System

Abstract

A system is provided.SOLUTION: This system is provided with an interface means for allowing a person in charge of a company to input the request or problem of the company, an analyzing means for analyzing the inputted request or problem, and for specifying insufficient information, a question generating means for generating an additional question based on the insufficient information, and a means for presenting the generated additional question to the person in charge of the company. This system is provided with a presentation and reception means for receiving an answer, a proposal generation means for analyzing the received answer, and for generating the combination of proper merchandise or service, an estimate generation means for automatically preparing a rough estimate based on proposal contents, and an output means for presenting the generated proposal contents and rough estimate to the person in charge of the enterprise.SELECTED DRAWING: Figure 1

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] In order for small and medium-sized enterprises to promote DX (digital transformation), they need specific plans and concrete measures. However, many small and medium-sized enterprises lack staff with specialized DX knowledge and do not know what improvements to make and how. Furthermore, many do not have sales representatives to consult with. This makes the initial steps to promoting DX extremely difficult, and there is a need for a system that allows even company personnel without advanced specialized knowledge to easily receive DX proposals. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing an interface means for company personnel to input their company's requests and issues, an analysis means for analyzing the input requests and issues to identify missing information, a question generation means for generating additional questions to supplement the necessary information, a presentation and reception means for presenting the generated questions to the company personnel and receiving answers, a proposal generation means for generating an optimal combination of products and services based on the received answers, an estimate generation means for automatically creating a rough estimate based on the proposal content, and an output means for presenting the generated proposal content and rough estimate to the company personnel.

[0006] Furthermore, the generated proposal and rough estimate are notified to the corporate sales representative, and notification and follow-up methods are also included to enable the corporate sales representative to follow up. In this way, the specific plans and estimates necessary for promoting DX for SMEs can be provided efficiently and easily.

[0007] "Company representative" refers to the user who enters their company's requests and issues into the system.

[0008] "Interface means" refers to user interfaces such as input screens and chatbots that users use to input their requests and issues.

[0009] "Analysis means" refers to the technical means used to analyze the content of requests and issues entered by users and identify missing information.

[0010] The "question generation means" refers to a means for automatically generating additional questions to fill in the missing information identified by the analysis means.

[0011] The "presentation and reception means" refers to a means for presenting the generated follow-up question to the user and receiving the answer thereto.

[0012] "Proposal generation means" refers to a means for generating an optimal combination of products and services based on additional information received from a user, and creating specific proposal content.

[0013] The "quote generating means" refers to a means for automatically creating a rough estimate based on the proposal content generated by the proposal generating means.

[0014] "Output means" refers to a means for presenting the generated proposal and rough estimate to the user.

[0015] The "notification means" refers to a means for notifying the corporate sales representative of the generated proposal content and rough estimate.

[0016] "Follow-up methods" refer to the means by which corporate sales representatives carry out follow-up activities such as providing detailed explanations of proposals to users and answering questions. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] ---

[0039] This invention is a system that enables small and medium-sized enterprises to easily promote DX (digital transformation). In this system, company personnel input their company's requests and challenges, and a generative AI then asks for additional information, ultimately presenting a specific DX plan and a rough estimate. The system mainly includes the following means:

[0040] First, a company representative (user) opens a web page and inputs their company's requests and issues into a chat-style interface. When inputting requests and issues, the company representative can describe specific problems, such as, "Our company's inventory management is not going well. We would like to improve efficiency."

[0041] Next, the input requests and issues are sent to a server and analyzed by an analysis means. The analysis means analyzes the content of the requests and issues using natural language analysis to identify missing information. This indicates the additional information required to generate a DX plan.

[0042] When the missing information is identified, the question generation means automatically generates an additional question to supplement the missing information. The generated question is presented to the user via the presentation and reception means. When the user answers the presented question and transmits the answer, the information is also received by the server.

[0043] Next, the proposal generation means generates the optimal combination of products and services based on all the information received from the user. The proposal generation means selects the optimal combination from a database of products and services that it has learned in advance and creates a specific DX plan.

[0044] Based on the created DX plan, the estimate generation means calculates the estimated costs. The estimate generation means automatically creates an approximate estimate based on the price information of the proposed products and services. The generated proposal details and approximate estimate are presented to the user in real time via the output means.

[0045] As a specific example, if a user inputs, "Our company's inventory management is not going well. We would like to improve efficiency," the generation AI will generate a question about the specific problem of insufficient inventory management, and present it as, "Which part of inventory management are you having trouble with?" If the user responds, "It's difficult to keep track of inventory numbers," the proposal generation means will suggest products such as an "inventory management system" and "RFID tags." Based on this proposal, the estimate generation means will create a rough estimate and present it to the user.

[0046] In this way, corporate representatives can easily input their company's requests and challenges, and by utilizing generative AI, they can quickly obtain a specific DX plan and rough estimate. Furthermore, as a follow-up, the generated proposal and rough estimate are notified to the corporate sales representative via the notification method. If necessary, the corporate sales representative can use the follow-up method to provide detailed explanations to the corporate representative and further advance the deal.

[0047] The system of the present invention aims to support small and medium-sized enterprises in efficiently promoting DX even without specialized knowledge.

[0048] The processing flow will be explained below.

[0049] ---

[0050] Step 1:

[0051] Users open a web page and enter their company's requests and issues through a chat interface. The device then sends the entered information to the server.

[0052] Step 2:

[0053] The server passes the information on requests and issues received from the user to the analysis means, which uses natural language processing technology to analyze the input information and identify any missing information.

[0054] Step 3:

[0055] The question generation means on the server generates additional questions to fill in the missing information based on the information from the analysis means. The generated questions are transferred to the server, and the server then presents the questions to the user via the terminal.

[0056] Step 4:

[0057] The user answers the additional questions and sends the answers via the terminal to the server, which receives the information.

[0058] Step 5:

[0059] The server passes all responses to the analysis means and proposal generation means, which determines the optimal combination of products and services based on all the input information and generates a specific DX plan.

[0060] Step 6:

[0061] The proposal generation means generates specific proposal content and passes it to the estimate generation means, which retrieves price information for the necessary products and services based on the proposal content and automatically creates a rough estimate.

[0062] Step 7:

[0063] The server receives the generated proposal and rough estimate and presents them to the user in real time via the output means. The user can check the proposal and estimate information on a browser.

[0064] Step 8:

[0065] The server notifies the corporate sales representative of the generated proposal and rough estimate information, and the necessary information is transferred to the sales team using the notification means.

[0066] Step 9:

[0067] The corporate sales representative will use the follow-up means to follow up with the user by providing detailed explanations and answering questions, etc. The user can then have specific discussions with the corporate sales representative about the proposal content and details of the transaction.

[0068] ---

[0069] The above is the specific flow of the program's processing. The specific operations at each step enable efficient digital transformation for small and medium-sized enterprises.

[0070] Example 1

[0071] 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."

[0072] To efficiently promote digital transformation, small and medium-sized enterprises need a system that is easy to manage even without specialized knowledge. However, current systems are complex, and few can be quickly customized to meet the specific challenges a company faces or provide appropriate proposals. This makes it difficult for many companies to promote DX. This invention aims to solve these problems by providing a system that allows company personnel to easily input requests and challenges and quickly obtain an appropriate DX plan and rough estimate.

[0073] 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.

[0074] In this invention, the server includes an input means for a company representative to input their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation means and a reception means for presenting the generated additional questions to the company representative and receiving answers, a proposal generation means for analyzing the answers received from the company representative and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, and an output means for presenting the generated proposal content and rough estimate to the company representative. This enables company representatives to quickly obtain an optimal DX plan and rough estimate that addresses their company's issues, even without specialized knowledge.

[0075] A "corporate representative" is someone involved in promoting digital transformation within a company, and is responsible for inputting the company's requests and challenges into the system.

[0076] The "input means" is an interface that allows company personnel to input their company's requests and issues into the system, and is primarily a chat-style interface.

[0077] "Analysis means" refers to a function used to analyze input requests and issues and identify missing information, and includes natural language processing technology.

[0078] The "question generation means" is a function that automatically generates additional questions necessary to fill in missing information.

[0079] The "presentation means and reception means" refers to the function of presenting the generated follow-up questions to the company personnel and receiving the answers, thereby enabling interactive communication.

[0080] The "proposal generation means" is a function that generates an appropriate combination of products and services based on all the information received from the company representative.

[0081] The "quote generation means" is a function that automatically creates a rough estimate based on the price information of the proposed products and services.

[0082] The "output means" is a function for presenting the generated proposal content and rough estimate to the company representative.

[0083] The "notification means" is a function for notifying the corporate sales representative of the generated proposal content and rough estimate.

[0084] The "follow-up means" is a function that enables a corporate sales representative to follow up with a company representative.

[0085] The "plan generation means" is a function that generates an optimal digital transformation plan based on the generated proposals and pricing information for products and services.

[0086] The "real-time presentation means" is a function for presenting the generated plan to the company representative in real time.

[0087] This invention is a system that enables small and medium-sized enterprises to efficiently promote digital transformation (DX). In this system, company personnel input their company's requests and challenges, and a generative AI then asks for additional information, ultimately presenting a specific DX plan and a rough estimate. The specific configuration and processing flow of this system are explained below.

[0088] The system mainly includes the following means:

[0089] 1. Input your requests and issues

[0090] Users can open a web page using a browser and input their company's requests and issues into a chat-style input device. For example, they can enter a specific problem such as, "Our company's inventory management is not going well. We would like to improve efficiency."

[0091] 2. Automatic analysis

[0092] The server receives the requests and issues entered by the user and performs natural language analysis using an analysis means. The analysis means preferably uses Google's (registered trademark) natural language API. As a result of the analysis, missing information is identified.

[0093] 3. Generating and Presenting Follow-Up Questions

[0094] The server's question generation means automatically generates additional questions to supplement the missing information. These questions are presented to the user through a chat interface. For example, a specific question such as "Which part of inventory management are you having trouble with?" is generated.

[0095] 4. Entering and Receiving Additional Information

[0096] The user answers the questions and submits the answer. For example, the user might type, "It's difficult to determine the inventory levels." This answer is also received by the server.

[0097] 5. Proposal Generation

[0098] The server's proposal generation means generates the optimal combination of products and services based on all the information received from the user. This proposal generation means uses a database of products and services that has been trained in advance. For example, products such as inventory management systems and RFID tags are proposed.

[0099] 6. Generate a quote

[0100] The server's quotation generation means automatically calculates the estimated costs based on the price information of the proposed products and services. For example, it creates a quotation that includes the cost of introducing an inventory management system or RFID tags.

[0101] 7. Proposal and quotation

[0102] The final DX plan and rough estimate are presented to the user in real time via the server's output means.

[0103] 8. Follow-up

[0104] The generated proposal and rough estimate are notified to the corporate sales representative by the server's notification means, and the corporate sales representative can provide a detailed explanation to the company representative using follow-up means as necessary.

[0105] Specific examples

[0106] The user enters a prompt like this into the generative AI model:

[0107] Our company's inventory management is not going well. We want to improve efficiency.

[0108] Based on this prompt, the system will:

[0109] 1. The user enters the above prompt sentence.

[0110] 2. The server performs automatic analysis and generates and presents an additional question: "Which part of inventory management are you having trouble with?"

[0111] 3. The user responds, "It's difficult to keep track of inventory."

[0112] 4. The server's proposal generation means proposes products such as inventory management systems and RFID tags.

[0113] 5. The server creates a rough estimate and generates an estimate such as "Inventory management system: ¥500,000, RFID tag: ¥200,000."

[0114] 6. The server presents the generated proposal and rough estimate to the user.

[0115] 7. The proposal and quotation will also be notified to the corporate sales representative, who will contact the user directly as necessary.

[0116] In this way, the server allows users to easily input their company's challenges and utilize the generative AI model to quickly obtain a specific DX plan and rough estimate.

[0117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0118] Step 1:

[0119] A user opens a web page and inputs their company's requests and issues into the chat interface. Specifically, they input something like, "Our inventory management is not going well. We want to improve efficiency." The input requests and issues are sent to the server.

[0120] input:

[0121] Users open a browser and enter their requests or issues into the chat interface.

[0122] output:

[0123] Requests and issues are sent to the server.

[0124] Specific behavior:

[0125] 1. The user opens a browser and accesses the system's web page.

[0126] 2. The user enters their request or issue into the chat interface and clicks the "Send" button.

[0127] Step 2:

[0128] The server receives requests and issues sent by users and performs natural language analysis using an analysis means. As a result of the analysis, missing information is identified.

[0129] input:

[0130] Requests and issues submitted by users.

[0131] output:

[0132] Analysis results identifying missing information.

[0133] Specific behavior:

[0134] 1. The server receives requests and issue data in real time.

[0135] 2. The server's analysis means analyzes the input data using Google's natural language API.

[0136] 3. The server uses the analysis results to identify the missing information.

[0137] Step 3:

[0138] The server's question generation means automatically generates an additional question to supplement the missing information. For example, this question might be, "Which part of inventory management are you having trouble with?" This generated question is then presented to the user.

[0139] input:

[0140] Analysis results based on missing information.

[0141] output:

[0142] Additional question.

[0143] Specific behavior:

[0144] 1. The server's question generation means generates an appropriate question to fill in the missing information.

[0145] 2. The server's output means displays the generated question on the chat interface.

[0146] Step 4:

[0147] The user answers the questions and sends the answer to the server. For example, the user might say, "It's difficult to know how many items are in stock." The answer is then received by the server.

[0148] input:

[0149] The response entered by the user.

[0150] output:

[0151] The user's response data.

[0152] Specific behavior:

[0153] 1. The user checks the question displayed in the chat interface.

[0154] 2. The user enters additional information and clicks the "Submit" button.

[0155] 3. The server receives the response from the user.

[0156] Step 5:

[0157] The server's proposal generator generates the optimal combination of products and services based on all the information received from the user. The proposal generator uses a pre-trained database. For example, it proposes products such as inventory management systems and RFID tags.

[0158] input:

[0159] All information received from the user (initial input and additional information).

[0160] output:

[0161] The best combination of products and services.

[0162] Specific behavior:

[0163] 1. The server's proposal generator consolidates all received user information.

[0164] 2. The server's proposal generation means searches the database for suitable products and services and selects the optimal combination.

[0165] Step 6:

[0166] The server's quotation generation means automatically calculates the estimated costs based on the price information of the proposed products and services. For example, it creates a quotation that includes the cost of introducing an inventory management system or RFID tags.

[0167] input:

[0168] Pricing information for proposed products and services.

[0169] output:

[0170] Rough estimate.

[0171] Specific behavior:

[0172] 1. The server's quote generation means obtains pricing information for the proposed product or service.

[0173] 2. The server calculates a rough estimate based on the price information.

[0174] 3. The server compiles the quotation information into a document format (e.g. PDF).

[0175] Step 7:

[0176] The generated DX plan and rough estimate are presented to the user in real time via the server's output means.

[0177] input:

[0178] Generated DX plan and rough estimate.

[0179] output:

[0180] The DX plan and rough estimate presented to the user.

[0181] Specific behavior:

[0182] 1. The server's output means displays the generated DX plan and quotation on a web page.

[0183] 2. The user refreshes the web page to see the new information.

[0184] Step 8:

[0185] The generated proposal and rough estimate are notified to the corporate sales representative via the server's notification means, who then provides a detailed explanation to the company representative using follow-up means as necessary.

[0186] input:

[0187] Proposals and estimates generated.

[0188] output:

[0189] Notification to Corporate Sales Representatives.

[0190] Specific behavior:

[0191] 1. The server's notification method will notify the corporate sales representative of the proposal details and quotation by email.

[0192] 2. Corporate sales representatives receive notifications and contact users as needed.

[0193] 3. Corporate sales representatives follow up with users to further develop the deal.

[0194] (Application example 1)

[0195] 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."

[0196] When small and medium-sized enterprises (SMEs) plan to introduce factory robots or promote digital transformation, it is difficult to efficiently select the appropriate systems and technologies and obtain quick and accurate estimates. In particular, when there is a lack of specialized knowledge, it can be difficult to determine the optimal combination of technologies and estimate their costs, which can result in delays in promoting digital transformation. There is a need for a support system that can solve these issues and enable SMEs to efficiently realize digital transformation.

[0197] 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.

[0198] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving responses, a proposal generation means for analyzing the received responses and generating an appropriate combination of technologies and systems, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, and an estimate generation means for automatically creating a proposal and a rough estimate for an optimal machine or system based on the analyzed information. This enables small and medium-sized enterprises to efficiently and quickly create DX plans and obtain rough estimates even without specialized knowledge.

[0199] "Corporate representative" refers to a person in a company who is responsible for promoting DX and introducing systems.

[0200] "Requests and issues" refer to business problems the company is facing and specific areas that it would like to improve.

[0201] "Interface means" refers to the user interface through which company personnel input their company's requests and issues.

[0202] "Analysis means" refers to a device or program that analyzes input requests and issues and performs processing to identify missing information.

[0203] "Question generation means" refers to a function that automatically generates additional questions based on missing information.

[0204] The term "presenting and receiving means" refers to a device or program that presents the generated follow-up questions to a company representative and receives the answers thereto.

[0205] "Proposal generation means" refers to a device or program that analyzes the received responses and generates the optimal combination of technologies and systems.

[0206] "Estimate generation means" refers to a function that automatically creates a rough estimate based on the proposal content.

[0207] "Output means" refers to a device or program that presents the generated proposal content and rough estimate to the company representative.

[0208] "Notification means" refers to the function of notifying the corporate sales representative of the generated proposal content and rough estimate.

[0209] "Follow-up means" refers to a device or program that allows a corporate sales representative to provide detailed explanations or additional support to a company representative.

[0210] "DX plan generation means" refers to the function that generates the optimal DX plan based on the generated proposal and pricing information for the technology and system.

[0211] "Real-time presentation means" refers to the function of presenting the generated DX plan to corporate personnel in real time.

[0212] This invention is a system for small and medium-sized enterprises to effectively promote DX (digital transformation), and its specific embodiment is shown below.

[0213] Overall system configuration

[0214] The system mainly includes the following means:

[0215] 1. Interface means: Provide a GUI (graphical user interface) for company personnel to input their company's requests and issues.

[0216] 2. Analysis: Use a natural language processing (NLP) engine to analyze the input request or issue and identify missing information.

[0217] 3. Question generator: A generative AI model to generate additional questions based on missing information.

[0218] 4. Presentation and Reception Means: A communication module for presenting the generated follow-up questions to company personnel and receiving their answers.

[0219] 5. Proposal generation means: A data analysis engine that analyzes the received responses and generates optimal combinations of technologies and systems.

[0220] 6. Estimate generation means: An estimate calculation module that automatically creates a rough estimate based on the proposal.

[0221] 7. Output means: A display module that presents the generated proposal and rough estimate to the company representative.

[0222] 8. Notification method: A notification system to notify corporate sales representatives of the generated proposals and rough estimates.

[0223] 9. Follow-up tool: A CRM (Customer Relationship Management) system that allows corporate sales representatives to follow up with corporate representatives.

[0224] 10. DX plan generation means: A plan generation module for generating an optimal DX plan based on the proposal and pricing information of technologies and systems.

[0225] 11. Real-time presentation method: A module that presents the generated DX plan to company personnel in real time.

[0226] Program processing

[0227] The above means are implemented using the following hardware and software.

[0228] Hardware: A tablet PC, server, smartphone, or computer installed on the robot itself.

[0229] Software: OpenAI® API, natural language processing libraries, data analysis tools, CRM systems.

[0230] Data processing and calculation

[0231] The server uses analysis means to perform natural language processing on the requests and issues received from the company representative and identifies any missing information. Next, it uses question generation means to generate additional questions based on the missing information and presents them to the company representative using presentation and reception means. The data is reanalyzed based on the company representative's answers, and the proposal generation means derives an appropriate combination of technologies and systems. The estimate generation means automatically calculates a rough estimate based on these proposals and presents it to the company representative using output means. The generated proposal and estimate are notified to the corporate sales representative using notification means, and detailed explanations and additional proposals are made using follow-up means. Finally, the DX plan generated by the DX plan generation means is presented to the company representative using real-time presentation means.

[0232] Specific examples

[0233] To illustrate, here is a usage scenario:

[0234] A factory worker enters through the application interface that "the frequency of failures on the current production line is high."

[0235] The generative AI model analyzes this and generates a follow-up question: "Which sections fail most frequently?"

[0236] If the person in charge answers "assembly line," the proposal generation means will propose "automated inspection system for assembly line" and "parts transport robot," and the estimate generation means will create a rough estimate.

[0237] An example prompt is:

[0238] text

[0239] Please enter your company's requirements and challenges: The current production line is failing frequently.

[0240] This invention enables small and medium-sized enterprises to efficiently and quickly create DX plans and obtain rough estimates, even without specialized knowledge.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] Users input requests and issues to the system

[0244] The user (company representative) uses the interface to input their company's requests and issues. For example, they might input "The frequency of breakdowns on the current production line is high." This input text data is then sent to the system.

[0245] Step 2:

[0246] The server analyzes requests and issues

[0247] The server performs natural language processing on the received text data of requests and issues using an analysis means. The analysis means tokenizes the input text data and identifies important keywords and missing information. For example, keywords such as "production line," "failure frequency," and "high" are extracted.

[0248] Step 3:

[0249] Server generates additional questions

[0250] Based on the analysis results, the server generates a follow-up question using a question generation means. A generative AI model is used to automatically generate a question that corresponds to the missing information. For example, a follow-up question such as "Which section has a high failure frequency?" is generated. This follow-up question is presented to the user via the presentation and reception means.

[0251] Step 4:

[0252] User answers additional questions

[0253] The user answers the additional question presented, for example, "assembly line," and this answer is sent back to the system.

[0254] Step 5:

[0255] The server analyzes the answers and generates suggestions

[0256] The server then analyzes the received responses again using its analysis means, and generates the optimal combination of technologies and systems using its proposal generation means. For example, it may propose an "automated inspection system for assembly lines" or a "parts transport robot." These proposals are then pulled from the database and combined.

[0257] Step 6:

[0258] The server creates a rough estimate

[0259] Based on the proposal, the server automatically creates a rough estimate using the estimate generation means. Price information for each proposed technology and system is retrieved from the database, and the estimate is created by adding them up. For example, the unit price of the "automated inspection system" + the unit price of the "parts transport robot" = the total amount of the rough estimate.

[0260] Step 7:

[0261] Present generated proposals and quotes

[0262] The generated proposal and rough estimate are presented to the user via the output means, and the user can review them and input further information or additional requests as necessary.

[0263] Step 8:

[0264] Proposal details and quotes will be sent to the corporate sales representative

[0265] The generated proposal and rough estimate are notified to the corporate sales representative by a notification means, so that the sales representative can prepare for follow-up.

[0266] Step 9:

[0267] Corporate sales representatives will follow up

[0268] Corporate sales reps use follow-up methods to provide further explanations or additional assistance to corporate representatives, such as using a CRM system to contact customers to answer questions or provide demonstrations.

[0269] Step 10:

[0270] Generate and present the final DX plan

[0271] Finally, the DX plan generation means generates an optimal DX plan based on all the information and suggestions. The generated DX plan is presented to the user through the real-time presentation means. The user can check the final DX plan in real time and proceed with preparations for implementation.

[0272] 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.

[0273] ---

[0274] This invention is a system that enables small and medium-sized enterprises to easily promote DX (digital transformation), and in particular combines generative AI and an emotion engine. In this system, company personnel input their company's requests and challenges, and the generative AI then asks for additional information and, taking into account the user's emotions, presents the optimal DX plan and rough estimate. The system mainly includes the following means:

[0275] First, a company representative (user) opens a web page and inputs their company's requests and issues into a chat-style interface. When inputting requests and issues, the user can describe specific problems, such as, "Our company's inventory management is not going well. We would like to improve efficiency." An emotion engine is installed that recognizes the user's emotions from the input, and this emotional information is also collected at the same time.

[0276] Next, the input requests, issues, and emotional data are sent to the server and passed to the analysis means. The analysis means uses natural language processing technology to analyze the input information and identify missing information. The emotion engine also provides the emotional data to the analysis means, which then analyzes the information according to the user's emotional state.

[0277] When missing information is identified, the question generation means automatically generates additional questions to supplement the missing information. The generated questions adaptively adjust the tone and content of the questions and answers based on the emotional information. These questions are then presented to the user from the server via the terminal. When the user answers the presented questions and submits their answers, the information is also received by the server.

[0278] Next, the proposal generation means generates an optimal combination of products and services based on all the information received from the user (requests, issues, and emotional data). The proposal generation means takes into account the data from the emotion engine and makes suggestions based on the user's emotional state. For example, if the user is feeling stressed, it can suggest a simple and intuitive solution, while if the emotional state is positive, it can provide a comprehensive plan with multiple options.

[0279] Based on the created DX plan, the estimate generation means calculates the estimated costs. The estimate generation means automatically creates a rough estimate based on the price information of the proposed products and services, and optimizes the estimate by taking emotional data into consideration. The generated proposal and rough estimate are presented to the user in real time via the output means.

[0280] As a specific example, if a user inputs "Our company's inventory management is not going well. We want to improve efficiency," and the emotion engine recognizes this as "high frustration," the generation AI will present a simple and specific question: "Which part of inventory management are you struggling with?" If the user answers, "It's difficult to keep track of inventory levels," the proposal generation means will suggest simple and intuitive products such as "inventory management systems" and "RFID tags." Based on this proposal, the estimate generation means will create a rough estimate and present it to the user.

[0281] Furthermore, the generated proposal and rough estimate are notified to the corporate sales representative by the notification means, and the corporate sales representative can use the follow-up means to provide a detailed explanation to the company representative as necessary and further advance the transaction.

[0282] The system of this invention aims to help small and medium-sized enterprises effectively promote DX even without specialized knowledge. By introducing an emotion engine, it provides a better user experience and realizes efficient DX promotion.

[0283] The processing flow will be explained below.

[0284] ---

[0285] Step 1:

[0286] The user opens a web page and inputs their company's requests and issues through a chat interface. The device sends the input information to the server. At the same time, the emotion engine collects emotional data from the user's input.

[0287] Step 2:

[0288] The server passes the information on requests and issues received from the user, as well as emotional data, to the analysis means. The analysis means uses natural language processing technology to analyze the input information and identify any missing information. At the same time, the emotion engine provides the emotional data to the analysis means, which then analyzes the information according to the user's emotional state.

[0289] Step 3:

[0290] The question generation means on the server generates additional questions to fill in the missing information based on the information from the analysis means and the emotional data. The tone and content of these questions are adjusted based on the user's emotional state. The generated questions are transferred to the server, which then presents the questions to the user via the terminal.

[0291] Step 4:

[0292] The user answers the additional questions and sends the answers to the server via the terminal, which receives the information and passes it to the analysis means and the proposal generation means.

[0293] Step 5:

[0294] The proposal generation means on the server generates the optimal combination of products and services based on all information received from the user (requests, issues, emotional data). The proposal generation means takes into account data from the emotion engine and makes suggestions based on the user's emotional state. For example, if the user is feeling frustrated, it will propose a simple and intuitive solution.

[0295] Step 6:

[0296] The proposal generation means generates specific proposal details and passes them to the quotation generation means. The quotation generation means incorporates price information for the necessary products and services based on the proposal details and automatically creates a rough quotation. Furthermore, based on data from the emotion engine, it presents the optimal price for the user.

[0297] Step 7:

[0298] The server receives the generated proposal and rough estimate and presents them to the user in real time via the output means. The user can check the proposal and estimate information on a browser.

[0299] Step 8:

[0300] The server notifies the corporate sales representative of the generated proposal and rough estimate information, and the necessary information is transferred to the sales team using the notification means.

[0301] Step 9:

[0302] The corporate sales representative will use the follow-up means to follow up with the user by providing detailed explanations and answering questions, etc. The user can then have specific discussions with the corporate sales representative about the proposal content and details of the transaction.

[0303] ---

[0304] The above is the specific processing flow of the system that combines the emotion engine. The operation at each step enables small and medium-sized enterprises to promote digital transformation in an efficient and emotionally considerate manner.

[0305] Example 2

[0306] 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."

[0307] When small and medium-sized enterprises (SMEs) promote digital transformation (DX), they require specialized knowledge to select and estimate appropriate products and services, and in the process, they require proposals that take the user's emotional state into consideration. This reduces user frustration and ensures efficient and effective DX promotion. However, conventional DX support systems do not take the user's emotional state into account when making proposals or optimizations, resulting in a poor user experience.

[0308] 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.

[0309] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving answers, a proposal generation means for analyzing the received answers and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, an emotion analysis means for collecting and analyzing user emotion data, and an optimization means for taking the emotion data into account in the generated proposal content and rough estimate. This makes it possible to generate optimal proposals and estimates that take the user's emotional state into account.

[0310] "Corporate representative" refers to a person within a company who inputs their company's requests and issues and requests proposals and estimates for promoting DX.

[0311] "Interface means" refers to input means such as a web page or chat interface through which company personnel can input their company's requests and issues.

[0312] "Analysis means" refers to natural language processing technology and analysis engines that analyze input requests and issues and identify missing information.

[0313] The "question generation means" refers to a function that automatically generates additional questions to fill in the missing information identified by the analysis means.

[0314] The "presentation and reception means" refers to a function for presenting the generated follow-up question to a company representative and receiving an answer from the company representative.

[0315] "Proposal generation means" refers to the function of analyzing the received responses and generating an appropriate combination of products and services.

[0316] "Estimate generation means" refers to a function that automatically creates a rough estimate based on the proposal content.

[0317] "Output means" refers to a function for presenting the generated proposal content and rough estimate to the company representative.

[0318] "Emotion analysis means" refers to emotion engines and analysis technologies for collecting and analyzing user emotion data.

[0319] "Optimization means" refers to a function that allows emotional data to be taken into account in the generated proposals and rough estimates.

[0320] "Notification means" refers to a function for notifying corporate sales representatives of the generated proposal contents and rough estimate.

[0321] "Follow-up measures" refers to functions that enable corporate sales representatives to provide additional support and explanations to company representatives and carry out follow-up.

[0322] "DX plan generation means" refers to the function of generating the optimal digital transformation plan based on the generated proposals and price information for products and services.

[0323] "Real-time presentation means" refers to the function for instantly presenting the generated DX plan to company personnel.

[0324] This invention is a system that allows small and medium-sized enterprises to easily promote digital transformation (DX). The system begins by having company personnel input their company's requests and challenges into an interface. The system uses a generative AI model to present an optimal DX plan and rough estimate, taking into account the emotional state of the company personnel.

[0325] First, the user uses the interface means via a web browser. This interface means is a chat interface that allows company personnel to input their company's requests and issues in text format. For example, a specific problem can be input, such as, "Our company's inventory management is not going well. We would like to improve efficiency." The input content is then analyzed by the emotion analysis means, which collects and analyzes the user's emotional data.

[0326] Next, the device transmits this input data and emotion data to a server. The server analyzes the input content using natural language processing technology (e.g., natural language processing API) and identifies missing information. The analysis means acquires the emotion data analyzed by an emotion engine (e.g., emotion analysis API) and performs information analysis according to the user's emotional state.

[0327] When the missing information is identified, a question generation means installed on the server automatically generates a follow-up question using a generative AI model (e.g., the generative AI model GPT-4 (registered trademark)). The generated question is adjusted based on the emotional data, and the tone and content are adaptively changed. The question is presented to the user via the terminal. When the user answers the presented question, the answer data is again sent to the server.

[0328] The server's proposal generation means then generates an appropriate combination of products and services based on all the data received from the user (requests, issues, and emotional data).The proposal generation means takes into account the data from the emotion analysis means and proposes a DX plan based on the user's emotional state.For example, if the user is feeling frustrated, a simple and intuitive solution will be proposed.

[0329] Based on the proposed DX plan, the estimate generation means automatically calculates the estimated costs. The estimate generation means optimizes the estimate based on price information for the proposed products and services, taking into account emotional data. The generated proposal and estimated estimate are presented to the user in real time by the output means.

[0330] As a specific example, if a user inputs "Our inventory management is not going well. We want to improve efficiency," and the sentiment analysis means recognizes this as "high frustration," the generative AI model (GPT-4) will generate a simple and specific question: "Which part of inventory management are you struggling with?" If the user answers, "It's difficult to keep track of inventory levels," the proposal generation means will suggest simple and intuitive products such as "inventory management systems" and "RFID tags." Based on this suggestion, the estimate generation means will create a rough estimate and present it to the user.

[0331] Furthermore, the generated proposal and rough estimate are notified to the corporate sales representative via a notification means, and the corporate sales representative can use the follow-up means to provide additional understanding or explanation to the company representative as necessary to support the progress of the transaction.

[0332] As an example of a prompt sentence, the generative AI model might be given the following prompt:

[0333] "A user wants to improve inventory management efficiency. His current emotional state is frustration. What inventory management system should we suggest?"

[0334] By integrating these methods, this system will help small and medium-sized enterprises effectively promote digital transformation without specialized knowledge, and will provide a better user experience by utilizing emotional data.

[0335] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0336] Step 1:

[0337] Users input their requests and issues

[0338] Input: The user inputs their company's requests and issues into the chat interface. For example, they might type, "Our inventory management is not going well. We want to improve efficiency."

[0339] Operation: The device receives user input and prepares it to be sent to the emotion engine.

[0340] Output: Text data of the input requests and issues.

[0341] Step 2:

[0342] Sending input data and emotion data

[0343] Input: The device sends user input and emotion data to the emotion engine.

[0344] Operation: The device generates an HTTP request to send data to the emotion engine and sends it to the server.

[0345] Output: Text data and emotion data received by the server.

[0346] Step 3:

[0347] The server receives and analyzes the data

[0348] Input: Text data and emotion data received by the server from the device.

[0349] How it works: The server uses a natural language processing module to analyze the text data and identify missing information. The emotion engine simultaneously analyzes the emotion data and understands the user's emotional state.

[0350] Output: Analysis results include missing information and the user's emotional state data.

[0351] Step 4:

[0352] The server generates a follow-up question

[0353] Input: Missing information and emotion data.

[0354] How it works: The server's generative AI model (e.g., GPT-4) generates follow-up questions to fill in missing information. The question generator uses emotional data to adaptively adjust the tone and content of the questions.

[0355] Output: Generated follow-up questions.

[0356] Step 5:

[0357] Submitting a generated question

[0358] Input: The generated follow-up question.

[0359] Operation: The server sends the additional question to the terminal as an HTTP response. The terminal displays the question to the user.

[0360] Output: A follow-up question that is presented to the user.

[0361] Step 6:

[0362] The user answers the question

[0363] Input: When a user sees a follow-up question, they input their answer through the chat interface.

[0364] Operation: The terminal receives the user's response and prepares to send the data to the server.

[0365] Output: The entered response data.

[0366] Step 7:

[0367] The server parses the answer

[0368] Input: User response data.

[0369] Operation: The server again uses the natural language processing module to analyze the response data and identify the appropriate combination of products and services.

[0370] Output: Potential products and services as analysis results.

[0371] Step 8:

[0372] Proposal and quote generation

[0373] Input: potential products and services, user sentiment data.

[0374] Operation: The server's proposal generation means generates optimal proposals taking into account emotion data. The estimate generation means calculates approximate costs based on the proposals. The estimate is also optimized.

[0375] Output: Generated proposal and quote.

[0376] Step 9:

[0377] Presentation by output means

[0378] Input: Generated proposal and quote.

[0379] How it works: The server sends these as HTTP responses to the device, which displays them to the user.

[0380] Output: Proposal and quote presented to the user.

[0381] Step 10:

[0382] Notification and follow-up

[0383] Input: Generated proposal and quote.

[0384] Operation: The server's notification means notifies the corporate sales representative of the generated proposal and quotation. The corporate sales representative uses the follow-up means to provide additional explanations or take action to the company representative as necessary.

[0385] Output: Proposal and quotation notified, and follow-up action taken.

[0386] (Application example 2)

[0387] 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."

[0388] Currently, many small and medium-sized enterprises and factories lack specialized knowledge about production line issues and methods for promoting DX (digital transformation), making it difficult to efficiently solve problems or select appropriate products. Furthermore, adaptive proposals that take into account employee emotions are often not made, often causing stress and confusion. In these cases, there is a need for automated creation of effective DX plans and quotation presentations, especially when promoting DX within factories.

[0389] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0390] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving responses, a proposal generation means for analyzing the received responses and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, a robot arranged on the production line for inputting issues within the factory, an emotion engine for collecting emotional data on the input issues, and an emotion analysis means for adjusting the tone of adaptive questions and proposals based on the emotional data. This enables small and medium-sized enterprises and factories to efficiently and adaptively create DX plans and present estimates while taking into account the emotions of their employees.

[0391] Definitions of important words

[0392] An "interface means" is an input means through which company personnel input their company's requests and issues.

[0393] The "analysis means" is a means for analyzing input requests and issues and identifying missing information.

[0394] The "question generation means" is a means for generating additional questions based on missing information.

[0395] The "presentation and reception means" is a means for presenting the generated follow-up questions to the company personnel and receiving the answers.

[0396] The "proposal generation means" is a means for analyzing the received responses and generating an appropriate combination of products and services.

[0397] The "quote generation means" is a means for automatically creating a rough estimate based on the proposal contents.

[0398] The "output means" is a means for presenting the generated proposal contents and rough estimate to the company representative.

[0399] "Robots installed on the production line" are machines installed in the factory to input tasks.

[0400] The "emotion engine" is a system for collecting emotional data for input tasks.

[0401] "Sentiment analysis means" means for adjusting the tone of adaptive questions and suggestions based on emotional data.

[0402] MODE FOR CARRYING OUT THE INVENTION

[0403] This invention is a system in which company personnel input their company's requests and challenges, and based on that, a generative AI and emotion engine present the optimal DX plan and rough estimate. This system also uses robots placed on production lines with the aim of promoting DX within factories.

[0404] The system mainly includes the following means:

[0405] 1. Interface Method

[0406] Users input their company's issues via voice input or a tablet via a robot placed on the production line.

[0407] The robot sends this input to the emotion engine.

[0408] 2. Emotion Engine

[0409] The emotion engine collects and analyzes the emotion data of the input task. The software used is a natural language processing library (e.g., TextBlob).

[0410] 3. Analysis and Question Generation Methods

[0411] The server analyzes the input request or problem and identifies missing information, using natural language processing technology (e.g., OpenAI's generative AI model).

[0412] A follow-up question is generated based on the missing information and the question is presented to the user through the interface means.

[0413] 4. Means of presentation and reception

[0414] The user answers the generated additional questions and sends them to the server via the robot.

[0415] The server receives this response and parses it again.

[0416] 5. Proposal generation means

[0417] The server generates an appropriate combination of products and services based on the received information and makes adaptive suggestions based on emotional data. For example, if the user is "highly frustrated," it will select a simple and intuitive solution.

[0418] 6. Estimate generation and output methods

[0419] The estimate generating means automatically generates a rough estimate based on the proposal and presents it to the user in real time via the output means, with the result being displayed on the robot's display and / or voice output.

[0420] Hardware and software used

[0421] Hardware: Robots, servers, tablets or voice input devices located on a production line.

[0422] Software: Natural language processing libraries (e.g., TextBlob), generative AI models (e.g., OpenAI's text-davinci-003).

[0423] Specific examples

[0424] For example, suppose a factory staff member inputs, "We want to eliminate the bottleneck on the production line. It's inefficient." In this case, the emotion engine recognizes this as "high frustration." The generation AI then generates a question, "Specifically, where is the problem occurring?" and presents it to the factory staff member. If the factory staff member answers, "Inventory management is difficult," the generation AI suggests introducing an "inventory management system" and "RFID tags." Finally, the estimate generation means creates a rough estimate and presents it to the factory staff member.

[0425] Prompt Sentence Examples

[0426] Please propose the best DX plan and estimate for the following issues: Eliminating bottlenecks in the production line. Inefficiency. Emotional state: High frustration

[0427] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0428] Program processing steps

[0429] Step 1:

[0430] Users input their company's issues using a robot placed on the production line. This input is done via a tablet or a voice recognition system. The input issue (e.g., "We want to eliminate the bottleneck on the production line. It's inefficient.") is sent to the emotion engine.

[0431] Input: User's assignment

[0432] Output: Issue data

[0433] Step 2:

[0434] The emotion engine receives the task data and analyzes the input for emotional data. The emotion engine uses a natural language processing library (e.g., TextBlob) to identify the task's emotional state. The analysis results are sent to the server as emotion tags such as "positive," "negative," or "neutral."

[0435] Input: Issue data

[0436] Data processing: Sentiment analysis

[0437] Output: Emotion tag

[0438] Step 3:

[0439] The server receives the task data and emotion tags, analyzes the task content using an analysis method, and uses a generative AI model (e.g., OpenAI's text-davinci-003) to identify missing information. It then generates follow-up questions based on the missing information.

[0440] Input: issue data, emotion tags

[0441] Data calculation: problem analysis, identification of missing information

[0442] Output: Additional questions

[0443] Step 4:

[0444] The server presents the generated follow-up questions to the user through the interface means (the robot's display or voice output), and the user answers the presented follow-up questions.

[0445] Input: Additional Question

[0446] Output: User's answer

[0447] Step 5:

[0448] The server receives the user's answers and analyzes them again using the analysis method. Based on the analysis results, the server generates an appropriate combination of products and services, taking into account the emotional data. During this generation process, the tone and content of the questions are also adjusted based on the emotional tags.

[0449] Input: User's answer

[0450] Data calculation: response analysis, product and service generation

[0451] Output: Product and service proposals

[0452] Step 6:

[0453] The quotation generation means automatically generates a rough quotation based on the proposal. This quotation information is calculated using the generation AI and includes necessary price information. The generated quotation is presented to the user via the output means.

[0454] Input: Product / service proposal

[0455] Data calculation: Estimate calculation

[0456] Output: Rough estimate

[0457] Step 7:

[0458] The user can review the generated proposal and estimate and follow up if necessary. This process is done in real time and allows the user to ask specific questions or request additional information.

[0459] Input: Proposal details, rough estimate

[0460] Output: User confirmation and follow-up

[0461] In this way, the system of the present invention as a whole enables small and medium-sized enterprises and factories to efficiently and adaptively create DX plans and present estimates while taking into account the feelings of their employees.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] [Second embodiment]

[0466] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0467] 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.

[0468] 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).

[0469] 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.

[0470] 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.

[0471] 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).

[0472] 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.

[0473] 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.

[0474] 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.

[0475] 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.

[0476] 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.

[0477] 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."

[0478] ---

[0479] This invention is a system that enables small and medium-sized enterprises to easily promote DX (digital transformation). In this system, company personnel input their company's requests and challenges, and a generative AI then asks for additional information, ultimately presenting a specific DX plan and a rough estimate. The system mainly includes the following means:

[0480] First, a company representative (user) opens a web page and inputs their company's requests and issues into a chat-style interface. When inputting requests and issues, the company representative can describe specific problems, such as, "Our company's inventory management is not going well. We would like to improve efficiency."

[0481] Next, the input requests and issues are sent to a server and analyzed by an analysis means. The analysis means analyzes the content of the requests and issues using natural language analysis to identify missing information. This indicates the additional information required to generate a DX plan.

[0482] When the missing information is identified, the question generation means automatically generates an additional question to supplement the missing information. The generated question is presented to the user via the presentation and reception means. When the user answers the presented question and transmits the answer, the information is also received by the server.

[0483] Next, the proposal generation means generates the optimal combination of products and services based on all the information received from the user. The proposal generation means selects the optimal combination from a database of products and services that it has learned in advance and creates a specific DX plan.

[0484] Based on the created DX plan, the estimate generation means calculates the estimated costs. The estimate generation means automatically creates an approximate estimate based on the price information of the proposed products and services. The generated proposal details and approximate estimate are presented to the user in real time via the output means.

[0485] As a specific example, if a user inputs, "Our company's inventory management is not going well. We would like to improve efficiency," the generation AI will generate a question about the specific problem of insufficient inventory management, and present it as, "Which part of inventory management are you having trouble with?" If the user responds, "It's difficult to keep track of inventory numbers," the proposal generation means will suggest products such as an "inventory management system" and "RFID tags." Based on this proposal, the estimate generation means will create a rough estimate and present it to the user.

[0486] In this way, corporate representatives can easily input their company's requests and challenges, and by utilizing generative AI, they can quickly obtain a specific DX plan and rough estimate. Furthermore, as a follow-up, the generated proposal and rough estimate are notified to the corporate sales representative via the notification method. If necessary, the corporate sales representative can use the follow-up method to provide detailed explanations to the corporate representative and further advance the deal.

[0487] The system of the present invention aims to support small and medium-sized enterprises in efficiently promoting DX even without specialized knowledge.

[0488] The processing flow will be explained below.

[0489] ---

[0490] Step 1:

[0491] Users open a web page and enter their company's requests and issues through a chat interface. The device then sends the entered information to the server.

[0492] Step 2:

[0493] The server passes the information on requests and issues received from the user to the analysis means, which uses natural language processing technology to analyze the input information and identify any missing information.

[0494] Step 3:

[0495] The question generation means on the server generates additional questions to fill in the missing information based on the information from the analysis means. The generated questions are transferred to the server, and the server then presents the questions to the user via the terminal.

[0496] Step 4:

[0497] The user answers the additional questions and sends the answers via the terminal to the server, which receives the information.

[0498] Step 5:

[0499] The server passes all responses to the analysis means and proposal generation means, which determines the optimal combination of products and services based on all the input information and generates a specific DX plan.

[0500] Step 6:

[0501] The proposal generation means generates specific proposal content and passes it to the estimate generation means, which retrieves price information for the necessary products and services based on the proposal content and automatically creates a rough estimate.

[0502] Step 7:

[0503] The server receives the generated proposal and rough estimate and presents them to the user in real time via the output means. The user can check the proposal and estimate information on a browser.

[0504] Step 8:

[0505] The server notifies the corporate sales representative of the generated proposal and rough estimate information, and the necessary information is transferred to the sales team using the notification means.

[0506] Step 9:

[0507] The corporate sales representative will use the follow-up means to follow up with the user by providing detailed explanations and answering questions, etc. The user can then have specific discussions with the corporate sales representative about the proposal content and details of the transaction.

[0508] ---

[0509] The above is the specific flow of the program's processing. The specific operations at each step enable efficient digital transformation for small and medium-sized enterprises.

[0510] Example 1

[0511] 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."

[0512] To efficiently promote digital transformation, small and medium-sized enterprises need a system that is easy to manage even without specialized knowledge. However, current systems are complex, and few can be quickly customized to meet the specific challenges a company faces or provide appropriate proposals. This makes it difficult for many companies to promote DX. This invention aims to solve these problems by providing a system that allows company personnel to easily input requests and challenges and quickly obtain an appropriate DX plan and rough estimate.

[0513] 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.

[0514] In this invention, the server includes an input means for a company representative to input their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation means and a reception means for presenting the generated additional questions to the company representative and receiving answers, a proposal generation means for analyzing the answers received from the company representative and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, and an output means for presenting the generated proposal content and rough estimate to the company representative. This enables company representatives to quickly obtain an optimal DX plan and rough estimate that addresses their company's issues, even without specialized knowledge.

[0515] A "corporate representative" is someone involved in promoting digital transformation within a company, and is responsible for inputting the company's requests and challenges into the system.

[0516] The "input means" is an interface that allows company personnel to input their company's requests and issues into the system, and is primarily a chat-style interface.

[0517] "Analysis means" refers to a function used to analyze input requests and issues and identify missing information, and includes natural language processing technology.

[0518] The "question generation means" is a function that automatically generates additional questions necessary to fill in missing information.

[0519] The "presentation means and reception means" refers to the function of presenting the generated follow-up questions to the company personnel and receiving the answers, thereby enabling interactive communication.

[0520] The "proposal generation means" is a function that generates an appropriate combination of products and services based on all the information received from the company representative.

[0521] The "quote generation means" is a function that automatically creates a rough estimate based on the price information of the proposed products and services.

[0522] The "output means" is a function for presenting the generated proposal content and rough estimate to the company representative.

[0523] The "notification means" is a function for notifying the corporate sales representative of the generated proposal content and rough estimate.

[0524] The "follow-up means" is a function that enables a corporate sales representative to follow up with a company representative.

[0525] The "plan generation means" is a function that generates an optimal digital transformation plan based on the generated proposals and pricing information for products and services.

[0526] The "real-time presentation means" is a function for presenting the generated plan to the company representative in real time.

[0527] This invention is a system that enables small and medium-sized enterprises to efficiently promote digital transformation (DX). In this system, company personnel input their company's requests and challenges, and a generative AI then asks for additional information, ultimately presenting a specific DX plan and a rough estimate. The specific configuration and processing flow of this system are explained below.

[0528] The system mainly includes the following means:

[0529] 1. Input your requests and issues

[0530] Users can open a web page using a browser and input their company's requests and issues into a chat-style input device. For example, they can enter a specific problem such as, "Our company's inventory management is not going well. We would like to improve efficiency."

[0531] 2. Automatic analysis

[0532] The server receives the requests and issues entered by the user and performs natural language analysis using an analysis means, preferably Google's natural language API. As a result of the analysis, missing information is identified.

[0533] 3. Generating and Presenting Follow-Up Questions

[0534] The server's question generation means automatically generates additional questions to supplement the missing information. These questions are presented to the user through a chat interface. For example, a specific question such as "Which part of inventory management are you having trouble with?" is generated.

[0535] 4. Entering and Receiving Additional Information

[0536] The user answers the questions and submits the answer. For example, the user might type, "It's difficult to determine the inventory levels." This answer is also received by the server.

[0537] 5. Proposal Generation

[0538] The server's proposal generation means generates the optimal combination of products and services based on all the information received from the user. This proposal generation means uses a database of products and services that has been trained in advance. For example, products such as inventory management systems and RFID tags are proposed.

[0539] 6. Generate a quote

[0540] The server's quotation generation means automatically calculates the estimated costs based on the price information of the proposed products and services. For example, it creates a quotation that includes the cost of introducing an inventory management system or RFID tags.

[0541] 7. Proposal and quotation

[0542] The final DX plan and rough estimate are presented to the user in real time via the server's output means.

[0543] 8. Follow-up

[0544] The generated proposal and rough estimate are notified to the corporate sales representative by the server's notification means, and the corporate sales representative can provide a detailed explanation to the company representative using follow-up means as necessary.

[0545] Specific examples

[0546] The user enters a prompt like this into the generative AI model:

[0547] Our company's inventory management is not going well. We want to improve efficiency.

[0548] Based on this prompt, the system will:

[0549] 1. The user enters the above prompt sentence.

[0550] 2. The server performs automatic analysis and generates and presents an additional question: "Which part of inventory management are you having trouble with?"

[0551] 3. The user responds, "It's difficult to keep track of inventory."

[0552] 4. The server's proposal generation means proposes products such as inventory management systems and RFID tags.

[0553] 5. The server creates a rough estimate and generates an estimate such as "Inventory management system: ¥500,000, RFID tag: ¥200,000."

[0554] 6. The server presents the generated proposal and rough estimate to the user.

[0555] 7. The proposal and quotation will also be notified to the corporate sales representative, who will contact the user directly as necessary.

[0556] In this way, the server allows users to easily input their company's challenges and utilize the generative AI model to quickly obtain a specific DX plan and rough estimate.

[0557] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0558] Step 1:

[0559] A user opens a web page and inputs their company's requests and issues into the chat interface. Specifically, they input something like, "Our inventory management is not going well. We want to improve efficiency." The input requests and issues are sent to the server.

[0560] input:

[0561] Users open a browser and enter their requests or issues into the chat interface.

[0562] output:

[0563] Requests and issues are sent to the server.

[0564] Specific behavior:

[0565] 1. The user opens a browser and accesses the system's web page.

[0566] 2. The user enters their request or issue into the chat interface and clicks the "Send" button.

[0567] Step 2:

[0568] The server receives requests and issues sent by users and performs natural language analysis using an analysis means. As a result of the analysis, missing information is identified.

[0569] input:

[0570] Requests and issues submitted by users.

[0571] output:

[0572] Analysis results identifying missing information.

[0573] Specific behavior:

[0574] 1. The server receives requests and issue data in real time.

[0575] 2. The server's analysis means analyzes the input data using Google's natural language API.

[0576] 3. The server uses the analysis results to identify the missing information.

[0577] Step 3:

[0578] The server's question generation means automatically generates an additional question to supplement the missing information. For example, this question might be, "Which part of inventory management are you having trouble with?" This generated question is then presented to the user.

[0579] input:

[0580] Analysis results based on missing information.

[0581] output:

[0582] Additional question.

[0583] Specific behavior:

[0584] 1. The server's question generation means generates an appropriate question to fill in the missing information.

[0585] 2. The server's output means displays the generated question on the chat interface.

[0586] Step 4:

[0587] The user answers the questions and sends the answer to the server. For example, the user might say, "It's difficult to know how many items are in stock." The answer is then received by the server.

[0588] input:

[0589] The response entered by the user.

[0590] output:

[0591] The user's response data.

[0592] Specific behavior:

[0593] 1. The user checks the question displayed in the chat interface.

[0594] 2. The user enters additional information and clicks the "Submit" button.

[0595] 3. The server receives the response from the user.

[0596] Step 5:

[0597] The server's proposal generator generates the optimal combination of products and services based on all the information received from the user. The proposal generator uses a pre-trained database. For example, it proposes products such as inventory management systems and RFID tags.

[0598] input:

[0599] All information received from the user (initial input and additional information).

[0600] output:

[0601] The best combination of products and services.

[0602] Specific behavior:

[0603] 1. The server's proposal generator consolidates all received user information.

[0604] 2. The server's proposal generation means searches the database for suitable products and services and selects the optimal combination.

[0605] Step 6:

[0606] The server's quotation generation means automatically calculates the estimated costs based on the price information of the proposed products and services. For example, it creates a quotation that includes the cost of introducing an inventory management system or RFID tags.

[0607] input:

[0608] Pricing information for proposed products and services.

[0609] output:

[0610] Rough estimate.

[0611] Specific behavior:

[0612] 1. The server's quote generation means obtains pricing information for the proposed product or service.

[0613] 2. The server calculates a rough estimate based on the price information.

[0614] 3. The server compiles the quotation information into a document format (e.g. PDF).

[0615] Step 7:

[0616] The generated DX plan and rough estimate are presented to the user in real time via the server's output means.

[0617] input:

[0618] Generated DX plan and rough estimate.

[0619] output:

[0620] The DX plan and rough estimate presented to the user.

[0621] Specific behavior:

[0622] 1. The server's output means displays the generated DX plan and quotation on a web page.

[0623] 2. The user refreshes the web page to see the new information.

[0624] Step 8:

[0625] The generated proposal and rough estimate are notified to the corporate sales representative via the server's notification means, who then provides a detailed explanation to the company representative using follow-up means as necessary.

[0626] input:

[0627] Proposals and estimates generated.

[0628] output:

[0629] Notification to Corporate Sales Representatives.

[0630] Specific behavior:

[0631] 1. The server's notification method will notify the corporate sales representative of the proposal details and quotation by email.

[0632] 2. Corporate sales representatives receive notifications and contact users as needed.

[0633] 3. Corporate sales representatives follow up with users to further develop the deal.

[0634] (Application example 1)

[0635] 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."

[0636] When small and medium-sized enterprises (SMEs) plan to introduce factory robots or promote digital transformation, it is difficult to efficiently select the appropriate systems and technologies and obtain quick and accurate estimates. In particular, when there is a lack of specialized knowledge, it can be difficult to determine the optimal combination of technologies and estimate their costs, which can result in delays in promoting digital transformation. There is a need for a support system that can solve these issues and enable SMEs to efficiently realize digital transformation.

[0637] 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.

[0638] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving responses, a proposal generation means for analyzing the received responses and generating an appropriate combination of technologies and systems, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, and an estimate generation means for automatically creating a proposal and a rough estimate for an optimal machine or system based on the analyzed information. This enables small and medium-sized enterprises to efficiently and quickly create DX plans and obtain rough estimates even without specialized knowledge.

[0639] "Corporate representative" refers to a person in a company who is responsible for promoting DX and introducing systems.

[0640] "Requests and issues" refer to business problems the company is facing and specific areas that it would like to improve.

[0641] "Interface means" refers to the user interface through which company personnel input their company's requests and issues.

[0642] "Analysis means" refers to a device or program that analyzes input requests and issues and performs processing to identify missing information.

[0643] "Question generation means" refers to a function that automatically generates additional questions based on missing information.

[0644] The term "presenting and receiving means" refers to a device or program that presents the generated follow-up questions to a company representative and receives the answers thereto.

[0645] "Proposal generation means" refers to a device or program that analyzes the received responses and generates the optimal combination of technologies and systems.

[0646] "Estimate generation means" refers to a function that automatically creates a rough estimate based on the proposal content.

[0647] "Output means" refers to a device or program that presents the generated proposal content and rough estimate to the company representative.

[0648] "Notification means" refers to the function of notifying the corporate sales representative of the generated proposal content and rough estimate.

[0649] "Follow-up means" refers to a device or program that allows a corporate sales representative to provide detailed explanations or additional support to a company representative.

[0650] "DX plan generation means" refers to the function that generates the optimal DX plan based on the generated proposal and pricing information for the technology and system.

[0651] "Real-time presentation means" refers to the function of presenting the generated DX plan to corporate personnel in real time.

[0652] This invention is a system for small and medium-sized enterprises to effectively promote DX (digital transformation), and its specific embodiment is shown below.

[0653] Overall system configuration

[0654] The system mainly includes the following means:

[0655] 1. Interface means: Provide a GUI (graphical user interface) for company personnel to input their company's requests and issues.

[0656] 2. Analysis: Use a natural language processing (NLP) engine to analyze the input request or issue and identify missing information.

[0657] 3. Question generator: A generative AI model to generate additional questions based on missing information.

[0658] 4. Presentation and Reception Means: A communication module for presenting the generated follow-up questions to company personnel and receiving their answers.

[0659] 5. Proposal generation means: A data analysis engine that analyzes the received responses and generates optimal combinations of technologies and systems.

[0660] 6. Estimate generation means: An estimate calculation module that automatically creates a rough estimate based on the proposal.

[0661] 7. Output means: A display module that presents the generated proposal and rough estimate to the company representative.

[0662] 8. Notification method: A notification system to notify corporate sales representatives of the generated proposals and rough estimates.

[0663] 9. Follow-up tool: A CRM (Customer Relationship Management) system that allows corporate sales representatives to follow up with corporate representatives.

[0664] 10. DX plan generation means: A plan generation module for generating an optimal DX plan based on the proposal and pricing information of technologies and systems.

[0665] 11. Real-time presentation method: A module that presents the generated DX plan to company personnel in real time.

[0666] Program processing

[0667] The above means are implemented using the following hardware and software.

[0668] Hardware: A tablet PC, server, smartphone, or computer installed on the robot itself.

[0669] Software: OpenAI API, natural language processing libraries, data analysis tools, CRM systems.

[0670] Data processing and calculation

[0671] The server uses analysis means to perform natural language processing on the requests and issues received from the company representative and identifies any missing information. Next, it uses question generation means to generate additional questions based on the missing information and presents them to the company representative using presentation and reception means. The data is reanalyzed based on the company representative's answers, and the proposal generation means derives an appropriate combination of technologies and systems. The estimate generation means automatically calculates a rough estimate based on these proposals and presents it to the company representative using output means. The generated proposal and estimate are notified to the corporate sales representative using notification means, and detailed explanations and additional proposals are made using follow-up means. Finally, the DX plan generated by the DX plan generation means is presented to the company representative using real-time presentation means.

[0672] Specific examples

[0673] To illustrate, here is a usage scenario:

[0674] A factory worker enters through the application interface that "the frequency of failures on the current production line is high."

[0675] The generative AI model analyzes this and generates a follow-up question: "Which sections fail most frequently?"

[0676] If the person in charge answers "assembly line," the proposal generation means will propose "automated inspection system for assembly line" and "parts transport robot," and the estimate generation means will create a rough estimate.

[0677] An example prompt is:

[0678] text

[0679] Please enter your company's requirements and challenges: The current production line is failing frequently.

[0680] This invention enables small and medium-sized enterprises to efficiently and quickly create DX plans and obtain rough estimates, even without specialized knowledge.

[0681] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0682] Step 1:

[0683] Users input requests and issues to the system

[0684] The user (company representative) uses the interface to input their company's requests and issues. For example, they might input "The frequency of breakdowns on the current production line is high." This input text data is then sent to the system.

[0685] Step 2:

[0686] The server analyzes requests and issues

[0687] The server performs natural language processing on the received text data of requests and issues using an analysis means. The analysis means tokenizes the input text data and identifies important keywords and missing information. For example, keywords such as "production line," "failure frequency," and "high" are extracted.

[0688] Step 3:

[0689] Server generates additional questions

[0690] Based on the analysis results, the server generates a follow-up question using a question generation means. A generative AI model is used to automatically generate a question that corresponds to the missing information. For example, a follow-up question such as "Which section has a high failure frequency?" is generated. This follow-up question is presented to the user via the presentation and reception means.

[0691] Step 4:

[0692] User answers additional questions

[0693] The user answers the additional question presented, for example, "assembly line," and this answer is sent back to the system.

[0694] Step 5:

[0695] The server analyzes the answers and generates suggestions

[0696] The server then analyzes the received responses again using its analysis means, and generates the optimal combination of technologies and systems using its proposal generation means. For example, it may propose an "automated inspection system for assembly lines" or a "parts transport robot." These proposals are then pulled from the database and combined.

[0697] Step 6:

[0698] The server creates a rough estimate

[0699] Based on the proposal, the server automatically creates a rough estimate using the estimate generation means. Price information for each proposed technology and system is retrieved from the database, and the estimate is created by adding them up. For example, the unit price of the "automated inspection system" + the unit price of the "parts transport robot" = the total amount of the rough estimate.

[0700] Step 7:

[0701] Present generated proposals and quotes

[0702] The generated proposal and rough estimate are presented to the user via the output means, and the user can review them and input further information or additional requests as necessary.

[0703] Step 8:

[0704] Proposal details and quotes will be sent to the corporate sales representative

[0705] The generated proposal and rough estimate are notified to the corporate sales representative by a notification means, so that the sales representative can prepare for follow-up.

[0706] Step 9:

[0707] Corporate sales representatives will follow up

[0708] Corporate sales reps use follow-up methods to provide further explanations or additional assistance to corporate representatives, such as using a CRM system to contact customers to answer questions or provide demonstrations.

[0709] Step 10:

[0710] Generate and present the final DX plan

[0711] Finally, the DX plan generation means generates an optimal DX plan based on all the information and suggestions. The generated DX plan is presented to the user through the real-time presentation means. The user can check the final DX plan in real time and proceed with preparations for implementation.

[0712] 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.

[0713] ---

[0714] This invention is a system that enables small and medium-sized enterprises to easily promote DX (digital transformation), and in particular combines generative AI and an emotion engine. In this system, company personnel input their company's requests and challenges, and the generative AI then asks for additional information and, taking into account the user's emotions, presents the optimal DX plan and rough estimate. The system mainly includes the following means:

[0715] First, a company representative (user) opens a web page and inputs their company's requests and issues into a chat-style interface. When inputting requests and issues, the user can describe specific problems, such as, "Our company's inventory management is not going well. We would like to improve efficiency." An emotion engine is installed that recognizes the user's emotions from the input, and this emotional information is also collected at the same time.

[0716] Next, the input requests, issues, and emotional data are sent to the server and passed to the analysis means. The analysis means uses natural language processing technology to analyze the input information and identify missing information. The emotion engine also provides the emotional data to the analysis means, which then analyzes the information according to the user's emotional state.

[0717] When missing information is identified, the question generation means automatically generates additional questions to supplement the missing information. The generated questions adaptively adjust the tone and content of the questions and answers based on the emotional information. These questions are then presented to the user from the server via the terminal. When the user answers the presented questions and submits their answers, the information is also received by the server.

[0718] Next, the proposal generation means generates an optimal combination of products and services based on all the information received from the user (requests, issues, and emotional data). The proposal generation means takes into account the data from the emotion engine and makes suggestions based on the user's emotional state. For example, if the user is feeling stressed, it can suggest a simple and intuitive solution, while if the emotional state is positive, it can provide a comprehensive plan with multiple options.

[0719] Based on the created DX plan, the estimate generation means calculates the estimated costs. The estimate generation means automatically creates a rough estimate based on the price information of the proposed products and services, and optimizes the estimate by taking emotional data into consideration. The generated proposal and rough estimate are presented to the user in real time via the output means.

[0720] As a specific example, if a user inputs "Our company's inventory management is not going well. We want to improve efficiency," and the emotion engine recognizes this as "high frustration," the generation AI will present a simple and specific question: "Which part of inventory management are you struggling with?" If the user answers, "It's difficult to keep track of inventory levels," the proposal generation means will suggest simple and intuitive products such as "inventory management systems" and "RFID tags." Based on this proposal, the estimate generation means will create a rough estimate and present it to the user.

[0721] Furthermore, the generated proposal and rough estimate are notified to the corporate sales representative by the notification means, and the corporate sales representative can use the follow-up means to provide a detailed explanation to the company representative as necessary and further advance the transaction.

[0722] The system of this invention aims to help small and medium-sized enterprises effectively promote DX even without specialized knowledge. By introducing an emotion engine, it provides a better user experience and realizes efficient DX promotion.

[0723] The processing flow will be explained below.

[0724] ---

[0725] Step 1:

[0726] The user opens a web page and inputs their company's requests and issues through a chat interface. The device sends the input information to the server. At the same time, the emotion engine collects emotional data from the user's input.

[0727] Step 2:

[0728] The server passes the information on requests and issues received from the user, as well as emotional data, to the analysis means. The analysis means uses natural language processing technology to analyze the input information and identify any missing information. At the same time, the emotion engine provides the emotional data to the analysis means, which then analyzes the information according to the user's emotional state.

[0729] Step 3:

[0730] The question generation means on the server generates additional questions to fill in the missing information based on the information from the analysis means and the emotional data. The tone and content of these questions are adjusted based on the user's emotional state. The generated questions are transferred to the server, which then presents the questions to the user via the terminal.

[0731] Step 4:

[0732] The user answers the additional questions and sends the answers to the server via the terminal, which receives the information and passes it to the analysis means and the proposal generation means.

[0733] Step 5:

[0734] The proposal generation means on the server generates the optimal combination of products and services based on all information received from the user (requests, issues, emotional data). The proposal generation means takes into account data from the emotion engine and makes suggestions based on the user's emotional state. For example, if the user is feeling frustrated, it will propose a simple and intuitive solution.

[0735] Step 6:

[0736] The proposal generation means generates specific proposal details and passes them to the quotation generation means. The quotation generation means incorporates price information for the necessary products and services based on the proposal details and automatically creates a rough quotation. Furthermore, based on data from the emotion engine, it presents the optimal price for the user.

[0737] Step 7:

[0738] The server receives the generated proposal and rough estimate and presents them to the user in real time via the output means. The user can check the proposal and estimate information on a browser.

[0739] Step 8:

[0740] The server notifies the corporate sales representative of the generated proposal and rough estimate information, and the necessary information is transferred to the sales team using the notification means.

[0741] Step 9:

[0742] The corporate sales representative will use the follow-up means to follow up with the user by providing detailed explanations and answering questions, etc. The user can then have specific discussions with the corporate sales representative about the proposal content and details of the transaction.

[0743] ---

[0744] The above is the specific processing flow of the system that combines the emotion engine. The operation at each step enables small and medium-sized enterprises to promote digital transformation in an efficient and emotionally considerate manner.

[0745] Example 2

[0746] 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."

[0747] When small and medium-sized enterprises (SMEs) promote digital transformation (DX), they require specialized knowledge to select and estimate appropriate products and services, and in the process, they require proposals that take the user's emotional state into consideration. This reduces user frustration and ensures efficient and effective DX promotion. However, conventional DX support systems do not take the user's emotional state into account when making proposals or optimizations, resulting in a poor user experience.

[0748] 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.

[0749] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving answers, a proposal generation means for analyzing the received answers and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, an emotion analysis means for collecting and analyzing user emotion data, and an optimization means for taking the emotion data into account in the generated proposal content and rough estimate. This makes it possible to generate optimal proposals and estimates that take the user's emotional state into account.

[0750] "Corporate representative" refers to a person within a company who inputs their company's requests and issues and requests proposals and estimates for promoting DX.

[0751] "Interface means" refers to input means such as a web page or chat interface through which company personnel can input their company's requests and issues.

[0752] "Analysis means" refers to natural language processing technology and analysis engines that analyze input requests and issues and identify missing information.

[0753] The "question generation means" refers to a function that automatically generates additional questions to fill in the missing information identified by the analysis means.

[0754] The "presentation and reception means" refers to a function for presenting the generated follow-up question to a company representative and receiving an answer from the company representative.

[0755] "Proposal generation means" refers to the function of analyzing the received responses and generating an appropriate combination of products and services.

[0756] "Estimate generation means" refers to a function that automatically creates a rough estimate based on the proposal content.

[0757] "Output means" refers to a function for presenting the generated proposal content and rough estimate to the company representative.

[0758] "Emotion analysis means" refers to emotion engines and analysis technologies for collecting and analyzing user emotion data.

[0759] "Optimization means" refers to a function that allows emotional data to be taken into account in the generated proposals and rough estimates.

[0760] "Notification means" refers to a function for notifying corporate sales representatives of the generated proposal contents and rough estimate.

[0761] "Follow-up measures" refers to functions that enable corporate sales representatives to provide additional support and explanations to company representatives and carry out follow-up.

[0762] "DX plan generation means" refers to the function of generating the optimal digital transformation plan based on the generated proposals and price information for products and services.

[0763] "Real-time presentation means" refers to the function for instantly presenting the generated DX plan to company personnel.

[0764] This invention is a system that allows small and medium-sized enterprises to easily promote digital transformation (DX). The system begins by having company personnel input their company's requests and challenges into an interface. The system uses a generative AI model to present an optimal DX plan and rough estimate, taking into account the emotional state of the company personnel.

[0765] First, the user uses the interface means via a web browser. This interface means is a chat interface that allows company personnel to input their company's requests and issues in text format. For example, a specific problem can be input, such as, "Our company's inventory management is not going well. We would like to improve efficiency." The input content is then analyzed by the emotion analysis means, which collects and analyzes the user's emotional data.

[0766] Next, the device transmits this input data and emotion data to a server. The server analyzes the input content using natural language processing technology (e.g., natural language processing API) and identifies missing information. The analysis means acquires the emotion data analyzed by an emotion engine (e.g., emotion analysis API) and performs information analysis according to the user's emotional state.

[0767] When missing information is identified, a question generation means installed on the server automatically generates additional questions using a generative AI model (e.g., the generative AI model GPT-4). The generated questions are adjusted based on the emotional data, and the tone and content are adaptively changed. The questions are presented to the user via the device. When the user answers the presented questions, the answer data is again sent to the server.

[0768] The server's proposal generation means then generates an appropriate combination of products and services based on all the data received from the user (requests, issues, and emotional data).The proposal generation means takes into account the data from the emotion analysis means and proposes a DX plan based on the user's emotional state.For example, if the user is feeling frustrated, a simple and intuitive solution will be proposed.

[0769] Based on the proposed DX plan, the estimate generation means automatically calculates the estimated costs. The estimate generation means optimizes the estimate based on price information for the proposed products and services, taking into account emotional data. The generated proposal and estimated estimate are presented to the user in real time by the output means.

[0770] As a specific example, if a user inputs "Our inventory management is not going well. We want to improve efficiency," and the sentiment analysis means recognizes this as "high frustration," the generative AI model (GPT-4) will generate a simple and specific question: "Which part of inventory management are you struggling with?" If the user answers, "It's difficult to keep track of inventory levels," the proposal generation means will suggest simple and intuitive products such as "inventory management systems" and "RFID tags." Based on this suggestion, the estimate generation means will create a rough estimate and present it to the user.

[0771] Furthermore, the generated proposal and rough estimate are notified to the corporate sales representative via a notification means, and the corporate sales representative can use the follow-up means to provide additional understanding or explanation to the company representative as necessary to support the progress of the transaction.

[0772] As an example of a prompt sentence, the generative AI model might be given the following prompt:

[0773] "A user wants to improve inventory management efficiency. His current emotional state is frustration. What inventory management system should we suggest?"

[0774] By integrating these methods, this system will help small and medium-sized enterprises effectively promote digital transformation without specialized knowledge, and will provide a better user experience by utilizing emotional data.

[0775] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0776] Step 1:

[0777] Users input their requests and issues

[0778] Input: The user inputs their company's requests and issues into the chat interface. For example, they might type, "Our inventory management is not going well. We want to improve efficiency."

[0779] Operation: The device receives user input and prepares it to be sent to the emotion engine.

[0780] Output: Text data of the input requests and issues.

[0781] Step 2:

[0782] Sending input data and emotion data

[0783] Input: The device sends user input and emotion data to the emotion engine.

[0784] Operation: The device generates an HTTP request to send data to the emotion engine and sends it to the server.

[0785] Output: Text data and emotion data received by the server.

[0786] Step 3:

[0787] The server receives and analyzes the data

[0788] Input: Text data and emotion data received by the server from the device.

[0789] How it works: The server uses a natural language processing module to analyze the text data and identify missing information. The emotion engine simultaneously analyzes the emotion data and understands the user's emotional state.

[0790] Output: Analysis results include missing information and the user's emotional state data.

[0791] Step 4:

[0792] The server generates a follow-up question

[0793] Input: Missing information and emotion data.

[0794] How it works: The server's generative AI model (e.g., GPT-4) generates follow-up questions to fill in missing information. The question generator uses emotional data to adaptively adjust the tone and content of the questions.

[0795] Output: Generated follow-up questions.

[0796] Step 5:

[0797] Submitting a generated question

[0798] Input: The generated follow-up question.

[0799] Operation: The server sends the additional question to the terminal as an HTTP response. The terminal displays the question to the user.

[0800] Output: A follow-up question that is presented to the user.

[0801] Step 6:

[0802] The user answers the question

[0803] Input: When a user sees a follow-up question, they input their answer through the chat interface.

[0804] Operation: The terminal receives the user's response and prepares to send the data to the server.

[0805] Output: The entered response data.

[0806] Step 7:

[0807] The server parses the answer

[0808] Input: User response data.

[0809] Operation: The server again uses the natural language processing module to analyze the response data and identify the appropriate combination of products and services.

[0810] Output: Potential products and services as analysis results.

[0811] Step 8:

[0812] Proposal and quote generation

[0813] Input: potential products and services, user sentiment data.

[0814] Operation: The server's proposal generation means generates optimal proposals taking into account emotion data. The estimate generation means calculates approximate costs based on the proposals. The estimate is also optimized.

[0815] Output: Generated proposal and quote.

[0816] Step 9:

[0817] Presentation by output means

[0818] Input: Generated proposal and quote.

[0819] How it works: The server sends these as HTTP responses to the device, which displays them to the user.

[0820] Output: Proposal and quote presented to the user.

[0821] Step 10:

[0822] Notification and follow-up

[0823] Input: Generated proposal and quote.

[0824] Operation: The server's notification means notifies the corporate sales representative of the generated proposal and quotation. The corporate sales representative uses the follow-up means to provide additional explanations or take action to the company representative as necessary.

[0825] Output: Proposal and quotation notified, and follow-up action taken.

[0826] (Application example 2)

[0827] 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."

[0828] Currently, many small and medium-sized enterprises and factories lack specialized knowledge about production line issues and methods for promoting DX (digital transformation), making it difficult to efficiently solve problems or select appropriate products. Furthermore, adaptive proposals that take into account employee emotions are often not made, often causing stress and confusion. In these cases, there is a need for automated creation of effective DX plans and quotation presentations, especially when promoting DX within factories.

[0829] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0830] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving responses, a proposal generation means for analyzing the received responses and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, a robot arranged on the production line for inputting issues within the factory, an emotion engine for collecting emotional data on the input issues, and an emotion analysis means for adjusting the tone of adaptive questions and proposals based on the emotional data. This enables small and medium-sized enterprises and factories to efficiently and adaptively create DX plans and present estimates while taking into account the emotions of their employees.

[0831] Definitions of important words

[0832] An "interface means" is an input means through which company personnel input their company's requests and issues.

[0833] The "analysis means" is a means for analyzing input requests and issues and identifying missing information.

[0834] The "question generation means" is a means for generating additional questions based on missing information.

[0835] The "presentation and reception means" is a means for presenting the generated follow-up questions to the company personnel and receiving the answers.

[0836] The "proposal generation means" is a means for analyzing the received responses and generating an appropriate combination of products and services.

[0837] The "quote generation means" is a means for automatically creating a rough estimate based on the proposal contents.

[0838] The "output means" is a means for presenting the generated proposal contents and rough estimate to the company representative.

[0839] "Robots installed on the production line" are machines installed in the factory to input tasks.

[0840] The "emotion engine" is a system for collecting emotional data for input tasks.

[0841] "Sentiment analysis means" means for adjusting the tone of adaptive questions and suggestions based on emotional data.

[0842] MODE FOR CARRYING OUT THE INVENTION

[0843] This invention is a system in which company personnel input their company's requests and challenges, and based on that, a generative AI and emotion engine present the optimal DX plan and rough estimate. This system also uses robots placed on production lines with the aim of promoting DX within factories.

[0844] The system mainly includes the following means:

[0845] 1. Interface Method

[0846] Users input their company's issues via voice input or a tablet via a robot placed on the production line.

[0847] The robot sends this input to the emotion engine.

[0848] 2. Emotion Engine

[0849] The emotion engine collects and analyzes the emotion data of the input task. The software used is a natural language processing library (e.g., TextBlob).

[0850] 3. Analysis and Question Generation Methods

[0851] The server analyzes the input request or problem and identifies missing information, using natural language processing technology (e.g., OpenAI's generative AI model).

[0852] A follow-up question is generated based on the missing information and the question is presented to the user through the interface means.

[0853] 4. Means of presentation and reception

[0854] The user answers the generated additional questions and sends them to the server via the robot.

[0855] The server receives this response and parses it again.

[0856] 5. Proposal generation means

[0857] The server generates an appropriate combination of products and services based on the received information and makes adaptive suggestions based on emotional data. For example, if the user is "highly frustrated," it will select a simple and intuitive solution.

[0858] 6. Estimate generation and output methods

[0859] The estimate generating means automatically generates a rough estimate based on the proposal and presents it to the user in real time via the output means, with the result being displayed on the robot's display and / or voice output.

[0860] Hardware and software used

[0861] Hardware: Robots, servers, tablets or voice input devices located on a production line.

[0862] Software: Natural language processing libraries (e.g., TextBlob), generative AI models (e.g., OpenAI's text-davinci-003).

[0863] Specific examples

[0864] For example, suppose a factory staff member inputs, "We want to eliminate the bottleneck on the production line. It's inefficient." In this case, the emotion engine recognizes this as "high frustration." The generation AI then generates a question, "Specifically, where is the problem occurring?" and presents it to the factory staff member. If the factory staff member answers, "Inventory management is difficult," the generation AI suggests introducing an "inventory management system" and "RFID tags." Finally, the estimate generation means creates a rough estimate and presents it to the factory staff member.

[0865] Prompt Sentence Examples

[0866] Please propose the best DX plan and estimate for the following issues: Eliminating bottlenecks in the production line. Inefficiency. Emotional state: High frustration

[0867] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0868] Program processing steps

[0869] Step 1:

[0870] Users input their company's issues using a robot placed on the production line. This input is done via a tablet or a voice recognition system. The input issue (e.g., "We want to eliminate the bottleneck on the production line. It's inefficient.") is sent to the emotion engine.

[0871] Input: User's assignment

[0872] Output: Issue data

[0873] Step 2:

[0874] The emotion engine receives the task data and analyzes the input for emotional data. The emotion engine uses a natural language processing library (e.g., TextBlob) to identify the task's emotional state. The analysis results are sent to the server as emotion tags such as "positive," "negative," or "neutral."

[0875] Input: Issue data

[0876] Data processing: Sentiment analysis

[0877] Output: Emotion tag

[0878] Step 3:

[0879] The server receives the task data and emotion tags, analyzes the task content using an analysis method, and uses a generative AI model (e.g., OpenAI's text-davinci-003) to identify missing information. It then generates follow-up questions based on the missing information.

[0880] Input: issue data, emotion tags

[0881] Data calculation: problem analysis, identification of missing information

[0882] Output: Additional questions

[0883] Step 4:

[0884] The server presents the generated follow-up questions to the user through the interface means (the robot's display or voice output), and the user answers the presented follow-up questions.

[0885] Input: Additional Question

[0886] Output: User's answer

[0887] Step 5:

[0888] The server receives the user's answers and analyzes them again using the analysis method. Based on the analysis results, the server generates an appropriate combination of products and services, taking into account the emotional data. During this generation process, the tone and content of the questions are also adjusted based on the emotional tags.

[0889] Input: User's answer

[0890] Data calculation: response analysis, product and service generation

[0891] Output: Product and service proposals

[0892] Step 6:

[0893] The quotation generation means automatically generates a rough quotation based on the proposal. This quotation information is calculated using the generation AI and includes necessary price information. The generated quotation is presented to the user via the output means.

[0894] Input: Product / service proposal

[0895] Data calculation: Estimate calculation

[0896] Output: Rough estimate

[0897] Step 7:

[0898] The user can review the generated proposal and estimate and follow up if necessary. This process is done in real time and allows the user to ask specific questions or request additional information.

[0899] Input: Proposal details, rough estimate

[0900] Output: User confirmation and follow-up

[0901] In this way, the system of the present invention as a whole enables small and medium-sized enterprises and factories to efficiently and adaptively create DX plans and present estimates while taking into account the feelings of their employees.

[0902] 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.

[0903] 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.

[0904] 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.

[0905] [Third embodiment]

[0906] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0907] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0908] 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).

[0909] 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.

[0910] 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.

[0911] 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).

[0912] 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.

[0913] 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.

[0914] 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.

[0915] 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.

[0916] 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.

[0917] 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."

[0918] ---

[0919] This invention is a system that enables small and medium-sized enterprises to easily promote DX (digital transformation). In this system, company personnel input their company's requests and challenges, and a generative AI then asks for additional information, ultimately presenting a specific DX plan and a rough estimate. The system mainly includes the following means:

[0920] First, a company representative (user) opens a web page and inputs their company's requests and issues into a chat-style interface. When inputting requests and issues, the company representative can describe specific problems, such as, "Our company's inventory management is not going well. We would like to improve efficiency."

[0921] Next, the input requests and issues are sent to a server and analyzed by an analysis means. The analysis means analyzes the content of the requests and issues using natural language analysis to identify missing information. This indicates the additional information required to generate a DX plan.

[0922] When the missing information is identified, the question generation means automatically generates an additional question to supplement the missing information. The generated question is presented to the user via the presentation and reception means. When the user answers the presented question and transmits the answer, the information is also received by the server.

[0923] Next, the proposal generation means generates the optimal combination of products and services based on all the information received from the user. The proposal generation means selects the optimal combination from a database of products and services that it has learned in advance and creates a specific DX plan.

[0924] Based on the created DX plan, the estimate generation means calculates the estimated costs. The estimate generation means automatically creates an approximate estimate based on the price information of the proposed products and services. The generated proposal details and approximate estimate are presented to the user in real time via the output means.

[0925] As a specific example, if a user inputs, "Our company's inventory management is not going well. We would like to improve efficiency," the generation AI will generate a question about the specific problem of insufficient inventory management, and present it as, "Which part of inventory management are you having trouble with?" If the user responds, "It's difficult to keep track of inventory numbers," the proposal generation means will suggest products such as an "inventory management system" and "RFID tags." Based on this proposal, the estimate generation means will create a rough estimate and present it to the user.

[0926] In this way, corporate representatives can easily input their company's requests and challenges, and by utilizing generative AI, they can quickly obtain a specific DX plan and rough estimate. Furthermore, as a follow-up, the generated proposal and rough estimate are notified to the corporate sales representative via the notification method. If necessary, the corporate sales representative can use the follow-up method to provide detailed explanations to the corporate representative and further advance the deal.

[0927] The system of the present invention aims to support small and medium-sized enterprises in efficiently promoting DX even without specialized knowledge.

[0928] The processing flow will be explained below.

[0929] ---

[0930] Step 1:

[0931] Users open a web page and enter their company's requests and issues through a chat interface. The device then sends the entered information to the server.

[0932] Step 2:

[0933] The server passes the information on requests and issues received from the user to the analysis means, which uses natural language processing technology to analyze the input information and identify any missing information.

[0934] Step 3:

[0935] The question generation means on the server generates additional questions to fill in the missing information based on the information from the analysis means. The generated questions are transferred to the server, and the server then presents the questions to the user via the terminal.

[0936] Step 4:

[0937] The user answers the additional questions and sends the answers via the terminal to the server, which receives the information.

[0938] Step 5:

[0939] The server passes all responses to the analysis means and proposal generation means, which determines the optimal combination of products and services based on all the input information and generates a specific DX plan.

[0940] Step 6:

[0941] The proposal generation means generates specific proposal content and passes it to the estimate generation means, which retrieves price information for the necessary products and services based on the proposal content and automatically creates a rough estimate.

[0942] Step 7:

[0943] The server receives the generated proposal and rough estimate and presents them to the user in real time via the output means. The user can check the proposal and estimate information on a browser.

[0944] Step 8:

[0945] The server notifies the corporate sales representative of the generated proposal and rough estimate information, and the necessary information is transferred to the sales team using the notification means.

[0946] Step 9:

[0947] The corporate sales representative will use the follow-up means to follow up with the user by providing detailed explanations and answering questions, etc. The user can then have specific discussions with the corporate sales representative about the proposal content and details of the transaction.

[0948] ---

[0949] The above is the specific flow of the program's processing. The specific operations at each step enable efficient digital transformation for small and medium-sized enterprises.

[0950] Example 1

[0951] 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."

[0952] To efficiently promote digital transformation, small and medium-sized enterprises need a system that is easy to manage even without specialized knowledge. However, current systems are complex, and few can be quickly customized to meet the specific challenges a company faces or provide appropriate proposals. This makes it difficult for many companies to promote DX. This invention aims to solve these problems by providing a system that allows company personnel to easily input requests and challenges and quickly obtain an appropriate DX plan and rough estimate.

[0953] 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.

[0954] In this invention, the server includes an input means for a company representative to input their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation means and a reception means for presenting the generated additional questions to the company representative and receiving answers, a proposal generation means for analyzing the answers received from the company representative and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, and an output means for presenting the generated proposal content and rough estimate to the company representative. This enables company representatives to quickly obtain an optimal DX plan and rough estimate that addresses their company's issues, even without specialized knowledge.

[0955] A "corporate representative" is someone involved in promoting digital transformation within a company, and is responsible for inputting the company's requests and challenges into the system.

[0956] The "input means" is an interface that allows company personnel to input their company's requests and issues into the system, and is primarily a chat-style interface.

[0957] "Analysis means" refers to a function used to analyze input requests and issues and identify missing information, and includes natural language processing technology.

[0958] The "question generation means" is a function that automatically generates additional questions necessary to fill in missing information.

[0959] The "presentation means and reception means" refers to the function of presenting the generated follow-up questions to the company personnel and receiving the answers, thereby enabling interactive communication.

[0960] The "proposal generation means" is a function that generates an appropriate combination of products and services based on all the information received from the company representative.

[0961] The "quote generation means" is a function that automatically creates a rough estimate based on the price information of the proposed products and services.

[0962] The "output means" is a function for presenting the generated proposal content and rough estimate to the company representative.

[0963] The "notification means" is a function for notifying the corporate sales representative of the generated proposal content and rough estimate.

[0964] The "follow-up means" is a function that enables a corporate sales representative to follow up with a company representative.

[0965] The "plan generation means" is a function that generates an optimal digital transformation plan based on the generated proposals and pricing information for products and services.

[0966] The "real-time presentation means" is a function for presenting the generated plan to the company representative in real time.

[0967] This invention is a system that enables small and medium-sized enterprises to efficiently promote digital transformation (DX). In this system, company personnel input their company's requests and challenges, and a generative AI then asks for additional information, ultimately presenting a specific DX plan and a rough estimate. The specific configuration and processing flow of this system are explained below.

[0968] The system mainly includes the following means:

[0969] 1. Input your requests and issues

[0970] Users can open a web page using a browser and input their company's requests and issues into a chat-style input device. For example, they can enter a specific problem such as, "Our company's inventory management is not going well. We would like to improve efficiency."

[0971] 2. Automatic analysis

[0972] The server receives the requests and issues entered by the user and performs natural language analysis using an analysis means, preferably Google's natural language API. As a result of the analysis, missing information is identified.

[0973] 3. Generating and Presenting Follow-Up Questions

[0974] The server's question generation means automatically generates additional questions to supplement the missing information. These questions are presented to the user through a chat interface. For example, a specific question such as "Which part of inventory management are you having trouble with?" is generated.

[0975] 4. Entering and Receiving Additional Information

[0976] The user answers the questions and submits the answer. For example, the user might type, "It's difficult to determine the inventory levels." This answer is also received by the server.

[0977] 5. Proposal Generation

[0978] The server's proposal generation means generates the optimal combination of products and services based on all the information received from the user. This proposal generation means uses a database of products and services that has been trained in advance. For example, products such as inventory management systems and RFID tags are proposed.

[0979] 6. Generate a quote

[0980] The server's quotation generation means automatically calculates the estimated costs based on the price information of the proposed products and services. For example, it creates a quotation that includes the cost of introducing an inventory management system or RFID tags.

[0981] 7. Proposal and quotation

[0982] The final DX plan and rough estimate are presented to the user in real time via the server's output means.

[0983] 8. Follow-up

[0984] The generated proposal and rough estimate are notified to the corporate sales representative by the server's notification means, and the corporate sales representative can provide a detailed explanation to the company representative using follow-up means as necessary.

[0985] Specific examples

[0986] The user enters a prompt like this into the generative AI model:

[0987] Our company's inventory management is not going well. We want to improve efficiency.

[0988] Based on this prompt, the system will:

[0989] 1. The user enters the above prompt sentence.

[0990] 2. The server performs automatic analysis and generates and presents an additional question: "Which part of inventory management are you having trouble with?"

[0991] 3. The user responds, "It's difficult to keep track of inventory."

[0992] 4. The server's proposal generation means proposes products such as inventory management systems and RFID tags.

[0993] 5. The server creates a rough estimate and generates an estimate such as "Inventory management system: ¥500,000, RFID tag: ¥200,000."

[0994] 6. The server presents the generated proposal and rough estimate to the user.

[0995] 7. The proposal and quotation will also be notified to the corporate sales representative, who will contact the user directly as necessary.

[0996] In this way, the server allows users to easily input their company's challenges and utilize the generative AI model to quickly obtain a specific DX plan and rough estimate.

[0997] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0998] Step 1:

[0999] A user opens a web page and inputs their company's requests and issues into the chat interface. Specifically, they input something like, "Our inventory management is not going well. We want to improve efficiency." The input requests and issues are sent to the server.

[1000] input:

[1001] Users open a browser and enter their requests or issues into the chat interface.

[1002] output:

[1003] Requests and issues are sent to the server.

[1004] Specific behavior:

[1005] 1. The user opens a browser and accesses the system's web page.

[1006] 2. The user enters their request or issue into the chat interface and clicks the "Send" button.

[1007] Step 2:

[1008] The server receives requests and issues sent by users and performs natural language analysis using an analysis means. As a result of the analysis, missing information is identified.

[1009] input:

[1010] Requests and issues submitted by users.

[1011] output:

[1012] Analysis results identifying missing information.

[1013] Specific behavior:

[1014] 1. The server receives requests and issue data in real time.

[1015] 2. The server's analysis means analyzes the input data using Google's natural language API.

[1016] 3. The server uses the analysis results to identify the missing information.

[1017] Step 3:

[1018] The server's question generation means automatically generates an additional question to supplement the missing information. For example, this question might be, "Which part of inventory management are you having trouble with?" This generated question is then presented to the user.

[1019] input:

[1020] Analysis results based on missing information.

[1021] output:

[1022] Additional question.

[1023] Specific behavior:

[1024] 1. The server's question generation means generates an appropriate question to fill in the missing information.

[1025] 2. The server's output means displays the generated question on the chat interface.

[1026] Step 4:

[1027] The user answers the questions and sends the answer to the server. For example, the user might say, "It's difficult to know how many items are in stock." The answer is then received by the server.

[1028] input:

[1029] The response entered by the user.

[1030] output:

[1031] The user's response data.

[1032] Specific behavior:

[1033] 1. The user checks the question displayed in the chat interface.

[1034] 2. The user enters additional information and clicks the "Submit" button.

[1035] 3. The server receives the response from the user.

[1036] Step 5:

[1037] The server's proposal generator generates the optimal combination of products and services based on all the information received from the user. The proposal generator uses a pre-trained database. For example, it proposes products such as inventory management systems and RFID tags.

[1038] input:

[1039] All information received from the user (initial input and additional information).

[1040] output:

[1041] The best combination of products and services.

[1042] Specific behavior:

[1043] 1. The server's proposal generator consolidates all received user information.

[1044] 2. The server's proposal generation means searches the database for suitable products and services and selects the optimal combination.

[1045] Step 6:

[1046] The server's quotation generation means automatically calculates the estimated costs based on the price information of the proposed products and services. For example, it creates a quotation that includes the cost of introducing an inventory management system or RFID tags.

[1047] input:

[1048] Pricing information for proposed products and services.

[1049] output:

[1050] Rough estimate.

[1051] Specific behavior:

[1052] 1. The server's quote generation means obtains pricing information for the proposed product or service.

[1053] 2. The server calculates a rough estimate based on the price information.

[1054] 3. The server compiles the quotation information into a document format (e.g. PDF).

[1055] Step 7:

[1056] The generated DX plan and rough estimate are presented to the user in real time via the server's output means.

[1057] input:

[1058] Generated DX plan and rough estimate.

[1059] output:

[1060] The DX plan and rough estimate presented to the user.

[1061] Specific behavior:

[1062] 1. The server's output means displays the generated DX plan and quotation on a web page.

[1063] 2. The user refreshes the web page to see the new information.

[1064] Step 8:

[1065] The generated proposal and rough estimate are notified to the corporate sales representative via the server's notification means, who then provides a detailed explanation to the company representative using follow-up means as necessary.

[1066] input:

[1067] Proposals and estimates generated.

[1068] output:

[1069] Notification to Corporate Sales Representatives.

[1070] Specific behavior:

[1071] 1. The server's notification method will notify the corporate sales representative of the proposal details and quotation by email.

[1072] 2. Corporate sales representatives receive notifications and contact users as needed.

[1073] 3. Corporate sales representatives follow up with users to further develop the deal.

[1074] (Application example 1)

[1075] 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."

[1076] When small and medium-sized enterprises (SMEs) plan to introduce factory robots or promote digital transformation, it is difficult to efficiently select the appropriate systems and technologies and obtain quick and accurate estimates. In particular, when there is a lack of specialized knowledge, it can be difficult to determine the optimal combination of technologies and estimate their costs, which can result in delays in promoting digital transformation. There is a need for a support system that can solve these issues and enable SMEs to efficiently realize digital transformation.

[1077] 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.

[1078] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving responses, a proposal generation means for analyzing the received responses and generating an appropriate combination of technologies and systems, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, and an estimate generation means for automatically creating a proposal and a rough estimate for an optimal machine or system based on the analyzed information. This enables small and medium-sized enterprises to efficiently and quickly create DX plans and obtain rough estimates even without specialized knowledge.

[1079] "Corporate representative" refers to a person in a company who is responsible for promoting DX and introducing systems.

[1080] "Requests and issues" refer to business problems the company is facing and specific areas that it would like to improve.

[1081] "Interface means" refers to the user interface through which company personnel input their company's requests and issues.

[1082] "Analysis means" refers to a device or program that analyzes input requests and issues and performs processing to identify missing information.

[1083] "Question generation means" refers to a function that automatically generates additional questions based on missing information.

[1084] The term "presenting and receiving means" refers to a device or program that presents the generated follow-up questions to a company representative and receives the answers thereto.

[1085] "Proposal generation means" refers to a device or program that analyzes the received responses and generates the optimal combination of technologies and systems.

[1086] "Estimate generation means" refers to a function that automatically creates a rough estimate based on the proposal content.

[1087] "Output means" refers to a device or program that presents the generated proposal content and rough estimate to the company representative.

[1088] "Notification means" refers to the function of notifying the corporate sales representative of the generated proposal content and rough estimate.

[1089] "Follow-up means" refers to a device or program that allows a corporate sales representative to provide detailed explanations or additional support to a company representative.

[1090] "DX plan generation means" refers to the function that generates the optimal DX plan based on the generated proposal and pricing information for the technology and system.

[1091] "Real-time presentation means" refers to the function of presenting the generated DX plan to corporate personnel in real time.

[1092] This invention is a system for small and medium-sized enterprises to effectively promote DX (digital transformation), and its specific embodiment is shown below.

[1093] Overall system configuration

[1094] The system mainly includes the following means:

[1095] 1. Interface means: Provide a GUI (graphical user interface) for company personnel to input their company's requests and issues.

[1096] 2. Analysis: Use a natural language processing (NLP) engine to analyze the input request or issue and identify missing information.

[1097] 3. Question generator: A generative AI model to generate additional questions based on missing information.

[1098] 4. Presentation and Reception Means: A communication module for presenting the generated follow-up questions to company personnel and receiving their answers.

[1099] 5. Proposal generation means: A data analysis engine that analyzes the received responses and generates optimal combinations of technologies and systems.

[1100] 6. Estimate generation means: An estimate calculation module that automatically creates a rough estimate based on the proposal.

[1101] 7. Output means: A display module that presents the generated proposal and rough estimate to the company representative.

[1102] 8. Notification method: A notification system to notify corporate sales representatives of the generated proposals and rough estimates.

[1103] 9. Follow-up tool: A CRM (Customer Relationship Management) system that allows corporate sales representatives to follow up with corporate representatives.

[1104] 10. DX plan generation means: A plan generation module for generating an optimal DX plan based on the proposal and pricing information of technologies and systems.

[1105] 11. Real-time presentation method: A module that presents the generated DX plan to company personnel in real time.

[1106] Program processing

[1107] The above means are implemented using the following hardware and software.

[1108] Hardware: A tablet PC, server, smartphone, or computer installed on the robot itself.

[1109] Software: OpenAI API, natural language processing libraries, data analysis tools, CRM systems.

[1110] Data processing and calculation

[1111] The server uses analysis means to perform natural language processing on the requests and issues received from the company representative and identifies any missing information. Next, it uses question generation means to generate additional questions based on the missing information and presents them to the company representative using presentation and reception means. The data is reanalyzed based on the company representative's answers, and the proposal generation means derives an appropriate combination of technologies and systems. The estimate generation means automatically calculates a rough estimate based on these proposals and presents it to the company representative using output means. The generated proposal and estimate are notified to the corporate sales representative using notification means, and detailed explanations and additional proposals are made using follow-up means. Finally, the DX plan generated by the DX plan generation means is presented to the company representative using real-time presentation means.

[1112] Specific examples

[1113] To illustrate, here is a usage scenario:

[1114] A factory worker enters through the application interface that "the frequency of failures on the current production line is high."

[1115] The generative AI model analyzes this and generates a follow-up question: "Which sections fail most frequently?"

[1116] If the person in charge answers "assembly line," the proposal generation means will propose "automated inspection system for assembly line" and "parts transport robot," and the estimate generation means will create a rough estimate.

[1117] An example prompt is:

[1118] text

[1119] Please enter your company's requirements and challenges: The current production line is failing frequently.

[1120] This invention enables small and medium-sized enterprises to efficiently and quickly create DX plans and obtain rough estimates, even without specialized knowledge.

[1121] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1122] Step 1:

[1123] Users input requests and issues to the system

[1124] The user (company representative) uses the interface to input their company's requests and issues. For example, they might input "The frequency of breakdowns on the current production line is high." This input text data is then sent to the system.

[1125] Step 2:

[1126] The server analyzes requests and issues

[1127] The server performs natural language processing on the received text data of requests and issues using an analysis means. The analysis means tokenizes the input text data and identifies important keywords and missing information. For example, keywords such as "production line," "failure frequency," and "high" are extracted.

[1128] Step 3:

[1129] Server generates additional questions

[1130] Based on the analysis results, the server generates a follow-up question using a question generation means. A generative AI model is used to automatically generate a question that corresponds to the missing information. For example, a follow-up question such as "Which section has a high failure frequency?" is generated. This follow-up question is presented to the user via the presentation and reception means.

[1131] Step 4:

[1132] User answers additional questions

[1133] The user answers the additional question presented, for example, "assembly line," and this answer is sent back to the system.

[1134] Step 5:

[1135] The server analyzes the answers and generates suggestions

[1136] The server then analyzes the received responses again using its analysis means, and generates the optimal combination of technologies and systems using its proposal generation means. For example, it may propose an "automated inspection system for assembly lines" or a "parts transport robot." These proposals are then pulled from the database and combined.

[1137] Step 6:

[1138] The server creates a rough estimate

[1139] Based on the proposal, the server automatically creates a rough estimate using the estimate generation means. Price information for each proposed technology and system is retrieved from the database, and the estimate is created by adding them up. For example, the unit price of the "automated inspection system" + the unit price of the "parts transport robot" = the total amount of the rough estimate.

[1140] Step 7:

[1141] Present generated proposals and quotes

[1142] The generated proposal and rough estimate are presented to the user via the output means, and the user can review them and input further information or additional requests as necessary.

[1143] Step 8:

[1144] Proposal details and quotes will be sent to the corporate sales representative

[1145] The generated proposal and rough estimate are notified to the corporate sales representative by a notification means, so that the sales representative can prepare for follow-up.

[1146] Step 9:

[1147] Corporate sales representatives will follow up

[1148] Corporate sales reps use follow-up methods to provide further explanations or additional assistance to corporate representatives, such as using a CRM system to contact customers to answer questions or provide demonstrations.

[1149] Step 10:

[1150] Generate and present the final DX plan

[1151] Finally, the DX plan generation means generates an optimal DX plan based on all the information and suggestions. The generated DX plan is presented to the user through the real-time presentation means. The user can check the final DX plan in real time and proceed with preparations for implementation.

[1152] 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.

[1153] ---

[1154] This invention is a system that enables small and medium-sized enterprises to easily promote DX (digital transformation), and in particular combines generative AI and an emotion engine. In this system, company personnel input their company's requests and challenges, and the generative AI then asks for additional information and, taking into account the user's emotions, presents the optimal DX plan and rough estimate. The system mainly includes the following means:

[1155] First, a company representative (user) opens a web page and inputs their company's requests and issues into a chat-style interface. When inputting requests and issues, the user can describe specific problems, such as, "Our company's inventory management is not going well. We would like to improve efficiency." An emotion engine is installed that recognizes the user's emotions from the input, and this emotional information is also collected at the same time.

[1156] Next, the input requests, issues, and emotional data are sent to the server and passed to the analysis means. The analysis means uses natural language processing technology to analyze the input information and identify missing information. The emotion engine also provides the emotional data to the analysis means, which then analyzes the information according to the user's emotional state.

[1157] When missing information is identified, the question generation means automatically generates additional questions to supplement the missing information. The generated questions adaptively adjust the tone and content of the questions and answers based on the emotional information. These questions are then presented to the user from the server via the terminal. When the user answers the presented questions and submits their answers, the information is also received by the server.

[1158] Next, the proposal generation means generates an optimal combination of products and services based on all the information received from the user (requests, issues, and emotional data). The proposal generation means takes into account the data from the emotion engine and makes suggestions based on the user's emotional state. For example, if the user is feeling stressed, it can suggest a simple and intuitive solution, while if the emotional state is positive, it can provide a comprehensive plan with multiple options.

[1159] Based on the created DX plan, the estimate generation means calculates the estimated costs. The estimate generation means automatically creates a rough estimate based on the price information of the proposed products and services, and optimizes the estimate by taking emotional data into consideration. The generated proposal and rough estimate are presented to the user in real time via the output means.

[1160] As a specific example, if a user inputs "Our company's inventory management is not going well. We want to improve efficiency," and the emotion engine recognizes this as "high frustration," the generation AI will present a simple and specific question: "Which part of inventory management are you struggling with?" If the user answers, "It's difficult to keep track of inventory levels," the proposal generation means will suggest simple and intuitive products such as "inventory management systems" and "RFID tags." Based on this proposal, the estimate generation means will create a rough estimate and present it to the user.

[1161] Furthermore, the generated proposal and rough estimate are notified to the corporate sales representative by the notification means, and the corporate sales representative can use the follow-up means to provide a detailed explanation to the company representative as necessary and further advance the transaction.

[1162] The system of this invention aims to help small and medium-sized enterprises effectively promote DX even without specialized knowledge. By introducing an emotion engine, it provides a better user experience and realizes efficient DX promotion.

[1163] The processing flow will be explained below.

[1164] ---

[1165] Step 1:

[1166] The user opens a web page and inputs their company's requests and issues through a chat interface. The device sends the input information to the server. At the same time, the emotion engine collects emotional data from the user's input.

[1167] Step 2:

[1168] The server passes the information on requests and issues received from the user, as well as emotional data, to the analysis means. The analysis means uses natural language processing technology to analyze the input information and identify any missing information. At the same time, the emotion engine provides the emotional data to the analysis means, which then analyzes the information according to the user's emotional state.

[1169] Step 3:

[1170] The question generation means on the server generates additional questions to fill in the missing information based on the information from the analysis means and the emotional data. The tone and content of these questions are adjusted based on the user's emotional state. The generated questions are transferred to the server, which then presents the questions to the user via the terminal.

[1171] Step 4:

[1172] The user answers the additional questions and sends the answers to the server via the terminal, which receives the information and passes it to the analysis means and the proposal generation means.

[1173] Step 5:

[1174] The proposal generation means on the server generates the optimal combination of products and services based on all information received from the user (requests, issues, emotional data). The proposal generation means takes into account data from the emotion engine and makes suggestions based on the user's emotional state. For example, if the user is feeling frustrated, it will propose a simple and intuitive solution.

[1175] Step 6:

[1176] The proposal generation means generates specific proposal details and passes them to the quotation generation means. The quotation generation means incorporates price information for the necessary products and services based on the proposal details and automatically creates a rough quotation. Furthermore, based on data from the emotion engine, it presents the optimal price for the user.

[1177] Step 7:

[1178] The server receives the generated proposal and rough estimate and presents them to the user in real time via the output means. The user can check the proposal and estimate information on a browser.

[1179] Step 8:

[1180] The server notifies the corporate sales representative of the generated proposal and rough estimate information, and the necessary information is transferred to the sales team using the notification means.

[1181] Step 9:

[1182] The corporate sales representative will use the follow-up means to follow up with the user by providing detailed explanations and answering questions, etc. The user can then have specific discussions with the corporate sales representative about the proposal content and details of the transaction.

[1183] ---

[1184] The above is the specific processing flow of the system that combines the emotion engine. The operation at each step enables small and medium-sized enterprises to promote digital transformation in an efficient and emotionally considerate manner.

[1185] Example 2

[1186] 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."

[1187] When small and medium-sized enterprises (SMEs) promote digital transformation (DX), they require specialized knowledge to select and estimate appropriate products and services, and in the process, they require proposals that take the user's emotional state into consideration. This reduces user frustration and ensures efficient and effective DX promotion. However, conventional DX support systems do not take the user's emotional state into account when making proposals or optimizations, resulting in a poor user experience.

[1188] 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.

[1189] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving answers, a proposal generation means for analyzing the received answers and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, an emotion analysis means for collecting and analyzing user emotion data, and an optimization means for taking the emotion data into account in the generated proposal content and rough estimate. This makes it possible to generate optimal proposals and estimates that take the user's emotional state into account.

[1190] "Corporate representative" refers to a person within a company who inputs their company's requests and issues and requests proposals and estimates for promoting DX.

[1191] "Interface means" refers to input means such as a web page or chat interface through which company personnel can input their company's requests and issues.

[1192] "Analysis means" refers to natural language processing technology and analysis engines that analyze input requests and issues and identify missing information.

[1193] The "question generation means" refers to a function that automatically generates additional questions to fill in the missing information identified by the analysis means.

[1194] The "presentation and reception means" refers to a function for presenting the generated follow-up question to a company representative and receiving an answer from the company representative.

[1195] "Proposal generation means" refers to the function of analyzing the received responses and generating an appropriate combination of products and services.

[1196] "Estimate generation means" refers to a function that automatically creates a rough estimate based on the proposal content.

[1197] "Output means" refers to a function for presenting the generated proposal content and rough estimate to the company representative.

[1198] "Emotion analysis means" refers to emotion engines and analysis technologies for collecting and analyzing user emotion data.

[1199] "Optimization means" refers to a function that allows emotional data to be taken into account in the generated proposals and rough estimates.

[1200] "Notification means" refers to a function for notifying corporate sales representatives of the generated proposal contents and rough estimate.

[1201] "Follow-up measures" refers to functions that enable corporate sales representatives to provide additional support and explanations to company representatives and carry out follow-up.

[1202] "DX plan generation means" refers to the function of generating the optimal digital transformation plan based on the generated proposals and price information for products and services.

[1203] "Real-time presentation means" refers to the function for instantly presenting the generated DX plan to company personnel.

[1204] This invention is a system that allows small and medium-sized enterprises to easily promote digital transformation (DX). The system begins by having company personnel input their company's requests and challenges into an interface. The system uses a generative AI model to present an optimal DX plan and rough estimate, taking into account the emotional state of the company personnel.

[1205] First, the user uses the interface means via a web browser. This interface means is a chat interface that allows company personnel to input their company's requests and issues in text format. For example, a specific problem can be input, such as, "Our company's inventory management is not going well. We would like to improve efficiency." The input content is then analyzed by the emotion analysis means, which collects and analyzes the user's emotional data.

[1206] Next, the device transmits this input data and emotion data to a server. The server analyzes the input content using natural language processing technology (e.g., natural language processing API) and identifies missing information. The analysis means acquires the emotion data analyzed by an emotion engine (e.g., emotion analysis API) and performs information analysis according to the user's emotional state.

[1207] When missing information is identified, a question generation means installed on the server automatically generates additional questions using a generative AI model (e.g., the generative AI model GPT-4). The generated questions are adjusted based on the emotional data, and the tone and content are adaptively changed. The questions are presented to the user via the device. When the user answers the presented questions, the answer data is again sent to the server.

[1208] The server's proposal generation means then generates an appropriate combination of products and services based on all the data received from the user (requests, issues, and emotional data).The proposal generation means takes into account the data from the emotion analysis means and proposes a DX plan based on the user's emotional state.For example, if the user is feeling frustrated, a simple and intuitive solution will be proposed.

[1209] Based on the proposed DX plan, the estimate generation means automatically calculates the estimated costs. The estimate generation means optimizes the estimate based on price information for the proposed products and services, taking into account emotional data. The generated proposal and estimated estimate are presented to the user in real time by the output means.

[1210] As a specific example, if a user inputs "Our inventory management is not going well. We want to improve efficiency," and the sentiment analysis means recognizes this as "high frustration," the generative AI model (GPT-4) will generate a simple and specific question: "Which part of inventory management are you struggling with?" If the user answers, "It's difficult to keep track of inventory levels," the proposal generation means will suggest simple and intuitive products such as "inventory management systems" and "RFID tags." Based on this suggestion, the estimate generation means will create a rough estimate and present it to the user.

[1211] Furthermore, the generated proposal and rough estimate are notified to the corporate sales representative via a notification means, and the corporate sales representative can use the follow-up means to provide additional understanding or explanation to the company representative as necessary to support the progress of the transaction.

[1212] As an example of a prompt sentence, the generative AI model might be given the following prompt:

[1213] "A user wants to improve inventory management efficiency. His current emotional state is frustration. What inventory management system should we suggest?"

[1214] By integrating these methods, this system will help small and medium-sized enterprises effectively promote digital transformation without specialized knowledge, and will provide a better user experience by utilizing emotional data.

[1215] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1216] Step 1:

[1217] Users input their requests and issues

[1218] Input: The user inputs their company's requests and issues into the chat interface. For example, they might type, "Our inventory management is not going well. We want to improve efficiency."

[1219] Operation: The device receives user input and prepares it to be sent to the emotion engine.

[1220] Output: Text data of the input requests and issues.

[1221] Step 2:

[1222] Sending input data and emotion data

[1223] Input: The device sends user input and emotion data to the emotion engine.

[1224] Operation: The device generates an HTTP request to send data to the emotion engine and sends it to the server.

[1225] Output: Text data and emotion data received by the server.

[1226] Step 3:

[1227] The server receives and analyzes the data

[1228] Input: Text data and emotion data received by the server from the device.

[1229] How it works: The server uses a natural language processing module to analyze the text data and identify missing information. The emotion engine simultaneously analyzes the emotion data and understands the user's emotional state.

[1230] Output: Analysis results include missing information and the user's emotional state data.

[1231] Step 4:

[1232] The server generates a follow-up question

[1233] Input: Missing information and emotion data.

[1234] How it works: The server's generative AI model (e.g., GPT-4) generates follow-up questions to fill in missing information. The question generator uses emotional data to adaptively adjust the tone and content of the questions.

[1235] Output: Generated follow-up questions.

[1236] Step 5:

[1237] Submitting a generated question

[1238] Input: The generated follow-up question.

[1239] Operation: The server sends the additional question to the terminal as an HTTP response. The terminal displays the question to the user.

[1240] Output: A follow-up question that is presented to the user.

[1241] Step 6:

[1242] The user answers the question

[1243] Input: When a user sees a follow-up question, they input their answer through the chat interface.

[1244] Operation: The terminal receives the user's response and prepares to send the data to the server.

[1245] Output: The entered response data.

[1246] Step 7:

[1247] The server parses the answer

[1248] Input: User response data.

[1249] Operation: The server again uses the natural language processing module to analyze the response data and identify the appropriate combination of products and services.

[1250] Output: Potential products and services as analysis results.

[1251] Step 8:

[1252] Proposal and quote generation

[1253] Input: potential products and services, user sentiment data.

[1254] Operation: The server's proposal generation means generates optimal proposals taking into account emotion data. The estimate generation means calculates approximate costs based on the proposals. The estimate is also optimized.

[1255] Output: Generated proposal and quote.

[1256] Step 9:

[1257] Presentation by output means

[1258] Input: Generated proposal and quote.

[1259] How it works: The server sends these as HTTP responses to the device, which displays them to the user.

[1260] Output: Proposal and quote presented to the user.

[1261] Step 10:

[1262] Notification and follow-up

[1263] Input: Generated proposal and quote.

[1264] Operation: The server's notification means notifies the corporate sales representative of the generated proposal and quotation. The corporate sales representative uses the follow-up means to provide additional explanations or take action to the company representative as necessary.

[1265] Output: Proposal and quotation notified, and follow-up action taken.

[1266] (Application example 2)

[1267] 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."

[1268] Currently, many small and medium-sized enterprises and factories lack specialized knowledge about production line issues and methods for promoting DX (digital transformation), making it difficult to efficiently solve problems or select appropriate products. Furthermore, adaptive proposals that take into account employee emotions are often not made, often causing stress and confusion. In these cases, there is a need for automated creation of effective DX plans and quotation presentations, especially when promoting DX within factories.

[1269] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1270] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving responses, a proposal generation means for analyzing the received responses and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, a robot arranged on the production line for inputting issues within the factory, an emotion engine for collecting emotional data on the input issues, and an emotion analysis means for adjusting the tone of adaptive questions and proposals based on the emotional data. This enables small and medium-sized enterprises and factories to efficiently and adaptively create DX plans and present estimates while taking into account the emotions of their employees.

[1271] Definitions of important words

[1272] An "interface means" is an input means through which company personnel input their company's requests and issues.

[1273] The "analysis means" is a means for analyzing input requests and issues and identifying missing information.

[1274] The "question generation means" is a means for generating additional questions based on missing information.

[1275] The "presentation and reception means" is a means for presenting the generated follow-up questions to the company personnel and receiving the answers.

[1276] The "proposal generation means" is a means for analyzing the received responses and generating an appropriate combination of products and services.

[1277] The "quote generation means" is a means for automatically creating a rough estimate based on the proposal contents.

[1278] The "output means" is a means for presenting the generated proposal contents and rough estimate to the company representative.

[1279] "Robots installed on the production line" are machines installed in the factory to input tasks.

[1280] The "emotion engine" is a system for collecting emotional data for input tasks.

[1281] "Sentiment analysis means" means for adjusting the tone of adaptive questions and suggestions based on emotional data.

[1282] MODE FOR CARRYING OUT THE INVENTION

[1283] This invention is a system in which company personnel input their company's requests and challenges, and based on that, a generative AI and emotion engine present the optimal DX plan and rough estimate. This system also uses robots placed on production lines with the aim of promoting DX within factories.

[1284] The system mainly includes the following means:

[1285] 1. Interface Method

[1286] Users input their company's issues via voice input or a tablet via a robot placed on the production line.

[1287] The robot sends this input to the emotion engine.

[1288] 2. Emotion Engine

[1289] The emotion engine collects and analyzes the emotion data of the input task. The software used is a natural language processing library (e.g., TextBlob).

[1290] 3. Analysis and Question Generation Methods

[1291] The server analyzes the input request or problem and identifies missing information, using natural language processing technology (e.g., OpenAI's generative AI model).

[1292] A follow-up question is generated based on the missing information and the question is presented to the user through the interface means.

[1293] 4. Means of presentation and reception

[1294] The user answers the generated additional questions and sends them to the server via the robot.

[1295] The server receives this response and parses it again.

[1296] 5. Proposal generation means

[1297] The server generates an appropriate combination of products and services based on the received information and makes adaptive suggestions based on emotional data. For example, if the user is "highly frustrated," it will select a simple and intuitive solution.

[1298] 6. Estimate generation and output methods

[1299] The estimate generating means automatically generates a rough estimate based on the proposal and presents it to the user in real time via the output means, with the result being displayed on the robot's display and / or voice output.

[1300] Hardware and software used

[1301] Hardware: Robots, servers, tablets or voice input devices located on a production line.

[1302] Software: Natural language processing libraries (e.g., TextBlob), generative AI models (e.g., OpenAI's text-davinci-003).

[1303] Specific examples

[1304] For example, suppose a factory staff member inputs, "We want to eliminate the bottleneck on the production line. It's inefficient." In this case, the emotion engine recognizes this as "high frustration." The generation AI then generates a question, "Specifically, where is the problem occurring?" and presents it to the factory staff member. If the factory staff member answers, "Inventory management is difficult," the generation AI suggests introducing an "inventory management system" and "RFID tags." Finally, the estimate generation means creates a rough estimate and presents it to the factory staff member.

[1305] Prompt Sentence Examples

[1306] Please propose the best DX plan and estimate for the following issues: Eliminating bottlenecks in the production line. Inefficiency. Emotional state: High frustration

[1307] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1308] Program processing steps

[1309] Step 1:

[1310] Users input their company's issues using a robot placed on the production line. This input is done via a tablet or a voice recognition system. The input issue (e.g., "We want to eliminate the bottleneck on the production line. It's inefficient.") is sent to the emotion engine.

[1311] Input: User's assignment

[1312] Output: Issue data

[1313] Step 2:

[1314] The emotion engine receives the task data and analyzes the input for emotional data. The emotion engine uses a natural language processing library (e.g., TextBlob) to identify the task's emotional state. The analysis results are sent to the server as emotion tags such as "positive," "negative," or "neutral."

[1315] Input: Issue data

[1316] Data processing: Sentiment analysis

[1317] Output: Emotion tag

[1318] Step 3:

[1319] The server receives the task data and emotion tags, analyzes the task content using an analysis method, and uses a generative AI model (e.g., OpenAI's text-davinci-003) to identify missing information. It then generates follow-up questions based on the missing information.

[1320] Input: issue data, emotion tags

[1321] Data calculation: problem analysis, identification of missing information

[1322] Output: Additional questions

[1323] Step 4:

[1324] The server presents the generated follow-up questions to the user through the interface means (the robot's display or voice output), and the user answers the presented follow-up questions.

[1325] Input: Additional Question

[1326] Output: User's answer

[1327] Step 5:

[1328] The server receives the user's answers and analyzes them again using the analysis method. Based on the analysis results, the server generates an appropriate combination of products and services, taking into account the emotional data. During this generation process, the tone and content of the questions are also adjusted based on the emotional tags.

[1329] Input: User's answer

[1330] Data calculation: response analysis, product and service generation

[1331] Output: Product and service proposals

[1332] Step 6:

[1333] The quotation generation means automatically generates a rough quotation based on the proposal. This quotation information is calculated using the generation AI and includes necessary price information. The generated quotation is presented to the user via the output means.

[1334] Input: Product / service proposal

[1335] Data calculation: Estimate calculation

[1336] Output: Rough estimate

[1337] Step 7:

[1338] The user can review the generated proposal and estimate and follow up if necessary. This process is done in real time and allows the user to ask specific questions or request additional information.

[1339] Input: Proposal details, rough estimate

[1340] Output: User confirmation and follow-up

[1341] In this way, the system of the present invention as a whole enables small and medium-sized enterprises and factories to efficiently and adaptively create DX plans and present estimates while taking into account the feelings of their employees.

[1342] 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.

[1343] 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.

[1344] 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.

[1345] [Fourth embodiment]

[1346] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1347] 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.

[1348] 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).

[1349] 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.

[1350] 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.

[1351] 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).

[1352] 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.

[1353] 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.

[1354] 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.

[1355] 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.

[1356] 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.

[1357] 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.

[1358] 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."

[1359] ---

[1360] This invention is a system that enables small and medium-sized enterprises to easily promote DX (digital transformation). In this system, company personnel input their company's requests and challenges, and a generative AI then asks for additional information, ultimately presenting a specific DX plan and a rough estimate. The system mainly includes the following means:

[1361] First, a company representative (user) opens a web page and inputs their company's requests and issues into a chat-style interface. When inputting requests and issues, the company representative can describe specific problems, such as, "Our company's inventory management is not going well. We would like to improve efficiency."

[1362] Next, the input requests and issues are sent to a server and analyzed by an analysis means. The analysis means analyzes the content of the requests and issues using natural language analysis to identify missing information. This indicates the additional information required to generate a DX plan.

[1363] When the missing information is identified, the question generation means automatically generates an additional question to supplement the missing information. The generated question is presented to the user via the presentation and reception means. When the user answers the presented question and transmits the answer, the information is also received by the server.

[1364] Next, the proposal generation means generates the optimal combination of products and services based on all the information received from the user. The proposal generation means selects the optimal combination from a database of products and services that it has learned in advance and creates a specific DX plan.

[1365] Based on the created DX plan, the estimate generation means calculates the estimated costs. The estimate generation means automatically creates an approximate estimate based on the price information of the proposed products and services. The generated proposal details and approximate estimate are presented to the user in real time via the output means.

[1366] As a specific example, if a user inputs, "Our company's inventory management is not going well. We would like to improve efficiency," the generation AI will generate a question about the specific problem of insufficient inventory management, and present it as, "Which part of inventory management are you having trouble with?" If the user responds, "It's difficult to keep track of inventory numbers," the proposal generation means will suggest products such as an "inventory management system" and "RFID tags." Based on this proposal, the estimate generation means will create a rough estimate and present it to the user.

[1367] In this way, corporate representatives can easily input their company's requests and challenges, and by utilizing generative AI, they can quickly obtain a specific DX plan and rough estimate. Furthermore, as a follow-up, the generated proposal and rough estimate are notified to the corporate sales representative via the notification method. If necessary, the corporate sales representative can use the follow-up method to provide detailed explanations to the corporate representative and further advance the deal.

[1368] The system of the present invention aims to support small and medium-sized enterprises in efficiently promoting DX even without specialized knowledge.

[1369] The processing flow will be explained below.

[1370] ---

[1371] Step 1:

[1372] Users open a web page and enter their company's requests and issues through a chat interface. The device then sends the entered information to the server.

[1373] Step 2:

[1374] The server passes the information on requests and issues received from the user to the analysis means, which uses natural language processing technology to analyze the input information and identify any missing information.

[1375] Step 3:

[1376] The question generation means on the server generates additional questions to fill in the missing information based on the information from the analysis means. The generated questions are transferred to the server, and the server then presents the questions to the user via the terminal.

[1377] Step 4:

[1378] The user answers the additional questions and sends the answers via the terminal to the server, which receives the information.

[1379] Step 5:

[1380] The server passes all responses to the analysis means and proposal generation means, which determines the optimal combination of products and services based on all the input information and generates a specific DX plan.

[1381] Step 6:

[1382] The proposal generation means generates specific proposal content and passes it to the estimate generation means, which retrieves price information for the necessary products and services based on the proposal content and automatically creates a rough estimate.

[1383] Step 7:

[1384] The server receives the generated proposal and rough estimate and presents them to the user in real time via the output means. The user can check the proposal and estimate information on a browser.

[1385] Step 8:

[1386] The server notifies the corporate sales representative of the generated proposal and rough estimate information, and the necessary information is transferred to the sales team using the notification means.

[1387] Step 9:

[1388] The corporate sales representative will use the follow-up means to follow up with the user by providing detailed explanations and answering questions, etc. The user can then have specific discussions with the corporate sales representative about the proposal content and details of the transaction.

[1389] ---

[1390] The above is the specific flow of the program's processing. The specific operations at each step enable efficient digital transformation for small and medium-sized enterprises.

[1391] Example 1

[1392] 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."

[1393] To efficiently promote digital transformation, small and medium-sized enterprises need a system that is easy to manage even without specialized knowledge. However, current systems are complex, and few can be quickly customized to meet the specific challenges a company faces or provide appropriate proposals. This makes it difficult for many companies to promote DX. This invention aims to solve these problems by providing a system that allows company personnel to easily input requests and challenges and quickly obtain an appropriate DX plan and rough estimate.

[1394] 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.

[1395] In this invention, the server includes an input means for a company representative to input their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation means and a reception means for presenting the generated additional questions to the company representative and receiving answers, a proposal generation means for analyzing the answers received from the company representative and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, and an output means for presenting the generated proposal content and rough estimate to the company representative. This enables company representatives to quickly obtain an optimal DX plan and rough estimate that addresses their company's issues, even without specialized knowledge.

[1396] A "corporate representative" is someone involved in promoting digital transformation within a company, and is responsible for inputting the company's requests and challenges into the system.

[1397] The "input means" is an interface that allows company personnel to input their company's requests and issues into the system, and is primarily a chat-style interface.

[1398] "Analysis means" refers to a function used to analyze input requests and issues and identify missing information, and includes natural language processing technology.

[1399] The "question generation means" is a function that automatically generates additional questions necessary to fill in missing information.

[1400] The "presentation means and reception means" refers to the function of presenting the generated follow-up questions to the company personnel and receiving the answers, thereby enabling interactive communication.

[1401] The "proposal generation means" is a function that generates an appropriate combination of products and services based on all the information received from the company representative.

[1402] The "quote generation means" is a function that automatically creates a rough estimate based on the price information of the proposed products and services.

[1403] The "output means" is a function for presenting the generated proposal content and rough estimate to the company representative.

[1404] The "notification means" is a function for notifying the corporate sales representative of the generated proposal content and rough estimate.

[1405] The "follow-up means" is a function that enables a corporate sales representative to follow up with a company representative.

[1406] The "plan generation means" is a function that generates an optimal digital transformation plan based on the generated proposals and pricing information for products and services.

[1407] The "real-time presentation means" is a function for presenting the generated plan to the company representative in real time.

[1408] This invention is a system that enables small and medium-sized enterprises to efficiently promote digital transformation (DX). In this system, company personnel input their company's requests and challenges, and a generative AI then asks for additional information, ultimately presenting a specific DX plan and a rough estimate. The specific configuration and processing flow of this system are explained below.

[1409] The system mainly includes the following means:

[1410] 1. Input your requests and issues

[1411] Users can open a web page using a browser and input their company's requests and issues into a chat-style input device. For example, they can enter a specific problem such as, "Our company's inventory management is not going well. We would like to improve efficiency."

[1412] 2. Automatic analysis

[1413] The server receives the requests and issues entered by the user and performs natural language analysis using an analysis means, preferably Google's natural language API. As a result of the analysis, missing information is identified.

[1414] 3. Generating and Presenting Follow-Up Questions

[1415] The server's question generation means automatically generates additional questions to supplement the missing information. These questions are presented to the user through a chat interface. For example, a specific question such as "Which part of inventory management are you having trouble with?" is generated.

[1416] 4. Entering and Receiving Additional Information

[1417] The user answers the questions and submits the answer. For example, the user might type, "It's difficult to determine the inventory levels." This answer is also received by the server.

[1418] 5. Proposal Generation

[1419] The server's proposal generation means generates the optimal combination of products and services based on all the information received from the user. This proposal generation means uses a database of products and services that has been trained in advance. For example, products such as inventory management systems and RFID tags are proposed.

[1420] 6. Generate a quote

[1421] The server's quotation generation means automatically calculates the estimated costs based on the price information of the proposed products and services. For example, it creates a quotation that includes the cost of introducing an inventory management system or RFID tags.

[1422] 7. Proposal and quotation

[1423] The final DX plan and rough estimate are presented to the user in real time via the server's output means.

[1424] 8. Follow-up

[1425] The generated proposal and rough estimate are notified to the corporate sales representative by the server's notification means, and the corporate sales representative can provide a detailed explanation to the company representative using follow-up means as necessary.

[1426] Specific examples

[1427] The user enters a prompt like this into the generative AI model:

[1428] Our company's inventory management is not going well. We want to improve efficiency.

[1429] Based on this prompt, the system will:

[1430] 1. The user enters the above prompt sentence.

[1431] 2. The server performs automatic analysis and generates and presents an additional question: "Which part of inventory management are you having trouble with?"

[1432] 3. The user responds, "It's difficult to keep track of inventory."

[1433] 4. The server's proposal generation means proposes products such as inventory management systems and RFID tags.

[1434] 5. The server creates a rough estimate and generates an estimate such as "Inventory management system: ¥500,000, RFID tag: ¥200,000."

[1435] 6. The server presents the generated proposal and rough estimate to the user.

[1436] 7. The proposal and quotation will also be notified to the corporate sales representative, who will contact the user directly as necessary.

[1437] In this way, the server allows users to easily input their company's challenges and utilize the generative AI model to quickly obtain a specific DX plan and rough estimate.

[1438] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1439] Step 1:

[1440] A user opens a web page and inputs their company's requests and issues into the chat interface. Specifically, they input something like, "Our inventory management is not going well. We want to improve efficiency." The input requests and issues are sent to the server.

[1441] input:

[1442] Users open a browser and enter their requests or issues into the chat interface.

[1443] output:

[1444] Requests and issues are sent to the server.

[1445] Specific behavior:

[1446] 1. The user opens a browser and accesses the system's web page.

[1447] 2. The user enters their request or issue into the chat interface and clicks the "Send" button.

[1448] Step 2:

[1449] The server receives requests and issues sent by users and performs natural language analysis using an analysis means. As a result of the analysis, missing information is identified.

[1450] input:

[1451] Requests and issues submitted by users.

[1452] output:

[1453] Analysis results identifying missing information.

[1454] Specific behavior:

[1455] 1. The server receives requests and issue data in real time.

[1456] 2. The server's analysis means analyzes the input data using Google's natural language API.

[1457] 3. The server uses the analysis results to identify the missing information.

[1458] Step 3:

[1459] The server's question generation means automatically generates an additional question to supplement the missing information. For example, this question might be, "Which part of inventory management are you having trouble with?" This generated question is then presented to the user.

[1460] input:

[1461] Analysis results based on missing information.

[1462] output:

[1463] Additional question.

[1464] Specific behavior:

[1465] 1. The server's question generation means generates an appropriate question to fill in the missing information.

[1466] 2. The server's output means displays the generated question on the chat interface.

[1467] Step 4:

[1468] The user answers the questions and sends the answer to the server. For example, the user might say, "It's difficult to know how many items are in stock." The answer is then received by the server.

[1469] input:

[1470] The response entered by the user.

[1471] output:

[1472] The user's response data.

[1473] Specific behavior:

[1474] 1. The user checks the question displayed in the chat interface.

[1475] 2. The user enters additional information and clicks the "Submit" button.

[1476] 3. The server receives the response from the user.

[1477] Step 5:

[1478] The server's proposal generator generates the optimal combination of products and services based on all the information received from the user. The proposal generator uses a pre-trained database. For example, it proposes products such as inventory management systems and RFID tags.

[1479] input:

[1480] All information received from the user (initial input and additional information).

[1481] output:

[1482] The best combination of products and services.

[1483] Specific behavior:

[1484] 1. The server's proposal generator consolidates all received user information.

[1485] 2. The server's proposal generation means searches the database for suitable products and services and selects the optimal combination.

[1486] Step 6:

[1487] The server's quotation generation means automatically calculates the estimated costs based on the price information of the proposed products and services. For example, it creates a quotation that includes the cost of introducing an inventory management system or RFID tags.

[1488] input:

[1489] Pricing information for proposed products and services.

[1490] output:

[1491] Rough estimate.

[1492] Specific behavior:

[1493] 1. The server's quote generation means obtains pricing information for the proposed product or service.

[1494] 2. The server calculates a rough estimate based on the price information.

[1495] 3. The server compiles the quotation information into a document format (e.g. PDF).

[1496] Step 7:

[1497] The generated DX plan and rough estimate are presented to the user in real time via the server's output means.

[1498] input:

[1499] Generated DX plan and rough estimate.

[1500] output:

[1501] The DX plan and rough estimate presented to the user.

[1502] Specific behavior:

[1503] 1. The server's output means displays the generated DX plan and quotation on a web page.

[1504] 2. The user refreshes the web page to see the new information.

[1505] Step 8:

[1506] The generated proposal and rough estimate are notified to the corporate sales representative via the server's notification means, who then provides a detailed explanation to the company representative using follow-up means as necessary.

[1507] input:

[1508] Proposals and estimates generated.

[1509] output:

[1510] Notification to Corporate Sales Representatives.

[1511] Specific behavior:

[1512] 1. The server's notification method will notify the corporate sales representative of the proposal details and quotation by email.

[1513] 2. Corporate sales representatives receive notifications and contact users as needed.

[1514] 3. Corporate sales representatives follow up with users to further develop the deal.

[1515] (Application example 1)

[1516] 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."

[1517] When small and medium-sized enterprises (SMEs) plan to introduce factory robots or promote digital transformation, it is difficult to efficiently select the appropriate systems and technologies and obtain quick and accurate estimates. In particular, when there is a lack of specialized knowledge, it can be difficult to determine the optimal combination of technologies and estimate their costs, which can result in delays in promoting digital transformation. There is a need for a support system that can solve these issues and enable SMEs to efficiently realize digital transformation.

[1518] 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.

[1519] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving responses, a proposal generation means for analyzing the received responses and generating an appropriate combination of technologies and systems, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, and an estimate generation means for automatically creating a proposal and a rough estimate for an optimal machine or system based on the analyzed information. This enables small and medium-sized enterprises to efficiently and quickly create DX plans and obtain rough estimates even without specialized knowledge.

[1520] "Corporate representative" refers to a person in a company who is responsible for promoting DX and introducing systems.

[1521] "Requests and issues" refer to business problems the company is facing and specific areas that it would like to improve.

[1522] "Interface means" refers to the user interface through which company personnel input their company's requests and issues.

[1523] "Analysis means" refers to a device or program that analyzes input requests and issues and performs processing to identify missing information.

[1524] "Question generation means" refers to a function that automatically generates additional questions based on missing information.

[1525] The term "presenting and receiving means" refers to a device or program that presents the generated follow-up questions to a company representative and receives the answers thereto.

[1526] "Proposal generation means" refers to a device or program that analyzes the received responses and generates the optimal combination of technologies and systems.

[1527] "Estimate generation means" refers to a function that automatically creates a rough estimate based on the proposal content.

[1528] "Output means" refers to a device or program that presents the generated proposal content and rough estimate to the company representative.

[1529] "Notification means" refers to the function of notifying the corporate sales representative of the generated proposal content and rough estimate.

[1530] "Follow-up means" refers to a device or program that allows a corporate sales representative to provide detailed explanations or additional support to a company representative.

[1531] "DX plan generation means" refers to the function that generates the optimal DX plan based on the generated proposal and pricing information for the technology and system.

[1532] "Real-time presentation means" refers to the function of presenting the generated DX plan to corporate personnel in real time.

[1533] This invention is a system for small and medium-sized enterprises to effectively promote DX (digital transformation), and its specific embodiment is shown below.

[1534] Overall system configuration

[1535] The system mainly includes the following means:

[1536] 1. Interface means: Provide a GUI (graphical user interface) for company personnel to input their company's requests and issues.

[1537] 2. Analysis: Use a natural language processing (NLP) engine to analyze the input request or issue and identify missing information.

[1538] 3. Question generator: A generative AI model to generate additional questions based on missing information.

[1539] 4. Presentation and Reception Means: A communication module for presenting the generated follow-up questions to company personnel and receiving their answers.

[1540] 5. Proposal generation means: A data analysis engine that analyzes the received responses and generates optimal combinations of technologies and systems.

[1541] 6. Estimate generation means: An estimate calculation module that automatically creates a rough estimate based on the proposal.

[1542] 7. Output means: A display module that presents the generated proposal and rough estimate to the company representative.

[1543] 8. Notification method: A notification system to notify corporate sales representatives of the generated proposals and rough estimates.

[1544] 9. Follow-up tool: A CRM (Customer Relationship Management) system that allows corporate sales representatives to follow up with corporate representatives.

[1545] 10. DX plan generation means: A plan generation module for generating an optimal DX plan based on the proposal and pricing information of technologies and systems.

[1546] 11. Real-time presentation method: A module that presents the generated DX plan to company personnel in real time.

[1547] Program processing

[1548] The above means are implemented using the following hardware and software.

[1549] Hardware: A tablet PC, server, smartphone, or computer installed on the robot itself.

[1550] Software: OpenAI API, natural language processing libraries, data analysis tools, CRM systems.

[1551] Data processing and calculation

[1552] The server uses analysis means to perform natural language processing on the requests and issues received from the company representative and identifies any missing information. Next, it uses question generation means to generate additional questions based on the missing information and presents them to the company representative using presentation and reception means. The data is reanalyzed based on the company representative's answers, and the proposal generation means derives an appropriate combination of technologies and systems. The estimate generation means automatically calculates a rough estimate based on these proposals and presents it to the company representative using output means. The generated proposal and estimate are notified to the corporate sales representative using notification means, and detailed explanations and additional proposals are made using follow-up means. Finally, the DX plan generated by the DX plan generation means is presented to the company representative using real-time presentation means.

[1553] Specific examples

[1554] To illustrate, here is a usage scenario:

[1555] A factory worker enters through the application interface that "the frequency of failures on the current production line is high."

[1556] The generative AI model analyzes this and generates a follow-up question: "Which sections fail most frequently?"

[1557] If the person in charge answers "assembly line," the proposal generation means will propose "automated inspection system for assembly line" and "parts transport robot," and the estimate generation means will create a rough estimate.

[1558] An example prompt is:

[1559] text

[1560] Please enter your company's requirements and challenges: The current production line is failing frequently.

[1561] This invention enables small and medium-sized enterprises to efficiently and quickly create DX plans and obtain rough estimates, even without specialized knowledge.

[1562] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1563] Step 1:

[1564] Users input requests and issues to the system

[1565] The user (company representative) uses the interface to input their company's requests and issues. For example, they might input "The frequency of breakdowns on the current production line is high." This input text data is then sent to the system.

[1566] Step 2:

[1567] The server analyzes requests and issues

[1568] The server performs natural language processing on the received text data of requests and issues using an analysis means. The analysis means tokenizes the input text data and identifies important keywords and missing information. For example, keywords such as "production line," "failure frequency," and "high" are extracted.

[1569] Step 3:

[1570] Server generates additional questions

[1571] Based on the analysis results, the server generates a follow-up question using a question generation means. A generative AI model is used to automatically generate a question that corresponds to the missing information. For example, a follow-up question such as "Which section has a high failure frequency?" is generated. This follow-up question is presented to the user via the presentation and reception means.

[1572] Step 4:

[1573] User answers additional questions

[1574] The user answers the additional question presented, for example, "assembly line," and this answer is sent back to the system.

[1575] Step 5:

[1576] The server analyzes the answers and generates suggestions

[1577] The server then analyzes the received responses again using its analysis means, and generates the optimal combination of technologies and systems using its proposal generation means. For example, it may propose an "automated inspection system for assembly lines" or a "parts transport robot." These proposals are then pulled from the database and combined.

[1578] Step 6:

[1579] The server creates a rough estimate

[1580] Based on the proposal, the server automatically creates a rough estimate using the estimate generation means. Price information for each proposed technology and system is retrieved from the database, and the estimate is created by adding them up. For example, the unit price of the "automated inspection system" + the unit price of the "parts transport robot" = the total amount of the rough estimate.

[1581] Step 7:

[1582] Present generated proposals and quotes

[1583] The generated proposal and rough estimate are presented to the user via the output means, and the user can review them and input further information or additional requests as necessary.

[1584] Step 8:

[1585] Proposal details and quotes will be sent to the corporate sales representative

[1586] The generated proposal and rough estimate are notified to the corporate sales representative by a notification means, so that the sales representative can prepare for follow-up.

[1587] Step 9:

[1588] Corporate sales representatives will follow up

[1589] Corporate sales reps use follow-up methods to provide further explanations or additional assistance to corporate representatives, such as using a CRM system to contact customers to answer questions or provide demonstrations.

[1590] Step 10:

[1591] Generate and present the final DX plan

[1592] Finally, the DX plan generation means generates an optimal DX plan based on all the information and suggestions. The generated DX plan is presented to the user through the real-time presentation means. The user can check the final DX plan in real time and proceed with preparations for implementation.

[1593] 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.

[1594] ---

[1595] This invention is a system that enables small and medium-sized enterprises to easily promote DX (digital transformation), and in particular combines generative AI and an emotion engine. In this system, company personnel input their company's requests and challenges, and the generative AI then asks for additional information and, taking into account the user's emotions, presents the optimal DX plan and rough estimate. The system mainly includes the following means:

[1596] First, a company representative (user) opens a web page and inputs their company's requests and issues into a chat-style interface. When inputting requests and issues, the user can describe specific problems, such as, "Our company's inventory management is not going well. We would like to improve efficiency." An emotion engine is installed that recognizes the user's emotions from the input, and this emotional information is also collected at the same time.

[1597] Next, the input requests, issues, and emotional data are sent to the server and passed to the analysis means. The analysis means uses natural language processing technology to analyze the input information and identify missing information. The emotion engine also provides the emotional data to the analysis means, which then analyzes the information according to the user's emotional state.

[1598] When missing information is identified, the question generation means automatically generates additional questions to supplement the missing information. The generated questions adaptively adjust the tone and content of the questions and answers based on the emotional information. These questions are then presented to the user from the server via the terminal. When the user answers the presented questions and submits their answers, the information is also received by the server.

[1599] Next, the proposal generation means generates an optimal combination of products and services based on all the information received from the user (requests, issues, and emotional data). The proposal generation means takes into account the data from the emotion engine and makes suggestions based on the user's emotional state. For example, if the user is feeling stressed, it can suggest a simple and intuitive solution, while if the emotional state is positive, it can provide a comprehensive plan with multiple options.

[1600] Based on the created DX plan, the estimate generation means calculates the estimated costs. The estimate generation means automatically creates a rough estimate based on the price information of the proposed products and services, and optimizes the estimate by taking emotional data into consideration. The generated proposal and rough estimate are presented to the user in real time via the output means.

[1601] As a specific example, if a user inputs "Our company's inventory management is not going well. We want to improve efficiency," and the emotion engine recognizes this as "high frustration," the generation AI will present a simple and specific question: "Which part of inventory management are you struggling with?" If the user answers, "It's difficult to keep track of inventory levels," the proposal generation means will suggest simple and intuitive products such as "inventory management systems" and "RFID tags." Based on this proposal, the estimate generation means will create a rough estimate and present it to the user.

[1602] Furthermore, the generated proposal and rough estimate are notified to the corporate sales representative by the notification means, and the corporate sales representative can use the follow-up means to provide a detailed explanation to the company representative as necessary and further advance the transaction.

[1603] The system of this invention aims to help small and medium-sized enterprises effectively promote DX even without specialized knowledge. By introducing an emotion engine, it provides a better user experience and realizes efficient DX promotion.

[1604] The processing flow will be explained below.

[1605] ---

[1606] Step 1:

[1607] The user opens a web page and inputs their company's requests and issues through a chat interface. The device sends the input information to the server. At the same time, the emotion engine collects emotional data from the user's input.

[1608] Step 2:

[1609] The server passes the information on requests and issues received from the user, as well as emotional data, to the analysis means. The analysis means uses natural language processing technology to analyze the input information and identify any missing information. At the same time, the emotion engine provides the emotional data to the analysis means, which then analyzes the information according to the user's emotional state.

[1610] Step 3:

[1611] The question generation means on the server generates additional questions to fill in the missing information based on the information from the analysis means and the emotional data. The tone and content of these questions are adjusted based on the user's emotional state. The generated questions are transferred to the server, which then presents the questions to the user via the terminal.

[1612] Step 4:

[1613] The user answers the additional questions and sends the answers to the server via the terminal, which receives the information and passes it to the analysis means and the proposal generation means.

[1614] Step 5:

[1615] The proposal generation means on the server generates the optimal combination of products and services based on all information received from the user (requests, issues, emotional data). The proposal generation means takes into account data from the emotion engine and makes suggestions based on the user's emotional state. For example, if the user is feeling frustrated, it will propose a simple and intuitive solution.

[1616] Step 6:

[1617] The proposal generation means generates specific proposal details and passes them to the quotation generation means. The quotation generation means incorporates price information for the necessary products and services based on the proposal details and automatically creates a rough quotation. Furthermore, based on data from the emotion engine, it presents the optimal price for the user.

[1618] Step 7:

[1619] The server receives the generated proposal and rough estimate and presents them to the user in real time via the output means. The user can check the proposal and estimate information on a browser.

[1620] Step 8:

[1621] The server notifies the corporate sales representative of the generated proposal and rough estimate information, and the necessary information is transferred to the sales team using the notification means.

[1622] Step 9:

[1623] The corporate sales representative will use the follow-up means to follow up with the user by providing detailed explanations and answering questions, etc. The user can then have specific discussions with the corporate sales representative about the proposal content and details of the transaction.

[1624] ---

[1625] The above is the specific processing flow of the system that combines the emotion engine. The operation at each step enables small and medium-sized enterprises to promote digital transformation in an efficient and emotionally considerate manner.

[1626] Example 2

[1627] 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."

[1628] When small and medium-sized enterprises (SMEs) promote digital transformation (DX), they require specialized knowledge to select and estimate appropriate products and services, and in the process, they require proposals that take the user's emotional state into consideration. This reduces user frustration and ensures efficient and effective DX promotion. However, conventional DX support systems do not take the user's emotional state into account when making proposals or optimizations, resulting in a poor user experience.

[1629] 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.

[1630] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving answers, a proposal generation means for analyzing the received answers and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, an emotion analysis means for collecting and analyzing user emotion data, and an optimization means for taking the emotion data into account in the generated proposal content and rough estimate. This makes it possible to generate optimal proposals and estimates that take the user's emotional state into account.

[1631] "Corporate representative" refers to a person within a company who inputs their company's requests and issues and requests proposals and estimates for promoting DX.

[1632] "Interface means" refers to input means such as a web page or chat interface through which company personnel can input their company's requests and issues.

[1633] "Analysis means" refers to natural language processing technology and analysis engines that analyze input requests and issues and identify missing information.

[1634] The "question generation means" refers to a function that automatically generates additional questions to fill in the missing information identified by the analysis means.

[1635] The "presentation and reception means" refers to a function for presenting the generated follow-up question to a company representative and receiving an answer from the company representative.

[1636] "Proposal generation means" refers to the function of analyzing the received responses and generating an appropriate combination of products and services.

[1637] "Estimate generation means" refers to a function that automatically creates a rough estimate based on the proposal content.

[1638] "Output means" refers to a function for presenting the generated proposal content and rough estimate to the company representative.

[1639] "Emotion analysis means" refers to emotion engines and analysis technologies for collecting and analyzing user emotion data.

[1640] "Optimization means" refers to a function that allows emotional data to be taken into account in the generated proposals and rough estimates.

[1641] "Notification means" refers to a function for notifying corporate sales representatives of the generated proposal contents and rough estimate.

[1642] "Follow-up measures" refers to functions that enable corporate sales representatives to provide additional support and explanations to company representatives and carry out follow-up.

[1643] "DX plan generation means" refers to the function of generating the optimal digital transformation plan based on the generated proposals and price information for products and services.

[1644] "Real-time presentation means" refers to the function for instantly presenting the generated DX plan to company personnel.

[1645] This invention is a system that allows small and medium-sized enterprises to easily promote digital transformation (DX). The system begins by having company personnel input their company's requests and challenges into an interface. The system uses a generative AI model to present an optimal DX plan and rough estimate, taking into account the emotional state of the company personnel.

[1646] First, the user uses the interface means via a web browser. This interface means is a chat interface that allows company personnel to input their company's requests and issues in text format. For example, a specific problem can be input, such as, "Our company's inventory management is not going well. We would like to improve efficiency." The input content is then analyzed by the emotion analysis means, which collects and analyzes the user's emotional data.

[1647] Next, the device transmits this input data and emotion data to a server. The server analyzes the input content using natural language processing technology (e.g., natural language processing API) and identifies missing information. The analysis means acquires the emotion data analyzed by an emotion engine (e.g., emotion analysis API) and performs information analysis according to the user's emotional state.

[1648] When missing information is identified, a question generation means installed on the server automatically generates additional questions using a generative AI model (e.g., the generative AI model GPT-4). The generated questions are adjusted based on the emotional data, and the tone and content are adaptively changed. The questions are presented to the user via the device. When the user answers the presented questions, the answer data is again sent to the server.

[1649] The server's proposal generation means then generates an appropriate combination of products and services based on all the data received from the user (requests, issues, and emotional data).The proposal generation means takes into account the data from the emotion analysis means and proposes a DX plan based on the user's emotional state.For example, if the user is feeling frustrated, a simple and intuitive solution will be proposed.

[1650] Based on the proposed DX plan, the estimate generation means automatically calculates the estimated costs. The estimate generation means optimizes the estimate based on price information for the proposed products and services, taking into account emotional data. The generated proposal and estimated estimate are presented to the user in real time by the output means.

[1651] As a specific example, if a user inputs "Our inventory management is not going well. We want to improve efficiency," and the sentiment analysis means recognizes this as "high frustration," the generative AI model (GPT-4) will generate a simple and specific question: "Which part of inventory management are you struggling with?" If the user answers, "It's difficult to keep track of inventory levels," the proposal generation means will suggest simple and intuitive products such as "inventory management systems" and "RFID tags." Based on this suggestion, the estimate generation means will create a rough estimate and present it to the user.

[1652] Furthermore, the generated proposal and rough estimate are notified to the corporate sales representative via a notification means, and the corporate sales representative can use the follow-up means to provide additional understanding or explanation to the company representative as necessary to support the progress of the transaction.

[1653] As an example of a prompt sentence, the generative AI model might be given the following prompt:

[1654] "A user wants to improve inventory management efficiency. His current emotional state is frustration. What inventory management system should we suggest?"

[1655] By integrating these methods, this system will help small and medium-sized enterprises effectively promote digital transformation without specialized knowledge, and will provide a better user experience by utilizing emotional data.

[1656] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1657] Step 1:

[1658] Users input their requests and issues

[1659] Input: The user inputs their company's requests and issues into the chat interface. For example, they might type, "Our inventory management is not going well. We want to improve efficiency."

[1660] Operation: The device receives user input and prepares it to be sent to the emotion engine.

[1661] Output: Text data of the input requests and issues.

[1662] Step 2:

[1663] Sending input data and emotion data

[1664] Input: The device sends user input and emotion data to the emotion engine.

[1665] Operation: The device generates an HTTP request to send data to the emotion engine and sends it to the server.

[1666] Output: Text data and emotion data received by the server.

[1667] Step 3:

[1668] The server receives and analyzes the data

[1669] Input: Text data and emotion data received by the server from the device.

[1670] How it works: The server uses a natural language processing module to analyze the text data and identify missing information. The emotion engine simultaneously analyzes the emotion data and understands the user's emotional state.

[1671] Output: Analysis results include missing information and the user's emotional state data.

[1672] Step 4:

[1673] The server generates a follow-up question

[1674] Input: Missing information and emotion data.

[1675] How it works: The server's generative AI model (e.g., GPT-4) generates follow-up questions to fill in missing information. The question generator uses emotional data to adaptively adjust the tone and content of the questions.

[1676] Output: Generated follow-up questions.

[1677] Step 5:

[1678] Submitting a generated question

[1679] Input: The generated follow-up question.

[1680] Operation: The server sends the additional question to the terminal as an HTTP response. The terminal displays the question to the user.

[1681] Output: A follow-up question that is presented to the user.

[1682] Step 6:

[1683] The user answers the question

[1684] Input: When a user sees a follow-up question, they input their answer through the chat interface.

[1685] Operation: The terminal receives the user's response and prepares to send the data to the server.

[1686] Output: The entered response data.

[1687] Step 7:

[1688] The server parses the answer

[1689] Input: User response data.

[1690] Operation: The server again uses the natural language processing module to analyze the response data and identify the appropriate combination of products and services.

[1691] Output: Potential products and services as analysis results.

[1692] Step 8:

[1693] Proposal and quote generation

[1694] Input: potential products and services, user sentiment data.

[1695] Operation: The server's proposal generation means generates optimal proposals taking into account emotion data. The estimate generation means calculates approximate costs based on the proposals. The estimate is also optimized.

[1696] Output: Generated proposal and quote.

[1697] Step 9:

[1698] Presentation by output means

[1699] Input: Generated proposal and quote.

[1700] How it works: The server sends these as HTTP responses to the device, which displays them to the user.

[1701] Output: Proposal and quote presented to the user.

[1702] Step 10:

[1703] Notification and follow-up

[1704] Input: Generated proposal and quote.

[1705] Operation: The server's notification means notifies the corporate sales representative of the generated proposal and quotation. The corporate sales representative uses the follow-up means to provide additional explanations or take action to the company representative as necessary.

[1706] Output: Proposal and quotation notified, and follow-up action taken.

[1707] (Application example 2)

[1708] 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."

[1709] Currently, many small and medium-sized enterprises and factories lack specialized knowledge about production line issues and methods for promoting DX (digital transformation), making it difficult to efficiently solve problems or select appropriate products. Furthermore, adaptive proposals that take into account employee emotions are often not made, often causing stress and confusion. In these cases, there is a need for automated creation of effective DX plans and quotation presentations, especially when promoting DX within factories.

[1710] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1711] In this invention, the server includes an interface means through which a company representative inputs their company's requests and issues, an analysis means for analyzing the input requests and issues and identifying missing information, a question generation means for generating additional questions based on the missing information, a presentation and reception means for presenting the generated additional questions to the company representative and receiving responses, a proposal generation means for analyzing the received responses and generating an appropriate combination of products and services, an estimate generation means for automatically creating a rough estimate based on the proposal content, an output means for presenting the generated proposal content and rough estimate to the company representative, a robot arranged on the production line for inputting issues within the factory, an emotion engine for collecting emotional data on the input issues, and an emotion analysis means for adjusting the tone of adaptive questions and proposals based on the emotional data. This enables small and medium-sized enterprises and factories to efficiently and adaptively create DX plans and present estimates while taking into account the emotions of their employees.

[1712] Definitions of important words

[1713] An "interface means" is an input means through which company personnel input their company's requests and issues.

[1714] The "analysis means" is a means for analyzing input requests and issues and identifying missing information.

[1715] The "question generation means" is a means for generating additional questions based on missing information.

[1716] The "presentation and reception means" is a means for presenting the generated follow-up questions to the company personnel and receiving the answers.

[1717] The "proposal generation means" is a means for analyzing the received responses and generating an appropriate combination of products and services.

[1718] The "quote generation means" is a means for automatically creating a rough estimate based on the proposal contents.

[1719] The "output means" is a means for presenting the generated proposal contents and rough estimate to the company representative.

[1720] "Robots installed on the production line" are machines installed in the factory to input tasks.

[1721] The "emotion engine" is a system for collecting emotional data for input tasks.

[1722] "Sentiment analysis means" means for adjusting the tone of adaptive questions and suggestions based on emotional data.

[1723] MODE FOR CARRYING OUT THE INVENTION

[1724] This invention is a system in which company personnel input their company's requests and challenges, and based on that, a generative AI and emotion engine present the optimal DX plan and rough estimate. This system also uses robots placed on production lines with the aim of promoting DX within factories.

[1725] The system mainly includes the following means:

[1726] 1. Interface Method

[1727] Users input their company's issues via voice input or a tablet via a robot placed on the production line.

[1728] The robot sends this input to the emotion engine.

[1729] 2. Emotion Engine

[1730] The emotion engine collects and analyzes the emotion data of the input task. The software used is a natural language processing library (e.g., TextBlob).

[1731] 3. Analysis and Question Generation Methods

[1732] The server analyzes the input request or problem and identifies missing information, using natural language processing technology (e.g., OpenAI's generative AI model).

[1733] A follow-up question is generated based on the missing information and the question is presented to the user through the interface means.

[1734] 4. Means of presentation and reception

[1735] The user answers the generated additional questions and sends them to the server via the robot.

[1736] The server receives this response and parses it again.

[1737] 5. Proposal generation means

[1738] The server generates an appropriate combination of products and services based on the received information and makes adaptive suggestions based on emotional data. For example, if the user is "highly frustrated," it will select a simple and intuitive solution.

[1739] 6. Estimate generation and output methods

[1740] The estimate generating means automatically generates a rough estimate based on the proposal and presents it to the user in real time via the output means, with the result being displayed on the robot's display and / or voice output.

[1741] Hardware and software used

[1742] Hardware: Robots, servers, tablets or voice input devices located on a production line.

[1743] Software: Natural language processing libraries (e.g., TextBlob), generative AI models (e.g., OpenAI's text-davinci-003).

[1744] Specific examples

[1745] For example, suppose a factory staff member inputs, "We want to eliminate the bottleneck on the production line. It's inefficient." In this case, the emotion engine recognizes this as "high frustration." The generation AI then generates a question, "Specifically, where is the problem occurring?" and presents it to the factory staff member. If the factory staff member answers, "Inventory management is difficult," the generation AI suggests introducing an "inventory management system" and "RFID tags." Finally, the estimate generation means creates a rough estimate and presents it to the factory staff member.

[1746] Prompt Sentence Examples

[1747] Please propose the best DX plan and estimate for the following issues: Eliminating bottlenecks in the production line. Inefficiency. Emotional state: High frustration

[1748] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1749] Program processing steps

[1750] Step 1:

[1751] Users input their company's issues using a robot placed on the production line. This input is done via a tablet or a voice recognition system. The input issue (e.g., "We want to eliminate the bottleneck on the production line. It's inefficient.") is sent to the emotion engine.

[1752] Input: User's assignment

[1753] Output: Issue data

[1754] Step 2:

[1755] The emotion engine receives the task data and analyzes the input for emotional data. The emotion engine uses a natural language processing library (e.g., TextBlob) to identify the task's emotional state. The analysis results are sent to the server as emotion tags such as "positive," "negative," or "neutral."

[1756] Input: Issue data

[1757] Data processing: Sentiment analysis

[1758] Output: Emotion tag

[1759] Step 3:

[1760] The server receives the task data and emotion tags, analyzes the task content using an analysis method, and uses a generative AI model (e.g., OpenAI's text-davinci-003) to identify missing information. It then generates follow-up questions based on the missing information.

[1761] Input: issue data, emotion tags

[1762] Data calculation: problem analysis, identification of missing information

[1763] Output: Additional questions

[1764] Step 4:

[1765] The server presents the generated follow-up questions to the user through the interface means (the robot's display or voice output), and the user answers the presented follow-up questions.

[1766] Input: Additional Question

[1767] Output: User's answer

[1768] Step 5:

[1769] The server receives the user's answers and analyzes them again using the analysis method. Based on the analysis results, the server generates an appropriate combination of products and services, taking into account the emotional data. During this generation process, the tone and content of the questions are also adjusted based on the emotional tags.

[1770] Input: User's answer

[1771] Data calculation: response analysis, product and service generation

[1772] Output: Product and service proposals

[1773] Step 6:

[1774] The quotation generation means automatically generates a rough quotation based on the proposal. This quotation information is calculated using the generation AI and includes necessary price information. The generated quotation is presented to the user via the output means.

[1775] Input: Product / service proposal

[1776] Data calculation: Estimate calculation

[1777] Output: Rough estimate

[1778] Step 7:

[1779] The user can review the generated proposal and estimate and follow up if necessary. This process is done in real time and allows the user to ask specific questions or request additional information.

[1780] Input: Proposal details, rough estimate

[1781] Output: User confirmation and follow-up

[1782] In this way, the system of the present invention as a whole enables small and medium-sized enterprises and factories to efficiently and adaptively create DX plans and present estimates while taking into account the feelings of their employees.

[1783] 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.

[1784] 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.

[1785] 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.

[1786] 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.

[1787] FIG. 9 is a diagram illustrating 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 actions 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.

[1788] 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.

[1789] 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).

[1790] 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.

[1791] 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."

[1792] 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.

[1793] 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).

[1794] 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.

[1795] 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.

[1796] 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.

[1797] 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.

[1798] 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.

[1799] 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.

[1800] 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.

[1801] 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.

[1802] 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.

[1803] 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.

[1804] The following is further disclosed regarding the above embodiment.

[1805] (Claim 1)

[1806] An interface for company personnel to input their company's requests and issues,

[1807] An analytical means for analyzing input requests and issues and identifying missing information;

[1808] a question generation means for generating additional questions based on the missing information;

[1809] a presentation and reception means for presenting the generated additional questions to a company representative and receiving answers;

[1810] A proposal generation means for analyzing the received responses and generating an appropriate combination of products and services;

[1811] an estimate generation means for automatically generating a rough estimate based on the proposal;

[1812] an output means for presenting the generated proposal content and rough estimate to the company representative;

[1813] A system including the above means.

[1814] (Claim 2)

[1815] a notification means for notifying the corporate sales representative of the generated proposal content and rough estimate;

[1816] A follow-up method for corporate sales representatives to follow up;

[1817] The system of claim 1 further comprising:

[1818] (Claim 3)

[1819] a DX plan generation means for generating an optimal DX plan based on the generated proposal and price information of products and services;

[1820] A real-time presentation method for presenting the generated DX plan to company personnel in real time,

[1821] The system of claim 1 further comprising:

[1822] "Example 1"

[1823] (Claim 1)

[1824] A means for company representatives to input their company's requests and issues,

[1825] An analytical means for analyzing input requ...

Claims

1. An interface for company personnel to input their company's requests and issues, An analytical means for analyzing input requests and issues and identifying missing information; a question generation means for generating additional questions based on the missing information; a presentation and reception means for presenting the generated additional questions to a company representative and receiving answers; A proposal generation means for analyzing the received responses and generating an appropriate combination of products and services; an estimate generation means for automatically generating a rough estimate based on the proposal; an output means for presenting the generated proposal content and rough estimate to the company representative; A system including the above means.

2. a notification means for notifying the corporate sales representative of the generated proposal content and rough estimate; A follow-up method for corporate sales representatives to follow up; The system of claim 1 further comprising:

3. a DX plan generation means for generating an optimal DX plan based on the generated proposal and price information of products and services; A real-time presentation method for presenting the generated DX plan to company personnel in real time, The system of claim 1 further comprising:

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