System

Abstract

A system is provided.SOLUTION: The system includes a means for receiving proposal information, a means for acquiring relevant information from a company database and a merchandise database on the basis of the proposal information, a means for matching a proposal merchandise with a proposal company on the basis of the acquired relevant information, a means for automatically generating a proposal material on the basis of a matching result, and a means for transmitting the generated proposal material to a user terminal.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 conventional sales activities, salespeople had to individually collect and analyze information on the proposed products and the proposing companies, and manually create proposal materials. This process was time-consuming, labor-intensive, and extremely inefficient. It also presented issues such as information omissions and variations in the quality of proposals. The present invention aims to solve these problems and improve the efficiency of sales activities and the quality of proposal materials. [Means for solving the problem]

[0005] The present invention provides a system including means for receiving proposal information, acquiring related information from a company database and a product database based on the proposal information, means for matching the proposed products with the proposing companies based on the acquired related information, means for automatically generating proposal materials based on the matching results, and means for transmitting the generated proposal materials to a user's terminal. This significantly reduces the manual workload of salespeople and enables the rapid and automatic generation of high-quality proposal materials.

[0006] "Proposal information" is detailed information about the product that the user is proposing and the company to which the proposal is being made.

[0007] A "corporate database" is a database that stores detailed information about companies, including their industry, size, performance, and specific challenges.

[0008] The "product database" is a database that stores detailed information about the products being proposed, including the product's performance, price, usage, track record, etc.

[0009] "Related information" is information related to the proposed product and company, obtained from the company database and product database.

[0010] "Matching" is the process of evaluating the needs and characteristics of the proposed products and the proposing company, and determining their compatibility.

[0011] "Proposal materials" are materials containing information about the proposed products and the proposing company, and are documents for specifically explaining proposals in sales activities.

[0012] "Automatic generation" refers to the process by which the system automatically generates proposal materials without manual intervention.

[0013] "User's Device" refers to the electronic device used to receive, display, and save the proposal materials, including, but not limited to, a PC, tablet, or smartphone. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system that mainly consists of the steps of receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, and transmitting the proposal materials. This system consists of three main elements: a server, a terminal, and a user.

[0036] 1. User Input Phase

[0037] Users access a dedicated application on their device and log in. After logging in, they enter detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and specific challenges.

[0038] 2. Data Collection Phase

[0039] The terminal sends the proposal information entered by the user to the server. Based on the proposal information, the server accesses the company database and product database to obtain related information. From the company database, information such as the company's industry trends, past proposal history, and current challenges is obtained. From the product database, information such as product performance and existing sales performance is obtained.

[0040] 3. Data Matching Phase

[0041] The server uses AI algorithms to match the relevant information it has acquired. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. For example, if a company making a proposal to a user is looking to improve the efficiency of customer service, it evaluates the extent to which an AI chatbot can solve that problem.

[0042] 4. Proposal material generation phase

[0043] After obtaining the matching results, the server uses an automatic generation program to create a proposal document that includes the product's characteristics, reasons for the proposal, and its benefits to the company. Furthermore, the document is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[0044] 5. Material distribution phase

[0045] The created proposal materials are sent from the server to the user's device, which displays the received proposal materials and allows the user to download and save them.

[0046] Specific examples

[0047] For example, if a user inputs "AI Chatbot" as the proposed product and "ABC Corporation" as the target company, the following process will be executed:

[0048] 1. The user enters information about the "AI chatbot" and "ABC Co., Ltd." into their device and sends it to the server.

[0049] 2. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about "ABC Co., Ltd." from the company database.

[0050] 3. The server uses an AI algorithm to evaluate how well the "AI Chatbot" meets the customer service needs of "ABC Co., Ltd."

[0051] 4. Based on the matching results, the server uses an automatic generation program to create a proposal document, which includes the product's performance, cost-effectiveness, reasons for the proposal, etc.

[0052] 5. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0053] In this way, the present invention improves sales efficiency and the quality and consistency of sales pitch materials.

[0054] The processing flow will be explained below.

[0055] Step 1:

[0056] The user accesses the dedicated application on the terminal and logs in.

[0057] Step 2:

[0058] The user inputs detailed information about the proposed product and the company to which the proposal is being made, including the product's characteristics, the industry and specific challenges of the proposing company, etc.

[0059] Step 3:

[0060] The terminal transmits the input proposal information to the server.

[0061] Step 4:

[0062] The server retrieves detailed information about the proposed product from the product database, including the product's performance, price, and existing sales record.

[0063] Step 5:

[0064] The server retrieves detailed information about the proposed company from a company database, including the company's industry, size, performance, and specific challenges.

[0065] Step 6:

[0066] The server uses the acquired product information and company information to apply an AI algorithm to evaluate the degree of match between the proposed product and the proposing company. Specifically, it calculates the extent to which the proposed product can solve the needs and problems of the proposing company.

[0067] Step 7:

[0068] Based on the evaluation results of the degree of matching, the server calls up a proposal template and uses an automatic generation program to create a proposal document, which includes the product name, product characteristics, reasons for proposing it, benefits to the company, etc.

[0069] Step 8:

[0070] The server generates the proposal in a visually appealing format (e.g., a PDF file or presentation slides).

[0071] Step 9:

[0072] The server transmits the generated proposal material to the user's terminal.

[0073] Step 10:

[0074] The terminal displays the received proposal materials and provides a function that allows the user to download and save them.

[0075] This allows users to efficiently receive high-quality proposal materials, enabling them to carry out sales activities effectively.

[0076] Example 1

[0077] 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."

[0078] In existing sales activities, the process of creating proposal materials is time-consuming and labor-intensive, and often lacks consistency and efficiency. Another issue is that it is difficult to fully evaluate the compatibility between the product and the company, making it difficult to make optimal proposals.

[0079] 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.

[0080] In this invention, the server includes means for receiving proposal information, means for acquiring related information from a database based on the proposal information, means for matching proposed products with proposing companies using an artificial intelligence algorithm based on the acquired related information, means for automatically generating proposal materials based on the matching results, means for transmitting the generated proposal materials to a user's terminal, and means for the terminal to provide the user with a function for displaying and saving the proposal materials. This makes it possible to create proposal materials more efficiently and to produce consistent, high-quality proposals.

[0081] "Proposal information" refers to detailed information about the products and the companies to which the proposal is being made, including, specifically, the characteristics and price of the products, the industry and size of the companies, and their specific challenges.

[0082] A "database" refers to a system that organizes and stores company and product information, and retrieves data in response to queries as needed.

[0083] An "artificial intelligence algorithm" is a computational method for data analysis and prediction, particularly using machine learning and deep learning models to evaluate the compatibility of proposed products with the proposing company.

[0084] A "proposal document" is a document that includes the characteristics of the product, the reasons for the proposal, the benefits to the company, etc., and is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[0085] The "suitability score" is a numerical indication of how well the proposed product matches the needs of the proposing company, and is calculated using an artificial intelligence algorithm.

[0086] "User's device" refers to a device used by the user, such as a computer, tablet, or smartphone, that has the function of displaying and saving proposal materials sent from the server.

[0087] An "automatic generation program" refers to software that automatically creates proposal materials in a specified format based on the acquired information and relevance scores.

[0088] MODE FOR CARRYING OUT THE INVENTION

[0089] The present invention is a system that consists of the steps of receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, and transmitting the proposal materials. This system is composed of three elements: a server, a terminal, and a user.

[0090] User Input Phase

[0091] The user accesses a dedicated application on their device and enters their login information. After logging in, the user enters detailed information (proposal information) about the product to be proposed and the company to which the proposal is being made. This information includes the product's characteristics, price, the company's industry, size, and specific challenges.

[0092] Data Collection Phase

[0093] The terminal sends the proposal information entered by the user to the server. At this time, the information is sent to the server in JSON format using an HTTP POST request. Based on this proposal information, the server accesses the company database and product database to retrieve related information. From the company database, it retrieves information such as the company's industry trends, past proposal history, and current issues, while from the product database, it retrieves information such as product performance and sales performance.

[0094] Data Matching Phase

[0095] The server runs an algorithm using the generative AI model based on the acquired related information to calculate the compatibility between the proposed product and the proposing company. For example, if the company to which the user is proposing is seeking to improve the efficiency of customer service, the generative AI model will score the extent to which it can solve that problem.

[0096] Proposal material generation phase

[0097] After obtaining the matching results, the server uses an automatic generation program to create a proposal document. The proposal document includes the product's characteristics, the reasons for the proposal, and its benefits to the company. The document is generated in a visually appealing format, such as a PDF or slide presentation. For example, it can be generated by embedding data in a template using a Python script.

[0098] Material distribution phase

[0099] The created proposal materials are sent from the server to the user's device. The device receives the materials and provides functions that allow the user to view, download, and save them. The user can use the proposal materials for sales activities and presentations.

[0100] Specific examples

[0101] For example, if a user inputs "AI chatbot" as the proposed product and "a certain company" as the target company, the following process will be executed:

[0102] 1. The user enters information about the "AI chatbot" and "a certain company" on their device and sends it to the server.

[0103] 2. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about a "certain company" from the company database.

[0104] 3. The server uses the generative AI model to evaluate how well the "AI chatbot" fits the customer service needs of "a certain company."

[0105] 4. The server uses an automatic generation program to create a proposal document, which includes the product's performance, cost-effectiveness, reasons for the proposal, etc.

[0106] 5. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0107] For example, the prompt for the generative AI model is as follows: "Please create a proposal document aimed at improving customer service at the target company. The product you are proposing is an 'AI chatbot,' and the company name is 'a certain company.'"

[0108] The above is an embodiment of the present invention, and this system makes the creation of proposal materials efficient and consistent, thereby greatly supporting sales activities.

[0109] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0110] Step 1:

[0111] The user accesses a dedicated application on the device and enters login information (username and password). The entered login information is sent from the device to the server via an HTTP POST request. The server compares the received login information with an authentication database and authenticates the user. If authentication is successful, the server generates an authentication token and sends it to the device. The device saves the authentication token and uses it for subsequent requests.

[0112] Input: Username, Password

[0113] Data processing: Login information authentication

[0114] Output: Authentication token

[0115] Step 2:

[0116] The user inputs proposal information (product name, company name, and other detailed information) into the application on the device. The device sends the input proposal information in JSON format to the server. The server stores the received proposal information in its internal database.

[0117] Input: Product name, company name, and other details

[0118] Data processing: Saving proposal information

[0119] Output: Confirmation of saving of proposal information to database

[0120] Step 3:

[0121] The server queries the company database and product database based on the proposal information. From the company database, it obtains information such as the company's industry trends, past proposal history, and current issues, and from the product database, it obtains information such as product performance and sales performance.

[0122] Input: Proposal information

[0123] Data manipulation: performing database queries

[0124] Output: Company information, product information

[0125] Step 4:

[0126] The server evaluates the compatibility of the proposed product with the proposing company using an algorithm that uses a generative AI model based on company and product information. The server uses the acquired data as input, provides a prompt to the generative AI model, and outputs a compatibility score as the evaluation result.

[0127] Input: Company information, product information, prompt text

[0128] Data processing: scoring with generative AI models

[0129] Output: Relevance score

[0130] Step 5:

[0131] The server selects a proposal template based on the relevance score and the acquired related information, and creates a proposal using an automatic generation program. This proposal includes the characteristics of the proposed product, the evaluation results, and its benefits to the company. The generated document is saved in PDF format or slide presentation format.

[0132] Input: Relevance score, related information

[0133] Data processing: Automatic generation of proposal materials

[0134] Output: Proposal materials (PDF or slide format)

[0135] Step 6:

[0136] The server sends the generated proposal materials to the user's device. The device displays the received proposal materials to the user and provides functions that allow the user to download or save them. The user can use the proposal materials in sales activities and presentations.

[0137] Input: Proposal materials

[0138] Data processing: Sending proposal materials

[0139] Output: Display and save proposal materials

[0140] Through the above steps, this system makes it possible for users to streamline their sales activities and quickly provide high-quality proposal materials.

[0141] (Application example 1)

[0142] 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."

[0143] When promoting or introducing new products in physical stores, it is difficult to quickly generate efficient and effective proposal materials. With conventional methods, creating proposal materials takes a great deal of time and effort, risking delays in the store's sales promotion activities. Furthermore, creating consistent, high-quality proposal materials requires specialized knowledge, which not all stores possess. This reduces the efficiency of physical stores in carrying out effective promotions and product introductions.

[0144] 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.

[0145] In this invention, the server includes means for receiving proposal information, means for acquiring related information from a company database and a product database based on the proposal information, means for matching the proposed products with the proposing companies based on the acquired related information, means for automatically generating proposal materials based on the matching results, means for transmitting the generated proposal materials to the user's information processing device, and means for generating the proposal materials in a visually appealing format, thereby enabling store managers and staff at physical stores to generate high-quality proposal materials in a short amount of time and to carry out rapid and effective promotions and product introductions.

[0146] "Proposal information" refers to information about the characteristics and price of the product and the company to which the proposal is being made.

[0147] A "corporate database" is a database that stores information such as a company's industry trends, past proposal history, and current issues.

[0148] The "product database" is a database that stores information such as product performance and sales performance.

[0149] "Matching" is the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating the degree of compatibility.

[0150] A "proposal document" is a document that is automatically generated to describe the characteristics, effects, and reasons for proposing a product, and is output in a visually appealing format.

[0151] The "user's information processing device" refers to a mobile terminal such as a smartphone or tablet that receives and displays proposal materials.

[0152] The "means for generating a proposal document in a visually appealing format" is a method for automatically generating a proposal document in a visually appealing format using charts and text.

[0153] A "generative AI model" is an artificial intelligence model used to automatically generate the content of proposal materials.

[0154] A "prompt sentence" is a specific instruction sentence input to a generative AI model.

[0155] This invention is a system that matches products with companies based on proposal information and automatically generates and transmits proposal materials. This system consists of three main elements: a server, a terminal, and a user. Its special feature is a method that enables store managers and staff at physical stores to efficiently generate proposal materials for promotions and new product introductions.

[0156] 1. System Program

[0157] The system program is structured as follows:

[0158] User Input Phase

[0159] Users access a dedicated application on their device (smartphone) and log in. After logging in, users enter detailed information about the products they are proposing and the target companies. This information includes, for example, the characteristics and price of the new product, the type and size of the target stores, and any challenges the stores are facing.

[0160] Data Collection Phase

[0161] The input proposal information is sent to the server. The server accesses the product database and company database based on the proposal information to obtain related information. The product database provides information such as product performance and past sales performance, while the company database provides information such as the company's industry trends, past proposal history, and current issues.

[0162] Data Matching Phase

[0163] Based on the acquired related information, the server uses an AI algorithm to perform matching. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. For example, it evaluates how well the proposed new product meets the customer service needs of the store in question.

[0164] Proposal material generation phase

[0165] After obtaining the matching results, the server uses an automatic generation program to create a proposal document, which includes the product's characteristics, reasons for the proposal, and its benefits to the company. The document is generated in a visually appealing format (e.g., PDF file format).

[0166] Material distribution phase

[0167] The created proposal materials are sent from the server to the user's terminal, which displays the received proposal materials and provides a function that allows the user to download and save them.

[0168] 2. Natural language description of the process

[0169] Hardware: Smartphone (iOS or Android), server equipment

[0170] Software: Python, FPDF library, Requests library for HTTP requests

[0171] A user logs into the smartphone app and enters information about new products and promotions. This data is sent to a server, which accesses a product database and a company database to collect the necessary information. The collected data is evaluated by an AI algorithm, which calculates the compatibility between the proposed product and the proposing company. Based on the results, the server uses an automatic generation program to create a proposal document, which is generated as a visually appealing PDF file. Finally, the generated proposal document is sent from the server to the user's smartphone, where the user can receive and view it.

[0172] 3. Specific Examples

[0173] Below is an example prompt, where the user enters the following information:

[0174] Username: Manager

[0175] Password: A secure password

[0176] Product ID: P12345

[0177] Store ID: S67890

[0178] Based on this information, the system evaluates the characteristics of the new product, the store's industry and size, and customer service needs, and calculates the degree of suitability. For example, it evaluates how much an AI chatbot for a new product can improve the efficiency of customer service at the store. As a result, a visually appealing PDF file is generated, detailing the new product's introduction, benefits, and reasons for proposing it.

[0179] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0180] Step 1:

[0181] The user opens a dedicated application on their device (smartphone) and enters their login information (username and password). The entered login information is sent to the server, where the user is authenticated. If authentication is successful, the user enters detailed information about the product they are proposing and the target company. The information entered includes the product ID, store ID, product characteristics, price, store size, and the challenges they are facing.

[0182] Input: Login information, product information, store information

[0183] Output: Proposal information (product ID, store ID, product characteristics, price, size, issues)

[0184] Step 2:

[0185] The terminal sends the proposal information entered by the user to the server. The server makes a request to obtain related information from the product database and company database based on the product ID and store ID contained in the received proposal information. Information such as product performance and sales performance is obtained from the product database, and information such as the store's industry trends, past proposal history, and current challenges is obtained from the company database.

[0186] Input: Proposal information

[0187] Output: Product information, company information

[0188] Step 3:

[0189] Based on the acquired product and company information, the server uses an AI algorithm to match the proposed product with the proposing company. Specifically, it evaluates the product's characteristics and effectiveness, as well as the store's needs and characteristics, and calculates its suitability. In this evaluation process, the AI ​​algorithm analyzes various data points to determine the extent to which the proposed product can solve the target store's problems.

[0190] Input: Product information, company information

[0191] Output: Matching degree (fit score)

[0192] Step 4:

[0193] After obtaining the matching results, the server uses an automatic generation program to create a proposal document. This document includes the product's characteristics, effects, reasons for proposing it, and benefits to the company, and is generated in a visually appealing format (e.g., PDF file format). The generative AI model creates prompts based on the product's detailed information and the matching results, and reflects them in the proposal document.

[0194] Input: Matching degree, product information, company information

[0195] Output: Proposal materials (PDF file)

[0196] Step 5:

[0197] The created proposal document is sent from the server to the user's device. The device displays the received proposal document and provides the user with the ability to download and save it. The user can also edit the proposal document as needed or share it with other parties.

[0198] Input: Proposal materials

[0199] Output: Proposal materials (PDF file) displayed on the device

[0200] 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.

[0201] The present invention is a system that combines receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system consists of four main elements: a server, a terminal, a user, and an emotion engine.

[0202] 1. User Input Phase

[0203] Users access a dedicated application on their device and log in. After logging in, they enter detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and specific challenges.

[0204] 2. Emotion Recognition Phase

[0205] When receiving the proposed information, the device uses an emotion engine to analyze the user's facial expressions, tone of voice, etc. to recognize the user's emotional state. This emotion information is also sent to the server as part of the proposed information.

[0206] 3. Data Collection Phase

[0207] The terminal sends the input proposal information and emotion information to the server. The server accesses the company database and product database to obtain related information based on the proposal information. From the company database, information such as the company's industry trends, past proposal history, and current challenges is obtained. From the product database, information such as product performance and existing sales performance is obtained.

[0208] 4. Data Matching Phase

[0209] The server uses an AI algorithm to match the acquired related information and emotional information. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. It also takes the user's emotional information into account to determine the optimal proposal.

[0210] 5. Proposal material generation phase

[0211] After obtaining the matching results and emotion information, the server uses an automatic generation program to create a proposal document. This proposal document includes the product's characteristics, reasons for proposing it, and its benefits to the company. Furthermore, based on the emotion information, the content and format of the document are optimized, and the document is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[0212] 6. Material distribution phase

[0213] The created proposal materials are sent from the server to the user's device. The device displays the received proposal materials and provides the user with the ability to download and save them. In addition, the generated materials are optimized based on the user's emotional information, making it easier for the user to feel satisfied.

[0214] Specific examples

[0215] For example, if a user inputs "AI Chatbot" as the proposed product and "XYZ Corporation" as the target company, the following process will be executed:

[0216] 1. The user enters information about the "AI chatbot" and "XYZ Corporation" on their device and sends it to the server.

[0217] 2. At the same time as receiving the input information, the device analyzes the user's facial expressions and tone of voice and sends emotional information to the server.

[0218] 3. The server retrieves detailed information about the "AI chatbot" from the product database and detailed information about "XYZ Corporation" from the company database.

[0219] 4. The server uses an AI algorithm to evaluate how well the AI ​​chatbot meets the customer service needs of XYZ Corporation, taking emotional information into account.

[0220] 5. The server uses an automatic generation program to create a proposal document based on the matching results and emotional information. This document includes the product's performance, cost-effectiveness, reasons for the proposal, etc. The presentation and design of the document are also adjusted based on the emotional information.

[0221] 6. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0222] In this way, the present invention aims to improve the efficiency of sales activities and realizes the provision of high-quality proposal materials that take into account the user's emotions.

[0223] The processing flow will be explained below.

[0224] Step 1:

[0225] The user accesses the dedicated application on the terminal and logs in.

[0226] Step 2:

[0227] The user inputs detailed information about the proposed product and the company to which the proposal is being made, including the product's characteristics, the industry and specific challenges of the proposing company, etc.

[0228] Step 3:

[0229] When receiving the proposed information, the terminal analyzes the user's facial expression and voice tone using an emotion engine to recognize the user's emotional state.

[0230] Step 4:

[0231] The terminal transmits the input suggestion information and emotion information to the server.

[0232] Step 5:

[0233] Based on the proposal information, the server retrieves related detailed information from the company database and product database. From the company database, it retrieves information such as the company's industry, size, past transaction history, and specific issues. From the product database, it retrieves information such as the product's performance, price, and existing sales performance.

[0234] Step 6:

[0235] The server uses an AI algorithm to analyze the acquired product and company information, evaluates the degree of match between the proposed product and the proposed company, and fine-tunes the content of the proposal taking into account the user's emotional information.

[0236] Step 7:

[0237] The server starts an automatic generation program based on the matching degree and emotion information to create a proposal document. The document contains information such as the product's characteristics, the reason for the proposal, and its benefits to the company. The presentation method and design of the document are also adjusted according to the user's emotion information.

[0238] Step 8:

[0239] The server stores the generated proposal in a visually appealing format (e.g., a PDF file or a slide presentation).

[0240] Step 9:

[0241] The server transmits the generated proposal material to the user's terminal.

[0242] Step 10:

[0243] The device displays the received proposal materials on the user interface and allows the user to download and save them. In addition, the content and format of the materials are optimized based on the user's emotional information, resulting in a higher level of satisfaction for the user.

[0244] In this way, the present invention combines proposal information with user emotion information to automatically generate more effective and satisfying proposal materials, thereby improving the efficiency of sales activities.

[0245] Example 2

[0246] 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."

[0247] Conventional recommendation systems are unable to consider the user's emotional state when evaluating the degree of match between products and companies, making it difficult to generate optimal proposal content. Furthermore, the generated proposal materials are often not visually appealing, making it difficult to increase user satisfaction.

[0248] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving proposal information, means for collecting and analyzing user emotion information based on the received proposal information, means for acquiring related information from a company database and a product database based on the proposal information and emotion information, means for evaluating the needs and characteristics of the proposed product and the proposing company based on the acquired related information and calculating the degree of suitability, means for automatically generating proposal materials based on the matching results and emotion information, and means for transmitting the generated proposal materials to the user's terminal. This makes it possible to generate optimal proposal content that takes the user's emotion information into consideration and provide visually appealing proposal materials.

[0249] "Proposal information" refers to detailed information about the products proposed by the user and the companies to which the proposal is made.

[0250] "Emotional information" refers to information about the user's emotional state obtained by analyzing the user's facial expressions, tone of voice, and the like.

[0251] A "corporate database" refers to a database that stores information such as a company's industry trends, past proposal history, and challenges it faces.

[0252] A "product database" refers to a database that stores information such as product performance, characteristics, price, and sales record.

[0253] "Related information" refers to data obtained from the company database and product database based on the proposal information.

[0254] "Matching" refers to the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating the degree of compatibility.

[0255] "Proposal materials" refer to materials that are automatically generated based on matching results and emotional information, and that describe the characteristics of the product, the reasons for proposing it, and its benefits to the company.

[0256] "Visually generated" refers to optimizing the content and format of the material so that the material is created in a format that is visually appealing to the user.

[0257] The present invention is a system that combines receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system consists of four main elements: a server, a terminal, a user, and an emotion engine.

[0258] User Input Phase

[0259] Users access a dedicated application and log in. After logging in, they enter detailed information (proposal information) about the products they are proposing and the companies they are proposing to into the terminal. The information entered includes the characteristics and price of the products, the company's industry and size, and specific challenges.

[0260] Emotion Recognition Phase

[0261] The device uses a camera and microphone to collect facial expressions and voice tones while the user is typing. This collected data is analyzed by an emotion engine to generate the user's emotion information. This emotion information is also sent to the server along with the suggestion information.

[0262] Data Collection Phase

[0263] The terminal transmits the proposal information and emotion information to the server. The server accesses the company database and product database to obtain related information based on the proposal information. The company database provides information on the company's industry trends and past proposal history, while the product database provides information on the product's performance and existing sales performance.

[0264] Data Matching Phase

[0265] The server uses an AI algorithm to match the acquired related information and emotional information. It evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates the degree of compatibility. It also evaluates the user's emotional information to determine the optimal proposal content.

[0266] Proposal material generation phase

[0267] Based on the matching results and the emotion information, the server uses an automatic generation program to create a proposal document. This document includes the product's characteristics, the reason for the proposal, its benefits to the company, etc. Furthermore, based on the emotion information, the content and format of the document are optimized, and the document is generated in a visually appealing format.

[0268] Material distribution phase

[0269] The created proposal materials are sent from the server to the user's device. The device displays the received proposal materials and provides the user with the ability to download and save them. Because the proposal materials are optimized based on the user's emotional information, the user can achieve a higher level of satisfaction.

[0270] Specific examples

[0271] For example, if a user inputs "AI Chatbot" as the proposed product and "ABC Company" as the target company, the following process will be executed:

[0272] 1. The user enters information about the "AI chatbot" and "ABC Company" into the device through a dedicated application and sends it to the server.

[0273] 2. At the same time as the information is entered, the device also analyzes the user's facial expressions and tone of voice to collect emotional information and send it to the server.

[0274] 3. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about "ABC Company" from the company database.

[0275] 4. The server uses an AI algorithm to evaluate the compatibility between the "AI chatbot" and "ABC Company," taking into account emotional information.

[0276] 5. The server uses an automatic generation program to create a proposal document based on the matching results and emotion information. This document includes the product's performance, cost-effectiveness, reasons for the proposal, etc. The presentation and design of the document are also adjusted based on the emotion information.

[0277] 6. The server sends the generated proposal materials to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0278] Prompt Sentence Examples

[0279] Below are some example prompts for input to a generative AI model:

[0280] Please enter "AI Chatbot" as the proposed product and "ABC Company" as the target company. After entering the information, it will be sent to the server. At this time, please log in through a dedicated application. In addition, facial expressions and tone of voice while entering information will also be recorded, and the user's emotional state will also be analyzed.

[0281] According to this prompt, the user provides the appropriate information and emotional state, and the system generates the optimal proposal material.

[0282] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0283] Step 1: User Input Phase

[0284] The user accesses the dedicated application to log in. They enter their username and password as login information and complete the login process. The input is from the dedicated application and the terminal, and the output is successful user authentication.

[0285] The user inputs detailed information about the product to be proposed and the target company (proposal information) into the terminal. The input here includes the product's characteristics, price, the company's industry and size, and specific issues, and the output is the temporarily saved proposal information.

[0286] Step 2: Emotion Recognition Phase

[0287] The device uses a camera and microphone to capture facial expressions and voice tones during user input, and this collected data is sent to the emotion engine as input.

[0288] The emotion engine analyzes the collected data and recognizes the user's emotional state. This analysis is data calculation, and the output is emotional information.

[0289] The device sends the generated emotion information together with the suggestion information to the server. The input is the emotion information and the suggestion information, and the output is data transmission to the server.

[0290] Step 3: Data collection phase

[0291] The server receives the proposal information and emotion information sent from the terminal. The input is the proposal information and emotion information, and the output is a receipt confirmation.

[0292] The server accesses the company database and product database to retrieve relevant information. This data retrieval process is called data processing. The input is a database query about the company and product, and the output is the retrieved relevant information.

[0293] Step 4: Data Matching Phase

[0294] The server uses an AI algorithm to match the acquired related information and emotional information. The input is related information and emotional information, and the output is a compatibility score.

[0295] The server evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates their compatibility. This evaluation process is data calculation, and the compatibility score is the output.

[0296] Step 5: Proposal generation phase

[0297] The server uses an automatic generation program to create proposal materials based on the matching results and emotion information. The input is the matching results and emotion information, and the output is the proposal materials.

[0298] The content of the proposal document includes the product's characteristics, the reason for the proposal, and its benefits to the company. Furthermore, the format and design of the document are optimized based on the emotional information. This process involves the actual data processing and content generation.

[0299] Step 6: Material distribution phase

[0300] The server sends the generated proposal materials to the user's terminal. The input is the proposal materials, and the output is a document file sent to the user's terminal.

[0301] The terminal unpacks and displays the received proposal documents. The user can then check them and download or save them as needed. The input is the document file, and the output is the displayed document and the saved file.

[0302] (Application example 2)

[0303] 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."

[0304] Conventional proposal material generation systems create proposal materials by simple data matching without considering the user's emotional state. As a result, many proposals do not match the user's true needs or emotions, and the effectiveness of the proposal materials is not fully realized. The present invention aims to solve this problem by automatically generating optimal proposal materials that take into account the user's emotional information, thereby realizing proposals that provide greater user satisfaction.

[0305] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving proposal information, means for acquiring related information from a company database and a product database, means for matching proposed products with proposing companies, means for acquiring emotion information using an emotion engine that recognizes the user's emotional state, means for automatically generating proposal materials based on the matching results and the emotion information, means for transmitting the generated proposal materials to the user's terminal, and means for providing the user with means for checking and saving the proposal materials or recommendation list. This enables optimal product proposals to be made according to the user's emotions.

[0306] "Proposal information" refers to detailed information about the product and the company to which the proposal is being made, including the product's characteristics and price, the company's industry and size, and specific challenges.

[0307] A "corporate database" refers to a database that stores various information about a company, including the company's industry trends, past proposal history, and current challenges it is facing.

[0308] "Product database" refers to a database that stores detailed information about various products, including product performance, price, and existing sales.

[0309] "Matching" refers to the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating their compatibility.

[0310] An "emotion engine" is a system that analyzes a user's facial expressions and tone of voice to recognize their emotional state. This system uses emotional information as part of its recommendations.

[0311] A "proposal document" refers to a document that includes the characteristics of the proposed product, the reasons for proposing it, its benefits to the company, etc. This document is generated in a visually appealing format based on emotional information.

[0312] "User's device" refers to the device used to input proposal information, view and save proposal materials, etc. This includes smartphones and computers.

[0313] "Emotional information" refers to the user's emotional state as recognized by the emotion engine. This information is used to generate and optimize recommendations.

[0314] A "visually appealing format" refers to a document format that applies design elements that match the user's emotional state, allowing the proposal to be more effectively communicated to the user.

[0315] The present invention is a system that combines receiving proposal information, acquiring related information, matching proposed products with proposing companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system includes means for receiving proposal information, means for acquiring related information from a company database and a product database, means for matching proposed products with proposing companies, an emotion engine for acquiring emotion information, means for automatically generating proposal materials based on the matching results and the emotion information, means for transmitting the generated proposal materials to a user terminal, and means for the user to review and save the proposal materials or recommendation list.

[0316] Overall outline of the system program

[0317] 1. User input phase:

[0318] Users input proposal information through a smartphone app, including data related to the products and companies they are proposing.

[0319] 2. Emotion Recognition Phase:

[0320] Using the emotion engine, the smartphone's camera and microphone are used to analyze the user's facial expressions and vocal tone as emotional information.

[0321] 3. Data collection phase:

[0322] The proposal information and emotion information are transmitted to a server, and related information is obtained from a company database and a product database.

[0323] 4. Data Matching Phase:

[0324] Using an AI algorithm, the proposed products and proposing companies are matched based on the acquired related information and emotional information.

[0325] 5. Proposal generation phase:

[0326] Based on the matching results and emotional information, an automatic generation program is used to create proposal materials, which include the product's characteristics, reasons for the proposal, and benefits to the company. Furthermore, based on the emotional information, the materials are generated in a visually appealing format.

[0327] 6. Material distribution phase:

[0328] The proposal materials are sent from the server to the user's smartphone, where the user can view, save, and complete the purchase procedure.

[0329] Hardware and Software Used

[0330] Hardware:

[0331] Smartphone (camera, microphone)

[0332] software:

[0333] Emotion Recognizer

[0334] Database Access API (database_access)

[0335] Proposal document generation program (proposal_generator)

[0336] Process Description

[0337] Receive Proposals:

[0338] The user inputs the proposal information via a smartphone app.

[0339] Acquiring emotional information:

[0340] The emotion engine analyzes facial expressions and voice tones via the user's camera and microphone to obtain emotional information.

[0341] Get related information:

[0342] Based on the proposal information, the server retrieves related information from the company database and the product database.

[0343] Data Matching:

[0344] Using an AI algorithm, the degree of match between the proposed products and the proposing company is calculated, and emotional information is also taken into account.

[0345] Proposal generation:

[0346] The automatic generation program generates proposal materials, including the characteristics of the proposed products and their benefits to the company, in a visually appealing format based on emotional information.

[0347] Distribution of materials:

[0348] The generated proposal materials are sent from the server to the user's smartphone, providing an interface for the user to review and save the materials.

[0349] Specific examples

[0350] for example:

[0351] User ID: user123, selected product ID: product456. Emotional state: positive (expression engine result: smiling, voice sign: cheerful). Proposal material generation.

[0352] This example makes it possible to build a system that takes into account the emotional state of the user and makes optimal product suggestions.

[0353] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0354] Step 1: The user opens the smartphone app and logs in. After logging in, the user enters detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and any unique challenges. The entered proposal information is sent from the device to the server. The input data is sent in JSON format, and a data consistency check is performed at that time.

[0355] Step 2: When the user inputs the suggestion information, the device uses the smartphone's camera and microphone to analyze the user's facial expressions and voice tone with an emotion engine to obtain emotional information. Emotional information is calculated based on changes in facial expressions (e.g., smiling or furrowing the brow) and changes in voice tone (e.g., cheerful or depressed voice). The analysis results are formatted as an emotion score and sent to the server along with the suggestion information.

[0356] Step 3: Based on the received proposal information and emotion information, the server accesses the company database and product database to retrieve relevant information. Specifically, detailed product information and price information about the proposed product is retrieved from the product database, and the company's industry, past transaction history, current issues, etc. are retrieved from the company database. In this process, the necessary information is filtered using database queries to efficiently retrieve data.

[0357] Step 4: The server uses an AI algorithm to perform matching based on the acquired related information and emotional information. It evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates their compatibility. It also takes into account the user's emotional information to determine the optimal proposal. Specifically, it uses a normalized scoring algorithm to calculate a compatibility score for each proposed product.

[0358] Step 5: The server uses an automatic generation program to create a proposal document based on the matching results and emotional information. This proposal document includes the product's characteristics, the reason for proposing it, and its benefits to the company. The presentation and design of the document are also adjusted based on the emotional information. For example, bright colors and friendly fonts are used in the case of a positive emotional state. The document is generated in PDF or slide format.

[0359] Step 6: The server sends the generated offer document to the user's device. The device displays the received offer document and allows the user to download and save it. The user can also view the offer document through the provided interface and complete the purchase procedure.

[0360] As a specific example, if user ID "user123" enters "Product ABC" as the proposed product and selects "Company XYZ" as the target company, proposal materials will be automatically generated through the process from steps 1 to 6 and sent to the user's smartphone. If the emotional state is recognized as positive, bright colors and friendly fonts will be used.

[0361] 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.

[0362] 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.

[0363] 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.

[0364] [Second embodiment]

[0365] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0366] 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.

[0367] 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).

[0368] 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.

[0369] 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.

[0370] 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).

[0371] 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.

[0372] 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.

[0373] 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.

[0374] 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.

[0375] In the smart glasses 214, the 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.

[0376] 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."

[0377] The present invention is a system that mainly consists of the steps of receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, and transmitting the proposal materials. This system consists of three main elements: a server, a terminal, and a user.

[0378] 1. User Input Phase

[0379] Users access a dedicated application on their device and log in. After logging in, they enter detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and specific challenges.

[0380] 2. Data Collection Phase

[0381] The terminal sends the proposal information entered by the user to the server. Based on the proposal information, the server accesses the company database and product database to obtain related information. From the company database, information such as the company's industry trends, past proposal history, and current challenges is obtained. From the product database, information such as product performance and existing sales performance is obtained.

[0382] 3. Data Matching Phase

[0383] The server uses AI algorithms to match the relevant information it has acquired. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. For example, if a company making a proposal to a user is looking to improve the efficiency of customer service, it evaluates the extent to which an AI chatbot can solve that problem.

[0384] 4. Proposal material generation phase

[0385] After obtaining the matching results, the server uses an automatic generation program to create a proposal document that includes the product's characteristics, reasons for the proposal, and its benefits to the company. Furthermore, the document is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[0386] 5. Material distribution phase

[0387] The created proposal materials are sent from the server to the user's device, which displays the received proposal materials and allows the user to download and save them.

[0388] Specific examples

[0389] For example, if a user inputs "AI Chatbot" as the proposed product and "ABC Corporation" as the target company, the following process will be executed:

[0390] 1. The user enters information about the "AI chatbot" and "ABC Co., Ltd." into their device and sends it to the server.

[0391] 2. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about "ABC Co., Ltd." from the company database.

[0392] 3. The server uses an AI algorithm to evaluate how well the "AI Chatbot" meets the customer service needs of "ABC Co., Ltd."

[0393] 4. Based on the matching results, the server uses an automatic generation program to create a proposal document, which includes the product's performance, cost-effectiveness, reasons for the proposal, etc.

[0394] 5. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0395] In this way, the present invention improves sales efficiency and the quality and consistency of sales pitch materials.

[0396] The processing flow will be explained below.

[0397] Step 1:

[0398] The user accesses the dedicated application on the terminal and logs in.

[0399] Step 2:

[0400] The user inputs detailed information about the proposed product and the company to which the proposal is being made, including the product's characteristics, the industry and specific challenges of the proposing company, etc.

[0401] Step 3:

[0402] The terminal transmits the input proposal information to the server.

[0403] Step 4:

[0404] The server retrieves detailed information about the proposed product from the product database, including the product's performance, price, and existing sales record.

[0405] Step 5:

[0406] The server retrieves detailed information about the proposed company from a company database, including the company's industry, size, performance, and specific challenges.

[0407] Step 6:

[0408] The server uses the acquired product information and company information to apply an AI algorithm to evaluate the degree of match between the proposed product and the proposing company. Specifically, it calculates the extent to which the proposed product can solve the needs and problems of the proposing company.

[0409] Step 7:

[0410] Based on the evaluation results of the degree of matching, the server calls up a proposal template and uses an automatic generation program to create a proposal document, which includes the product name, product characteristics, reasons for proposing it, benefits to the company, etc.

[0411] Step 8:

[0412] The server generates the proposal in a visually appealing format (e.g., a PDF file or presentation slides).

[0413] Step 9:

[0414] The server transmits the generated proposal material to the user's terminal.

[0415] Step 10:

[0416] The terminal displays the received proposal materials and provides a function that allows the user to download and save them.

[0417] This allows users to efficiently receive high-quality proposal materials, enabling them to carry out sales activities effectively.

[0418] Example 1

[0419] 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."

[0420] In existing sales activities, the process of creating proposal materials is time-consuming and labor-intensive, and often lacks consistency and efficiency. Another issue is that it is difficult to fully evaluate the compatibility between the product and the company, making it difficult to make optimal proposals.

[0421] 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.

[0422] In this invention, the server includes means for receiving proposal information, means for acquiring related information from a database based on the proposal information, means for matching proposed products with proposing companies using an artificial intelligence algorithm based on the acquired related information, means for automatically generating proposal materials based on the matching results, means for transmitting the generated proposal materials to a user's terminal, and means for the terminal to provide the user with a function for displaying and saving the proposal materials. This makes it possible to create proposal materials more efficiently and to produce consistent, high-quality proposals.

[0423] "Proposal information" refers to detailed information about the products and the companies to which the proposal is being made, including, specifically, the characteristics and price of the products, the industry and size of the companies, and their specific challenges.

[0424] A "database" refers to a system that organizes and stores company and product information, and retrieves data in response to queries as needed.

[0425] An "artificial intelligence algorithm" is a computational method for data analysis and prediction, particularly using machine learning and deep learning models to evaluate the compatibility of proposed products with the proposing company.

[0426] A "proposal document" is a document that includes the characteristics of the product, the reasons for the proposal, the benefits to the company, etc., and is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[0427] The "suitability score" is a numerical indication of how well the proposed product matches the needs of the proposing company, and is calculated using an artificial intelligence algorithm.

[0428] "User's device" refers to a device used by the user, such as a computer, tablet, or smartphone, that has the function of displaying and saving proposal materials sent from the server.

[0429] An "automatic generation program" refers to software that automatically creates proposal materials in a specified format based on the acquired information and relevance scores.

[0430] MODE FOR CARRYING OUT THE INVENTION

[0431] The present invention is a system that consists of the steps of receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, and transmitting the proposal materials. This system is composed of three elements: a server, a terminal, and a user.

[0432] User Input Phase

[0433] The user accesses a dedicated application on their device and enters their login information. After logging in, the user enters detailed information (proposal information) about the product to be proposed and the company to which the proposal is being made. This information includes the product's characteristics, price, the company's industry, size, and specific challenges.

[0434] Data Collection Phase

[0435] The terminal sends the proposal information entered by the user to the server. At this time, the information is sent to the server in JSON format using an HTTP POST request. Based on this proposal information, the server accesses the company database and product database to retrieve related information. From the company database, it retrieves information such as the company's industry trends, past proposal history, and current issues, while from the product database, it retrieves information such as product performance and sales performance.

[0436] Data Matching Phase

[0437] The server runs an algorithm using the generative AI model based on the acquired related information to calculate the compatibility between the proposed product and the proposing company. For example, if the company to which the user is proposing is seeking to improve the efficiency of customer service, the generative AI model will score the extent to which it can solve that problem.

[0438] Proposal material generation phase

[0439] After obtaining the matching results, the server uses an automatic generation program to create a proposal document. The proposal document includes the product's characteristics, the reasons for the proposal, and its benefits to the company. The document is generated in a visually appealing format, such as a PDF or slide presentation. For example, it can be generated by embedding data in a template using a Python script.

[0440] Material distribution phase

[0441] The created proposal materials are sent from the server to the user's device. The device receives the materials and provides functions that allow the user to view, download, and save them. The user can use the proposal materials for sales activities and presentations.

[0442] Specific examples

[0443] For example, if a user inputs "AI chatbot" as the proposed product and "a certain company" as the target company, the following process will be executed:

[0444] 1. The user enters information about the "AI chatbot" and "a certain company" on their device and sends it to the server.

[0445] 2. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about a "certain company" from the company database.

[0446] 3. The server uses the generative AI model to evaluate how well the "AI chatbot" fits the customer service needs of "a certain company."

[0447] 4. The server uses an automatic generation program to create a proposal document, which includes the product's performance, cost-effectiveness, reasons for the proposal, etc.

[0448] 5. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0449] For example, the prompt for the generative AI model is as follows: "Please create a proposal document aimed at improving customer service at the target company. The product you are proposing is an 'AI chatbot,' and the company name is 'a certain company.'"

[0450] The above is an embodiment of the present invention, and this system makes the creation of proposal materials efficient and consistent, thereby greatly supporting sales activities.

[0451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0452] Step 1:

[0453] The user accesses a dedicated application on the device and enters login information (username and password). The entered login information is sent from the device to the server via an HTTP POST request. The server compares the received login information with an authentication database and authenticates the user. If authentication is successful, the server generates an authentication token and sends it to the device. The device saves the authentication token and uses it for subsequent requests.

[0454] Input: Username, Password

[0455] Data processing: Login information authentication

[0456] Output: Authentication token

[0457] Step 2:

[0458] The user inputs proposal information (product name, company name, and other detailed information) into the application on the device. The device sends the input proposal information in JSON format to the server. The server stores the received proposal information in its internal database.

[0459] Input: Product name, company name, and other details

[0460] Data processing: Saving proposal information

[0461] Output: Confirmation of saving of proposal information to database

[0462] Step 3:

[0463] The server queries the company database and product database based on the proposal information. From the company database, it obtains information such as the company's industry trends, past proposal history, and current issues, and from the product database, it obtains information such as product performance and sales performance.

[0464] Input: Proposal information

[0465] Data manipulation: performing database queries

[0466] Output: Company information, product information

[0467] Step 4:

[0468] The server evaluates the compatibility of the proposed product with the proposing company using an algorithm that uses a generative AI model based on company and product information. The server uses the acquired data as input, provides a prompt to the generative AI model, and outputs a compatibility score as the evaluation result.

[0469] Input: Company information, product information, prompt text

[0470] Data processing: scoring with generative AI models

[0471] Output: Relevance score

[0472] Step 5:

[0473] The server selects a proposal template based on the relevance score and the acquired related information, and creates a proposal using an automatic generation program. This proposal includes the characteristics of the proposed product, the evaluation results, and its benefits to the company. The generated document is saved in PDF format or slide presentation format.

[0474] Input: Relevance score, related information

[0475] Data processing: Automatic generation of proposal materials

[0476] Output: Proposal materials (PDF or slide format)

[0477] Step 6:

[0478] The server sends the generated proposal materials to the user's device. The device displays the received proposal materials to the user and provides functions that allow the user to download or save them. The user can use the proposal materials in sales activities and presentations.

[0479] Input: Proposal materials

[0480] Data processing: Sending proposal materials

[0481] Output: Display and save proposal materials

[0482] Through the above steps, this system makes it possible for users to streamline their sales activities and quickly provide high-quality proposal materials.

[0483] (Application example 1)

[0484] 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."

[0485] When promoting or introducing new products in physical stores, it is difficult to quickly generate efficient and effective proposal materials. With conventional methods, creating proposal materials takes a great deal of time and effort, risking delays in the store's sales promotion activities. Furthermore, creating consistent, high-quality proposal materials requires specialized knowledge, which not all stores possess. This reduces the efficiency of physical stores in carrying out effective promotions and product introductions.

[0486] 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.

[0487] In this invention, the server includes means for receiving proposal information, means for acquiring related information from a company database and a product database based on the proposal information, means for matching the proposed products with the proposing companies based on the acquired related information, means for automatically generating proposal materials based on the matching results, means for transmitting the generated proposal materials to the user's information processing device, and means for generating the proposal materials in a visually appealing format, thereby enabling store managers and staff at physical stores to generate high-quality proposal materials in a short amount of time and to carry out rapid and effective promotions and product introductions.

[0488] "Proposal information" refers to information about the characteristics and price of the product and the company to which the proposal is being made.

[0489] A "corporate database" is a database that stores information such as a company's industry trends, past proposal history, and current issues.

[0490] The "product database" is a database that stores information such as product performance and sales performance.

[0491] "Matching" is the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating the degree of compatibility.

[0492] A "proposal document" is a document that is automatically generated to describe the characteristics, effects, and reasons for proposing a product, and is output in a visually appealing format.

[0493] The "user's information processing device" refers to a mobile terminal such as a smartphone or tablet that receives and displays proposal materials.

[0494] The "means for generating a proposal document in a visually appealing format" is a method for automatically generating a proposal document in a visually appealing format using charts and text.

[0495] A "generative AI model" is an artificial intelligence model used to automatically generate the content of proposal materials.

[0496] A "prompt sentence" is a specific instruction sentence input to a generative AI model.

[0497] This invention is a system that matches products with companies based on proposal information and automatically generates and transmits proposal materials. This system consists of three main elements: a server, a terminal, and a user. Its special feature is a method that enables store managers and staff at physical stores to efficiently generate proposal materials for promotions and new product introductions.

[0498] 1. System Program

[0499] The system program is structured as follows:

[0500] User Input Phase

[0501] Users access a dedicated application on their device (smartphone) and log in. After logging in, users enter detailed information about the products they are proposing and the target companies. This information includes, for example, the characteristics and price of the new product, the type and size of the target stores, and any challenges the stores are facing.

[0502] Data Collection Phase

[0503] The input proposal information is sent to the server. The server accesses the product database and company database based on the proposal information to obtain related information. The product database provides information such as product performance and past sales performance, while the company database provides information such as the company's industry trends, past proposal history, and current issues.

[0504] Data Matching Phase

[0505] Based on the acquired related information, the server uses an AI algorithm to perform matching. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. For example, it evaluates how well the proposed new product meets the customer service needs of the store in question.

[0506] Proposal material generation phase

[0507] After obtaining the matching results, the server uses an automatic generation program to create a proposal document, which includes the product's characteristics, reasons for the proposal, and its benefits to the company. The document is generated in a visually appealing format (e.g., PDF file format).

[0508] Material distribution phase

[0509] The created proposal materials are sent from the server to the user's terminal, which displays the received proposal materials and provides a function that allows the user to download and save them.

[0510] 2. Natural language description of the process

[0511] Hardware: Smartphone (iOS or Android), server equipment

[0512] Software: Python, FPDF library, Requests library for HTTP requests

[0513] A user logs into the smartphone app and enters information about new products and promotions. This data is sent to a server, which accesses a product database and a company database to collect the necessary information. The collected data is evaluated by an AI algorithm, which calculates the compatibility between the proposed product and the proposing company. Based on the results, the server uses an automatic generation program to create a proposal document, which is generated as a visually appealing PDF file. Finally, the generated proposal document is sent from the server to the user's smartphone, where the user can receive and view it.

[0514] 3. Specific Examples

[0515] Below is an example prompt, where the user enters the following information:

[0516] Username: Manager

[0517] Password: A secure password

[0518] Product ID: P12345

[0519] Store ID: S67890

[0520] Based on this information, the system evaluates the characteristics of the new product, the store's industry and size, and customer service needs, and calculates the degree of suitability. For example, it evaluates how much an AI chatbot for a new product can improve the efficiency of customer service at the store. As a result, a visually appealing PDF file is generated, detailing the new product's introduction, benefits, and reasons for proposing it.

[0521] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0522] Step 1:

[0523] The user opens a dedicated application on their device (smartphone) and enters their login information (username and password). The entered login information is sent to the server, where the user is authenticated. If authentication is successful, the user enters detailed information about the product they are proposing and the target company. The information entered includes the product ID, store ID, product characteristics, price, store size, and the challenges they are facing.

[0524] Input: Login information, product information, store information

[0525] Output: Proposal information (product ID, store ID, product characteristics, price, size, issues)

[0526] Step 2:

[0527] The terminal sends the proposal information entered by the user to the server. The server makes a request to obtain related information from the product database and company database based on the product ID and store ID contained in the received proposal information. Information such as product performance and sales performance is obtained from the product database, and information such as the store's industry trends, past proposal history, and current challenges is obtained from the company database.

[0528] Input: Proposal information

[0529] Output: Product information, company information

[0530] Step 3:

[0531] Based on the acquired product and company information, the server uses an AI algorithm to match the proposed product with the proposing company. Specifically, it evaluates the product's characteristics and effectiveness, as well as the store's needs and characteristics, and calculates its suitability. In this evaluation process, the AI ​​algorithm analyzes various data points to determine the extent to which the proposed product can solve the target store's problems.

[0532] Input: Product information, company information

[0533] Output: Matching degree (fit score)

[0534] Step 4:

[0535] After obtaining the matching results, the server uses an automatic generation program to create a proposal document. This document includes the product's characteristics, effects, reasons for proposing it, and benefits to the company, and is generated in a visually appealing format (e.g., PDF file format). The generative AI model creates prompts based on the product's detailed information and the matching results, and reflects them in the proposal document.

[0536] Input: Matching degree, product information, company information

[0537] Output: Proposal materials (PDF file)

[0538] Step 5:

[0539] The created proposal document is sent from the server to the user's device. The device displays the received proposal document and provides the user with the ability to download and save it. The user can also edit the proposal document as needed or share it with other parties.

[0540] Input: Proposal materials

[0541] Output: Proposal materials (PDF file) displayed on the device

[0542] 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.

[0543] The present invention is a system that combines receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system consists of four main elements: a server, a terminal, a user, and an emotion engine.

[0544] 1. User Input Phase

[0545] Users access a dedicated application on their device and log in. After logging in, they enter detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and specific challenges.

[0546] 2. Emotion Recognition Phase

[0547] When receiving the proposed information, the device uses an emotion engine to analyze the user's facial expressions, tone of voice, etc. to recognize the user's emotional state. This emotion information is also sent to the server as part of the proposed information.

[0548] 3. Data Collection Phase

[0549] The terminal sends the input proposal information and emotion information to the server. The server accesses the company database and product database to obtain related information based on the proposal information. From the company database, information such as the company's industry trends, past proposal history, and current challenges is obtained. From the product database, information such as product performance and existing sales performance is obtained.

[0550] 4. Data Matching Phase

[0551] The server uses an AI algorithm to match the acquired related information and emotional information. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. It also takes the user's emotional information into account to determine the optimal proposal.

[0552] 5. Proposal material generation phase

[0553] After obtaining the matching results and emotion information, the server uses an automatic generation program to create a proposal document. This proposal document includes the product's characteristics, reasons for proposing it, and its benefits to the company. Furthermore, based on the emotion information, the content and format of the document are optimized, and the document is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[0554] 6. Material distribution phase

[0555] The created proposal materials are sent from the server to the user's device. The device displays the received proposal materials and provides the user with the ability to download and save them. In addition, the generated materials are optimized based on the user's emotional information, making it easier for the user to feel satisfied.

[0556] Specific examples

[0557] For example, if a user inputs "AI Chatbot" as the proposed product and "XYZ Corporation" as the target company, the following process will be executed:

[0558] 1. The user enters information about the "AI chatbot" and "XYZ Corporation" on their device and sends it to the server.

[0559] 2. At the same time as receiving the input information, the device analyzes the user's facial expressions and tone of voice and sends emotional information to the server.

[0560] 3. The server retrieves detailed information about the "AI chatbot" from the product database and detailed information about "XYZ Corporation" from the company database.

[0561] 4. The server uses an AI algorithm to evaluate how well the AI ​​chatbot meets the customer service needs of XYZ Corporation, taking emotional information into account.

[0562] 5. The server uses an automatic generation program to create a proposal document based on the matching results and emotional information. This document includes the product's performance, cost-effectiveness, reasons for the proposal, etc. The presentation and design of the document are also adjusted based on the emotional information.

[0563] 6. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0564] In this way, the present invention aims to improve the efficiency of sales activities and realizes the provision of high-quality proposal materials that take into account the user's emotions.

[0565] The processing flow will be explained below.

[0566] Step 1:

[0567] The user accesses the dedicated application on the terminal and logs in.

[0568] Step 2:

[0569] The user inputs detailed information about the proposed product and the company to which the proposal is being made, including the product's characteristics, the industry and specific challenges of the proposing company, etc.

[0570] Step 3:

[0571] When receiving the proposed information, the terminal analyzes the user's facial expression and voice tone using an emotion engine to recognize the user's emotional state.

[0572] Step 4:

[0573] The terminal transmits the input suggestion information and emotion information to the server.

[0574] Step 5:

[0575] Based on the proposal information, the server retrieves related detailed information from the company database and product database. From the company database, it retrieves information such as the company's industry, size, past transaction history, and specific issues. From the product database, it retrieves information such as the product's performance, price, and existing sales performance.

[0576] Step 6:

[0577] The server uses an AI algorithm to analyze the acquired product and company information, evaluates the degree of match between the proposed product and the proposed company, and fine-tunes the content of the proposal taking into account the user's emotional information.

[0578] Step 7:

[0579] The server starts an automatic generation program based on the matching degree and emotion information to create a proposal document. The document contains information such as the product's characteristics, the reason for the proposal, and its benefits to the company. The presentation method and design of the document are also adjusted according to the user's emotion information.

[0580] Step 8:

[0581] The server stores the generated proposal in a visually appealing format (e.g., a PDF file or a slide presentation).

[0582] Step 9:

[0583] The server transmits the generated proposal material to the user's terminal.

[0584] Step 10:

[0585] The device displays the received proposal materials on the user interface and allows the user to download and save them. In addition, the content and format of the materials are optimized based on the user's emotional information, resulting in a higher level of satisfaction for the user.

[0586] In this way, the present invention combines proposal information with user emotion information to automatically generate more effective and satisfying proposal materials, thereby improving the efficiency of sales activities.

[0587] Example 2

[0588] 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."

[0589] Conventional recommendation systems are unable to consider the user's emotional state when evaluating the degree of match between products and companies, making it difficult to generate optimal proposal content. Furthermore, the generated proposal materials are often not visually appealing, making it difficult to increase user satisfaction.

[0590] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving proposal information, means for collecting and analyzing user emotion information based on the received proposal information, means for acquiring related information from a company database and a product database based on the proposal information and emotion information, means for evaluating the needs and characteristics of the proposed product and the proposing company based on the acquired related information and calculating the degree of suitability, means for automatically generating proposal materials based on the matching results and emotion information, and means for transmitting the generated proposal materials to the user's terminal. This makes it possible to generate optimal proposal content that takes the user's emotion information into consideration and provide visually appealing proposal materials.

[0591] "Proposal information" refers to detailed information about the products proposed by the user and the companies to which the proposal is made.

[0592] "Emotional information" refers to information about the user's emotional state obtained by analyzing the user's facial expressions, tone of voice, and the like.

[0593] A "corporate database" refers to a database that stores information such as a company's industry trends, past proposal history, and challenges it faces.

[0594] A "product database" refers to a database that stores information such as product performance, characteristics, price, and sales record.

[0595] "Related information" refers to data obtained from the company database and product database based on the proposal information.

[0596] "Matching" refers to the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating the degree of compatibility.

[0597] "Proposal materials" refer to materials that are automatically generated based on matching results and emotional information, and that describe the characteristics of the product, the reasons for proposing it, and its benefits to the company.

[0598] "Visually generated" refers to optimizing the content and format of the material so that the material is created in a format that is visually appealing to the user.

[0599] The present invention is a system that combines receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system consists of four main elements: a server, a terminal, a user, and an emotion engine.

[0600] User Input Phase

[0601] Users access a dedicated application and log in. After logging in, they enter detailed information (proposal information) about the products they are proposing and the companies they are proposing to into the terminal. The information entered includes the characteristics and price of the products, the company's industry and size, and specific challenges.

[0602] Emotion Recognition Phase

[0603] The device uses a camera and microphone to collect facial expressions and voice tones while the user is typing. This collected data is analyzed by an emotion engine to generate the user's emotion information. This emotion information is also sent to the server along with the suggestion information.

[0604] Data Collection Phase

[0605] The terminal transmits the proposal information and emotion information to the server. The server accesses the company database and product database to obtain related information based on the proposal information. The company database provides information on the company's industry trends and past proposal history, while the product database provides information on the product's performance and existing sales performance.

[0606] Data Matching Phase

[0607] The server uses an AI algorithm to match the acquired related information and emotional information. It evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates the degree of compatibility. It also evaluates the user's emotional information to determine the optimal proposal content.

[0608] Proposal material generation phase

[0609] Based on the matching results and the emotion information, the server uses an automatic generation program to create a proposal document. This document includes the product's characteristics, the reason for the proposal, its benefits to the company, etc. Furthermore, based on the emotion information, the content and format of the document are optimized, and the document is generated in a visually appealing format.

[0610] Material distribution phase

[0611] The created proposal materials are sent from the server to the user's device. The device displays the received proposal materials and provides the user with the ability to download and save them. Because the proposal materials are optimized based on the user's emotional information, the user can achieve a higher level of satisfaction.

[0612] Specific examples

[0613] For example, if a user inputs "AI Chatbot" as the proposed product and "ABC Company" as the target company, the following process will be executed:

[0614] 1. The user enters information about the "AI chatbot" and "ABC Company" into the device through a dedicated application and sends it to the server.

[0615] 2. At the same time as the information is entered, the device also analyzes the user's facial expressions and tone of voice to collect emotional information and send it to the server.

[0616] 3. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about "ABC Company" from the company database.

[0617] 4. The server uses an AI algorithm to evaluate the compatibility between the "AI chatbot" and "ABC Company," taking into account emotional information.

[0618] 5. The server uses an automatic generation program to create a proposal document based on the matching results and emotion information. This document includes the product's performance, cost-effectiveness, reasons for the proposal, etc. The presentation and design of the document are also adjusted based on the emotion information.

[0619] 6. The server sends the generated proposal materials to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0620] Prompt Sentence Examples

[0621] Below are some example prompts for input to a generative AI model:

[0622] Please enter "AI Chatbot" as the proposed product and "ABC Company" as the target company. After entering the information, it will be sent to the server. At this time, please log in through a dedicated application. In addition, facial expressions and tone of voice while entering information will also be recorded, and the user's emotional state will also be analyzed.

[0623] According to this prompt, the user provides the appropriate information and emotional state, and the system generates the optimal proposal material.

[0624] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0625] Step 1: User Input Phase

[0626] The user accesses the dedicated application to log in. They enter their username and password as login information and complete the login process. The input is from the dedicated application and the terminal, and the output is successful user authentication.

[0627] The user inputs detailed information about the product to be proposed and the target company (proposal information) into the terminal. The input here includes the product's characteristics, price, the company's industry and size, and specific issues, and the output is the temporarily saved proposal information.

[0628] Step 2: Emotion Recognition Phase

[0629] The device uses a camera and microphone to capture facial expressions and voice tones during user input, and this collected data is sent to the emotion engine as input.

[0630] The emotion engine analyzes the collected data and recognizes the user's emotional state. This analysis is data calculation, and the output is emotional information.

[0631] The device sends the generated emotion information together with the suggestion information to the server. The input is the emotion information and the suggestion information, and the output is data transmission to the server.

[0632] Step 3: Data collection phase

[0633] The server receives the proposal information and emotion information sent from the terminal. The input is the proposal information and emotion information, and the output is a receipt confirmation.

[0634] The server accesses the company database and product database to retrieve relevant information. This data retrieval process is called data processing. The input is a database query about the company and product, and the output is the retrieved relevant information.

[0635] Step 4: Data Matching Phase

[0636] The server uses an AI algorithm to match the acquired related information and emotional information. The input is related information and emotional information, and the output is a compatibility score.

[0637] The server evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates their compatibility. This evaluation process is data calculation, and the compatibility score is the output.

[0638] Step 5: Proposal generation phase

[0639] The server uses an automatic generation program to create proposal materials based on the matching results and emotion information. The input is the matching results and emotion information, and the output is the proposal materials.

[0640] The content of the proposal document includes the product's characteristics, the reason for the proposal, and its benefits to the company. Furthermore, the format and design of the document are optimized based on the emotional information. This process involves the actual data processing and content generation.

[0641] Step 6: Material distribution phase

[0642] The server sends the generated proposal materials to the user's terminal. The input is the proposal materials, and the output is a document file sent to the user's terminal.

[0643] The terminal unpacks and displays the received proposal documents. The user can then check them and download or save them as needed. The input is the document file, and the output is the displayed document and the saved file.

[0644] (Application example 2)

[0645] 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."

[0646] Conventional proposal material generation systems create proposal materials by simple data matching without considering the user's emotional state. As a result, many proposals do not match the user's true needs or emotions, and the effectiveness of the proposal materials is not fully realized. The present invention aims to solve this problem by automatically generating optimal proposal materials that take into account the user's emotional information, thereby realizing proposals that provide greater user satisfaction.

[0647] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving proposal information, means for acquiring related information from a company database and a product database, means for matching proposed products with proposing companies, means for acquiring emotion information using an emotion engine that recognizes the user's emotional state, means for automatically generating proposal materials based on the matching results and the emotion information, means for transmitting the generated proposal materials to the user's terminal, and means for providing the user with means for checking and saving the proposal materials or recommendation list. This enables optimal product proposals to be made according to the user's emotions.

[0648] "Proposal information" refers to detailed information about the product and the company to which the proposal is being made, including the product's characteristics and price, the company's industry and size, and specific challenges.

[0649] A "corporate database" refers to a database that stores various information about a company, including the company's industry trends, past proposal history, and current challenges it is facing.

[0650] "Product database" refers to a database that stores detailed information about various products, including product performance, price, and existing sales.

[0651] "Matching" refers to the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating their compatibility.

[0652] An "emotion engine" is a system that analyzes a user's facial expressions and tone of voice to recognize their emotional state. This system uses emotional information as part of its recommendations.

[0653] A "proposal document" refers to a document that includes the characteristics of the proposed product, the reasons for proposing it, its benefits to the company, etc. This document is generated in a visually appealing format based on emotional information.

[0654] "User's device" refers to the device used to input proposal information, view and save proposal materials, etc. This includes smartphones and computers.

[0655] "Emotional information" refers to the user's emotional state as recognized by the emotion engine. This information is used to generate and optimize recommendations.

[0656] A "visually appealing format" refers to a document format that applies design elements that match the user's emotional state, allowing the proposal to be more effectively communicated to the user.

[0657] The present invention is a system that combines receiving proposal information, acquiring related information, matching proposed products with proposing companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system includes means for receiving proposal information, means for acquiring related information from a company database and a product database, means for matching proposed products with proposing companies, an emotion engine for acquiring emotion information, means for automatically generating proposal materials based on the matching results and the emotion information, means for transmitting the generated proposal materials to a user terminal, and means for the user to review and save the proposal materials or recommendation list.

[0658] Overall outline of the system program

[0659] 1. User input phase:

[0660] Users input proposal information through a smartphone app, including data related to the products and companies they are proposing.

[0661] 2. Emotion Recognition Phase:

[0662] Using the emotion engine, the smartphone's camera and microphone are used to analyze the user's facial expressions and vocal tone as emotional information.

[0663] 3. Data collection phase:

[0664] The proposal information and emotion information are transmitted to a server, and related information is obtained from a company database and a product database.

[0665] 4. Data Matching Phase:

[0666] Using an AI algorithm, the proposed products and proposing companies are matched based on the acquired related information and emotional information.

[0667] 5. Proposal generation phase:

[0668] Based on the matching results and emotional information, an automatic generation program is used to create proposal materials, which include the product's characteristics, reasons for the proposal, and benefits to the company. Furthermore, based on the emotional information, the materials are generated in a visually appealing format.

[0669] 6. Material distribution phase:

[0670] The proposal materials are sent from the server to the user's smartphone, where the user can view, save, and complete the purchase procedure.

[0671] Hardware and Software Used

[0672] Hardware:

[0673] Smartphone (camera, microphone)

[0674] software:

[0675] Emotion Recognizer

[0676] Database Access API (database_access)

[0677] Proposal document generation program (proposal_generator)

[0678] Process Description

[0679] Receive Proposals:

[0680] The user inputs the proposal information via a smartphone app.

[0681] Acquiring emotional information:

[0682] The emotion engine analyzes facial expressions and voice tones via the user's camera and microphone to obtain emotional information.

[0683] Get related information:

[0684] Based on the proposal information, the server retrieves related information from the company database and the product database.

[0685] Data Matching:

[0686] Using an AI algorithm, the degree of match between the proposed products and the proposing company is calculated, and emotional information is also taken into account.

[0687] Proposal generation:

[0688] The automatic generation program generates proposal materials, including the characteristics of the proposed products and their benefits to the company, in a visually appealing format based on emotional information.

[0689] Distribution of materials:

[0690] The generated proposal materials are sent from the server to the user's smartphone, providing an interface for the user to review and save the materials.

[0691] Specific examples

[0692] for example:

[0693] User ID: user123, selected product ID: product456. Emotional state: positive (expression engine result: smiling, voice sign: cheerful). Proposal material generation.

[0694] This example makes it possible to build a system that takes into account the emotional state of the user and makes optimal product suggestions.

[0695] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0696] Step 1: The user opens the smartphone app and logs in. After logging in, the user enters detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and any unique challenges. The entered proposal information is sent from the device to the server. The input data is sent in JSON format, and a data consistency check is performed at that time.

[0697] Step 2: When the user inputs the suggestion information, the device uses the smartphone's camera and microphone to analyze the user's facial expressions and voice tone with an emotion engine to obtain emotional information. Emotional information is calculated based on changes in facial expressions (e.g., smiling or furrowing the brow) and changes in voice tone (e.g., cheerful or depressed voice). The analysis results are formatted as an emotion score and sent to the server along with the suggestion information.

[0698] Step 3: Based on the received proposal information and emotion information, the server accesses the company database and product database to retrieve relevant information. Specifically, detailed product information and price information about the proposed product is retrieved from the product database, and the company's industry, past transaction history, current issues, etc. are retrieved from the company database. In this process, the necessary information is filtered using database queries to efficiently retrieve data.

[0699] Step 4: The server uses an AI algorithm to perform matching based on the acquired related information and emotional information. It evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates their compatibility. It also takes into account the user's emotional information to determine the optimal proposal. Specifically, it uses a normalized scoring algorithm to calculate a compatibility score for each proposed product.

[0700] Step 5: The server uses an automatic generation program to create a proposal document based on the matching results and emotional information. This proposal document includes the product's characteristics, the reason for proposing it, and its benefits to the company. The presentation and design of the document are also adjusted based on the emotional information. For example, bright colors and friendly fonts are used in the case of a positive emotional state. The document is generated in PDF or slide format.

[0701] Step 6: The server sends the generated offer document to the user's device. The device displays the received offer document and allows the user to download and save it. The user can also view the offer document through the provided interface and complete the purchase procedure.

[0702] As a specific example, if user ID "user123" enters "Product ABC" as the proposed product and selects "Company XYZ" as the target company, proposal materials will be automatically generated through the process from steps 1 to 6 and sent to the user's smartphone. If the emotional state is recognized as positive, bright colors and friendly fonts will be used.

[0703] 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.

[0704] 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.

[0705] 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.

[0706] [Third embodiment]

[0707] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0708] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0709] 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).

[0710] 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.

[0711] 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.

[0712] 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).

[0713] 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.

[0714] 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.

[0715] 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.

[0716] 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.

[0717] 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.

[0718] 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."

[0719] The present invention is a system that mainly consists of the steps of receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, and transmitting the proposal materials. This system consists of three main elements: a server, a terminal, and a user.

[0720] 1. User Input Phase

[0721] Users access a dedicated application on their device and log in. After logging in, they enter detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and specific challenges.

[0722] 2. Data Collection Phase

[0723] The terminal sends the proposal information entered by the user to the server. Based on the proposal information, the server accesses the company database and product database to obtain related information. From the company database, information such as the company's industry trends, past proposal history, and current challenges is obtained. From the product database, information such as product performance and existing sales performance is obtained.

[0724] 3. Data Matching Phase

[0725] The server uses AI algorithms to match the relevant information it has acquired. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. For example, if a company making a proposal to a user is looking to improve the efficiency of customer service, it evaluates the extent to which an AI chatbot can solve that problem.

[0726] 4. Proposal material generation phase

[0727] After obtaining the matching results, the server uses an automatic generation program to create a proposal document that includes the product's characteristics, reasons for the proposal, and its benefits to the company. Furthermore, the document is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[0728] 5. Material distribution phase

[0729] The created proposal materials are sent from the server to the user's device, which displays the received proposal materials and allows the user to download and save them.

[0730] Specific examples

[0731] For example, if a user inputs "AI Chatbot" as the proposed product and "ABC Corporation" as the target company, the following process will be executed:

[0732] 1. The user enters information about the "AI chatbot" and "ABC Co., Ltd." into their device and sends it to the server.

[0733] 2. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about "ABC Co., Ltd." from the company database.

[0734] 3. The server uses an AI algorithm to evaluate how well the "AI Chatbot" meets the customer service needs of "ABC Co., Ltd."

[0735] 4. Based on the matching results, the server uses an automatic generation program to create a proposal document, which includes the product's performance, cost-effectiveness, reasons for the proposal, etc.

[0736] 5. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0737] In this way, the present invention improves sales efficiency and the quality and consistency of sales pitch materials.

[0738] The processing flow will be explained below.

[0739] Step 1:

[0740] The user accesses the dedicated application on the terminal and logs in.

[0741] Step 2:

[0742] The user inputs detailed information about the proposed product and the company to which the proposal is being made, including the product's characteristics, the industry and specific challenges of the proposing company, etc.

[0743] Step 3:

[0744] The terminal transmits the input proposal information to the server.

[0745] Step 4:

[0746] The server retrieves detailed information about the proposed product from the product database, including the product's performance, price, and existing sales record.

[0747] Step 5:

[0748] The server retrieves detailed information about the proposed company from a company database, including the company's industry, size, performance, and specific challenges.

[0749] Step 6:

[0750] The server uses the acquired product information and company information to apply an AI algorithm to evaluate the degree of match between the proposed product and the proposing company. Specifically, it calculates the extent to which the proposed product can solve the needs and problems of the proposing company.

[0751] Step 7:

[0752] Based on the evaluation results of the degree of matching, the server calls up a proposal template and uses an automatic generation program to create a proposal document, which includes the product name, product characteristics, reasons for proposing it, benefits to the company, etc.

[0753] Step 8:

[0754] The server generates the proposal in a visually appealing format (e.g., a PDF file or presentation slides).

[0755] Step 9:

[0756] The server transmits the generated proposal material to the user's terminal.

[0757] Step 10:

[0758] The terminal displays the received proposal materials and provides a function that allows the user to download and save them.

[0759] This allows users to efficiently receive high-quality proposal materials, enabling them to carry out sales activities effectively.

[0760] Example 1

[0761] 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."

[0762] In existing sales activities, the process of creating proposal materials is time-consuming and labor-intensive, and often lacks consistency and efficiency. Another issue is that it is difficult to fully evaluate the compatibility between the product and the company, making it difficult to make optimal proposals.

[0763] 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.

[0764] In this invention, the server includes means for receiving proposal information, means for acquiring related information from a database based on the proposal information, means for matching proposed products with proposing companies using an artificial intelligence algorithm based on the acquired related information, means for automatically generating proposal materials based on the matching results, means for transmitting the generated proposal materials to a user's terminal, and means for the terminal to provide the user with a function for displaying and saving the proposal materials. This makes it possible to create proposal materials more efficiently and to produce consistent, high-quality proposals.

[0765] "Proposal information" refers to detailed information about the products and the companies to which the proposal is being made, including, specifically, the characteristics and price of the products, the industry and size of the companies, and their specific challenges.

[0766] A "database" refers to a system that organizes and stores company and product information, and retrieves data in response to queries as needed.

[0767] An "artificial intelligence algorithm" is a computational method for data analysis and prediction, particularly using machine learning and deep learning models to evaluate the compatibility of proposed products with the proposing company.

[0768] A "proposal document" is a document that includes the characteristics of the product, the reasons for the proposal, the benefits to the company, etc., and is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[0769] The "suitability score" is a numerical indication of how well the proposed product matches the needs of the proposing company, and is calculated using an artificial intelligence algorithm.

[0770] "User's device" refers to a device used by the user, such as a computer, tablet, or smartphone, that has the function of displaying and saving proposal materials sent from the server.

[0771] An "automatic generation program" refers to software that automatically creates proposal materials in a specified format based on the acquired information and relevance scores.

[0772] MODE FOR CARRYING OUT THE INVENTION

[0773] The present invention is a system that consists of the steps of receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, and transmitting the proposal materials. This system is composed of three elements: a server, a terminal, and a user.

[0774] User Input Phase

[0775] The user accesses a dedicated application on their device and enters their login information. After logging in, the user enters detailed information (proposal information) about the product to be proposed and the company to which the proposal is being made. This information includes the product's characteristics, price, the company's industry, size, and specific challenges.

[0776] Data Collection Phase

[0777] The terminal sends the proposal information entered by the user to the server. At this time, the information is sent to the server in JSON format using an HTTP POST request. Based on this proposal information, the server accesses the company database and product database to retrieve related information. From the company database, it retrieves information such as the company's industry trends, past proposal history, and current issues, while from the product database, it retrieves information such as product performance and sales performance.

[0778] Data Matching Phase

[0779] The server runs an algorithm using the generative AI model based on the acquired related information to calculate the compatibility between the proposed product and the proposing company. For example, if the company to which the user is proposing is seeking to improve the efficiency of customer service, the generative AI model will score the extent to which it can solve that problem.

[0780] Proposal material generation phase

[0781] After obtaining the matching results, the server uses an automatic generation program to create a proposal document. The proposal document includes the product's characteristics, the reasons for the proposal, and its benefits to the company. The document is generated in a visually appealing format, such as a PDF or slide presentation. For example, it can be generated by embedding data in a template using a Python script.

[0782] Material distribution phase

[0783] The created proposal materials are sent from the server to the user's device. The device receives the materials and provides functions that allow the user to view, download, and save them. The user can use the proposal materials for sales activities and presentations.

[0784] Specific examples

[0785] For example, if a user inputs "AI chatbot" as the proposed product and "a certain company" as the target company, the following process will be executed:

[0786] 1. The user enters information about the "AI chatbot" and "a certain company" on their device and sends it to the server.

[0787] 2. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about a "certain company" from the company database.

[0788] 3. The server uses the generative AI model to evaluate how well the "AI chatbot" fits the customer service needs of "a certain company."

[0789] 4. The server uses an automatic generation program to create a proposal document, which includes the product's performance, cost-effectiveness, reasons for the proposal, etc.

[0790] 5. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0791] For example, the prompt for the generative AI model is as follows: "Please create a proposal document aimed at improving customer service at the target company. The product you are proposing is an 'AI chatbot,' and the company name is 'a certain company.'"

[0792] The above is an embodiment of the present invention, and this system makes the creation of proposal materials efficient and consistent, thereby greatly supporting sales activities.

[0793] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0794] Step 1:

[0795] The user accesses a dedicated application on the device and enters login information (username and password). The entered login information is sent from the device to the server via an HTTP POST request. The server compares the received login information with an authentication database and authenticates the user. If authentication is successful, the server generates an authentication token and sends it to the device. The device saves the authentication token and uses it for subsequent requests.

[0796] Input: Username, Password

[0797] Data processing: Login information authentication

[0798] Output: Authentication token

[0799] Step 2:

[0800] The user inputs proposal information (product name, company name, and other detailed information) into the application on the device. The device sends the input proposal information in JSON format to the server. The server stores the received proposal information in its internal database.

[0801] Input: Product name, company name, and other details

[0802] Data processing: Saving proposal information

[0803] Output: Confirmation of saving of proposal information to database

[0804] Step 3:

[0805] The server queries the company database and product database based on the proposal information. From the company database, it obtains information such as the company's industry trends, past proposal history, and current issues, and from the product database, it obtains information such as product performance and sales performance.

[0806] Input: Proposal information

[0807] Data manipulation: performing database queries

[0808] Output: Company information, product information

[0809] Step 4:

[0810] The server evaluates the compatibility of the proposed product with the proposing company using an algorithm that uses a generative AI model based on company and product information. The server uses the acquired data as input, provides a prompt to the generative AI model, and outputs a compatibility score as the evaluation result.

[0811] Input: Company information, product information, prompt text

[0812] Data processing: scoring with generative AI models

[0813] Output: Relevance score

[0814] Step 5:

[0815] The server selects a proposal template based on the relevance score and the acquired related information, and creates a proposal using an automatic generation program. This proposal includes the characteristics of the proposed product, the evaluation results, and its benefits to the company. The generated document is saved in PDF format or slide presentation format.

[0816] Input: Relevance score, related information

[0817] Data processing: Automatic generation of proposal materials

[0818] Output: Proposal materials (PDF or slide format)

[0819] Step 6:

[0820] The server sends the generated proposal materials to the user's device. The device displays the received proposal materials to the user and provides functions that allow the user to download or save them. The user can use the proposal materials in sales activities and presentations.

[0821] Input: Proposal materials

[0822] Data processing: Sending proposal materials

[0823] Output: Display and save proposal materials

[0824] Through the above steps, this system makes it possible for users to streamline their sales activities and quickly provide high-quality proposal materials.

[0825] (Application example 1)

[0826] 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."

[0827] When promoting or introducing new products in physical stores, it is difficult to quickly generate efficient and effective proposal materials. With conventional methods, creating proposal materials takes a great deal of time and effort, risking delays in the store's sales promotion activities. Furthermore, creating consistent, high-quality proposal materials requires specialized knowledge, which not all stores possess. This reduces the efficiency of physical stores in carrying out effective promotions and product introductions.

[0828] 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.

[0829] In this invention, the server includes means for receiving proposal information, means for acquiring related information from a company database and a product database based on the proposal information, means for matching the proposed products with the proposing companies based on the acquired related information, means for automatically generating proposal materials based on the matching results, means for transmitting the generated proposal materials to the user's information processing device, and means for generating the proposal materials in a visually appealing format, thereby enabling store managers and staff at physical stores to generate high-quality proposal materials in a short amount of time and to carry out rapid and effective promotions and product introductions.

[0830] "Proposal information" refers to information about the characteristics and price of the product and the company to which the proposal is being made.

[0831] A "corporate database" is a database that stores information such as a company's industry trends, past proposal history, and current issues.

[0832] The "product database" is a database that stores information such as product performance and sales performance.

[0833] "Matching" is the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating the degree of compatibility.

[0834] A "proposal document" is a document that is automatically generated to describe the characteristics, effects, and reasons for proposing a product, and is output in a visually appealing format.

[0835] The "user's information processing device" refers to a mobile terminal such as a smartphone or tablet that receives and displays proposal materials.

[0836] The "means for generating a proposal document in a visually appealing format" is a method for automatically generating a proposal document in a visually appealing format using charts and text.

[0837] A "generative AI model" is an artificial intelligence model used to automatically generate the content of proposal materials.

[0838] A "prompt sentence" is a specific instruction sentence input to a generative AI model.

[0839] This invention is a system that matches products with companies based on proposal information and automatically generates and transmits proposal materials. This system consists of three main elements: a server, a terminal, and a user. Its special feature is a method that enables store managers and staff at physical stores to efficiently generate proposal materials for promotions and new product introductions.

[0840] 1. System Program

[0841] The system program is structured as follows:

[0842] User Input Phase

[0843] Users access a dedicated application on their device (smartphone) and log in. After logging in, users enter detailed information about the products they are proposing and the target companies. This information includes, for example, the characteristics and price of the new product, the type and size of the target stores, and any challenges the stores are facing.

[0844] Data Collection Phase

[0845] The input proposal information is sent to the server. The server accesses the product database and company database based on the proposal information to obtain related information. The product database provides information such as product performance and past sales performance, while the company database provides information such as the company's industry trends, past proposal history, and current issues.

[0846] Data Matching Phase

[0847] Based on the acquired related information, the server uses an AI algorithm to perform matching. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. For example, it evaluates how well the proposed new product meets the customer service needs of the store in question.

[0848] Proposal material generation phase

[0849] After obtaining the matching results, the server uses an automatic generation program to create a proposal document, which includes the product's characteristics, reasons for the proposal, and its benefits to the company. The document is generated in a visually appealing format (e.g., PDF file format).

[0850] Material distribution phase

[0851] The created proposal materials are sent from the server to the user's terminal, which displays the received proposal materials and provides a function that allows the user to download and save them.

[0852] 2. Natural language description of the process

[0853] Hardware: Smartphone (iOS or Android), server equipment

[0854] Software: Python, FPDF library, Requests library for HTTP requests

[0855] A user logs into the smartphone app and enters information about new products and promotions. This data is sent to a server, which accesses a product database and a company database to collect the necessary information. The collected data is evaluated by an AI algorithm, which calculates the compatibility between the proposed product and the proposing company. Based on the results, the server uses an automatic generation program to create a proposal document, which is generated as a visually appealing PDF file. Finally, the generated proposal document is sent from the server to the user's smartphone, where the user can receive and view it.

[0856] 3. Specific Examples

[0857] Below is an example prompt, where the user enters the following information:

[0858] Username: Manager

[0859] Password: A secure password

[0860] Product ID: P12345

[0861] Store ID: S67890

[0862] Based on this information, the system evaluates the characteristics of the new product, the store's industry and size, and customer service needs, and calculates the degree of suitability. For example, it evaluates how much an AI chatbot for a new product can improve the efficiency of customer service at the store. As a result, a visually appealing PDF file is generated, detailing the new product's introduction, benefits, and reasons for proposing it.

[0863] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0864] Step 1:

[0865] The user opens a dedicated application on their device (smartphone) and enters their login information (username and password). The entered login information is sent to the server, where the user is authenticated. If authentication is successful, the user enters detailed information about the product they are proposing and the target company. The information entered includes the product ID, store ID, product characteristics, price, store size, and the challenges they are facing.

[0866] Input: Login information, product information, store information

[0867] Output: Proposal information (product ID, store ID, product characteristics, price, size, issues)

[0868] Step 2:

[0869] The terminal sends the proposal information entered by the user to the server. The server makes a request to obtain related information from the product database and company database based on the product ID and store ID contained in the received proposal information. Information such as product performance and sales performance is obtained from the product database, and information such as the store's industry trends, past proposal history, and current challenges is obtained from the company database.

[0870] Input: Proposal information

[0871] Output: Product information, company information

[0872] Step 3:

[0873] Based on the acquired product and company information, the server uses an AI algorithm to match the proposed product with the proposing company. Specifically, it evaluates the product's characteristics and effectiveness, as well as the store's needs and characteristics, and calculates its suitability. In this evaluation process, the AI ​​algorithm analyzes various data points to determine the extent to which the proposed product can solve the target store's problems.

[0874] Input: Product information, company information

[0875] Output: Matching degree (fit score)

[0876] Step 4:

[0877] After obtaining the matching results, the server uses an automatic generation program to create a proposal document. This document includes the product's characteristics, effects, reasons for proposing it, and benefits to the company, and is generated in a visually appealing format (e.g., PDF file format). The generative AI model creates prompts based on the product's detailed information and the matching results, and reflects them in the proposal document.

[0878] Input: Matching degree, product information, company information

[0879] Output: Proposal materials (PDF file)

[0880] Step 5:

[0881] The created proposal document is sent from the server to the user's device. The device displays the received proposal document and provides the user with the ability to download and save it. The user can also edit the proposal document as needed or share it with other parties.

[0882] Input: Proposal materials

[0883] Output: Proposal materials (PDF file) displayed on the device

[0884] 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.

[0885] The present invention is a system that combines receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system consists of four main elements: a server, a terminal, a user, and an emotion engine.

[0886] 1. User Input Phase

[0887] Users access a dedicated application on their device and log in. After logging in, they enter detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and specific challenges.

[0888] 2. Emotion Recognition Phase

[0889] When receiving the proposed information, the device uses an emotion engine to analyze the user's facial expressions, tone of voice, etc. to recognize the user's emotional state. This emotion information is also sent to the server as part of the proposed information.

[0890] 3. Data Collection Phase

[0891] The terminal sends the input proposal information and emotion information to the server. The server accesses the company database and product database to obtain related information based on the proposal information. From the company database, information such as the company's industry trends, past proposal history, and current challenges is obtained. From the product database, information such as product performance and existing sales performance is obtained.

[0892] 4. Data Matching Phase

[0893] The server uses an AI algorithm to match the acquired related information and emotional information. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. It also takes the user's emotional information into account to determine the optimal proposal.

[0894] 5. Proposal material generation phase

[0895] After obtaining the matching results and emotion information, the server uses an automatic generation program to create a proposal document. This proposal document includes the product's characteristics, reasons for proposing it, and its benefits to the company. Furthermore, based on the emotion information, the content and format of the document are optimized, and the document is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[0896] 6. Material distribution phase

[0897] The created proposal materials are sent from the server to the user's device. The device displays the received proposal materials and provides the user with the ability to download and save them. In addition, the generated materials are optimized based on the user's emotional information, making it easier for the user to feel satisfied.

[0898] Specific examples

[0899] For example, if a user inputs "AI Chatbot" as the proposed product and "XYZ Corporation" as the target company, the following process will be executed:

[0900] 1. The user enters information about the "AI chatbot" and "XYZ Corporation" on their device and sends it to the server.

[0901] 2. At the same time as receiving the input information, the device analyzes the user's facial expressions and tone of voice and sends emotional information to the server.

[0902] 3. The server retrieves detailed information about the "AI chatbot" from the product database and detailed information about "XYZ Corporation" from the company database.

[0903] 4. The server uses an AI algorithm to evaluate how well the AI ​​chatbot meets the customer service needs of XYZ Corporation, taking emotional information into account.

[0904] 5. The server uses an automatic generation program to create a proposal document based on the matching results and emotional information. This document includes the product's performance, cost-effectiveness, reasons for the proposal, etc. The presentation and design of the document are also adjusted based on the emotional information.

[0905] 6. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0906] In this way, the present invention aims to improve the efficiency of sales activities and realizes the provision of high-quality proposal materials that take into account the user's emotions.

[0907] The processing flow will be explained below.

[0908] Step 1:

[0909] The user accesses the dedicated application on the terminal and logs in.

[0910] Step 2:

[0911] The user inputs detailed information about the proposed product and the company to which the proposal is being made, including the product's characteristics, the industry and specific challenges of the proposing company, etc.

[0912] Step 3:

[0913] When receiving the proposed information, the terminal analyzes the user's facial expression and voice tone using an emotion engine to recognize the user's emotional state.

[0914] Step 4:

[0915] The terminal transmits the input suggestion information and emotion information to the server.

[0916] Step 5:

[0917] Based on the proposal information, the server retrieves related detailed information from the company database and product database. From the company database, it retrieves information such as the company's industry, size, past transaction history, and specific issues. From the product database, it retrieves information such as the product's performance, price, and existing sales performance.

[0918] Step 6:

[0919] The server uses an AI algorithm to analyze the acquired product and company information, evaluates the degree of match between the proposed product and the proposed company, and fine-tunes the content of the proposal taking into account the user's emotional information.

[0920] Step 7:

[0921] The server starts an automatic generation program based on the matching degree and emotion information to create a proposal document. The document contains information such as the product's characteristics, the reason for the proposal, and its benefits to the company. The presentation method and design of the document are also adjusted according to the user's emotion information.

[0922] Step 8:

[0923] The server stores the generated proposal in a visually appealing format (e.g., a PDF file or a slide presentation).

[0924] Step 9:

[0925] The server transmits the generated proposal material to the user's terminal.

[0926] Step 10:

[0927] The device displays the received proposal materials on the user interface and allows the user to download and save them. In addition, the content and format of the materials are optimized based on the user's emotional information, resulting in a higher level of satisfaction for the user.

[0928] In this way, the present invention combines proposal information with user emotion information to automatically generate more effective and satisfying proposal materials, thereby improving the efficiency of sales activities.

[0929] Example 2

[0930] 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."

[0931] Conventional recommendation systems are unable to consider the user's emotional state when evaluating the degree of match between products and companies, making it difficult to generate optimal proposal content. Furthermore, the generated proposal materials are often not visually appealing, making it difficult to increase user satisfaction.

[0932] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving proposal information, means for collecting and analyzing user emotion information based on the received proposal information, means for acquiring related information from a company database and a product database based on the proposal information and emotion information, means for evaluating the needs and characteristics of the proposed product and the proposing company based on the acquired related information and calculating the degree of suitability, means for automatically generating proposal materials based on the matching results and emotion information, and means for transmitting the generated proposal materials to the user's terminal. This makes it possible to generate optimal proposal content that takes the user's emotion information into consideration and provide visually appealing proposal materials.

[0933] "Proposal information" refers to detailed information about the products proposed by the user and the companies to which the proposal is made.

[0934] "Emotional information" refers to information about the user's emotional state obtained by analyzing the user's facial expressions, tone of voice, and the like.

[0935] A "corporate database" refers to a database that stores information such as a company's industry trends, past proposal history, and challenges it faces.

[0936] A "product database" refers to a database that stores information such as product performance, characteristics, price, and sales record.

[0937] "Related information" refers to data obtained from the company database and product database based on the proposal information.

[0938] "Matching" refers to the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating the degree of compatibility.

[0939] "Proposal materials" refer to materials that are automatically generated based on matching results and emotional information, and that describe the characteristics of the product, the reasons for proposing it, and its benefits to the company.

[0940] "Visually generated" refers to optimizing the content and format of the material so that the material is created in a format that is visually appealing to the user.

[0941] The present invention is a system that combines receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system consists of four main elements: a server, a terminal, a user, and an emotion engine.

[0942] User Input Phase

[0943] Users access a dedicated application and log in. After logging in, they enter detailed information (proposal information) about the products they are proposing and the companies they are proposing to into the terminal. The information entered includes the characteristics and price of the products, the company's industry and size, and specific challenges.

[0944] Emotion Recognition Phase

[0945] The device uses a camera and microphone to collect facial expressions and voice tones while the user is typing. This collected data is analyzed by an emotion engine to generate the user's emotion information. This emotion information is also sent to the server along with the suggestion information.

[0946] Data Collection Phase

[0947] The terminal transmits the proposal information and emotion information to the server. The server accesses the company database and product database to obtain related information based on the proposal information. The company database provides information on the company's industry trends and past proposal history, while the product database provides information on the product's performance and existing sales performance.

[0948] Data Matching Phase

[0949] The server uses an AI algorithm to match the acquired related information and emotional information. It evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates the degree of compatibility. It also evaluates the user's emotional information to determine the optimal proposal content.

[0950] Proposal material generation phase

[0951] Based on the matching results and the emotion information, the server uses an automatic generation program to create a proposal document. This document includes the product's characteristics, the reason for the proposal, its benefits to the company, etc. Furthermore, based on the emotion information, the content and format of the document are optimized, and the document is generated in a visually appealing format.

[0952] Material distribution phase

[0953] The created proposal materials are sent from the server to the user's device. The device displays the received proposal materials and provides the user with the ability to download and save them. Because the proposal materials are optimized based on the user's emotional information, the user can achieve a higher level of satisfaction.

[0954] Specific examples

[0955] For example, if a user inputs "AI Chatbot" as the proposed product and "ABC Company" as the target company, the following process will be executed:

[0956] 1. The user enters information about the "AI chatbot" and "ABC Company" into the device through a dedicated application and sends it to the server.

[0957] 2. At the same time as the information is entered, the device also analyzes the user's facial expressions and tone of voice to collect emotional information and send it to the server.

[0958] 3. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about "ABC Company" from the company database.

[0959] 4. The server uses an AI algorithm to evaluate the compatibility between the "AI chatbot" and "ABC Company," taking into account emotional information.

[0960] 5. The server uses an automatic generation program to create a proposal document based on the matching results and emotion information. This document includes the product's performance, cost-effectiveness, reasons for the proposal, etc. The presentation and design of the document are also adjusted based on the emotion information.

[0961] 6. The server sends the generated proposal materials to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[0962] Prompt Sentence Examples

[0963] Below are some example prompts for input to a generative AI model:

[0964] Please enter "AI Chatbot" as the proposed product and "ABC Company" as the target company. After entering the information, it will be sent to the server. At this time, please log in through a dedicated application. In addition, facial expressions and tone of voice while entering information will also be recorded, and the user's emotional state will also be analyzed.

[0965] According to this prompt, the user provides the appropriate information and emotional state, and the system generates the optimal proposal material.

[0966] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0967] Step 1: User Input Phase

[0968] The user accesses the dedicated application to log in. They enter their username and password as login information and complete the login process. The input is from the dedicated application and the terminal, and the output is successful user authentication.

[0969] The user inputs detailed information about the product to be proposed and the target company (proposal information) into the terminal. The input here includes the product's characteristics, price, the company's industry and size, and specific issues, and the output is the temporarily saved proposal information.

[0970] Step 2: Emotion Recognition Phase

[0971] The device uses a camera and microphone to capture facial expressions and voice tones during user input, and this collected data is sent to the emotion engine as input.

[0972] The emotion engine analyzes the collected data and recognizes the user's emotional state. This analysis is data calculation, and the output is emotional information.

[0973] The device sends the generated emotion information together with the suggestion information to the server. The input is the emotion information and the suggestion information, and the output is data transmission to the server.

[0974] Step 3: Data collection phase

[0975] The server receives the proposal information and emotion information sent from the terminal. The input is the proposal information and emotion information, and the output is a receipt confirmation.

[0976] The server accesses the company database and product database to retrieve relevant information. This data retrieval process is called data processing. The input is a database query about the company and product, and the output is the retrieved relevant information.

[0977] Step 4: Data Matching Phase

[0978] The server uses an AI algorithm to match the acquired related information and emotional information. The input is related information and emotional information, and the output is a compatibility score.

[0979] The server evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates their compatibility. This evaluation process is data calculation, and the compatibility score is the output.

[0980] Step 5: Proposal generation phase

[0981] The server uses an automatic generation program to create proposal materials based on the matching results and emotion information. The input is the matching results and emotion information, and the output is the proposal materials.

[0982] The content of the proposal document includes the product's characteristics, the reason for the proposal, and its benefits to the company. Furthermore, the format and design of the document are optimized based on the emotional information. This process involves the actual data processing and content generation.

[0983] Step 6: Material distribution phase

[0984] The server sends the generated proposal materials to the user's terminal. The input is the proposal materials, and the output is a document file sent to the user's terminal.

[0985] The terminal unpacks and displays the received proposal documents. The user can then check them and download or save them as needed. The input is the document file, and the output is the displayed document and the saved file.

[0986] (Application example 2)

[0987] 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."

[0988] Conventional proposal material generation systems create proposal materials by simple data matching without considering the user's emotional state. As a result, many proposals do not match the user's true needs or emotions, and the effectiveness of the proposal materials is not fully realized. The present invention aims to solve this problem by automatically generating optimal proposal materials that take into account the user's emotional information, thereby realizing proposals that provide greater user satisfaction.

[0989] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving proposal information, means for acquiring related information from a company database and a product database, means for matching proposed products with proposing companies, means for acquiring emotion information using an emotion engine that recognizes the user's emotional state, means for automatically generating proposal materials based on the matching results and the emotion information, means for transmitting the generated proposal materials to the user's terminal, and means for providing the user with means for checking and saving the proposal materials or recommendation list. This enables optimal product proposals to be made according to the user's emotions.

[0990] "Proposal information" refers to detailed information about the product and the company to which the proposal is being made, including the product's characteristics and price, the company's industry and size, and specific challenges.

[0991] A "corporate database" refers to a database that stores various information about a company, including the company's industry trends, past proposal history, and current challenges it is facing.

[0992] "Product database" refers to a database that stores detailed information about various products, including product performance, price, and existing sales.

[0993] "Matching" refers to the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating their compatibility.

[0994] An "emotion engine" is a system that analyzes a user's facial expressions and tone of voice to recognize their emotional state. This system uses emotional information as part of its recommendations.

[0995] A "proposal document" refers to a document that includes the characteristics of the proposed product, the reasons for proposing it, its benefits to the company, etc. This document is generated in a visually appealing format based on emotional information.

[0996] "User's device" refers to the device used to input proposal information, view and save proposal materials, etc. This includes smartphones and computers.

[0997] "Emotional information" refers to the user's emotional state as recognized by the emotion engine. This information is used to generate and optimize recommendations.

[0998] A "visually appealing format" refers to a document format that applies design elements that match the user's emotional state, allowing the proposal to be more effectively communicated to the user.

[0999] The present invention is a system that combines receiving proposal information, acquiring related information, matching proposed products with proposing companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system includes means for receiving proposal information, means for acquiring related information from a company database and a product database, means for matching proposed products with proposing companies, an emotion engine for acquiring emotion information, means for automatically generating proposal materials based on the matching results and the emotion information, means for transmitting the generated proposal materials to a user terminal, and means for the user to review and save the proposal materials or recommendation list.

[1000] Overall outline of the system program

[1001] 1. User input phase:

[1002] Users input proposal information through a smartphone app, including data related to the products and companies they are proposing.

[1003] 2. Emotion Recognition Phase:

[1004] Using the emotion engine, the smartphone's camera and microphone are used to analyze the user's facial expressions and vocal tone as emotional information.

[1005] 3. Data collection phase:

[1006] The proposal information and emotion information are transmitted to a server, and related information is obtained from a company database and a product database.

[1007] 4. Data Matching Phase:

[1008] Using an AI algorithm, the proposed products and proposing companies are matched based on the acquired related information and emotional information.

[1009] 5. Proposal generation phase:

[1010] Based on the matching results and emotional information, an automatic generation program is used to create proposal materials, which include the product's characteristics, reasons for the proposal, and benefits to the company. Furthermore, based on the emotional information, the materials are generated in a visually appealing format.

[1011] 6. Material distribution phase:

[1012] The proposal materials are sent from the server to the user's smartphone, where the user can view, save, and complete the purchase procedure.

[1013] Hardware and Software Used

[1014] Hardware:

[1015] Smartphone (camera, microphone)

[1016] software:

[1017] Emotion Recognizer

[1018] Database Access API (database_access)

[1019] Proposal document generation program (proposal_generator)

[1020] Process Description

[1021] Receive Proposals:

[1022] The user inputs the proposal information via a smartphone app.

[1023] Acquiring emotional information:

[1024] The emotion engine analyzes facial expressions and voice tones via the user's camera and microphone to obtain emotional information.

[1025] Get related information:

[1026] Based on the proposal information, the server retrieves related information from the company database and the product database.

[1027] Data Matching:

[1028] Using an AI algorithm, the degree of match between the proposed products and the proposing company is calculated, and emotional information is also taken into account.

[1029] Proposal generation:

[1030] The automatic generation program generates proposal materials, including the characteristics of the proposed products and their benefits to the company, in a visually appealing format based on emotional information.

[1031] Distribution of materials:

[1032] The generated proposal materials are sent from the server to the user's smartphone, providing an interface for the user to review and save the materials.

[1033] Specific examples

[1034] for example:

[1035] User ID: user123, selected product ID: product456. Emotional state: positive (expression engine result: smiling, voice sign: cheerful). Proposal material generation.

[1036] This example makes it possible to build a system that takes into account the emotional state of the user and makes optimal product suggestions.

[1037] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1038] Step 1: The user opens the smartphone app and logs in. After logging in, the user enters detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and any unique challenges. The entered proposal information is sent from the device to the server. The input data is sent in JSON format, and a data consistency check is performed at that time.

[1039] Step 2: When the user inputs the suggestion information, the device uses the smartphone's camera and microphone to analyze the user's facial expressions and voice tone with an emotion engine to obtain emotional information. Emotional information is calculated based on changes in facial expressions (e.g., smiling or furrowing the brow) and changes in voice tone (e.g., cheerful or depressed voice). The analysis results are formatted as an emotion score and sent to the server along with the suggestion information.

[1040] Step 3: Based on the received proposal information and emotion information, the server accesses the company database and product database to retrieve relevant information. Specifically, detailed product information and price information about the proposed product is retrieved from the product database, and the company's industry, past transaction history, current issues, etc. are retrieved from the company database. In this process, the necessary information is filtered using database queries to efficiently retrieve data.

[1041] Step 4: The server uses an AI algorithm to perform matching based on the acquired related information and emotional information. It evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates their compatibility. It also takes into account the user's emotional information to determine the optimal proposal. Specifically, it uses a normalized scoring algorithm to calculate a compatibility score for each proposed product.

[1042] Step 5: The server uses an automatic generation program to create a proposal document based on the matching results and emotional information. This proposal document includes the product's characteristics, the reason for proposing it, and its benefits to the company. The presentation and design of the document are also adjusted based on the emotional information. For example, bright colors and friendly fonts are used in the case of a positive emotional state. The document is generated in PDF or slide format.

[1043] Step 6: The server sends the generated offer document to the user's device. The device displays the received offer document and allows the user to download and save it. The user can also view the offer document through the provided interface and complete the purchase procedure.

[1044] As a specific example, if user ID "user123" enters "Product ABC" as the proposed product and selects "Company XYZ" as the target company, proposal materials will be automatically generated through the process from steps 1 to 6 and sent to the user's smartphone. If the emotional state is recognized as positive, bright colors and friendly fonts will be used.

[1045] 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.

[1046] 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.

[1047] 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.

[1048] [Fourth embodiment]

[1049] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1050] 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.

[1051] 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).

[1052] 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.

[1053] 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.

[1054] 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).

[1055] 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.

[1056] 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.

[1057] 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.

[1058] 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.

[1059] 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.

[1060] 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.

[1061] 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."

[1062] The present invention is a system that mainly consists of the steps of receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, and transmitting the proposal materials. This system consists of three main elements: a server, a terminal, and a user.

[1063] 1. User Input Phase

[1064] Users access a dedicated application on their device and log in. After logging in, they enter detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and specific challenges.

[1065] 2. Data Collection Phase

[1066] The terminal sends the proposal information entered by the user to the server. Based on the proposal information, the server accesses the company database and product database to obtain related information. From the company database, information such as the company's industry trends, past proposal history, and current challenges is obtained. From the product database, information such as product performance and existing sales performance is obtained.

[1067] 3. Data Matching Phase

[1068] The server uses AI algorithms to match the relevant information it has acquired. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. For example, if a company making a proposal to a user is looking to improve the efficiency of customer service, it evaluates the extent to which an AI chatbot can solve that problem.

[1069] 4. Proposal material generation phase

[1070] After obtaining the matching results, the server uses an automatic generation program to create a proposal document that includes the product's characteristics, reasons for the proposal, and its benefits to the company. Furthermore, the document is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[1071] 5. Material distribution phase

[1072] The created proposal materials are sent from the server to the user's device, which displays the received proposal materials and allows the user to download and save them.

[1073] Specific examples

[1074] For example, if a user inputs "AI Chatbot" as the proposed product and "ABC Corporation" as the target company, the following process will be executed:

[1075] 1. The user enters information about the "AI chatbot" and "ABC Co., Ltd." into their device and sends it to the server.

[1076] 2. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about "ABC Co., Ltd." from the company database.

[1077] 3. The server uses an AI algorithm to evaluate how well the "AI Chatbot" meets the customer service needs of "ABC Co., Ltd."

[1078] 4. Based on the matching results, the server uses an automatic generation program to create a proposal document, which includes the product's performance, cost-effectiveness, reasons for the proposal, etc.

[1079] 5. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[1080] In this way, the present invention improves sales efficiency and the quality and consistency of sales pitch materials.

[1081] The processing flow will be explained below.

[1082] Step 1:

[1083] The user accesses the dedicated application on the terminal and logs in.

[1084] Step 2:

[1085] The user inputs detailed information about the proposed product and the company to which the proposal is being made, including the product's characteristics, the industry and specific challenges of the proposing company, etc.

[1086] Step 3:

[1087] The terminal transmits the input proposal information to the server.

[1088] Step 4:

[1089] The server retrieves detailed information about the proposed product from the product database, including the product's performance, price, and existing sales record.

[1090] Step 5:

[1091] The server retrieves detailed information about the proposed company from a company database, including the company's industry, size, performance, and specific challenges.

[1092] Step 6:

[1093] The server uses the acquired product information and company information to apply an AI algorithm to evaluate the degree of match between the proposed product and the proposing company. Specifically, it calculates the extent to which the proposed product can solve the needs and problems of the proposing company.

[1094] Step 7:

[1095] Based on the evaluation results of the degree of matching, the server calls up a proposal template and uses an automatic generation program to create a proposal document, which includes the product name, product characteristics, reasons for proposing it, benefits to the company, etc.

[1096] Step 8:

[1097] The server generates the proposal in a visually appealing format (e.g., a PDF file or presentation slides).

[1098] Step 9:

[1099] The server transmits the generated proposal material to the user's terminal.

[1100] Step 10:

[1101] The terminal displays the received proposal materials and provides a function that allows the user to download and save them.

[1102] This allows users to efficiently receive high-quality proposal materials, enabling them to carry out sales activities effectively.

[1103] Example 1

[1104] 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."

[1105] In existing sales activities, the process of creating proposal materials is time-consuming and labor-intensive, and often lacks consistency and efficiency. Another issue is that it is difficult to fully evaluate the compatibility between the product and the company, making it difficult to make optimal proposals.

[1106] 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.

[1107] In this invention, the server includes means for receiving proposal information, means for acquiring related information from a database based on the proposal information, means for matching proposed products with proposing companies using an artificial intelligence algorithm based on the acquired related information, means for automatically generating proposal materials based on the matching results, means for transmitting the generated proposal materials to a user's terminal, and means for the terminal to provide the user with a function for displaying and saving the proposal materials. This makes it possible to create proposal materials more efficiently and to produce consistent, high-quality proposals.

[1108] "Proposal information" refers to detailed information about the products and the companies to which the proposal is being made, including, specifically, the characteristics and price of the products, the industry and size of the companies, and their specific challenges.

[1109] A "database" refers to a system that organizes and stores company and product information, and retrieves data in response to queries as needed.

[1110] An "artificial intelligence algorithm" is a computational method for data analysis and prediction, particularly using machine learning and deep learning models to evaluate the compatibility of proposed products with the proposing company.

[1111] A "proposal document" is a document that includes the characteristics of the product, the reasons for the proposal, the benefits to the company, etc., and is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[1112] The "suitability score" is a numerical indication of how well the proposed product matches the needs of the proposing company, and is calculated using an artificial intelligence algorithm.

[1113] "User's device" refers to a device used by the user, such as a computer, tablet, or smartphone, that has the function of displaying and saving proposal materials sent from the server.

[1114] An "automatic generation program" refers to software that automatically creates proposal materials in a specified format based on the acquired information and relevance scores.

[1115] MODE FOR CARRYING OUT THE INVENTION

[1116] The present invention is a system that consists of the steps of receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, and transmitting the proposal materials. This system is composed of three elements: a server, a terminal, and a user.

[1117] User Input Phase

[1118] The user accesses a dedicated application on their device and enters their login information. After logging in, the user enters detailed information (proposal information) about the product to be proposed and the company to which the proposal is being made. This information includes the product's characteristics, price, the company's industry, size, and specific challenges.

[1119] Data Collection Phase

[1120] The terminal sends the proposal information entered by the user to the server. At this time, the information is sent to the server in JSON format using an HTTP POST request. Based on this proposal information, the server accesses the company database and product database to retrieve related information. From the company database, it retrieves information such as the company's industry trends, past proposal history, and current issues, while from the product database, it retrieves information such as product performance and sales performance.

[1121] Data Matching Phase

[1122] The server runs an algorithm using the generative AI model based on the acquired related information to calculate the compatibility between the proposed product and the proposing company. For example, if the company to which the user is proposing is seeking to improve the efficiency of customer service, the generative AI model will score the extent to which it can solve that problem.

[1123] Proposal material generation phase

[1124] After obtaining the matching results, the server uses an automatic generation program to create a proposal document. The proposal document includes the product's characteristics, the reasons for the proposal, and its benefits to the company. The document is generated in a visually appealing format, such as a PDF or slide presentation. For example, it can be generated by embedding data in a template using a Python script.

[1125] Material distribution phase

[1126] The created proposal materials are sent from the server to the user's device. The device receives the materials and provides functions that allow the user to view, download, and save them. The user can use the proposal materials for sales activities and presentations.

[1127] Specific examples

[1128] For example, if a user inputs "AI chatbot" as the proposed product and "a certain company" as the target company, the following process will be executed:

[1129] 1. The user enters information about the "AI chatbot" and "a certain company" on their device and sends it to the server.

[1130] 2. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about a "certain company" from the company database.

[1131] 3. The server uses the generative AI model to evaluate how well the "AI chatbot" fits the customer service needs of "a certain company."

[1132] 4. The server uses an automatic generation program to create a proposal document, which includes the product's performance, cost-effectiveness, reasons for the proposal, etc.

[1133] 5. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[1134] For example, the prompt for the generative AI model is as follows: "Please create a proposal document aimed at improving customer service at the target company. The product you are proposing is an 'AI chatbot,' and the company name is 'a certain company.'"

[1135] The above is an embodiment of the present invention, and this system makes the creation of proposal materials efficient and consistent, thereby greatly supporting sales activities.

[1136] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1137] Step 1:

[1138] The user accesses a dedicated application on the device and enters login information (username and password). The entered login information is sent from the device to the server via an HTTP POST request. The server compares the received login information with an authentication database and authenticates the user. If authentication is successful, the server generates an authentication token and sends it to the device. The device saves the authentication token and uses it for subsequent requests.

[1139] Input: Username, Password

[1140] Data processing: Login information authentication

[1141] Output: Authentication token

[1142] Step 2:

[1143] The user inputs proposal information (product name, company name, and other detailed information) into the application on the device. The device sends the input proposal information in JSON format to the server. The server stores the received proposal information in its internal database.

[1144] Input: Product name, company name, and other details

[1145] Data processing: Saving proposal information

[1146] Output: Confirmation of saving of proposal information to database

[1147] Step 3:

[1148] The server queries the company database and product database based on the proposal information. From the company database, it obtains information such as the company's industry trends, past proposal history, and current issues, and from the product database, it obtains information such as product performance and sales performance.

[1149] Input: Proposal information

[1150] Data manipulation: performing database queries

[1151] Output: Company information, product information

[1152] Step 4:

[1153] The server evaluates the compatibility of the proposed product with the proposing company using an algorithm that uses a generative AI model based on company and product information. The server uses the acquired data as input, provides a prompt to the generative AI model, and outputs a compatibility score as the evaluation result.

[1154] Input: Company information, product information, prompt text

[1155] Data processing: scoring with generative AI models

[1156] Output: Relevance score

[1157] Step 5:

[1158] The server selects a proposal template based on the relevance score and the acquired related information, and creates a proposal using an automatic generation program. This proposal includes the characteristics of the proposed product, the evaluation results, and its benefits to the company. The generated document is saved in PDF format or slide presentation format.

[1159] Input: Relevance score, related information

[1160] Data processing: Automatic generation of proposal materials

[1161] Output: Proposal materials (PDF or slide format)

[1162] Step 6:

[1163] The server sends the generated proposal materials to the user's device. The device displays the received proposal materials to the user and provides functions that allow the user to download or save them. The user can use the proposal materials in sales activities and presentations.

[1164] Input: Proposal materials

[1165] Data processing: Sending proposal materials

[1166] Output: Display and save proposal materials

[1167] Through the above steps, this system makes it possible for users to streamline their sales activities and quickly provide high-quality proposal materials.

[1168] (Application example 1)

[1169] 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."

[1170] When promoting or introducing new products in physical stores, it is difficult to quickly generate efficient and effective proposal materials. With conventional methods, creating proposal materials takes a great deal of time and effort, risking delays in the store's sales promotion activities. Furthermore, creating consistent, high-quality proposal materials requires specialized knowledge, which not all stores possess. This reduces the efficiency of physical stores in carrying out effective promotions and product introductions.

[1171] 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.

[1172] In this invention, the server includes means for receiving proposal information, means for acquiring related information from a company database and a product database based on the proposal information, means for matching the proposed products with the proposing companies based on the acquired related information, means for automatically generating proposal materials based on the matching results, means for transmitting the generated proposal materials to the user's information processing device, and means for generating the proposal materials in a visually appealing format, thereby enabling store managers and staff at physical stores to generate high-quality proposal materials in a short amount of time and to carry out rapid and effective promotions and product introductions.

[1173] "Proposal information" refers to information about the characteristics and price of the product and the company to which the proposal is being made.

[1174] A "corporate database" is a database that stores information such as a company's industry trends, past proposal history, and current issues.

[1175] The "product database" is a database that stores information such as product performance and sales performance.

[1176] "Matching" is the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating the degree of compatibility.

[1177] A "proposal document" is a document that is automatically generated to describe the characteristics, effects, and reasons for proposing a product, and is output in a visually appealing format.

[1178] The "user's information processing device" refers to a mobile terminal such as a smartphone or tablet that receives and displays proposal materials.

[1179] The "means for generating a proposal document in a visually appealing format" is a method for automatically generating a proposal document in a visually appealing format using charts and text.

[1180] A "generative AI model" is an artificial intelligence model used to automatically generate the content of proposal materials.

[1181] A "prompt sentence" is a specific instruction sentence input to a generative AI model.

[1182] This invention is a system that matches products with companies based on proposal information and automatically generates and transmits proposal materials. This system consists of three main elements: a server, a terminal, and a user. Its special feature is a method that enables store managers and staff at physical stores to efficiently generate proposal materials for promotions and new product introductions.

[1183] 1. System Program

[1184] The system program is structured as follows:

[1185] User Input Phase

[1186] Users access a dedicated application on their device (smartphone) and log in. After logging in, users enter detailed information about the products they are proposing and the target companies. This information includes, for example, the characteristics and price of the new product, the type and size of the target stores, and any challenges the stores are facing.

[1187] Data Collection Phase

[1188] The input proposal information is sent to the server. The server accesses the product database and company database based on the proposal information to obtain related information. The product database provides information such as product performance and past sales performance, while the company database provides information such as the company's industry trends, past proposal history, and current issues.

[1189] Data Matching Phase

[1190] Based on the acquired related information, the server uses an AI algorithm to perform matching. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. For example, it evaluates how well the proposed new product meets the customer service needs of the store in question.

[1191] Proposal material generation phase

[1192] After obtaining the matching results, the server uses an automatic generation program to create a proposal document, which includes the product's characteristics, reasons for the proposal, and its benefits to the company. The document is generated in a visually appealing format (e.g., PDF file format).

[1193] Material distribution phase

[1194] The created proposal materials are sent from the server to the user's terminal, which displays the received proposal materials and provides a function that allows the user to download and save them.

[1195] 2. Natural language description of the process

[1196] Hardware: Smartphone (iOS or Android), server equipment

[1197] Software: Python, FPDF library, Requests library for HTTP requests

[1198] A user logs into the smartphone app and enters information about new products and promotions. This data is sent to a server, which accesses a product database and a company database to collect the necessary information. The collected data is evaluated by an AI algorithm, which calculates the compatibility between the proposed product and the proposing company. Based on the results, the server uses an automatic generation program to create a proposal document, which is generated as a visually appealing PDF file. Finally, the generated proposal document is sent from the server to the user's smartphone, where the user can receive and view it.

[1199] 3. Specific Examples

[1200] Below is an example prompt, where the user enters the following information:

[1201] Username: Manager

[1202] Password: A secure password

[1203] Product ID: P12345

[1204] Store ID: S67890

[1205] Based on this information, the system evaluates the characteristics of the new product, the store's industry and size, and customer service needs, and calculates the degree of suitability. For example, it evaluates how much an AI chatbot for a new product can improve the efficiency of customer service at the store. As a result, a visually appealing PDF file is generated, detailing the new product's introduction, benefits, and reasons for proposing it.

[1206] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1207] Step 1:

[1208] The user opens a dedicated application on their device (smartphone) and enters their login information (username and password). The entered login information is sent to the server, where the user is authenticated. If authentication is successful, the user enters detailed information about the product they are proposing and the target company. The information entered includes the product ID, store ID, product characteristics, price, store size, and the challenges they are facing.

[1209] Input: Login information, product information, store information

[1210] Output: Proposal information (product ID, store ID, product characteristics, price, size, issues)

[1211] Step 2:

[1212] The terminal sends the proposal information entered by the user to the server. The server makes a request to obtain related information from the product database and company database based on the product ID and store ID contained in the received proposal information. Information such as product performance and sales performance is obtained from the product database, and information such as the store's industry trends, past proposal history, and current challenges is obtained from the company database.

[1213] Input: Proposal information

[1214] Output: Product information, company information

[1215] Step 3:

[1216] Based on the acquired product and company information, the server uses an AI algorithm to match the proposed product with the proposing company. Specifically, it evaluates the product's characteristics and effectiveness, as well as the store's needs and characteristics, and calculates its suitability. In this evaluation process, the AI ​​algorithm analyzes various data points to determine the extent to which the proposed product can solve the target store's problems.

[1217] Input: Product information, company information

[1218] Output: Matching degree (fit score)

[1219] Step 4:

[1220] After obtaining the matching results, the server uses an automatic generation program to create a proposal document. This document includes the product's characteristics, effects, reasons for proposing it, and benefits to the company, and is generated in a visually appealing format (e.g., PDF file format). The generative AI model creates prompts based on the product's detailed information and the matching results, and reflects them in the proposal document.

[1221] Input: Matching degree, product information, company information

[1222] Output: Proposal materials (PDF file)

[1223] Step 5:

[1224] The created proposal document is sent from the server to the user's device. The device displays the received proposal document and provides the user with the ability to download and save it. The user can also edit the proposal document as needed or share it with other parties.

[1225] Input: Proposal materials

[1226] Output: Proposal materials (PDF file) displayed on the device

[1227] 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.

[1228] The present invention is a system that combines receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system consists of four main elements: a server, a terminal, a user, and an emotion engine.

[1229] 1. User Input Phase

[1230] Users access a dedicated application on their device and log in. After logging in, they enter detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and specific challenges.

[1231] 2. Emotion Recognition Phase

[1232] When receiving the proposed information, the device uses an emotion engine to analyze the user's facial expressions, tone of voice, etc. to recognize the user's emotional state. This emotion information is also sent to the server as part of the proposed information.

[1233] 3. Data Collection Phase

[1234] The terminal sends the input proposal information and emotion information to the server. The server accesses the company database and product database to obtain related information based on the proposal information. From the company database, information such as the company's industry trends, past proposal history, and current challenges is obtained. From the product database, information such as product performance and existing sales performance is obtained.

[1235] 4. Data Matching Phase

[1236] The server uses an AI algorithm to match the acquired related information and emotional information. Specifically, it evaluates the needs and characteristics of the proposed products and the proposing company, and calculates their compatibility. It also takes the user's emotional information into account to determine the optimal proposal.

[1237] 5. Proposal material generation phase

[1238] After obtaining the matching results and emotion information, the server uses an automatic generation program to create a proposal document. This proposal document includes the product's characteristics, reasons for proposing it, and its benefits to the company. Furthermore, based on the emotion information, the content and format of the document are optimized, and the document is generated in a visually appealing format (e.g., PDF file or slide presentation format).

[1239] 6. Material distribution phase

[1240] The created proposal materials are sent from the server to the user's device. The device displays the received proposal materials and provides the user with the ability to download and save them. In addition, the generated materials are optimized based on the user's emotional information, making it easier for the user to feel satisfied.

[1241] Specific examples

[1242] For example, if a user inputs "AI Chatbot" as the proposed product and "XYZ Corporation" as the target company, the following process will be executed:

[1243] 1. The user enters information about the "AI chatbot" and "XYZ Corporation" on their device and sends it to the server.

[1244] 2. At the same time as receiving the input information, the device analyzes the user's facial expressions and tone of voice and sends emotional information to the server.

[1245] 3. The server retrieves detailed information about the "AI chatbot" from the product database and detailed information about "XYZ Corporation" from the company database.

[1246] 4. The server uses an AI algorithm to evaluate how well the AI ​​chatbot meets the customer service needs of XYZ Corporation, taking emotional information into account.

[1247] 5. The server uses an automatic generation program to create a proposal document based on the matching results and emotional information. This document includes the product's performance, cost-effectiveness, reasons for the proposal, etc. The presentation and design of the document are also adjusted based on the emotional information.

[1248] 6. The server sends the generated proposal materials in PDF format to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[1249] In this way, the present invention aims to improve the efficiency of sales activities and realizes the provision of high-quality proposal materials that take into account the user's emotions.

[1250] The processing flow will be explained below.

[1251] Step 1:

[1252] The user accesses the dedicated application on the terminal and logs in.

[1253] Step 2:

[1254] The user inputs detailed information about the proposed product and the company to which the proposal is being made, including the product's characteristics, the industry and specific challenges of the proposing company, etc.

[1255] Step 3:

[1256] When receiving the proposed information, the terminal analyzes the user's facial expression and voice tone using an emotion engine to recognize the user's emotional state.

[1257] Step 4:

[1258] The terminal transmits the input suggestion information and emotion information to the server.

[1259] Step 5:

[1260] Based on the proposal information, the server retrieves related detailed information from the company database and product database. From the company database, it retrieves information such as the company's industry, size, past transaction history, and specific issues. From the product database, it retrieves information such as the product's performance, price, and existing sales performance.

[1261] Step 6:

[1262] The server uses an AI algorithm to analyze the acquired product and company information, evaluates the degree of match between the proposed product and the proposed company, and fine-tunes the content of the proposal taking into account the user's emotional information.

[1263] Step 7:

[1264] The server starts an automatic generation program based on the matching degree and emotion information to create a proposal document. The document contains information such as the product's characteristics, the reason for the proposal, and its benefits to the company. The presentation method and design of the document are also adjusted according to the user's emotion information.

[1265] Step 8:

[1266] The server stores the generated proposal in a visually appealing format (e.g., a PDF file or a slide presentation).

[1267] Step 9:

[1268] The server transmits the generated proposal material to the user's terminal.

[1269] Step 10:

[1270] The device displays the received proposal materials on the user interface and allows the user to download and save them. In addition, the content and format of the materials are optimized based on the user's emotional information, resulting in a higher level of satisfaction for the user.

[1271] In this way, the present invention combines proposal information with user emotion information to automatically generate more effective and satisfying proposal materials, thereby improving the efficiency of sales activities.

[1272] Example 2

[1273] 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."

[1274] Conventional recommendation systems are unable to consider the user's emotional state when evaluating the degree of match between products and companies, making it difficult to generate optimal proposal content. Furthermore, the generated proposal materials are often not visually appealing, making it difficult to increase user satisfaction.

[1275] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for receiving proposal information, means for collecting and analyzing user emotion information based on the received proposal information, means for acquiring related information from a company database and a product database based on the proposal information and emotion information, means for evaluating the needs and characteristics of the proposed product and the proposing company based on the acquired related information and calculating the degree of suitability, means for automatically generating proposal materials based on the matching results and emotion information, and means for transmitting the generated proposal materials to the user's terminal. This makes it possible to generate optimal proposal content that takes the user's emotion information into consideration and provide visually appealing proposal materials.

[1276] "Proposal information" refers to detailed information about the products proposed by the user and the companies to which the proposal is made.

[1277] "Emotional information" refers to information about the user's emotional state obtained by analyzing the user's facial expressions, tone of voice, and the like.

[1278] A "corporate database" refers to a database that stores information such as a company's industry trends, past proposal history, and challenges it faces.

[1279] A "product database" refers to a database that stores information such as product performance, characteristics, price, and sales record.

[1280] "Related information" refers to data obtained from the company database and product database based on the proposal information.

[1281] "Matching" refers to the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating the degree of compatibility.

[1282] "Proposal materials" refer to materials that are automatically generated based on matching results and emotional information, and that describe the characteristics of the product, the reasons for proposing it, and its benefits to the company.

[1283] "Visually generated" refers to optimizing the content and format of the material so that the material is created in a format that is visually appealing to the user.

[1284] The present invention is a system that combines receiving proposal information, retrieving related information from a database, matching products with companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system consists of four main elements: a server, a terminal, a user, and an emotion engine.

[1285] User Input Phase

[1286] Users access a dedicated application and log in. After logging in, they enter detailed information (proposal information) about the products they are proposing and the companies they are proposing to into the terminal. The information entered includes the characteristics and price of the products, the company's industry and size, and specific challenges.

[1287] Emotion Recognition Phase

[1288] The device uses a camera and microphone to collect facial expressions and voice tones while the user is typing. This collected data is analyzed by an emotion engine to generate the user's emotion information. This emotion information is also sent to the server along with the suggestion information.

[1289] Data Collection Phase

[1290] The terminal transmits the proposal information and emotion information to the server. The server accesses the company database and product database to obtain related information based on the proposal information. The company database provides information on the company's industry trends and past proposal history, while the product database provides information on the product's performance and existing sales performance.

[1291] Data Matching Phase

[1292] The server uses an AI algorithm to match the acquired related information and emotional information. It evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates the degree of compatibility. It also evaluates the user's emotional information to determine the optimal proposal content.

[1293] Proposal material generation phase

[1294] Based on the matching results and the emotion information, the server uses an automatic generation program to create a proposal document. This document includes the product's characteristics, the reason for the proposal, its benefits to the company, etc. Furthermore, based on the emotion information, the content and format of the document are optimized, and the document is generated in a visually appealing format.

[1295] Material distribution phase

[1296] The created proposal materials are sent from the server to the user's device. The device displays the received proposal materials and provides the user with the ability to download and save them. Because the proposal materials are optimized based on the user's emotional information, the user can achieve a higher level of satisfaction.

[1297] Specific examples

[1298] For example, if a user inputs "AI Chatbot" as the proposed product and "ABC Company" as the target company, the following process will be executed:

[1299] 1. The user enters information about the "AI chatbot" and "ABC Company" into the device through a dedicated application and sends it to the server.

[1300] 2. At the same time as the information is entered, the device also analyzes the user's facial expressions and tone of voice to collect emotional information and send it to the server.

[1301] 3. The server retrieves detailed information about the "AI chatbot" from the product database and retrieves detailed information about "ABC Company" from the company database.

[1302] 4. The server uses an AI algorithm to evaluate the compatibility between the "AI chatbot" and "ABC Company," taking into account emotional information.

[1303] 5. The server uses an automatic generation program to create a proposal document based on the matching results and emotion information. This document includes the product's performance, cost-effectiveness, reasons for the proposal, etc. The presentation and design of the document are also adjusted based on the emotion information.

[1304] 6. The server sends the generated proposal materials to the user's terminal, and the terminal provides the user with an interface for displaying and saving the materials.

[1305] Prompt Sentence Examples

[1306] Below are some example prompts for input to a generative AI model:

[1307] Please enter "AI Chatbot" as the proposed product and "ABC Company" as the target company. After entering the information, it will be sent to the server. At this time, please log in through a dedicated application. In addition, facial expressions and tone of voice while entering information will also be recorded, and the user's emotional state will also be analyzed.

[1308] According to this prompt, the user provides the appropriate information and emotional state, and the system generates the optimal proposal material.

[1309] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1310] Step 1: User Input Phase

[1311] The user accesses the dedicated application to log in. They enter their username and password as login information and complete the login process. The input is from the dedicated application and the terminal, and the output is successful user authentication.

[1312] The user inputs detailed information about the product to be proposed and the target company (proposal information) into the terminal. The input here includes the product's characteristics, price, the company's industry and size, and specific issues, and the output is the temporarily saved proposal information.

[1313] Step 2: Emotion Recognition Phase

[1314] The device uses a camera and microphone to capture facial expressions and voice tones during user input, and this collected data is sent to the emotion engine as input.

[1315] The emotion engine analyzes the collected data and recognizes the user's emotional state. This analysis is data calculation, and the output is emotional information.

[1316] The device sends the generated emotion information together with the suggestion information to the server. The input is the emotion information and the suggestion information, and the output is data transmission to the server.

[1317] Step 3: Data collection phase

[1318] The server receives the proposal information and emotion information sent from the terminal. The input is the proposal information and emotion information, and the output is a receipt confirmation.

[1319] The server accesses the company database and product database to retrieve relevant information. This data retrieval process is called data processing. The input is a database query about the company and product, and the output is the retrieved relevant information.

[1320] Step 4: Data Matching Phase

[1321] The server uses an AI algorithm to match the acquired related information and emotional information. The input is related information and emotional information, and the output is a compatibility score.

[1322] The server evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates their compatibility. This evaluation process is data calculation, and the compatibility score is the output.

[1323] Step 5: Proposal generation phase

[1324] The server uses an automatic generation program to create proposal materials based on the matching results and emotion information. The input is the matching results and emotion information, and the output is the proposal materials.

[1325] The content of the proposal document includes the product's characteristics, the reason for the proposal, and its benefits to the company. Furthermore, the format and design of the document are optimized based on the emotional information. This process involves the actual data processing and content generation.

[1326] Step 6: Material distribution phase

[1327] The server sends the generated proposal materials to the user's terminal. The input is the proposal materials, and the output is a document file sent to the user's terminal.

[1328] The terminal unpacks and displays the received proposal documents. The user can then check them and download or save them as needed. The input is the document file, and the output is the displayed document and the saved file.

[1329] (Application example 2)

[1330] 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."

[1331] Conventional proposal material generation systems create proposal materials by simple data matching without considering the user's emotional state. As a result, many proposals do not match the user's true needs or emotions, and the effectiveness of the proposal materials is not fully realized. The present invention aims to solve this problem by automatically generating optimal proposal materials that take into account the user's emotional information, thereby realizing proposals that provide greater user satisfaction.

[1332] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving proposal information, means for acquiring related information from a company database and a product database, means for matching proposed products with proposing companies, means for acquiring emotion information using an emotion engine that recognizes the user's emotional state, means for automatically generating proposal materials based on the matching results and the emotion information, means for transmitting the generated proposal materials to the user's terminal, and means for providing the user with means for checking and saving the proposal materials or recommendation list. This enables optimal product proposals to be made according to the user's emotions.

[1333] "Proposal information" refers to detailed information about the product and the company to which the proposal is being made, including the product's characteristics and price, the company's industry and size, and specific challenges.

[1334] A "corporate database" refers to a database that stores various information about a company, including the company's industry trends, past proposal history, and current challenges it is facing.

[1335] "Product database" refers to a database that stores detailed information about various products, including product performance, price, and existing sales.

[1336] "Matching" refers to the process of evaluating the needs and characteristics of the proposed products and the proposing company, and calculating their compatibility.

[1337] An "emotion engine" is a system that analyzes a user's facial expressions and tone of voice to recognize their emotional state. This system uses emotional information as part of its recommendations.

[1338] A "proposal document" refers to a document that includes the characteristics of the proposed product, the reasons for proposing it, its benefits to the company, etc. This document is generated in a visually appealing format based on emotional information.

[1339] "User's device" refers to the device used to input proposal information, view and save proposal materials, etc. This includes smartphones and computers.

[1340] "Emotional information" refers to the user's emotional state as recognized by the emotion engine. This information is used to generate and optimize recommendations.

[1341] A "visually appealing format" refers to a document format that applies design elements that match the user's emotional state, allowing the proposal to be more effectively communicated to the user.

[1342] The present invention is a system that combines receiving proposal information, acquiring related information, matching proposed products with proposing companies, automatically generating proposal materials, transmitting proposal materials, and an emotion engine. This system includes means for receiving proposal information, means for acquiring related information from a company database and a product database, means for matching proposed products with proposing companies, an emotion engine for acquiring emotion information, means for automatically generating proposal materials based on the matching results and the emotion information, means for transmitting the generated proposal materials to a user terminal, and means for the user to review and save the proposal materials or recommendation list.

[1343] Overall outline of the system program

[1344] 1. User input phase:

[1345] Users input proposal information through a smartphone app, including data related to the products and companies they are proposing.

[1346] 2. Emotion Recognition Phase:

[1347] Using the emotion engine, the smartphone's camera and microphone are used to analyze the user's facial expressions and vocal tone as emotional information.

[1348] 3. Data collection phase:

[1349] The proposal information and emotion information are transmitted to a server, and related information is obtained from a company database and a product database.

[1350] 4. Data Matching Phase:

[1351] Using an AI algorithm, the proposed products and proposing companies are matched based on the acquired related information and emotional information.

[1352] 5. Proposal generation phase:

[1353] Based on the matching results and emotional information, an automatic generation program is used to create proposal materials, which include the product's characteristics, reasons for the proposal, and benefits to the company. Furthermore, based on the emotional information, the materials are generated in a visually appealing format.

[1354] 6. Material distribution phase:

[1355] The proposal materials are sent from the server to the user's smartphone, where the user can view, save, and complete the purchase procedure.

[1356] Hardware and Software Used

[1357] Hardware:

[1358] Smartphone (camera, microphone)

[1359] software:

[1360] Emotion Recognizer

[1361] Database Access API (database_access)

[1362] Proposal document generation program (proposal_generator)

[1363] Process Description

[1364] Receive Proposals:

[1365] The user inputs the proposal information via a smartphone app.

[1366] Acquiring emotional information:

[1367] The emotion engine analyzes facial expressions and voice tones via the user's camera and microphone to obtain emotional information.

[1368] Get related information:

[1369] Based on the proposal information, the server retrieves related information from the company database and the product database.

[1370] Data Matching:

[1371] Using an AI algorithm, the degree of match between the proposed products and the proposing company is calculated, and emotional information is also taken into account.

[1372] Proposal generation:

[1373] The automatic generation program generates proposal materials, including the characteristics of the proposed products and their benefits to the company, in a visually appealing format based on emotional information.

[1374] Distribution of materials:

[1375] The generated proposal materials are sent from the server to the user's smartphone, providing an interface for the user to review and save the materials.

[1376] Specific examples

[1377] for example:

[1378] User ID: user123, selected product ID: product456. Emotional state: positive (expression engine result: smiling, voice sign: cheerful). Proposal material generation.

[1379] This example makes it possible to build a system that takes into account the emotional state of the user and makes optimal product suggestions.

[1380] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1381] Step 1: The user opens the smartphone app and logs in. After logging in, the user enters detailed information (proposal information) about the product they wish to propose and the company to which they are making the proposal. This information includes the product's characteristics and price, the company's industry and size, and any unique challenges. The entered proposal information is sent from the device to the server. The input data is sent in JSON format, and a data consistency check is performed at that time.

[1382] Step 2: When the user inputs the suggestion information, the device uses the smartphone's camera and microphone to analyze the user's facial expressions and voice tone with an emotion engine to obtain emotional information. Emotional information is calculated based on changes in facial expressions (e.g., smiling or furrowing the brow) and changes in voice tone (e.g., cheerful or depressed voice). The analysis results are formatted as an emotion score and sent to the server along with the suggestion information.

[1383] Step 3: Based on the received proposal information and emotion information, the server accesses the company database and product database to retrieve relevant information. Specifically, detailed product information and price information about the proposed product is retrieved from the product database, and the company's industry, past transaction history, current issues, etc. are retrieved from the company database. In this process, the necessary information is filtered using database queries to efficiently retrieve data.

[1384] Step 4: The server uses an AI algorithm to perform matching based on the acquired related information and emotional information. It evaluates the needs and characteristics of the proposed products and the proposing companies, and calculates their compatibility. It also takes into account the user's emotional information to determine the optimal proposal. Specifically, it uses a normalized scoring algorithm to calculate a compatibility score for each proposed product.

[1385] Step 5: The server uses an automatic generation program to create a proposal document based on the matching results and emotional information. This proposal document includes the product's characteristics, the reason for proposing it, and its benefits to the company. The presentation and design of the document are also adjusted based on the emotional information. For example, bright colors and friendly fonts are used in the case of a positive emotional state. The document is generated in PDF or slide format.

[1386] Step 6: The server sends the generated offer document to the user's device. The device displays the received offer document and allows the user to download and save it. The user can also view the offer document through the provided interface and complete the purchase procedure.

[1387] As a specific example, if user ID "user123" enters "Product ABC" as the proposed product and selects "Company XYZ" as the target company, proposal materials will be automatically generated through the process from steps 1 to 6 and sent to the user's smartphone. If the emotional state is recognized as positive, bright colors and friendly fonts will be used.

[1388] 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.

[1389] 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.

[1390] 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 robot 414.

[1391] 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.

[1392] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1393] 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.

[1394] 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).

[1395] 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.

[1396] 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."

[1397] 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.

[1398] 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).

[1399] 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.

[1400] 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.

[1401] 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.

[1402] 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.

[1403] 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.

[1404] 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.

[1405] 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.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] The following is further disclosed regarding the above embodiment.

[1410] (Claim 1)

[1411] means for receiving proposal information;

[1412] means for acquiring related information from a company database and a product database based on the proposal information;

[1413] A means of matching proposed products with proposing companies based on the acquired related information;

[1414] A means for automatically generating proposal materials based on the matching results;

[1415] The system includes a means for transmitting the generated proposal material to a user's terminal.

[1416] (Claim 2)

[1417] 2. The system according to claim 1, further comprising means for evaluating the degree of matching between the proposed product and the proposing company based on company information obtained from the company database and product information obtained from the product database.

[1418] (Claim 3)

[1419] 2. The system according to claim 1, further comprising means for visually generating proposal materials in a predetermined format based on the matching results.

[1420] "Example 1"

[1421] (Claim 1)

[1422] means for receiving proposal information;

[1423] means for acquiring related information from a database based on the proposed information;

[1424] A means for matching proposed products with proposing companies using an artificial intelligence algorithm based on the acquired related information;

[1425] A means for automatically generating proposal materials based on the matching results;

[1426] means for transmitting the generated proposal material to a user's terminal;

[1427] The system includes a means for providing the user with the ability to display and save proposal materials.

[1428] (Claim 2)

[1429] 2. The system according to claim 1, further comprising means for acquiring relevant information from a company information database and a product information database, and calculating a compatibility score based on the acquired company information and product information.

[1430] (Claim 3)

[1431] The system according to claim 1, further comprising means for automatically generating proposal materials in a predetermined format based on the matching results, and visually generating them in PDF or slide presentation format.

[1432] "Application Example 1"

[1433] (Claim 1)

[1434] means for receiving proposal information;

[1435] means for acquiring related information from a company database and a product database based on the proposal information;

[1436] A means of matching proposed products with proposing companies based on the acquired related information;

[1437] A means for automatically generating proposal materials based on the matching results;

[1438] means for transmitting the generated proposal material to the user's information processing device;

[1439] A system including a means for generating pitch decks in a visually appealing format.

[1440] (Claim 2)

[1441] A means for evaluating the degree of matching between the proposed product and the proposing company based on company information obtained from the company database and product information obtained from the product database;

[1442] 2. The system according to claim 1, further comprising means for storing and displaying the generated proposal materials in the user's information processing device.

[1443] (Claim 3)

[1444] The system of claim 1, further comprising a means for automatically generating proposal materials including the characteristics and effects of a product and reasons for proposing it using a generative AI model.

[1445] "Example 2: Combining Emotion Engines"

[1446] (Claim 1)

[1447] means for receiving proposal information;

[1448] means for collecting and analyzing user emotion information based on the received proposal information;

[1449] means for acquiring related information from a company database and a product database based on the proposal information and emotion information;

[1450] A means for evaluating the needs and characteristics of the proposed products and the proposing company based on the acquired related information, and calculating the degree of suitability;

[1451] a means for automatically generating proposal materials based on the matching results and emotion information;

[1452] The system includes a means for transmitting the generated proposal material to a user's terminal.

[1453] (Claim 2)

[1454] The system of claim 1 includes a means for evaluating the suitability of the proposed product and the proposing company based on company information obtained from the company database and product information obtained from the product database, and determining the optimal proposal content taking into account the user's emotional information.

[1455] (Claim 3)

[1456] 2. The system according to claim 1, further comprising means for visually generating a proposal document in a predetermined format based on the matching result and the user's emotion information, and optimizing the content and format of the document.

[1457] "Application example 2 when combining emotion engines"

[1458] (Claim 1)

[1459] means for receiving proposal information;

[1460] means for acquiring related information from a company database and a product database based on the proposal information;

[1461] A means of matching proposed products with proposing companies based on the acquired related information;

[1462] a means for acquiring emotion information by utilizing an emotion engine that recognizes the user's emotional state;

[1463] a means for automatically generating proposal materials based on the matching results and emotion information;

[1464] means for transmitting the generated proposal material to a user's terminal;

[1465] A system including a means for providing a means for a user to review and save the proposal materials or recommendation list.

[1466] (Claim 2)

[1467] A means for evaluating the degree of matching between the proposed product and the proposing company based on company information obtained from the company database and product information obtained from the product database;

[1468] The system according to claim 1, further comprising means for adjusting the degree of matching by combining emotion information.

[1469] (Claim 3)

[1470] a means for visually generating proposal materials in a predetermined format based on the matching results and the emotion information;

[1471] 10. The system of claim 1, further comprising means for applying visually appealing design elements to the proposal that are tailored to the user's emotional state. [Explanation of symbols]

[1472] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving proposal information; means for acquiring related information from a company database and a product database based on the proposal information; A means of matching proposed products with proposing companies based on the acquired related information; A means for automatically generating proposal materials based on the matching results; The system includes a means for transmitting the generated proposal material to a user's terminal.

2. 2. The system according to claim 1, further comprising means for evaluating the degree of matching between the proposed product and the proposing company based on company information obtained from the company database and product information obtained from the product database.

3. 2. The system according to claim 1, further comprising means for visually generating proposal materials in a predetermined format based on the matching results.

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