KR20210096367A - Electronic device, method, and computer readable medium for assisting development of product - Google Patents

Abstract

An objective of the present invention is to enhance convenience when developing a product. According to various embodiments of the present invention, an electronic device comprises a communication circuit, at least one memory configured to store instructions, and at least one processor. The at least one processor can be configured to, when executing the instructions, receive information on a product requested to be developed from an external electronic device through the communication circuit, identify image data from the information on the product, identify text data from the information on the product, recognize the image data and the text data by using a trained neural network, acquire first data on the shape of the product, second data on at least one raw material of the product, third data on at least one function of the product, and fourth data on components of the product based on the recognition, use the first data, the second data, the third data, and the fourth data to identify at least one candidate developer capable of developing the product among a plurality of developers registered in a database associated with the electronic device, and transmit information on the at least one candidate developer to the external electronic device through the communication circuit.

Description

ELECTRONIC DEVICE, METHOD, AND COMPUTER READABLE MEDIUM FOR ASSISTING DEVELOPMENT OF PRODUCT

Various embodiments described below relate to electronic devices, methods, and computer readable media for assisting in product development.

An artificial neural network may have a form in which a plurality of neurons corresponding to a computing unit are connected by a weighted link, wherein the weighted link may adjust weights to adapt to a given environment.

On the other hand, artificial neural networks, such as self-organizing map (SOM), recurrent neural network (RNN), convolutional neural network (CNN), deep neural network (DNN), such as It may be referred to as a generic term for various models.

Users can explore developers to develop products that implement their ideas. In addition, when there are many components constituting the product, the user may search for a developer capable of developing each component. Because such a search can present a hassle to a user, electronic devices, methods, and computer readable media to assist in product development may be required.

The technical problems to be achieved in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned can be clearly understood by those of ordinary skill in the art to which the present invention belongs from the description below. There will be.

An electronic device according to various embodiments may include a communication circuit, at least one memory configured to store instructions, and at least one processor, wherein the at least one processor is configured to execute the instructions. Receive information about a product requesting development from an external electronic device through the communication circuit, identify image data from the information about the product, identify text data from the information about the product, Recognize the image data and the text data using a trained neural network, and based on the recognition, first data about the shape of the product, second data about at least one raw material of the product, at least the product Obtaining third data for one function and fourth data for components of the product, and using the first data, the second data, the third data, and the fourth data, the electronic identify at least one candidate developer capable of developing the product from among a plurality of developers registered in a database associated with a device, and transmit information about the at least one candidate developer to the external electronic device through the communication circuit can be

According to various embodiments, a method for operating an electronic device having a communication circuit includes: receiving information about a product requesting development from an external electronic device through the communication circuit; an operation of identifying image data from, an operation of identifying text data from the information about the product, an operation of recognizing the image data and the text data using a trained neural network, and based on the recognition, the obtaining first data on the shape of a product, second data on at least one raw material of the product, third data on at least one function of the product, and fourth data on components of the product operation, and at least one candidate capable of developing the product from among a plurality of developers registered in a database associated with the electronic device by using the first data, the second data, the third data, and the fourth data The method may include identifying a developer and transmitting information on the at least one candidate developer to the external electronic device through the communication circuit.

A non-transitory computer readable storage medium according to various embodiments is, when executed by one or more processors of an electronic device having a communication circuit, from an external electronic device through the communication circuit, Receive information about a product requesting development, identify image data from the information about the product, identify text data from the information about the product, and use a trained neural network to identify the image data and the text recognize data, and based on the recognition, first data about a shape of the product, second data about at least one raw material of the product, third data about at least one function of the product, and the product obtains fourth data for the components of , and uses the first data, the second data, the third data, and the fourth data from among a plurality of developers registered in a database associated with the electronic device. one or more comprising instructions that cause the electronic device to identify at least one candidate developer capable of developing the product, and transmit information about the at least one candidate developer to the external electronic device via the communication circuitry. Programs can be saved.

An electronic device, a method, and a computer-readable medium according to various embodiments obtain image data and text data from information about a product to be developed, and recognize the obtained image data and text data using a neural network, thereby providing a product Convenience during development can be enhanced.

Effects obtainable in the present disclosure are not limited to the above-mentioned effects, and other effects not mentioned may be clearly understood by those of ordinary skill in the art to which the present disclosure belongs from the description below. will be.

1 illustrates an example of a network environment in accordance with various embodiments;
2 is a simplified block diagram of an electronic device according to various embodiments of the present disclosure;
3 is a system diagram illustrating an operation of an electronic device according to various embodiments of the present disclosure;
4 illustrates an example of an interface displayed on a display of an external electronic device.
5 is a flow diagram illustrating a method of determining a development schedule for a product in accordance with various embodiments.
6 is a system diagram illustrating a method of transmitting information about a development schedule in accordance with various embodiments.

Terms used in the present disclosure are used only to describe specific embodiments, and may not be intended to limit the scope of other embodiments. The singular expression may include the plural expression unless the context clearly dictates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by one of ordinary skill in the art described in the present disclosure. Among the terms used in the present disclosure, terms defined in a general dictionary may be interpreted with the same or similar meaning as the meaning in the context of the related art, and unless explicitly defined in the present disclosure, ideal or excessively formal meanings is not interpreted as In some cases, even terms defined in the present disclosure cannot be construed to exclude embodiments of the present disclosure.

In various embodiments of the present disclosure described below, a hardware approach method will be described as an example. However, since various embodiments of the present disclosure include technology using both hardware and software, various embodiments of the present disclosure do not exclude a software-based approach.

1 illustrates an example of a network environment in accordance with various embodiments;

Referring to FIG. 1 , an environment 10 may include an electronic device 100 , an electronic device 101 , and an electronic device 102 .

In various embodiments, the electronic device 100 may receive information about a product requesting development from the electronic device 101 . In various embodiments, the information about the product may include image data and text data. For example, the information about the product may include image data for an expected shape of the product, image data for the shape of components of the product, and text including a description of at least one raw material of the product It may include data, text data including a description of a function of the product, and text data including a description of components of the product.

In various embodiments, the electronic device 100 may obtain the image data and the text data from information about the product. For example, the electronic device 100 may extract the image data and extract the text data by parsing information about the product in a markup language.

In various embodiments, the electronic device 100 may recognize the image data and the text data using a trained neural network. For example, the electronic device 100 may include an artificial neural network or a device operatively coupled to the artificial neural network. For example, the electronic device 100 may be a device for controlling the artificial neural network that performs training using learning data received from the outside and performs inference on input data based on the training.

In various embodiments, the electronic device 100 may provide first data on the shape of the product, second data on at least one raw material of the product, and at least one function of the product, based on the recognition. It is possible to obtain third data for the product and fourth data for the components of the product.

In various embodiments, the electronic device 100 includes a plurality of developers registered in a database associated with the electronic device 100 using the first data, the second data, the third data, and the fourth data. Among them, at least one candidate developer capable of developing the product may be identified. For example, the electronic device 100 may include a first developer who develops an acrylic product, a second developer who develops a metal product, a third developer who develops a circuit board, a fourth developer who develops a woven product, and 3D A database including information about a fifth developer who develops a product using the printer may be stored. The electronic device 100 may identify the at least one candidate developer corresponding to at least one of the first data, the second data, the third data, and the fourth data in the database. For example, when the product is a desk including metal legs and an acrylic top plate including a light whose brightness is changed according to illumination, the electronic device 100 may include the first developer, the second developer, and the second developer. 3 developers may be identified as the at least one candidate developer. However, it is not limited thereto.

In various embodiments, the electronic device 100 may transmit information on the identified at least one candidate developer to the electronic device 101 . The electronic device 101 may receive information on the at least one candidate developer from the electronic device 100 .

In various embodiments, the electronic device 101 may display information on the at least one candidate developer through the display of the electronic device 101 . In various embodiments, the electronic device 101 may receive a user input for information on the at least one candidate developer displayed through the display. For example, when the user input indicates that the at least one candidate developer authorizes the development of the product, the electronic device 101 transmits a message indicating that the product is developed to the electronic device 100 . can do.

In various embodiments, in response to receiving the message, the electronic device 100 may transmit a message indicating that development of the product is in progress to the electronic device 102 . In various embodiments, since the electronic device 102 may be at least one electronic device associated with the at least one candidate developer, the electronic device 100 transmits a message indicating that development of the product is proceeding to the electronic device. 102 .

1 illustrates an example in which the electronic device 100, the electronic device 101, and the electronic device 102 are configured as different electronic devices, this is for convenience of description. At least one of the electronic device 100 , the electronic device 101 , and the electronic device 102 may be implemented as one device according to embodiments.

2 is a simplified block diagram of an electronic device according to various embodiments of the present disclosure; This simplified block diagram may represent functional configurations of the electronic device 100 in the environment 10 shown in FIG. 1 .

The artificial neural network may refer to hardware, software, or a combination thereof for providing a generalized output with respect to an input.

For example, an artificial neural network is used to simulate a convolutional neural network (CNN), a Markov chain, a deep neural network (DNN), or a binarized neural network (BNN), etc. It may operate based on an application for and a processor for executing the application.

Referring to FIG. 2 , the electronic device 100 is a device capable of performing machine learning through training (or training), and may include a device that learns using a model composed of an artificial neural network. For example, the electronic device 100 may be configured to input, output, build a database, and store information used for data mining, data analysis, and machine learning algorithms (eg, deep learning algorithms). can

The electronic device 100 may transmit/receive data to and from an external electronic device (not shown in FIG. 2 , for example, the electronic device 101 or the electronic device 102) through a communication circuit (not shown), and the external electronic device Results can be derived by analyzing or learning the data received from The electronic device 100 may distribute and process the calculation of the external electronic device.

The electronic device 100 may be implemented as a server. The electronic device 100 may be configured in plurality to form a neural network device set. Each of the electronic devices 100 may disperse and process the calculation, and may derive a result value through data analysis and learning based on the distributed processed data. The electronic device 100 may transmit a result obtained by using a machine learning algorithm or the like to an external electronic device or another neural network device.

According to various embodiments, the electronic device 100 may include an input unit 110 , a processor 120 , a memory 130 , and a learning processor 140 .

According to various embodiments, the input unit 110 may obtain input data for deriving an output value through artificial neural network model learning. The input unit 110 may obtain raw input data.

The processor 120 or the learning processor 140 may preprocess the raw input data to generate training data that can be input to the artificial neural network model learning. The preprocessing may be to extract a feature point from the input data. As described above, the input unit 110 may receive data through a communication circuit (not shown) to obtain input data or pre-process the data.

According to various embodiments, the processor 120 may collect usage history information from the electronic device 100 and store it in the memory 130 . The processor 120 may determine the best combination for executing a specific function through the stored usage history information and predictive modeling. The processor 120 may receive image information, audio information, data, information about an initial solution, characteristic information, or user input information from the input unit 110 .

According to various embodiments, the processor 120 collects information in real time, processes or classifies the information, and stores the processed information in the memory 130 , a database of the memory 130 , or the learning processor 140 . can

According to various embodiments, when the operation of the electronic device 100 is determined based on data analysis and machine learning algorithm, the processor 120 may control components of the electronic device 100 to execute the determined operation. there is. According to various embodiments, the processor 120 may control the electronic device 100 according to a control command to perform a determined operation.

When a specific operation is performed, the processor 120 analyzes historical information indicating the execution of the specific operation through data analysis and machine learning algorithms and techniques, and updates previously learned information based on the analyzed information. can The processor 120, together with the learning processor 140, may improve the accuracy of data analysis and machine learning algorithms and performance based on the updated information.

According to various embodiments, the memory 130 may store input data obtained from the input unit 110 , learned data, or a learning history. The memory 130 may store the artificial neural network model 131 .

According to various embodiments, the artificial neural network model 131 may be stored in a space allocated to the memory 130 . The space allocated to the memory 130 stores the artificial neural network model 131 that is being trained or learned through the learning processor 140, and when the artificial neural network model 131 is updated through learning, the updated artificial neural network model ( 131) can be stored. The space allocated to the memory 130 may store the learned model by dividing it into a plurality of versions according to a learning time point or learning progress.

According to various embodiments, the memory 130 may include a database capable of storing and classifying input data obtained from the input unit 110 and the learned data.

According to various embodiments, the learning processor 140 learns the artificial neural network model 131 by directly acquiring data obtained by preprocessing the input data obtained by the processor 120 through the input unit 110 , or the memory 130 . The artificial neural network model 131 may be trained by acquiring preprocessed input data stored in the database of . For example, the learning processor 140 may acquire the parameters of the optimized neural network model 131 by repeatedly learning the artificial neural network model 131 using various learning techniques.

According to various embodiments, the trained model may update the artificial neural network model 131 in the database. The learning processor 140 may be integrated into the electronic device 100 or implemented in the memory 130 . For example, the learning processor 140 may be implemented using the memory 130 .

According to various embodiments, the learning processor 140 generally identifies, indexes, categorizes, manipulates, stores, retrieves, and outputs data for use in supervised or unsupervised learning, data mining, predictive analytics, or other devices. to store data in one or more databases. Here, the database may be implemented using the memory 130 , a memory maintained in a cloud computing environment, or another remote memory location accessible by the terminal through a communication method such as a network. The information stored in the learning processor 140 may be utilized by the processor 120 using any of a variety of different types of data analysis algorithms and machine learning algorithms. For example, examples of such algorithms include k-recent adjacency systems, fuzzy logic (eg likelihood theory), neural networks, Boltzmann machines, vector quantization, pulse neural networks, support vector machines, maximum margin classifiers, hill climbing, derivation Logical Systems Bayesian Networks, Peritnets (e.g. Finite State Machines, Milli Machines, Moore Finite State Machines), Classifier Trees (e.g. Perceptron Trees, Support Vector Trees, Markov Trees, Decision Tree Forests, Arbitrary Forests), Read Models and systems, artificial fusion, sensor fusion, image fusion, reinforcement learning, augmented reality, pattern recognition, automated planning, and the like.

Hereinafter, for convenience of description, the processor 120 and the learning processor 140 may be referred to as at least one processor or processor. In various embodiments, the at least one processor may be configured to execute the operations illustrated through FIGS. 3 and 5 to 6 .

3 is a system diagram illustrating an operation of an electronic device according to various embodiments of the present disclosure; Such an operation may be performed by the electronic device 100 shown in FIGS. 1 and 2 and the electronic device 101 shown in FIG. 1 .

4 illustrates an example of an interface displayed on a display of an external electronic device. Such an interface may be displayed through the display of the electronic device 101 shown in FIG. 1 .

Referring to FIG. 3 , in operation 301 , the electronic device 101 may obtain information on a product requesting development through a development request interface. For example, the electronic device 101 may acquire the information about the product including a description of the product requested to be developed, based on the reception of the user input. For example, referring to FIG. 4 , the electronic device 101 may display the interface 400 through the display of the electronic device 101 . The interface 400 inputs a field (eg, title) for inputting a name of a product requesting development, a field for inputting identification information of a user requesting development (eg, name), and information for subsequent management It may include a field (eg, password) for inputting a description of the product for which development is requested (eg, content). In various embodiments, the information about the product requesting development may include image data and text data to describe the product.

In various embodiments, in response to obtaining the information about the product, the electronic device 101 may transmit the information about the product to the electronic device 100 in operation 303 . For example, referring to FIG. 4 , in response to receiving a user input for an executable object (eg, storage) in the interface 400 , the electronic device 101 performs acquisition of the information about the product. Upon completion, the information on the product may be transmitted to the electronic device 100 . The electronic device 100 may receive the information about the product from the electronic device 101 .

In operation 305, the electronic device 100 may identify image data from the information about the product. For example, the electronic device 100 may be configured to obtain information about at least one of components of the product, a function of the product, a shape of the product, or a raw material of the product, the information on the product The image data can be identified from For example, when the information about the product is configured in a markup language, the electronic device 100 may identify the image data by parsing the markup language.

In operation 307, the electronic device 100 may identify text data from the information on the product. For example, the electronic device 100 may be configured to obtain information about at least one of components of the product, a function of the product, a shape of the product, or a raw material of the product, from the information of the product. The text data may be identified. For example, when the information on the product is configured in a markup language, the electronic device 100 may identify the text data by parsing the markup language.

3 illustrates an example of executing operation 307 after executing operation 305, but this is for convenience of description. Operations 305 and 307 may be executed simultaneously as independent operations or may be executed out of order.

In operation 309, the electronic device 100 may recognize image data and text data using a trained neural network. For example, the electronic device 100 may extract feature points from the image data using a trained neural network. The electronic device 100 may recognize the image data by inputting information on the feature points extracted from the image data into the neural network. For example, the electronic device 100 may extract feature points from the text data using the neural network. The electronic device 100 may recognize the text data by inputting information on the feature points extracted from the text data into the neural network.

In operation 311 , the electronic device 100 performs first data on the shape of the product, second data on at least one raw material of the product, and second data on at least one function of the product based on the recognition. 3 data, and fourth data for components of the product may be obtained.

For example, the electronic device 100 may acquire information about what the product looks like as the first data, based on the recognition of the image data. For example, the electronic device 100 extracts texture information from the image data, estimates at least one raw material of the product based on the extracted texture information, and obtains the second data can do. For example, the electronic device 100 may estimate what the function of the product is based on the recognition of the image data, and acquire the third data based on the estimation. For example, the electronic device 100 obtains fourth data (eg, what the components are, information about shapes of the components, etc.) on the components constituting the product from the image data. can do.

For example, the electronic device 100 may acquire the second data for at least one raw material of the product by performing natural language interpretation based on the recognition of the text data. For example, the electronic device 100 may acquire information about the function of the product as the third data by performing natural language interpretation based on the recognition of the text data. For example, the electronic device 100 may acquire the fourth data on the components of the product by performing natural language interpretation based on the recognition of the text data.

In operation 313 , the electronic device 100 uses the first data, the second data, the third data, and the fourth data from among a plurality of developers registered in the database associated with the electronic device 100 . At least one candidate developer capable of developing the product may be identified. In various embodiments, the database may include information about a plurality of developers. For example, the database includes product information having a history developed by each of the plurality of developers, raw material information of a product developed by each of the plurality of developers, and product information developed by each of the plurality of developers. It may include shape information, function information of products developed by each of the plurality of developers, and the like. The electronic device 100 identifies the at least one candidate developer capable of developing the product by searching the database using the first data, the second data, the third data, and the fourth data. can do.

In operation 315 , the electronic device 100 may transmit information on the at least one candidate developer to the electronic device 101 . The electronic device 101 may receive information on the at least one candidate developer from the electronic device 100 . Since the user of the electronic device 101 can obtain the information on the at least one candidate developer recommended by the electronic device 100 without extra effort after inputting the information on the product, various implementations are possible. The electronic device 100 according to examples may provide an enhanced user experience.

5 is a flow diagram illustrating a method of determining a development schedule for a product in accordance with various embodiments. This method may be executed by the electronic device 100 shown in FIG. 1 , the electronic device 100 shown in FIG. 2 , the electronic device 100 shown in FIG. 3 , and a processor of the electronic device 100 . .

In operation 510 , the electronic device 100 may determine a developer of each of the components of the product that has requested development from among at least one candidate developer. For example, the product requested for development includes a circuit board as a first component, an acrylic housing used for mounting the circuit board as a second component, and an iron for supporting the housing Assume that we include legs as a third component. The electronic device 100 identifies developer A capable of developing the first component from among at least one candidate developer, and identifies developer B capable of developing the second component from among the at least one candidate developer, Developer C capable of developing the third component may be identified from among the at least one candidate developer.

In operation 520, the electronic device 100 may determine a development order of the components based on the first data, the second data, the third data, and the fourth data. For example, according to the above-described example, the electronic device 100 determines the first component corresponding to the circuit board as a component that needs to be developed first, and sets the second component corresponding to the housing as the first component. It may be determined as a component to be developed after the development of the first component, and a third component corresponding to the iron legs may be determined as a component to be developed after the development of the second component.

In operation 530 , the electronic device 100 may determine a product development schedule based on the developer's determination of each of the components and the development order. For example, the electronic device 100 arranges the development schedule of the first component to the earliest date, and the development schedule of the second component from a point in time when it is determined that the first component will be delivered to developer B may be arranged, and the development schedule of the third component may be arranged from a point in time when it is determined that the second component will be delivered to developer C.

In operation 540 , the electronic device 100 may transmit the information on the development schedule together with the information on the at least one candidate developer to the electronic device 101 . The electronic device 101 may receive the information on the development schedule together with the information on the at least one candidate developer. The electronic device 101 may display information on the at least one candidate developer and information on the development schedule through the display of the electronic device 101 so that the user can proceed with the development of the product.

6 is a system diagram illustrating a method of transmitting information about a development schedule in accordance with various embodiments. Such an operation may be performed by the electronic device 100 shown in FIGS. 1 and 2 , the electronic device 101 shown in FIG. 1 , and the electronic device 102 shown in FIG. 1 .

Referring to FIG. 6 , in operation 315 , the electronic device 100 may transmit information on at least one candidate developer to the electronic device 101 . The electronic device 101 may receive information on the at least one candidate developer from the electronic device 100 .

In operation 601 , the electronic device 100 may transmit information about the development schedule to the electronic device 101 . The electronic device 101 may receive information on the development schedule from the electronic device 100 .

In operation 603 , the electronic device 101 may transmit a message indicating that a product is being developed to the electronic device 100 . For example, the electronic device 101 may display information on a development schedule of the electronic device 101 and information on the at least one candidate developer through a display. The electronic device 101 receives a user input for information displayed through the display, and based on identifying that the user input indicates that the product is being developed, the electronic device transmits the message indicating that the product is being developed. (100) can be transmitted. The electronic device 100 may receive the message from the electronic device 101 .

In operation 605, the electronic device 100 receives, in response to the reception of the message, information on the development schedule and information on the at least one component required to be developed by each of the at least one candidate developer. It may transmit to the electronic device 102 associated with the at least one candidate developer. The electronic device 102 may receive, from the electronic device 100 , information on the development schedule and information on the at least one component required to be developed by each of the at least one candidate developer.

As described above, the electronic device 100 according to various embodiments may provide an enhanced user experience by not only introducing a developer capable of developing a product but also arranging a development schedule for the product.

As described above, an electronic device according to various embodiments may include a communication circuit, at least one memory configured to store instructions, and at least one processor, wherein the at least one processor comprises: , when executing the instructions, receive information about a product requesting development from an external electronic device through the communication circuit, identify image data from the information about the product, and text from the information about the product Identify data, recognize the image data and the text data using a trained neural network, and based on the recognition, first data about the shape of the product, second data about at least one raw material of the product , to obtain third data for at least one function of the product, and fourth data for components of the product, wherein the first data, the second data, the third data, and the fourth data are obtained. to identify at least one candidate developer capable of developing the product from among a plurality of developers registered in a database associated with the electronic device using may be configured to transmit to a device.

In various embodiments, the at least one processor, when executing the instructions, determines a developer of each of the components from among the at least one candidate developer, and the first data, the second data, and the third Based on data, and the fourth data, a development order of the components is determined, and a development schedule of the product is determined based on the determination and the development order of the developer of each of the components, and the determined The information on the development schedule may be further configured to be transmitted to the external electronic device through the communication circuit together with the information on the at least one candidate developer.

In various embodiments, when the at least one processor executes the instructions, a message indicating that the product is developed through the communication circuit from the external electronic device receiving the information on the at least one candidate developer to each of the at least one other electronic device respectively associated with the at least one candidate developer through the communication circuit, and in response to the reception, information about the development schedule and the development by each of the at least one candidate developer It may be further configured to transmit information about the required at least one component.

According to various embodiments, a method for operating an electronic device having a communication circuit includes: receiving information about a product requesting development from an external electronic device through the communication circuit; an operation of identifying image data from, an operation of identifying text data from the information about the product, an operation of recognizing the image data and the text data using a trained neural network, and based on the recognition, the obtaining first data on the shape of a product, second data on at least one raw material of the product, third data on at least one function of the product, and fourth data on components of the product operation, and at least one candidate capable of developing the product from among a plurality of developers registered in a database associated with the electronic device by using the first data, the second data, the third data, and the fourth data The method may include identifying a developer and transmitting information on the at least one candidate developer to the external electronic device through the communication circuit.

A non-transitory computer readable storage medium according to various embodiments is, when executed by one or more processors of an electronic device having a communication circuit, from an external electronic device through the communication circuit, Receive information about a product requesting development, identify image data from the information about the product, identify text data from the information about the product, and use a trained neural network to identify the image data and the text recognize data, and based on the recognition, first data about a shape of the product, second data about at least one raw material of the product, third data about at least one function of the product, and the product obtains fourth data for the components of , and uses the first data, the second data, the third data, and the fourth data from among a plurality of developers registered in a database associated with the electronic device. one or more comprising instructions that cause the electronic device to identify at least one candidate developer capable of developing the product, and transmit information about the at least one candidate developer to the external electronic device via the communication circuitry. Programs can be saved.

Methods according to the embodiments described in the claims or specifications of the present disclosure may be implemented in the form of hardware, software, or a combination of hardware and software.

When implemented in software, a computer-readable storage medium storing one or more programs (software modules) may be provided. One or more programs stored in the computer-readable storage medium are configured to be executable by one or more processors in an electronic device (device). One or more programs include instructions for causing an electronic device to execute methods according to embodiments described in a claim or specification of the present disclosure.

Such programs (software modules, software) include random access memory, non-volatile memory including flash memory, read only memory (ROM), electrically erasable programmable ROM (EEPROM: Electrically Erasable Programmable Read Only Memory), magnetic disc storage device, Compact Disc-ROM (CD-ROM), Digital Versatile Discs (DVDs), or any other form of It may be stored in an optical storage device or a magnetic cassette. Alternatively, it may be stored in a memory composed of a combination of some or all thereof. In addition, each configuration memory may be included in plurality.

In addition, the program is transmitted through a communication network consisting of a communication network such as the Internet, Intranet, Local Area Network (LAN), Wide LAN (WLAN), or Storage Area Network (SAN), or a combination thereof. It may be stored on an attachable storage device that can be accessed. Such a storage device may be connected to a device implementing an embodiment of the present disclosure through an external port. In addition, a separate storage device on the communication network may be connected to the device implementing the embodiment of the present disclosure.

In the specific embodiments of the present disclosure described above, components included in the disclosure are expressed in the singular or plural according to the specific embodiments presented. However, the singular or plural expression is appropriately selected for the context presented for convenience of description, and the present disclosure is not limited to the singular or plural element, and even if the element is expressed in plural, it is composed of the singular or singular. Even an expressed component may be composed of a plurality of components.

Meanwhile, although specific embodiments have been described in the detailed description of the present disclosure, various modifications are possible without departing from the scope of the present disclosure. Therefore, the scope of the present disclosure should not be limited to the described embodiments and should be defined by the claims described below as well as the claims and equivalents.

Claims (3)

In an electronic device,
communication circuit;
at least one memory configured to store instructions; and
At least one processor, wherein the at least one processor, when executing the instructions,
Receive information about a product requesting development from an external electronic device through the communication circuit,
identify image data from the information about the product;
identify textual data from said information about said product;
Recognizing the image data and the text data using a trained neural network,
Based on the recognition, first data about the shape of the product, second data about at least one raw material of the product, third data about at least one function of the product, and components of the product to obtain the fourth data for
Identify at least one candidate developer capable of developing the product from among a plurality of developers registered in a database associated with the electronic device by using the first data, the second data, the third data, and the fourth data do,
and transmit information on the at least one candidate developer to the external electronic device through the communication circuit.
The method according to claim 1, wherein the at least one processor, when executing the instructions,
Determining a developer of each of the components from among the at least one candidate developer,
determining a development order of the components based on the first data, the second data, the third data, and the fourth data;
Based on the determination and the development order of the developer of each of the components, determine a development schedule of the product,
The electronic device further configured to transmit information on the determined development schedule together with information on the at least one candidate developer to the external electronic device through the communication circuit.
The method according to claim 2, wherein the at least one processor, when executing the instructions,
Receiving a message indicating that the product is being developed through the communication circuit from the external electronic device that has received the information on the at least one candidate developer,
In response to the reception, information on the development schedule and at least a development request by each of the at least one candidate developer are provided to each of the at least one other electronic device respectively associated with the at least one candidate developer through the communication circuit An electronic device further configured to transmit information about one component.

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