure may provide a method and an apparatus for generating an artificial intelligence (AI) predictive model, based on component input data, obtaining fail result data according to the AI predictive model, obtaining pass result data according to the fail result data, and updating the AI predictive model, based on at least one of the component input data, the fail result data, or the pass result data.

An electronic device according to various example embodiments of the disclosure may include: a communication module comprising communication circuitry, a memory, and at least one processor comprising processing circuitry operatively connected to the communication module and/or the memory, wherein at least one processor may be configured to: generate an artificial intelligence (AI) predictive model, based on component input data, obtain fail result data according to the AI predictive model, obtain pass result data according to the fail result data, and update the AI predictive model, based on at least one of the component input data, the fail result data, and the pass result data.

An AI prediction system according to various example embodiments of the disclosure may include: an electronic device, comprising circuitry, configured to obtain fail result data according to an AI predictive model, obtain pass result data according to the fail result data, and transmit at least one of component input data, the fail result data, or the pass result data to a server, wherein the server is configured to generate and/or update the AI predictive model, based on at least one of the component input data, the fail result data, or the pass result data, and transmit the AI predictive model to the electronic device.

An operating method of an electronic device according to various example embodiments of the disclosure may include: generating an artificial intelligence (AI) predictive model, based on component input data, obtaining fail result data according to the AI predictive model, obtaining pass result data according to the fail result data, and updating the AI predictive model, based on at least one of the component input data, the fail result data, and the pass result data.

According to various example embodiments, an AI predictive model of a complete product (e.g., an electronic device) may be updated based on at least one of component input data, fail result data, or pass result data, thereby maintaining accuracy of the AI predictive model predicting normal and failure.

According to various example embodiments, when data is predicted as being normal but is determined as a failure as a result of testing a complete product, an AI predictive model may be trained using the fail result data predicted as normal, thereby improving performance of predicting a complete product subsequently produced.

According to various example embodiments, when data is predicted as a failure but is determined as being normal as a result of testing a complete product, a sampling rate (or sampling number) of complete products tested to determine failure and normal may be adjusted, thereby saving time or cost for testing a complete product.

DETAILED DESCRIPTION

FIG.1is a block diagram illustrating an example electronic device101in a network environment100according to certain embodiments.

As used herein, the term “module” may include a unit implemented in hardware, software, or firmware, or any combination thereof, and may be interchangeably used with other terms, for example, “logic,” “logic block,” “component,” or “circuit”. The “module” may be a minimum unit of a single integrated component adapted to perform one or more functions, or a part thereof. For example, according to an embodiment, the “module” may be implemented in the form of an application-specific integrated circuit (ASIC).

According to various embodiments, each element (e.g., a module or a program) of the above-described elements may include a single entity or multiple entities, and some of the multiple entities mat be separately disposed in any other element. According to various embodiments, one or more of the above-described elements may be omitted, or one or more other elements may be added. Alternatively or additionally, a plurality of elements (e.g., modules or programs) may be integrated into a single element. In such a case, according to various embodiments, the integrated element may still perform one or more functions of each of the plurality of elements in the same or similar manner as they are performed by a corresponding one of the plurality of elements before the integration. According to various embodiments, operations performed by the module, the program, or another element may be carried out sequentially, in parallel, repeatedly, or heuristically, or one or more of the operations may be executed in a different order or omitted, or one or more other operations may be added.

FIG.2A and2Bare block diagrams illustrating example configurations of an AI prediction system including an electronic device and a server according to various embodiments.

FIG.2Aillustrates an example of generating an AI predictive model in an AI prediction system200according to various embodiments.

Referring toFIG.2A, the artificial intelligence (AI) prediction system200according to various embodiments may include an electronic device (e.g., the electronic device101ofFIG.1) and a server (e.g., the server108ofFIG.1). The electronic device101may include at least one of a component data acquisition module210, an AI predictive model control module230, and a data processing module250. Each of these modules may include various processing circuitry and/or executable program instructions. The component data acquisition module210, the AI predictive model control module230, and the data processing module250may be included in a processor (e.g., including processing circuitry) (e.g., the processor120ofFIG.1) of the electronic device101. The component data acquisition module210, the AI predictive model control module230, and the data processing module250may be configured as modules operatively connected to the processor (e.g., the processor120ofFIG.1) of the electronic device101. The server108may include a learning data module270and a machine learning module290, each of which may include various processing circuitry and/or executable program instructions.

The component data acquisition module210may obtain component input data in real time. The component input data may include test information (e.g., a specification, performance, and a test value) about a plurality of components (or electronic components) (e.g., a processor and a camera) included in a complete product (e.g., an electronic device). The component data acquisition module210may provide the obtain component input data to the data processing module250.

The AI predictive model control module230may generate an AI predictive model, based on the component input data. For example, the AI predictive model control module230may upload (or receive) the AI predictive model from the server108in real time. The generated (or uploaded) AI predictive model may be a first AI predictive model generated without fail result data or pass result data. The first AI predictive model may be generated based on the component input data. The AI predictive model control module230may predict product result data (e.g., pass result data201and fail result data203) with component input data273(e.g., a cause) using the first AI predictive model. According to various embodiments, the first AI predictive model may be generated using an entire complete product made of a component corresponding to the component input data273as fail result data271. The first AI predictive model may be generated based on the component input data273and the fail result data271.

The data processing module250may transmit at least one of the pass result data201, the fail result data203, or the component input data205to the server108in real time. The data processing module250may obtain the component input data205from the component data acquisition module210. The data processing module250may obtain (or receive) the pass result data201or the fail result data203from the AI predictive model control module230. The pass result data201may be data determined as being normal by the AI predictive model and determined as being normal as a result of an actual test. A complete product that is determined as being normal as a result of a test may refer, for example, to the product having no abnormality (or error or malfunction) in product performance (or use) and may be normally used by a user. The pass result data201may be determined as being a failure by the AI predictive model but determined as being normal as a result of an actual test. The fail result data203may be data predicted as being faulty by the AI predictive model and determined as being faulty as a result of an actual test. A complete product that is determined as being a failure as a result of a test may refer, for example, to the product having an abnormality (or error or malfunction) in product performance (or use). The fail result data203may be data predicted as being normal by the AI predictive model but determined as being faulty as a result of an actual test.

The learning data module270of the server108may learn the component input data273. The learning data module270may convert the component input data273into learning data for machine learning. The learning data module270may convert the component input data273into the learning data before the pass result data201and the fail result data203are generated. The learning data module270may convert fail result data271about all complete products made with the component input data273and the component input data273into learning data. The learning data module270may transmit the converted learning data to the machine learning module290.

The machine learning module290may generate an AI predictive model by learning the learning data. For example, the machine learning module290may generate an AI predictive model by learning the component input data273. The machine learning module290may generate an AI predictive model by learning the component input data273and the fail result data271. The machine learning module290may transmit the generated AI predictive model to the electronic device101. The generated AI predictive model may be the first AI predictive model.

FIG.2Billustrates an example of updating the AI predictive model in the AI prediction system200according to various embodiments.

Referring toFIG.2B, the component data acquisition module210may obtain component input data in real time. The AI predictive model control module230may update the AI predictive model (e.g., the first AI predictive model) generated byFIG.2A. The AI predictive model control module230may update the AI predictive model, based on at least one of pass result data201, fail result data203, or component input data205. According to various embodiments, the AI predictive model control module230may update the AI predictive model using a set ratio of pass result data207among the pass result data201.

When the pass result data201and the fail result data203are obtained by the first AI predictive model, the pass result data201and the fail result data203may be transmitted to the server108in real time through the data processing module250. The AI predictive model control module230may obtain (or receive) an updated AI predictive model from the machine learning module290included in the server108. The AI predictive model obtained from the machine learning module290after the first AI predictive model may be a second AI predictive model. The AI predictive model control module230may predict product result data (e.g., the pass result data201and the fail result data203) with component input data273, pass result data275, and fail result data271(e.g., cause) using the second AI predictive model. The second AI predictive model may be generated based on the fail result data271, the component input data273, and the pass result data275. When the pass result data201and the fail result data203are obtained by the second AI predictive model, the pass result data201and the fail result data203may be transmitted to the server108in real time through the data processing module250. The AI predictive model control module230may obtain an updated AI predictive model from the machine learning module290included in the server108. The AI predictive model control module230may update the AI predictive model whenever pass result data201, fail result data203, or component input data205is obtained.

The data processing module250may transmit at least one of the set ratio of pass result data207, the fail result data203, or the component input data205to the server108in real time. The data processing module250may obtain the component input data205from the component data acquisition module210. The data processing module250may obtain (or receive) the pass result data201or the fail result data203from the AI predictive model control module230. The set ratio of pass result data207may refer to some pass result data extracted from the pass result data201, based on the fail result data203. For example, when ten pieces of fail result data203are detected (or extracted), ten pieces of pass result data207at the same or similar ratio (e.g., 1:1) may be used to update the AI predictive model.

The learning data module270may convert the fail result data271, the component input data273, and the pass result data275into learning data for machine learning. The fail result data271may correspond to the fail result data203, the component input data273may correspond to the component input data205, and the pass result data275may correspond to the set ratio of pass result data207. The learning data module270may transmit the converted learning data to the machine learning module290.

The machine learning module290may update the AI predictive model, based on the learning data. For example, the machine learning module290may update the AI predictive model, based on at least one of the fail result data271, the component input data273, and the pass result data275. The machine learning module290may transmit the updated AI predictive model to the electronic device101. The generated AI predictive model may be the second AI predictive model. The learning data module270may obtain data from the data processing module250in real time, and may convert the obtained data into learning data. The machine learning module290may update the AI predictive model, based on the learning data converted by the learning data module270to transmit the same to the electronic device101in real time.

According to various embodiments, the electronic device101and the server108may update the AI predictive model in conjunction with each other. The electronic device101and the server108may update the AI predictive model in real time whenever component input data, pass result data, and fail result data are obtained. When the AI predictive model is updated, pass result data and fail result data may be generated by the updated AI predictive model.

An electronic device (e.g., the electronic device101ofFIG.1) according to various example embodiments of the disclosure may include: a communication module comprising communication circuitry (e.g., the communication module190ofFIG.1), a memory (e.g., the memory130ofFIG.1), and at least one processor comprising processing circuitry (e.g., the processor120ofFIG.1) operatively connected to the communication module and/or the memory, wherein at least one processor may be configured to: generate an artificial intelligence (AI) predictive model, based on component input data, obtain fail result data according to the AI predictive model, obtain pass result data according to the fail result data, and update the AI predictive model, based on at least one of the component input data, the fail result data, and the pass result data.

At least one processor may be configured to: transmit at least one of the component input data, the fail result data, or the pass result data to a server (e.g., the server108ofFIG.1) through the communication module, and receive the AI predictive model from the server.

At least one processor may be configured to update the AI predictive model based on the component input data, the fail result data, or the pass result data being obtained, and obtain fail result data and pass result data predicted by the updated AI predictive model.

The fail result data may be data predicted as being normal or faulty by the AI predictive model and determined as being faulty as a result of an actual test, and the pass result data may be data determined as being normal or faulty by the AI predictive model and determined as being normal as a result of the actual test.

At least one processor may be configured to obtain the pass result data, based on a number of pieces of the fail result data.

At least one processor may be configured to: obtain the pass result data according to a set ratio based on the number of pieces of the fail result data being less than or equal to a threshold value, and change an extraction ratio (or the set ratio) of the pass result data and obtain the pass result data according to the changed extraction ratio based on the number of pieces of the fail result data exceeding the threshold value.

At least one processor may be configured to: update the AI predictive model, based on fail result data predicted as being normal by the AI predictive model and determined as a failure as a result of an actual test.

At least one processor may be configured to: obtain the pass result data according to a configured sampling rate based on there being fail result data predicted as being a failure by the AI predictive model and determined as a failure as a result of an actual test.

At least one processor may be configured to: control a sampling rate at which the fail result data and the pass result data are inspected, based on pass result data predicted as being a failure by the AI predictive model and determined as being normal as a result of an actual test.

At least one processor may be configured to: change the sampling rate based on the pass result data predicted as being the failure by the AI predictive model and determined as being normal as the result of the actual test being detected, and maintain the sampling ratio based on the pass result data predicted as being the failure by the AI predictive model and determined as being normal as the result of the actual test not being detected.

An AI prediction system (e.g., the AI prediction system200ofFIG.2AandFIG.2B) according to various example embodiments of the disclosure may include an electronic device comprising circuitry (e.g., the electronic device101ofFIG.1) configured to: obtain fail result data according to an AI predictive model, obtain pass result data according to the fail result data, and transmit at least one of component input data, the fail result data, or the pass result data to a server, wherein the server (e.g., the server108ofFIG.1) is configured to: generate or update the AI predictive model, based on at least one of the component input data, the fail result data, or the pass result data, and transmit the AI predictive model to the electronic device.

The server may be configured to: update the AI predictive model whenever the component input data, the fail result data, or the pass result data is obtained from the electronic device, and transmit the AI predictive model to the electronic device, and the electronic device may be configured to obtain fail result data and pass result data predicted by the AI predictive model received from the server.

The electronic device may be configured to: obtain the pass result data according to a set ratio based on the number of pieces of the fail result data being less than or equal to a threshold value, and change an extraction ratio (or the set ratio) of the pass result data and obtain the pass result data according to the changed extraction ratio based on the number of pieces of the fail result data exceeding the threshold value.

FIG.3is a flowchart300illustrating an example method of operating an electronic device according to various embodiments.

Referring toFIG.3, in operation301, at least one processor (e.g., the processor120ofFIG.1) of the electronic device (e.g., the electronic device101ofFIG.1) according to various embodiments may generate an AI predictive model, based on component input data. The generated AI predictive model may be a first AI predictive model generated without fail result data or pass result data. At least one processor120may obtain component input data in real time. The component input data may include test information (e.g., a specification, performance, and a test value) about a plurality of components (or electronic components) (e.g., a processor and a camera) included in a complete product (e.g., an electronic device). According to various embodiments, the first AI predictive model may be generated using an entire complete product made with the component input data as fail result data. For example, the first AI predictive model may be generated based on the component input data and the fail result data. At least one processor120may receive (or upload) the AI predictive model from a server (e.g., the server108ofFIG.1) through a communication module (e.g., the communication module190ofFIG.1).

In operation303, at least one processor120may obtain fail result data, based on the generated AI predictive model. Pass result data and fail result data may be generated by the generated AI predictive model. The fail result data may be data predicted as being faulty by the generated AI predictive model and determined as being faulty as a result of an actual test. A complete product that is determined as being a failure as a result of a test may refer, for example, to the product having an abnormality (or error or malfunction) in product performance (or use). Alternatively, the fail result data may be data predicted as being normal by the AI predictive model but determined as being faulty as a result of an actual test.

In operation305, at least one processor120may obtain pass result data, based on the fail result data. The pass result data may be data determined as being normal by the AI predictive model and determined as being normal as a result of an actual test. A complete product that is determined as being normal as a result of a test may refer, for example, to the product having no abnormality (or error or malfunction) in product performance (or use) and may be normally used by a user. The pass result data may be determined as being a failure by the AI predictive model but determined as being normal as a result of an actual test. At least one processor120may obtain pass result data, based on the number of pieces of fail result data. For example, when the number of pieces of fail result data is10, the processor120may obtain ten pieces of pass result data at the same or similar ratio (e.g., 1:1).

According to various embodiments, at least one processor120may obtain pass result data, based on whether the number of pieces of fail result data is less than or equal to a threshold value. When the number of pieces of fail result data is less than or equal to the threshold value, at least one processor120may obtain pass result data according to a set ratio (e.g., 1:1). The number of pieces of fail result data being less than or equal to the threshold value may refer, for example, to fail result data having occurred the same as or similar to that predicted by the AI predictive model. When the number of pieces of fail result data exceeds the threshold value, the processor120may change (or adjust) an extraction ratio (or the set ratio) of pass result data. The number of pieces of fail result data exceeding the threshold value may refer, for example, to a greater number of pieces of fail result data having occurred than predicted by the AI predictive model. When the number of pieces of fail result data exceeds the threshold value, the processor120may increase the extraction ratio of pass result data. At least one processor120may obtain pass result data according to the changed extraction ratio (e.g., 1:1.5).

In operation307, at least one processor120may update the AI predictive model, based on the obtained data. The obtained data may include at least one of component input data, fail result data, or pass result data. At least one processor120may update the AI predictive model, based on at least one of the component input data, the fail result data, or the pass result data. At least one processor120may receive an updated AI predictive model from the server108through the communication module190.

According to various embodiments, at least one processor120may obtain component input data in real time, may obtain fail result data or pass result data, based on an updated AI predictive model, and may update the AI predictive model, based on at least one of the component input data, the fail result data, or the pass result data. At least one processor120may update the AI predictive model in real time by repeatedly performing operation303to operation307.

FIG.4Ais a diagram illustrating a normal and failure prediction rate of an electronic device according to various embodiments.

Referring toFIG.4A, according to various embodiments, a defect rate415may be reduced in the disclosure420in which an AI predictive model is updated in real time compared to predicting fail result data and pass result data using a first AI predictive model410. For example, a defect rate401by the first AI predictive model410may be greater than a defect rate421by the disclosure420. When complete products are made with the same component input data, the defect rate401may not be reduced by the first AI predictive model410, while the disclosure420may update the AI predictive model in real time, thus reducing the defect rate415. An AI predictive model may be regarded as having good prediction performance when a defect rate is reduced.

FIG.4Bis a graph illustrating a prediction success rate according to a comparative example.

Referring toFIG.4B, the graph450according to the comparative example shows that the prediction success rate decreases over time. For example, a prediction success rate451of a predictive model at a time t0may decrease over time. Conventionally, once a predictive model is produced, the same prediction result may always be obtained as a result of testing the same component. Even though results of testing components are the same, results of testing complete products may be different due to a change in external factors (e.g., development of s/w technology and strengthening of a specification). To reflect a change in external factors in a prediction result, a new predictive model needs to be produced each time, and a person directly tracks a change in external factors to determine when to produce a predictive model. For example, conventionally, when a prediction success rate is a success threshold value (P1), the predictive model may be regenerated at a time t1. When the predictive model is regenerated, a prediction success rate453may be increased. To regenerate the predictive model, it may be necessary to test and relearn all complete products for a period.

FIG.4Cis a graph illustrating a prediction success rate according to various embodiments.

Referring toFIG.4C, the graph470according to the disclosure shows that the prediction success rate changes over time but has a regular success rate (P0). For example, a prediction success rate471of a first AI predictive model at a time t0may be close to P0. In the disclosure, the first AI predictive model may be updated (473) based on component input data, and fail result data and pass result data obtained by the first AI predictive model. The first AI predictive model may be updated (473) to a second AI predictive model. In the disclosure, the second predictive model may be updated (475) based on component input data, and fail result data and pass result data obtained by the second predictive model. The second AI predictive model may be updated (475) to a third AI predictive model. In the disclosure, the third predictive model may be updated (477) based on component input data, and fail result data and pass result data obtained by the third predictive model. The third AI predictive model may be updated (477) to a fourth AI predictive model. In the disclosure, an AI predictive model may be updated in real time, thereby maintaining a prediction success rate at a regular success rate (P0) over time.

FIG.5is a diagram illustrating data obtained by an electronic device according to various embodiments.

Referring toFIG.5, the electronic device (e.g., the electronic device101ofFIG.1) according to various embodiments may obtain a first table510, a second table530, and a third table550. The first table510may be component input data obtained in real time. The component input data may include test information (e.g., a specification, performance, and a test value) about a plurality of components (or electronic components) (e.g., a processor and a camera) included in a complete product (e.g., an electronic device). In the first table510, a module ID511may be an identifier assigned to each component. For example, the electronic device101may obtain component input data with a module ID511of OVNL31C8A140402 (513).

The second table530may include result data about a complete product including various components. The second table530may include result data about a complete product corresponding to camera chip information531(Chip_ID_Camera) about the complete product. The result data may include fail result data and pass result data. The electronic device101may match the module ID511of the first table510and the camera chip information531of the second table530, thereby combining the component input data with the fail result data and the pass result data, based on the module ID511.

The third table550may include fail result data and pass result data corresponding to component result data. The electronic device101may transmit the third table550to a server (e.g., the server108inFIG.1). The server108may convert the third table550into learning data, and may generate and update an AI predictive model, based on the learning data. The machine learning module290of the server108may separate the third table550into cause learning data (e.g., component input data551, fail result data, and pass result data) and result learning data (e.g., fail result data and pass result data) to learn the data.

FIG.6is a flowchart600illustrating an example method in which an electronic device controls an extraction ratio of pass result data according to various embodiments.

Referring toFIG.6, in operation601, at least one processor (e.g., the processor120ofFIG.1) of the electronic device (e.g., the electronic device101ofFIG.1) according to various embodiments may obtain component input data and fail result data. The component input data may include test information (e.g., a specification, performance, and a test value) about a plurality of components (or electronic components) (e.g., a processor and a camera) included in a complete product (e.g., an electronic device). The fail result data may be data predicted as being faulty by an AI predictive model and determined as being faulty as a result of an actual test. A complete product that is determined as being a failure as a result of a test may refer, for example, to the product having an abnormality (or error or malfunction) in product performance (or use). Alternatively, the fail result data may be data predicted as being normal by the AI predictive model but determined as being faulty as a result of an actual test.

In operation603, at least one processor120may determine whether the number of failures is less than or equal to a threshold value. The number of failures may refer to the number of pieces of fail result data. At least one processor120may obtain pass result data, based on whether the number of pieces of fail result data is less than or equal to the threshold value. At least one processor120may perform operation605when the number of failures is less than or equal to the threshold value, and may perform operation604when the number of failures exceeds the threshold value.

When the number of failures is less than or equal to the threshold value, at least one processor120may obtain pass result data according to a set ratio (e.g., 1:1) in operation605. The number of pieces of fail result data being less than or equal to the threshold value may refer, for example, to fail result data has occurred the same as or similar to that predicted by the AI predictive model. The set ratio may be determined (or configured) in consideration of a number required to update the AI predictive model.

When the number of failures exceeds the threshold value, at least one processor120may change an extraction ratio (or the set ratio) of pass result data in operation604. The number of pieces of fail result data exceeding the threshold value may refer, for example, to a greater number of pieces of fail result data having occurred than predicted by the AI predictive model. A greater number of pieces of fail result data than predicted by the AI predictive model occurring may refer, for example, to prediction performance being poor. At least one processor120may increase the number (quantity) of pieces of data for training the AI predictive model to improve the performance of the AI predictive model. When the number of pieces of fail result data exceeds the threshold value, at least one processor120may increase the extraction ratio of pass result data.

In operation606, at least one processor120may obtain pass result data according to the changed extraction ratio. At least one processor120may obtain a greater number of pieces of pass result data than the set ratio. At least one processor120may update the AI predictive model by training the AI predictive model with the pass result data obtained greater than the set ratio together with the component input data and the fail result data. When operation606is completed, at least one processor120may perform operation607.

In operation607, at least one processor120may update the AI predictive model, based on the obtained data. The obtained data may include at least one of component input data, fail result data, or pass result data. At least one processor120may update the AI predictive model, based on at least one of the component input data, the fail result data, or the pass result data. The processor120may receive an updated AI predictive model from a server108through a communication module190.

According to various embodiments, at least one processor120may obtain component input data in real time, may obtain fail result data or pass result data, based on an updated AI predictive model, and may update the AI predictive model, based on at least one of the component input data, the fail result data, or the pass result data. At least one processor120may update the AI predictive model in real time by repeatedly performing operation601to operation607.

FIG.7is a diagram illustrating an example in which an electronic device controls an extraction ratio of pass result data according to various embodiments.

Referring toFIG.7, the electronic device (e.g., the electronic device101ofFIG.1) according to various embodiments may predict pass result data711and fail result data713using an AI predictive model710. The AI predictive model710may be generated based on component input data. Alternatively, the AI predictive model710may be generated based on component input data and fail result data about an entire complete product made of a component corresponding to the component input data.

The electronic device101may obtain result data730as a result of actually testing a complete product predicted by the AI predictive model710. The result data730may include pass result data and fail result data731. The result data may be predicted as a failure by the AI predictive model710but may be included in the pass result data a result of an actual test, and thus the number of pieces of fail result data715may be reduced. The electronic device101may learn the result data730, which is predicted as a failure by the AI predictive model710but is normal result data as a result of the actual test, to update the AI predictive model.

The updated AI predictive model750may be generated based on the component input data and the result data730. The electronic device101may predict pass result data751and fail result data753using the updated AI predictive model750. As a result of prediction, the number of pieces of fail result data753may be reduced. When predicting normal or failure of a complete product using the updated AI predictive model750, failure predictions may be reduced and prediction performance may be improved.

FIG.8is a flowchart800illustrating an example method in which an electronic device updates an AI predictive model using fail result data according to various embodiments.

Referring toFIG.8, in operation801, at least one processor (e.g., the processor120ofFIG.1) of the electronic device (e.g., the electronic device101ofFIG.1) according to various embodiments may obtain fail result data. The fail result data may be data predicted as being faulty by an AI predictive model and determined as being defective as a result of an actual test. The fail result data may be data predicted as being normal by the AI predictive model but determined as being faulty as a result of an actual test.

In operation803, at least one processor120may determine whether the fail result data is predicted as being normal. The fail result data may be data predicted as being faulty or as being normal by the AI predictive model. At least one processor120may perform operation805when the fail result data is predicted as being normal, and may perform operation807when the fail result data is predicted as being not normal.

When the fail result data is predicted as being normal, at least one processor120may update the AI predictive model with the fail result data predicted as being normal in operation805. When the fail result data is predicted as being normal by the AI predictive model but is a failure as a result of the actual test, at least one processor120may determine that there is a change in external conditions. When an increasing number of defects occur due to the change in external conditions (e.g., a change in specifications determined as being normal), the processor120may update the AI predictive model in real time. At least one processor120may use fail result data predicted as being faulty by an AI predictive model and pass result data predicted as being normal as learning data. The trained AI predictive model may be updated (or generated) into an AI predictive model that reflects the change in external conditions in a next prediction by reflecting the result of the test. When producing a product, repeatedly performing predict->test->learn->update the AI predictive model->predict->test->learn->update the AI predictive model may update the AI predictive model to perform more accurate prediction.

When the fail result data is predicted as not being normal (e.g., the fail result data is predicted as being faulty), at least one processor120may obtain pass result data according to a configured sampling rate in operation807. The sampling rate may refer to a sampling rate (or number) at which fail result data or pass result data is actually tested. The sampling rate may be different from the extraction ratio, which represents the ratio of the number of pass result data to the number of fail result data. At least one processor120may configure a rate appropriate for each production facility environment by adjusting the sampling rate at which products predicted as being normal is actually tested. For example, at least one processor120may randomly sample and test some of pass result data predicted as being normal, and may match and learn the same with component input data to reflect the change in external conditions in the AI predictive model. When there is an increase in fail result data due to an upward adjustment of a specification, updating the AI predictive model may increase failure prediction, and may improve the accuracy of the AI predictive model. Repeating this process may make it possible to more accurately predict normal or failure.

In operation809, at least one processor120may update the AI predictive model, based on the obtained data. The obtained data may include at least one of component input data, fail result data, or pass result data. At least one processor120may update the AI predictive model, based on at least one of the component input data, the fail result data, or the pass result data. At least one processor120may receive an updated AI predictive model from a server108through a communication module190.

FIG.9Ais a diagram illustrating an example in which a failure prediction rate increases due to an external factor according to various embodiments.

Referring toFIG.9A, before an external factor is changed (910), pass result data predicted as being normal and fail result data911predicted as being faulty may be obtained according to an AI predictive model. After the external factor is changed (920), there may be an increase in pass result data predicted as being normal by the AI predictive model may decrease and there may be a decrease in fail result data predicted as being faulty may increase (915). After the external factor is changed (920), there may be an increase in fail result data921obtained as a result of an actual test increases and prediction accuracy may decrease. In the disclosure, to address this problem, some of pass result data predicted as being normal may be randomly sampled, tested, and matched with component input data to be learned, thereby reflecting a change in external conditions in the AI predictive model. When there is an increase in fail result data due to an upward adjustment of a specification, updating the AI predictive model may increase failure prediction, and may improve the accuracy of the AI predictive model.

FIG.9Bis a diagram illustrating an example in which an electronic device updates an AI predictive model using fail result data according to various embodiments.

Referring toFIG.9B, the electronic device (e.g., the electronic device101ofFIG.1) according to various embodiments may predict pass result data and fail result data951using an AI predictive model950. The AI predictive model950may be generated based on at least one of component input data, pass result data, and fail result data. The electronic device101may obtain result data970as a result of actually testing a complete product predicted by the AI predictive model950. The result data970may include pass result data and fail result data975. The result data970may include pass result data971that is predicted as being normal by the AI predictive model950and is normal as a result of an actual test and fail result data973that is predicted as being normal by the AI predictive model950but is a failure as a result of the actual test.

The electronic device101may update the AI predictive model by randomly sampling the pass result data and matching and learning the pass result data971and the fail result data973with component input data. The updated AI predictive model990may be generated based on the component input data, the pass result data971, and the fail result data973. The electronic device101may predict pass result data and fail result data using the updated AI predictive model990. As a result of prediction, the number of pieces of fail result data991may increase. When predicting normal or failure of a complete product using the updated AI predictive model990, failure predictions may be reduced and prediction performance may be improved.

FIG.10is a flowchart1000illustrating an example method in which an electronic device controls a sampling rate according to various embodiments.

Referring toFIG.10, in operation1001, at least one processor (e.g., the processor120ofFIG.1) of the electronic device (e.g., the electronic device101ofFIG.1) according to various embodiments may obtain pass result data. The pass result data may be data determined as being normal by an AI predictive model and determined as being normal as a result of an actual test. A complete product that is determined as being normal as a result of a test may refer, for example, to the product having no abnormality (or error or malfunction) in product performance (or use) and may be normally used by a user. The pass result data may be determined as being a failure by the AI predictive model but determined as being normal as a result of an actual test.

In operation1003, at least one processor120may determine whether there is any data predicted as a failure by the AI predictive model among the obtained pass result data. At least one processor120may perform operation1005when there is pass result data determined as being a failure by the AI predictive model but determined as being normal as a result of the actual test. At least one processor120may perform operation1007when there is no pass result data determined as being a failure by the AI predictive model but determined as being normal as a result of the actual test.

In operation1005, at least one processor120may change a sampling rate. When there is pass result data determined as being a failure by the AI predictive model but determined as being normal as a result of the actual test, the processor120may change the sampling rate. In the disclosure, a sampling rate (or sampling number) of complete products to be tested to determine whether a complete product is faulty or normal may be adjusted, thereby saving time or cost for testing a complete product. For example, at least one processor120may increase or decrease the sampling rate at which fail result data and pass result data are inspected (or tested).

In operation1007, at least one processor120may maintain the sampling rate. When there is no pass result data determined as being a failure by the AI predictive model but determined as being normal as a result of the actual test, the processor120may maintain the sampling rate. In a case of a product with no change in external factors, a predicted result is the same or similar to an actual test result, and thus the processor120may maintain the sampling rate at which fail result data and pass result data are inspected (or tested).

FIG.11AandFIG.11Bare diagrams illustrating examples in which an electronic device controls a sampling rate according to various embodiments.

Referring toFIG.11A, the electronic device (e.g., the electronic device101ofFIG.1) according to various embodiments may predict pass result data1111and fail result data1113using an AI predictive model1110. The AI predictive model1110may be generated based on at least one of component input data, pass result data, and fail result data. The electronic device101may obtain result data1120as a result of actually testing a complete product predicted by the AI predictive model1110. The result data1120may include pass result data and fail result data. The result data1120may include pass result data1123and1125that are predicted as being normal by the AI predictive model1110and are normal as a result of an actual test and fail result data1121and1127that are predicted as being normal by the AI predictive model1110but are failures as a result of the actual test.

The electronic device101may update the AI predictive model by randomly sampling the pass result data and matching and learning the pass result data1123and1125and the fail result data1121and1127with component input data. The electronic device101may predict pass result data1131and fail result data1133using the updated AI predictive model1130. As a result of prediction, the number of pieces of fail result data1133may increase. When predicting normal or failure of a complete product using the updated AI predictive model1130, failure predictions may be reduced and prediction performance may be improved.

Referring toFIG.11B, the electronic device101may predict pass result data1151and fail result data1153using an AI predictive model1150. The electronic device101may obtain result data1160as a result of actually testing a complete product predicted by the AI predictive model1150. The result data1160may include pass result data and fail result data. The result data1160may include pass result data1161and1162that are predicted as being normal by the AI predictive model1150and are normal as a result of an actual test and fail result data1163and1164that are predicted as being normal by the AI predictive model1150but are failures as a result of the actual test.

When there is fail result data determined as being normal by the AI predictive model1150but determined as being a failure as a result of the actual test among the result data1160, the electronic device101may increase a sampling rate. Alternatively, when an external change factor occurs, the electronic device101may increase the sampling rate. The electronic device101may update the AI predictive model by randomly sampling the pass result data and matching and learning the pass result data1161and1162and the fail result data1163and1164with component input data. The electronic device101may predict pass result data1171and fail result data1173using the updated AI predictive model1170. As a result of prediction, the number of pieces of fail result data1173may increase.

A method of operating an electronic device (e.g., the electronic device101ofFIG.1) according to various example embodiments of the disclosure may include: generating an artificial intelligence (AI) predictive model, based on component input data, obtaining fail result data according to the AI predictive model, obtaining pass result data according to the fail result data, and updating the AI predictive model, based on at least one of the component input data, the fail result data, and the pass result data.

The method may further include: transmitting at least one of the component input data, the fail result data, or the pass result data to a server through the communication module, and receiving the AI predictive model from the server.

The obtaining of the pass result data may include: obtaining the pass result data according to a set ratio based on a number of pieces of the fail result data being less than or equal to a threshold value, and changing an extraction ratio of the pass result data and obtaining the pass result data according to the changed extraction ratio based on the number of pieces of the fail result data exceeding the threshold value.

The updating may include: updating the AI predictive model, based on fail result data predicted as being normal by the AI predictive model and determined as a failure as a result of an actual test.

The obtaining of the pass result data may include: obtaining the pass result data according to a configured sampling rate based on there being fail result data predicted as being a failure by the AI predictive model and determined as a failure as a result of an actual test.

The method may further include: controlling a sampling rate at which the fail result data and the pass result data are inspected, based on pass result data predicted as being a failure by the AI predictive model and determined as being normal as a result of an actual test.

The controlling may include: changing the sampling rate based on the pass result data predicted as being the failure by the AI predictive model and determined as being normal as the result of the actual test being detected, and maintaining the sampling ratio based on the pass result data predicted as being the failure by the AI predictive model and determined as being normal as the result of the actual test not being detected.

Various example embodiments of the disclosure and drawings are only intended to provide various examples for easily describing the technical content of the disclosure and for assisting understanding of the disclosure, and are not intended to limit the scope of the disclosure. Therefore, it should be understood that the scope of the disclosure includes all changes or modifications derived based on the technical idea of the disclosure in addition to the various example embodiments disclosed herein including the appended claims and their equivalents. It will also be understood that any of the embodiment(s) described herein may be used in conjunction with any other embodiment(s) described herein.