Apparatus for providing corporate information for merger and acquisition mediation based on artificial intelligence and method thereof

Abstract

A corporate information providing device for AI-based M&A brokerage according to the present disclosure comprises: a memory storing at least one process for performing an AI-based M&A brokerage operation; and at least one processor that performs the operation according to the process; wherein the at least one processor is configured to extract text within an uploaded business card image and assign a member identification code based on the user's company affiliation information, provide a teaser containing basic information about a company available for acquisition to the user assigned the member identification code, provide a screen containing first detailed information about the company available for acquisition to the user who has confirmed the teaser and completed an electronic NDA with the company available for acquisition, and provide a screen containing second key information about the company available for acquisition to the user who has completed an electronic corporate acquisition brokerage contract among the users with an intention to acquire. The teaser, the first detailed information, and the second key information may be a document automatically generated by an AI model by processing corporate data received based on the contract information entered by the user with an intention to acquire and the key factors of the company available for acquisition.

Description

Technology Field

[0001] The present invention relates to an artificial intelligence-based information provision device and method, and more specifically, to an artificial intelligence-based corporate information provision device and method for M&A brokerage. Background Technology

[0002] M&A (Mergers and Acquisitions) is a term referring to corporate acquisitions and mergers. It encompasses an acquisition, in which one company gains management control by acquiring the shares or assets of another, and a merger, in which two or more companies are integrated into a single entity legally or substantially.

[0003] M&A is conducted to secure market dominance through growth in scale, expand into new areas by entering new markets, or enter new businesses; for an M&A to proceed, the conditions between the prospective buyer and the acquiring company must be mutually satisfied. To this end, detailed corporate information of the acquiring company and the requirements of the prospective buyer must be exchanged.

[0004] However, specific corporate information or requirements are confidential and difficult to disclose indiscriminately, which may impose restrictions on the exchange of information and the negotiation of terms. Negotiations for confidentiality must be conducted prior to full-scale M&A talks, and even after signing a confidentiality agreement, a process of coordination or continuous updating regarding information to be disclosed and conditions versus information not to be disclosed is necessary.

[0005] Therefore, the reality is that a significant amount of time and effort is required to collect, evaluate, and review various data for M&A.

[0006] Conventional patent literature presents strategies for M&A by collecting and matching corporate information and corporate issue information, and strategies for matching companies to proceed with M&A based on corporate preference voting; however, prior to this, there was a problem in that it failed to disclose how corporate information or corporate issue information, which must be kept confidential, is managed and provided to the counterparty. Prior art literature

[0007] Republic of Korea Registered Patent Publication 10-2749539 B1 (December 27, 2024) The problem to be solved

[0008] The purpose of the embodiments disclosed in this disclosure is to provide an AI-based corporate information provision device and method for M&A brokerage, which controls a screen configuration to be provided to a user-based interface using key factors set to reflect contract information entered into by a user and the advantages of a company available for acquisition.

[0009] The problems that this disclosure aims to solve are not limited to those mentioned above, and other unmentioned problems will be clearly understood by a person skilled in the art from the description below. means of solving the problem

[0010] A corporate information providing device for AI-based M&A brokerage according to the present disclosure for achieving the technical problem described above comprises: a memory in which at least one process for performing an AI-based M&A brokerage operation is stored; and at least one processor that performs the operation according to the process; wherein the at least one processor is configured to extract text within an uploaded business card image and assign a member identification code based on the user's company affiliation information, provide a teaser containing basic information about a company available for acquisition to the user assigned the member identification code, provide a screen containing first detailed information about the company available for acquisition to the user who has confirmed the teaser and completed an electronic NDA with the company available for acquisition, and provide a screen containing second key information about the company available for acquisition to the user who has completed an electronic corporate acquisition brokerage contract among the users with an intention to acquire. The teaser, the first detailed information, and the second key information may be a document automatically generated by an AI model by processing corporate data received based on the contract information entered by the user with an intention to acquire and the key factors of the company available for acquisition.

[0011] A method for a device for providing corporate information for AI-based M&A brokerage according to the present disclosure, for achieving the technical problem described above, comprising: a step of extracting text within an uploaded business card image and assigning a member identification code based on the user's company affiliation information; a step of providing a teaser containing basic information about a company available for acquisition to a user who has been assigned the member identification code; a step of additionally providing a screen containing first detailed information about the company available for acquisition to a user who has confirmed the teaser and completed an electronic NDA with the company available for acquisition; and a step of providing a screen containing second key information about the company available for acquisition to a user who has completed an electronic corporate acquisition brokerage contract among users who have an intention to acquire; wherein the teaser, the first detailed information, and the second key information may be characterized as being a document automatically generated by an artificial intelligence model by processing corporate data received based on the contract information concluded by the user with an intention to acquire and the key factors of the company available for acquisition.

[0012] In addition to this, a computer program stored on a computer-readable recording medium for implementing the present disclosure may be further provided.

[0013] In addition to this, a computer-readable recording medium for recording a computer program for implementing the present disclosure may be further provided. Effects of the invention

[0014] According to the aforementioned means for solving the problem of the present disclosure, by disclosing a teaser, first detailed information, and second key information in stages, the effect of simultaneously handling confidentiality and efficient M&A contract proceedings is provided.

[0015] Furthermore, according to the aforementioned means for solving the problem of the present disclosure, by generating automatic reports that effectively process corporate data based on key factors, promotional materials for a company eligible for M&A are quickly prepared, and the effect of facilitating the identification of information on the prospective buyer company is provided through the provision of objective and consistent information.

[0016] The effects of the present disclosure are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description below. Brief explanation of the drawing

[0017] FIG. 1 is a block diagram briefly illustrating the configuration of a corporate information provision device for artificial intelligence-based M&A brokerage according to the present disclosure. FIG. 2 is a block diagram briefly illustrating the configuration of a processor of a corporate information providing device for artificial intelligence-based M&A brokerage according to the present disclosure. FIG. 3 is a flowchart illustrating a method of a corporate information provision device for artificial intelligence-based M&A brokerage according to the present disclosure. FIG. 4 is a process diagram illustrating the screen output process based on contract information and key factors entered into by a user of an AI-based corporate information provision device for M&A brokerage according to the present disclosure. FIG. 5 is a process diagram illustrating a self-classification process of an artificial intelligence model of a corporate information provision device for AI-based M&A brokerage according to the present disclosure. FIG. 6 is a process diagram illustrating the process of outputting companies available for purchase based on the priority of a recommendation model of a corporate information providing device for artificial intelligence-based M&A brokerage according to the present disclosure. FIG. 7 is a conceptual diagram briefly illustrating the output screen of a user-based interface of a corporate information provision device for artificial intelligence-based M&A brokerage according to the present disclosure. Specific details for implementing the invention

[0018] Throughout this disclosure, the same reference numerals denote the same components. This disclosure does not describe all elements of the embodiments, and general content in the art to which this disclosure pertains or content that overlaps between embodiments is omitted. The terms 'part, module, component, block' as used in the specification may be implemented in software or hardware, and depending on the embodiments, a plurality of 'parts, modules, components, blocks' may be implemented as a single component, or a single 'part, module, component, block' may include a plurality of components.

[0019] Throughout the specification, when a part is described as being "connected" to another part, this includes not only cases where they are directly connected but also cases where they are indirectly connected, and indirect connections include connections made via a wireless communication network.

[0020] Furthermore, when it is stated that a part "includes" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0021] Throughout the specification, when it is stated that a component is located "on" another component, this includes not only cases where a component is in contact with another component, but also cases where another component exists between the two components.

[0022] The terms first, second, etc. are used to distinguish one component from another, and the components are not limited by the aforementioned terms.

[0023] Singular expressions include plural expressions unless there is an obvious exception in the context.

[0024] In each step, identification codes are used for convenience of explanation and do not describe the order of the steps; the steps may be performed differently from the specified order unless a specific order is clearly indicated in the context.

[0025] The operating principles and embodiments of the present disclosure will be described below with reference to the attached drawings.

[0026] In this specification, the term "device according to the present disclosure" includes all various devices capable of performing computational processing and providing results to a user. For example, the device according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may be in the form of any one of these.

[0027] Here, the computer may include, for example, a notebook, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

[0028] The above server device is a server that processes information by communicating with an external device, and may include an application server, a computing server, a database server, a file server, a game server, a mail server, a proxy server, and a web server.

[0029] The above portable terminal may include, for example, all types of handheld-based wireless communication devices such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), PDA (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminals, smartphones, etc., as well as wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted devices (HMDs).

[0030] Functions related to artificial intelligence according to the present disclosure are operated through a processor and memory. The processor may be composed of one or more processors. In this case, the one or more processors may be general-purpose processors such as CPUs, APs, and DSPs (Digital Signal Processors), graphics-dedicated processors such as GPUs and VPUs (Vision Processing Units), or artificial intelligence-dedicated processors such as NPUs. The one or more processors control the processing of input data according to predefined operation rules or artificial intelligence models stored in memory. Alternatively, if the one or more processors are artificial intelligence-dedicated processors, the artificial intelligence-dedicated processors may be designed with a hardware structure specialized for processing a specific artificial intelligence model.

[0031] The predefined operating rules or artificial intelligence models are characterized by being created through learning. Here, being created through learning means that a predefined operating rules or artificial intelligence models configured to perform desired characteristics (or objectives) are created by a basic artificial intelligence model being trained using multiple learning data by a learning algorithm. Such learning may be performed on the device itself where the artificial intelligence according to the present disclosure is executed, or it may be performed through a separate server and / or system. Examples of learning algorithms include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but are not limited to the examples described above.

[0032] An artificial intelligence model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through operations between the results of previous layers and the multiple weights. The multiple weights possessed by the multiple neural network layers can be optimized based on the learning results of the artificial intelligence model. For example, the multiple weights may be updated so that the loss value or cost value obtained from the artificial intelligence model during the learning process is reduced or minimized. Artificial neural networks may include deep neural networks (DNNs), such as Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), Bidirectional Recurrent Deep Neural Networks (BRDNNs), or Deep Q-Networks, but are not limited to the examples mentioned above.

[0033] According to an exemplary embodiment of the present disclosure, a processor can implement artificial intelligence. Artificial intelligence refers to a machine learning method based on an artificial neural network that enables a machine to learn by mimicking human biological neurons. Methodologies of artificial intelligence can be classified according to the learning method into supervised learning, where input and output data are provided together as training data and the solution (output data) to the problem (input data) is predetermined; unsupervised learning, where only input data is provided without output data and the solution (output data) to the problem (input data) is not predetermined; and reinforcement learning, where a reward is given from an external environment whenever an action is taken from the current state, and learning proceeds in a direction that maximizes such reward. In addition, artificial intelligence methodologies can be classified according to the architecture, which is the structure of the learning model. The architectures of widely used deep learning technologies can be classified into Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Transformers, and Generative Adversarial Networks (GAN).

[0034] The device and system may include an artificial intelligence model. The artificial intelligence model may be a single model or may be implemented as multiple models. The artificial intelligence model may be composed of a neural network (or artificial neural network) and may include statistical learning algorithms that mimic biological neurons in machine learning and cognitive science. A neural network may refer to a model that possesses problem-solving capabilities by having artificial neurons (nodes) that form a network through synaptic connections and change the strength of synaptic connections through learning. The neurons of a neural network may include combinations of weights or biases. A neural network may include one or more layers composed of one or more neurons or nodes. For example, the device may include an input layer, a hidden layer, and an output layer. The neural network constituting the device can infer a result (output) to be predicted from an arbitrary input by changing the weights of the neurons through learning.

[0035] The processor can create neural networks, train or learn neural networks, perform computations based on received input data, generate information signals based on the results of the computation, or retrain neural networks. Neural network models may include, but are not limited to, various types of models such as Convolutional Neural Networks (CNN), Region with Convolutional Neural Networks (R-CNN), Region Proposal Networks (RPN), Recurrent Neural Networks (RNN), Stacking-based Deep Neural Networks (S-DNN), State-Space Dynamic Neural Networks (S-SDNN), Deconvolution Networks, Deep Belief Networks (DBN), Restructured Boltzmann Machines (RBM), Fully Convolutional Networks, Long Short-Term Memory Networks (LSTM), and Classification Networks, such as GoogleNet, AlexNet, and VGG Network. The processor may include one or more processors to perform computations according to neural network models. For example, a neural network is a deep neural network It may include a (Deep Neural Network).

[0036] Neural networks include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Function), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto) Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network), DCN (Deep Convolutional Network), DN (Deconvolutional Network), DCIGN (Deep Convolutional Inverse Graphics Network), GAN (Generative Adversarial Network), LSM (Liquid State Machine), ELM (Extreme Learning Machine), ESN (Echo It will be understood by a person skilled in the art that any neural network may be included, but is not limited to, State Network, Deep Residual Network, Differential Neural Computer, Neural Turning Machine, Capsule Network, Kohonen Network, and Attention Network.

[0037] According to an exemplary embodiment of the present disclosure, the processor comprises a Convolutional Neural Network (CNN) such as GoogleNet, AlexNet, VGG Network, Region with Convolutional Neural Network (R-CNN), Region Proposal Network (RPN), Recurrent Neural Network (RNN), Stacking-based Deep Neural Network (S-DNN), State-Space Dynamic Neural Network (S-SDNN), Deconvolution Network, Deep Belief Network (DBN), Restructured Boltzmann Machine (RBM), Fully Convolutional Network, Long Short-Term Memory (LSTM) Network, Classification Network, Generative Modeling, eXplainable AI, Continual AI, Representation Learning, AI for Material Design, BERT, SP-BERT, MRC / QA, Text Analysis, Dialog System, GPT-3, GPT-4 for Natural Language Processing, Visual Analytics, Visual Understanding, Video Synthesis for Vision Processing, Anomaly Detection, Prediction, Time-Series Forecasting, Optimization for ResNet Data Intelligence, Various artificial intelligence structures and algorithms, such as recommendation and data creation, may be used, but are not limited thereto. Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0038] FIG. 1 is a block diagram briefly illustrating the configuration of a corporate information providing device (100) for artificial intelligence-based M&A brokerage according to the present disclosure.

[0039] Referring to FIG. 1, the corporate information providing device (100) according to the present disclosure may include an input / output module (110), a communication module (120), a memory (130), and a processor (140). Hereinafter, the corporate information providing device (100) is a terminal or server that provides a service for M&A brokerage, and the method thereof is assumed to be implemented through the corporate information providing device (100).

[0040] The input / output module (110) may be various interfaces or connection ports that receive user input or output information to the user. The input / output module (110) may be divided into an input module and an output module.

[0041] The input module receives user input from the user. The input module is for inputting video information (or signals), audio information (or signals), data, or information input by the user, and may include at least one of at least one camera, at least one microphone, and a user input unit. Voice data or image data collected by the input unit may be analyzed and processed into a user control command.

[0042] User input can take various forms, including key input, touch input, and voice input. Examples of input modules capable of receiving such user input include traditional keypads, keyboards, and mice; as well as touch sensors that detect user touch; microphones that receive voice signals; cameras that recognize gestures through image recognition; proximity sensors consisting of light or infrared sensors that detect user approach; motion sensors that recognize user movements using accelerometers or gyroscopes; and all other diverse forms of input means that detect or receive various types of user input. This is a comprehensive concept.

[0043] Here, the touch sensor can be implemented as a piezoelectric or capacitive touch sensor that detects touch through a touch panel or touch film attached to the display panel, or as an optical touch sensor that detects touch by an optical method. In addition, the input module may be implemented in the form of an input interface (USB port, PS / 2 port, etc.) that connects an external input device to receive user input, instead of a device that detects user input itself.

[0044] The output module can output various types of information and provide it to the user. The output module is a comprehensive concept that includes a display for outputting video, a speaker for outputting sound (and / or an amplifier connected thereto), a haptic device for generating vibration, and various other forms of output means. In addition, the output module may be implemented in the form of a port-type output interface that connects the individual output means described above.

[0045] For example, an output module in the form of a display can display text, still images, and videos. The term "display" refers to a broad concept of an image display device that includes all types of devices capable of performing image output functions, such as Liquid Crystal Displays (LCDs), Light Emitting Diode (LED) displays, Organic Light Emitting Diode (OLED) displays, Flat Panel Displays (FPDs), transparent displays, Curved Displays, flexible displays, 1D displays, holographic displays, projectors, and others. Such a display may also take the form of a touch display integrated with the touch sensor of an input module.

[0046] In other words, the input / output module (110) can receive user input or provide output to the user based on a user interface.

[0047] The communication module (120) can communicate with an external device. Accordingly, the device can transmit and receive information with an external device through the communication module. For example, the device can communicate with an external device using the communication module so that information stored and generated within the electric vehicle charging management system is shared. The communication module (120) may include, for example, at least one of a wired communication module, a wireless communication module, a short-range communication module, and a location information module.

[0048] Here, communication, that is, the transmission and reception of data, can be performed via wired or wireless means. To this end, the communication module may be composed of a wired communication module that connects to the Internet, etc., via a Local Area Network (LAN); a mobile communication module that connects to a mobile communication network via a mobile communication base station to transmit and receive data; a short-range communication module that uses a Wireless Local Area Network (WLAN) family communication method such as Wi-Fi or a Wireless Personal Area Network (WPAN) family communication method such as Bluetooth or Zigbee; a satellite communication module that uses a Global Navigation Satellite System (GNSS) such as GPS; or a combination thereof. The wireless communication technology used for communication may include Narrowband Internet of Things (NB-IoT) for low-power communication. In this case, for example, NB-IoT technology may be an example of LPWAN (Low Power Wide Area Network) technology and may be implemented according to standards such as LTE Cat (category) NB1 and / or LTE Cat NB2, but is not limited to the names mentioned above. Additionally, or generally, wireless communication technology implemented in wireless devices according to various embodiments may perform communication based on LTE-M technology. In this case, for example, LTE-M technology may be an example of LPWAN technology and may be referred to by various names such as eMTC (enhanced Machine Type Communication).For example, LTE-M technology may be implemented in at least one of various standards such as 1) LTE CAT 0, 2) LTE Cat M1, 3) LTE Cat M2, 4) LTE non-BL (non-Bandwidth Limited), 5) LTE-MTC, 6) LTE Machine Type Communication, and / or 7) LTE M, and is not limited to the names mentioned above. Additionally or generally, wireless communication technology implemented in wireless devices according to various embodiments may include at least one of ZigBee, Bluetooth, and Low Power Wide Area Network (LPWAN) for low-power communication, and is not limited to the names mentioned above. As an example, ZigBee technology can create personal area networks (PANs) related to small / low-power digital communication based on various standards such as IEEE 802.15.4 and may be referred to by various names.

[0049] The memory (130) can store various types of information. The memory can store data temporarily or semi-permanently. For example, the memory may store an operating system (OS) for operating the first device and / or the second device, data for hosting a website, or data regarding a program or application (e.g., a web application) for generating Braille. In addition, the memory may store modules in the form of computer code as described above.

[0050] Examples of memory (130) may include a hard disk drive (HDD), a solid state drive (SSD), flash memory, ROM (Read-Only Memory), and RAM (Random Access Memory). These memories may be provided as built-in or removable types.

[0051] The processor (140) controls the overall operation of the enterprise information providing device (100). To this end, the processor (140) performs calculations and processing of various information and can control the operation of the components of the first device and / or the second device.

[0052] The processor (140) may be implemented as a computer or a similar device according to hardware, software, or a combination thereof. Hardware-wise, the processor (140) may be provided in the form of an electronic circuit that processes electrical signals to perform control functions, and software-wise, it may be provided in the form of a program that drives the hardware processor. Meanwhile, unless otherwise specifically mentioned in the following description, the operation of the first device and / or the second device may be interpreted as being performed by the control of the processor (140). That is, the modules may be interpreted as the processor (140) controlling the first device and / or the second device to perform the following operations.

[0053] The processor (140) may be implemented with a memory that stores data for an algorithm or a program that reproduces the algorithm for controlling the operation of components within the device, and at least one sub-processor (not shown) that performs the aforementioned operation using the data stored in the memory. In this case, the memory and the processor may each be implemented as separate chips. Alternatively, the memory and the processor may be implemented as a single chip.

[0054] Additionally, the processor (140) can control one or a combination of the components described above in order to implement various embodiments according to the present disclosure, which will be described in the drawings below, on the device.

[0055] FIG. 2 is a block diagram briefly illustrating the configuration of a processor (140) of an artificial intelligence-based corporate information provision device (100) for M&A brokerage according to the present disclosure, and FIG. 3 is a flowchart illustrating a method of an artificial intelligence-based corporate information provision device (100) for M&A brokerage according to the present disclosure.

[0056] Additionally, FIG. 4 is a process diagram illustrating the process of outputting a screen based on contract information and key factors entered into by a user of an AI-based corporate information providing device (100) for M&A brokerage according to the present disclosure, FIG. 5 is a process diagram illustrating the process of classifying industries using an AI model, FIG. 6 is a process diagram illustrating the process of outputting companies available for acquisition based on priority according to a recommendation model, and FIG. 7 is a conceptual diagram briefly illustrating the output screen of a user-based interface. Hereinafter, an AI-based corporate information providing device (100) for M&A brokerage according to the present disclosure and a method thereof will be described with reference to FIG. 2 to FIG. 7.

[0057] A corporate information providing device (100) for AI-based M&A brokerage according to the present disclosure may include a memory (130) in which at least one process for performing an AI-based M&A brokerage operation is stored, and at least one processor (140) that performs the operation according to the process.

[0058] At least one processor (140) may be configured to extract text within an uploaded business card image and assign a member identification code based on the user's company affiliation information, provide a teaser containing basic information about a company available for purchase to the user assigned the member identification code, provide a screen containing first detailed information about the company available for purchase to the user who has confirmed the teaser and completed an electronic NDA with the company available for purchase, and provide a screen containing second key information about the company available for purchase to the user who has completed an electronic company acquisition brokerage contract among the users who have an intention to purchase.

[0059] In one embodiment, the teaser, the first detailed information, and the second key information may be a chart automatically generated by an artificial intelligence model by processing corporate data received based on contract information entered by a user with the intention to purchase and the key factors of the company available for purchase.

[0060] Specifically, as illustrated in FIG. 2, at least one processor (140) may include a membership registration module (141), a teaser provision module (142), a first detail provision module (143), a second key information provision module (144), and a corporate information provision module (145) depending on the function.

[0061] The membership registration module (141) can extract text from the uploaded business card image and assign a member identification code based on the user's company affiliation information.

[0062] When a user uploads a business card image, text within the business card image can be automatically extracted using an artificial intelligence-based optical character recognition (OCR) technique, and the extracted text, such as company name, person in charge, job title, and contact information, can be stored in a corporate information providing device (100).

[0063] By introducing a business card-based member authentication method, accurate verification of member identity and security can be maximized.

[0064] In addition, corporate information or corporate business card image information can be obtained through a database within the corporate information providing device (100) or a separately connected server, and after verifying the authenticity of the business card according to the obtained corporate information or corporate business card image information, text within the business card image verified to be a genuine business card can be extracted and a member identification code can be assigned based on the user's company affiliation information.

[0065] When the user is assigned a member identification code and the user's membership registration is completed, the teaser providing module (142) can provide a teaser of a company available for purchase to the user interface (200).

[0066] A teaser may contain limited information that allows verification of basic details about a company available for acquisition. For example, it may include minimal information such as the company name, business summary, and revenue size.

[0067] At this time, a watermark can be inserted into the user interface (200) containing the teaser to prevent even minimal information from being illegally leaked or shared without permission.

[0068] After the user checks the teaser information, they can enter into an electronic Non-Disclosure Agreement (NDA) with an acquireable company of interest.

[0069] By connecting a button or link to the user interface (200) that allows for the signing of an NDA along with a teaser, it is possible to check the details of the company available for purchase while simultaneously ensuring the security of confidentiality.

[0070] When the user is assigned a member identification code and confirms that they are a user who has entered into an NDA, the first detail information provision module (143) can provide the first detail information of a purchasable company to the user interface (200).

[0071] The first detailed information is corporate information provided following the teaser, and may include more detailed internal information in addition to the information disclosed in the teaser. For example, the first detailed information may include key financial statement information and status confirmation information of the acquireable company.

[0072] After verifying the details up to the first detail, a user who intends to enter into an M&A may enter into a corporate acquisition brokerage agreement.

[0073] By connecting a button or link to the user interface (200) that allows for the conclusion of a corporate acquisition brokerage contract along with the first detailed information, it is possible to proceed with an electronic contract-based M&A without separate offline activities, along with verifying corporate information.

[0074] When it is confirmed that a user who has been assigned a member identification code and entered into an NDA is a user who has also entered into a corporate acquisition brokerage agreement, the second core information provision module (144) can provide second core information of the acquireable company to the user interface (200) of the user who is the acquisition candidate, and can provide transfer conditions for the acquisition or merger of the acquirer company to the user interface (200) of the acquireable company.

[0075] The second core information is corporate information provided following the first detailed information, and may include corporate information that is more confidential than the first detailed information. For example, it may include detailed financial analysis data, specific employee status and key personnel information, core technology data, etc.

[0076] The teaser provision module (142), the first detailed information provision module (143), and the second key information provision module (144) can be automatically linked systemically depending on whether the user has completed membership registration, the conclusion of an NDA, or the conclusion of a corporate acquisition brokerage contract, thereby providing the next output screen of the user interface (200). Safe and systematic transaction processing is possible without separate offline document processing, and the service can be provided to enable efficient M&A contracts while maintaining security by controlling the level and amount of information provided at each stage.

[0077] Meanwhile, the corporate information provision module (145) is connected to a corporate database (300) that stores corporate information of each acquireable company or acquisition candidate company, and can obtain corporate information included in the teaser, first detailed information, and second key information.

[0078] Corporate information obtained from the corporate database (300) can be processed and edited based on the major factors of each purchaseable company and output to the user interface (200) as a chart in a specific format.

[0079] The corporate database (300) may include various sources such as the company's accounting system, public data portal, or internal ERP. The corporate information provision module (145) can quickly collect the latest corporate information from the corporate database (300) by performing real-time or periodic updates.

[0080] Accordingly, as illustrated in FIG. 3, at least one processor (140) of the artificial intelligence-based corporate information provision device (100) for M&A brokerage according to the present disclosure may perform the steps of: extracting text within an uploaded business card image and assigning a member identification code based on the user's company affiliation information (S310); providing a teaser containing basic information about a company available for acquisition to a user who has been assigned the member identification code (S320); providing a screen containing first detailed information about the company available for acquisition to a user who has confirmed the teaser and completed an electronic NDA with the company available for acquisition (S330); and providing a screen containing second key information about the company available for acquisition to a user who has completed an electronic corporate acquisition brokerage contract among users who have an intention to acquire (S340).

[0081] And, as illustrated in FIG. 4, the teaser, the first detail, and the second key information may be a chart automatically generated by an artificial intelligence model (400) by processing corporate data received based on contract information entered by the user with the intention to purchase and the major factors of the company available for purchase.

[0082] Referring to FIG. 4, the user can determine which stage the user has proceeded with is the membership completion stage (410), the NDA signing stage (420), or the corporate acquisition brokerage contract signing stage (430), and determine the screen information to be displayed according to the contract information the user has signed.

[0083] If the contract concluded by the user is at the membership completion stage (410), a teaser output (210) can be performed; if it is at the NDA conclusion stage (420), a first detailed information output (220) can be performed; and if it is at the corporate acquisition brokerage contract conclusion stage (430), a second key information output (230) can be performed.

[0084] At this time, the form of outputting each teaser, first detail information, and second key information can be determined according to the main factor (440).

[0085] At least one processor (140) may be configured such that the major factor (440) is set based on at least one of the competitive factors, key performance indicators, and technological capabilities of the acquireable company analyzed by the artificial intelligence model, or is set based on factors manually selected by a manager.

[0086] The major factors (400) can be set differently for each company available for purchase, depending on the advantages to be emphasized, and can be set automatically using an artificial intelligence model or manually set by an administrator.

[0087] Additionally, at least one processor (140) may be configured to automatically extract and obtain corporate data including basic company information and financial company information from the corporate database, and to selectively provide a screen edited into a format corresponding to the type and content of the major factors according to the contract entered into by the user.

[0088] For example, for an acquireable company with strong technological capabilities, technological capability can be set as a key factor. The presentation can be structured by highlighting and prominently placing corporate data that demonstrates this technological capability, such as the status of certifications for new technologies and products in the market, patent acquisition, and key talent acquisition.

[0089] In addition, for acquireable companies with a majority market share as a strength, competitive factors can be established as key factors. As competitive factors, the economic scale of the market, the ratio of market share within that market, and the difference in market share with the next company can be placed at the forefront to structure the presentation.

[0090] As an example, when technological capability is the main factor, the slide may be composed by attaching product images or patent claims as images, and when competitive factors are the main factor, the slide may be composed by creating a chart showing the difference in market share with competitors.

[0091] Accordingly, by using an artificial intelligence model, the strengths of companies available for acquisition can be analyzed by receiving corporate information obtained from a corporate database (300), crawled web-based data, homepage data, social media announcements, etc., and the analyzed strengths can be set as major factors, or elements directly entered by the manager of companies available for acquisition can be set as major factors.

[0092] The artificial intelligence model (400) can learn the optimal PDF or PPT format based on strengths and output a slide containing a corresponding teaser, first detail information, or second key information based on the set key factors.

[0093] Therefore, instead of simply displaying a screen in which corporate data is listed as text on the user interface (200), a processed chart can be displayed by assigning different weights to each corporate data according to major factors and editing the weight or arrangement.

[0094] Meanwhile, at least one processor (140) may be configured to generate a proprietary industry classification code (530) that subdivides existing industry codes using an artificial intelligence model learned from the corporate data, the actual business content of the company, IR (Investor Relations) materials, website materials, and crawled web-based materials, and to classify the company of a user assigned the member identification code according to the proprietary industry classification code.

[0095] As illustrated in FIG. 5, an artificial intelligence model (500) that receives a corporate database (510) and crawl data (520) can output its own industry classification code (530) of the corporate information providing device (100) according to the present disclosure.

[0096] Here, the corporate database (510) may be the same as the corporate database (300) of FIG. 2. The crawling data (520) may be data obtained by crawling the homepage, social media, articles, news, etc. of companies available for purchase.

[0097] Here, the artificial intelligence model (500) may be a separate artificial intelligence model trained with separate training data from the artificial intelligence model (400) of FIG. 4.

[0098] The artificial intelligence model (500) learns from the company data including the company's actual business content, financial indicators, website, IR materials, and social network analysis data, and the company's own industry classification code (530) by labeling them as training data, and can output the company's own industry classification code (530) that can be purchased based on the data obtained from the company database (510) and crawl data (520).

[0099] Since existing national standard industrial classifications (KSIC) or international standard codes such as NAICS and SIC do not fully reflect the complex business areas or diversified areas of actual companies, the company information providing device (100) according to the present disclosure generates its own industry classification code (530) to subdivide industry categories and can ensure accurate industry identification of companies available for purchase and improved matching accuracy between similar companies.

[0100] At least one processor (140) can quantify the potential acquisition of a candidate company by matching the industry of a company that can be acquired with the industry of interest that the candidate company wishes to acquire, based on its own industry class code (530).

[0101] In addition, acquisition potential can be quantified by reflecting additional factors such as business scope, market share, and financial strength in the acquisition synergy.

[0102] In one embodiment, a teaser, first detailed information, and second key information are generated, and the possibility of M&A between companies is scored using corporate data and a proprietary industry classification code (530) reflected in the chart, and the scored possibility of M&A can be presented to prospective buyers.

[0103] Alternatively, as one embodiment, at least one processor (140) may generate a personalized profile based on the industry of the prospective buyer company, its history of previous M&A, areas of interest, market expansion strategies, investment objectives, and investment propensity.

[0104] As an example, the company profile for the M&A success probability of the company may be obtained by combining the personalized profile of the candidate company and the self-industry classification code (530).

[0105] At least one processor (140) may be configured to output the similarity between the corporate information of each acquireable company and the corporate profile of the acquire candidate company using a recommendation model that learns the self-industry classification code (530) of the acquireable company and the vector values ​​embedding the size, financial health, technology level, and customer group of the acquireable company.

[0106] Referring to FIG. 6, a deep learning-based recommendation model (600) can learn embedding vector values ​​that embedding corporate data of companies that can be acquired and output candidates for companies that can be acquired based on similarity with the corporate profile of candidate companies.

[0107] When the corporate profile of a candidate company for acquisition is input into the recommendation model (600), the company profile of the candidate company for acquisition and the embedding vector value of the top-ranked companies A, B, and C that are eligible for acquisition can be output with a similarity score of 60 or more.

[0108] At this time, companies available for purchase can be output sequentially according to the priority corresponding to the similarity score.

[0109] In addition, similarity scores may also be provided in the user interface (200) so that the user can check the similarity score between the corporate profile of the prospective buyer company and the companies available for acquisition and use it as a reference for determining the possibility of M&A.

[0110] Accordingly, at least one processor (140) may be configured to output a score for the probability of a successful M&A based on the similarity between the acquireable company and the acquisition candidate company, and to sequentially output the acquireable company based on priority according to the output score.

[0111] At least one processor (140) may be configured to distinguish the purchaseable companies as a dashboard or list and output them according to priority, and to automatically insert a watermark (530) into the screen containing the purchaseable company information and output it.

[0112] As illustrated in FIG. 7, each purchaseable company A, B, and C can be distinguished and output sequentially. The recommendation results of the recommendation model (600) can be provided to the user interface (200) in the form of a dashboard or list within the platform.

[0113] A filtering button (not shown) based on similarity score can be added to the user interface (200) so that companies available for purchase can be systematically searched based on similarity score.

[0114] Referring to FIGS. 6 and 7, the company information button (710), purchase button (720), and watermark (730) of companies A, B, and C available for purchase can be inserted and output, and additionally, similarity scores for companies A, B, and C available for purchase can be output together.

[0115] The company information button (710) may provide a link to view detailed company information, or it may be a dashboard that directly outputs the company information itself.

[0116] The buy button (720) can provide a link to an electronic contract page where a corporate purchase brokerage contract can be concluded.

[0117] As an example, when corporate information such as FIG. 7 is provided during the NDA signing stage, instead of a buy button (720), an NDA signing button may be provided, and a link may be provided to an electronic contract signing page where the NDA can be signed immediately.

[0118] The system can process signing, contract document issuance, and access authorization in batches.

[0119] A watermark (730) can be automatically inserted into a user interface (200) as a marker containing identification information, including a member identification code and the time of access, for accessing a screen containing corporate information. Although it is labeled as WATERMARK in FIG. 7, this is an exemplary display, and identification information that can identify the accessing user may be repeated across the entire screen or inserted in the center or on one side of the screen.

[0120] Notifications of major events such as contract requests, completion of signing, and disclosure of details can be automatically sent via push messages or emails through the user interface (200), and the status of corporate information provision, contract progress, and waiting status can be monitored in real time through the administrator interface.

[0121] In one embodiment, at least one processor (140) may be configured to calculate the similarity between the acquireable company and the acquirer company by weighting the investment purpose or market expansion strategy in the company profile of the acquirer company.

[0122] Since the pool of potential acquisition targets can vary significantly depending on the acquisition target's reasons for M&A, it is possible to identify investment objectives or market expansion strategies—the reasons for proceeding with the M&A—based on the acquisition target's corporate profile, and output a similarity level with potential acquisition targets by considering these factors.

[0123] Accordingly, the corporate information providing device (100) according to the present disclosure can proceed with the teaser, first detailed information, and second key information according to the contract status and key factors, thereby enabling the simultaneous maintenance of confidentiality and efficient M&A transaction progress. Since the acquireable company and the prospective buyer company can proceed with the contract in a single process while maintaining security, user convenience can be maximized.

[0124] Furthermore, potential acquisition targets can automatically generate presentation slides based on key factors using an artificial intelligence model without having to prepare separate corporate promotional materials, and prospective buyers can search for companies using objective and verified slides.

[0125] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. The instructions may be stored in the form of program code and, when executed by a processor, may generate a program module to perform the operation of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0126] Computer-readable recording media include all types of recording media that store instructions that can be decoded by a computer. Examples include ROM (Read Only Memory), RAM (Random Access Memory), magnetic tape, magnetic disk, flash memory, optical data storage devices, etc.

[0127] As described above, the disclosed embodiments have been explained with reference to the attached drawings. Those skilled in the art will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical spirit or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be interpreted restrictively. Explanation of the symbols

[0128] 100: Electronic device 110: I / O module 120: Communication module 130: Memory 140: Processor 141: Membership Registration Module 142: Teaser Provider Module 143: 1st Detail Provision Module 144: 2nd Core Information Provision Module 145: Corporate Information Provision Module 200: User Interface 300: Corporate Database 400, 500: Artificial Intelligence Model 600: Recommended Model

Claims

Claim 1 A corporate information providing device for AI-based M&A brokerage, comprising: a memory storing at least one process for performing an AI-based M&A brokerage operation; and at least one processor that performs the operation according to the process; wherein the at least one processor is configured to extract text within an uploaded business card image and assign a member identification code based on the user's company affiliation information, provide a teaser containing basic information about a company available for acquisition to the user assigned the member identification code, provide a screen containing first detailed information about the company available for acquisition to the user who has confirmed the teaser and completed an electronic NDA with the company available for acquisition, and provide a screen containing second key information about the company available for acquisition to the user who has completed an electronic corporate acquisition brokerage contract among the users with an intention to acquire; wherein the teaser, the first detailed information, and the second key information are sheets automatically generated by an AI model by processing corporate data received based on the contract information concluded by the user with an intention to acquire and the key factors of the company available for acquisition. Claim 2 In claim 1, the at least one processor is configured such that the major factor is set based on at least one of the competitive elements, key performance indicators, and technological capabilities of the acquireable company analyzed by the artificial intelligence model, or is set based on an element manually selected by a manager. Claim 3 In claim 2, the AI-based corporate information providing device for M&A brokerage is configured such that at least one processor automatically extracts and obtains corporate data including basic company information and financial company information from the corporate database, and selectively provides a screen edited into a form corresponding to the type and content of the major factors according to the contract concluded by the user. Claim 4 In claim 3, the above-mentioned at least one processor is configured to generate a proprietary industry classification code by subdividing existing industry codes using an artificial intelligence model learned using the above-mentioned corporate data, the actual business content of the company, IR (Investor Relations) materials, website materials, and crawled web-based materials, and to classify the company of a user assigned the above-mentioned member identification code according to the said proprietary industry classification code, thereby providing corporate information for AI-based M&A brokerage. Claim 5 In claim 4, the AI-based corporate information provision device for M&A brokerage is configured such that at least one processor generates a personalized profile based on the industry, existing M&A history, areas of interest, market expansion strategy, investment objectives, and investment propensity of the candidate company, and combines the personalized profile of the candidate company with the proprietary industry classification code to obtain a corporate profile for outputting the likelihood of M&A success of the candidate company. Claim 6 In claim 5, the above-mentioned at least one processor is configured to output the similarity between the corporate information of each acquireable company and the corporate profile of the acquirer company using a recommendation model that learns the self-industry classification code of the acquireable company and the vector values ​​embedding the size, financial health, technology level, and customer group of the acquireable company. This is an AI-based corporate information providing device for M&A brokerage. Claim 7 In claim 6, the above-mentioned at least one processor is configured to score and output the probability of M&A success based on the similarity between the acquireable company and the acquisition candidate company, and to sequentially output the acquireable company based on priority according to the output score, thereby providing corporate information for M&A brokerage based on artificial intelligence. Claim 8 In claim 7, the above-mentioned at least one processor is configured to calculate the similarity between the acquireable company and the acquirer company by weighting the investment purpose or market expansion strategy in the corporate profile of the acquirer company, an artificial intelligence-based corporate information providing device for M&A brokerage. Claim 9 In claim 8, the AI-based corporate information providing device for M&A brokerage is configured such that at least one processor distinguishes the acquireable companies into a dashboard or list and outputs them according to priority, and automatically inserts a watermark into a screen containing the acquireable company information and outputs it. Claim 10 A method of a device for providing corporate information for AI-based M&A brokerage performed by a processor of a device, the method comprising: a step of extracting text within an uploaded business card image and assigning a member identification code based on the user's company affiliation information; a step of providing a teaser containing basic information about a company available for acquisition to a user who has been assigned the member identification code; a step of additionally providing a screen containing first detailed information about the company available for acquisition to a user who has confirmed the teaser and completed concluding an electronic NDA with the company available for acquisition; and a step of providing a screen containing second key information about the company available for acquisition to a user who has completed concluding an electronic corporate acquisition brokerage contract among users with an intention to acquire; wherein the teaser, the first detailed information, and the second key information are a table automatically generated by an artificial intelligence model by processing corporate data received based on the contract information concluded by the user with an intention to acquire and the key factors of the company available for acquisition.

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