system

Abstract

Provide a system. [Solution] means for accepting user information; means for generating a procedural assistance wizard based on a user's experience level; A means to generate the necessary documents by calling generation AI based on data collected from users; means for submitting a user's request to a network of experts and obtaining advice from appropriate experts; and means for evaluating the cost of the procedure and generating cost reduction suggestions.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional M&A procedures are complex and require many steps, making the complexity of the procedures a challenge, especially for beginners. Furthermore, the advice of experts and advisors is essential to proceed with the procedures, resulting in high costs. This creates a major barrier to M&A for small and medium-sized enterprises and individuals. [Means for solving the problem]

[0005] This invention provides a system that supports M&A processes by utilizing generative AI. Specifically, it includes a means for accepting user information and a means for generating a procedure support wizard based on the information. It also includes a means for automatically generating necessary documents by calling the generative AI based on collected data. It also provides a means for sending user requests to an expert network and obtaining advice from appropriate experts. Finally, it includes a means for evaluating the cost of the procedure and generating cost-reduction proposals. This system achieves both procedure simplification and cost reduction.

[0006] "User information" refers to information necessary for the system to identify the user and provide appropriate assistance, such as the user's name, contact information, and level of experience.

[0007] The "Procedure Support Wizard" is an interface that provides users with guidance and questions as they go through M&A procedures, simplifying the process by allowing users to enter answers and options.

[0008] "Generative AI" is artificial intelligence that automatically generates necessary legal and procedural documents based on data collected from users.

[0009] The means of generating "documents" is a process by which the generation AI automatically generates the necessary contracts and other procedural documents based on user input information.

[0010] The "Expert Network" is a collection of legal, accounting, and M&A experts that users can use when they need additional professional advice.

[0011] "Cost assessment" is a method for assessing the costs associated with M&A procedures and is a process used to make proposals for cost reduction.

[0012] The means for generating a "proposal" is a process for presenting specific cost reduction methods to the user based on the results of cost evaluation. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention relates to a system using a generative AI for supporting M&A procedures. Specific embodiments for implementing this system will be described below.

[0035] User Registration

[0036] A user accesses the system.

[0037] The terminal displays a form for the user to enter basic information such as name, contact details, and level of experience.

[0038] The user enters the necessary information into the input form and submits it.

[0039] The server stores the transmitted information in a database.

[0040] Providing a procedure assistance wizard

[0041] The server generates a procedure assistance wizard that matches the user's experience level based on the information entered by the user.

[0042] The terminal displays the interface of this wizard to the user.

[0043] The user answers the wizard's questions, for example, "What is the purpose of the transaction?"

[0044] The server collects the user's answers and stores them in a database for use in the next step.

[0045] Automatic Document Generation

[0046] The server calls the generation AI based on the collected user response data.

[0047] Generative AI generates templates for required legal and procedural documents (e.g., acquisition agreements).

[0048] The server transmits the generated document to the user's terminal.

[0049] The terminal displays the generated document to the user.

[0050] The user checks the displayed document and corrects it if necessary.

[0051] When the user modifies the document and presses the save button, the server saves the modified document in the database.

[0052] Access to expert networks

[0053] The user requests "additional expert advice" within the wizard.

[0054] The server receives this request and uses generative AI to select the most suitable expert.

[0055] The server transmits the user's consultation details to the expert.

[0056] The expert receives the user's inquiry and provides advice.

[0057] The server forwards this advice to the user's terminal.

[0058] The terminal displays the expert advice to the user.

[0059] Cost evaluation and optimization

[0060] The server calls the generation AI based on the user's procedure response data.

[0061] The generative AI evaluates the costs of procedures and generates proposals for cost reduction (e.g., cost reduction through digitization of documents).

[0062] The server sends the evaluation results and suggestions to the user's terminal.

[0063] The terminal displays the evaluation results and suggestions to the user.

[0064] If the user accepts the offer, they can choose the most appropriate method for proceeding, for example, by using an electronic signature.

[0065] Specific examples

[0066] User Registration

[0067] A user logs into the system and enters their name and contact information.

[0068] The server stores this information in a database.

[0069] Providing a procedure assistance wizard

[0070] The server generates a wizard that displays, "We will support you in your first M&A procedure."

[0071] The terminal displays a wizard and asks, "What type of M&A transaction would you like to undertake?"

[0072] The user selects "Acquisition" and presses the "Next" button.

[0073] The server stores the answer in a database and generates the next question.

[0074] Automatic Document Generation

[0075] The server calls a generation AI based on the user's answers and generates an "acquisition agreement."

[0076] The file is sent to the user's terminal and displayed.

[0077] The user checks the document and makes any necessary corrections.

[0078] The server stores the saved documents in a database.

[0079] Access to expert networks

[0080] The user requests advice on the details of the contract terms.

[0081] The server uses generative AI to select the most suitable expert.

[0082] The server sends a request to the selected expert and obtains advice.

[0083] The terminal displays the advice from the expert to the user.

[0084] Cost evaluation and optimization

[0085] The server calls a generation AI based on the user's data and evaluates the cost of the procedure.

[0086] The generative AI generates a suggestion that "using electronic signatures will reduce costs."

[0087] The server sends the proposal to the user's terminal.

[0088] The user reviews the proposal and selects "Use electronic signature" to proceed.

[0089] In this manner, the present invention provides a system that simplifies the M&A process and reduces costs by minimizing the use of experts.

[0090] The processing flow will be explained below.

[0091] Step 1:

[0092] A user accesses the system.

[0093] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[0094] Step 2:

[0095] The user enters the required information into the input form and submits it.

[0096] The server stores the transmitted information in a database.

[0097] Step 3:

[0098] The server generates a procedure assistance wizard that matches the user's experience level based on the information input by the user.

[0099] The terminal displays the wizard's interface to the user.

[0100] Step 4:

[0101] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[0102] The server collects the user's answers and stores them in a database for use in the next step.

[0103] Step 5:

[0104] The server calls the generation AI based on the user response data collected.

[0105] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[0106] Step 6:

[0107] The server sends the generated document to the user's terminal.

[0108] The terminal displays the generated document to the user.

[0109] Step 7:

[0110] The user checks the displayed document and corrects it if necessary.

[0111] The server saves the modified document to the database.

[0112] Step 8:

[0113] The user requests "additional expert advice" within the wizard.

[0114] The server uses generated AI to select the most suitable expert.

[0115] Step 9:

[0116] The server transmits the user's consultation details to the expert.

[0117] An expert will receive the user's inquiry and provide advice.

[0118] The server transfers the advice from the expert to the user's terminal.

[0119] Step 10:

[0120] The terminal displays expert advice to the user.

[0121] The user reviews the proposal and takes action if necessary.

[0122] Step 11:

[0123] The server calls the generation AI based on the user's procedural data.

[0124] Generative AI assesses the costs of procedures and generates cost-saving proposals.

[0125] Step 12:

[0126] The server sends the proposal to the user's terminal.

[0127] The terminal displays the suggestions to the user.

[0128] If the user accepts the proposal, the system will select the most suitable method and proceed with the procedure.

[0129] Example 1

[0130] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0131] Traditional M&A procedures are complicated and require extensive specialized knowledge and expensive expert fees. As a result, the procedures are often difficult and costly, especially for beginners and small companies. In addition, the preparation of necessary documents and cost assessments are often time-consuming, hindering efficient progress.

[0132] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0133] In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for generating the necessary documents by calling a generation AI based on data collected from the user, means for transmitting the generated documents to the user's terminal and displaying them to the user, means for the user to modify and save the generated documents, means for transmitting the user's request to an expert network and obtaining advice from appropriate experts, and means for evaluating the cost of the procedures and generating cost reduction proposals. This enables users to proceed with M&A procedures efficiently and at low cost even if they do not have specialized knowledge.

[0134] "User information" refers to basic information such as the name, contact details, and level of experience of the user accessing the system.

[0135] "Procedure Assistance Wizard" means an interface that provides a series of questions and instructions to assist a user in the M&A process, generated based on the user's experience level.

[0136] "Generative AI" refers to artificial intelligence that automatically generates necessary documents based on data collected from users.

[0137] "Expert Network" means a collection of experts within the system to provide appropriate expert advice based on a user's request.

[0138] "Cost assessment" means the process of assessing the costs of a procedure and making recommendations to reduce those costs in the most appropriate manner.

[0139] "Documents" means legal and procedural documents required for the M&A process (e.g., acquisition agreement).

[0140] "Terminal" means a device such as a computer or smartphone that a user uses to access the system and perform operations or input data.

[0141] "Database" means a digital storage device for storing user and procedural information.

[0142] The present invention relates to a system using a generative AI for supporting M&A procedures. A specific embodiment of this system will be described below.

[0143] The user first accesses the system using a device such as a personal computer or smartphone. The user enters basic information such as their name, contact details, and level of experience into an input form and submits it. The device sends this information to the server, which then stores it in a database.

[0144] The server then generates a procedural assistance wizard based on the user's experience level. This wizard is created using a front-end framework such as React or Vue.js and is displayed on the device. The wizard presents the user with questions such as "What is the purpose of the transaction?" and collects the user's answers.

[0145] Based on the collected data, the server calls a generative AI to generate the required document. The generative AI used here is an AI model such as OpenAI's GPT-3. An example of a prompt is as follows:

[0146] The user has selected Acquisition. Now generate the required Acquisition Agreement template.

[0147] Transaction objective: Entering new markets

[0148] Based on this prompt, the generation AI generates a template for the acquisition agreement. The generated document is sent from the server to the user's device and displayed on the device. The user checks the document and makes any necessary corrections. The corrected document is saved by the user and then saved back to the database by the server.

[0149] Furthermore, if the user requests additional expert advice, the server uses the generation AI to select the most suitable expert. The user's consultation details are sent to the selected expert, who then provides advice. The provided advice is transferred to the user's device via the server and displayed to the user.

[0150] Finally, the server calls the generation AI based on the collected user procedure response data to evaluate the cost of the procedure. The generation AI generates proposals for cost reduction and sends the evaluation results and proposals to the user's device. The user reviews the displayed proposals and, if they accept them, selects the optimal method. For example, they can choose to use an electronic signature.

[0151] In this manner, the present invention provides a system that simplifies the M&A process and reduces costs by minimizing the use of experts.

[0152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0153] Detailed explanation of the program's processing flow

[0154] Step 1:

[0155] A user accesses the system. The terminal displays an input form for basic information such as name, contact details, and level of experience to the user. The user enters the basic information into the input form and presses the submit button. Based on this input, the terminal sends the data to the server, which stores it in a database.

[0156] Step 2:

[0157] The server generates a procedure assistance wizard based on the user's experience level. In this case, the server generates a wizard containing questions and instructions for beginners based on the information entered by the user. The terminal displays the wizard interface to the user and waits for the user's response. For example, the question "What type of M&A procedure do you want to carry out?" is displayed. The user's input is saved in a database, and the data becomes the output for the next step.

[0158] Step 3:

[0159] The user answers the wizard's questions. The user enters their answers into the input form and clicks "Next." The server receives this data and stores it in a database. This data is used to understand the user's specific needs.

[0160] Step 4:

[0161] The server calls the generative AI based on the collected user response data. For example, if the answer in the previous step was "acquisition," the server sends the following prompt to the generative AI model:

[0162] The user has selected Acquisition. Now generate the required Acquisition Agreement template.

[0163] Transaction objective: Entering new markets

[0164] The generation AI generates the necessary legal and procedural documents based on these prompts and returns the output to the server.

[0165] Step 5:

[0166] The server sends the generated document to the user's terminal. The terminal displays the received document file to the user. For example, the document is displayed using a PDF viewer or text editor. The user checks the displayed document and makes corrections as necessary. When the user presses the save button, the corrected document is sent back to the server.

[0167] Step 6:

[0168] When a user saves a modified document, the server stores the document in a database, making it valuable information for future reference.

[0169] Step 7:

[0170] When a user requests additional expert advice, the server uses the generated AI to select the most suitable expert. The server analyzes the expert profiles using the generated AI and selects the most suitable expert.

[0171] Step 8:

[0172] The server sends the user's consultation details to the selected expert via email or message, and the expert provides advice based on the received consultation details and returns the advice to the server.

[0173] Step 9:

[0174] The server receives the advice from the expert and transfers it to the user's terminal, which displays the advice to the user, who can refer to it and use it to progress through the procedure.

[0175] Step 10:

[0176] The server calls the generation AI based on the user's procedure response data and evaluates the cost of the procedure. For example, it sends the following prompt to the generation AI:

[0177] Evaluate the cost of the procedure based on the following data:

[0178] Procedure: Acquisition

[0179] Budget: 5 million yen

[0180] Recommendation: Use of electronic signatures

[0181] The generation AI generates evaluation results and returns them to the server.

[0182] Step 11:

[0183] The server sends the evaluation results and cost reduction proposals to the user's terminal, which displays the evaluation results and proposals, and the user confirms the proposals.

[0184] Step 12:

[0185] If the user accepts the proposal, they select a specific method for proceeding, such as using an electronic signature. The user clicks a selection button, and this information is sent to the server, which automatically sets up the optimal procedure.

[0186] As described above, detailed processing including specific actions, inputs, and outputs is carried out at each step. This system enables users to proceed with M&A procedures efficiently and at low cost.

[0187] (Application example 1)

[0188] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0189] Factory operators are seeking a system that can streamline M&A (mergers and acquisitions) procedures, reduce costs, and allow smooth procedures even when they lack specialized knowledge or experience. In particular, the challenge is to reduce the burden on operators and improve the efficiency of the entire process through procedure automation, document generation, and access to expert networks.

[0190] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0191] In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for generating necessary documents by calling a generation AI based on data collected from the user, means for sending the user's request to an expert network and obtaining advice from appropriate experts, means for evaluating the cost of the procedure and generating cost-reduction proposals, means for providing procedure assistance to a factory operator via a robot, means for viewing and modifying documents generated by the generation AI through the robot's interface, and means for evaluating and optimizing costs related to the factory M&A procedure. This enables efficient M&A procedures and cost reductions even for factory operators who lack specialized knowledge or experience.

[0192] "User information" is basic data about individuals and organizations using the system, including names, contact information, experience level, etc.

[0193] A "procedure assistance wizard" is a guide that assists users with a procedure based on their level of experience, and includes a series of questions and instructions to help users proceed smoothly through the procedure.

[0194] "Generative AI" refers to a system that uses artificial intelligence (AI) to automatically perform specific tasks, in this case, the technology to generate the necessary documents.

[0195] An "expert network" is a collection of individuals or organizations with specialized knowledge and experience in a particular field, from which users can receive appropriate advice by sending a request.

[0196] "Cost evaluation" refers to the process of analyzing and evaluating the costs incurred in a particular procedure or project.

[0197] A "cost reduction proposal" is a proposal that shows specific methods and means for reducing costs based on the results of analysis.

[0198] "Factory operator" means the person or organization responsible for the operation and management of the factory.

[0199] "Robot" means an automated device or system, including those used to assist factory operators in performing procedures.

[0200] "Interface" refers to the means or device for interaction between the user and the system, and in this case documents are viewed and modified through the robot's interface.

[0201] "M&A procedures" refers to the set of procedures and activities related to mergers and acquisitions, including the production of necessary documents and obtaining expert advice.

[0202] "Optimization" refers to the process of making something the most effective and efficient for a particular goal.

[0203] The system for implementing this invention is composed of a server, a terminal, and a user. The server accepts user information, generates a procedure assistance wizard based on the user's experience level, and generates the necessary documents by calling a generation AI based on data collected from the user. It also sends the user's request to an expert network to obtain advice from appropriate experts, evaluates the cost of the procedure, and generates cost-reduction proposals.

[0204] In this system, users can view and edit documents generated by AI through the robot's interface, allowing factory operators to proceed with M&A procedures without interrupting actual factory operations.In addition, the server automatically performs cost evaluations and optimization proposals, reducing the operator's burden.

[0205] The server runs Python programs and stores user data in an SQLite database. The generative AI model includes artificial intelligence algorithms for document generation, expert search, cost evaluation, and optimization. Based on user input, the model generates prompts and executes appropriate actions.

[0206] For example, in a scenario where a user is acquiring a factory, the user registers their name, contact information, and the fact that this is their first M&A experience, and the system displays a wizard to assist with the first-time M&A process. Following the wizard, the user answers the necessary questions, and the system generates an "acquisition agreement" based on the answers. The user reviews the generated agreement and enters any amendments into the system. If expert advice is needed, the user accesses the expert network, and the system introduces an appropriate expert. Finally, the system evaluates the cost of the procedure and suggests that "using electronic signatures would be effective."

[0207] Examples of prompts to input to a generative AI model include:

[0208] "Generate a wizard to assist users in M&A for the first time."

[0209] "Generate a template acquisition agreement based on the user's answers."

[0210] "Please introduce me to an expert on M&A procedures."

[0211] "Generate cost assessments and optimization suggestions related to procedures."

[0212] This system enables efficient and effective M&A procedures to be carried out through cooperation between the server, terminal, and user elements.

[0213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0214] Step 1:

[0215] A user accesses the system and enters basic information (name, contact details, experience level).

[0216] Input: Name, Contact Information, Experience Level.

[0217] Processing: The terminal displays an input form, and the user enters the required information. When the user submits the information, the data is sent from the terminal to the server.

[0218] Output: The server stores the submitted information in a database.

[0219] Step 2:

[0220] The server generates a procedure assistance wizard based on the user information.

[0221] Input: User basic information, experience level.

[0222] Processing: The server obtains the user's experience level from the database and uses generation AI to generate a procedure assistance wizard suitable for the user.

[0223] Output: The generated procedure assistance wizard is sent to the terminal and displayed to the user.

[0224] Step 3:

[0225] The user answers questions according to the procedure assistance wizard.

[0226] Input: The user's answer.

[0227] Processing: The terminal displays the assistance wizard, and the user answers each question. The answer data is sent from the terminal to the server and stored in a database.

[0228] Output: Saved response data.

[0229] Step 4:

[0230] The server calls a generation AI based on the user's response data to generate the necessary documents.

[0231] Input: User response data.

[0232] Processing: The server uses generation AI to analyze the response data and generate the appropriate document (e.g., acquisition agreement).

[0233] Output: The generated document is sent to the terminal and displayed to the user.

[0234] Step 5:

[0235] The user reviews the generated document and corrects it if necessary.

[0236] Input: Generated document, user modifications.

[0237] Processing: The terminal displays the generated document, and the user inputs the modifications. The modified document is sent from the terminal to the server and stored in the database.

[0238] Output: The modified document is saved.

[0239] Step 6:

[0240] A user requests expert advice.

[0241] Input: Request for advice.

[0242] Processing: The device sends the user's advice request to the server. The server uses the generative AI to select an appropriate expert and send the request.

[0243] Output: The advice from the expert is sent to the terminal via the server and displayed to the user.

[0244] Step 7:

[0245] The server evaluates the cost of the procedure and generates cost-saving suggestions.

[0246] Input: User response data, procedural data.

[0247] Processing: The server uses the generation AI to perform cost assessment and generate cost reduction proposals.

[0248] Output: The evaluation results and cost reduction proposals are sent from the server to the terminal and displayed to the user.

[0249] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0250] The purpose of this invention is to improve the user experience by combining an emotion engine with a system that uses generative AI to support M&A procedures. Specific embodiments for implementing this system are described below.

[0251] User Registration

[0252] A user accesses the system.

[0253] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[0254] The user enters the necessary information into the input form and submits it.

[0255] The server stores the transmitted information in a database.

[0256] Providing a procedure assistance wizard

[0257] Based on the information entered by the user, the server generates a procedure assistance wizard suited to the user's experience level.

[0258] The terminal displays the wizard's interface to the user.

[0259] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[0260] The server collects the user's answers and then activates an emotion engine to recognize the user's emotions.

[0261] The emotion engine collects emotional data from the user's facial expressions, tone of voice, etc.

[0262] The server stores the collected response data and emotion data in a database.

[0263] Automatic Document Generation

[0264] The server calls the generation AI based on the user response data collected.

[0265] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[0266] The server sends the generated document to the user's terminal.

[0267] The terminal displays the generated document to the user.

[0268] The user checks the displayed document and corrects it if necessary.

[0269] The server saves the modified document to the database.

[0270] Access to expert networks

[0271] The user requests "additional expert advice" within the wizard.

[0272] The server uses generated AI to select the most suitable expert.

[0273] The emotion engine takes into account the user's emotional data and routes the request to the appropriate expert.

[0274] An expert will receive the user's inquiry and provide advice.

[0275] The server transfers the advice from the expert to the user's terminal.

[0276] Cost evaluation and optimization

[0277] The server calls the generation AI based on the user's procedural data and emotional data.

[0278] The generative AI evaluates the cost of each procedure and generates suggestions for reducing costs (for example, reducing costs by digitizing documents).

[0279] The server sends the evaluation results and suggestions to the user's terminal.

[0280] The terminal displays the evaluation results and suggestions to the user.

[0281] If the user accepts the proposal, they can proceed based on the proposal, for example, by choosing to use an electronic signature.

[0282] Specific examples

[0283] User Registration

[0284] A user logs into the system and enters their name and contact information.

[0285] The server stores this information in a database.

[0286] Providing a procedure assistance wizard

[0287] The server generates a wizard that says, "We will support your first M&A procedure."

[0288] The terminal displays a wizard and asks, "What type of M&A transaction would you like to undertake?"

[0289] The user selects "Acquisition" and presses the "Next" button.

[0290] The server collects the user's answers in the wizard and activates the emotion engine to obtain emotion data.

[0291] The emotion engine analyzes the user's emotions and determines the appropriate next question or response.

[0292] Automatic Document Generation

[0293] The server calls a generation AI based on the user's answers and emotional data, and generates an "acquisition contract."

[0294] The server sends the generated document to the user's terminal for display.

[0295] The user checks the document and clicks the save button without making any changes.

[0296] The server stores the saved document in a database.

[0297] Access to expert networks

[0298] The user requests advice on the details of the contract terms.

[0299] The server uses generative AI and an emotion engine to select the most suitable expert and send the request.

[0300] An expert will receive the user's inquiry and provide advice.

[0301] The server transfers the advice from the expert to the user's terminal and displays it.

[0302] Cost evaluation and optimization

[0303] The server calls a generative AI based on user data and emotion data and evaluates the cost of the procedure.

[0304] The generative AI generates a suggestion that "using electronic signatures will reduce costs."

[0305] The server sends the proposal to the user's terminal for display.

[0306] The user reviews the proposal and selects "Use electronic signature" to proceed.

[0307] This system makes it possible to proceed with the process while taking into consideration the user's feelings, resulting in more user-friendly M&A support.

[0308] The processing flow will be explained below.

[0309] Step 1:

[0310] A user accesses the system.

[0311] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[0312] Step 2:

[0313] The user enters the required information into the input form and submits it.

[0314] The server stores the transmitted information in a database.

[0315] Step 3:

[0316] The server generates a procedure assistance wizard suited to the user's experience level based on the user's basic information.

[0317] The terminal displays the wizard's interface to the user.

[0318] Step 4:

[0319] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[0320] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[0321] The server collects user response data and emotion data and stores them in a database.

[0322] Step 5:

[0323] The server calls the generation AI based on the user response data collected.

[0324] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[0325] Step 6:

[0326] The server sends the generated document to the user's terminal.

[0327] The terminal displays the generated document to the user.

[0328] Step 7:

[0329] The user reviews the displayed document and makes any necessary corrections, for example by modifying a particular clause.

[0330] The server saves the modified document to the database.

[0331] Step 8:

[0332] The user requests "additional expert advice" within the wizard.

[0333] The emotion engine determines the urgency of the request based on the user's emotion data.

[0334] The server uses generated AI to select the most suitable expert.

[0335] Step 9:

[0336] The server transmits the user's consultation details to the specialist and also provides emotion data at the same time.

[0337] Experts receive the user's consultation details and emotional data and provide advice.

[0338] The server transfers the advice from the expert to the user's terminal.

[0339] Step 10:

[0340] The terminal displays expert advice to the user.

[0341] The user confirms the displayed advice.

[0342] Step 11:

[0343] The server calls the generation AI based on the user's procedural data and emotional data.

[0344] Generative AI evaluates the costs of procedures and generates proposals for cost reduction (e.g., cost reduction through digitization of documents).

[0345] Step 12:

[0346] The server sends the proposal to the user's terminal.

[0347] The terminal displays the suggestions to the user.

[0348] The user reviews the proposal and makes a selection if necessary, for example, choosing to use electronic signatures.

[0349] Example 2

[0350] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0351] Conventional M&A procedure support systems only provide uniform procedural support without considering the user's experience level or emotional state, which has the drawback of not improving the user experience. Furthermore, document generation and expert support are handled without considering the user's emotions, which increases the stress and anxiety felt by the user and prevents the procedure from progressing smoothly. Furthermore, the system lacks the functionality to evaluate and optimize the overall cost of the procedure.

[0352] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0353] In this invention, the server includes means for accepting user information, means for generating a procedure support wizard based on the user's experience level, means for generating necessary documents by calling a generation AI based on data and emotion data collected from the user, means for sending the user's request to an expert network and obtaining advice from appropriate experts, means for evaluating the cost of the procedure and generating cost reduction proposals, and means including an emotion engine that recognizes the user's emotions and responds appropriately. This enables smooth and efficient support for M&A procedures while improving the user experience.

[0354] "User information" is basic data such as name, contact details, and level of experience that users enter into the system.

[0355] The "procedure assistance wizard" is an interface that is generated based on the user's experience level and that continuously presents questions and guidance necessary for the procedure.

[0356] "Generative AI" refers to artificial intelligence technology that automatically generates necessary legal and procedural documents based on user response data.

[0357] "Emotion data" is information about emotions collected from the user's facial expressions, tone of voice, and the like.

[0358] An "emotion engine" refers to an analysis system that recognizes a user's emotions and responds appropriately accordingly.

[0359] An "expert network" is a collection of people or organizations with expertise in a particular field, and is a system that provides advice in response to user requests.

[0360] "Cost assessment" is the process of measuring the costs of each procedure and making proposals for cost reduction based on the results.

[0361] A "database" is a system for storing collected user information, emotional data, generated documents, etc.

[0362] This invention is a system for supporting M&A procedures, which utilizes a combination of generative AI and an emotion engine to improve the user experience. A specific embodiment of this system will be described below.

[0363] User Registration

[0364] A user accesses the system and begins registration.

[0365] The terminal displays a basic information input form (e.g., name, contact information, level of experience) to the user on the browser. This is implemented using HTML forms and JavaScript (registered trademark).

[0366] The user fills in the form with the required information and clicks the submit button, which sends the data as an HTTP POST request.

[0367] The server analyzes the received data (for example, using Python's Flask or Django) and stores it in a database (for example, MySQL (registered trademark) or PostgreSQL).

[0368] Providing a procedure assistance wizard

[0369] The server generates a procedure assistance wizard based on the user's experience level. The generated wizard displays, for example, "We will support you in your first M&A procedure."

[0370] The device dynamically displays the wizard interface using JavaScript and React.

[0371] The user answers each question in the wizard, for example, "What is the purpose of your transaction?"

[0372] The server receives the user's answer and activates the emotion engine, which is implemented using OpenCV and the Facial Emotion Recognition API.

[0373] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data and return it to the server.

[0374] The server stores the response data and emotion data in a database.

[0375] Automatic Document Generation

[0376] The server calls the AI ​​generator based on the user's response data. For example, the following sentences can be used as prompts:

[0377] "Generate the necessary acquisition agreements based on your name and contact information to help you with your first M&A transaction."

[0378] The generation AI generates the necessary legal documents (e.g., acquisition agreements) based on the prompts and returns them to the server. The generation AI uses OpenAI GPT and Google® BERT.

[0379] The server sends the generated document to the user's device, which displays it, for example, by email or via a digital download link.

[0380] The user reviews the document and makes any necessary corrections, using the GUI of a PDF viewer or word processor to view and edit the document.

[0381] The server saves the modified document back into the database.

[0382] Access to expert networks

[0383] The user requests "additional expert advice" through the interface within the wizard.

[0384] The server uses generative AI and an emotion engine to analyze the request content and select the appropriate expert.

[0385] The emotion engine considers the request content and the user's emotion data and sends the request to the appropriate expert.

[0386] Experts provide advice based on requests received.

[0387] The server transfers the advice from the expert to the user's terminal, which displays it.

[0388] Cost evaluation and optimization

[0389] The server calls a generation AI based on the user's procedure data and emotion data, and evaluates the cost of the procedure.

[0390] The generative AI generates specific cost reduction proposals (for example, cost reductions through the use of electronic signatures) along with the cost assessment results.

[0391] The server sends the evaluation results and suggestions to the user's terminal, which displays them.

[0392] The user reviews the proposal and, if they agree, proceeds to the next step, for example, choosing to use an electronic signature.

[0393] This system will enable the provision of efficient and user-friendly M&A procedural support, taking into consideration the user's experience and feelings.

[0394] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0395] Step 1: Beginning user registration

[0396] A user accesses the system and begins registration by entering basic information such as the user's name, contact details, and experience level.

[0397] The device displays a basic information input form in the browser. This form is written in HTML and JavaScript.

[0398] Step 2: Submit user information

[0399] The user fills in the form with the required information and clicks the submit button. Input data includes inputs such as name, contact details, experience level, etc.

[0400] The device sends the form data to the server as an HTTP POST request.

[0401] Step 3: Save user information

[0402] The server parses the received data and extracts information such as name, contact details, experience level, etc. This is done using Python's Flask or Django.

[0403] The server stores the parsed data in a database (e.g., MySQL or PostgreSQL), and generates database records as output.

[0404] Step 4: Generate the procedure assistance wizard

[0405] The server retrieves the user's experience level from the database and generates a procedure assistance wizard based on that experience level.

[0406] Create a prompt for the server-generated wizard and generate interface data for the wizard as output data. The prompt will say, "We will support you in your first M&A procedure."

[0407] Step 5: Display the Wizard

[0408] The device uses JavaScript and React to display the wizard interface to the user. The input data is the interface data received from the server.

[0409] The user answers the wizard's questions, such as "What is the purpose of the transaction?"

[0410] Step 6: Collect and analyze user responses

[0411] The server receives and analyzes the user's answers through the wizard, including data such as experience level and trading objectives.

[0412] The server runs the emotion engine, which uses OpenCV and the Facial Emotion Recognition API.

[0413] Step 7: Collect emotion data

[0414] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotion data, and uses the user's camera footage and voice as input data.

[0415] The server receives the emotion data and stores the user's response data and emotion data in a database. As an output, the analysis results are stored in the database.

[0416] Step 8: Invoke automatic document generation

[0417] The server sends a prompt to the generation AI based on the user's response data, such as "To support your first M&A procedure, please generate the necessary acquisition agreement based on the user's name and contact information."

[0418] The input data is the user's response data, and the output data is a prompt sentence sent to the generation AI.

[0419] Step 9: Generate Documents

[0420] The generation AI generates the necessary legal documents (e.g., acquisition contracts) based on the prompt sentences and returns them to the server. The generation AI uses OpenAI GPT and Google BERT.

[0421] The server receives the generated document and sends it to the user's device. The input data is the document data from the generation AI, and the output data is the document data sent to the user's device.

[0422] Step 10: View and modify the document

[0423] The terminal displays the generated document to the user, using a PDF viewer or a word processor GUI.

[0424] The user reviews the document and makes corrections as necessary. The input data are the user's corrections, and the corrected document is generated as output data.

[0425] Step 11: Save the revised document

[0426] The server saves the modified document to the database. The input data is the modified document, and the output data is the updated database record.

[0427] Step 12: Request from your expert network

[0428] The user requests "additional expert advice" through the wizard interface. The input data is the user's request.

[0429] The server analyzes the request using the generative AI and emotion engine, and selects the appropriate expert. The output data is the selected expert.

[0430] Step 13: Providing expert advice

[0431] The emotion engine considers the request content and the user's emotion data and sends the request to the appropriate expert. The input data is the user's emotion data.

[0432] The expert provides advice based on the received request, and the output data is the advice.

[0433] Step 14: Forwarding Advice

[0434] The server receives advice from the expert and transfers it to the user's terminal. The input data is the expert's advice, and the output data is the data transferred to the user's terminal.

[0435] The device displays the advice.

[0436] Step 15: Cost assessment and proposal

[0437] The server calls the generation AI based on the user's procedure data and emotion data, and evaluates the cost of the procedure. The input data is the user's procedure data and emotion data.

[0438] The generation AI generates specific cost reduction proposals along with the cost assessment results. The output data is the cost reduction proposals.

[0439] Step 16: View cost assessment results and recommendations

[0440] The server sends the evaluation results and proposals to the user's terminal, which displays them. The input data are the cost evaluation results and proposals.

[0441] The user reviews the proposal and, if they agree, proceeds to the next step, for example, choosing to use an electronic signature.

[0442] This will enable smooth and efficient support for M&A procedures that are tailored to the user's experience and emotions.

[0443] (Application example 2)

[0444] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0445] There is a need to improve the passenger experience in autonomous vehicles. Specifically, the challenge is to analyze passenger emotions in real time and provide appropriate services to make them feel safe and comfortable. Providing services that meet the individual needs of passengers is also an important element.

[0446] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for calling a generation AI based on data collected from the user to generate the required document, means for collecting and analyzing the user's emotions, means for proposing services based on the emotions, means for sending the user's request to an expert network and obtaining advice from appropriate experts, and means for evaluating the cost of the procedure and generating cost-reduction suggestions. This makes it possible to analyze passenger emotions in real time and provide comfortable services tailored to individual needs.

[0447] - "User information receiving means" is a function that allows the user to input basic information such as name, contact information, and experience level, and stores this data on the cloud server.

[0448] The "procedure assistance wizard generation means" is a function for generating a wizard that provides appropriate procedures and guidance based on the user's experience level and input information.

[0449] The "document generation means" is a function that calls a generation AI based on data collected from the user and automatically generates the necessary documents.

[0450] "Emotion collection and analysis means" is a function for analyzing the user's facial expressions and tone of voice to collect and analyze emotional data.

[0451] The "service suggestion means" is a function for suggesting optimal services and information to users based on collected and analyzed emotional data.

[0452] The "expert network access means" is a function for transmitting a user's request to the expert network and obtaining advice from an appropriate expert.

[0453] The "cost evaluation means" is a function for evaluating the cost of a procedure and generating cost reduction proposals based on the results.

[0454] overview

[0455] This invention relates to a smartphone application called "Smart Drive Assistant" designed to improve the passenger experience in autonomous vehicles. The system combines emotion collection and analysis methods with generative AI to provide real-time services tailored to passengers' emotions and needs.

[0456] Hardware and software used

[0457] Hardware: smartphone, in-vehicle camera, microphone

[0458] Software: emotion recognition engines (e.g., Affectiva), generative AI models (e.g., OpenAI GPT-4 (registered trademark)), mobile app development frameworks (e.g., React Native)

[0459] Data processing and calculation

[0460] 1. Acceptance of user information

[0461] The server prompts the user to enter basic information such as name, contact details, and experience level, and stores this data on the cloud server. The user enters this information via a smartphone app.

[0462] 2. Emotional Data Collection

[0463] The device (smartphone) uses cameras and microphones installed in the vehicle to capture passengers' facial expressions and tone of voice, and the collected data is analyzed by an emotion recognition engine to generate emotion data.

[0464] 3. Service proposal

[0465] Based on the emotion data and the riding situation (destination, expected arrival time, etc.), the server sends prompts to the generative AI model to generate optimal services and information for the user. The generated service suggestions are displayed to the user via a smartphone app.

[0466] 4. Feedback Loop

[0467] By providing feedback on the proposed services and information from users, the server can update the generative AI model and improve the accuracy of future suggestions.

[0468] Specific examples

[0469] User Registration

[0470] A user launches the app for the first time and enters information such as their name, contact information, and frequent destinations.

[0471] Emotional Data Collection

[0472] When a user gets into an autonomous vehicle, an in-car camera captures their facial expressions and a microphone records their conversation. An emotion recognition engine analyzes this data to identify the user's current emotion (e.g., nervousness, anxiety, joy).

[0473] Service proposal

[0474] If an autonomous vehicle is traveling through rush hour and the server detects that the user is nervous, it will use a generative AI model to suggest relaxing music or information about nearby cafes.

[0475] feedback

[0476] If the user is satisfied with the suggestions, they provide feedback, which improves the accuracy of the suggestions in future.

[0477] Example prompts to input to the generative AI model

[0478] "Users are feeling a bit uneasy."

[0479] "Our destination is 5km away and it will take approximately 15 minutes to arrive."

[0480] "Given this situation, what services should we offer our users?"

[0481] This will enable real-time analysis of passenger emotions and the provision of comfortable services tailored to individual needs.

[0482] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0483] Step 1:

[0484] Users launch the smartphone app and enter basic information such as their name, contact details, and experience level.

[0485] The input information is transmitted from the terminal to the server.

[0486] The server stores this information in a database located in the cloud.

[0487] Input: User basic information (name, contact details, experience level)

[0488] Output: Basic information stored in a cloud database

[0489] Step 2:

[0490] When a user gets into a self-driving vehicle, the cameras and microphones inside the vehicle start working.

[0491] The device captures the user's facial expressions and voice through a camera and microphone.

[0492] The collected data is transmitted from the terminal to a server.

[0493] Input: User's facial expression data, voice data

[0494] Output: Emotion data sent to the server

[0495] Step 3:

[0496] The server receives the emotion data and analyzes it using an emotion recognition engine.

[0497] The user's current emotional state is analyzed and output as emotional data.

[0498] Input: Emotion data sent to the server

[0499] Output: Analyzed emotion data (e.g., anxiety, tension, joy, etc.)

[0500] Step 4:

[0501] The server sends the ride status, including emotion data, destination information, expected arrival time, etc., to the generative AI model.

[0502] A generative AI model generates optimal service suggestions based on these prompts.

[0503] The generated service offer is returned to the server.

[0504] Input: Emotion data, riding situation (destination information, expected arrival time, etc.)

[0505] Output: Service proposals from the generative AI model

[0506] Step 5:

[0507] The server transmits the generated service offer to the terminal.

[0508] The terminal displays the proposed services and information to the user.

[0509] Input: Service proposals from a generative AI model

[0510] Output: Service proposals displayed on the user's smartphone app

[0511] Step 6:

[0512] Users provide feedback on the proposed services through a smartphone app.

[0513] The feedback is sent from the terminal to the server.

[0514] The server uses this feedback to update the generative AI model and improve the accuracy of its suggestions.

[0515] Input: User feedback

[0516] Output: Updated generative AI model

[0517] This process will enable passengers' emotions to be analyzed in real time, making it possible to provide comfortable services tailored to their individual needs.

[0518] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0519] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0520] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0521] [Second embodiment]

[0522] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0523] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0524] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0525] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0526] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0527] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0528] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0529] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0530] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0531] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0532] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0533] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0534] The present invention relates to a system using a generative AI for supporting M&A procedures. Specific embodiments for implementing this system will be described below.

[0535] User Registration

[0536] A user accesses the system.

[0537] The terminal displays a form for the user to enter basic information such as name, contact details, and level of experience.

[0538] The user enters the necessary information into the input form and submits it.

[0539] The server stores the transmitted information in a database.

[0540] Providing a procedure assistance wizard

[0541] The server generates a procedure assistance wizard that matches the user's experience level based on the information entered by the user.

[0542] The terminal displays the interface of this wizard to the user.

[0543] The user answers the wizard's questions, for example, "What is the purpose of the transaction?"

[0544] The server collects the user's answers and stores them in a database for use in the next step.

[0545] Automatic Document Generation

[0546] The server calls the generation AI based on the collected user response data.

[0547] Generative AI generates templates for required legal and procedural documents (e.g., acquisition agreements).

[0548] The server transmits the generated document to the user's terminal.

[0549] The terminal displays the generated document to the user.

[0550] The user checks the displayed document and corrects it if necessary.

[0551] When the user modifies the document and presses the save button, the server saves the modified document in the database.

[0552] Access to expert networks

[0553] The user requests "additional expert advice" within the wizard.

[0554] The server receives this request and uses generative AI to select the most suitable expert.

[0555] The server transmits the user's consultation details to the expert.

[0556] The expert receives the user's inquiry and provides advice.

[0557] The server forwards this advice to the user's terminal.

[0558] The terminal displays the expert advice to the user.

[0559] Cost evaluation and optimization

[0560] The server calls the generation AI based on the user's procedure response data.

[0561] The generative AI evaluates the costs of procedures and generates proposals for cost reduction (e.g., cost reduction through digitization of documents).

[0562] The server sends the evaluation results and suggestions to the user's terminal.

[0563] The terminal displays the evaluation results and suggestions to the user.

[0564] If the user accepts the offer, they can choose the most appropriate method for proceeding, for example, by using an electronic signature.

[0565] Specific examples

[0566] User Registration

[0567] A user logs into the system and enters their name and contact information.

[0568] The server stores this information in a database.

[0569] Providing a procedure assistance wizard

[0570] The server generates a wizard that displays, "We will support you in your first M&A procedure."

[0571] The terminal displays a wizard and asks, "What type of M&A transaction would you like to undertake?"

[0572] The user selects "Acquisition" and presses the "Next" button.

[0573] The server stores the answer in a database and generates the next question.

[0574] Automatic Document Generation

[0575] The server calls a generation AI based on the user's answers and generates an "acquisition agreement."

[0576] The file is sent to the user's terminal and displayed.

[0577] The user checks the document and makes any necessary corrections.

[0578] The server stores the saved documents in a database.

[0579] Access to expert networks

[0580] The user requests advice on the details of the contract terms.

[0581] The server uses generative AI to select the most suitable expert.

[0582] The server sends a request to the selected expert and obtains advice.

[0583] The terminal displays the advice from the expert to the user.

[0584] Cost evaluation and optimization

[0585] The server calls a generation AI based on the user's data and evaluates the cost of the procedure.

[0586] The generative AI generates a suggestion that "using electronic signatures will reduce costs."

[0587] The server sends the proposal to the user's terminal.

[0588] The user reviews the proposal and selects "Use electronic signature" to proceed.

[0589] In this manner, the present invention provides a system that simplifies the M&A process and reduces costs by minimizing the use of experts.

[0590] The processing flow will be explained below.

[0591] Step 1:

[0592] A user accesses the system.

[0593] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[0594] Step 2:

[0595] The user enters the required information into the input form and submits it.

[0596] The server stores the transmitted information in a database.

[0597] Step 3:

[0598] The server generates a procedure assistance wizard that matches the user's experience level based on the information input by the user.

[0599] The terminal displays the wizard's interface to the user.

[0600] Step 4:

[0601] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[0602] The server collects the user's answers and stores them in a database for use in the next step.

[0603] Step 5:

[0604] The server calls the generation AI based on the user response data collected.

[0605] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[0606] Step 6:

[0607] The server sends the generated document to the user's terminal.

[0608] The terminal displays the generated document to the user.

[0609] Step 7:

[0610] The user checks the displayed document and corrects it if necessary.

[0611] The server saves the modified document to the database.

[0612] Step 8:

[0613] The user requests "additional expert advice" within the wizard.

[0614] The server uses generated AI to select the most suitable expert.

[0615] Step 9:

[0616] The server transmits the user's consultation details to the expert.

[0617] An expert will receive the user's inquiry and provide advice.

[0618] The server transfers the advice from the expert to the user's terminal.

[0619] Step 10:

[0620] The terminal displays expert advice to the user.

[0621] The user reviews the proposal and takes action if necessary.

[0622] Step 11:

[0623] The server calls the generation AI based on the user's procedural data.

[0624] Generative AI assesses the costs of procedures and generates cost-saving proposals.

[0625] Step 12:

[0626] The server sends the proposal to the user's terminal.

[0627] The terminal displays the suggestions to the user.

[0628] If the user accepts the proposal, the system will select the most suitable method and proceed with the procedure.

[0629] Example 1

[0630] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0631] Traditional M&A procedures are complicated and require extensive specialized knowledge and expensive expert fees. As a result, the procedures are often difficult and costly, especially for beginners and small companies. In addition, the preparation of necessary documents and cost assessments are often time-consuming, hindering efficient progress.

[0632] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0633] In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for generating the necessary documents by calling a generation AI based on data collected from the user, means for transmitting the generated documents to the user's terminal and displaying them to the user, means for the user to modify and save the generated documents, means for transmitting the user's request to an expert network and obtaining advice from appropriate experts, and means for evaluating the cost of the procedures and generating cost reduction proposals. This enables users to proceed with M&A procedures efficiently and at low cost even if they do not have specialized knowledge.

[0634] "User information" refers to basic information such as the name, contact details, and level of experience of the user accessing the system.

[0635] "Procedure Assistance Wizard" means an interface that provides a series of questions and instructions to assist a user in the M&A process, generated based on the user's experience level.

[0636] "Generative AI" refers to artificial intelligence that automatically generates necessary documents based on data collected from users.

[0637] "Expert Network" means a collection of experts within the system to provide appropriate expert advice based on a user's request.

[0638] "Cost assessment" means the process of assessing the costs of a procedure and making recommendations to reduce those costs in the most appropriate manner.

[0639] "Documents" means legal and procedural documents required for the M&A process (e.g., acquisition agreement).

[0640] "Terminal" means a device such as a computer or smartphone that a user uses to access the system and perform operations or input data.

[0641] "Database" means a digital storage device for storing user and procedural information.

[0642] The present invention relates to a system using a generative AI for supporting M&A procedures. A specific embodiment of this system will be described below.

[0643] The user first accesses the system using a device such as a personal computer or smartphone. The user enters basic information such as their name, contact details, and level of experience into an input form and submits it. The device sends this information to the server, which then stores it in a database.

[0644] The server then generates a procedural assistance wizard based on the user's experience level. This wizard is created using a front-end framework such as React or Vue.js and is displayed on the device. The wizard presents the user with questions such as "What is the purpose of the transaction?" and collects the user's answers.

[0645] Based on the collected data, the server calls a generative AI to generate the required document. The generative AI used here is an AI model such as OpenAI's GPT-3. An example of a prompt is as follows:

[0646] The user has selected Acquisition. Now generate the required Acquisition Agreement template.

[0647] Transaction objective: Entering new markets

[0648] Based on this prompt, the generation AI generates a template for the acquisition agreement. The generated document is sent from the server to the user's device and displayed on the device. The user checks the document and makes any necessary corrections. The corrected document is saved by the user and then saved back to the database by the server.

[0649] Furthermore, if the user requests additional expert advice, the server uses the generation AI to select the most suitable expert. The user's consultation details are sent to the selected expert, who then provides advice. The provided advice is transferred to the user's device via the server and displayed to the user.

[0650] Finally, the server calls the generation AI based on the collected user procedure response data to evaluate the cost of the procedure. The generation AI generates proposals for cost reduction and sends the evaluation results and proposals to the user's device. The user reviews the displayed proposals and, if they accept them, selects the optimal method. For example, they can choose to use an electronic signature.

[0651] In this manner, the present invention provides a system that simplifies the M&A process and reduces costs by minimizing the use of experts.

[0652] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0653] Detailed explanation of the program's processing flow

[0654] Step 1:

[0655] A user accesses the system. The terminal displays an input form for basic information such as name, contact details, and level of experience to the user. The user enters the basic information into the input form and presses the submit button. Based on this input, the terminal sends the data to the server, which stores it in a database.

[0656] Step 2:

[0657] The server generates a procedure assistance wizard based on the user's experience level. In this case, the server generates a wizard containing questions and instructions for beginners based on the information entered by the user. The terminal displays the wizard interface to the user and waits for the user's response. For example, the question "What type of M&A procedure do you want to carry out?" is displayed. The user's input is saved in a database, and the data becomes the output for the next step.

[0658] Step 3:

[0659] The user answers the wizard's questions. The user enters their answers into the input form and clicks "Next." The server receives this data and stores it in a database. This data is used to understand the user's specific needs.

[0660] Step 4:

[0661] The server calls the generative AI based on the collected user response data. For example, if the answer in the previous step was "acquisition," the server sends the following prompt to the generative AI model:

[0662] The user has selected Acquisition. Now generate the required Acquisition Agreement template.

[0663] Transaction objective: Entering new markets

[0664] The generation AI generates the necessary legal and procedural documents based on these prompts and returns the output to the server.

[0665] Step 5:

[0666] The server sends the generated document to the user's terminal. The terminal displays the received document file to the user. For example, the document is displayed using a PDF viewer or text editor. The user checks the displayed document and makes corrections as necessary. When the user presses the save button, the corrected document is sent back to the server.

[0667] Step 6:

[0668] When a user saves a modified document, the server stores the document in a database, making it valuable information for future reference.

[0669] Step 7:

[0670] When a user requests additional expert advice, the server uses the generated AI to select the most suitable expert. The server analyzes the expert profiles using the generated AI and selects the most suitable expert.

[0671] Step 8:

[0672] The server sends the user's consultation details to the selected expert via email or message, and the expert provides advice based on the received consultation details and returns the advice to the server.

[0673] Step 9:

[0674] The server receives the advice from the expert and transfers it to the user's terminal, which displays the advice to the user, who can refer to it and use it to progress through the procedure.

[0675] Step 10:

[0676] The server calls the generation AI based on the user's procedure response data and evaluates the cost of the procedure. For example, it sends the following prompt to the generation AI:

[0677] Evaluate the cost of the procedure based on the following data:

[0678] Procedure: Acquisition

[0679] Budget: 5 million yen

[0680] Recommendation: Use of electronic signatures

[0681] The generation AI generates evaluation results and returns them to the server.

[0682] Step 11:

[0683] The server sends the evaluation results and cost reduction proposals to the user's terminal, which displays the evaluation results and proposals, and the user confirms the proposals.

[0684] Step 12:

[0685] If the user accepts the proposal, they select a specific method for proceeding, such as using an electronic signature. The user clicks a selection button, and this information is sent to the server, which automatically sets up the optimal procedure.

[0686] As described above, detailed processing including specific actions, inputs, and outputs is carried out at each step. This system enables users to proceed with M&A procedures efficiently and at low cost.

[0687] (Application example 1)

[0688] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0689] Factory operators are seeking a system that can streamline M&A (mergers and acquisitions) procedures, reduce costs, and allow smooth procedures even when they lack specialized knowledge or experience. In particular, the challenge is to reduce the burden on operators and improve the efficiency of the entire process through procedure automation, document generation, and access to expert networks.

[0690] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0691] In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for generating necessary documents by calling a generation AI based on data collected from the user, means for sending the user's request to an expert network and obtaining advice from appropriate experts, means for evaluating the cost of the procedure and generating cost-reduction proposals, means for providing procedure assistance to a factory operator via a robot, means for viewing and modifying documents generated by the generation AI through the robot's interface, and means for evaluating and optimizing costs related to the factory M&A procedure. This enables efficient M&A procedures and cost reductions even for factory operators who lack specialized knowledge or experience.

[0692] "User information" is basic data about individuals and organizations using the system, including names, contact information, experience level, etc.

[0693] A "procedure assistance wizard" is a guide that assists users with a procedure based on their level of experience, and includes a series of questions and instructions to help users proceed smoothly through the procedure.

[0694] "Generative AI" refers to a system that uses artificial intelligence (AI) to automatically perform specific tasks, in this case, the technology to generate the necessary documents.

[0695] An "expert network" is a collection of individuals or organizations with specialized knowledge and experience in a particular field, from which users can receive appropriate advice by sending a request.

[0696] "Cost evaluation" refers to the process of analyzing and evaluating the costs incurred in a particular procedure or project.

[0697] A "cost reduction proposal" is a proposal that shows specific methods and means for reducing costs based on the results of analysis.

[0698] "Factory operator" means the person or organization responsible for the operation and management of the factory.

[0699] "Robot" means an automated device or system, including those used to assist factory operators in performing procedures.

[0700] "Interface" refers to the means or device for interaction between the user and the system, and in this case documents are viewed and modified through the robot's interface.

[0701] "M&A procedures" refers to the set of procedures and activities related to mergers and acquisitions, including the production of necessary documents and obtaining expert advice.

[0702] "Optimization" refers to the process of making something the most effective and efficient for a particular goal.

[0703] The system for implementing this invention is composed of a server, a terminal, and a user. The server accepts user information, generates a procedure assistance wizard based on the user's experience level, and generates the necessary documents by calling a generation AI based on data collected from the user. It also sends the user's request to an expert network to obtain advice from appropriate experts, evaluates the cost of the procedure, and generates cost-reduction proposals.

[0704] In this system, users can view and edit documents generated by AI through the robot's interface, allowing factory operators to proceed with M&A procedures without interrupting actual factory operations.In addition, the server automatically performs cost evaluations and optimization proposals, reducing the operator's burden.

[0705] The server runs Python programs and stores user data in an SQLite database. The generative AI model includes artificial intelligence algorithms for document generation, expert search, cost evaluation, and optimization. Based on user input, the model generates prompts and executes appropriate actions.

[0706] For example, in a scenario where a user is acquiring a factory, the user registers their name, contact information, and the fact that this is their first M&A experience, and the system displays a wizard to assist with the first-time M&A process. Following the wizard, the user answers the necessary questions, and the system generates an "acquisition agreement" based on the answers. The user reviews the generated agreement and enters any amendments into the system. If expert advice is needed, the user accesses the expert network, and the system introduces an appropriate expert. Finally, the system evaluates the cost of the procedure and suggests that "using electronic signatures would be effective."

[0707] Examples of prompts to input to a generative AI model include:

[0708] "Generate a wizard to assist users in M&A for the first time."

[0709] "Generate a template acquisition agreement based on the user's answers."

[0710] "Please introduce me to an expert on M&A procedures."

[0711] "Generate cost assessments and optimization suggestions related to procedures."

[0712] This system enables efficient and effective M&A procedures to be carried out through cooperation between the server, terminal, and user elements.

[0713] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0714] Step 1:

[0715] A user accesses the system and enters basic information (name, contact details, experience level).

[0716] Input: Name, Contact Information, Experience Level.

[0717] Processing: The terminal displays an input form, and the user enters the required information. When the user submits the information, the data is sent from the terminal to the server.

[0718] Output: The server stores the submitted information in a database.

[0719] Step 2:

[0720] The server generates a procedure assistance wizard based on the user information.

[0721] Input: User basic information, experience level.

[0722] Processing: The server obtains the user's experience level from the database and uses generation AI to generate a procedure assistance wizard suitable for the user.

[0723] Output: The generated procedure assistance wizard is sent to the terminal and displayed to the user.

[0724] Step 3:

[0725] The user answers questions according to the procedure assistance wizard.

[0726] Input: The user's answer.

[0727] Processing: The terminal displays the assistance wizard, and the user answers each question. The answer data is sent from the terminal to the server and stored in a database.

[0728] Output: Saved response data.

[0729] Step 4:

[0730] The server calls a generation AI based on the user's response data to generate the necessary documents.

[0731] Input: User response data.

[0732] Processing: The server uses generation AI to analyze the response data and generate the appropriate document (e.g., acquisition agreement).

[0733] Output: The generated document is sent to the terminal and displayed to the user.

[0734] Step 5:

[0735] The user reviews the generated document and corrects it if necessary.

[0736] Input: Generated document, user modifications.

[0737] Processing: The terminal displays the generated document, and the user inputs the modifications. The modified document is sent from the terminal to the server and stored in the database.

[0738] Output: The modified document is saved.

[0739] Step 6:

[0740] A user requests expert advice.

[0741] Input: Request for advice.

[0742] Processing: The device sends the user's advice request to the server. The server uses the generative AI to select an appropriate expert and send the request.

[0743] Output: The advice from the expert is sent to the terminal via the server and displayed to the user.

[0744] Step 7:

[0745] The server evaluates the cost of the procedure and generates cost-saving suggestions.

[0746] Input: User response data, procedural data.

[0747] Processing: The server uses the generation AI to perform cost assessment and generate cost reduction proposals.

[0748] Output: The evaluation results and cost reduction proposals are sent from the server to the terminal and displayed to the user.

[0749] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0750] The purpose of this invention is to improve the user experience by combining an emotion engine with a system that uses generative AI to support M&A procedures. Specific embodiments for implementing this system are described below.

[0751] User Registration

[0752] A user accesses the system.

[0753] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[0754] The user enters the necessary information into the input form and submits it.

[0755] The server stores the transmitted information in a database.

[0756] Providing a procedure assistance wizard

[0757] Based on the information entered by the user, the server generates a procedure assistance wizard suited to the user's experience level.

[0758] The terminal displays the wizard's interface to the user.

[0759] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[0760] The server collects the user's answers and then activates an emotion engine to recognize the user's emotions.

[0761] The emotion engine collects emotional data from the user's facial expressions, tone of voice, etc.

[0762] The server stores the collected response data and emotion data in a database.

[0763] Automatic Document Generation

[0764] The server calls the generation AI based on the user response data collected.

[0765] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[0766] The server sends the generated document to the user's terminal.

[0767] The terminal displays the generated document to the user.

[0768] The user checks the displayed document and corrects it if necessary.

[0769] The server saves the modified document to the database.

[0770] Access to expert networks

[0771] The user requests "additional expert advice" within the wizard.

[0772] The server uses generated AI to select the most suitable expert.

[0773] The emotion engine takes into account the user's emotional data and routes the request to the appropriate expert.

[0774] An expert will receive the user's inquiry and provide advice.

[0775] The server transfers the advice from the expert to the user's terminal.

[0776] Cost evaluation and optimization

[0777] The server calls the generation AI based on the user's procedural data and emotional data.

[0778] The generative AI evaluates the cost of each procedure and generates suggestions for reducing costs (for example, reducing costs by digitizing documents).

[0779] The server sends the evaluation results and suggestions to the user's terminal.

[0780] The terminal displays the evaluation results and suggestions to the user.

[0781] If the user accepts the proposal, they can proceed based on the proposal, for example, by choosing to use an electronic signature.

[0782] Specific examples

[0783] User Registration

[0784] A user logs into the system and enters their name and contact information.

[0785] The server stores this information in a database.

[0786] Providing a procedure assistance wizard

[0787] The server generates a wizard that says, "We will support your first M&A procedure."

[0788] The terminal displays a wizard and asks, "What type of M&A transaction would you like to undertake?"

[0789] The user selects "Acquisition" and presses the "Next" button.

[0790] The server collects the user's answers in the wizard and activates the emotion engine to obtain emotion data.

[0791] The emotion engine analyzes the user's emotions and determines the appropriate next question or response.

[0792] Automatic Document Generation

[0793] The server calls a generation AI based on the user's answers and emotional data, and generates an "acquisition contract."

[0794] The server sends the generated document to the user's terminal for display.

[0795] The user checks the document and clicks the save button without making any changes.

[0796] The server stores the saved document in a database.

[0797] Access to expert networks

[0798] The user requests advice on the details of the contract terms.

[0799] The server uses generative AI and an emotion engine to select the most suitable expert and send the request.

[0800] An expert will receive the user's inquiry and provide advice.

[0801] The server transfers the advice from the expert to the user's terminal and displays it.

[0802] Cost evaluation and optimization

[0803] The server calls a generative AI based on user data and emotion data and evaluates the cost of the procedure.

[0804] The generative AI generates a suggestion that "using electronic signatures will reduce costs."

[0805] The server sends the proposal to the user's terminal for display.

[0806] The user reviews the proposal and selects "Use electronic signature" to proceed.

[0807] This system makes it possible to proceed with the process while taking into consideration the user's feelings, resulting in more user-friendly M&A support.

[0808] The processing flow will be explained below.

[0809] Step 1:

[0810] A user accesses the system.

[0811] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[0812] Step 2:

[0813] The user enters the required information into the input form and submits it.

[0814] The server stores the transmitted information in a database.

[0815] Step 3:

[0816] The server generates a procedure assistance wizard suited to the user's experience level based on the user's basic information.

[0817] The terminal displays the wizard's interface to the user.

[0818] Step 4:

[0819] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[0820] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[0821] The server collects user response data and emotion data and stores them in a database.

[0822] Step 5:

[0823] The server calls the generation AI based on the user response data collected.

[0824] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[0825] Step 6:

[0826] The server sends the generated document to the user's terminal.

[0827] The terminal displays the generated document to the user.

[0828] Step 7:

[0829] The user reviews the displayed document and makes any necessary corrections, for example by modifying a particular clause.

[0830] The server saves the modified document to the database.

[0831] Step 8:

[0832] The user requests "additional expert advice" within the wizard.

[0833] The emotion engine determines the urgency of the request based on the user's emotion data.

[0834] The server uses generated AI to select the most suitable expert.

[0835] Step 9:

[0836] The server transmits the user's consultation details to the specialist and also provides emotion data at the same time.

[0837] Experts receive the user's consultation details and emotional data and provide advice.

[0838] The server transfers the advice from the expert to the user's terminal.

[0839] Step 10:

[0840] The terminal displays expert advice to the user.

[0841] The user confirms the displayed advice.

[0842] Step 11:

[0843] The server calls the generation AI based on the user's procedural data and emotional data.

[0844] Generative AI evaluates the costs of procedures and generates proposals for cost reduction (e.g., cost reduction through digitization of documents).

[0845] Step 12:

[0846] The server sends the proposal to the user's terminal.

[0847] The terminal displays the suggestions to the user.

[0848] The user reviews the proposal and makes a selection if necessary, for example, choosing to use electronic signatures.

[0849] Example 2

[0850] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0851] Conventional M&A procedure support systems only provide uniform procedural support without considering the user's experience level or emotional state, which has the drawback of not improving the user experience. Furthermore, document generation and expert support are handled without considering the user's emotions, which increases the stress and anxiety felt by the user and prevents the procedure from progressing smoothly. Furthermore, the system lacks the functionality to evaluate and optimize the overall cost of the procedure.

[0852] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0853] In this invention, the server includes means for accepting user information, means for generating a procedure support wizard based on the user's experience level, means for generating necessary documents by calling a generation AI based on data and emotion data collected from the user, means for sending the user's request to an expert network and obtaining advice from appropriate experts, means for evaluating the cost of the procedure and generating cost reduction proposals, and means including an emotion engine that recognizes the user's emotions and responds appropriately. This enables smooth and efficient support for M&A procedures while improving the user experience.

[0854] "User information" is basic data such as name, contact details, and level of experience that users enter into the system.

[0855] The "procedure assistance wizard" is an interface that is generated based on the user's experience level and that continuously presents questions and guidance necessary for the procedure.

[0856] "Generative AI" refers to artificial intelligence technology that automatically generates necessary legal and procedural documents based on user response data.

[0857] "Emotion data" is information about emotions collected from the user's facial expressions, tone of voice, and the like.

[0858] An "emotion engine" refers to an analysis system that recognizes a user's emotions and responds appropriately accordingly.

[0859] An "expert network" is a collection of people or organizations with expertise in a particular field, and is a system that provides advice in response to user requests.

[0860] "Cost assessment" is the process of measuring the costs of each procedure and making proposals for cost reduction based on the results.

[0861] A "database" is a system for storing collected user information, emotional data, generated documents, etc.

[0862] This invention is a system for supporting M&A procedures, which utilizes a combination of generative AI and an emotion engine to improve the user experience. A specific embodiment of this system will be described below.

[0863] User Registration

[0864] A user accesses the system and begins registration.

[0865] The device displays a basic information input form (e.g., name, contact information, experience level) to the user in a browser. This is implemented using HTML forms and JavaScript.

[0866] The user fills in the form with the required information and clicks the submit button, which sends the data as an HTTP POST request.

[0867] The server parses the received data (for example, using Python's Flask or Django) and stores it in a database (for example, MySQL or PostgreSQL).

[0868] Providing a procedure assistance wizard

[0869] The server generates a procedure assistance wizard based on the user's experience level. The generated wizard displays, for example, "We will support you in your first M&A procedure."

[0870] The device dynamically displays the wizard interface using JavaScript and React.

[0871] The user answers each question in the wizard, for example, "What is the purpose of your transaction?"

[0872] The server receives the user's answer and activates the emotion engine, which is implemented using OpenCV and the Facial Emotion Recognition API.

[0873] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data and return it to the server.

[0874] The server stores the response data and emotion data in a database.

[0875] Automatic Document Generation

[0876] The server calls the AI ​​generator based on the user's response data. For example, the following sentences can be used as prompts:

[0877] "Generate the necessary acquisition agreements based on your name and contact information to help you with your first M&A transaction."

[0878] The generative AI generates the necessary legal documents (e.g., acquisition contracts) based on the prompts and returns them to the server. The generative AI uses OpenAI GPT and Google BERT.

[0879] The server sends the generated document to the user's device, which displays it, for example, by email or via a digital download link.

[0880] The user reviews the document and makes any necessary corrections, using the GUI of a PDF viewer or word processor to view and edit the document.

[0881] The server saves the modified document back into the database.

[0882] Access to expert networks

[0883] The user requests "additional expert advice" through the interface within the wizard.

[0884] The server uses generative AI and an emotion engine to analyze the request content and select the appropriate expert.

[0885] The emotion engine considers the request content and the user's emotion data and sends the request to the appropriate expert.

[0886] Experts provide advice based on requests received.

[0887] The server transfers the advice from the expert to the user's terminal, which displays it.

[0888] Cost evaluation and optimization

[0889] The server calls a generation AI based on the user's procedure data and emotion data, and evaluates the cost of the procedure.

[0890] The generative AI generates specific cost reduction proposals (for example, cost reductions through the use of electronic signatures) along with the cost assessment results.

[0891] The server sends the evaluation results and suggestions to the user's terminal, which displays them.

[0892] The user reviews the proposal and, if they agree, proceeds to the next step, for example, choosing to use an electronic signature.

[0893] This system will enable the provision of efficient and user-friendly M&A procedural support, taking into consideration the user's experience and feelings.

[0894] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0895] Step 1: Beginning user registration

[0896] A user accesses the system and begins registration by entering basic information such as the user's name, contact details, and experience level.

[0897] The device displays a basic information input form in the browser. This form is written in HTML and JavaScript.

[0898] Step 2: Submit user information

[0899] The user fills in the form with the required information and clicks the submit button. Input data includes inputs such as name, contact details, experience level, etc.

[0900] The device sends the form data to the server as an HTTP POST request.

[0901] Step 3: Save user information

[0902] The server parses the received data and extracts information such as name, contact details, experience level, etc. This is done using Python's Flask or Django.

[0903] The server stores the parsed data in a database (e.g., MySQL or PostgreSQL), and generates database records as output.

[0904] Step 4: Generate the procedure assistance wizard

[0905] The server retrieves the user's experience level from the database and generates a procedure assistance wizard based on that experience level.

[0906] Create a prompt for the server-generated wizard and generate interface data for the wizard as output data. The prompt will say, "We will support you in your first M&A procedure."

[0907] Step 5: Display the Wizard

[0908] The device uses JavaScript and React to display the wizard interface to the user. The input data is the interface data received from the server.

[0909] The user answers the wizard's questions, such as "What is the purpose of the transaction?"

[0910] Step 6: Collect and analyze user responses

[0911] The server receives and analyzes the user's answers through the wizard, including data such as experience level and trading objectives.

[0912] The server runs the emotion engine, which uses OpenCV and the Facial Emotion Recognition API.

[0913] Step 7: Collect emotion data

[0914] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotion data, and uses the user's camera footage and voice as input data.

[0915] The server receives the emotion data and stores the user's response data and emotion data in a database. As an output, the analysis results are stored in the database.

[0916] Step 8: Invoke automatic document generation

[0917] The server sends a prompt to the generation AI based on the user's response data, such as "To support your first M&A procedure, please generate the necessary acquisition agreement based on the user's name and contact information."

[0918] The input data is the user's response data, and the output data is a prompt sentence sent to the generation AI.

[0919] Step 9: Generate Documents

[0920] The generation AI generates the necessary legal documents (e.g., acquisition contracts) based on the prompt sentences and returns them to the server. The generation AI uses OpenAI GPT and Google BERT.

[0921] The server receives the generated document and sends it to the user's device. The input data is the document data from the generation AI, and the output data is the document data sent to the user's device.

[0922] Step 10: View and modify the document

[0923] The terminal displays the generated document to the user, using a PDF viewer or a word processor GUI.

[0924] The user reviews the document and makes corrections as necessary. The input data are the user's corrections, and the corrected document is generated as output data.

[0925] Step 11: Save the revised document

[0926] The server saves the modified document to the database. The input data is the modified document, and the output data is the updated database record.

[0927] Step 12: Request from your expert network

[0928] The user requests "additional expert advice" through the wizard interface. The input data is the user's request.

[0929] The server analyzes the request using the generative AI and emotion engine, and selects the appropriate expert. The output data is the selected expert.

[0930] Step 13: Providing expert advice

[0931] The emotion engine considers the request content and the user's emotion data and sends the request to the appropriate expert. The input data is the user's emotion data.

[0932] The expert provides advice based on the received request, and the output data is the advice.

[0933] Step 14: Forwarding Advice

[0934] The server receives advice from the expert and transfers it to the user's terminal. The input data is the expert's advice, and the output data is the data transferred to the user's terminal.

[0935] The device displays the advice.

[0936] Step 15: Cost assessment and proposal

[0937] The server calls the generation AI based on the user's procedure data and emotion data, and evaluates the cost of the procedure. The input data is the user's procedure data and emotion data.

[0938] The generation AI generates specific cost reduction proposals along with the cost assessment results. The output data is the cost reduction proposals.

[0939] Step 16: View cost assessment results and recommendations

[0940] The server sends the evaluation results and proposals to the user's terminal, which displays them. The input data are the cost evaluation results and proposals.

[0941] The user reviews the proposal and, if they agree, proceeds to the next step, for example, choosing to use an electronic signature.

[0942] This will enable smooth and efficient support for M&A procedures that are tailored to the user's experience and emotions.

[0943] (Application example 2)

[0944] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0945] There is a need to improve the passenger experience in autonomous vehicles. Specifically, the challenge is to analyze passenger emotions in real time and provide appropriate services to make them feel safe and comfortable. Providing services that meet the individual needs of passengers is also an important element.

[0946] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for calling a generation AI based on data collected from the user to generate the required document, means for collecting and analyzing the user's emotions, means for proposing services based on the emotions, means for sending the user's request to an expert network and obtaining advice from appropriate experts, and means for evaluating the cost of the procedure and generating cost-reduction suggestions. This makes it possible to analyze passenger emotions in real time and provide comfortable services tailored to individual needs.

[0947] - "User information receiving means" is a function that allows the user to input basic information such as name, contact information, and experience level, and stores this data on the cloud server.

[0948] The "procedure assistance wizard generation means" is a function for generating a wizard that provides appropriate procedures and guidance based on the user's experience level and input information.

[0949] The "document generation means" is a function that calls a generation AI based on data collected from the user and automatically generates the necessary documents.

[0950] "Emotion collection and analysis means" is a function for analyzing the user's facial expressions and tone of voice to collect and analyze emotional data.

[0951] The "service suggestion means" is a function for suggesting optimal services and information to users based on collected and analyzed emotional data.

[0952] The "expert network access means" is a function for transmitting a user's request to the expert network and obtaining advice from an appropriate expert.

[0953] The "cost evaluation means" is a function for evaluating the cost of a procedure and generating cost reduction proposals based on the results.

[0954] overview

[0955] This invention relates to a smartphone application called "Smart Drive Assistant" designed to improve the passenger experience in autonomous vehicles. The system combines emotion collection and analysis methods with generative AI to provide real-time services tailored to passengers' emotions and needs.

[0956] Hardware and software used

[0957] Hardware: smartphone, in-vehicle camera, microphone

[0958] Software: emotion recognition engines (e.g., Affectiva), generative AI models (e.g., OpenAI GPT-4), mobile app development frameworks (e.g., React Native)

[0959] Data processing and calculation

[0960] 1. Acceptance of user information

[0961] The server prompts the user to enter basic information such as name, contact details, and experience level, and stores this data on the cloud server. The user enters this information via a smartphone app.

[0962] 2. Emotional Data Collection

[0963] The device (smartphone) uses cameras and microphones installed in the vehicle to capture passengers' facial expressions and tone of voice, and the collected data is analyzed by an emotion recognition engine to generate emotion data.

[0964] 3. Service proposal

[0965] Based on the emotion data and the riding situation (destination, expected arrival time, etc.), the server sends prompts to the generative AI model to generate optimal services and information for the user. The generated service suggestions are displayed to the user via a smartphone app.

[0966] 4. Feedback Loop

[0967] By providing feedback on the proposed services and information from users, the server can update the generative AI model and improve the accuracy of future suggestions.

[0968] Specific examples

[0969] User Registration

[0970] A user launches the app for the first time and enters information such as their name, contact information, and frequent destinations.

[0971] Emotional Data Collection

[0972] When a user gets into an autonomous vehicle, an in-car camera captures their facial expressions and a microphone records their conversation. An emotion recognition engine analyzes this data to identify the user's current emotion (e.g., nervousness, anxiety, joy).

[0973] Service proposal

[0974] If an autonomous vehicle is traveling through rush hour and the server detects that the user is nervous, it will use a generative AI model to suggest relaxing music or information about nearby cafes.

[0975] feedback

[0976] If the user is satisfied with the suggestions, they provide feedback, which improves the accuracy of the suggestions in future.

[0977] Example prompts to input to the generative AI model

[0978] "Users are feeling a bit uneasy."

[0979] "Our destination is 5km away and it will take approximately 15 minutes to arrive."

[0980] "Given this situation, what services should we offer our users?"

[0981] This will enable real-time analysis of passenger emotions and the provision of comfortable services tailored to individual needs.

[0982] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0983] Step 1:

[0984] Users launch the smartphone app and enter basic information such as their name, contact details, and experience level.

[0985] The input information is transmitted from the terminal to the server.

[0986] The server stores this information in a database located in the cloud.

[0987] Input: User basic information (name, contact details, experience level)

[0988] Output: Basic information stored in a cloud database

[0989] Step 2:

[0990] When a user gets into a self-driving vehicle, the cameras and microphones inside the vehicle start working.

[0991] The device captures the user's facial expressions and voice through a camera and microphone.

[0992] The collected data is transmitted from the terminal to a server.

[0993] Input: User's facial expression data, voice data

[0994] Output: Emotion data sent to the server

[0995] Step 3:

[0996] The server receives the emotion data and analyzes it using an emotion recognition engine.

[0997] The user's current emotional state is analyzed and output as emotional data.

[0998] Input: Emotion data sent to the server

[0999] Output: Analyzed emotion data (e.g., anxiety, tension, joy, etc.)

[1000] Step 4:

[1001] The server sends the ride status, including emotion data, destination information, expected arrival time, etc., to the generative AI model.

[1002] A generative AI model generates optimal service suggestions based on these prompts.

[1003] The generated service offer is returned to the server.

[1004] Input: Emotion data, riding situation (destination information, expected arrival time, etc.)

[1005] Output: Service proposals from the generative AI model

[1006] Step 5:

[1007] The server transmits the generated service offer to the terminal.

[1008] The terminal displays the proposed services and information to the user.

[1009] Input: Service proposals from a generative AI model

[1010] Output: Service proposals displayed on the user's smartphone app

[1011] Step 6:

[1012] Users provide feedback on the proposed services through a smartphone app.

[1013] The feedback is sent from the terminal to the server.

[1014] The server uses this feedback to update the generative AI model and improve the accuracy of its suggestions.

[1015] Input: User feedback

[1016] Output: Updated generative AI model

[1017] This process will enable passengers' emotions to be analyzed in real time, making it possible to provide comfortable services tailored to their individual needs.

[1018] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1019] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1020] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1021] [Third embodiment]

[1022] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1023] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1025] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1026] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1027] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1029] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1030] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1032] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1033] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1034] The present invention relates to a system using a generative AI for supporting M&A procedures. Specific embodiments for implementing this system will be described below.

[1035] User Registration

[1036] A user accesses the system.

[1037] The terminal displays a form for the user to enter basic information such as name, contact details, and level of experience.

[1038] The user enters the necessary information into the input form and submits it.

[1039] The server stores the transmitted information in a database.

[1040] Providing a procedure assistance wizard

[1041] The server generates a procedure assistance wizard that matches the user's experience level based on the information entered by the user.

[1042] The terminal displays the interface of this wizard to the user.

[1043] The user answers the wizard's questions, for example, "What is the purpose of the transaction?"

[1044] The server collects the user's answers and stores them in a database for use in the next step.

[1045] Automatic Document Generation

[1046] The server calls the generation AI based on the collected user response data.

[1047] Generative AI generates templates for required legal and procedural documents (e.g., acquisition agreements).

[1048] The server transmits the generated document to the user's terminal.

[1049] The terminal displays the generated document to the user.

[1050] The user checks the displayed document and corrects it if necessary.

[1051] When the user modifies the document and presses the save button, the server saves the modified document in the database.

[1052] Access to expert networks

[1053] The user requests "additional expert advice" within the wizard.

[1054] The server receives this request and uses generative AI to select the most suitable expert.

[1055] The server transmits the user's consultation details to the expert.

[1056] The expert receives the user's inquiry and provides advice.

[1057] The server forwards this advice to the user's terminal.

[1058] The terminal displays the expert advice to the user.

[1059] Cost evaluation and optimization

[1060] The server calls the generation AI based on the user's procedure response data.

[1061] The generative AI evaluates the costs of procedures and generates proposals for cost reduction (e.g., cost reduction through digitization of documents).

[1062] The server sends the evaluation results and suggestions to the user's terminal.

[1063] The terminal displays the evaluation results and suggestions to the user.

[1064] If the user accepts the offer, they can choose the most appropriate method for proceeding, for example, by using an electronic signature.

[1065] Specific examples

[1066] User Registration

[1067] A user logs into the system and enters their name and contact information.

[1068] The server stores this information in a database.

[1069] Providing a procedure assistance wizard

[1070] The server generates a wizard that displays, "We will support you in your first M&A procedure."

[1071] The terminal displays a wizard and asks, "What type of M&A transaction would you like to undertake?"

[1072] The user selects "Acquisition" and presses the "Next" button.

[1073] The server stores the answer in a database and generates the next question.

[1074] Automatic Document Generation

[1075] The server calls a generation AI based on the user's answers and generates an "acquisition agreement."

[1076] The file is sent to the user's terminal and displayed.

[1077] The user checks the document and makes any necessary corrections.

[1078] The server stores the saved documents in a database.

[1079] Access to expert networks

[1080] The user requests advice on the details of the contract terms.

[1081] The server uses generative AI to select the most suitable expert.

[1082] The server sends a request to the selected expert and obtains advice.

[1083] The terminal displays the advice from the expert to the user.

[1084] Cost evaluation and optimization

[1085] The server calls a generation AI based on the user's data and evaluates the cost of the procedure.

[1086] The generative AI generates a suggestion that "using electronic signatures will reduce costs."

[1087] The server sends the proposal to the user's terminal.

[1088] The user reviews the proposal and selects "Use electronic signature" to proceed.

[1089] In this manner, the present invention provides a system that simplifies the M&A process and reduces costs by minimizing the use of experts.

[1090] The processing flow will be explained below.

[1091] Step 1:

[1092] A user accesses the system.

[1093] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[1094] Step 2:

[1095] The user enters the required information into the input form and submits it.

[1096] The server stores the transmitted information in a database.

[1097] Step 3:

[1098] The server generates a procedure assistance wizard that matches the user's experience level based on the information input by the user.

[1099] The terminal displays the wizard's interface to the user.

[1100] Step 4:

[1101] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[1102] The server collects the user's answers and stores them in a database for use in the next step.

[1103] Step 5:

[1104] The server calls the generation AI based on the user response data collected.

[1105] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[1106] Step 6:

[1107] The server sends the generated document to the user's terminal.

[1108] The terminal displays the generated document to the user.

[1109] Step 7:

[1110] The user checks the displayed document and corrects it if necessary.

[1111] The server saves the modified document to the database.

[1112] Step 8:

[1113] The user requests "additional expert advice" within the wizard.

[1114] The server uses generated AI to select the most suitable expert.

[1115] Step 9:

[1116] The server transmits the user's consultation details to the expert.

[1117] An expert will receive the user's inquiry and provide advice.

[1118] The server transfers the advice from the expert to the user's terminal.

[1119] Step 10:

[1120] The terminal displays expert advice to the user.

[1121] The user reviews the proposal and takes action if necessary.

[1122] Step 11:

[1123] The server calls the generation AI based on the user's procedural data.

[1124] Generative AI assesses the costs of procedures and generates cost-saving proposals.

[1125] Step 12:

[1126] The server sends the proposal to the user's terminal.

[1127] The terminal displays the suggestions to the user.

[1128] If the user accepts the proposal, the system will select the most suitable method and proceed with the procedure.

[1129] Example 1

[1130] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1131] Traditional M&A procedures are complicated and require extensive specialized knowledge and expensive expert fees. As a result, the procedures are often difficult and costly, especially for beginners and small companies. In addition, the preparation of necessary documents and cost assessments are often time-consuming, hindering efficient progress.

[1132] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1133] In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for generating the necessary documents by calling a generation AI based on data collected from the user, means for transmitting the generated documents to the user's terminal and displaying them to the user, means for the user to modify and save the generated documents, means for transmitting the user's request to an expert network and obtaining advice from appropriate experts, and means for evaluating the cost of the procedures and generating cost reduction proposals. This enables users to proceed with M&A procedures efficiently and at low cost even if they do not have specialized knowledge.

[1134] "User information" refers to basic information such as the name, contact details, and level of experience of the user accessing the system.

[1135] "Procedure Assistance Wizard" means an interface that provides a series of questions and instructions to assist a user in the M&A process, generated based on the user's experience level.

[1136] "Generative AI" refers to artificial intelligence that automatically generates necessary documents based on data collected from users.

[1137] "Expert Network" means a collection of experts within the system to provide appropriate expert advice based on a user's request.

[1138] "Cost assessment" means the process of assessing the costs of a procedure and making recommendations to reduce those costs in the most appropriate manner.

[1139] "Documents" means legal and procedural documents required for the M&A process (e.g., acquisition agreement).

[1140] "Terminal" means a device such as a computer or smartphone that a user uses to access the system and perform operations or input data.

[1141] "Database" means a digital storage device for storing user and procedural information.

[1142] The present invention relates to a system using a generative AI for supporting M&A procedures. A specific embodiment of this system will be described below.

[1143] The user first accesses the system using a device such as a personal computer or smartphone. The user enters basic information such as their name, contact details, and level of experience into an input form and submits it. The device sends this information to the server, which then stores it in a database.

[1144] The server then generates a procedural assistance wizard based on the user's experience level. This wizard is created using a front-end framework such as React or Vue.js and is displayed on the device. The wizard presents the user with questions such as "What is the purpose of the transaction?" and collects the user's answers.

[1145] Based on the collected data, the server calls a generative AI to generate the required document. The generative AI used here is an AI model such as OpenAI's GPT-3. An example of a prompt is as follows:

[1146] The user has selected Acquisition. Now generate the required Acquisition Agreement template.

[1147] Transaction objective: Entering new markets

[1148] Based on this prompt, the generation AI generates a template for the acquisition agreement. The generated document is sent from the server to the user's device and displayed on the device. The user checks the document and makes any necessary corrections. The corrected document is saved by the user and then saved back to the database by the server.

[1149] Furthermore, if the user requests additional expert advice, the server uses the generation AI to select the most suitable expert. The user's consultation details are sent to the selected expert, who then provides advice. The provided advice is transferred to the user's device via the server and displayed to the user.

[1150] Finally, the server calls the generation AI based on the collected user procedure response data to evaluate the cost of the procedure. The generation AI generates proposals for cost reduction and sends the evaluation results and proposals to the user's device. The user reviews the displayed proposals and, if they accept them, selects the optimal method. For example, they can choose to use an electronic signature.

[1151] In this manner, the present invention provides a system that simplifies the M&A process and reduces costs by minimizing the use of experts.

[1152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1153] Detailed explanation of the program's processing flow

[1154] Step 1:

[1155] A user accesses the system. The terminal displays an input form for basic information such as name, contact details, and level of experience to the user. The user enters the basic information into the input form and presses the submit button. Based on this input, the terminal sends the data to the server, which stores it in a database.

[1156] Step 2:

[1157] The server generates a procedure assistance wizard based on the user's experience level. In this case, the server generates a wizard containing questions and instructions for beginners based on the information entered by the user. The terminal displays the wizard interface to the user and waits for the user's response. For example, the question "What type of M&A procedure do you want to carry out?" is displayed. The user's input is saved in a database, and the data becomes the output for the next step.

[1158] Step 3:

[1159] The user answers the wizard's questions. The user enters their answers into the input form and clicks "Next." The server receives this data and stores it in a database. This data is used to understand the user's specific needs.

[1160] Step 4:

[1161] The server calls the generative AI based on the collected user response data. For example, if the answer in the previous step was "acquisition," the server sends the following prompt to the generative AI model:

[1162] The user has selected Acquisition. Now generate the required Acquisition Agreement template.

[1163] Transaction objective: Entering new markets

[1164] The generation AI generates the necessary legal and procedural documents based on these prompts and returns the output to the server.

[1165] Step 5:

[1166] The server sends the generated document to the user's terminal. The terminal displays the received document file to the user. For example, the document is displayed using a PDF viewer or text editor. The user checks the displayed document and makes corrections as necessary. When the user presses the save button, the corrected document is sent back to the server.

[1167] Step 6:

[1168] When a user saves a modified document, the server stores the document in a database, making it valuable information for future reference.

[1169] Step 7:

[1170] When a user requests additional expert advice, the server uses the generated AI to select the most suitable expert. The server analyzes the expert profiles using the generated AI and selects the most suitable expert.

[1171] Step 8:

[1172] The server sends the user's consultation details to the selected expert via email or message, and the expert provides advice based on the received consultation details and returns the advice to the server.

[1173] Step 9:

[1174] The server receives the advice from the expert and transfers it to the user's terminal, which displays the advice to the user, who can refer to it and use it to progress through the procedure.

[1175] Step 10:

[1176] The server calls the generation AI based on the user's procedure response data and evaluates the cost of the procedure. For example, it sends the following prompt to the generation AI:

[1177] Evaluate the cost of the procedure based on the following data:

[1178] Procedure: Acquisition

[1179] Budget: 5 million yen

[1180] Recommendation: Use of electronic signatures

[1181] The generation AI generates evaluation results and returns them to the server.

[1182] Step 11:

[1183] The server sends the evaluation results and cost reduction proposals to the user's terminal, which displays the evaluation results and proposals, and the user confirms the proposals.

[1184] Step 12:

[1185] If the user accepts the proposal, they select a specific method for proceeding, such as using an electronic signature. The user clicks a selection button, and this information is sent to the server, which automatically sets up the optimal procedure.

[1186] As described above, detailed processing including specific actions, inputs, and outputs is carried out at each step. This system enables users to proceed with M&A procedures efficiently and at low cost.

[1187] (Application example 1)

[1188] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1189] Factory operators are seeking a system that can streamline M&A (mergers and acquisitions) procedures, reduce costs, and allow smooth procedures even when they lack specialized knowledge or experience. In particular, the challenge is to reduce the burden on operators and improve the efficiency of the entire process through procedure automation, document generation, and access to expert networks.

[1190] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1191] In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for generating necessary documents by calling a generation AI based on data collected from the user, means for sending the user's request to an expert network and obtaining advice from appropriate experts, means for evaluating the cost of the procedure and generating cost-reduction proposals, means for providing procedure assistance to a factory operator via a robot, means for viewing and modifying documents generated by the generation AI through the robot's interface, and means for evaluating and optimizing costs related to the factory M&A procedure. This enables efficient M&A procedures and cost reductions even for factory operators who lack specialized knowledge or experience.

[1192] "User information" is basic data about individuals and organizations using the system, including names, contact information, experience level, etc.

[1193] A "procedure assistance wizard" is a guide that assists users with a procedure based on their level of experience, and includes a series of questions and instructions to help users proceed smoothly through the procedure.

[1194] "Generative AI" refers to a system that uses artificial intelligence (AI) to automatically perform specific tasks, in this case, the technology to generate the necessary documents.

[1195] An "expert network" is a collection of individuals or organizations with specialized knowledge and experience in a particular field, from which users can receive appropriate advice by sending a request.

[1196] "Cost evaluation" refers to the process of analyzing and evaluating the costs incurred in a particular procedure or project.

[1197] A "cost reduction proposal" is a proposal that shows specific methods and means for reducing costs based on the results of analysis.

[1198] "Factory operator" means the person or organization responsible for the operation and management of the factory.

[1199] "Robot" means an automated device or system, including those used to assist factory operators in performing procedures.

[1200] "Interface" refers to the means or device for interaction between the user and the system, and in this case documents are viewed and modified through the robot's interface.

[1201] "M&A procedures" refers to the set of procedures and activities related to mergers and acquisitions, including the production of necessary documents and obtaining expert advice.

[1202] "Optimization" refers to the process of making something the most effective and efficient for a particular goal.

[1203] The system for implementing this invention is composed of a server, a terminal, and a user. The server accepts user information, generates a procedure assistance wizard based on the user's experience level, and generates the necessary documents by calling a generation AI based on data collected from the user. It also sends the user's request to an expert network to obtain advice from appropriate experts, evaluates the cost of the procedure, and generates cost-reduction proposals.

[1204] In this system, users can view and edit documents generated by AI through the robot's interface, allowing factory operators to proceed with M&A procedures without interrupting actual factory operations.In addition, the server automatically performs cost evaluations and optimization proposals, reducing the operator's burden.

[1205] The server runs Python programs and stores user data in an SQLite database. The generative AI model includes artificial intelligence algorithms for document generation, expert search, cost evaluation, and optimization. Based on user input, the model generates prompts and executes appropriate actions.

[1206] For example, in a scenario where a user is acquiring a factory, the user registers their name, contact information, and the fact that this is their first M&A experience, and the system displays a wizard to assist with the first-time M&A process. Following the wizard, the user answers the necessary questions, and the system generates an "acquisition agreement" based on the answers. The user reviews the generated agreement and enters any amendments into the system. If expert advice is needed, the user accesses the expert network, and the system introduces an appropriate expert. Finally, the system evaluates the cost of the procedure and suggests that "using electronic signatures would be effective."

[1207] Examples of prompts to input to a generative AI model include:

[1208] "Generate a wizard to assist users in M&A for the first time."

[1209] "Generate a template acquisition agreement based on the user's answers."

[1210] "Please introduce me to an expert on M&A procedures."

[1211] "Generate cost assessments and optimization suggestions related to procedures."

[1212] This system enables efficient and effective M&A procedures to be carried out through cooperation between the server, terminal, and user elements.

[1213] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1214] Step 1:

[1215] A user accesses the system and enters basic information (name, contact details, experience level).

[1216] Input: Name, Contact Information, Experience Level.

[1217] Processing: The terminal displays an input form, and the user enters the required information. When the user submits the information, the data is sent from the terminal to the server.

[1218] Output: The server stores the submitted information in a database.

[1219] Step 2:

[1220] The server generates a procedure assistance wizard based on the user information.

[1221] Input: User basic information, experience level.

[1222] Processing: The server obtains the user's experience level from the database and uses generation AI to generate a procedure assistance wizard suitable for the user.

[1223] Output: The generated procedure assistance wizard is sent to the terminal and displayed to the user.

[1224] Step 3:

[1225] The user answers questions according to the procedure assistance wizard.

[1226] Input: The user's answer.

[1227] Processing: The terminal displays the assistance wizard, and the user answers each question. The answer data is sent from the terminal to the server and stored in a database.

[1228] Output: Saved response data.

[1229] Step 4:

[1230] The server calls a generation AI based on the user's response data to generate the necessary documents.

[1231] Input: User response data.

[1232] Processing: The server uses generation AI to analyze the response data and generate the appropriate document (e.g., acquisition agreement).

[1233] Output: The generated document is sent to the terminal and displayed to the user.

[1234] Step 5:

[1235] The user reviews the generated document and corrects it if necessary.

[1236] Input: Generated document, user modifications.

[1237] Processing: The terminal displays the generated document, and the user inputs the modifications. The modified document is sent from the terminal to the server and stored in the database.

[1238] Output: The modified document is saved.

[1239] Step 6:

[1240] A user requests expert advice.

[1241] Input: Request for advice.

[1242] Processing: The device sends the user's advice request to the server. The server uses the generative AI to select an appropriate expert and send the request.

[1243] Output: The advice from the expert is sent to the terminal via the server and displayed to the user.

[1244] Step 7:

[1245] The server evaluates the cost of the procedure and generates cost-saving suggestions.

[1246] Input: User response data, procedural data.

[1247] Processing: The server uses the generation AI to perform cost assessment and generate cost reduction proposals.

[1248] Output: The evaluation results and cost reduction proposals are sent from the server to the terminal and displayed to the user.

[1249] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1250] The purpose of this invention is to improve the user experience by combining an emotion engine with a system that uses generative AI to support M&A procedures. Specific embodiments for implementing this system are described below.

[1251] User Registration

[1252] A user accesses the system.

[1253] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[1254] The user enters the necessary information into the input form and submits it.

[1255] The server stores the transmitted information in a database.

[1256] Providing a procedure assistance wizard

[1257] Based on the information entered by the user, the server generates a procedure assistance wizard suited to the user's experience level.

[1258] The terminal displays the wizard's interface to the user.

[1259] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[1260] The server collects the user's answers and then activates an emotion engine to recognize the user's emotions.

[1261] The emotion engine collects emotional data from the user's facial expressions, tone of voice, etc.

[1262] The server stores the collected response data and emotion data in a database.

[1263] Automatic Document Generation

[1264] The server calls the generation AI based on the user response data collected.

[1265] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[1266] The server sends the generated document to the user's terminal.

[1267] The terminal displays the generated document to the user.

[1268] The user checks the displayed document and corrects it if necessary.

[1269] The server saves the modified document to the database.

[1270] Access to expert networks

[1271] The user requests "additional expert advice" within the wizard.

[1272] The server uses generated AI to select the most suitable expert.

[1273] The emotion engine takes into account the user's emotional data and routes the request to the appropriate expert.

[1274] An expert will receive the user's inquiry and provide advice.

[1275] The server transfers the advice from the expert to the user's terminal.

[1276] Cost evaluation and optimization

[1277] The server calls the generation AI based on the user's procedural data and emotional data.

[1278] The generative AI evaluates the cost of each procedure and generates suggestions for reducing costs (for example, reducing costs by digitizing documents).

[1279] The server sends the evaluation results and suggestions to the user's terminal.

[1280] The terminal displays the evaluation results and suggestions to the user.

[1281] If the user accepts the proposal, they can proceed based on the proposal, for example, by choosing to use an electronic signature.

[1282] Specific examples

[1283] User Registration

[1284] A user logs into the system and enters their name and contact information.

[1285] The server stores this information in a database.

[1286] Providing a procedure assistance wizard

[1287] The server generates a wizard that says, "We will support your first M&A procedure."

[1288] The terminal displays a wizard and asks, "What type of M&A transaction would you like to undertake?"

[1289] The user selects "Acquisition" and presses the "Next" button.

[1290] The server collects the user's answers in the wizard and activates the emotion engine to obtain emotion data.

[1291] The emotion engine analyzes the user's emotions and determines the appropriate next question or response.

[1292] Automatic Document Generation

[1293] The server calls a generation AI based on the user's answers and emotional data, and generates an "acquisition contract."

[1294] The server sends the generated document to the user's terminal for display.

[1295] The user checks the document and clicks the save button without making any changes.

[1296] The server stores the saved document in a database.

[1297] Access to expert networks

[1298] The user requests advice on the details of the contract terms.

[1299] The server uses generative AI and an emotion engine to select the most suitable expert and send the request.

[1300] An expert will receive the user's inquiry and provide advice.

[1301] The server transfers the advice from the expert to the user's terminal and displays it.

[1302] Cost evaluation and optimization

[1303] The server calls a generative AI based on user data and emotion data and evaluates the cost of the procedure.

[1304] The generative AI generates a suggestion that "using electronic signatures will reduce costs."

[1305] The server sends the proposal to the user's terminal for display.

[1306] The user reviews the proposal and selects "Use electronic signature" to proceed.

[1307] This system makes it possible to proceed with the process while taking into consideration the user's feelings, resulting in more user-friendly M&A support.

[1308] The processing flow will be explained below.

[1309] Step 1:

[1310] A user accesses the system.

[1311] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[1312] Step 2:

[1313] The user enters the required information into the input form and submits it.

[1314] The server stores the transmitted information in a database.

[1315] Step 3:

[1316] The server generates a procedure assistance wizard suited to the user's experience level based on the user's basic information.

[1317] The terminal displays the wizard's interface to the user.

[1318] Step 4:

[1319] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[1320] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[1321] The server collects user response data and emotion data and stores them in a database.

[1322] Step 5:

[1323] The server calls the generation AI based on the user response data collected.

[1324] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[1325] Step 6:

[1326] The server sends the generated document to the user's terminal.

[1327] The terminal displays the generated document to the user.

[1328] Step 7:

[1329] The user reviews the displayed document and makes any necessary corrections, for example by modifying a particular clause.

[1330] The server saves the modified document to the database.

[1331] Step 8:

[1332] The user requests "additional expert advice" within the wizard.

[1333] The emotion engine determines the urgency of the request based on the user's emotion data.

[1334] The server uses generated AI to select the most suitable expert.

[1335] Step 9:

[1336] The server transmits the user's consultation details to the specialist and also provides emotion data at the same time.

[1337] Experts receive the user's consultation details and emotional data and provide advice.

[1338] The server transfers the advice from the expert to the user's terminal.

[1339] Step 10:

[1340] The terminal displays expert advice to the user.

[1341] The user confirms the displayed advice.

[1342] Step 11:

[1343] The server calls the generation AI based on the user's procedural data and emotional data.

[1344] Generative AI evaluates the costs of procedures and generates proposals for cost reduction (e.g., cost reduction through digitization of documents).

[1345] Step 12:

[1346] The server sends the proposal to the user's terminal.

[1347] The terminal displays the suggestions to the user.

[1348] The user reviews the proposal and makes a selection if necessary, for example, choosing to use electronic signatures.

[1349] Example 2

[1350] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1351] Conventional M&A procedure support systems only provide uniform procedural support without considering the user's experience level or emotional state, which has the drawback of not improving the user experience. Furthermore, document generation and expert support are handled without considering the user's emotions, which increases the stress and anxiety felt by the user and prevents the procedure from progressing smoothly. Furthermore, the system lacks the functionality to evaluate and optimize the overall cost of the procedure.

[1352] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1353] In this invention, the server includes means for accepting user information, means for generating a procedure support wizard based on the user's experience level, means for generating necessary documents by calling a generation AI based on data and emotion data collected from the user, means for sending the user's request to an expert network and obtaining advice from appropriate experts, means for evaluating the cost of the procedure and generating cost reduction proposals, and means including an emotion engine that recognizes the user's emotions and responds appropriately. This enables smooth and efficient support for M&A procedures while improving the user experience.

[1354] "User information" is basic data such as name, contact details, and level of experience that users enter into the system.

[1355] The "procedure assistance wizard" is an interface that is generated based on the user's experience level and that continuously presents questions and guidance necessary for the procedure.

[1356] "Generative AI" refers to artificial intelligence technology that automatically generates necessary legal and procedural documents based on user response data.

[1357] "Emotion data" is information about emotions collected from the user's facial expressions, tone of voice, and the like.

[1358] An "emotion engine" refers to an analysis system that recognizes a user's emotions and responds appropriately accordingly.

[1359] An "expert network" is a collection of people or organizations with expertise in a particular field, and is a system that provides advice in response to user requests.

[1360] "Cost assessment" is the process of measuring the costs of each procedure and making proposals for cost reduction based on the results.

[1361] A "database" is a system for storing collected user information, emotional data, generated documents, etc.

[1362] This invention is a system for supporting M&A procedures, which utilizes a combination of generative AI and an emotion engine to improve the user experience. A specific embodiment of this system will be described below.

[1363] User Registration

[1364] A user accesses the system and begins registration.

[1365] The device displays a basic information input form (e.g., name, contact information, experience level) to the user in a browser. This is implemented using HTML forms and JavaScript.

[1366] The user fills in the form with the required information and clicks the submit button, which sends the data as an HTTP POST request.

[1367] The server parses the received data (for example, using Python's Flask or Django) and stores it in a database (for example, MySQL or PostgreSQL).

[1368] Providing a procedure assistance wizard

[1369] The server generates a procedure assistance wizard based on the user's experience level. The generated wizard displays, for example, "We will support you in your first M&A procedure."

[1370] The device dynamically displays the wizard interface using JavaScript and React.

[1371] The user answers each question in the wizard, for example, "What is the purpose of your transaction?"

[1372] The server receives the user's answer and activates the emotion engine, which is implemented using OpenCV and the Facial Emotion Recognition API.

[1373] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data and return it to the server.

[1374] The server stores the response data and emotion data in a database.

[1375] Automatic Document Generation

[1376] The server calls the AI ​​generator based on the user's response data. For example, the following sentences can be used as prompts:

[1377] "Generate the necessary acquisition agreements based on your name and contact information to help you with your first M&A transaction."

[1378] The generative AI generates the necessary legal documents (e.g., acquisition contracts) based on the prompts and returns them to the server. The generative AI uses OpenAI GPT and Google BERT.

[1379] The server sends the generated document to the user's device, which displays it, for example, by email or via a digital download link.

[1380] The user reviews the document and makes any necessary corrections, using the GUI of a PDF viewer or word processor to view and edit the document.

[1381] The server saves the modified document back into the database.

[1382] Access to expert networks

[1383] The user requests "additional expert advice" through the interface within the wizard.

[1384] The server uses generative AI and an emotion engine to analyze the request content and select the appropriate expert.

[1385] The emotion engine considers the request content and the user's emotion data and sends the request to the appropriate expert.

[1386] Experts provide advice based on requests received.

[1387] The server transfers the advice from the expert to the user's terminal, which displays it.

[1388] Cost evaluation and optimization

[1389] The server calls a generation AI based on the user's procedure data and emotion data, and evaluates the cost of the procedure.

[1390] The generative AI generates specific cost reduction proposals (for example, cost reductions through the use of electronic signatures) along with the cost assessment results.

[1391] The server sends the evaluation results and suggestions to the user's terminal, which displays them.

[1392] The user reviews the proposal and, if they agree, proceeds to the next step, for example, choosing to use an electronic signature.

[1393] This system will enable the provision of efficient and user-friendly M&A procedural support, taking into consideration the user's experience and feelings.

[1394] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1395] Step 1: Beginning user registration

[1396] A user accesses the system and begins registration by entering basic information such as the user's name, contact details, and experience level.

[1397] The device displays a basic information input form in the browser. This form is written in HTML and JavaScript.

[1398] Step 2: Submit user information

[1399] The user fills in the form with the required information and clicks the submit button. Input data includes inputs such as name, contact details, experience level, etc.

[1400] The device sends the form data to the server as an HTTP POST request.

[1401] Step 3: Save user information

[1402] The server parses the received data and extracts information such as name, contact details, experience level, etc. This is done using Python's Flask or Django.

[1403] The server stores the parsed data in a database (e.g., MySQL or PostgreSQL), and generates database records as output.

[1404] Step 4: Generate the procedure assistance wizard

[1405] The server retrieves the user's experience level from the database and generates a procedure assistance wizard based on that experience level.

[1406] Create a prompt for the server-generated wizard and generate interface data for the wizard as output data. The prompt will say, "We will support you in your first M&A procedure."

[1407] Step 5: Display the Wizard

[1408] The device uses JavaScript and React to display the wizard interface to the user. The input data is the interface data received from the server.

[1409] The user answers the wizard's questions, such as "What is the purpose of the transaction?"

[1410] Step 6: Collect and analyze user responses

[1411] The server receives and analyzes the user's answers through the wizard, including data such as experience level and trading objectives.

[1412] The server runs the emotion engine, which uses OpenCV and the Facial Emotion Recognition API.

[1413] Step 7: Collect emotion data

[1414] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotion data, and uses the user's camera footage and voice as input data.

[1415] The server receives the emotion data and stores the user's response data and emotion data in a database. As an output, the analysis results are stored in the database.

[1416] Step 8: Invoke automatic document generation

[1417] The server sends a prompt to the generation AI based on the user's response data, such as "To support your first M&A procedure, please generate the necessary acquisition agreement based on the user's name and contact information."

[1418] The input data is the user's response data, and the output data is a prompt sentence sent to the generation AI.

[1419] Step 9: Generate Documents

[1420] The generation AI generates the necessary legal documents (e.g., acquisition contracts) based on the prompt sentences and returns them to the server. The generation AI uses OpenAI GPT and Google BERT.

[1421] The server receives the generated document and sends it to the user's device. The input data is the document data from the generation AI, and the output data is the document data sent to the user's device.

[1422] Step 10: View and modify the document

[1423] The terminal displays the generated document to the user, using a PDF viewer or a word processor GUI.

[1424] The user reviews the document and makes corrections as necessary. The input data are the user's corrections, and the corrected document is generated as output data.

[1425] Step 11: Save the revised document

[1426] The server saves the modified document to the database. The input data is the modified document, and the output data is the updated database record.

[1427] Step 12: Request from your expert network

[1428] The user requests "additional expert advice" through the wizard interface. The input data is the user's request.

[1429] The server analyzes the request using the generative AI and emotion engine, and selects the appropriate expert. The output data is the selected expert.

[1430] Step 13: Providing expert advice

[1431] The emotion engine considers the request content and the user's emotion data and sends the request to the appropriate expert. The input data is the user's emotion data.

[1432] The expert provides advice based on the received request, and the output data is the advice.

[1433] Step 14: Forwarding Advice

[1434] The server receives advice from the expert and transfers it to the user's terminal. The input data is the expert's advice, and the output data is the data transferred to the user's terminal.

[1435] The device displays the advice.

[1436] Step 15: Cost assessment and proposal

[1437] The server calls the generation AI based on the user's procedure data and emotion data, and evaluates the cost of the procedure. The input data is the user's procedure data and emotion data.

[1438] The generation AI generates specific cost reduction proposals along with the cost assessment results. The output data is the cost reduction proposals.

[1439] Step 16: View cost assessment results and recommendations

[1440] The server sends the evaluation results and proposals to the user's terminal, which displays them. The input data are the cost evaluation results and proposals.

[1441] The user reviews the proposal and, if they agree, proceeds to the next step, for example, choosing to use an electronic signature.

[1442] This will enable smooth and efficient support for M&A procedures that are tailored to the user's experience and emotions.

[1443] (Application example 2)

[1444] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1445] There is a need to improve the passenger experience in autonomous vehicles. Specifically, the challenge is to analyze passenger emotions in real time and provide appropriate services to make them feel safe and comfortable. Providing services that meet the individual needs of passengers is also an important element.

[1446] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for calling a generation AI based on data collected from the user to generate the required document, means for collecting and analyzing the user's emotions, means for proposing services based on the emotions, means for sending the user's request to an expert network and obtaining advice from appropriate experts, and means for evaluating the cost of the procedure and generating cost-reduction suggestions. This makes it possible to analyze passenger emotions in real time and provide comfortable services tailored to individual needs.

[1447] - "User information receiving means" is a function that allows the user to input basic information such as name, contact information, and experience level, and stores this data on the cloud server.

[1448] The "procedure assistance wizard generation means" is a function for generating a wizard that provides appropriate procedures and guidance based on the user's experience level and input information.

[1449] The "document generation means" is a function that calls a generation AI based on data collected from the user and automatically generates the necessary documents.

[1450] "Emotion collection and analysis means" is a function for analyzing the user's facial expressions and tone of voice to collect and analyze emotional data.

[1451] The "service suggestion means" is a function for suggesting optimal services and information to users based on collected and analyzed emotional data.

[1452] The "expert network access means" is a function for transmitting a user's request to the expert network and obtaining advice from an appropriate expert.

[1453] The "cost evaluation means" is a function for evaluating the cost of a procedure and generating cost reduction proposals based on the results.

[1454] overview

[1455] This invention relates to a smartphone application called "Smart Drive Assistant" designed to improve the passenger experience in autonomous vehicles. The system combines emotion collection and analysis methods with generative AI to provide real-time services tailored to passengers' emotions and needs.

[1456] Hardware and software used

[1457] Hardware: smartphone, in-vehicle camera, microphone

[1458] Software: emotion recognition engines (e.g., Affectiva), generative AI models (e.g., OpenAI GPT-4), mobile app development frameworks (e.g., React Native)

[1459] Data processing and calculation

[1460] 1. Acceptance of user information

[1461] The server prompts the user to enter basic information such as name, contact details, and experience level, and stores this data on the cloud server. The user enters this information via a smartphone app.

[1462] 2. Emotional Data Collection

[1463] The device (smartphone) uses cameras and microphones installed in the vehicle to capture passengers' facial expressions and tone of voice, and the collected data is analyzed by an emotion recognition engine to generate emotion data.

[1464] 3. Service proposal

[1465] Based on the emotion data and the riding situation (destination, expected arrival time, etc.), the server sends prompts to the generative AI model to generate optimal services and information for the user. The generated service suggestions are displayed to the user via a smartphone app.

[1466] 4. Feedback Loop

[1467] By providing feedback on the proposed services and information from users, the server can update the generative AI model and improve the accuracy of future suggestions.

[1468] Specific examples

[1469] User Registration

[1470] A user launches the app for the first time and enters information such as their name, contact information, and frequent destinations.

[1471] Emotional Data Collection

[1472] When a user gets into an autonomous vehicle, an in-car camera captures their facial expressions and a microphone records their conversation. An emotion recognition engine analyzes this data to identify the user's current emotion (e.g., nervousness, anxiety, joy).

[1473] Service proposal

[1474] If an autonomous vehicle is traveling through rush hour and the server detects that the user is nervous, it will use a generative AI model to suggest relaxing music or information about nearby cafes.

[1475] feedback

[1476] If the user is satisfied with the suggestions, they provide feedback, which improves the accuracy of the suggestions in future.

[1477] Example prompts to input to the generative AI model

[1478] "Users are feeling a bit uneasy."

[1479] "Our destination is 5km away and it will take approximately 15 minutes to arrive."

[1480] "Given this situation, what services should we offer our users?"

[1481] This will enable real-time analysis of passenger emotions and the provision of comfortable services tailored to individual needs.

[1482] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1483] Step 1:

[1484] Users launch the smartphone app and enter basic information such as their name, contact details, and experience level.

[1485] The input information is transmitted from the terminal to the server.

[1486] The server stores this information in a database located in the cloud.

[1487] Input: User basic information (name, contact details, experience level)

[1488] Output: Basic information stored in a cloud database

[1489] Step 2:

[1490] When a user gets into a self-driving vehicle, the cameras and microphones inside the vehicle start working.

[1491] The device captures the user's facial expressions and voice through a camera and microphone.

[1492] The collected data is transmitted from the terminal to a server.

[1493] Input: User's facial expression data, voice data

[1494] Output: Emotion data sent to the server

[1495] Step 3:

[1496] The server receives the emotion data and analyzes it using an emotion recognition engine.

[1497] The user's current emotional state is analyzed and output as emotional data.

[1498] Input: Emotion data sent to the server

[1499] Output: Analyzed emotion data (e.g., anxiety, tension, joy, etc.)

[1500] Step 4:

[1501] The server sends the ride status, including emotion data, destination information, expected arrival time, etc., to the generative AI model.

[1502] A generative AI model generates optimal service suggestions based on these prompts.

[1503] The generated service offer is returned to the server.

[1504] Input: Emotion data, riding situation (destination information, expected arrival time, etc.)

[1505] Output: Service proposals from the generative AI model

[1506] Step 5:

[1507] The server transmits the generated service offer to the terminal.

[1508] The terminal displays the proposed services and information to the user.

[1509] Input: Service proposals from a generative AI model

[1510] Output: Service proposals displayed on the user's smartphone app

[1511] Step 6:

[1512] Users provide feedback on the proposed services through a smartphone app.

[1513] The feedback is sent from the terminal to the server.

[1514] The server uses this feedback to update the generative AI model and improve the accuracy of its suggestions.

[1515] Input: User feedback

[1516] Output: Updated generative AI model

[1517] This process will enable passengers' emotions to be analyzed in real time, making it possible to provide comfortable services tailored to their individual needs.

[1518] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1519] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1520] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1521] [Fourth embodiment]

[1522] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1523] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1524] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1525] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1526] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1527] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1528] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1529] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1530] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1531] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1532] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1533] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1534] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1535] The present invention relates to a system using a generative AI for supporting M&A procedures. Specific embodiments for implementing this system will be described below.

[1536] User Registration

[1537] A user accesses the system.

[1538] The terminal displays a form for the user to enter basic information such as name, contact details, and level of experience.

[1539] The user enters the necessary information into the input form and submits it.

[1540] The server stores the transmitted information in a database.

[1541] Providing a procedure assistance wizard

[1542] The server generates a procedure assistance wizard that matches the user's experience level based on the information entered by the user.

[1543] The terminal displays the interface of this wizard to the user.

[1544] The user answers the wizard's questions, for example, "What is the purpose of the transaction?"

[1545] The server collects the user's answers and stores them in a database for use in the next step.

[1546] Automatic Document Generation

[1547] The server calls the generation AI based on the collected user response data.

[1548] Generative AI generates templates for required legal and procedural documents (e.g., acquisition agreements).

[1549] The server transmits the generated document to the user's terminal.

[1550] The terminal displays the generated document to the user.

[1551] The user checks the displayed document and corrects it if necessary.

[1552] When the user modifies the document and presses the save button, the server saves the modified document in the database.

[1553] Access to expert networks

[1554] The user requests "additional expert advice" within the wizard.

[1555] The server receives this request and uses generative AI to select the most suitable expert.

[1556] The server transmits the user's consultation details to the expert.

[1557] The expert receives the user's inquiry and provides advice.

[1558] The server forwards this advice to the user's terminal.

[1559] The terminal displays the expert advice to the user.

[1560] Cost evaluation and optimization

[1561] The server calls the generation AI based on the user's procedure response data.

[1562] The generative AI evaluates the costs of procedures and generates proposals for cost reduction (e.g., cost reduction through digitization of documents).

[1563] The server sends the evaluation results and suggestions to the user's terminal.

[1564] The terminal displays the evaluation results and suggestions to the user.

[1565] If the user accepts the offer, they can choose the most appropriate method for proceeding, for example, by using an electronic signature.

[1566] Specific examples

[1567] User Registration

[1568] A user logs into the system and enters their name and contact information.

[1569] The server stores this information in a database.

[1570] Providing a procedure assistance wizard

[1571] The server generates a wizard that displays, "We will support you in your first M&A procedure."

[1572] The terminal displays a wizard and asks, "What type of M&A transaction would you like to undertake?"

[1573] The user selects "Acquisition" and presses the "Next" button.

[1574] The server stores the answer in a database and generates the next question.

[1575] Automatic Document Generation

[1576] The server calls a generation AI based on the user's answers and generates an "acquisition agreement."

[1577] The file is sent to the user's terminal and displayed.

[1578] The user checks the document and makes any necessary corrections.

[1579] The server stores the saved documents in a database.

[1580] Access to expert networks

[1581] The user requests advice on the details of the contract terms.

[1582] The server uses generative AI to select the most suitable expert.

[1583] The server sends a request to the selected expert and obtains advice.

[1584] The terminal displays the advice from the expert to the user.

[1585] Cost evaluation and optimization

[1586] The server calls a generation AI based on the user's data and evaluates the cost of the procedure.

[1587] The generative AI generates a suggestion that "using electronic signatures will reduce costs."

[1588] The server sends the proposal to the user's terminal.

[1589] The user reviews the proposal and selects "Use electronic signature" to proceed.

[1590] In this manner, the present invention provides a system that simplifies the M&A process and reduces costs by minimizing the use of experts.

[1591] The processing flow will be explained below.

[1592] Step 1:

[1593] A user accesses the system.

[1594] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[1595] Step 2:

[1596] The user enters the required information into the input form and submits it.

[1597] The server stores the transmitted information in a database.

[1598] Step 3:

[1599] The server generates a procedure assistance wizard that matches the user's experience level based on the information input by the user.

[1600] The terminal displays the wizard's interface to the user.

[1601] Step 4:

[1602] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[1603] The server collects the user's answers and stores them in a database for use in the next step.

[1604] Step 5:

[1605] The server calls the generation AI based on the user response data collected.

[1606] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[1607] Step 6:

[1608] The server sends the generated document to the user's terminal.

[1609] The terminal displays the generated document to the user.

[1610] Step 7:

[1611] The user checks the displayed document and corrects it if necessary.

[1612] The server saves the modified document to the database.

[1613] Step 8:

[1614] The user requests "additional expert advice" within the wizard.

[1615] The server uses generated AI to select the most suitable expert.

[1616] Step 9:

[1617] The server transmits the user's consultation details to the expert.

[1618] An expert will receive the user's inquiry and provide advice.

[1619] The server transfers the advice from the expert to the user's terminal.

[1620] Step 10:

[1621] The terminal displays expert advice to the user.

[1622] The user reviews the proposal and takes action if necessary.

[1623] Step 11:

[1624] The server calls the generation AI based on the user's procedural data.

[1625] Generative AI assesses the costs of procedures and generates cost-saving proposals.

[1626] Step 12:

[1627] The server sends the proposal to the user's terminal.

[1628] The terminal displays the suggestions to the user.

[1629] If the user accepts the proposal, the system will select the most suitable method and proceed with the procedure.

[1630] Example 1

[1631] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1632] Traditional M&A procedures are complicated and require extensive specialized knowledge and expensive expert fees. As a result, the procedures are often difficult and costly, especially for beginners and small companies. In addition, the preparation of necessary documents and cost assessments are often time-consuming, hindering efficient progress.

[1633] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1634] In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for generating the necessary documents by calling a generation AI based on data collected from the user, means for transmitting the generated documents to the user's terminal and displaying them to the user, means for the user to modify and save the generated documents, means for transmitting the user's request to an expert network and obtaining advice from appropriate experts, and means for evaluating the cost of the procedures and generating cost reduction proposals. This enables users to proceed with M&A procedures efficiently and at low cost even if they do not have specialized knowledge.

[1635] "User information" refers to basic information such as the name, contact details, and level of experience of the user accessing the system.

[1636] "Procedure Assistance Wizard" means an interface that provides a series of questions and instructions to assist a user in the M&A process, generated based on the user's experience level.

[1637] "Generative AI" refers to artificial intelligence that automatically generates necessary documents based on data collected from users.

[1638] "Expert Network" means a collection of experts within the system to provide appropriate expert advice based on a user's request.

[1639] "Cost assessment" means the process of assessing the costs of a procedure and making recommendations to reduce those costs in the most appropriate manner.

[1640] "Documents" means legal and procedural documents required for the M&A process (e.g., acquisition agreement).

[1641] "Terminal" means a device such as a computer or smartphone that a user uses to access the system and perform operations or input data.

[1642] "Database" means a digital storage device for storing user and procedural information.

[1643] The present invention relates to a system using a generative AI for supporting M&A procedures. A specific embodiment of this system will be described below.

[1644] The user first accesses the system using a device such as a personal computer or smartphone. The user enters basic information such as their name, contact details, and level of experience into an input form and submits it. The device sends this information to the server, which then stores it in a database.

[1645] The server then generates a procedural assistance wizard based on the user's experience level. This wizard is created using a front-end framework such as React or Vue.js and is displayed on the device. The wizard presents the user with questions such as "What is the purpose of the transaction?" and collects the user's answers.

[1646] Based on the collected data, the server calls a generative AI to generate the required document. The generative AI used here is an AI model such as OpenAI's GPT-3. An example of a prompt is as follows:

[1647] The user has selected Acquisition. Now generate the required Acquisition Agreement template.

[1648] Transaction objective: Entering new markets

[1649] Based on this prompt, the generation AI generates a template for the acquisition agreement. The generated document is sent from the server to the user's device and displayed on the device. The user checks the document and makes any necessary corrections. The corrected document is saved by the user and then saved back to the database by the server.

[1650] Furthermore, if the user requests additional expert advice, the server uses the generation AI to select the most suitable expert. The user's consultation details are sent to the selected expert, who then provides advice. The provided advice is transferred to the user's device via the server and displayed to the user.

[1651] Finally, the server calls the generation AI based on the collected user procedure response data to evaluate the cost of the procedure. The generation AI generates proposals for cost reduction and sends the evaluation results and proposals to the user's device. The user reviews the displayed proposals and, if they accept them, selects the optimal method. For example, they can choose to use an electronic signature.

[1652] In this manner, the present invention provides a system that simplifies the M&A process and reduces costs by minimizing the use of experts.

[1653] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1654] Detailed explanation of the program's processing flow

[1655] Step 1:

[1656] A user accesses the system. The terminal displays an input form for basic information such as name, contact details, and level of experience. The user enters the basic information into the input form and presses the submit button. Based on this input, the terminal sends the data to the server, which stores it in a database.

[1657] Step 2:

[1658] The server generates a procedure assistance wizard based on the user's experience level. In this case, the server generates a wizard containing questions and instructions for beginners based on the information entered by the user. The terminal displays the wizard interface to the user and waits for the user's response. For example, the question "What type of M&A procedure do you want to carry out?" is displayed. The user's input is saved in a database, and the data becomes the output for the next step.

[1659] Step 3:

[1660] The user answers the wizard's questions. The user enters their answers into the input form and clicks "Next." The server receives this data and stores it in a database. This data is used to understand the user's specific needs.

[1661] Step 4:

[1662] The server calls the generative AI based on the collected user response data. For example, if the answer in the previous step was "acquisition," the server sends the following prompt to the generative AI model:

[1663] The user has selected Acquisition. Now generate the required Acquisition Agreement template.

[1664] Transaction objective: Entering new markets

[1665] The generation AI generates the necessary legal and procedural documents based on these prompts and returns the output to the server.

[1666] Step 5:

[1667] The server sends the generated document to the user's terminal. The terminal displays the received document file to the user. For example, the document is displayed using a PDF viewer or text editor. The user checks the displayed document and makes corrections as necessary. When the user presses the save button, the corrected document is sent back to the server.

[1668] Step 6:

[1669] When a user saves a modified document, the server stores the document in a database, making it valuable information for future reference.

[1670] Step 7:

[1671] When a user requests additional expert advice, the server uses the generated AI to select the most suitable expert. The server analyzes the expert profiles using the generated AI and selects the most suitable expert.

[1672] Step 8:

[1673] The server sends the user's consultation details to the selected expert via email or message, and the expert provides advice based on the received consultation details and returns the advice to the server.

[1674] Step 9:

[1675] The server receives the advice from the expert and transfers it to the user's terminal, which displays the advice to the user, who can refer to it and use it to progress through the procedure.

[1676] Step 10:

[1677] The server calls the generation AI based on the user's procedure response data and evaluates the cost of the procedure. For example, it sends the following prompt to the generation AI:

[1678] Evaluate the cost of the procedure based on the following data:

[1679] Procedure: Acquisition

[1680] Budget: 5 million yen

[1681] Recommendation: Use of electronic signatures

[1682] The generation AI generates evaluation results and returns them to the server.

[1683] Step 11:

[1684] The server sends the evaluation results and cost reduction proposals to the user's terminal, which displays the evaluation results and proposals, and the user confirms the proposals.

[1685] Step 12:

[1686] If the user accepts the proposal, they select a specific method for proceeding, such as using an electronic signature. The user clicks a selection button, and this information is sent to the server, which automatically sets up the optimal procedure.

[1687] As described above, detailed processing including specific actions, inputs, and outputs is carried out at each step. This system enables users to proceed with M&A procedures efficiently and at low cost.

[1688] (Application example 1)

[1689] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1690] Factory operators are seeking a system that can streamline M&A (mergers and acquisitions) procedures, reduce costs, and allow smooth procedures even when they lack specialized knowledge or experience. In particular, the challenge is to reduce the burden on operators and improve the efficiency of the entire process through procedure automation, document generation, and access to expert networks.

[1691] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1692] In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for generating necessary documents by calling a generation AI based on data collected from the user, means for sending the user's request to an expert network and obtaining advice from appropriate experts, means for evaluating the cost of the procedure and generating cost-reduction proposals, means for providing procedure assistance to a factory operator via a robot, means for viewing and modifying documents generated by the generation AI through the robot's interface, and means for evaluating and optimizing costs related to the factory M&A procedure. This enables efficient M&A procedures and cost reductions even for factory operators who lack specialized knowledge or experience.

[1693] "User information" is basic data about individuals and organizations using the system, including names, contact information, experience level, etc.

[1694] A "procedure assistance wizard" is a guide that assists users with a procedure based on their level of experience, and includes a series of questions and instructions to help users proceed smoothly through the procedure.

[1695] "Generative AI" refers to a system that uses artificial intelligence (AI) to automatically perform specific tasks, in this case, the technology to generate the necessary documents.

[1696] An "expert network" is a collection of individuals or organizations with specialized knowledge and experience in a particular field, from which users can receive appropriate advice by sending a request.

[1697] "Cost evaluation" refers to the process of analyzing and evaluating the costs incurred in a particular procedure or project.

[1698] A "cost reduction proposal" is a proposal that shows specific methods and means for reducing costs based on the results of analysis.

[1699] "Factory operator" means the person or organization responsible for the operation and management of the factory.

[1700] "Robot" means an automated device or system, including those used to assist factory operators in performing procedures.

[1701] "Interface" refers to the means or device for interaction between the user and the system, and in this case documents are viewed and modified through the robot's interface.

[1702] "M&A procedures" refers to the set of procedures and activities related to mergers and acquisitions, including the production of necessary documents and obtaining expert advice.

[1703] "Optimization" refers to the process of making something the most effective and efficient for a particular goal.

[1704] The system for implementing this invention is composed of a server, a terminal, and a user. The server accepts user information, generates a procedure assistance wizard based on the user's experience level, and generates the necessary documents by calling a generation AI based on data collected from the user. It also sends the user's request to an expert network to obtain advice from appropriate experts, evaluates the cost of the procedure, and generates cost-reduction proposals.

[1705] In this system, users can view and edit documents generated by AI through the robot's interface, allowing factory operators to proceed with M&A procedures without interrupting actual factory operations.In addition, the server automatically performs cost evaluations and optimization proposals, reducing the operator's burden.

[1706] The server runs Python programs and stores user data in an SQLite database. The generative AI model includes artificial intelligence algorithms for document generation, expert search, cost evaluation, and optimization. Based on user input, the model generates prompts and executes appropriate actions.

[1707] For example, in a scenario where a user is acquiring a factory, the user registers their name, contact information, and the fact that this is their first M&A experience, and the system displays a wizard to assist with the first-time M&A process. Following the wizard, the user answers the necessary questions, and the system generates an "acquisition agreement" based on the answers. The user reviews the generated agreement and enters any amendments into the system. If expert advice is needed, the user accesses the expert network, and the system introduces an appropriate expert. Finally, the system evaluates the cost of the procedure and suggests that "using electronic signatures would be effective."

[1708] Examples of prompts to input to a generative AI model include:

[1709] "Generate a wizard to assist users in M&A for the first time."

[1710] "Generate a template acquisition agreement based on the user's answers."

[1711] "Please introduce me to an expert on M&A procedures."

[1712] "Generate cost assessments and optimization suggestions related to procedures."

[1713] This system enables efficient and effective M&A procedures to be carried out through cooperation between the server, terminal, and user elements.

[1714] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1715] Step 1:

[1716] A user accesses the system and enters basic information (name, contact details, experience level).

[1717] Input: Name, Contact Information, Experience Level.

[1718] Processing: The terminal displays an input form, and the user enters the required information. When the user submits the information, the data is sent from the terminal to the server.

[1719] Output: The server stores the submitted information in a database.

[1720] Step 2:

[1721] The server generates a procedure assistance wizard based on the user information.

[1722] Input: User basic information, experience level.

[1723] Processing: The server obtains the user's experience level from the database and uses generation AI to generate a procedure assistance wizard suitable for the user.

[1724] Output: The generated procedure assistance wizard is sent to the terminal and displayed to the user.

[1725] Step 3:

[1726] The user answers questions according to the procedure assistance wizard.

[1727] Input: The user's answer.

[1728] Processing: The terminal displays the assistance wizard, and the user answers each question. The answer data is sent from the terminal to the server and stored in a database.

[1729] Output: Saved response data.

[1730] Step 4:

[1731] The server calls a generation AI based on the user's response data to generate the necessary documents.

[1732] Input: User response data.

[1733] Processing: The server uses generation AI to analyze the response data and generate the appropriate document (e.g., acquisition agreement).

[1734] Output: The generated document is sent to the terminal and displayed to the user.

[1735] Step 5:

[1736] The user reviews the generated document and corrects it if necessary.

[1737] Input: Generated document, user modifications.

[1738] Processing: The terminal displays the generated document, and the user inputs the modifications. The modified document is sent from the terminal to the server and stored in the database.

[1739] Output: The modified document is saved.

[1740] Step 6:

[1741] A user requests expert advice.

[1742] Input: Request for advice.

[1743] Processing: The device sends the user's advice request to the server. The server uses the generative AI to select an appropriate expert and send the request.

[1744] Output: The advice from the expert is sent to the terminal via the server and displayed to the user.

[1745] Step 7:

[1746] The server evaluates the cost of the procedure and generates cost-saving suggestions.

[1747] Input: User response data, procedural data.

[1748] Processing: The server uses the generation AI to perform cost assessment and generate cost reduction proposals.

[1749] Output: The evaluation results and cost reduction proposals are sent from the server to the terminal and displayed to the user.

[1750] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1751] The purpose of this invention is to improve the user experience by combining an emotion engine with a system that uses generative AI to support M&A procedures. Specific embodiments for implementing this system are described below.

[1752] User Registration

[1753] A user accesses the system.

[1754] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[1755] The user enters the necessary information into the input form and submits it.

[1756] The server stores the transmitted information in a database.

[1757] Providing a procedure assistance wizard

[1758] Based on the information entered by the user, the server generates a procedure assistance wizard suited to the user's experience level.

[1759] The terminal displays the wizard's interface to the user.

[1760] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[1761] The server collects the user's answers and then activates an emotion engine to recognize the user's emotions.

[1762] The emotion engine collects emotional data from the user's facial expressions, tone of voice, etc.

[1763] The server stores the collected response data and emotion data in a database.

[1764] Automatic Document Generation

[1765] The server calls the generation AI based on the user response data collected.

[1766] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[1767] The server sends the generated document to the user's terminal.

[1768] The terminal displays the generated document to the user.

[1769] The user checks the displayed document and corrects it if necessary.

[1770] The server saves the modified document to the database.

[1771] Access to expert networks

[1772] The user requests "additional expert advice" within the wizard.

[1773] The server uses generated AI to select the most suitable expert.

[1774] The emotion engine takes into account the user's emotional data and routes the request to the appropriate expert.

[1775] An expert will receive the user's inquiry and provide advice.

[1776] The server transfers the advice from the expert to the user's terminal.

[1777] Cost evaluation and optimization

[1778] The server calls the generation AI based on the user's procedural data and emotional data.

[1779] The generative AI evaluates the cost of each procedure and generates suggestions for reducing costs (for example, reducing costs by digitizing documents).

[1780] The server sends the evaluation results and suggestions to the user's terminal.

[1781] The terminal displays the evaluation results and suggestions to the user.

[1782] If the user accepts the proposal, they can proceed based on the proposal, for example, by choosing to use an electronic signature.

[1783] Specific examples

[1784] User Registration

[1785] A user logs into the system and enters their name and contact information.

[1786] The server stores this information in a database.

[1787] Providing a procedure assistance wizard

[1788] The server generates a wizard that says, "We will support your first M&A procedure."

[1789] The terminal displays a wizard and asks, "What type of M&A transaction would you like to undertake?"

[1790] The user selects "Acquisition" and presses the "Next" button.

[1791] The server collects the user's answers in the wizard and activates the emotion engine to obtain emotion data.

[1792] The emotion engine analyzes the user's emotions and determines the appropriate next question or response.

[1793] Automatic Document Generation

[1794] The server calls a generation AI based on the user's answers and emotional data, and generates an "acquisition contract."

[1795] The server sends the generated document to the user's terminal for display.

[1796] The user checks the document and clicks the save button without making any changes.

[1797] The server stores the saved document in a database.

[1798] Access to expert networks

[1799] The user requests advice on the details of the contract terms.

[1800] The server uses generative AI and an emotion engine to select the most suitable expert and send the request.

[1801] An expert will receive the user's inquiry and provide advice.

[1802] The server transfers the advice from the expert to the user's terminal and displays it.

[1803] Cost evaluation and optimization

[1804] The server calls a generative AI based on user data and emotion data and evaluates the cost of the procedure.

[1805] The generative AI generates a suggestion that "using electronic signatures will reduce costs."

[1806] The server sends the proposal to the user's terminal for display.

[1807] The user reviews the proposal and selects "Use electronic signature" to proceed.

[1808] This system makes it possible to proceed with the process while taking into consideration the user's feelings, resulting in more user-friendly M&A support.

[1809] The processing flow will be explained below.

[1810] Step 1:

[1811] A user accesses the system.

[1812] The device displays a form for the user to enter basic information such as name, contact details, and level of experience.

[1813] Step 2:

[1814] The user enters the required information into the input form and submits it.

[1815] The server stores the transmitted information in a database.

[1816] Step 3:

[1817] The server generates a procedure assistance wizard suited to the user's experience level based on the user's basic information.

[1818] The terminal displays the wizard's interface to the user.

[1819] Step 4:

[1820] The user answers the wizard's questions, such as "What is the purpose of your transaction?"

[1821] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data.

[1822] The server collects user response data and emotion data and stores them in a database.

[1823] Step 5:

[1824] The server calls the generation AI based on the user response data collected.

[1825] Generative AI generates the necessary legal and procedural documents (e.g., acquisition agreements).

[1826] Step 6:

[1827] The server sends the generated document to the user's terminal.

[1828] The terminal displays the generated document to the user.

[1829] Step 7:

[1830] The user reviews the displayed document and makes any necessary corrections, for example by modifying a particular clause.

[1831] The server saves the modified document to the database.

[1832] Step 8:

[1833] The user requests "additional expert advice" within the wizard.

[1834] The emotion engine determines the urgency of the request based on the user's emotion data.

[1835] The server uses generated AI to select the most suitable expert.

[1836] Step 9:

[1837] The server transmits the user's consultation details to the specialist and also provides emotion data at the same time.

[1838] Experts receive the user's consultation details and emotional data and provide advice.

[1839] The server transfers the advice from the expert to the user's terminal.

[1840] Step 10:

[1841] The terminal displays expert advice to the user.

[1842] The user confirms the displayed advice.

[1843] Step 11:

[1844] The server calls the generation AI based on the user's procedural data and emotional data.

[1845] Generative AI evaluates the costs of procedures and generates proposals for cost reduction (e.g., cost reduction through digitization of documents).

[1846] Step 12:

[1847] The server sends the proposal to the user's terminal.

[1848] The terminal displays the suggestions to the user.

[1849] The user reviews the proposal and makes a selection if necessary, for example, choosing to use electronic signatures.

[1850] Example 2

[1851] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1852] Conventional M&A procedure support systems only provide uniform procedural support without considering the user's experience level or emotional state, which has the drawback of not improving the user experience. Furthermore, document generation and expert support are handled without considering the user's emotions, which increases the stress and anxiety felt by the user and prevents the procedure from progressing smoothly. Furthermore, the system lacks the functionality to evaluate and optimize the overall cost of the procedure.

[1853] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1854] In this invention, the server includes means for accepting user information, means for generating a procedure support wizard based on the user's experience level, means for generating necessary documents by calling a generation AI based on data and emotion data collected from the user, means for sending the user's request to an expert network and obtaining advice from appropriate experts, means for evaluating the cost of the procedure and generating cost reduction proposals, and means including an emotion engine that recognizes the user's emotions and responds appropriately. This enables smooth and efficient support for M&A procedures while improving the user experience.

[1855] "User information" is basic data such as name, contact details, and level of experience that users enter into the system.

[1856] The "procedure assistance wizard" is an interface that is generated based on the user's experience level and that continuously presents questions and guidance necessary for the procedure.

[1857] "Generative AI" refers to artificial intelligence technology that automatically generates necessary legal and procedural documents based on user response data.

[1858] "Emotion data" is information about emotions collected from the user's facial expressions, tone of voice, and the like.

[1859] An "emotion engine" refers to an analysis system that recognizes a user's emotions and responds appropriately accordingly.

[1860] An "expert network" is a collection of people or organizations with expertise in a particular field, and is a system that provides advice in response to user requests.

[1861] "Cost assessment" is the process of measuring the costs of each procedure and making proposals for cost reduction based on the results.

[1862] A "database" is a system for storing collected user information, emotional data, generated documents, etc.

[1863] This invention is a system for supporting M&A procedures, which utilizes a combination of generative AI and an emotion engine to improve the user experience. A specific embodiment of this system will be described below.

[1864] User Registration

[1865] A user accesses the system and begins registration.

[1866] The device displays a basic information input form (e.g., name, contact information, experience level) to the user in a browser. This is implemented using HTML forms and JavaScript.

[1867] The user fills in the form with the required information and clicks the submit button, which sends the data as an HTTP POST request.

[1868] The server parses the received data (for example, using Python's Flask or Django) and stores it in a database (for example, MySQL or PostgreSQL).

[1869] Providing a procedure assistance wizard

[1870] The server generates a procedure assistance wizard based on the user's experience level. The generated wizard displays, for example, "We will support you in your first M&A procedure."

[1871] The device dynamically displays the wizard interface using JavaScript and React.

[1872] The user answers each question in the wizard, for example, "What is the purpose of your transaction?"

[1873] The server receives the user's answer and activates the emotion engine, which is implemented using OpenCV and the Facial Emotion Recognition API.

[1874] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotional data and return it to the server.

[1875] The server stores the response data and emotion data in a database.

[1876] Automatic Document Generation

[1877] The server calls the AI ​​generator based on the user's response data. For example, the following sentences can be used as prompts:

[1878] "Generate the necessary acquisition agreements based on your name and contact information to help you with your first M&A transaction."

[1879] The generative AI generates the necessary legal documents (e.g., acquisition contracts) based on the prompts and returns them to the server. The generative AI uses OpenAI GPT and Google BERT.

[1880] The server sends the generated document to the user's device, which displays it, for example, by email or via a digital download link.

[1881] The user reviews the document and makes any necessary corrections, using the GUI of a PDF viewer or word processor to view and edit the document.

[1882] The server saves the modified document back into the database.

[1883] Access to expert networks

[1884] The user requests "additional expert advice" through the interface within the wizard.

[1885] The server uses generative AI and an emotion engine to analyze the request content and select the appropriate expert.

[1886] The emotion engine considers the request content and the user's emotion data and sends the request to the appropriate expert.

[1887] Experts provide advice based on requests received.

[1888] The server transfers the advice from the expert to the user's terminal, which displays it.

[1889] Cost evaluation and optimization

[1890] The server calls a generation AI based on the user's procedure data and emotion data, and evaluates the cost of the procedure.

[1891] The generative AI generates specific cost reduction proposals (for example, cost reductions through the use of electronic signatures) along with the cost assessment results.

[1892] The server sends the evaluation results and suggestions to the user's terminal, which displays them.

[1893] The user reviews the proposal and, if they agree, proceeds to the next step, for example, choosing to use an electronic signature.

[1894] This system will enable the provision of efficient and user-friendly M&A procedural support, taking into consideration the user's experience and feelings.

[1895] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1896] Step 1: Beginning user registration

[1897] A user accesses the system and begins registration by entering basic information such as the user's name, contact details, and experience level.

[1898] The device displays a basic information input form in the browser. This form is written in HTML and JavaScript.

[1899] Step 2: Submit user information

[1900] The user fills in the form with the required information and clicks the submit button. Input data includes inputs such as name, contact details, experience level, etc.

[1901] The device sends the form data to the server as an HTTP POST request.

[1902] Step 3: Save user information

[1903] The server parses the received data and extracts information such as name, contact details, experience level, etc. This is done using Python's Flask or Django.

[1904] The server stores the parsed data in a database (e.g., MySQL or PostgreSQL), and generates database records as output.

[1905] Step 4: Generate the procedure assistance wizard

[1906] The server retrieves the user's experience level from the database and generates a procedure assistance wizard based on that experience level.

[1907] Create a prompt for the server-generated wizard and generate interface data for the wizard as output data. The prompt will say, "We will support you in your first M&A procedure."

[1908] Step 5: Display the Wizard

[1909] The device uses JavaScript and React to display the wizard interface to the user. The input data is the interface data received from the server.

[1910] The user answers the wizard's questions, such as "What is the purpose of the transaction?"

[1911] Step 6: Collect and analyze user responses

[1912] The server receives and analyzes the user's answers through the wizard, including data such as experience level and trading objectives.

[1913] The server runs the emotion engine, which uses OpenCV and the Facial Emotion Recognition API.

[1914] Step 7: Collect emotion data

[1915] The emotion engine analyzes the user's facial expressions and tone of voice to collect emotion data, and uses the user's camera footage and voice as input data.

[1916] The server receives the emotion data and stores the user's response data and emotion data in a database. As an output, the analysis results are stored in the database.

[1917] Step 8: Invoke automatic document generation

[1918] The server sends a prompt to the generation AI based on the user's response data, such as "To support your first M&A procedure, please generate the necessary acquisition agreement based on the user's name and contact information."

[1919] The input data is the user's response data, and the output data is a prompt sentence sent to the generation AI.

[1920] Step 9: Generate Documents

[1921] The generation AI generates the necessary legal documents (e.g., acquisition contracts) based on the prompt sentences and returns them to the server. The generation AI uses OpenAI GPT and Google BERT.

[1922] The server receives the generated document and sends it to the user's device. The input data is the document data from the generation AI, and the output data is the document data sent to the user's device.

[1923] Step 10: View and modify the document

[1924] The terminal displays the generated document to the user, using a PDF viewer or a word processor GUI.

[1925] The user reviews the document and makes corrections as necessary. The input data are the user's corrections, and the corrected document is generated as output data.

[1926] Step 11: Save the revised document

[1927] The server saves the modified document to the database. The input data is the modified document, and the output data is the updated database record.

[1928] Step 12: Request from your expert network

[1929] The user requests "additional expert advice" through the wizard interface. The input data is the user's request.

[1930] The server analyzes the request using the generative AI and emotion engine, and selects the appropriate expert. The output data is the selected expert.

[1931] Step 13: Providing expert advice

[1932] The emotion engine considers the request content and the user's emotion data and sends the request to the appropriate expert. The input data is the user's emotion data.

[1933] The expert provides advice based on the received request, and the output data is the advice.

[1934] Step 14: Forwarding Advice

[1935] The server receives advice from the expert and transfers it to the user's terminal. The input data is the expert's advice, and the output data is the data transferred to the user's terminal.

[1936] The device displays the advice.

[1937] Step 15: Cost assessment and proposal

[1938] The server calls the generation AI based on the user's procedure data and emotion data, and evaluates the cost of the procedure. The input data is the user's procedure data and emotion data.

[1939] The generation AI generates specific cost reduction proposals along with the cost assessment results. The output data is the cost reduction proposals.

[1940] Step 16: View cost assessment results and recommendations

[1941] The server sends the evaluation results and proposals to the user's terminal, which displays them. The input data are the cost evaluation results and proposals.

[1942] The user reviews the proposal and, if they agree, proceeds to the next step, for example, choosing to use an electronic signature.

[1943] This will enable smooth and efficient support for M&A procedures that are tailored to the user's experience and emotions.

[1944] (Application example 2)

[1945] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1946] There is a need to improve the passenger experience in autonomous vehicles. Specifically, the challenge is to analyze passenger emotions in real time and provide appropriate services to make them feel safe and comfortable. Providing services that meet the individual needs of passengers is also an important element.

[1947] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting user information, means for generating a procedure assistance wizard based on the user's experience level, means for calling a generation AI based on data collected from the user to generate the required document, means for collecting and analyzing the user's emotions, means for proposing services based on the emotions, means for sending the user's request to an expert network and obtaining advice from appropriate experts, and means for evaluating the cost of the procedure and generating cost-reduction suggestions. This makes it possible to analyze passenger emotions in real time and provide comfortable services tailored to individual needs.

[1948] - "User information receiving means" is a function that allows the user to input basic information such as name, contact information, and experience level, and stores this data on the cloud server.

[1949] The "procedure assistance wizard generation means" is a function for generating a wizard that provides appropriate procedures and guidance based on the user's experience level and input information.

[1950] The "document generation means" is a function that calls a generation AI based on data collected from the user and automatically generates the necessary documents.

[1951] "Emotion collection and analysis means" is a function for analyzing the user's facial expressions and tone of voice to collect and analyze emotional data.

[1952] The "service suggestion means" is a function for suggesting optimal services and information to users based on collected and analyzed emotional data.

[1953] The "expert network access means" is a function for transmitting a user's request to the expert network and obtaining advice from an appropriate expert.

[1954] The "cost evaluation means" is a function for evaluating the cost of a procedure and generating cost reduction proposals based on the results.

[1955] overview

[1956] This invention relates to a smartphone application called "Smart Drive Assistant" designed to improve the passenger experience in autonomous vehicles. The system combines emotion collection and analysis methods with generative AI to provide real-time services tailored to passengers' emotions and needs.

[1957] Hardware and software used

[1958] Hardware: smartphone, in-vehicle camera, microphone

[1959] Software: emotion recognition engines (e.g., Affectiva), generative AI models (e.g., OpenAI GPT-4), mobile app development frameworks (e.g., React Native)

[1960] Data processing and calculation

[1961] 1. Acceptance of user information

[1962] The server prompts the user to enter basic information such as name, contact details, and experience level, and stores this data on the cloud server. The user enters this information via a smartphone app.

[1963] 2. Emotional Data Collection

[1964] The device (smartphone) uses cameras and microphones installed in the vehicle to capture passengers' facial expressions and tone of voice, and the collected data is analyzed by an emotion recognition engine to generate emotion data.

[1965] 3. Service proposal

[1966] Based on the emotion data and the riding situation (destination, expected arrival time, etc.), the server sends prompts to the generative AI model to generate optimal services and information for the user. The generated service suggestions are displayed to the user via a smartphone app.

[1967] 4. Feedback Loop

[1968] By providing feedback on the proposed services and information from users, the server can update the generative AI model and improve the accuracy of future suggestions.

[1969] Specific examples

[1970] User Registration

[1971] A user launches the app for the first time and enters information such as their name, contact information, and frequent destinations.

[1972] Emotional Data Collection

[1973] When a user gets into an autonomous vehicle, an in-car camera captures their facial expressions and a microphone records their conversation. An emotion recognition engine analyzes this data to identify the user's current emotion (e.g., nervousness, anxiety, joy).

[1974] Service proposal

[1975] If an autonomous vehicle is traveling through rush hour and the server detects that the user is nervous, it will use a generative AI model to suggest relaxing music or information about nearby cafes.

[1976] feedback

[1977] If the user is satisfied with the suggestions, they provide feedback, which improves the accuracy of the suggestions in future.

[1978] Example prompts to input to the generative AI model

[1979] "Users are feeling a bit uneasy."

[1980] "Our destination is 5km away and it will take approximately 15 minutes to arrive."

[1981] "Given this situation, what services should we offer our users?"

[1982] This will enable real-time analysis of passenger emotions and the provision of comfortable services tailored to individual needs.

[1983] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1984] Step 1:

[1985] Users launch the smartphone app and enter basic information such as their name, contact details, and experience level.

[1986] The input information is transmitted from the terminal to the server.

[1987] The server stores this information in a database located in the cloud.

[1988] Input: User basic information (name, contact details, experience level)

[1989] Output: Basic information stored in a cloud database

[1990] Step 2:

[1991] When a user gets into a self-driving vehicle, the cameras and microphones inside the vehicle start working.

[1992] The device captures the user's facial expressions and voice through a camera and microphone.

[1993] The collected data is transmitted from the terminal to a server.

[1994] Input: User's facial expression data, voice data

[1995] Output: Emotion data sent to the server

[1996] Step 3:

[1997] The server receives the emotion data and analyzes it using an emotion recognition engine.

[1998] The user's current emotional state is analyzed and output as emotional data.

[1999] Input: Emotion data sent to the server

[2000] Output: Analyzed emotion data (e.g., anxiety, tension, joy, etc.)

[2001] Step 4:

[2002] The server sends the ride status, including emotion data, destination information, expected arrival time, etc., to the generative AI model.

[2003] A generative AI model generates optimal service suggestions based on these prompts.

[2004] The generated service offer is returned to the server.

[2005] Input: Emotion data, riding situation (destination information, expected arrival time, etc.)

[2006] Output: Service proposals from the generative AI model

[2007] Step 5:

[2008] The server transmits the generated service offer to the terminal.

[2009] The terminal displays the proposed services and information to the user.

[2010] Input: Service proposals from a generative AI model

[2011] Output: Service proposals displayed on the user's smartphone app

[2012] Step 6:

[2013] Users provide feedback on the proposed services through a smartphone app.

[2014] The feedback is sent from the terminal to the server.

[2015] The server uses this feedback to update the generative AI model and improve the accuracy of its suggestions.

[2016] Input: User feedback

[2017] Output: Updated generative AI model

[2018] This process will enable passengers' emotions to be analyzed in real time, making it possible to provide comfortable services tailored to their individual needs.

[2019] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2020] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2021] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2022] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2023] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2024] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2025] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2026] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2027] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2028] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2029] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2030] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2031] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2032] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2033] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2034] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2035] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2036] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2037] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2038] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2039] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2040] The following is further disclosed regarding the above embodiment.

[2041] (Claim 1)

[2042] means for accepting user information;

[2043] means for generating a procedural assistance wizard based on a user's experience level;

[2044] A means to generate the necessary documents by calling generation AI based on data collected from users;

[2045] means for submitting a user's request to a network of experts and obtaining advice from appropriate experts;

[2046] A system that includes a means for assessing the cost of procedures and generating cost-saving proposals.

[2047] (Claim 2)

[2048] 10. The system of claim 1, wherein the user input information is stored in a database.

[2049] (Claim 3)

[2050] 2. The system according to claim 1, wherein the generated document is sent to the user's terminal and displayed to the user.

[2051] "Example 1"

[2052] (Claim 1)

[2053] means for accepting user information;

[2054] means for generating a procedural assistance wizard based on a user's experience level;

[2055] A means to generate the necessary documents by calling generation AI based on data collected from users;

[2056] means for transmitting the generated document to a user's terminal and displaying it to the user;

[2057] a means for a user to modify and save the generated document;

[2058] means for submitting a user's request to a network of experts and obtaining advice from appropriate experts;

[2059] A system that includes a means for assessing the cost of procedures and generating cost-saving proposals.

[2060] (Claim 2)

[2061] 10. The system of claim 1, wherein the user input information is stored in a database.

[2062] (Claim 3)

[2063] 10. The system of claim 1, further comprising storing the modified document in a database.

[2064] "Application Example 1"

[2065] (Claim 1)

[2066] means for accepting user information;

[2067] means for generating a procedural assistance wizard based on a user's experience level;

[2068] A means to generate the necessary documents by calling generation AI based on data collected from users;

[2069] means for submitting a user's request to a network of experts and obtaining advice from appropriate experts;

[2070] a means of assessing the costs of procedures and generating cost-saving proposals;

[2071] A means to assist factory operators with procedures via robots;

[2072] A means to view and modify generative AI documents through a robotic interface;

[2073] A system that includes means for evaluating and optimizing costs associated with factory M&A procedures.

[2074] (Claim 2)

[2075] 10. The system of claim 1, wherein the user input information is stored in a database.

[2076] (Claim 3)

[2077] 2. The system according to claim 1, wherein the generated document is sent to a terminal of a factory operator and displayed to the operator.

[2078] "Example 2: Combining Emotion Engines"

[2079] (Claim 1)

[2080] means for accepting user information;

[2081] means for generating a procedural assistance wizard based on a user's experience level;

[2082] A means for generating the required documents by calling a generation AI based on data and emotion data collected from users;

[2083] means for submitting a user's request to a network of experts and obtaining advice from appropriate experts;

[2084] a means of assessing the costs of procedures and generating cost-saving proposals;

[2085] A system including means including an emotion engine for recognizing a user's emotions and providing an appropriate response.

[2086] (Claim 2)

[2087] 10. The system of claim 1, wherein the user input information is stored in a database.

[2088] (Claim 3)

[2089] 2. The system according to claim 1, wherein the generated document is sent to the user's terminal and displayed to the user.

[2090] "Application example 2 when combining emotion engines"

[2091] (Claim 1)

[2092] means for accepting user information;

[2093] means for generating a procedural assistance wizard based on a user's experience level;

[2094] A means to generate the necessary documents by calling generation AI based on data collected from users;

[2095] A means of collecting and analyzing user emotions;

[2096] A means for suggesting services based on emotions;

[2097] means for submitting a user's request to a network of experts and obtaining advice from appropriate experts;

[2098] A system that includes a means for assessing the cost of procedures and generating cost-saving proposals.

[2099] (Claim 2)

[2100] 2. The system of claim 1, wherein the user's input information and emotion data are stored in a database.

[2101] (Claim 3)

[2102] 2. The system according to claim 1, wherein the generated document is sent to the user's terminal and displayed to the user. [Explanation of symbols]

[2103] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for accepting user information; means for generating a procedural assistance wizard based on a user's experience level; A means to generate the necessary documents by calling generation AI based on data collected from users; means for submitting a user's request to a network of experts and obtaining advice from appropriate experts; and means for evaluating the cost of the procedure and generating cost reduction suggestions.

2. 2. The system of claim 1, wherein the user input information is stored in a database.

3. 2. The system according to claim 1, wherein the generated document is transmitted to a user's terminal and displayed to the user.

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