A system and method for automatically generating business model reports using artificial intelligence.

Abstract

This invention provides a system and method for automatically generating business model reports using artificial intelligence. [Solution] The system includes receiving information about the business model from a user terminal S310, calculating a validity index S320 via a validity evaluation model that utilizes a first neural network based on the information about the business model, the feasibility index includes a plurality of individual indicators, the plurality of individual indicators include a global capability index and a global business attractiveness index, assigning weights S330, assigning a second weight set on a country basis to the global business attractiveness index S340, adjusting the first weight and the second weight based on relationship information between the plurality of individual indicators S350, creating a report using a second neural network based on the information about the business model and the plurality of individual indicators, and transmitting it to the terminal S370.

Description

Technical Field

[0001] Relates to a system and method for automatically generating a report on a business model through artificial intelligence. Background Art of the Invention

[0002] Recently, the business environment has become globalized, and customization strategies tailored to each country and culture have become important. In order to succeed in the global market, companies are evaluating the validity of business models in each region and putting a lot of effort into analysis work to gain strategic insights. In particular, the ability to quickly and accurately generate complex reports such as validity evaluations can greatly assist corporate decision-making and resource allocation. However, creating these validity evaluation reports manually not only takes a lot of time and cost, but also has limitations in reflecting all market characteristics, legal requirements, consumer needs, etc. that vary by country.

[0003] Existing validity evaluation methods have mainly relied on methods where experts manually create reports or analyze structured data. This method has limitations in simultaneously analyzing large-scale data and various market factors, and it is difficult to provide insights specialized for the global market. Also, it cannot comprehensively reflect market data and industry-specific characteristics, legal regulations, etc. for each country, and it may be difficult to conduct a comprehensive evaluation of multinational business models.

[0004] There is a need for a system and method for automatically generating a report on a business model through artificial intelligence.

Summary of the Invention

Problems to be Solved by the Invention

[0005] Embodiments of the present disclosure can provide a system and method for automatically generating a report on a business model through artificial intelligence.

[0006] The technical challenges to be addressed by these embodiments are not limited to those described above, and other technical challenges not mentioned may be considered by those skilled in the art from the various embodiments described below. [Means for solving the problem]

[0007] A method by which a server provides a report on a business model to a user terminal according to one embodiment may include receiving information on the business model from the user terminal, calculating a validity index through a validity evaluation model utilizing a first neural network based on the information on the business model, the validity index including a plurality of individual indices, assigning a predetermined first weight to the plurality of individual indices according to global industry, assigning a predetermined second weight to the global business attractiveness index according to country, adjusting the first and second weights based on relationship information between the plurality of individual indices, and generating and transmitting a report on the business model to the user terminal. [Effects of the Invention]

[0008] According to one embodiment, the server can quickly and accurately provide companies with the information they need to establish a global market entry strategy by automatically evaluating the validity of a business model via artificial intelligence and generating a report based on that evaluation.

[0009] According to the embodiment, the server analyzes various input data for the business model, calculates industry- and country-specific validity indicators, and automatically generates reports based on them. This reduces report generation time compared to traditional manual analysis methods, provides precise evaluation results that reflect the characteristics of each market, and allows users to gain higher quality strategic insights.

[0010] The effects that can be obtained from the examples are not limited to those mentioned above, and other effects not mentioned can be clearly derived and understood by those ordinary skill in the art based on the detailed description below. [Brief explanation of the drawing]

[0011] [Figure 1] The accompanying drawings, included as part of the detailed description to facilitate understanding of the embodiments, provide various embodiments and, together with the detailed description, illustrate the technical features of the various embodiments. This is a diagram showing the configuration of an electronic device according to one embodiment. [Figure 2] This figure shows the configuration of a program according to one embodiment. [Figure 3] This document describes a method by which a server provides a report on a business model to a user terminal, according to one embodiment. [Figure 4] This block diagram shows the server configuration according to one embodiment. [Modes for carrying out the invention]

[0012] The following embodiments combine the components and features of the embodiments in a predetermined manner. Each component or feature can be considered optional unless otherwise explicitly mentioned. Each component or feature can be implemented in a form that is not combined with other components or features. Furthermore, several components and / or features can be combined to form various embodiments. The order of operations described in the various embodiments may be changed. Some components or features of some embodiments may be included in other embodiments or may be substituted for corresponding components or features of other embodiments.

[0013] The description of the drawings does not include any procedures or steps that could obscure the essence of the various embodiments, nor does it describe any procedures or steps that can be understood at the level of a person with ordinary skill in the art.

[0014] Throughout this specification, where a part "comprising" or "including" a component, this means, unless otherwise stated, that it may further include other components rather than excluding them. Furthermore, terms such as "part," "base," and "module" as used herein mean a unit that performs at least one function or operation, which can be implemented in hardware, software, or a combination of hardware and software. In addition, "a" or "an," "one," "it," and similar related terms may be used in the context describing various embodiments (particularly in the context of the following claims) to include both singular and plural meanings unless explicitly stated otherwise in this specification.

[0015] Hereinafter, embodiments of various models will be described in detail with reference to the accompanying drawings. The detailed description disclosed below, along with the accompanying drawings, is an exemplary embodiment of various models. It is intended to describe the forms and not to show only one embodiment.

[0016] Furthermore, specific terms used in various embodiments are provided to facilitate understanding of those embodiments, and the use of these specific terms can be modified in other ways without departing from the technical idea of ​​the various embodiments.

[0017] This figure shows the configuration of an electronic device according to one embodiment.

[0018] Figure 1 is a block diagram of an electronic device 101 in a network environment 100 according to various embodiments. Referring to Figure 1, in the network environment 100, the electronic device 101 can communicate with an electronic device 102 via a first network 198 (e.g., a short-range wireless communication network) or with at least one of an electronic device 104 or a server 108 via a second network 199 (e.g., a long-range wireless communication network). According to one embodiment, the electronic device 101 can communicate with an electronic device 104 via a server 108. According to one embodiment, the electronic device 101 may include a processor 120, memory 130, input module 150, acoustic output module 155, display module 160, audio module 170, sensor module 176, interface 177, connection terminal 178, haptic module 8, 708, haptic module 8, 77, battery 189, communication module 190, subscriber identification module 196, or antenna module 197. In some embodiments, the electronic device 101 may omit at least one of these components (e.g., connection terminal 178) or may have one or more other components added. In some embodiments, some of these components (e.g., sensor module 176, camera module 180, or antenna module 197) may be incorporated into a single component (e.g., display module 160). The electronic device 101 may be referred to as a client, terminal, or peer.

[0019] The processor 120 can, for example, execute software (e.g., program 140) to control at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120, and can perform various data processing or calculations. According to one embodiment, as at least part of the data processing or calculation, the processor 120 stores instructions or data received from other components (e.g., sensor module 176 or communication module 190) in volatile memory 132, processes the instructions or data stored in volatile memory 132, and stores the resulting data in non-volatile memory. According to one embodiment, the processor 120 may include a main processor 121 (e.g., central processing unit or application processor) or an auxiliary processor 123 (e.g., graphics processing unit, neural network processing unit (NPU), image signal processor, sensor hub processor, or communication processor) that can operate independently or together with it. For example, if the electronic device 101 includes a main processor 121 and an auxiliary processor 123, the auxiliary processor 123 may use less power than the main processor 121 or be configured to specialize in a specified function. The auxiliary processor 123 can be implemented separately from or as part of the main processor 121.

[0020] The auxiliary processor 123 can, for example, control at least one of the components of the electronic device 101's component 1, module 176, or communication module 190, together with the main processor 121, either on behalf of the main processor 121 when the main processor 121 is inactive (e.g., in sleep mode) or when the main processor 121 is active (e.g., running an application). According to one embodiment, the auxiliary processor 123 (e.g., an image signal processor or a communication processor) can be implemented as part of other functionally related components (e.g., a camera module 180 or a communication module 190). According to one embodiment, the auxiliary processor 123 (e.g., a neural network processing unit) may include hardware structures specialized for processing artificial intelligence models.

[0021] Artificial intelligence models can be generated through machine learning. Such learning may be performed, for example, on the electronic device 101 on which the artificial intelligence model is run, or via a separate server (e.g., server 108). The learning algorithm may include, but is not limited to, guided learning, unguided learning, semi-guided learning, or reinforcement learning. The artificial intelligence model may include multiple artificial neural network layers. The artificial neural network may be, but is not limited to, one of the following: deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted Boltzmann machine (RBM), deep belief network (DBN), bidirectional recurrent deep neural network (BRDNN), or a combination of two or more deep neural networks. In addition to the hardware structure, the artificial intelligence model may include additional or alternative software structures.

[0022] Memory 130 can store various data used by at least one component of the electronic device 101 (e.g., the processor 120 or the sensor module 176). The data can include, for example, software (e.g., program 140), and input data or output data for related instructions. Memory 130 can include volatile memory 132 or non-volatile memory 134.

[0023] Program 140 can be stored as software in memory 130 and can include, for example, an operating system 142, middleware 144, or an application 146.

[0024] Input module 150 can receive instructions or data used by a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., the user). Input module 150 can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0025] Acoustic output module 155 can output an acoustic signal to the outside of the electronic device 101. Acoustic output module 155 can include, for example, a speaker or a receiver. The speaker can be used for general purposes such as multimedia playback and recording playback. The receiver can be used to receive an incoming call. According to one embodiment, the receiver can be implemented separately from or as part of the speaker.

[0026] Module 160 can visually provide information to the outside of the electronic device 101 (e.g., the user). Display module 160 can include, for example, a display, a hologram device, or a projector and a control circuit for controlling the corresponding device. According to one embodiment, display module 160 can include a touch sensor set to sense touch or a pressure sensor set to measure the intensity of the force generated by touch.

[0027] The audio module 170 can convert sound into electrical signals, or conversely, convert electrical signals into sound. According to one embodiment, the audio module 170 can acquire sound via the input module 150, or output sound via the sound output module 155, or via an external electronic device (e.g., electronic device 102) (e.g., speaker or headphones) directly or wirelessly connected to the electronic device 101.

[0028] The sensor module 176 can sense the operating state of the electronic device 101 (e.g., power or temperature) or the external environmental state (e.g., user state) and generate an electrical signal or data value corresponding to the sensed state. According to one embodiment, the sensor module 176 may include, for example, a gesture sensor, a gyroscope sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an infrared sensor, a biosensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0029] Interface 177 can support one or more designated protocols that can be used for the electronic device 101 to connect directly or wirelessly to an external electronic device (e.g., electronic device 102). According to one embodiment, interface 177 may include, for example, a high-resolution multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0030] The connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (e.g., electronic device 102). According to one embodiment, the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0031] The tactile module 179 can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that the user can perceive through touch or kinesthetic sense. According to one embodiment, the tactile module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulator.

[0032] The camera module 180 can capture still images and video. According to one embodiment, the camera module 180 may include one or more lenses, an image sensor, an image signal processor, or a flash.

[0033] The power management module 188 can manage the power supplied to the electronic device 101. According to one embodiment, the power management module 188 can be implemented, for example, as at least part of a power management integration circuit (PMIC).

[0034] The battery 189 can supply power to at least one component of the electronic device 101. According to one embodiment, the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0035] The communication module 190 can support the establishment of a direct (e.g., wired) or wireless communication channel between the electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108), and the execution of communication over the established communication channel. The communication module 190 operates independently of the processor 120 (e.g., the application processor) and may include one or more communication processors that support direct (e.g., wired) or wireless communication. According to one embodiment, the communication module 190 may include a wireless communication module 192 (e.g., a cellular communication module, a near-field wireless communication module, or a GNSS (global navigation satellite system) communication module) or a wired communication module 194 (e.g., a LAN (local area network) communication module, or a power line communication module). Of these communication modules, the corresponding communication module can communicate with a first network 198 (e.g., a short-range communication network such as Bluetooth, WiFi (wireless fidelity) direct, or IrDA (infrared data association)) or a second network 199 (e.g., a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network, e.g., an electronic device 104). Some of these types of communication modules may be integrated into a single component (e.g., a single chip) or implemented in multiple separate components (e.g., multiple chips). The wireless communication module 192 can verify or authenticate the electronic device 101 within a communication network such as the first network 198 or the second network 199 using subscriber information (e.g., an International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196.

[0036] The wireless communication module 192 can support 5G networks following 4G networks and next-generation communication technologies, such as NR connectivity technology. NR connectivity technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module 192 can support high-frequency bands (e.g., mmWave bands) to achieve high data rates. The wireless communication module 192 can support various technologies to ensure performance in high-frequency bands, such as beamforming, massive array multiple-input and multiple-output (massive MIMO (multiple-input and multiple-output)), full-dimensional multiple-input / output (FD-MIMO), array antenna beamforming, or large-scale antennas. The wireless communication module 192 can support various requirements specified by the electronic device 101, external electronic devices (e.g., electronic device 104), or network systems (e.g., second network 199). According to one embodiment, the wireless communication module 192 has a peak data rate of 0.5 for eMBB realization (e.g., 20 Gbps or more), loss coverage of 0.5 for mMTC realization (e.g., 164 dB or less), or U-plane latency (e.g., downlink (DL) and uplink (UL)) for URLRC realization.

[0037] The antenna module 197 can transmit or receive signals or power to an external device (e.g., an external electronic device). According to one embodiment, the antenna module 197 may include an antenna comprising a radiator consisting of a conductor or conductive pattern formed on a substrate (e.g., a PCB). According to one embodiment, the antenna module 197 may include multiple antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication scheme used in a communication network, such as a first network 198 or a second network 199, can be selected from the multiple antennas, for example, by the communication module 190. Signals or power can be transmitted or received between the communication module 190 and an external electronic device via the selected at least one antenna. According to some embodiments, in addition to the radiator, other components (e.g., a radio frequency integrated circuit) (RFIC) may be further formed as part of the antenna module 197.

[0038] According to various embodiments, the antenna module 197 can form an mmWave antenna module. According to one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent to a first surface of the printed circuit board (e.g., the bottom surface) and capable of supporting a specified high-frequency band (e.g., the mmWave band), and an antenna (e.g., an array antenna) disposed on a second surface of the printed circuit board (e.g., the top surface or multiple frequency bands or multiple surfaces of the side bands).

[0039] At least some of the aforementioned components are connected to each other via a peripheral device communication method (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)) and can exchange signals (e.g., instructions or data) with each other.

[0040] According to one embodiment, commands or data can be transmitted to or received between the electronic device 101 and an external electronic device 104 via a server 108 connected to a second network 199. Each of the external electronic devices 102 or 104 may be the same or a different type of device as the electronic device 101. According to one embodiment, all or part of the operation performed by the electronic device 101 can be performed by one or more of the external electronic devices 102, 104, or 108. For example, if the electronic device 101 must automatically perform a function or service, or automatically in response to a request from a user or other device, the electronic device 101 may, instead of performing the function or service itself, or additionally, request one or more external electronic devices to perform at least part of that function or service. One or more external electronic devices that receive the request may perform at least part of the requested function or service, or additional functions or services related to the request, and communicate the results of the execution to the electronic device 101. The electronic device 101 may process the results as they are or additionally and provide them as at least part of the response to the request. For this purpose, for example, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technologies can be used. The electronic device 101 can, for example, provide ultra-low latency services using distributed computing or mobile edge computing. In another embodiment, the external electronic device 104 may include an IoT (Internet of Swing) device. The server 108 may be an intelligent server using machine learning and / or neural networks. According to one embodiment, the external electronic device 104 or the server 108 can be included in the second network 199. The electronic device 101 can be applied to intelligent services (e.g., smart homes, smart cities, smart cars, or healthcare) based on 5G communication technology and IoT-related technologies.

[0041] Server 108 is connected to electronic devices 101 and can provide services to the connected electronic devices 101. Server 108 can also store and manage various information of users who have completed the membership registration procedure and have joined as members, and can provide various purchase and payment functions related to the service. Furthermore, Server 108 may share execution data of service applications running on multiple electronic devices 101 in real time so that services can be shared among users. Such a server 108 can have the same hardware configuration as a conventional web server or service server. However, in terms of software, it can be implemented through some language such as C, C++, Java, Python, Golang, or Kotlin, and can include program modules that perform various functions. Generally, Server 108 refers to a computer system and the computer software (server program) installed for this purpose that is connected to an unspecified number of clients and / or other servers via an open computer network such as the Internet, receives work requests from clients or other servers, derives and provides the results of those work. Furthermore, server 108 should be understood as a broader concept that includes not only the server program described above, but also a series of application programs running on server 108, and various databases (DB: Database, hereinafter referred to as "DB") that may be built internally or externally. Therefore, server 108 classifies and stores and manages member registration information and various information and data related to the game in the DB. This DB can be implemented internally or externally to server 108.Furthermore, Server 108 can be implemented using server programs provided by various operating systems such as Windows, Linux, Unix, and Macintosh on general-purpose server hardware. Typical examples include IIS (Internet Information Server), CUN, and NCPA used in Windows environments, which can be used to implement web services. Server 108 may also be linked to authentication and payment systems for user authentication of services and for purchase settlements related to services.

[0042] The first network 198 and the second network 199 refer to a connection structure that enables information exchange between each node, such as terminals and servers, or a network connecting server 108 and electronic devices 101 and 104. The first network 198 and the second network 199 include, but are not limited to, the Internet, LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), 3G, 4G, LTE, 5G, Wi-Fi, etc. The first network 198 and the second network 199 may be closed networks such as LAN and WAN, but it is preferable that they be open networks such as the Internet. The Internet refers to a global open computing network structure consisting of the TCP / IP protocol, TCP, UDP (user datagram protocol), and multiple services that exist at a higher layer, namely HTTP (HyperText Transfer Protocol), Telnet, FTP (File Transfer Protocol), DNS (Domain Name System), SMTP (Simple Mail Transfer Protocol), and SNMP (Simple Network Management Protocol).

[0043] A database can have a general data structure implemented in the storage space (hard disk or memory) of a computer system using a database management program (DBMS). A database can have a data storage form that allows for free retrieval (extraction), deletion, editing, and addition of data. A database can be implemented for the purposes of one embodiment of this disclosure using relational database management systems (RDBMS) such as Oracle, Infomix, Sybase, and DB2, object-oriented database management systems (OODBMS) such as Gemston, Orion, and O2, and XML native databases such as Excelon and Sekaiju, and can have appropriate fields or elements to achieve its own functionality.

[0044] Figure 2 shows the configuration of a program according to one embodiment.

[0045] Figure 2 is a block diagram 200 showing program 140 according to various embodiments. According to one embodiment, program 140 may include an operating system 142, middleware 144, or an application 146 executable on the operating system 142 for controlling one or more resources of the electronic device 101. The operating system 142 may include, for example, Android™, iOS™, Windows™, Symbian™, Tizen™, or Bada™. At least some of the programs of program 140 may be preloaded onto the electronic device 101 at the time of manufacture, for example, or may be downloaded or updated from an external electronic device (e.g., electronic device 102 or 104, or server 108) when used by a user. All or part of program 140 may include a neural network.

[0046] The operating system 142 can control the management (such as allocation or retrieval) of one or more system resources (e.g., processes, memory, or power) of the electronic device 101. The operating system 142 may additionally or alternatively include one or more driver programs for driving other hardware devices of the electronic device 101, such as the input module 150, the acoustic output module 155, the display module 160, the audio module 170, the sensor module 176, the interface 177, the haptic module 179, the 8 modules 8, the camera module 18, the communication module 190, the subscriber identification module 196, or the antenna module 197.

[0047] Middleware 144 can provide various functions to application 146 so that application 146 can use functions or information provided from one or more resources of electronic device 101. Middleware 144 may include, for example, application manager 201, window manager 203, multimedia manager 205, resource manager 207, power manager 209, database manager 211, package manager 213, connectivity manager 2, connectivity manager 2, location manager 219, graphics manager 221, security manager 223, call manager 225, or speech recognition manager 227.

[0048] The application manager 201 can, for example, manage the lifecycle of application 146. The window manager 203 can, for example, manage one or more GUI resources used on the screen. The multimedia manager 205 can, for example, determine one or more formats required for playback of media files and perform encoding or decoding of the media files using codecs that conform to the selected corresponding formats. The resource manager 207 can, for example, manage the source code of application 146 or the memory space of memory 130. The power manager 209 can, for example, manage the capacity, temperature, or power of battery 189 and use that information to determine or provide relevant information necessary for the operation of electronic device 101. According to one embodiment, the power manager 209 can be linked with the BIOS (basic input / output system) (not shown) of the electronic device 101.

[0049] The database manager 211 can, for example, create, search, or modify databases used by application 146. The package manager 213 can, for example, manage the installation or updates of applications distributed in the form of package files. The connectivity manager 215 can, for example, manage wireless or direct connections between electronic device 101 and external electronic devices. The call management manager 217 can, for example, provide a function to notify the user of the occurrence of specified events (e.g., incoming calls, messages, or alarms). The location manager 219 can, for example, manage the location information of electronic device 101. The graphics manager 221 can, for example, manage one or more graphic effects or associated user interfaces to be provided to the user.

[0050] The security manager 223 can, for example, provide system security or user authentication. The call manager 225 can, for example, manage voice call functions or video call functions provided by the electronic device 101. The voice recognition manager 227 can, for example, send user voice data to the server 108 and receive from the server 108 instructions corresponding to functions executed on the electronic device 101 based on at least part of the voice data, or character data converted based on at least part of the voice data. According to one embodiment, the middleware 244 can dynamically remove some existing components or add new components. According to one embodiment, at least part of the middleware 144 may be included as part of the operating system 142, or it may be implemented in separate software from the operating system 142.

[0051] Application 146 may include, for example, applications such as Home 251, Dialer 253, SMS / MMS 255, IM (instant message) 257, Browser 259, Camera 261, Alarm 263, Contact 265, Voice Recognition 267, Media 327, Email 269, Album 275, Watch 277, Health 279 (e.g., measuring biometric information such as exercise level or blood glucose), or Environmental Information 281 (e.g., measuring atmospheric pressure, humidity, or temperature information). According to one embodiment, application 146 may further include an information exchange application (not shown) that can support information exchange between the electronic device 101 and an external electronic device. The information exchange application may include, for example, a notification relay application configured to transmit specified information (e.g., calls, messages, or alarms) to an external electronic device, or a device management application configured to manage an external electronic device. The notification relay application may transmit notification information to an external electronic device in response to a specified event (e.g., receiving a mail) that has occurred in another application of the electronic device 101 (e.g., an email application 269). Additionally or alternatively, the notification relay application can receive notification information from an external electronic device and provide it to the user of the electronic device 101.

[0052] The device management application can, for example, control the power (e.g., turn on or turn off) or functions (e.g., brightness, resolution, or focus) of an external electronic device or some component thereof (e.g., a display module or camera module of an external electronic device) that communicates with the electronic device 101. The device management application can also, either additionally or alternatively, support the installation, removal, or updating of applications running on the external electronic device.

[0053] In this specification, the terms neural network, neural network network, and network function may be used interchangeably. A neural network can consist of a set of interconnected computational units, sometimes commonly called “nodes.” These “nodes” may also be called “neurons.” A neural network is configured to contain at least two or more nodes. The nodes (or neurons) that make up a neural network may be interconnected by one or more “links.”

[0054] Within a neural network, two or more nodes connected via links can form a relative input-output node relationship. The concepts of input and output nodes are relative; any node that is an output node to another node is an input node to another node, and vice versa. As mentioned above, the relationship between input and output nodes can be generated around links. One input node can be connected to one or more output nodes via links, and vice versa.

[0055] In a relationship between input and output nodes connected via a single link, the output node can determine its value based on the data input to the input node. Here, the nodes interconnecting the input and output nodes may have weights. These weights can be variable and may be changed by the user or algorithm to enable the neural network to perform a desired function. Here, the edges or links interconnecting the input and output nodes have weights that can be variably applied by the user or algorithm to enable the neural network to perform a desired function. For example, if one or more input nodes are interconnected to one output node by each link, the output node can determine its value based on the values ​​input to the input nodes connected to the output node and the weights set for the links corresponding to each input node.

[0056] As described above, a neural network is formed when two or more nodes are interconnected via one or more links, creating a relationship between input and output nodes within the neural network. The number of nodes and links in the neural network, the relationships between nodes and links, and the weight values ​​assigned to each link can determine the characteristics of the neural network. For example, if there are two neural networks with the same number of nodes and links but different weight values ​​between links, the two neural networks may be perceived as different from each other.

[0057] This shows one embodiment of how a server provides a report on a business model to a user terminal. The embodiment in Figure 3 can be combined with various embodiments of this disclosure.

[0058] Referring to Figure 3, in step S310, the server (for example, server 108 in Figure 1) can receive information about the business model from the user terminal.

[0059] The server automatically generates reports on business models via artificial intelligence. For example, the server can provide a report generation service to user terminals that creates business model reports. For example, an application that provides the report generation service can be pre-installed on user terminals.

[0060] For example, a user terminal can send information about the business model to a server via an application that provides a report generation service.

[0061] Information about the business model includes the name of the business model, a description of the product or service, the target market, key competitors, projected revenue, and expenses.

[0062] In step S320, the server can calculate a validity metric based on information about the business model, via a validity evaluation model that uses a first neural network.

[0063] For example, a server can generate a business vector containing values ​​for each of several items by performing data preprocessing on information about a business model. For example, a server can calculate a validity index by inputting the business vector into a validity assessment model.

[0064] For example, feasibility indicators may include multiple individual indicators. These multiple individual indicators may include global capability indicators and global business attractiveness indicators. For example, global capability indicators may include indicators that assess the resources or capabilities required for a company to enter international markets. For example, global business attractiveness indicators may include indicators that assess the business attractiveness of a target market, taking into account factors such as the growth rate of the target market, consumer demand, and the level of competition.

[0065] In step S33O, the server can assign a first weight to the global capability index, which is predetermined according to the industry.

[0066] For example, a server can apply a first weight to a global capability metric using pre-configured weight values ​​specific to its industry. For instance, because technical expertise may be crucial in the IT industry, the server might assign a high weight to the industry's global capability metric.

[0067] In step S3 4 0, the server can assign a second weight to the global business attractiveness index, which is predetermined according to the country.

[0068] For example, the server can apply a second, pre-assigned weight to a global business attractiveness index depending on the target country of the business model. For instance, the server can assign a higher weight to countries with higher economic growth rates.

[0069] In step S350, the server can adjust the first and second weights based on relationship information between multiple individual indicators.

[0070] For example, the server can adjust the first and second weights by analyzing the correlation between the global capability index and the global business attractiveness index.

[0071] For example, if a particular industry has high international competitiveness, the weight given to the global capability index can be increased, and conversely, if the local market is unattractive, the weight given to the global business attractiveness index can be decreased to balance the evaluation.

[0072] In step S360, the server can generate a report on the business model via a reporting model that utilizes a second neural network based on information about the business model and multiple individual metrics.

[0073] For example, a server can generate an indicator vector containing the values ​​of multiple individual indicators. For example, a server can generate a business model report by inputting a business vector and an indicator vector into a reporting model.

[0074] For example, a business model report could specifically describe the strengths and weaknesses of the business model and include a customized global expansion strategy with country-specific feasibility analysis results. For instance, a business model report could be structured in an easy-to-understand manner, including information such as market growth potential, competitor analysis, cost and sales forecasts, and legal regulations.

[0075] In step S370, the server can send a report on the business model to the user terminal.

[0076] According to one embodiment, for example, the validity assessment model can use a transformer model. Transformer models are effective in multidimensionally analyzing various factors (industry, market, competitiveness, etc.) and synthesizing them to generate validity indicators. For example, a transformer model can process input text to determine the weight of each element and understand the complex relationships between various business-related factors.

[0077] For example, the server can pre-collect business data such as global market trends, industry trends, competitor analysis, and economic growth rates, as well as data on successful business models. For instance, the server can pre-collect various industry data related to feasibility assessment.

[0078] For example, a server is a vector that can input collected data into a transformation model. It can be preprocessed. For instance, a server can build a dataset that matches information about a business model with validity metrics. For example, a server can tokenize factors related to a business model and generate embedding vectors that highlight the weight or relevance of each factor.

[0079] For example, a transformer model can encode and decode input data to determine the relationships between each element. Here, we can learn how business model-related elements interact using a transformer model.

[0080] For example, a trans model can learn to identify the importance of each element and predict validity metrics via a multi-head attention mechanism. For instance, learning can be performed by setting validity metrics (e.g., probability of success, competitive advantage, market fit, etc.) as target variables, and the model parameters can be adjusted to minimize the difference between predicted and actual results.

[0081] According to one embodiment, for example, the report generation model can use a BERT (bidirectional encoder representations from transformers) model. BERT can be a pre-trained model for natural language processing with transformer-based bidirectional characteristics. That is, BERT can compute embeddings for each word considering all bidirectional context of the input sentence. BERT includes a pre-training stage and a fine-tuning stage, in which the language model can be pre-trained using a large amount of text data. In the fine-tuning step, the pre-trained model can be fine-tuned to suit a specific natural language processing task by using the data required for that task. At this time, in the pre-training stage, BERT can perform an operation to mask and restore information about the business model and several individual metrics. This improves BERT's ability to understand context and generate reports based on masked words. Furthermore, BERT can perform an NSP (Next Sentence Prediction) operation to predict whether two sentences are consecutive. This allows BERT to learn the relationships between sentences and improve its natural language processing capabilities.

[0082] For example, a server can determine a report on a business model by generating at least one sentence based on information about the business model and several individual metrics, generating a sentence vector for at least one sentence, and inputting at least one sentence vector into a BERT-based natural language processing model. For example, a BERT-based natural language processing model can be trained on reports on multiple business models.

[0083] For example, a BERT model may include a token embedding layer, a segment embedding layer, and a position embedding layer for converting at least one sentence into multiple embedding vectors.

[0084] For example, natural language sentences, each containing item information, can be transformed into sentence vectors containing multiple tokens via torquening. Each sentence can contain natural language sentences represented by the respective terms of multiple metrics. For example, multiple sentences can be collected in advance for training a natural language processing model. For example, typographical errors and unnecessary characters can be removed from natural language sentences that make up multiple sentences.

[0085] For example, in each of multiple sentence vectors, it is possible to annotate tokens corresponding to information about each of multiple indicators with standardized names.

[0086] For example, each of multiple sentence vectors may be masked for the tokens annotated in each sentence vector via a token embedding layer. For example, the token embedding layer can perform masking for the tokens annotated in each sentence vector. For example, the server can mask the tokens corresponding to information about each of multiple metrics via a token embedding layer. That is, the server can replace the tokens corresponding to information about each of multiple metrics with masked tokens via a token embedding layer.

[0087] For example, each of multiple sentence vectors can be generated from multiple intermediate representation vectors by adding a start token to indicate the beginning of the first sentence and a delimiter token to distinguish sentences via a token embedding layer. For example, a server can generate intermediate representation vectors by adding a start token and a delimiter token via a token embedding layer to a masked vector.

[0088] For example, each of the multiple intermediate representation vectors is generated as multiple embedding vectors by setting a segment identifier for each of the sentence vectors contained in the intermediate representation vector and an order identifier for each of the tokens contained in the sentence vector via a segment embedding layer and a position embedding layer. For example, a server can generate multiple intermediate representation vectors as multiple embedding vectors by setting a segment identifier for each of the multiple intermediate representation vectors contained in the intermediate representation vector and an order identifier for each of the tokens contained in the sentence vector via a segment embedding layer and a position embedding layer.

[0089] For example, in the case of a token masked based on multiple embedding vectors, the BERT model can be trained through the process of determining information about each of the multiple metrics.

[0090] For example, by inputting embedding vectors into the encoder layer included in the BERT model, the encoder layer can learn to predict intent information and individual information corresponding to mask tokens.

[0091] This is a block diagram showing the configuration of a server according to one embodiment. One embodiment shown in Figure 4 can be combined with various embodiments of this disclosure.

[0092] As shown in Figure 4, the server 400 may include a processor 410, a communication unit 420, and memory 430. However, not all of the components shown in Figure 4 are essential components of the server 400. The server 400 can be implemented with more components than those shown in Figure 4, and can also be implemented with fewer components than those shown in Figure 4. For example, the server 400 in some embodiments includes a processor 410 and a communication unit 420. In addition to memory 430, it may further include a user input interface (not shown), an output unit (not shown), and so on.

[0093] The processor 410 typically controls the overall operation of the server 400. The processor 410 comprises one or more processors and can control other components included in the server 400. For example, the processor 410 can control the communication unit 420 and memory 430, etc., by executing a program stored in memory 430. The processor 410 can also perform the functions of the server 400 shown in Figure 3 by executing a program stored in memory 430.

[0094] The communication unit 420 may include one or more components that enable the server 400 to communicate with other devices (not shown) and other servers (not shown). Other devices (not shown) may be, but are not limited to, computing devices such as the server 400 or sensing devices. The communication unit 420 may receive user input from other electronic devices via a network or receive data stored in external devices from external devices.

[0095] For example, the communication unit 420 can send and receive messages to establish a connection with at least one device. The communication unit 420 can transmit information generated by the processor 410 to at least one device connected to the server. The communication unit 420 can receive information from at least one device connected to the server. The communication unit 420 can transmit information related to the received information in response to the information received from at least one device.

[0096] Memory 430 can store programs for processing and controlling the processor 410. For example, memory 430 can store information input to the server or information received from other devices via the network. Furthermore, memory 430 can store data generated by the processor 410. Memory 430 can store information input to or output from the server 400.

[0097] Memory 430 may include at least one type of storage medium from among flash memory type, hard disk type, multimedia card micro type, card type memory (e.g., SD or XD memory), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory, magnetic disk, and optical disk.

[0098] These can be implemented in combination. For example, the apparatus, methods, and components described in the embodiments can be implemented using one or more general-purpose or special-purpose computers, such as a processor, controller, arithmetic logic unit (ALU), digital signal processor (DIGENSOR), microcomputer, field-programmable gate array (FPGA), programmable logic unit (PLU), microprocessor, or microprocessor or apparatus. The processing apparatus can run an operating system (OS) and one or more software applications that run on the OS. Furthermore, the processing apparatus can access, store, manipulate, process, and generate data in response to the execution of the software. For convenience of understanding, it has sometimes been described that one processing apparatus is used, but a person with ordinary skill in the art will see that the processing apparatus can include multiple processing elements and / or multiple types of processing elements. For example, the processing apparatus can include multiple processors or one processor and one controller. Furthermore, other processing configurations such as parallel processors are also possible.

[0099] Software can include computer programs, code, instructions, or a combination of one or more of these, which can configure a processing unit to operate as desired, or which can instruct the processing unit independently or in combination. Software and / or data can be permanently or temporarily embodied in any kind of machine, component, physical device, virtual device, computer storage medium or device, or transmitted signal wave, in order to be interpreted by a processing unit or to provide instructions or data to a processing unit. Software can be distributed across a network of computer systems and stored or executed in a distributed manner. Software and data can be stored on one or more computer-readable recording media.

[0100] The methods according to the embodiments are carried out in the form of program instructions that can be executed via various computer means and can be recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., individually or in combination. The program instructions recorded on the medium may be specifically designed and configured for the embodiments, or they may be known and usable by those skilled in the computer software art. Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as optical discs, and program instructions such as ROM, RAM, and flash memory. Examples of program instructions include not only machine code, such as that produced by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like. The hardware devices described above can be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

[0101] Although embodiments have been described above with limited drawings, a person with ordinary skill in the art can apply various technical modifications and variations based on the above. For example, the described techniques may be performed in a different order than described, and / or the components of the described systems, structures, devices, circuits, etc. may be combined or assembled in a different manner than described, or replaced or substituted by other components or equivalents, and still satisfactory results may be achieved.

[0102] Therefore, other embodiments, other forms, and those equivalent to the claims described below also fall within the scope of the claims.

Claims

[Claim 1] In a method by which a server provides a report on a business model to a user terminal, Receive information about the business model from the user terminal. The validity index is calculated via a validity evaluation model that uses a first neural network based on information about the business model. Feasibility indicators include multiple individual indicators, The aforementioned multiple individual indicators include global capability indicators and global business attractiveness indicators. Assign a first weight to the global competency index, which is set based on industry. A second weight, set for each country, is added to the global business attractiveness index. The first and second weights are adjusted based on relationship information between multiple individual indicators. A business model report is generated through a reporting model that utilizes a second neural network based on information about the business model and multiple individual metrics. This includes the step of sending a report on the business model to the user's terminal. method.

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