system

Abstract

To provide a system.SOLUTION: A system includes first means for a user to input basic information including a project name, purpose, budget, and period, second means for receiving the basic information input by the user and analyzing it using natural language processing technology, third means for generating a first prompt sentence for outputting a project proposal based on a result of the analysis of the basic information, fourth means for inputting the generated first prompt sentence into a generative AI model to generate the proposal, fifth means for sending the generated proposal to the user's terminal, and sixth means for accepting edits to the proposal by the user.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 product proposals, it was difficult to record past success stories and sales growth, and then use that information to propose optimal plans and service combinations. It was also difficult to create proposal documents, proposal materials, proposed prices, etc. all at once in a timely manner, compare them with similar services offered by competitors, and present them to customers in a single visit. [Means for solving the problem]

[0005] The system of the present invention provides a means for recording product service information, proposal examples for past orders, and which service combinations have been successful and led to increased sales, a means for proposing optimal plans and service combinations according to industry and customer needs, and a means for using natural language processing technology to provide proposal documents, proposal materials, proposed fees, etc. all in one timely package when making proposals. This makes it possible to compare services with equivalent services from competitors, and to propose differences in quotes and functionality with other companies to customers in a single visit. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention is equipped with a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales. This database provides information for proposing optimal plans and service combinations tailored to the type of industry and customer needs. Specifically, it has the function of analyzing past success stories and sales growth, and proposing optimal plans and service combinations based on that.

[0029] "Example 2"

[0030] Furthermore, the system of the present invention has the function of using natural language processing technology to create proposal documents, proposal materials, proposed fees, etc. all at once and in a timely manner when making a proposal. Specifically, the system uses natural language processing technology to automatically generate the information necessary for the proposal and reflect it in the proposal documents and proposal materials. This improves the efficiency of the proposal process.

[0031] "Example 3"

[0032] The system of the present invention also allows comparison with similar services offered by competitors. Specifically, it has the function of storing information on other companies' services in a database and comparing it with your own service based on that information. This makes it possible to clearly demonstrate to customers the superiority of your company's services.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The system references a database that records service information for the product, proposal examples from past orders, and which service combinations have been successful and led to increased sales. Step 2: Next, the system retrieves information from the database to propose optimal plans and service combinations tailored to the type of industry and customer needs.

[0036] Step 3: Finally, the system will suggest the optimal plan and combination of services based on the information obtained.

[0037] "Example 2"

[0038] Step 1: When making a proposal, the system uses natural language processing technology to create proposal documents, proposal materials, proposed fees, etc. in a timely manner all at once.

[0039] Step 2: Specifically, the system uses natural language processing technology to automatically generate the information necessary for the proposal.

[0040] Step 3: Finally, the system reflects the generated information in proposal documents and proposal materials.

[0041] "Example 3"

[0042] Step 1: The system retrieves service information from a database of competitors to compare with similar services offered by competitors.

[0043] Step 2: Next, the system compares the acquired service information of other companies with the service information of the company.

[0044] Step 3: Finally, the system presents the comparison results to the customer.

[0045] Example 1

[0046] Next, a description will be given of Example 1 of Form 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."

[0047] With the conventional proposal system, it was difficult to effectively utilize product service information and past success stories, making it difficult to quickly propose plans and service combinations that best fit customer needs. Furthermore, proposal documents and materials could not be generated in a timely manner in bulk, reducing the efficiency of proposals. Furthermore, it was difficult to compare services with equivalent competitors' services, or to present the differences in quotes and features to customers in a single visit.

[0048] 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.

[0049] In this invention, the server includes: means for recording product service information, proposal examples for past orders, and which service combinations have been successful and increased sales; means for proposing optimal plans and service combinations tailored to industry and customer needs; means for using natural language processing technology to quickly and collectively provide proposal documents, proposal materials, and proposed fees; means for a user to log in to the system and input information for creating a proposal; means for the server to receive the input information and retrieve related data from a database; means for the server to analyze the retrieved data and generate optimal plans and service combinations; means for the server to display the generated proposal to the user; means for the user to review the proposal and modify it as necessary; and means for the user to finalize the proposal and send it to the customer. This enables the server to quickly propose plans and service combinations that best suit customer needs, improving proposal efficiency. It also enables the server to compare services with similar services from competitors and propose differences in quotes and features to customers in a single visit.

[0050] "Product service information" is detailed information about the products and services offered.

[0051] "Examples of proposals for previously accepted projects" are records of specific plans and combinations of services proposed for previously accepted projects.

[0052] "A means of recording which combinations of services have been successfully proposed and have resulted in increased sales" is a function that records in a database the combinations of services that have been successfully proposed and the resulting increase in sales.

[0053] "A means of proposing optimal plans and service combinations tailored to the industry and customer needs" is a function that automatically generates and proposes optimal service combinations based on the customer's industry and specific needs.

[0054] "A means of using natural language processing technology to provide proposal documents, materials, pricing, etc. in a timely manner at the time of proposal" is a function that uses natural language processing technology to quickly generate the documents, materials, and pricing information required for a proposal and provide them all at once.

[0055] The "means for a user to log in to the system and input information for creating a proposal" refers to an interface that allows a user to access the system and input information necessary to create a proposal.

[0056] "Means for the server to receive the input information and retrieve related data from the database" refers to the function by which the server searches for and retrieves related data from the database based on the information entered by the user.

[0057] "Means for analyzing data acquired by the server and generating optimal plans and service combinations" refers to a function that analyzes data acquired by the server and automatically generates optimal service combinations.

[0058] The "means for displaying the proposal generated by the server to the user" is a function for transmitting the content of the proposal generated by the server to the user's terminal and displaying it.

[0059] The "means for the user to check the proposal and make corrections as necessary" is an interface that allows the user to check the displayed proposal content and make corrections as necessary.

[0060] The "means for the user to finalize the proposal and transmit it to the client" is a function that allows the user to finalize the content of the proposal and transmit the proposal to the client.

[0061] This system is equipped with a database that records product service information, proposal examples from past orders, and which service combinations have been successful and led to increased sales. This system has the function of proposing optimal plans and service combinations tailored to the type of industry and customer needs. Furthermore, when making proposals, natural language processing technology is used to provide timely proposals that include proposal documents, proposal materials, and proposed fees all at once.

[0062] Hardware and software used

[0063] Hardware

[0064] Database server (e.g. MySQL server)

[0065] Application server (e.g., Apache (registered trademark))

[0066] User device (e.g. PC, tablet)

[0067] software

[0068] Database Management System (DBMS)

[0069] Data Analysis Tools

[0070] Natural Language Processing Tools

[0071] Specific operation of the system

[0072] A user logs into the system

[0073] The user accesses the system's login screen from their terminal and enters their user ID and password to log in. The server checks the entered authentication information against the database, and if authentication is successful, transitions the user to the dashboard screen.

[0074] A user enters information to create a new suggestion

[0075] The user clicks the "Create a new proposal" button on the dashboard screen to move to the proposal creation screen, where they enter information such as the customer's industry, needs, and budget.

[0076] The server receives the entered information and retrieves the relevant data from the database.

[0077] The server takes the information entered by the user and queries a database, which returns data on past successes and sales growth.

[0078] The server analyzes the acquired data and generates the optimal plan and service combination.

[0079] The server analyzes the acquired data using publicly known libraries, specifically generating a combination of plans and services that are best suited to the customer's industry and needs based on past success stories and sales growth.

[0080] Displaying server-generated suggestions to the user

[0081] The server sends the generated plan and service combination to the user's device, which displays the proposed content on its screen.

[0082] The user reviews the proposal and makes any necessary corrections.

[0083] The user checks the displayed suggestions and makes corrections as necessary. Once corrections are complete, the user clicks the "Confirm Proposal" button.

[0084] The user finalizes the proposal and sends it to the customer

[0085] Once the user confirms the proposal, the server saves it in a database and sends it to the customer, who can receive it via email or a dedicated portal.

[0086] Examples of concrete examples and prompts

[0087] As a concrete example, consider the following scenario:

[0088] Suppose a user wants to propose the optimal service plan for a new customer. The user inputs the customer's industry and needs into the system. The server retrieves data on past successes and sales growth from the database and generates the optimal plan and combination of services. The generated plan is then proposed to the user.

[0089] Example prompt sentence:

[0090] "We would like to propose the optimal service plan for a new client. The client's industry is manufacturing, and their needs are cost reduction and efficiency. Please propose the optimal plan and combination of services based on past successes and sales growth."

[0091] By inputting this prompt into a generative AI model, the system can suggest the optimal plan and combination of services.

[0092] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0093] Step 1:

[0094] A user logs in to the system.

[0095] Input: User ID and password

[0096] Output: Authentication result (success or failure)

[0097] Specific operation: The user accesses the system's login screen from their terminal and enters their user ID and password. The server compares the entered authentication information with the database, and if authentication is successful, the user is transferred to the dashboard screen. If authentication fails, an error message is displayed.

[0098] Step 2:

[0099] A user enters information to create a new proposal.

[0100] Input: Information about the customer's industry, needs, budget, etc.

[0101] Output: Confirmation screen of the entered information

[0102] Specific operation: The user clicks the "Create a new proposal" button on the dashboard screen to move to the proposal creation screen. On the proposal creation screen, the user enters information such as the customer's industry, needs, and budget, and clicks the "Next" button. The server temporarily saves the entered information and displays a confirmation screen.

[0103] Step 3:

[0104] The server receives the entered information and retrieves relevant data from a database.

[0105] Input: Customer information entered by the user

[0106] Output: Relevant data (past success stories, sales data, etc.)

[0107] What it does: The server queries the database based on the information the user enters. The database returns data about past successes and sales growth. The server temporarily stores the retrieved data.

[0108] Step 4:

[0109] The server analyzes the data it acquires and generates the optimal plan and combination of services.

[0110] Input: Relevant data (past success stories, sales data, etc.)

[0111] Output: Best combination of plans and services

[0112] Specific operation: The server analyzes the acquired data using publicly known libraries. Specifically, it generates a combination of plans and services that are best suited to the customer's industry and needs based on past successes and sales growth. The generated plans are saved on the server.

[0113] Step 5:

[0114] The server generates suggestions and displays them to the user.

[0115] Input: Best plan and service combination

[0116] Output: Proposal display screen

[0117] Specific operation: The server sends the generated plan and service combination to the user's device, which then displays the received proposal on its screen.

[0118] Step 6:

[0119] The user reviews the proposal and corrects it if necessary.

[0120] Input: User modifications

[0121] Output: Revised proposal

[0122] Specific operation: The user checks the displayed proposal and makes any necessary corrections. Once the corrections are complete, the user clicks the "Confirm Proposal" button. The server temporarily saves the corrections and generates the final proposal.

[0123] Step 7:

[0124] The user finalizes the proposal and sends it to the customer.

[0125] Input: Final proposal

[0126] Output: Proposal sent to customer

[0127] Specific operation: When the user clicks the "Confirm Proposal" button, the server saves the proposal in the database and sends it to the customer, who can receive it via email or a dedicated portal.

[0128] (Application example 1)

[0129] Next, a description will be given of Application Example 1 of Embodiment 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."

[0130] Conventional product proposal systems had difficulty proposing optimal plans and service combinations based on past success stories and sales growth. Furthermore, they were unable to generate proposal documents and materials in a timely manner using natural language processing technology, preventing quick and effective proposals to customers. Furthermore, they lacked the functionality to suggest optimal product and service combinations based on past purchase history and success stories of other users, making it difficult to improve customer satisfaction.

[0131] 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.

[0132] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for proposing optimal product and service combinations based on past purchase history and success stories of other users, and means for displaying optimal services and proposal contents. This enables prompt and effective proposals to customers, improving customer satisfaction.

[0133] "Product service information" is detailed information about the products and services offered.

[0134] "Example proposals for projects accepted in the past" are examples of proposals created based on projects accepted in the past.

[0135] "A means of recording which service combinations have been successful in proposing and resulting in increased sales" is a means of recording successful service combinations and the resulting increase in sales.

[0136] "Means for proposing optimal plans and service combinations tailored to industry and customer needs" refers to means for proposing optimal plans and service combinations based on specific industry and customer needs.

[0137] "A means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. at the time of proposal" refers to a means for rapidly generating and proposing proposal documents, proposal materials, proposed fees, etc. at once using natural language processing technology.

[0138] "A means for proposing optimal combinations of products and services based on past purchase history and success stories of other users" refers to a means for proposing optimal combinations of products and services by referring to past purchase history and success stories of other users.

[0139] The "means for displaying the most suitable service and its proposed content" is a means for displaying the most suitable service and its proposed content to the user.

[0140] The system for implementing this invention has a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful and increased sales. Based on this information, the server proposes optimal plans and service combinations tailored to the type of industry and customer needs.

[0141] The server uses natural language processing technology to generate proposal documents, proposal materials, proposed fees, etc. in a timely manner, and provides users with prompt and effective proposals. Specifically, the server uses publicly known software to manage the database, and uses a generative AI model for natural language processing.

[0142] The server also suggests optimal combinations of products and services based on past purchase history and success stories of other users, allowing users to easily find the products and services that best suit their needs. Furthermore, the system also has a function to display the optimal services and suggestions on the user's device.

[0143] For example, if a user is searching for a product in the "Logistics" category, the server will suggest a service called "Premium Delivery" and display the suggestion, "Premium Delivery is highly recommended for faster shipping." In this way, it is possible to suggest the optimal combination of products and services to the user.

[0144] An example of a prompt is as follows:

[0145] "When a user searches for a product in the 'Logistics' category, show them the best services and suggestions."

[0146] This system allows users to quickly and effectively receive recommendations for optimal products and services, which is expected to improve customer satisfaction.

[0147] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0148] Step 1:

[0149] The server records in a database service information for the merchandise, examples of proposals for past orders, and which services were used in combination with the proposals to increase sales.

[0150] Input: Product service information, proposal examples for past orders, successful service combinations and sales data

[0151] Output: Information recorded in the database

[0152] Specific operation: The server receives service information for the product, past proposal examples, successful service combinations and sales data, and inserts this information into the database.

[0153] Step 2:

[0154] The server will propose the optimal combination of plans and services based on the industry and customer needs.

[0155] Input: Industry information, customer needs

[0156] Output: Best combination of plans and services

[0157] Specific operation: The server extracts data related to industry information and customer needs from the database and generates optimal plans and service combinations based on past success stories.

[0158] Step 3:

[0159] The server uses natural language processing technology to generate proposal documents, proposal materials, proposed prices, etc. in a timely manner all at once.

[0160] Input: Best plan and service combination

[0161] Output: Proposal document, proposal materials, proposal fee

[0162] Specific operation: The server uses the generative AI model to generate proposal documents, proposal materials, and proposed prices based on the optimal plan and service combination.

[0163] Step 4:

[0164] The server suggests optimal combinations of products and services based on past purchase history and success stories of other users.

[0165] Input: past purchase history, success stories of other users

[0166] Output: Optimal product and service combinations

[0167] Specific operation: The server extracts past purchase history and success stories of other users from the database and generates the optimal combination of products and services based on this data.

[0168] Step 5:

[0169] The server displays the optimal services and suggestions on the user's device.

[0170] Input: Best service and proposal

[0171] Output: The suggestions displayed on the user's device

[0172] Specific operation: The server sends the generated optimal service and proposal content to the user's terminal, which displays it.

[0173] Step 6:

[0174] The user checks the displayed proposals and makes selections or purchases as necessary.

[0175] Input: User choices and purchase intentions

[0176] Output: Selected products and services, purchase information

[0177] Specific operation: The user checks the offers displayed on the terminal and makes a selection or purchase. The server receives the user's selection and purchase information and records it in the database.

[0178] Example 2

[0179] Next, a description will be given of Example 2 of Form 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."

[0180] Traditional proposal work required a lot of time and effort to create proposal documents and materials, resulting in inefficiency. It was also difficult to maintain a consistent quality in the proposal, which could lead to a lower success rate. Furthermore, it was difficult to compare proposals with competitors and propose optimal plans tailored to customer needs.

[0181] The specific processing by the specific 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 recording service information of merchandise, proposal examples of past orders, and which service combinations have been successful in proposals and increased sales; means for proposing optimal plans and service combinations tailored to the type of industry and customer needs; means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc.; means for the user to input information required for the proposal; means for the server to receive the input information and analyze it using natural language processing technology; means for the server to send prompt sentences to the generative AI model based on the analysis results; means for the generative AI model to generate proposal documents and materials; means for the server to send the generated documents and materials to the user's terminal; and means for the user to review and edit the documents and materials. This enables the efficiency and quality of proposal work to be improved.

[0182] "Product service information" is detailed information about the products and services offered.

[0183] "Examples of proposals for previously accepted projects" are examples of proposals based on previously accepted projects.

[0184] The "means for recording whether proposals are successful and sales are increasing" refers to a method or device for recording the success rate of proposals and increases in sales.

[0185] "Means for proposing optimal plans and service combinations tailored to the needs of each industry and customer" refers to a method or device for proposing optimal plans and service combinations tailored to specific industry types and customer requests.

[0186] "Natural language processing technology" is a technology that uses computers to understand and analyze human language.

[0187] "Means for proposing proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner" refers to a method or device for quickly providing documents, materials, and fee information required for a proposal all at once.

[0188] The "means for the user to input information necessary for the proposal" refers to a method or device that allows the user to input information necessary for creating a proposal.

[0189] "Means for the server to receive input information and analyze it using natural language processing technology" refers to a method or device for the server to receive input information from a user and analyze that information using natural language processing technology.

[0190] "Means for the server to send a prompt sentence to the generative AI model based on the analysis results" refers to a method or device for the server to send a prompt sentence to the generative AI model based on the analysis results.

[0191] "Means for a generative AI model to generate proposal documents or materials" refers to a method or device for a generative AI model to generate proposal documents or materials based on a prompt sentence.

[0192] The "means for transmitting documents or materials generated by the server to the user's terminal" refers to a method or device for transmitting documents or materials generated by the server to the user's terminal.

[0193] "Means for users to check and edit documents and materials" refers to methods and devices that allow users to check the generated documents and materials and edit them as necessary.

[0194] The present invention provides a system for improving the efficiency of proposal work and quickly generating high-quality proposal documents and materials. Specific embodiments of this system will be described below.

[0195] First, the user enters the information necessary for the proposal. The user enters basic information such as the project name, purpose, budget, and period into an input form on a web browser. For example, the user might enter information such as "Next Generation AI Development Project," "AI Technology Research and Development," "5 million yen," and "6 months."

[0196] Next, the server receives the information entered by the user and analyzes it using natural language processing technology. The server uses publicly known libraries to analyze the text data and extract the information necessary for the proposal. For example, it extracts the "project name" from the phrase "next-generation AI development project" and the "purpose" from the phrase "research and development of AI technology."

[0197] The server then sends a prompt to the generative AI model based on the analysis results. The server creates a prompt based on the analysis results and sends it to the generative AI model (for example, OpenAI's (registered trademark) GPT-3 (registered trademark)). An example of a prompt would be, "Please create a proposal for a new project. The project name is 'Next Generation AI Development Project' and the purpose is research and development of AI technology. The budget is 5 million yen and the duration is 6 months."

[0198] The generative AI model generates proposal documents and materials based on prompts sent from the server. The generated documents include the project's objectives, budget, period, detailed plans, and more. For example, it generates a document with the following content: "Next-generation AI development project proposal," "Objective: Research and development of AI technology," "Budget: 5 million yen," "Period: 6 months," "Details: Research and implement the latest AI algorithms and develop a prototype for practical use."

[0199] The server then sends the generated documents and materials to the user's device, where the user can view the documents through a web browser or a dedicated application.

[0200] Finally, the user reviews the generated proposal document and materials and edits them as necessary. The user can modify the content of the document or enter additional information. Finally, the user saves and submits the completed proposal.

[0201] This system will improve the efficiency and quality of proposal work, allowing users to quickly create high-quality proposal documents and increase the success rate of proposals.

[0202] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0203] Step 1:

[0204] The user enters the information required for the proposal. The user enters basic information such as the project name, purpose, budget, and period into an input form on a web browser. For example, the user enters information such as "Next Generation AI Development Project," "AI Technology Research and Development," "5 million yen," and "6 months." Once the input is complete, the user clicks the "Submit" button. The input data is sent to the server.

[0205] Step 2:

[0206] The server receives the input information and analyzes it using natural language processing technology. The server uses publicly known libraries to analyze the text data and extract the information necessary for the proposal. For example, it extracts the "project name" from the phrase "next-generation AI development project" and the "purpose" from the phrase "research and development of AI technology." The input data undergoes text analysis and is output as extracted keywords and phrases.

[0207] Step 3:

[0208] The server sends a prompt to the generative AI model based on the analysis results. The server creates a prompt based on the analysis results and sends it to the generative AI model (for example, OpenAI's GPT-3). An example of a prompt might be, "Please create a proposal for a new project. The project name is 'Next Generation AI Development Project' and the purpose is research and development of AI technology. The budget is 5 million yen and the duration is 6 months." The analysis results are output as a prompt.

[0209] Step 4:

[0210] The generative AI model generates proposal documents and materials. The generative AI model generates proposal documents and materials based on prompt text sent from the server. The generated document includes the project's objectives, budget, period, detailed plan, etc. For example, it generates a document with the following content: "Next-generation AI development project proposal," "Objective: Research and development of AI technology," "Budget: 5 million yen," "Period: 6 months," "Details: Research and implement the latest AI algorithms and develop a prototype for practical use." The prompt text is output as the proposal document.

[0211] Step 5:

[0212] The server sends the generated documents and materials to the user's device. The server sends the proposed documents and materials received from the generative AI model to the user's device. The user can check the generated documents through a web browser or dedicated application. The generated documents are sent to the user's device.

[0213] Step 6:

[0214] The user reviews and edits the documents and materials. The user reviews the proposal documents and materials sent from the server and edits them as necessary. For example, they can modify the content of the document or enter additional information. Finally, the user saves and submits the completed proposal. The edited documents are output as the final proposal.

[0215] (Application example 2)

[0216] Next, a description will be given of Application Example 2 of Form 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."

[0217] With conventional proposal systems, proposing the optimal plan based on product service information and past proposal examples required a lot of manual work, resulting in inefficiency. It was also difficult to create proposal documents, proposal materials, and proposed prices all at once in a timely manner, making it difficult to provide prompt and accurate proposals to customers. Furthermore, it was difficult to compare with competitors or propose differences in quotes with other companies in a single visit. A new system to solve these issues was needed.

[0218] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0219] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., at the time of proposal, means for inputting product information and customer information and automatically generating proposal documents using natural language processing technology, means for automatically generating proposal materials using a generative AI model, and means for automatically calculating the prices of the proposed products and reflecting them in the proposal documents.This improves the efficiency of proposal work and enables quick and accurate proposals.

[0220] "Product service information" is detailed information about the products and services offered.

[0221] "Example proposals for previously accepted projects" are examples of proposals created based on previously accepted projects.

[0222] "Means for recording which combinations of services have been successfully proposed and have resulted in increased sales" refers to a method or device for recording combinations of services that have been successfully proposed and the resulting increase in sales.

[0223] "Means for proposing optimal plans and service combinations tailored to the needs of each industry and customer" refers to a method or device for proposing optimal plans and service combinations tailored to specific industry types and customer requests.

[0224] "A means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. at the time of proposal" refers to a method or device for quickly creating proposal documents, proposal materials, proposed fees, etc. at the time of proposal using natural language processing technology.

[0225] "Means for inputting product information and customer information and automatically generating a proposal document using natural language processing technology" refers to a method or device for inputting product information and customer information and automatically generating a proposal document based on that information using natural language processing technology.

[0226] A "means for automatically generating proposal materials using a generative AI model" is a method or device for automatically generating proposal materials using a generative AI model.

[0227] "Means for automatically calculating the price of the proposed product and reflecting it in the proposal document" refers to a method or device for automatically calculating the price of the proposed product and reflecting the result in the proposal document.

[0228] A system for implementing this invention includes means for recording service information on commercial products, proposal examples for past orders, and which services have been successfully proposed and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to propose proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner when making proposals, means for inputting product information and customer information and automatically generating proposal documents using natural language processing technology, means for automatically generating proposal materials using a generative AI model, and means for automatically calculating the price of the proposed product and reflecting it in the proposal document.

[0229] Hardware and software used

[0230] Hardware

[0231] server

[0232] Smartphone

[0233] software

[0234] Python (registered trademark)

[0235] Natural Language Processing Library

[0236] GPT-3 (generative AI model)

[0237] Data processing and calculation

[0238] The server first records the service information for the product and proposal examples from past orders in a database. Next, it runs an algorithm to propose the optimal combination of plans and services based on the industry and customer needs. When making a proposal, it uses natural language processing technology to create proposal documents, proposal materials, and proposed prices all at once in a timely manner.

[0239] Specifically, when a user inputs product and customer information, the server analyzes this information using natural language processing technology and automatically generates a proposal document.The proposal document is automatically generated using the generative AI model GPT-3, and the price of the proposed product is automatically calculated and reflected in the proposal document.

[0240] Specific examples

[0241] For example, a user enters the following product and customer information:

[0242] Product information: "Product name: Smartphone, Price: 50,000 yen, Features: High-performance camera, Long-lasting battery"

[0243] Customer information: "Customer name: Taro Tanaka, Address: Tokyo, Purchase history: Smartwatch, Wireless earphones"

[0244] With this information, the server generates the following prompt:

[0245] Product information: Smartphone, Price: 50,000 yen, Features: High-performance camera, Long-lasting battery

[0246] Customer: Taro Tanaka, Tokyo, Smartwatch, Wireless Earphones

[0247] By inputting this prompt into GPT-3, a proposal document is automatically generated. The generated proposal document includes detailed product information, the proposal to the customer, pricing information, etc. This allows users to make quick and accurate proposals.

[0248] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0249] Step 1:

[0250] The user enters product and customer information.

[0251] Input: Product information (e.g., "Product name: Smartphone, Price: 50,000 yen, Features: High-performance camera, Long-lasting battery"), Customer information (e.g., "Customer name: Taro Tanaka, Address: Tokyo, Purchase history: Smartwatch, Wireless earphones")

[0252] Output: The entered product information and customer information is sent to the server.

[0253] Step 2:

[0254] The product information and customer information received by the server is analyzed using natural language processing technology.

[0255] Input: Product and customer information submitted by the user

[0256] Data processing: Perform text analysis.

[0257] Output: Parsed text data

[0258] Step 3:

[0259] Based on the analyzed text data, the server automatically generates a proposal document using a generative AI model (GPT-3).

[0260] Input: Parsed text data

[0261] Data calculation: Input a prompt sentence into GPT-3 and generate a proposed document.

[0262] Output: Generated proposal document

[0263] Step 4:

[0264] The server automatically generates proposal materials using a generative AI model.

[0265] Input: Parsed text data

[0266] Data calculation: Input a prompt sentence into GPT-3 and generate a proposal document.

[0267] Output: Generated proposal

[0268] Step 5:

[0269] The server automatically calculates the price of the proposed product and reflects it in the proposal document.

[0270] Input: Product information (price)

[0271] Data calculation: Calculate the price based on the product price information and add it to the proposal document.

[0272] Output: Proposal document with pricing information reflected

[0273] Step 6:

[0274] The server sends the generated proposal document and proposal materials to the user.

[0275] Input: Generated proposal documents and proposal materials

[0276] Output: Proposal document and proposal materials sent to the user's device

[0277] Step 7:

[0278] The user checks the received proposal document and proposal materials and makes a proposal to the customer.

[0279] Input: Proposal document and proposal materials sent from the server

[0280] Output: Proposal to the customer

[0281] Example 3

[0282] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0283] Conventional proposal systems mainly based their proposals on product service information and past proposal examples, without sufficient comparison with competitors' services. This made it difficult to clearly demonstrate the superiority of a company's services to customers. Furthermore, creating proposal documents and materials took time, making it difficult to make timely proposals. This resulted in problems such as being unable to respond quickly to customer needs and losing competitiveness.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0285] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry type and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for storing competitor service information in a database and comparing it with the company's own services, and means for generating a report showing the advantages of the company's services based on the comparison results and sending it to the user. This makes it possible to clearly demonstrate the advantages of the company's services compared with those of competitors, enabling quick and effective proposals to be made to customers.

[0286] "Product service information" refers to detailed information about the products and services being offered.

[0287] "Examples of proposals for previously accepted projects" refers to examples of specific plans and services proposed for previously accepted projects.

[0288] "Recording means" refers to a method or device for storing information for future reference.

[0289] "Means for proposing optimal plans and service combinations tailored to specific industries and customer needs" refers to methods and devices for selecting and proposing optimal services and plans tailored to specific industries and customer requests.

[0290] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[0291] "Means for proposing proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner" refers to a method or device for quickly providing documents, materials, and fee information required for a proposal all at once.

[0292] "Competitor service information" refers to detailed information about products and services offered by other competing companies.

[0293] "Keeping in a database" means storing information in a database so that it can be accessed as needed.

[0294] "Means for comparing with one's own services" refers to methods or devices for comparing the services provided by one's own company with the services of competitors.

[0295] "Means for generating a report showing the superiority of one's own services based on the results of the comparison" refers to a method or device for using the results of the comparison to create a report that clearly shows the advantages of one's own services over those of competitors.

[0296] "Means for sending to user" refers to a method or device for delivering generated reports or information to a user.

[0297] This invention is a system that records service information for products, proposal examples from past orders, and which service combinations have been successful and increased sales, and then proposes optimal plans and service combinations tailored to the industry and customer needs. Furthermore, when making proposals, natural language processing technology is used to provide proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner.

[0298] Furthermore, the system has the function of storing information on competitors' services in a database and comparing it with the company's own services, making it possible to clearly demonstrate to customers the superiority of the company's services.

[0299] Hardware and software used

[0300] Hardware: Server (e.g. general-purpose server)

[0301] Software: Database management systems (e.g., MySQL), comparison algorithms (e.g., custom algorithms implemented in Python), natural language processing techniques (e.g., generative AI models)

[0302] Specific operation of the system

[0303] Server Operation

[0304] 1. Data recording and acquisition

[0305] The server records in a database service information for the products and proposal examples from past orders, as well as which services have been successfully combined with the proposals and resulted in increased sales.

[0306] Upon receiving a request from a user, the server retrieves the necessary information from the database.

[0307] 2. Proposing optimal plans and service combinations

[0308] The server proposes the optimal plan and service combination based on industry and customer needs, including analysis based on past success stories and sales data.

[0309] 3. Proposals using natural language processing technology

[0310] The server uses natural language processing technology to generate proposal documents, proposal materials, proposed fees, etc. all at once, allowing users to quickly submit proposals.

[0311] 4. Comparison with competitors

[0312] The server stores information about competitors' services in a database, compares it with its own service, and generates a report showing the superiority of its own service based on the comparison results.

[0313] 5. Submitting the report

[0314] The server sends the generated report to the user's device, where the user can check the report and understand the advantages of their company's service.

[0315] Specific examples

[0316] For example, suppose a user wants to compare their company's cloud storage service with a competitor's. The user sends a request from their device saying, "I want to compare cloud storage services." The server retrieves the following information from the database:

[0317] In-house cloud storage service: 1TB storage capacity, 500 yen per month, AES-256 bit encryption

[0318] Competitor A's cloud storage service: 500GB storage, ¥600 per month, AES-128 bit encryption

[0319] Competitor B's cloud storage service: 2TB storage capacity, 1000 yen per month, AES-256 bit encryption

[0320] The server compares this information and concludes that its service is "better than competitor A in terms of storage capacity and security features, but better than competitor B in terms of price." The server generates a report based on this conclusion and sends it to the user.

[0321] Prompt Sentence Examples

[0322] "Compare your cloud storage service with your competitors' cloud storage services. Compare storage capacity, price, and security features."

[0323] In this way, the server can clearly demonstrate the superiority of its service to the user. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0324] Step 1:

[0325] User submits a comparison request

[0326] Input: The user uses the device to input information about the services they want to compare and submit a comparison request. For example, they input a request such as "I want to compare cloud storage services."

[0327] Output: The device sends a request to the server.

[0328] Step 2:

[0329] The server receives the request and retrieves the information from the database.

[0330] Input: The server receives a request from the user, which includes the type of service to be compared and the specific comparison criteria.

[0331] Output: The server uses a database management system (e.g., MySQL) to retrieve service information from a database about its own and its competitors, such as cloud storage services, including storage capacity, pricing, and security features.

[0332] Step 3:

[0333] The server compares the information

[0334] Input: The server receives as input the service information of the company and its competitors retrieved from the database.

[0335] Output: The server uses a comparison algorithm (e.g., a custom algorithm implemented in Python) to compare your service with your competitors' services, specifically rating how superior your service is in each dimension (storage capacity, price, security features, etc.).

[0336] Step 4:

[0337] The server generates the comparison results

[0338] Input: The server receives the result of the comparison algorithm as input.

[0339] Output: The server generates a report to the user showing the advantages of the company's service. The report clearly shows the strengths of the company's service in each comparison category and its advantages over competitors.

[0340] Step 5:

[0341] The server sends the results to the user

[0342] Input: The server receives the generated report as input.

[0343] Output: The server sends the generated report to the user's device, where the user can check the report and understand the advantages of their company's service.

[0344] Adding specific actions

[0345] For example, when a user sends a request to "compare cloud storage services," the server retrieves the following information from the database:

[0346] In-house cloud storage service: 1TB storage capacity, 500 yen per month, AES-256 bit encryption

[0347] Competitor A's cloud storage service: 500GB storage, ¥600 per month, AES-128 bit encryption

[0348] Competitor B's cloud storage service: 2TB storage capacity, 1000 yen per month, AES-256 bit encryption

[0349] The server compares this information and concludes that its service is "better than competitor A in terms of storage capacity and security features, but better than competitor B in terms of price." The server generates a report based on this conclusion and sends it to the user.

[0350] (Application example 3)

[0351] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0352] The previous system recorded product service information and past proposal examples, and was able to propose optimal plans tailored to each industry and customer needs. However, it was not easy to compare plans with similar services offered by competitors. It was also difficult to clearly demonstrate the company's advantages when proposing plans, making it difficult to effectively appeal to customers. Furthermore, the system lacked the functionality to provide links that allowed users to easily purchase products and services that interested them.

[0353] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0354] In this invention, the server includes: means for recording product service information, proposal examples for past orders, and which service combinations have been successful and increased sales; means for proposing optimal plans and service combinations tailored to the type of industry and customer needs; means for using natural language processing technology to provide proposal documents, proposal materials, proposed fees, etc. in a timely manner when making proposals; means for storing information on equivalent products and services from other companies in a database and comparing them with the company's own products and services; means for presenting the comparison results to users and emphasizing the company's advantages; and means for providing links to easily purchase products and services that the user finds interesting. This makes it easy to compare products and services from competitors and clearly demonstrate the company's advantages. Furthermore, providing links to easily purchase products and services that the user finds interesting allows for effective appeal to customers.

[0355] "Product service information" refers to detailed information about the products and services being offered.

[0356] "Examples of proposals for previously accepted projects" are records of specific content and methods proposed in previously accepted projects.

[0357] "Means for recording which services have been successfully proposed and have increased sales" refers to a method or device for recording success stories and increased sales when multiple services are proposed in combination.

[0358] "Means for proposing optimal plans and service combinations tailored to specific industries and customer needs" refers to a method or device for proposing optimal plans and service combinations tailored to specific industries and customer requests.

[0359] "A means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. at the time of proposal" refers to a method or device for quickly generating and presenting proposal documents, materials, fees, etc. at the time of proposal using natural language processing technology.

[0360] "Means for storing information on comparable products and services of other companies in a database and comparing them with one's own products and services" refers to a method or device for storing information on similar products and services of competitors in a database and using that information to compare one's own products and services.

[0361] "Means for presenting the results of the comparison to users and emphasizing the advantages of your company" refers to methods and devices for presenting the results of the comparison to users and emphasizing the superiority of your company's products and services.

[0362] "Means for providing links that allow users to easily purchase products or services that interest them" refers to a method or device that provides links that allow users to easily purchase products or services that interest them.

[0363] The system for implementing this invention includes a server, a user terminal, and a database. The server has a means for recording service information for commercial products, proposal examples for past orders, and which service combinations have been successful and led to increased sales. The system also includes a means for proposing optimal plans and service combinations tailored to the type of business and customer needs.

[0364] The server uses natural language processing technology to generate and present proposal documents, proposal materials, proposed fees, etc. in a timely manner at the time of proposal submission, allowing users to submit proposals quickly and efficiently.

[0365] Furthermore, the server stores information on similar products and services from other companies in a database, and has a means of comparing its own products and services with those of other companies. The results of the comparison are presented to the user, allowing them to emphasize their own company's advantages. This allows users to easily compare their products with those of their competitors and appeal to customers about their company's strengths.

[0366] The user terminal receives the information provided by the server and displays it to the user. It also includes a means for providing links that allow the user to easily purchase products and services that interest them, allowing the user to quickly purchase the products and services that interest them.

[0367] For example, a user opens a "Service Comparison Assistant" app and adds their company's new service, "Premium Delivery." The app then compares it with similar services from competitors and displays the differences in price and features. The user confirms that their service is cheaper than their competitors' and creates materials to highlight its advantages to customers.

[0368] An example of a prompt is as follows:

[0369] "Add a new service called 'Premium Delivery' and compare it with a similar service from a competitor. Show us the comparison and tell us how you would highlight the advantages of your service."

[0370] The system is built using a database management system and a Python web framework. The database stores product service information, past proposal examples, and competitor service information, and the server processes and calculates the data based on this information. User devices include smartphones and tablets.

[0371] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0372] Step 1:

[0373] The user enters new service information.

[0374] Input: The user enters information for a new service called "Premium Shipping."

[0375] Specific operation: Enter information such as the service name, description, and price on the application screen of the user's terminal.

[0376] Output: The entered service information is sent to the server.

[0377] Step 2:

[0378] The server stores the entered service information in a database.

[0379] Input: New service information sent from the user terminal.

[0380] Specific operation: The server analyzes the received service information and stores it in a database.

[0381] Output: The new service information is added to the database.

[0382] Step 3:

[0383] The server retrieves information on comparable services from competitors from a database.

[0384] Input: A search query based on the new service information.

[0385] Specific operation: The server searches the service information of competitors in the database and obtains information on comparable services.

[0386] Output: Obtained competitor service information.

[0387] Step 4:

[0388] The server compares its services with those of its competitors.

[0389] Input: Your company's new service information and competitors' equivalent service information.

[0390] What it does: The server compares its services with those of competitors on items such as price, features, and functionality.

[0391] Output: The comparison results are generated.

[0392] Step 5:

[0393] The server transmits the comparison result to the user terminal.

[0394] Input: The generated comparison results.

[0395] Specific operation: The server converts the comparison result into a data format suitable for transmission to the user terminal, and transmits it.

[0396] Output: The comparison results are displayed on the user's terminal.

[0397] Step 6:

[0398] The user checks the comparison results and, if necessary, creates materials to present to the customer.

[0399] Input: The comparison results displayed on the user's terminal.

[0400] Specific operation: The user checks the comparison results and creates proposals or presentation materials as necessary.

[0401] Output: Materials to present to the customer.

[0402] Step 7:

[0403] Provide links that allow users to easily purchase products or services that interest them.

[0404] Input: Information about the products or services in which the user is interested.

[0405] Specific operation: The user terminal generates a purchase link for the product or service of interest and provides it to the user.

[0406] Output: A purchase link is displayed on the user's device.

[0407] The above is the flow of processing of the program of the system that realizes the application example.

[0408] 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.

[0409] "Example 1"

[0410] One embodiment of the present invention is a system incorporating an emotion engine. This system recognizes a user's emotions and adjusts suggestions based on those emotions. Specifically, during a dialogue with the user, the emotion engine analyzes the user's emotions from their tone of voice, facial expressions, and word choice. If the emotion is positive, the system strengthens the suggestions, and if it is negative, the system softens the suggestions. For example, if the user expresses joy, the system makes more aggressive suggestions. Conversely, if the user expresses dissatisfaction or confusion, the system tone down the suggestions and suggests solutions to the user's problems and concerns.

[0411] "Example 2"

[0412] The emotion engine can also track changes in a user's emotions in real time and dynamically adjust the content of its suggestions in response to those changes. For example, if the system senses that a user is initially very interested but gradually loses interest, it can change the direction of its suggestions and make new suggestions to recapture the user's interest. In this way, a system that incorporates an emotion engine can sensitively detect a user's emotions and make optimal suggestions based on those emotions.

[0413] "Example 3"

[0414] Furthermore, the emotion engine can learn a user's emotional patterns and predict future emotional changes based on those patterns. For example, if the system learns that a particular user always expresses anger when receiving a particular suggestion, it can avoid making that suggestion to that user or change the way it makes the suggestion. In this way, a system combined with an emotion engine can understand and adapt to the user's emotions, thereby making more effective suggestions.

[0415] The processing flow of each embodiment will be described below.

[0416] "Example 1"

[0417] Step 1: A user interaction is initiated.

[0418] Step 2: The emotion engine analyzes the user's emotions based on their tone of voice, facial expressions, and choice of words.

[0419] Step 3: Based on the analysis results, if the user's sentiment is positive, the suggestion is strengthened, and if it is negative, the suggestion is softened.

[0420] "Example 2"

[0421] Step 1: A user interaction is initiated.

[0422] Step 2: The emotion engine tracks the user's emotional changes in real time.

[0423] Step 3: Dynamically adjust your offers as sentiment changes.

[0424] "Example 3"

[0425] Step 1: A user interaction is initiated.

[0426] Step 2: The emotion engine learns the user's emotional patterns.

[0427] Step 3: Based on the learned patterns, predict future changes in emotion and adjust the suggestions accordingly.

[0428] Example 1

[0429] Next, a description will be given of Example 1 of Form 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."

[0430] Conventional recommendation systems made suggestions based on product service information and past success stories, but lacked the ability to adjust the content of the suggestions based on the user's emotions. This resulted in the inability to make optimal suggestions based on the user's emotions, which led to a low success rate for the suggestions. Furthermore, the system lacked the ability to reflect user feedback in real time and readjust the content of the suggestions, making it difficult to respond quickly to user needs.

[0431] 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.

[0432] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful and increased sales; means for proposing optimal plans and service combinations tailored to industry and customer needs; means for using natural language processing technology to provide timely proposal documents, proposal materials, proposed fees, etc. all at once when making a proposal; means for analyzing user emotions and adjusting the proposal content based on those emotions; and means for receiving user feedback and readjusting the proposal content. This enables optimal proposals based on user emotions and improves the success rate of proposals. It also makes it possible to quickly readjust the proposal content by reflecting user feedback in real time.

[0433] "Product service information" refers to detailed information about the products and services being offered.

[0434] "Examples of proposals for previously accepted projects" refers to specific plans and combinations of services proposed for previously accepted projects.

[0435] "Means for recording which services are successfully combined with proposals and increase sales" refers to a means for recording successful proposal combinations and the resulting sales data.

[0436] "Means of proposing optimal plans and service combinations tailored to industry and customer needs" refers to means of making optimal proposals based on specific industry and customer requirements.

[0437] "Natural language processing technology" refers to technology for understanding and generating human language.

[0438] "A means of proposing proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner" refers to a means of quickly providing the documents, materials, and fee information required for a proposal all at once.

[0439] The term "means for analyzing the user's emotions and adjusting the content of the proposal based on the emotions" refers to a means for analyzing the user's emotions and changing the content of the proposal based on the results of the analysis.

[0440] "Means for receiving user feedback and readjusting the content of the proposal" refers to means for receiving opinions and reactions from users and readjusting the content of the proposal based on those opinions and reactions.

[0441] MODE FOR CARRYING OUT THE INVENTION

[0442] The system of the present invention is equipped with a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales. This database provides information for proposing optimal plans and service combinations tailored to the type of industry and customer needs. Specifically, it has the function of analyzing past success stories and sales growth, and proposing optimal plans and service combinations based on that.

[0443] Hardware and software used

[0444] Hardware

[0445] Server: Manages the database and generates proposals.

[0446] Terminal: Provides an interface with the user and performs sentiment analysis.

[0447] software

[0448] Database management system (DBMS): Manages product service information and past proposal examples.

[0449] Natural Language Processing Engine: Generates proposal documents, proposal materials, proposed pricing, etc.

[0450] Sentiment analysis engine: Analyzes the user's tone of voice, facial expressions, and word choice.

[0451] Specific operation of the system

[0452] 1. Accepting user input

[0453] The user inputs a question or request to the system through the terminal, for example, a prompt such as "Please propose an implementation plan for a new marketing tool."

[0454] 2. Retrieve relevant information from the database

[0455] When the server receives the user's input, it searches the database for service information, past success stories, and sales data for related products. For example, it retrieves success stories from companies that have introduced similar marketing tools in the past.

[0456] 3. Analyze user emotions with an emotion engine

[0457] The device uses an emotion engine to analyze the user's emotions during a conversation, specifically by analyzing the user's tone of voice, facial expressions, and word choice in real time to determine whether the emotion is positive or negative.

[0458] 4. Generate proposals

[0459] The server generates optimal suggestions based on the information retrieved from the database and the analysis results of the emotion engine. For example, if the user expresses positive emotions, it will make proactive suggestions including additional services and options.

[0460] 5. Present the suggestions to the user

[0461] The device then presents the generated proposal to the user, providing specific suggestions such as, "Companies that have implemented similar tools in the past have seen a 20% increase in sales. Additionally, additional support plans are available."

[0462] 6. Get user feedback

[0463] The user provides feedback on the presented suggestions, which the device receives and sends to the server, which then adjusts the suggestions or provides additional information based on the feedback.

[0464] Prompt Sentence Examples

[0465] "Please propose a plan for implementing marketing tools based on past success stories."

[0466] "Please suggest what to do if a user expresses concern."

[0467] The above is a specific embodiment for implementing the system of the present invention. This system enables optimal suggestions based on the user's emotions, improving the success rate of suggestions. It also makes it possible to quickly readjust the content of suggestions by reflecting user feedback in real time.

[0468] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0469] Step 1:

[0470] The user inputs a question or request to the system through the terminal. For example, the user inputs a prompt such as "Please propose an implementation plan for a new marketing tool." The input prompt is sent from the terminal to the server.

[0471] Step 2:

[0472] The server analyzes the received prompt and searches the database for relevant product service information, past success stories, and sales data. Specifically, it accesses the database using an SQL query to retrieve the relevant information. The retrieved information is temporarily stored on the server.

[0473] Step 3:

[0474] The device uses an emotion engine to analyze the user's emotions during a conversation. Specifically, it analyzes the user's tone of voice, facial expressions, and word choice in real time to determine whether the emotion is positive or negative. The analysis results are sent from the device to the server.

[0475] Step 4:

[0476] The server generates optimal proposal content based on information obtained from the database and the analysis results of the emotion engine. Specifically, it uses a generative AI model to generate proposal documents, proposal materials, and proposed prices that correspond to the user's emotions, while referring to past success stories and sales data. The generated proposal content is temporarily stored on the server.

[0477] Step 5:

[0478] The terminal then presents the generated proposal to the user. Specifically, it displays the proposal documents and materials on the screen and presents the proposed price in text format. For example, it makes a specific proposal such as, "Companies that have implemented a similar tool in the past have seen a 20% increase in sales. Additionally, additional support plans are available."

[0479] Step 6:

[0480] The user provides feedback on the presented proposal. The feedback is sent from the device to the server. The server analyzes the received feedback and readjusts the proposal as necessary. Specifically, it analyzes the feedback and generates new proposals using the generative AI model again. The readjusted proposals are sent back to the device and presented to the user.

[0481] The above are the specific processing steps of the program of this system.

[0482] (Application example 1)

[0483] Next, a description will be given of Application Example 1 of Embodiment 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."

[0484] Conventional product proposal systems have difficulty proposing optimal plans and service combinations tailored to customer needs, and because the proposals are uniform, they are unable to respond flexibly to the customer's emotions or circumstances. Furthermore, because the proposals are not provided in a timely manner, there is also the problem of not being able to stimulate the customer's desire to purchase. To solve these issues, a system is needed that can recognize the customer's emotions and adjust the proposal content based on them.

[0485] 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.

[0486] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for recognizing user emotions and adjusting the proposal content based on those emotions, means implemented as a smartphone application to propose optimal product and service combinations based on the user's purchase history and browsing history, and means for analyzing user emotions using an emotion engine and adjusting the proposal content. This enables flexible proposals according to the customer's emotions and situation, thereby increasing the customer's desire to purchase.

[0487] "Product service information" refers to detailed information about the products and services being offered.

[0488] "Examples of proposals for previously accepted projects" refers to specific examples of plans and services proposed for previously accepted projects.

[0489] "Means for recording which service combinations have been successful in proposing and increasing sales" refers to a means for recording successful service combinations and the resulting sales data.

[0490] "Means for proposing optimal plans and service combinations tailored to specific industries and customer needs" refers to means for proposing optimal plans and service combinations based on specific industries and customer needs.

[0491] "A means of using natural language processing technology to propose proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner at the time of proposal" refers to a means of using natural language processing technology to quickly generate and propose proposal documents, materials, fees, etc. all at once.

[0492] "Means for recognizing the user's emotions and adjusting the content of suggestions based on those emotions" refers to means for analyzing the user's emotions and appropriately adjusting the content of suggestions based on the results.

[0493] "A means implemented as a smartphone application that suggests optimal combinations of products and services based on a user's purchase history and browsing history" refers to a means that operates as a smartphone application and suggests optimal combinations of products and services based on a user's past purchase history and browsing history.

[0494] "Means for analyzing a user's emotions using an emotion engine and adjusting the content of suggestions" refers to means for analyzing a user's emotions using an emotion engine and appropriately adjusting the content of suggestions based on the results of that analysis.

[0495] The system for implementing this invention includes a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful and led to increased sales. The system has the function of proposing optimal plans and service combinations tailored to the type of industry and customer needs. Furthermore, when making a proposal, natural language processing technology can be used to generate and propose proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner.

[0496] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and adjusts the content of suggestions based on those emotions. Specifically, during a conversation with the user, the emotion engine analyzes the user's emotions from their tone of voice, facial expressions, and choice of words, and strengthens the suggestions if the emotion is positive, and softens the suggestions if the emotion is negative.

[0497] This system is implemented as a smartphone application that proposes optimal combinations of products and services based on the user's purchase and browsing history. By analyzing the user's emotions using an emotion engine and adjusting the proposals accordingly, it is possible to increase the user's purchasing motivation.

[0498] Hardware and software used

[0499] Hardware: Smartphone

[0500] Software: Python, Library A, Library B

[0501] Data processing and calculation

[0502] The server does the following:

[0503] 1. Sentiment analysis: Library A is used to analyze sentiment from user input (e.g., "I would like to know more about this product.").

[0504] 2. Proposal generation: Library B is used to generate optimal product and service proposals based on the user ID and analyzed emotions.

[0505] 3. Output: Provide the analyzed sentiment and suggestions to the user.

[0506] Specific examples

[0507] For example, if a user types "I'd like to know more about this product," the emotion engine will analyze the user's interests and proactively suggest related products and services. Similarly, if a user types "This product is a bit expensive," the emotion engine will analyze the user's dissatisfaction and suggest alternative products at a more affordable price.

[0508] Prompt Sentence Examples

[0509] If a user types, "I'd like to know more about this product," the sentiment engine will analyze the user's interests and proactively suggest related products and services.

[0510] In this way, a personalized shopping assistant application for online shopping sites can be realized that makes optimal suggestions based on the user's emotions.

[0511] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0512] Step 1:

[0513] A user launches a smartphone application and inputs a question or request regarding a product or service.

[0514] Input: User text input (e.g., "I'd like to learn more about this product.")

[0515] Output: User's text input data

[0516] Specific behavior: A user enters text into an input field in an application and presses the submit button.

[0517] Step 2:

[0518] The terminal receives the user's input and sends it to the emotion engine.

[0519] Input: User text input data

[0520] Output: Text data sent to the emotion engine

[0521] What happens: The application sends the user's input to the emotion engine's API.

[0522] Step 3:

[0523] The server analyzes the user's emotions using an emotion engine.

[0524] Input: Text data sent to the emotion engine

[0525] Output: User emotion data (e.g., interest, joy, dissatisfaction, etc.)

[0526] What it does: The emotion engine analyzes text data and identifies the user's emotions.

[0527] Step 4:

[0528] The server receives the user's emotion data and sends it to Engine A.

[0529] Input: User emotion data

[0530] Output: Emotion data sent to Engine A

[0531] Specific operation: The emotion engine sends the analysis results to the API of engine A.

[0532] Step 5:

[0533] The server uses engine A to suggest optimal combinations of products and services based on the user's purchase history and browsing history.

[0534] Input: User emotional data, purchase history, browsing history

[0535] Output: Data proposing optimal products and services

[0536] Specific operation: Engine A retrieves the user's history from the database and generates optimal suggestions based on the emotional data.

[0537] Step 6:

[0538] The server transmits the generated proposal data to the terminal.

[0539] Input: Data on optimal products and services

[0540] Output: Proposal data sent to the device

[0541] What happens: The server sends the proposal data to the application's API.

[0542] Step 7:

[0543] The terminal receives the proposal data and displays it to the user.

[0544] Input: Proposal data sent from the server

[0545] Output: The suggestions that are displayed to the user

[0546] What happens: The app displays the suggestion data in the user interface.

[0547] In this way, optimal products and services are proposed based on the user's emotions.

[0548] Example 2

[0549] Next, a description will be given of Example 2 of Form 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."

[0550] With conventional proposal systems, creating proposal documents and materials required a lot of time and effort, making it difficult to make proposals efficiently. Furthermore, it was not possible to dynamically adjust proposals in response to changes in user sentiment, making it difficult to maximize the effectiveness of proposals. Furthermore, comparing with competitors and collecting and re-adjusting feedback was time-consuming, making them insufficient in today's business environment, where rapid response is required.

[0551] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0552] In this invention, the server includes means for recording service information of merchandise, proposal examples of past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for tracking user emotions in real time and dynamically adjusting the proposal content in response to changes in the emotions, means for automatically generating proposal documents and materials using a generative AI model, and means for collecting user feedback and readjusting the proposal content. This enables efficient and effective proposal work and allows optimal proposals to be made in response to user emotions.

[0553] "Product service information" refers to detailed information about the products and services being offered.

[0554] "Examples of proposals for previously accepted projects" refers to information that records proposal content and success stories for previously accepted projects.

[0555] "Natural language processing technology" refers to technology that allows computers to understand, interpret, and generate human language.

[0556] A "generative AI model" refers to an artificial intelligence model that generates new data or sentences based on given input data.

[0557] "Means for tracking user emotions in real time" refers to technology that analyzes a user's facial expressions, tone of voice, etc., and detects changes in emotions in real time.

[0558] "Dynamic adjustment means" refers to technology that changes the content of suggestions in real time in response to changes in the user's emotions.

[0559] "Means for collecting feedback" refers to techniques for collecting opinions and requests from users.

[0560] "Means for readjusting proposals" refers to techniques for readjusting proposals based on collected feedback.

[0561] This invention is a system that creates proposal documents, proposal materials, proposed fees, etc. in a timely manner all at once. Specifically, it combines natural language processing technology with an emotion engine to streamline proposal work and make optimal proposals based on the user's emotions.

[0562] The server has a means of recording product service information, proposal examples from past orders, and which services have been successfully combined with proposals to increase sales, making it possible to make effective proposals based on past data.

[0563] The server has a means for proposing the optimum plan and service combination according to the type of business or customer needs, which enables customized proposals to meet the specific needs of the customer.

[0564] The server has a means to use natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. Specifically, it uses a generative AI model (e.g., GPT-4 (registered trademark)) to generate natural-sounding sentences based on data provided by the user.

[0565] The device uses a camera and microphone to track the user's emotions in real time. The emotion engine analyzes the user's facial expressions and vocal tone to detect changes in emotion, allowing it to dynamically adjust recommendations based on the user's changing interests.

[0566] The server has a means for automatically generating proposal documents and materials using a generative AI model. Specifically, the server generates proposal documents by inputting the following prompt sentences into the generative AI model:

[0567] "A user is looking for suggestions for a new marketing strategy. Use market data and past campaign information to generate a proposal document based on the latest trends in the target market. Also include the ability to dynamically adjust the proposal based on user sentiment."

[0568] The server has a means for collecting feedback from users and readjusting the proposed content. The users input their opinions and requests regarding the proposed content through their terminals, and the server analyzes the feedback and readjusts the proposed content.

[0569] For example, if a user requests a proposal for a new marketing strategy, the server collects and analyzes market data and past campaign information, and uses a generative AI model to generate a proposal document like this:

[0570] "This new marketing strategy is designed based on the latest trends in our target market. Specifically, we aim to attract the attention of younger demographics by strengthening social media advertising and leveraging influencer marketing."

[0571] If the user starts to lose interest as they read through the suggestions, the emotion engine detects this change, and the server uses a generative AI model to generate new suggestions to bring the user back into the loop:

[0572] "Furthermore, by implementing the latest data analysis tools, we can measure the effectiveness of campaigns in real time and quickly adjust our strategies."

[0573] In this way, the system enables efficient and effective proposal work through collaboration between the server, terminals, and users.

[0574] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0575] Step 1:

[0576] Collecting input from the user

[0577] The user inputs the basic information and past data required for the proposal through the terminal. Specifically, the user provides market data and past campaign information.

[0578] Input: Market data, past campaign information

[0579] Output: Collected data

[0580] Specific operation: The user enters the necessary information into the input form on the terminal and presses the send button. The terminal then sends the entered data to the server.

[0581] Step 2:

[0582] Data analysis and information extraction

[0583] The server receives the data provided by the user and analyzes it using natural language processing technology. Specifically, the server performs the following processes:

[0584] Data preprocessing: noise removal and data normalization.

[0585] Information extraction: Extract important keywords and phrases.

[0586] Contextual understanding: Understand the context of the data and identify relevant information.

[0587] Input: Collected data

[0588] Output: Analysis results (important keywords, phrases, contextual information)

[0589] How it works: The server feeds the collected data into an analysis algorithm to remove noise and normalize it, then uses natural language processing techniques to extract key information and understand the context.

[0590] Step 3:

[0591] Proposal and document generation

[0592] The server generates proposal documents and materials using a generative AI model (e.g., GPT-4) based on the analysis results. The specific operations are as follows:

[0593] Prompt Generation: Create a prompt based on the suggestions.

[0594] Sentence generation: A prompt sentence is input into the generative AI model to generate natural-sounding sentences.

[0595] Document creation: Proposal materials (e.g., presentation slides) are created based on the generated text.

[0596] Input: Analysis results (important keywords, phrases, contextual information)

[0597] Output: Proposal documents, proposal materials

[0598] Specific operation: The server generates a prompt sentence based on the analysis results and inputs it into the generative AI model. A proposal document is created based on the generated sentence.

[0599] Step 4:

[0600] Emotion Tracking and Dynamic Adjustment

[0601] The device uses a camera and microphone to track the user's emotions in real time. The emotion engine analyzes the user's facial expressions and voice tone to detect changes in emotions. Specifically, it works as follows:

[0602] Facial expression analysis: Analyzes camera footage and estimates emotions from the user's facial expressions.

[0603] Voice analysis: Analyzes voice data collected by a microphone and estimates emotions from the tone and speed of the voice.

[0604] Dynamic Adjustment: When the emotion engine detects a change in emotion, the server uses a generative AI model to generate new suggestions to recapture the user's interest.

[0605] Input: Camera video, audio data

[0606] Output: Sentiment analysis results, new proposed document

[0607] Specific operation: The device uses a camera and microphone to collect the user's facial expressions and voice, and sends them to the emotion engine. The emotion engine then sends the analysis results to the server, which then generates a new proposal document.

[0608] Step 5:

[0609] Submitting suggestions and providing feedback

[0610] The server sends the generated proposal document and materials to the terminal and presents them to the user. The user checks the proposal and provides feedback. The specific operations are as follows:

[0611] Proposal presentation: The terminal displays the proposal document and materials.

[0612] Feedback collection: Users input their opinions and requests regarding the proposal.

[0613] Feedback Analysis: The server analyzes the user's feedback and readjusts the suggestions if necessary.

[0614] Input: Proposal documents, proposal materials, user feedback

[0615] Output: Reworked proposal document, proposal materials

[0616] Specific operation: The terminal displays the proposal document and materials, and the user inputs feedback. The server analyzes the feedback and adjusts the proposal content as necessary.

[0617] (Application example 2)

[0618] Next, a description will be given of Application Example 2 of Form 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."

[0619] Conventional recommendation systems have difficulty responding to changes in users' emotions and interests, making it difficult for the content of their recommendations to continue to attract the user's attention. Creating proposal documents and materials also requires a lot of time and effort, making it difficult to make efficient recommendations. Furthermore, because they are unable to utilize users' purchase and search histories to make optimal product recommendations, they are unable to maximize the user's purchasing motivation.

[0620] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0621] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful and increased sales, means for proposing optimal plans and service combinations tailored to the type of industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., means for tracking user emotions in real time and dynamically adjusting the proposal content in response to changes in those emotions, means for generating optimal product proposals based on the user's purchase history and search history, means for automatically generating proposal documents using a generative AI model, and means for capturing camera footage and performing emotion analysis. This enables efficient and effective proposals that respond to changes in the user's emotions and interests.

[0622] "Product service information" refers to detailed information about the products and services being offered.

[0623] "Example proposals for previously accepted projects" refers to examples of proposals created based on previously accepted projects.

[0624] "Means for recording whether a proposal in combination with a service is successful and sales are increasing" refers to a method or device for recording which service combinations are successful and contribute to sales.

[0625] "Means for proposing optimal plans and service combinations tailored to specific industries and customer needs" refers to methods and devices for proposing optimal plans and service combinations tailored to specific industries and customer requests.

[0626] "Natural language processing technology" refers to the technology that allows computers to understand and process human language.

[0627] "Means for proposing proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner" refers to a method or device for quickly preparing and proposing the documents, materials, and fees required for a proposal all at once.

[0628] "Means for tracking a user's emotions in real time and dynamically adjusting the content of suggestions in response to those changes" refers to a method or device for monitoring changes in a user's emotions in real time and appropriately changing the content of suggestions in response to those changes.

[0629] "Means for generating optimal product suggestions based on a user's purchase history and search history" refers to a method or device for analyzing a user's past purchase history and search history and suggesting optimal products based on that.

[0630] A "generative AI model" refers to a model that uses artificial intelligence to generate new information and suggestions from data.

[0631] "Means for capturing camera footage and performing emotion analysis" refers to a method or device for capturing footage using a camera and analyzing user emotions from the footage.

[0632] A system for carrying out this invention has the following configuration: The server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry type and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., at the time of proposal, means for tracking user emotions in real time and dynamically adjusting the proposal content in response to changes in those emotions, means for generating optimal product proposals based on the user's purchase history and search history, means for automatically generating proposal documents using a generative AI model, and means for capturing camera footage and performing sentiment analysis.

[0633] Hardware and software used

[0634] Hardware: Smartphone camera, smartphone display

[0635] Software: OpenAI GPT-3 API, camera footage capture and processing

[0636] Data processing and calculation

[0637] 1. Camera image capture: Capture the user's image in real time using the smartphone camera. Library X is used to acquire the image and process it frame by frame.

[0638] 2. Emotion analysis: Library Y is used to analyze the user's emotions from the captured video. Specifically, it analyzes the user's facial expressions and voice and tracks changes in emotions in real time.

[0639] 3. Proposal Document Generation: Using the OpenAI GPT-3 API, optimal product proposals are generated based on the user's purchase and search history. The generated proposal documents and materials are dynamically adjusted according to the user's emotions.

[0640] 4. Dynamic Adjustment: Adapting recommendations in real time based on sentiment analysis, for example, suggesting new products or services if the user is losing interest.

[0641] Specific examples

[0642] If the system detects a loss of interest in a user's facial expression while browsing products through a smartphone camera, it will suggest new products. For example, if a user searches for "smartphone cases" but loses interest, it will suggest related products such as "smartwatches" and "wireless earphones."

[0643] Prompt Sentence Examples

[0644] User data: {'Purchase history': ['Smartphone case', 'Charger'], 'Search history': ['Smartwatch', 'Wireless earphones']}

[0645] Generate optimal product suggestions.

[0646] In this way, it is possible to respond to changes in the user's emotions and interests and make efficient and effective suggestions.

[0647] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0648] Step 1:

[0649] The user activates the smartphone camera and views the product.

[0650] Input: User video

[0651] Output: Captured video frames

[0652] Specific operation: The smartphone camera captures the user's video in real time and obtains video frames.

[0653] Step 2:

[0654] The device processes the captured video frames and prepares the data for sentiment analysis.

[0655] Input: Captured video frames

[0656] Output: Data for sentiment analysis

[0657] Specific operation: Library X is used to analyze the video frame and extract the user's face.

[0658] Step 3:

[0659] The device uses library Y to analyze the user's emotions from the extracted facial parts.

[0660] Input: Data for emotion analysis (face area)

[0661] Output: Sentiment analysis results (e.g., interest, indifference, joy, etc.)

[0662] Specific operation: Using the emotion analysis model of library Y, analyze the user's emotion from the extracted facial parts in real time.

[0663] Step 4:

[0664] The server uses the OpenAI GPT-3 API to generate optimal product suggestions based on the user's purchase history and search history.

[0665] Input: User's purchase history, search history

[0666] Output: Proposal document

[0667] Specific operation: The server sends the user's purchase history and search history as prompts to the GPT-3 API, which generates optimal product suggestions.

[0668] Step 5:

[0669] The server dynamically adjusts the generated suggested documents based on the sentiment analysis results.

[0670] Input: Sentiment analysis results, proposal document

[0671] Output: Adjusted proposal document

[0672] Specific operation: The server evaluates the sentiment analysis results and dynamically adjusts the proposal document, such as adding new product proposals if the user is losing interest.

[0673] Step 6:

[0674] The terminal displays the adjusted proposal document to the user.

[0675] Input: Adjusted proposal document

[0676] Output: The proposal document that is displayed to the user

[0677] Specific operation: The adjusted proposal document is displayed on the smartphone display and provided to the user.

[0678] In this way, it is possible to respond to changes in the user's emotions and interests and make efficient and effective suggestions.

[0679] Example 3

[0680] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0681] With conventional systems, it was difficult not only to record product service information and past proposal examples, but also to compare them with competitors' services and learn user emotional patterns to predict future changes in emotion. It was also difficult to use natural language processing technology to create proposal documents and materials in a timely manner at the time of proposal. This led to the issue of being unable to make efficient and effective proposals when proposing optimal plans and service combinations to customers.

[0682] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0683] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for storing competitor service information in a database and comparing it with the company's own services, and means for learning user emotional patterns and predicting future emotional changes based on those patterns. This enables efficient and effective proposals to be made to customers.

[0684] "Product service information" is detailed data about the products and services offered.

[0685] "Examples of proposals for previously accepted orders" are specific proposals that have been previously submitted to clients and have been successfully accepted.

[0686] "A means of recording which services were successfully combined with the proposal and resulted in increased sales" refers to a method of saving successful cases where multiple services were combined and proposed, along with the sales data.

[0687] "Means of proposing optimal plans and service combinations tailored to the industry and customer needs" refers to a method of selecting and proposing the most appropriate plans and service combinations based on the customer's industry and specific needs.

[0688] "A means of using natural language processing technology to provide proposal documents, materials, fees, etc. all at once in a timely manner at the time of proposal" is a method of using natural language processing technology to quickly generate and provide the documents, materials, and fee information required for a proposal.

[0689] "Method of storing information about competitors' services in a database and comparing it with one's own service" refers to a method of storing information about competitors' services in a database and using that information to compare one's own service with another's.

[0690] "Means for learning a user's emotional patterns and predicting future emotional changes based on those patterns" refers to a method for analyzing a user's past emotional responses and predicting future emotional changes based on that data.

[0691] MODE FOR CARRYING OUT THE INVENTION

[0692] This invention is a system that includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales; means for proposing optimal plans and service combinations according to industry and customer needs; means for using natural language processing technology to provide proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner when making a proposal; means for storing competitor service information in a database and comparing it with one's own services; and means for learning user emotional patterns and predicting future emotional changes based on those patterns.

[0693] Server Roles

[0694] The server uses a database management system (e.g., MySQL) to record service information for products and proposal examples for past orders. The server uses a web scraping tool to collect service information from competitors and stores it in a database. In addition, the server uses a machine learning algorithm (e.g., TENSORFLOW (registered trademark)) to learn users' emotional patterns and predict future emotional changes.

[0695] Device Role

[0696] The terminal provides an interface for users to compare their company's services with those of competitors. The terminal uses front-end technology such as JavaScript (registered trademark) to display data in a user-friendly format. When a user requests a comparison, the terminal sends a request to the server to obtain the required data.

[0697] User Roles

[0698] Users can compare their company's services with those of competitors through their devices. By checking the comparison results, users can understand the advantages of their own services. In addition, when making a proposal, users can use natural language processing technology to generate proposal documents, proposal materials, and proposed prices all at once in a timely manner.

[0699] Specific examples

[0700] For example, if a user wants to compare their company's cloud storage service with a competitor's, they can input the following prompt into the generative AI model:

[0701] Example prompt sentence:

[0702] "Compare your cloud storage service with your competitors' services."

[0703] When the user enters this prompt, the system sends a request to the server to retrieve the necessary information from the database. The terminal visually displays the retrieved information, demonstrating the advantages of the company's services to the user.

[0704] Also, if the model learns a pattern of users expressing anger toward a particular suggestion, it can input prompt sentences like the following into the generative AI model:

[0705] Example prompt sentence:

[0706] "Please predict how User A will react to the new pricing proposal."

[0707] When this prompt is entered, the emotion engine predicts User A's emotions based on past data, and the system adjusts to change the way it makes suggestions or avoid making suggestions.

[0708] The above is an embodiment of the present invention. This system makes it possible to provide efficient and effective proposals to customers. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0709] Step 1:

[0710] The server collects service information of competitors and stores it in a database.

[0711] Input: Competitor's website URL

[0712] What it does: The server uses a web scraping tool to collect service information from competitors' websites.

[0713] Data processing: Organize the collected data and store it in a MySQL database.

[0714] Output: Updated database

[0715] Step 2:

[0716] The user sends a request via the terminal to compare the company's services with those of competitors.

[0717] Input: The type of service you want to compare (e.g., cloud storage services)

[0718] Specific actions: The user selects a service type on the device interface and clicks the "Compare" button.

[0719] Data processing: The device sends this request in JSON format to the server.

[0720] Output: Request to server

[0721] Step 3:

[0722] The server retrieves the necessary information from the database and returns it to the terminal.

[0723] Input: User request (JSON format)

[0724] What happens: The server parses the incoming request and queries the MySQL database for the corresponding service information.

[0725] Data processing: Convert the acquired data into JSON format.

[0726] Output: Response to the terminal (JSON format)

[0727] Step 4:

[0728] The terminal visually displays the acquired data.

[0729] Input: Response from the server (JSON format)

[0730] Specific behavior: The device parses the received JSON data and uses a front-end framework to display it in a user-friendly format.

[0731] Data processing: Convert JSON data into tabular or graph format.

[0732] Output: The comparison results that are displayed to the user

[0733] Step 5:

[0734] The emotion engine learns the user's emotional patterns.

[0735] Input: User's past reaction data (e.g., anger, joy, sadness)

[0736] What it does: The emotion engine uses machine learning algorithms (e.g., TENSORFLOW) to learn the user's emotional patterns.

[0737] Data processing: The collected emotion data is input into the neural network model and the model is trained.

[0738] Output: A trained emotion prediction model

[0739] Step 6:

[0740] The emotion engine predicts future emotional changes of the user.

[0741] Input: Trained emotion prediction model, new user suggestion data

[0742] Specific behavior: The emotion engine predicts how a user will feel about a particular suggestion based on learned emotion patterns.

[0743] Data processing: New proposed data is input into the model to predict sentiment changes.

[0744] Output: Emotion prediction result

[0745] The above are the specific processing steps of the program of this system.

[0746] (Application example 3)

[0747] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0748] Conventional online shopping sites suggest products without considering the user's emotions, making it difficult to maximize the user's purchasing motivation. Furthermore, they are unable to demonstrate the superiority of their products compared to similar products from competitors, making it difficult to make effective suggestions to users. Furthermore, they are unable to learn the user's emotional patterns and predict future emotional changes, making it difficult to make appropriate product suggestions to users.

[0749] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0750] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for analyzing user emotions and predicting future emotional changes based on emotional patterns, and means for comparing the company's products with similar products from competitors and demonstrating the superiority of the company's products. This enables effective product proposals that take user emotions into consideration, and enables the company to demonstrate the superiority of its products through comparisons with competitors.

[0751] "Product service information" is detailed information about the products and services offered.

[0752] "Examples of proposals for previously accepted projects" are examples of specific plans and services proposed for previously accepted projects.

[0753] "Means for recording which services have been successfully proposed in combination with other services and have resulted in increased sales" refers to a method or device for recording successful cases where a particular service has been proposed in combination with other services and the resulting increase in sales.

[0754] "Means for proposing optimal plans and service combinations tailored to industry and customer needs" refers to a method or device for proposing the most appropriate plans and service combinations based on specific industry and customer requirements.

[0755] "A means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. at the time of proposal" refers to a method or device for quickly generating and presenting proposal documents, materials, fees, etc. at the time of proposal using natural language processing technology.

[0756] "Means for analyzing a user's emotions and predicting future changes in emotions based on emotional patterns" refers to a method or device for analyzing a user's emotions and predicting future changes in emotions based on the analysis results.

[0757] "Means for demonstrating the superiority of one's own products in comparison with similar products offered by competitors" refers to methods or devices for comparing one's own products with similar products offered by competitors and clearly demonstrating the superiority of one's own products.

[0758] A system for carrying out this invention has the following configuration: The server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making a proposal, means for analyzing user emotions and predicting future emotional changes based on emotion patterns, and means for comparing the company's products with similar products from competitors and demonstrating their superiority.

[0759] Hardware and software used

[0760] Hardware: Smartphone

[0761] software:

[0762] Library E: A library for analyzing user sentiment

[0763] Library F: A library for comparing products with competitors' equivalent products

[0764] Library G: An HTTP request library for retrieving user emotion data from a database.

[0765] Data processing and calculation

[0766] The server first loads the user's emotional data from the database. Then, it receives the user's input and analyzes the emotion using Library E. Based on the analysis results, it suggests appropriate products. The suggested products are compared with similar products from competitors using Library F to demonstrate the superiority of the company's products.

[0767] Specific examples

[0768] For example, if a user types "I'm looking for a new smartphone," Library E analyzes the user's emotions and suggests alternative products if it detects anger. Otherwise, it compares the company's smartphone with competitors' smartphones to demonstrate their superiority.

[0769] Prompt Sentence Examples

[0770] If a user types in "I'm looking for a new smartphone," the emotion engine analyzes the user's emotions and suggests alternative products if anger is detected, otherwise it compares the company's smartphone with competitors' smartphones to demonstrate their superiority.

[0771] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0772] Step 1:

[0773] The server loads the user's emotion data from the database. Specifically, it uses the HTTP request library (library G) to retrieve the emotion data based on the user ID. The input is the user ID, and the output is the user's past emotion data.

[0774] Step 2:

[0775] The terminal receives user input. Specifically, the user inputs questions or requests about products through a smartphone application. The input is the user's text input, and the output is that text data.

[0776] Step 3:

[0777] The server uses library E to analyze emotions from the user's input text. Specifically, it passes the text data to library E and performs emotion analysis. The input is the user's text data, and the output is the analyzed emotion data.

[0778] Step 4:

[0779] The server updates the user's emotional patterns based on the analyzed emotional data. Specifically, it integrates the new emotional data with past emotional data to learn the emotional patterns. The input is the new emotional data and past emotional data, and the output is the updated emotional patterns.

[0780] Step 5:

[0781] The server predicts future emotional changes based on the emotional patterns. Specifically, it uses the learned emotional patterns to predict how the user will feel about a particular suggestion. The input is the updated emotional patterns, and the output is the predicted emotional changes.

[0782] Step 6:

[0783] The server suggests appropriate products based on the predicted user emotion change. Specifically, if the predicted emotion is negative, it suggests a different product, but if it is positive, it continues to suggest the same product. The input is the predicted emotion change, and the output is a list of suggested products.

[0784] Step 7:

[0785] The server uses Library F to compare its own products with similar products from competitors. Specifically, it passes the list of products to be proposed to Library F, which compares them with the competitors' products. The input is the list of products to be proposed, and the output is the comparison result.

[0786] Step 8:

[0787] The server generates information showing the superiority of the company's products based on the comparison results. Specifically, it analyzes the comparison results and generates information emphasizing the superiority of the company's products. The input is the comparison results, and the output is information showing the superiority of the company's products.

[0788] Step 9:

[0789] The terminal presents the generated information to the user. Specifically, it displays information on the smartphone screen that shows the superiority of the company's products. The input is information showing the superiority of the company's products, and the output is what is displayed to the user.

[0790] 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.

[0791] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0792] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0793] 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.

[0794] [Second embodiment]

[0795] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0796] 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.

[0797] 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).

[0798] 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.

[0799] 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.

[0800] 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).

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0807] "Example 1"

[0808] The system of the present invention is equipped with a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales. This database provides information for proposing optimal plans and service combinations tailored to the type of industry and customer needs. Specifically, it has the function of analyzing past success stories and sales growth, and proposing optimal plans and service combinations based on that.

[0809] "Example 2"

[0810] Furthermore, the system of the present invention has the function of using natural language processing technology to create proposal documents, proposal materials, proposed fees, etc. all at once and in a timely manner when making a proposal. Specifically, the system uses natural language processing technology to automatically generate the information necessary for the proposal and reflect it in the proposal documents and proposal materials. This improves the efficiency of the proposal process.

[0811] "Example 3"

[0812] The system of the present invention also allows comparison with similar services offered by competitors. Specifically, it has the function of storing information on other companies' services in a database and comparing it with your own service based on that information. This makes it possible to clearly demonstrate to customers the superiority of your company's services.

[0813] The processing flow of each embodiment will be described below.

[0814] "Example 1"

[0815] Step 1: The system references a database that records service information for the product, proposal examples from past orders, and which service combinations have been successful and led to increased sales. Step 2: Next, the system retrieves information from the database to propose optimal plans and service combinations tailored to the type of industry and customer needs.

[0816] Step 3: Finally, the system will suggest the optimal plan and combination of services based on the information obtained.

[0817] "Example 2"

[0818] Step 1: When making a proposal, the system uses natural language processing technology to create proposal documents, proposal materials, proposed fees, etc. in a timely manner all at once.

[0819] Step 2: Specifically, the system uses natural language processing technology to automatically generate the information necessary for the proposal.

[0820] Step 3: Finally, the system reflects the generated information in proposal documents and proposal materials.

[0821] "Example 3"

[0822] Step 1: The system retrieves service information from a database of competitors to compare with similar services offered by competitors.

[0823] Step 2: Next, the system compares the acquired service information of other companies with the service information of the company.

[0824] Step 3: Finally, the system presents the comparison results to the customer.

[0825] Example 1

[0826] Next, a description will be given of Example 1 of Form 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."

[0827] With the conventional proposal system, it was difficult to effectively utilize product service information and past success stories, making it difficult to quickly propose plans and service combinations that best fit customer needs. Furthermore, proposal documents and materials could not be generated in a timely manner in bulk, reducing the efficiency of proposals. Furthermore, it was difficult to compare services with equivalent competitors' services, or to present the differences in quotes and features to customers in a single visit.

[0828] 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.

[0829] In this invention, the server includes: means for recording product service information, proposal examples for past orders, and which service combinations have been successful and increased sales; means for proposing optimal plans and service combinations tailored to industry and customer needs; means for using natural language processing technology to quickly and collectively provide proposal documents, proposal materials, and proposed fees; means for a user to log in to the system and input information for creating a proposal; means for the server to receive the input information and retrieve related data from a database; means for the server to analyze the retrieved data and generate optimal plans and service combinations; means for the server to display the generated proposal to the user; means for the user to review the proposal and modify it as necessary; and means for the user to finalize the proposal and send it to the customer. This enables the server to quickly propose plans and service combinations that best suit customer needs, improving proposal efficiency. It also enables the server to compare services with similar services from competitors and propose differences in quotes and features to customers in a single visit.

[0830] "Product service information" is detailed information about the products and services offered.

[0831] "Examples of proposals for previously accepted projects" are records of specific plans and combinations of services proposed for previously accepted projects.

[0832] "A means of recording which combinations of services have been successfully proposed and have resulted in increased sales" is a function that records in a database the combinations of services that have been successfully proposed and the resulting increase in sales.

[0833] "A means of proposing optimal plans and service combinations tailored to the industry and customer needs" is a function that automatically generates and proposes optimal service combinations based on the customer's industry and specific needs.

[0834] "A means of using natural language processing technology to provide proposal documents, materials, pricing, etc. in a timely manner at the time of proposal" is a function that uses natural language processing technology to quickly generate the documents, materials, and pricing information required for a proposal and provide them all at once.

[0835] The "means for a user to log in to the system and input information for creating a proposal" refers to an interface that allows a user to access the system and input information necessary to create a proposal.

[0836] "Means for the server to receive the input information and retrieve related data from the database" refers to the function by which the server searches for and retrieves related data from the database based on the information entered by the user.

[0837] "Means for analyzing data acquired by the server and generating optimal plans and service combinations" refers to a function that analyzes data acquired by the server and automatically generates optimal service combinations.

[0838] The "means for displaying the proposal generated by the server to the user" is a function for transmitting the content of the proposal generated by the server to the user's terminal and displaying it.

[0839] The "means for the user to check the proposal and make corrections as necessary" is an interface that allows the user to check the displayed proposal content and make corrections as necessary.

[0840] The "means for the user to finalize the proposal and transmit it to the client" is a function that allows the user to finalize the content of the proposal and transmit the proposal to the client.

[0841] This system is equipped with a database that records product service information, proposal examples from past orders, and which service combinations have been successful and led to increased sales. This system has the function of proposing optimal plans and service combinations tailored to the type of industry and customer needs. Furthermore, when making proposals, natural language processing technology is used to provide timely proposals that include proposal documents, proposal materials, and proposed fees all at once.

[0842] Hardware and software used

[0843] Hardware

[0844] Database server (e.g. MySQL server)

[0845] Application server (e.g. Apache)

[0846] User device (e.g. PC, tablet)

[0847] software

[0848] Database Management System (DBMS)

[0849] Data Analysis Tools

[0850] Natural Language Processing Tools

[0851] Specific operation of the system

[0852] A user logs into the system

[0853] The user accesses the system's login screen from their terminal and enters their user ID and password to log in. The server checks the entered authentication information against the database, and if authentication is successful, transitions the user to the dashboard screen.

[0854] A user enters information to create a new suggestion

[0855] The user clicks the "Create a new proposal" button on the dashboard screen to move to the proposal creation screen, where they enter information such as the customer's industry, needs, and budget.

[0856] The server receives the entered information and retrieves the relevant data from the database.

[0857] The server takes the information entered by the user and queries a database, which returns data on past successes and sales growth.

[0858] The server analyzes the acquired data and generates the optimal plan and service combination.

[0859] The server analyzes the acquired data using publicly known libraries, specifically generating a combination of plans and services that are best suited to the customer's industry and needs based on past success stories and sales growth.

[0860] Displaying server-generated suggestions to the user

[0861] The server sends the generated plan and service combination to the user's device, which displays the proposed content on its screen.

[0862] The user reviews the proposal and makes any necessary corrections.

[0863] The user checks the displayed suggestions and makes corrections as necessary. Once corrections are complete, the user clicks the "Confirm Proposal" button.

[0864] The user finalizes the proposal and sends it to the customer

[0865] Once the user confirms the proposal, the server saves it in a database and sends it to the customer, who can receive it via email or a dedicated portal.

[0866] Examples of concrete examples and prompts

[0867] As a concrete example, consider the following scenario:

[0868] Suppose a user wants to propose the optimal service plan for a new customer. The user inputs the customer's industry and needs into the system. The server retrieves data on past successes and sales growth from the database and generates the optimal plan and combination of services. The generated plan is then proposed to the user.

[0869] Example prompt sentence:

[0870] "We would like to propose the optimal service plan for a new client. The client's industry is manufacturing, and their needs are cost reduction and efficiency. Please propose the optimal plan and combination of services based on past successes and sales growth."

[0871] By inputting this prompt into a generative AI model, the system can suggest the optimal plan and combination of services.

[0872] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0873] Step 1:

[0874] A user logs in to the system.

[0875] Input: User ID and password

[0876] Output: Authentication result (success or failure)

[0877] Specific operation: The user accesses the system's login screen from their terminal and enters their user ID and password. The server compares the entered authentication information with the database, and if authentication is successful, the user is transferred to the dashboard screen. If authentication fails, an error message is displayed.

[0878] Step 2:

[0879] A user enters information to create a new proposal.

[0880] Input: Information about the customer's industry, needs, budget, etc.

[0881] Output: Confirmation screen of the entered information

[0882] Specific operation: The user clicks the "Create a new proposal" button on the dashboard screen to move to the proposal creation screen. On the proposal creation screen, the user enters information such as the customer's industry, needs, and budget, and clicks the "Next" button. The server temporarily saves the entered information and displays a confirmation screen.

[0883] Step 3:

[0884] The server receives the entered information and retrieves relevant data from a database.

[0885] Input: Customer information entered by the user

[0886] Output: Relevant data (past success stories, sales data, etc.)

[0887] What it does: The server queries the database based on the information the user enters. The database returns data about past successes and sales growth. The server temporarily stores the retrieved data.

[0888] Step 4:

[0889] The server analyzes the data it acquires and generates the optimal plan and combination of services.

[0890] Input: Relevant data (past success stories, sales data, etc.)

[0891] Output: Best combination of plans and services

[0892] Specific operation: The server analyzes the acquired data using publicly known libraries. Specifically, it generates a combination of plans and services that are best suited to the customer's industry and needs based on past successes and sales growth. The generated plans are saved on the server.

[0893] Step 5:

[0894] The server generates suggestions and displays them to the user.

[0895] Input: Best plan and service combination

[0896] Output: Proposal display screen

[0897] Specific operation: The server sends the generated plan and service combination to the user's device, which then displays the received proposal on its screen.

[0898] Step 6:

[0899] The user reviews the proposal and corrects it if necessary.

[0900] Input: User modifications

[0901] Output: Revised proposal

[0902] Specific operation: The user checks the displayed proposal and makes any necessary corrections. Once the corrections are complete, the user clicks the "Confirm Proposal" button. The server temporarily saves the corrections and generates the final proposal.

[0903] Step 7:

[0904] The user finalizes the proposal and sends it to the customer.

[0905] Input: Final proposal

[0906] Output: Proposal sent to customer

[0907] Specific operation: When the user clicks the "Confirm Proposal" button, the server saves the proposal in the database and sends it to the customer, who can receive it via email or a dedicated portal.

[0908] (Application example 1)

[0909] Next, a description will be given of Application Example 1 of Form 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."

[0910] Conventional product proposal systems had difficulty proposing optimal plans and service combinations based on past success stories and sales growth. Furthermore, they were unable to generate proposal documents and materials in a timely manner using natural language processing technology, preventing quick and effective proposals to customers. Furthermore, they lacked the functionality to suggest optimal product and service combinations based on past purchase history and success stories of other users, making it difficult to improve customer satisfaction.

[0911] 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.

[0912] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for proposing optimal product and service combinations based on past purchase history and success stories of other users, and means for displaying optimal services and proposal contents. This enables prompt and effective proposals to customers, improving customer satisfaction.

[0913] "Product service information" is detailed information about the products and services offered.

[0914] "Example proposals for projects accepted in the past" are examples of proposals created based on projects accepted in the past.

[0915] "A means of recording which service combinations have been successful in proposing and resulting in increased sales" is a means of recording successful service combinations and the resulting increase in sales.

[0916] "Means for proposing optimal plans and service combinations tailored to industry and customer needs" refers to means for proposing optimal plans and service combinations based on specific industry and customer needs.

[0917] "A means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. at the time of proposal" refers to a means for rapidly generating and proposing proposal documents, proposal materials, proposed fees, etc. at once using natural language processing technology.

[0918] "A means for proposing optimal combinations of products and services based on past purchase history and success stories of other users" refers to a means for proposing optimal combinations of products and services by referring to past purchase history and success stories of other users.

[0919] The "means for displaying the most suitable service and its proposed content" is a means for displaying the most suitable service and its proposed content to the user.

[0920] The system for implementing this invention has a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful and increased sales. Based on this information, the server proposes optimal plans and service combinations tailored to the type of industry and customer needs.

[0921] The server uses natural language processing technology to generate proposal documents, proposal materials, proposed fees, etc. in a timely manner, and provides users with prompt and effective proposals. Specifically, the server uses publicly known software to manage the database, and uses a generative AI model for natural language processing.

[0922] The server also suggests optimal combinations of products and services based on past purchase history and success stories of other users, allowing users to easily find the products and services that best suit their needs. Furthermore, the system also has a function to display the optimal services and suggestions on the user's device.

[0923] For example, if a user is searching for a product in the "Logistics" category, the server will suggest a service called "Premium Delivery" and display the suggestion, "Premium Delivery is highly recommended for faster shipping." In this way, it is possible to suggest the optimal combination of products and services to the user.

[0924] An example of a prompt is as follows:

[0925] "When a user searches for a product in the 'Logistics' category, show them the best services and suggestions."

[0926] This system allows users to quickly and effectively receive recommendations for optimal products and services, which is expected to improve customer satisfaction.

[0927] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0928] Step 1:

[0929] The server records in a database service information for the merchandise, examples of proposals for past orders, and which services were used in combination with the proposals to increase sales.

[0930] Input: Product service information, proposal examples for past orders, successful service combinations and sales data

[0931] Output: Information recorded in the database

[0932] Specific operation: The server receives service information for the product, past proposal examples, successful service combinations and sales data, and inserts this information into the database.

[0933] Step 2:

[0934] The server will propose the optimal combination of plans and services based on the industry and customer needs.

[0935] Input: Industry information, customer needs

[0936] Output: Best combination of plans and services

[0937] Specific operation: The server extracts data related to industry information and customer needs from the database and generates optimal plans and service combinations based on past success stories.

[0938] Step 3:

[0939] The server uses natural language processing technology to generate proposal documents, proposal materials, proposed prices, etc. in a timely manner all at once.

[0940] Input: Best plan and service combination

[0941] Output: Proposal document, proposal materials, proposal fee

[0942] Specific operation: The server uses the generative AI model to generate proposal documents, proposal materials, and proposed prices based on the optimal plan and service combination.

[0943] Step 4:

[0944] The server suggests optimal combinations of products and services based on past purchase history and success stories of other users.

[0945] Input: past purchase history, success stories of other users

[0946] Output: Optimal product and service combinations

[0947] Specific operation: The server extracts past purchase history and success stories of other users from the database and generates the optimal combination of products and services based on this data.

[0948] Step 5:

[0949] The server displays the optimal services and suggestions on the user's device.

[0950] Input: Best service and proposal

[0951] Output: The suggestions displayed on the user's device

[0952] Specific operation: The server sends the generated optimal service and proposal content to the user's terminal, which displays it.

[0953] Step 6:

[0954] The user checks the displayed proposals and makes selections or purchases as necessary.

[0955] Input: User choices and purchase intentions

[0956] Output: Selected products and services, purchase information

[0957] Specific operation: The user checks the offers displayed on the terminal and makes a selection or purchase. The server receives the user's selection and purchase information and records it in the database.

[0958] Example 2

[0959] Next, a description will be given of Example 2 of Form 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."

[0960] Traditional proposal work required a lot of time and effort to create proposal documents and materials, resulting in inefficiency. It was also difficult to maintain a consistent quality in the proposal, which could lead to a lower success rate. Furthermore, it was difficult to compare proposals with competitors and propose optimal plans tailored to customer needs.

[0961] The specific processing by the specific 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 recording service information of merchandise, proposal examples of past orders, and which service combinations have been successful in proposals and increased sales; means for proposing optimal plans and service combinations tailored to the type of industry and customer needs; means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc.; means for the user to input information required for the proposal; means for the server to receive the input information and analyze it using natural language processing technology; means for the server to send prompt sentences to the generative AI model based on the analysis results; means for the generative AI model to generate proposal documents and materials; means for the server to send the generated documents and materials to the user's terminal; and means for the user to review and edit the documents and materials. This enables the efficiency and quality of proposal work to be improved.

[0962] "Product service information" is detailed information about the products and services offered.

[0963] "Examples of proposals for previously accepted projects" are examples of proposals based on previously accepted projects.

[0964] The "means for recording whether proposals are successful and sales are increasing" refers to a method or device for recording the success rate of proposals and increases in sales.

[0965] "Means for proposing optimal plans and service combinations tailored to the needs of each industry and customer" refers to a method or device for proposing optimal plans and service combinations tailored to specific industry types and customer requests.

[0966] "Natural language processing technology" is a technology that uses computers to understand and analyze human language.

[0967] "Means for proposing proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner" refers to a method or device for quickly providing documents, materials, and fee information required for a proposal all at once.

[0968] The "means for the user to input information necessary for the proposal" refers to a method or device that allows the user to input information necessary for creating a proposal.

[0969] "Means for the server to receive input information and analyze it using natural language processing technology" refers to a method or device for the server to receive input information from a user and analyze that information using natural language processing technology.

[0970] "Means for the server to send a prompt sentence to the generative AI model based on the analysis results" refers to a method or device for the server to send a prompt sentence to the generative AI model based on the analysis results.

[0971] "Means for a generative AI model to generate proposal documents or materials" refers to a method or device for a generative AI model to generate proposal documents or materials based on a prompt sentence.

[0972] The "means for transmitting documents or materials generated by the server to the user's terminal" refers to a method or device for transmitting documents or materials generated by the server to the user's terminal.

[0973] "Means for users to check and edit documents and materials" refers to methods and devices that allow users to check the generated documents and materials and edit them as necessary.

[0974] The present invention provides a system for improving the efficiency of proposal work and quickly generating high-quality proposal documents and materials. Specific embodiments of this system will be described below.

[0975] First, the user enters the information necessary for the proposal. The user enters basic information such as the project name, purpose, budget, and period into an input form on a web browser. For example, the user might enter information such as "Next Generation AI Development Project," "AI Technology Research and Development," "5 million yen," and "6 months."

[0976] Next, the server receives the information entered by the user and analyzes it using natural language processing technology. The server uses publicly known libraries to analyze the text data and extract the information necessary for the proposal. For example, it extracts the "project name" from the phrase "next-generation AI development project" and the "purpose" from the phrase "research and development of AI technology."

[0977] The server then sends a prompt to the generative AI model based on the analysis results. The server creates a prompt based on the analysis results and sends it to the generative AI model (for example, OpenAI's GPT-3). An example of a prompt would be, "Please create a proposal for a new project. The project name is 'Next Generation AI Development Project' and the purpose is research and development of AI technology. The budget is 5 million yen and the duration is 6 months."

[0978] The generative AI model generates proposal documents and materials based on prompts sent from the server. The generated documents include the project's objectives, budget, period, detailed plans, and more. For example, it generates a document with the following content: "Next-generation AI development project proposal," "Objective: Research and development of AI technology," "Budget: 5 million yen," "Period: 6 months," "Details: Research and implement the latest AI algorithms and develop a prototype for practical use."

[0979] The server then sends the generated documents and materials to the user's device, where the user can view the documents through a web browser or a dedicated application.

[0980] Finally, the user reviews the generated proposal document and materials and edits them as necessary. The user can modify the content of the document or enter additional information. Finally, the user saves and submits the completed proposal.

[0981] This system will improve the efficiency and quality of proposal work, allowing users to quickly create high-quality proposal documents and increase the success rate of proposals.

[0982] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0983] Step 1:

[0984] The user enters the information required for the proposal. The user enters basic information such as the project name, purpose, budget, and period into an input form on a web browser. For example, the user enters information such as "Next Generation AI Development Project," "AI Technology Research and Development," "5 million yen," and "6 months." Once the input is complete, the user clicks the "Submit" button. The input data is sent to the server.

[0985] Step 2:

[0986] The server receives the input information and analyzes it using natural language processing technology. The server uses publicly known libraries to analyze the text data and extract the information necessary for the proposal. For example, it extracts the "project name" from the phrase "next-generation AI development project" and the "purpose" from the phrase "research and development of AI technology." The input data undergoes text analysis and is output as extracted keywords and phrases.

[0987] Step 3:

[0988] The server sends a prompt to the generative AI model based on the analysis results. The server creates a prompt based on the analysis results and sends it to the generative AI model (for example, OpenAI's GPT-3). An example of a prompt might be, "Please create a proposal for a new project. The project name is 'Next Generation AI Development Project' and the purpose is research and development of AI technology. The budget is 5 million yen and the duration is 6 months." The analysis results are output as a prompt.

[0989] Step 4:

[0990] The generative AI model generates proposal documents and materials. The generative AI model generates proposal documents and materials based on prompt text sent from the server. The generated document includes the project's objectives, budget, period, detailed plan, etc. For example, it generates a document with the following content: "Next-generation AI development project proposal," "Objective: Research and development of AI technology," "Budget: 5 million yen," "Period: 6 months," "Details: Research and implement the latest AI algorithms and develop a prototype for practical use." The prompt text is output as the proposal document.

[0991] Step 5:

[0992] The server sends the generated documents and materials to the user's device. The server sends the proposed documents and materials received from the generative AI model to the user's device. The user can check the generated documents through a web browser or dedicated application. The generated documents are sent to the user's device.

[0993] Step 6:

[0994] The user reviews and edits the documents and materials. The user reviews the proposal documents and materials sent from the server and edits them as necessary. For example, they can modify the content of the document or enter additional information. Finally, the user saves and submits the completed proposal. The edited documents are output as the final proposal.

[0995] (Application example 2)

[0996] Next, a description will be given of Application Example 2 of Form 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."

[0997] With conventional proposal systems, proposing the optimal plan based on product service information and past proposal examples required a lot of manual work, resulting in inefficiency. It was also difficult to create proposal documents, proposal materials, and proposed prices all at once in a timely manner, making it difficult to provide prompt and accurate proposals to customers. Furthermore, it was difficult to compare with competitors or propose differences in quotes with other companies in a single visit. A new system to solve these issues was needed.

[0998] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0999] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., at the time of proposal, means for inputting product information and customer information and automatically generating proposal documents using natural language processing technology, means for automatically generating proposal materials using a generative AI model, and means for automatically calculating the prices of the proposed products and reflecting them in the proposal documents.This improves the efficiency of proposal work and enables quick and accurate proposals.

[1000] "Product service information" is detailed information about the products and services offered.

[1001] "Example proposals for previously accepted projects" are examples of proposals created based on previously accepted projects.

[1002] "Means for recording which combinations of services have been successfully proposed and have resulted in increased sales" refers to a method or device for recording combinations of services that have been successfully proposed and the resulting increase in sales.

[1003] "Means for proposing optimal plans and service combinations tailored to the needs of each industry and customer" refers to a method or device for proposing optimal plans and service combinations tailored to specific industry types and customer requests.

[1004] "A means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. at the time of proposal" refers to a method or device for quickly creating proposal documents, proposal materials, proposed fees, etc. at the time of proposal using natural language processing technology.

[1005] "Means for inputting product information and customer information and automatically generating a proposal document using natural language processing technology" refers to a method or device for inputting product information and customer information and automatically generating a proposal document based on that information using natural language processing technology.

[1006] A "means for automatically generating proposal materials using a generative AI model" is a method or device for automatically generating proposal materials using a generative AI model.

[1007] "Means for automatically calculating the price of the proposed product and reflecting it in the proposal document" refers to a method or device for automatically calculating the price of the proposed product and reflecting the result in the proposal document.

[1008] A system for implementing this invention includes means for recording service information on commercial products, proposal examples for past orders, and which services have been successfully proposed and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to propose proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner when making proposals, means for inputting product information and customer information and automatically generating proposal documents using natural language processing technology, means for automatically generating proposal materials using a generative AI model, and means for automatically calculating the price of the proposed product and reflecting it in the proposal document.

[1009] Hardware and software used

[1010] Hardware

[1011] server

[1012] Smartphone

[1013] software

[1014] Python

[1015] Natural Language Processing Library

[1016] GPT-3 (generative AI model)

[1017] Data processing and calculation

[1018] The server first records the service information for the product and proposal examples from past orders in a database. Next, it runs an algorithm to propose the optimal combination of plans and services based on the industry and customer needs. When making a proposal, it uses natural language processing technology to create proposal documents, proposal materials, and proposed prices all at once in a timely manner.

[1019] Specifically, when a user inputs product and customer information, the server analyzes this information using natural language processing technology and automatically generates a proposal document.The proposal document is automatically generated using the generative AI model GPT-3, and the price of the proposed product is automatically calculated and reflected in the proposal document.

[1020] Specific examples

[1021] For example, a user enters the following product and customer information:

[1022] Product information: "Product name: Smartphone, Price: 50,000 yen, Features: High-performance camera, Long-lasting battery"

[1023] Customer information: "Customer name: Taro Tanaka, Address: Tokyo, Purchase history: Smartwatch, Wireless earphones"

[1024] With this information, the server generates the following prompt:

[1025] Product information: Smartphone, Price: 50,000 yen, Features: High-performance camera, Long-lasting battery

[1026] Customer: Taro Tanaka, Tokyo, Smartwatch, Wireless Earphones

[1027] By inputting this prompt into GPT-3, a proposal document is automatically generated. The generated proposal document includes detailed product information, the proposal to the customer, pricing information, etc. This allows users to make quick and accurate proposals.

[1028] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1029] Step 1:

[1030] The user enters product and customer information.

[1031] Input: Product information (e.g., "Product name: Smartphone, Price: 50,000 yen, Features: High-performance camera, Long-lasting battery"), Customer information (e.g., "Customer name: Taro Tanaka, Address: Tokyo, Purchase history: Smartwatch, Wireless earphones")

[1032] Output: The entered product information and customer information is sent to the server.

[1033] Step 2:

[1034] The product information and customer information received by the server is analyzed using natural language processing technology.

[1035] Input: Product and customer information submitted by the user

[1036] Data processing: Perform text analysis.

[1037] Output: Parsed text data

[1038] Step 3:

[1039] Based on the analyzed text data, the server automatically generates a proposal document using a generative AI model (GPT-3).

[1040] Input: Parsed text data

[1041] Data calculation: Input a prompt sentence into GPT-3 and generate a proposed document.

[1042] Output: Generated proposal document

[1043] Step 4:

[1044] The server automatically generates proposal materials using a generative AI model.

[1045] Input: Parsed text data

[1046] Data calculation: Input a prompt sentence into GPT-3 and generate a proposal document.

[1047] Output: Generated proposal

[1048] Step 5:

[1049] The server automatically calculates the price of the proposed product and reflects it in the proposal document.

[1050] Input: Product information (price)

[1051] Data calculation: Calculate the price based on the product price information and add it to the proposal document.

[1052] Output: Proposal document with pricing information reflected

[1053] Step 6:

[1054] The server sends the generated proposal document and proposal materials to the user.

[1055] Input: Generated proposal documents and proposal materials

[1056] Output: Proposal document and proposal materials sent to the user's device

[1057] Step 7:

[1058] The user checks the received proposal document and proposal materials and makes a proposal to the customer.

[1059] Input: Proposal document and proposal materials sent from the server

[1060] Output: Proposal to the customer

[1061] Example 3

[1062] Next, a description will be given of Example 3 of Form Example 3. 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."

[1063] Conventional proposal systems mainly based their proposals on product service information and past proposal examples, without sufficient comparison with competitors' services. This made it difficult to clearly demonstrate the superiority of a company's services to customers. Furthermore, creating proposal documents and materials took time, making it difficult to make timely proposals. This resulted in problems such as being unable to respond quickly to customer needs and losing competitiveness.

[1064] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1065] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry type and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for storing competitor service information in a database and comparing it with the company's own services, and means for generating a report showing the advantages of the company's services based on the comparison results and sending it to the user. This makes it possible to clearly demonstrate the advantages of the company's services compared with those of competitors, enabling quick and effective proposals to be made to customers.

[1066] "Product service information" refers to detailed information about the products and services being offered.

[1067] "Examples of proposals for previously accepted projects" refers to examples of specific plans and services proposed for previously accepted projects.

[1068] "Recording means" refers to a method or device for storing information for future reference.

[1069] "Means for proposing optimal plans and service combinations tailored to specific industries and customer needs" refers to methods and devices for selecting and proposing optimal services and plans tailored to specific industries and customer requests.

[1070] "Natural language processing technology" refers to technology that enables computers to understand and process human language.

[1071] "Means for proposing proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner" refers to a method or device for quickly providing documents, materials, and fee information required for a proposal all at once.

[1072] "Competitor service information" refers to detailed information about products and services offered by other competing companies.

[1073] "Keeping in a database" means storing information in a database so that it can be accessed as needed.

[1074] "Means for comparing with one's own services" refers to methods or devices for comparing the services provided by one's own company with the services of competitors.

[1075] "Means for generating a report showing the superiority of one's own services based on the results of the comparison" refers to a method or device for using the results of the comparison to create a report that clearly shows the advantages of one's own services over those of competitors.

[1076] "Means for sending to user" refers to a method or device for delivering generated reports or information to a user.

[1077] This invention is a system that records service information for products, proposal examples from past orders, and which service combinations have been successful and increased sales, and then proposes optimal plans and service combinations tailored to the industry and customer needs. Furthermore, when making proposals, natural language processing technology is used to provide proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner.

[1078] Furthermore, the system has the function of storing information on competitors' services in a database and comparing it with the company's own services, making it possible to clearly demonstrate to customers the superiority of the company's services.

[1079] Hardware and software used

[1080] Hardware: Server (e.g. general-purpose server)

[1081] Software: Database management systems (e.g., MySQL), comparison algorithms (e.g., custom algorithms implemented in Python), natural language processing techniques (e.g., generative AI models)

[1082] Specific operation of the system

[1083] Server Operation

[1084] 1. Data recording and acquisition

[1085] The server records in a database service information for the products and proposal examples from past orders, as well as which services have been successfully combined with the proposals and resulted in increased sales.

[1086] Upon receiving a request from a user, the server retrieves the necessary information from the database.

[1087] 2. Proposing optimal plans and service combinations

[1088] The server proposes the optimal plan and service combination based on industry and customer needs, including analysis based on past success stories and sales data.

[1089] 3. Proposals using natural language processing technology

[1090] The server uses natural language processing technology to generate proposal documents, proposal materials, proposed fees, etc. all at once, allowing users to quickly submit proposals.

[1091] 4. Comparison with competitors

[1092] The server stores information about competitors' services in a database, compares it with its own service, and generates a report showing the superiority of its own service based on the comparison results.

[1093] 5. Submitting the report

[1094] The server sends the generated report to the user's device, where the user can check the report and understand the advantages of their company's service.

[1095] Specific examples

[1096] For example, suppose a user wants to compare their company's cloud storage service with a competitor's. The user sends a request from their device saying, "I want to compare cloud storage services." The server retrieves the following information from the database:

[1097] In-house cloud storage service: 1TB storage capacity, 500 yen per month, AES-256 bit encryption

[1098] Competitor A's cloud storage service: 500GB storage, ¥600 per month, AES-128 bit encryption

[1099] Competitor B's cloud storage service: 2TB storage capacity, 1000 yen per month, AES-256 bit encryption

[1100] The server compares this information and concludes that its service is "better than competitor A in terms of storage capacity and security features, but better than competitor B in terms of price." The server generates a report based on this conclusion and sends it to the user.

[1101] Prompt Sentence Examples

[1102] "Compare your cloud storage service with your competitors' cloud storage services. Compare storage capacity, price, and security features."

[1103] In this way, the server can clearly demonstrate the superiority of its service to the user. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1104] Step 1:

[1105] User submits a comparison request

[1106] Input: The user uses the device to input information about the services they want to compare and submit a comparison request. For example, they input a request such as "I want to compare cloud storage services."

[1107] Output: The device sends a request to the server.

[1108] Step 2:

[1109] The server receives the request and retrieves the information from the database.

[1110] Input: The server receives a request from the user, which includes the type of service to be compared and the specific comparison criteria.

[1111] Output: The server uses a database management system (e.g., MySQL) to retrieve service information from a database about its own and its competitors, such as cloud storage services, including storage capacity, pricing, and security features.

[1112] Step 3:

[1113] The server compares the information

[1114] Input: The server receives as input the service information of the company and its competitors retrieved from the database.

[1115] Output: The server uses a comparison algorithm (e.g., a custom algorithm implemented in Python) to compare your service with your competitors' services, specifically rating how superior your service is in each dimension (storage capacity, price, security features, etc.).

[1116] Step 4:

[1117] The server generates the comparison results

[1118] Input: The server receives the result of the comparison algorithm as input.

[1119] Output: The server generates a report to the user showing the advantages of the company's service. The report clearly shows the strengths of the company's service in each comparison category and its advantages over competitors.

[1120] Step 5:

[1121] The server sends the results to the user

[1122] Input: The server receives the generated report as input.

[1123] Output: The server sends the generated report to the user's device, where the user can check the report and understand the advantages of their company's service.

[1124] Adding specific actions

[1125] For example, when a user sends a request to "compare cloud storage services," the server retrieves the following information from the database:

[1126] In-house cloud storage service: 1TB storage capacity, 500 yen per month, AES-256 bit encryption

[1127] Competitor A's cloud storage service: 500GB storage, ¥600 per month, AES-128 bit encryption

[1128] Competitor B's cloud storage service: 2TB storage capacity, 1000 yen per month, AES-256 bit encryption

[1129] The server compares this information and concludes that its service is "better than competitor A in terms of storage capacity and security features, but better than competitor B in terms of price." The server generates a report based on this conclusion and sends it to the user.

[1130] (Application example 3)

[1131] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1132] The previous system recorded product service information and past proposal examples, and was able to propose optimal plans tailored to each industry and customer needs. However, it was not easy to compare plans with similar services offered by competitors. It was also difficult to clearly demonstrate the company's advantages when proposing plans, making it difficult to effectively appeal to customers. Furthermore, the system lacked the functionality to provide links that allowed users to easily purchase products and services that interested them.

[1133] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1134] In this invention, the server includes: means for recording product service information, proposal examples for past orders, and which service combinations have been successful and increased sales; means for proposing optimal plans and service combinations tailored to the type of industry and customer needs; means for using natural language processing technology to provide proposal documents, proposal materials, proposed fees, etc. in a timely manner when making proposals; means for storing information on equivalent products and services from other companies in a database and comparing them with the company's own products and services; means for presenting the comparison results to users and emphasizing the company's advantages; and means for providing links to easily purchase products and services that the user finds interesting. This makes it easy to compare products and services from competitors and clearly demonstrate the company's advantages. Furthermore, providing links to easily purchase products and services that the user finds interesting allows for effective appeal to customers.

[1135] "Product service information" refers to detailed information about the products and services being offered.

[1136] "Examples of proposals for previously accepted projects" are records of specific content and methods proposed in previously accepted projects.

[1137] "Means for recording which services have been successfully proposed and have increased sales" refers to a method or device for recording success stories and increased sales when multiple services are proposed in combination.

[1138] "Means for proposing optimal plans and service combinations tailored to specific industries and customer needs" refers to a method or device for proposing optimal plans and service combinations tailored to specific industries and customer requests.

[1139] "A means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. at the time of proposal" refers to a method or device for quickly generating and presenting proposal documents, materials, fees, etc. at the time of proposal using natural language processing technology.

[1140] "Means for storing information on comparable products and services of other companies in a database and comparing them with one's own products and services" refers to a method or device for storing information on similar products and services of competitors in a database and using that information to compare one's own products and services.

[1141] "Means for presenting the results of the comparison to users and emphasizing the advantages of your company" refers to methods and devices for presenting the results of the comparison to users and emphasizing the superiority of your company's products and services.

[1142] "Means for providing links that allow users to easily purchase products or services that interest them" refers to a method or device that provides links that allow users to easily purchase products or services that interest them.

[1143] The system for implementing this invention includes a server, a user terminal, and a database. The server has a means for recording service information for commercial products, proposal examples for past orders, and which service combinations have been successful and led to increased sales. The system also includes a means for proposing optimal plans and service combinations tailored to the type of business and customer needs.

[1144] The server uses natural language processing technology to generate and present proposal documents, proposal materials, proposed fees, etc. in a timely manner at the time of proposal submission, allowing users to submit proposals quickly and efficiently.

[1145] Furthermore, the server stores information on similar products and services from other companies in a database, and has a means of comparing its own products and services with those of other companies. The results of the comparison are presented to the user, allowing them to emphasize their own company's advantages. This allows users to easily compare their products with those of their competitors and appeal to customers about their company's strengths.

[1146] The user terminal receives the information provided by the server and displays it to the user. It also includes a means for providing links that allow the user to easily purchase products and services that interest them, allowing the user to quickly purchase the products and services that interest them.

[1147] For example, a user opens a "Service Comparison Assistant" app and adds their company's new service, "Premium Delivery." The app then compares it with similar services from competitors and displays the differences in price and features. The user confirms that their service is cheaper than their competitors' and creates materials to highlight its advantages to customers.

[1148] An example of a prompt is as follows:

[1149] "Add a new service called 'Premium Delivery' and compare it with a similar service from a competitor. Show us the comparison and tell us how you would highlight the advantages of your service."

[1150] The system is built using a database management system and a Python web framework. The database stores product service information, past proposal examples, and competitor service information, and the server processes and calculates the data based on this information. User devices include smartphones and tablets.

[1151] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1152] Step 1:

[1153] The user enters new service information.

[1154] Input: The user enters information for a new service called "Premium Shipping."

[1155] Specific operation: Enter information such as the service name, description, and price on the application screen of the user's terminal.

[1156] Output: The entered service information is sent to the server.

[1157] Step 2:

[1158] The server stores the entered service information in a database.

[1159] Input: New service information sent from the user terminal.

[1160] Specific operation: The server analyzes the received service information and stores it in a database.

[1161] Output: The new service information is added to the database.

[1162] Step 3:

[1163] The server retrieves information on comparable services from competitors from a database.

[1164] Input: A search query based on the new service information.

[1165] Specific operation: The server searches the service information of competitors in the database and obtains information on comparable services.

[1166] Output: Obtained competitor service information.

[1167] Step 4:

[1168] The server compares its services with those of its competitors.

[1169] Input: Your company's new service information and competitors' equivalent service information.

[1170] What it does: The server compares its services with those of competitors on items such as price, features, and functionality.

[1171] Output: The comparison results are generated.

[1172] Step 5:

[1173] The server transmits the comparison result to the user terminal.

[1174] Input: The generated comparison results.

[1175] Specific operation: The server converts the comparison result into a data format suitable for transmission to the user terminal, and transmits it.

[1176] Output: The comparison results are displayed on the user's terminal.

[1177] Step 6:

[1178] The user checks the comparison results and, if necessary, creates materials to present to the customer.

[1179] Input: The comparison results displayed on the user's terminal.

[1180] Specific operation: The user checks the comparison results and creates proposals or presentation materials as necessary.

[1181] Output: Materials to present to the customer.

[1182] Step 7:

[1183] Provide links that allow users to easily purchase products or services that interest them.

[1184] Input: Information about the products or services in which the user is interested.

[1185] Specific operation: The user terminal generates a purchase link for the product or service of interest and provides it to the user.

[1186] Output: A purchase link is displayed on the user's device.

[1187] The above is the flow of processing of the program of the system that realizes the application example.

[1188] 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.

[1189] "Example 1"

[1190] One embodiment of the present invention is a system incorporating an emotion engine. This system recognizes a user's emotions and adjusts suggestions based on those emotions. Specifically, during a dialogue with the user, the emotion engine analyzes the user's emotions from their tone of voice, facial expressions, and word choice. If the emotion is positive, the system strengthens the suggestions, and if it is negative, the system softens the suggestions. For example, if the user expresses joy, the system makes more aggressive suggestions. Conversely, if the user expresses dissatisfaction or confusion, the system tone down the suggestions and suggests solutions to the user's problems and concerns.

[1191] "Example 2"

[1192] The emotion engine can also track changes in a user's emotions in real time and dynamically adjust the content of its suggestions in response to those changes. For example, if the system senses that a user is initially very interested but gradually loses interest, it can change the direction of its suggestions and make new suggestions to recapture the user's interest. In this way, a system that incorporates an emotion engine can sensitively detect a user's emotions and make optimal suggestions based on those emotions.

[1193] "Example 3"

[1194] Furthermore, the emotion engine can learn a user's emotional patterns and predict future emotional changes based on those patterns. For example, if the system learns that a particular user always expresses anger when receiving a particular suggestion, it can avoid making that suggestion to that user or change the way it makes the suggestion. In this way, a system combined with an emotion engine can understand and adapt to the user's emotions, thereby making more effective suggestions.

[1195] The processing flow of each embodiment will be described below.

[1196] "Example 1"

[1197] Step 1: A user interaction is initiated.

[1198] Step 2: The emotion engine analyzes the user's emotions based on their tone of voice, facial expressions, and choice of words.

[1199] Step 3: Based on the analysis results, if the user's sentiment is positive, the suggestion is strengthened, and if it is negative, the suggestion is softened.

[1200] "Example 2"

[1201] Step 1: A user interaction is initiated.

[1202] Step 2: The emotion engine tracks the user's emotional changes in real time.

[1203] Step 3: Dynamically adjust your offers as sentiment changes.

[1204] "Example 3"

[1205] Step 1: A user interaction is initiated.

[1206] Step 2: The emotion engine learns the user's emotional patterns.

[1207] Step 3: Based on the learned patterns, predict future changes in emotion and adjust the suggestions accordingly.

[1208] Example 1

[1209] Next, a description will be given of Example 1 of Form 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."

[1210] Conventional recommendation systems made suggestions based on product service information and past success stories, but lacked the ability to adjust the content of the suggestions based on the user's emotions. This resulted in the inability to make optimal suggestions based on the user's emotions, which led to a low success rate for the suggestions. Furthermore, the system lacked the ability to reflect user feedback in real time and readjust the content of the suggestions, making it difficult to respond quickly to user needs.

[1211] 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.

[1212] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful and increased sales; means for proposing optimal plans and service combinations tailored to industry and customer needs; means for using natural language processing technology to provide timely proposal documents, proposal materials, proposed fees, etc. all at once when making a proposal; means for analyzing user emotions and adjusting the proposal content based on those emotions; and means for receiving user feedback and readjusting the proposal content. This enables optimal proposals based on user emotions and improves the success rate of proposals. It also makes it possible to quickly readjust the proposal content by reflecting user feedback in real time.

[1213] "Product service information" refers to detailed information about the products and services being offered.

[1214] "Examples of proposals for previously accepted projects" refers to specific plans and combinations of services proposed for previously accepted projects.

[1215] "Means for recording which services are successfully combined with proposals and increase sales" refers to a means for recording successful proposal combinations and the resulting sales data.

[1216] "Means of proposing optimal plans and service combinations tailored to industry and customer needs" refers to means of making optimal proposals based on specific industry and customer requirements.

[1217] "Natural language processing technology" refers to technology for understanding and generating human language.

[1218] "A means of proposing proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner" refers to a means of quickly providing the documents, materials, and fee information required for a proposal all at once.

[1219] The term "means for analyzing the user's emotions and adjusting the content of the proposal based on the emotions" refers to a means for analyzing the user's emotions and changing the content of the proposal based on the results of the analysis.

[1220] "Means for receiving user feedback and readjusting the content of the proposal" refers to means for receiving opinions and reactions from users and readjusting the content of the proposal based on those opinions and reactions.

[1221] MODE FOR CARRYING OUT THE INVENTION

[1222] The system of the present invention is equipped with a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales. This database provides information for proposing optimal plans and service combinations tailored to the type of industry and customer needs. Specifically, it has the function of analyzing past success stories and sales growth, and proposing optimal plans and service combinations based on that.

[1223] Hardware and software used

[1224] Hardware

[1225] Server: Manages the database and generates proposals.

[1226] Terminal: Provides an interface with the user and performs sentiment analysis.

[1227] software

[1228] Database management system (DBMS): Manages product service information and past proposal examples.

[1229] Natural Language Processing Engine: Generates proposal documents, proposal materials, proposed pricing, etc.

[1230] Sentiment analysis engine: Analyzes the user's tone of voice, facial expressions, and word choice.

[1231] Specific operation of the system

[1232] 1. Accepting user input

[1233] The user inputs a question or request to the system through the terminal, for example, a prompt such as "Please propose an implementation plan for a new marketing tool."

[1234] 2. Retrieve relevant information from the database

[1235] When the server receives the user's input, it searches the database for service information, past success stories, and sales data for related products. For example, it retrieves success stories from companies that have introduced similar marketing tools in the past.

[1236] 3. Analyze user emotions with an emotion engine

[1237] The device uses an emotion engine to analyze the user's emotions during a conversation, specifically by analyzing the user's tone of voice, facial expressions, and word choice in real time to determine whether the emotion is positive or negative.

[1238] 4. Generate proposals

[1239] The server generates optimal suggestions based on the information retrieved from the database and the analysis results of the emotion engine. For example, if the user expresses positive emotions, it will make proactive suggestions including additional services and options.

[1240] 5. Present the suggestions to the user

[1241] The device then presents the generated proposal to the user, providing specific suggestions such as, "Companies that have implemented similar tools in the past have seen a 20% increase in sales. Additionally, additional support plans are available."

[1242] 6. Get user feedback

[1243] The user provides feedback on the presented suggestions, which the device receives and sends to the server, which then adjusts the suggestions or provides additional information based on the feedback.

[1244] Prompt Sentence Examples

[1245] "Please propose a plan for implementing marketing tools based on past success stories."

[1246] "Please suggest what to do if a user expresses concern."

[1247] The above is a specific embodiment for implementing the system of the present invention. This system enables optimal suggestions based on the user's emotions, improving the success rate of suggestions. It also makes it possible to quickly readjust the content of suggestions by reflecting user feedback in real time.

[1248] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1249] Step 1:

[1250] The user inputs a question or request to the system through the terminal. For example, the user inputs a prompt such as "Please propose an implementation plan for a new marketing tool." The input prompt is sent from the terminal to the server.

[1251] Step 2:

[1252] The server analyzes the received prompt and searches the database for relevant product service information, past success stories, and sales data. Specifically, it accesses the database using an SQL query to retrieve the relevant information. The retrieved information is temporarily stored on the server.

[1253] Step 3:

[1254] The device uses an emotion engine to analyze the user's emotions during a conversation. Specifically, it analyzes the user's tone of voice, facial expressions, and word choice in real time to determine whether the emotion is positive or negative. The analysis results are sent from the device to the server.

[1255] Step 4:

[1256] The server generates optimal proposal content based on information obtained from the database and the analysis results of the emotion engine. Specifically, it uses a generative AI model to generate proposal documents, proposal materials, and proposed prices that correspond to the user's emotions, while referring to past success stories and sales data. The generated proposal content is temporarily stored on the server.

[1257] Step 5:

[1258] The terminal then presents the generated proposal to the user. Specifically, it displays the proposal documents and materials on the screen and presents the proposed price in text format. For example, it makes a specific proposal such as, "Companies that have implemented a similar tool in the past have seen a 20% increase in sales. Additionally, additional support plans are available."

[1259] Step 6:

[1260] The user provides feedback on the presented proposal. The feedback is sent from the device to the server. The server analyzes the received feedback and readjusts the proposal as necessary. Specifically, it analyzes the feedback and generates new proposals using the generative AI model again. The readjusted proposals are sent back to the device and presented to the user.

[1261] The above are the specific processing steps of the program of this system.

[1262] (Application example 1)

[1263] Next, a description will be given of Application Example 1 of Form 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."

[1264] Conventional product proposal systems have difficulty proposing optimal plans and service combinations tailored to customer needs, and because the proposals are uniform, they are unable to respond flexibly to the customer's emotions or circumstances. Furthermore, because the proposals are not provided in a timely manner, there is also the problem of not being able to stimulate the customer's desire to purchase. To solve these issues, a system is needed that can recognize the customer's emotions and adjust the proposal content based on them.

[1265] 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.

[1266] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for recognizing user emotions and adjusting the proposal content based on those emotions, means implemented as a smartphone application to propose optimal product and service combinations based on the user's purchase history and browsing history, and means for analyzing user emotions using an emotion engine and adjusting the proposal content. This enables flexible proposals according to the customer's emotions and situation, thereby increasing the customer's desire to purchase.

[1267] "Product service information" refers to detailed information about the products and services being offered.

[1268] "Examples of proposals for previously accepted projects" refers to specific examples of plans and services proposed for previously accepted projects.

[1269] "Means for recording which service combinations have been successful in proposing and increasing sales" refers to a means for recording successful service combinations and the resulting sales data.

[1270] "Means for proposing optimal plans and service combinations tailored to specific industries and customer needs" refers to means for proposing optimal plans and service combinations based on specific industries and customer needs.

[1271] "A means of using natural language processing technology to propose proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner at the time of proposal" refers to a means of using natural language processing technology to quickly generate and propose proposal documents, materials, fees, etc. all at once.

[1272] "Means for recognizing the user's emotions and adjusting the content of suggestions based on those emotions" refers to means for analyzing the user's emotions and appropriately adjusting the content of suggestions based on the results.

[1273] "A means implemented as a smartphone application that suggests optimal combinations of products and services based on a user's purchase history and browsing history" refers to a means that operates as a smartphone application and suggests optimal combinations of products and services based on a user's past purchase history and browsing history.

[1274] "Means for analyzing a user's emotions using an emotion engine and adjusting the content of suggestions" refers to means for analyzing a user's emotions using an emotion engine and appropriately adjusting the content of suggestions based on the results of that analysis.

[1275] The system for implementing this invention includes a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful and led to increased sales. The system has the function of proposing optimal plans and service combinations tailored to the type of industry and customer needs. Furthermore, when making a proposal, natural language processing technology can be used to generate and propose proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner.

[1276] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions and adjusts the content of suggestions based on those emotions. Specifically, during a conversation with the user, the emotion engine analyzes the user's emotions from their tone of voice, facial expressions, and choice of words, and strengthens the suggestions if the emotion is positive, and softens the suggestions if the emotion is negative.

[1277] This system is implemented as a smartphone application that proposes optimal combinations of products and services based on the user's purchase and browsing history. By analyzing the user's emotions using an emotion engine and adjusting the proposals accordingly, it is possible to increase the user's purchasing motivation.

[1278] Hardware and software used

[1279] Hardware: Smartphone

[1280] Software: Python, Library A, Library B

[1281] Data processing and calculation

[1282] The server does the following:

[1283] 1. Sentiment analysis: Library A is used to analyze sentiment from user input (e.g., "I would like to know more about this product.").

[1284] 2. Proposal generation: Library B is used to generate optimal product and service proposals based on the user ID and analyzed emotions.

[1285] 3. Output: Provide the analyzed sentiment and suggestions to the user.

[1286] Specific examples

[1287] For example, if a user types "I'd like to know more about this product," the emotion engine will analyze the user's interests and proactively suggest related products and services. Similarly, if a user types "This product is a bit expensive," the emotion engine will analyze the user's dissatisfaction and suggest alternative products at a more affordable price.

[1288] Prompt Sentence Examples

[1289] If a user types, "I'd like to know more about this product," the sentiment engine will analyze the user's interests and proactively suggest related products and services.

[1290] In this way, a personalized shopping assistant application for online shopping sites can be realized that makes optimal suggestions based on the user's emotions.

[1291] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1292] Step 1:

[1293] A user launches a smartphone application and inputs a question or request regarding a product or service.

[1294] Input: User text input (e.g., "I'd like to learn more about this product.")

[1295] Output: User's text input data

[1296] Specific behavior: A user enters text into an input field in an application and presses the submit button.

[1297] Step 2:

[1298] The terminal receives the user's input and sends it to the emotion engine.

[1299] Input: User text input data

[1300] Output: Text data sent to the emotion engine

[1301] What happens: The application sends the user's input to the emotion engine's API.

[1302] Step 3:

[1303] The server analyzes the user's emotions using an emotion engine.

[1304] Input: Text data sent to the emotion engine

[1305] Output: User emotion data (e.g., interest, joy, dissatisfaction, etc.)

[1306] What it does: The emotion engine analyzes text data and identifies the user's emotions.

[1307] Step 4:

[1308] The server receives the user's emotion data and sends it to Engine A.

[1309] Input: User emotion data

[1310] Output: Emotion data sent to Engine A

[1311] Specific operation: The emotion engine sends the analysis results to the API of engine A.

[1312] Step 5:

[1313] The server uses engine A to suggest optimal combinations of products and services based on the user's purchase history and browsing history.

[1314] Input: User emotional data, purchase history, browsing history

[1315] Output: Data proposing optimal products and services

[1316] Specific operation: Engine A retrieves the user's history from the database and generates optimal suggestions based on the emotional data.

[1317] Step 6:

[1318] The server transmits the generated proposal data to the terminal.

[1319] Input: Data on optimal products and services

[1320] Output: Proposal data sent to the device

[1321] What happens: The server sends the proposal data to the application's API.

[1322] Step 7:

[1323] The terminal receives the proposal data and displays it to the user.

[1324] Input: Proposal data sent from the server

[1325] Output: The suggestions that are displayed to the user

[1326] What happens: The app displays the suggestion data in the user interface.

[1327] In this way, optimal products and services are proposed based on the user's emotions.

[1328] Example 2

[1329] Next, a description will be given of Example 2 of Form 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."

[1330] With conventional proposal systems, creating proposal documents and materials required a lot of time and effort, making it difficult to make proposals efficiently. Furthermore, it was not possible to dynamically adjust proposals in response to changes in user sentiment, making it difficult to maximize the effectiveness of proposals. Furthermore, comparing with competitors and collecting and re-adjusting feedback was time-consuming, making them insufficient in today's business environment, where rapid response is required.

[1331] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1332] In this invention, the server includes means for recording service information of merchandise, proposal examples of past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for tracking user emotions in real time and dynamically adjusting the proposal content in response to changes in the emotions, means for automatically generating proposal documents and materials using a generative AI model, and means for collecting user feedback and readjusting the proposal content. This enables efficient and effective proposal work and allows optimal proposals to be made in response to user emotions.

[1333] "Product service information" refers to detailed information about the products and services being offered.

[1334] "Examples of proposals for previously accepted projects" refers to information that records proposal content and success stories for previously accepted projects.

[1335] "Natural language processing technology" refers to technology that allows computers to understand, interpret, and generate human language.

[1336] A "generative AI model" refers to an artificial intelligence model that generates new data or sentences based on given input data.

[1337] "Means for tracking user emotions in real time" refers to technology that analyzes a user's facial expressions, tone of voice, etc., and detects changes in emotions in real time.

[1338] "Dynamic adjustment means" refers to technology that changes the content of suggestions in real time in response to changes in the user's emotions.

[1339] "Means for collecting feedback" refers to techniques for collecting opinions and requests from users.

[1340] "Means for readjusting proposals" refers to techniques for readjusting proposals based on collected feedback.

[1341] This invention is a system that creates proposal documents, proposal materials, proposed fees, etc. in a timely manner all at once. Specifically, it combines natural language processing technology with an emotion engine to streamline proposal work and make optimal proposals based on the user's emotions.

[1342] The server has a means of recording product service information, proposal examples from past orders, and which services have been successfully combined with proposals to increase sales, making it possible to make effective proposals based on past data.

[1343] The server has a means for proposing the optimum plan and service combination according to the type of business or customer needs, which enables customized proposals to meet the specific needs of the customer.

[1344] The server has a means of using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. Specifically, it uses a generative AI model (e.g., GPT-4) to generate natural-sounding sentences based on data provided by the user.

[1345] The device uses a camera and microphone to track the user's emotions in real time. The emotion engine analyzes the user's facial expressions and vocal tone to detect changes in emotion, allowing it to dynamically adjust recommendations based on the user's changing interests.

[1346] The server has a means for automatically generating proposal documents and materials using a generative AI model. Specifically, the server generates proposal documents by inputting the following prompt sentences into the generative AI model:

[1347] "A user is looking for suggestions for a new marketing strategy. Use market data and past campaign information to generate a proposal document based on the latest trends in the target market. Also include the ability to dynamically adjust the proposal based on user sentiment."

[1348] The server has a means for collecting feedback from users and readjusting the proposed content. The users input their opinions and requests regarding the proposed content through their terminals, and the server analyzes the feedback and readjusts the proposed content.

[1349] For example, if a user requests a proposal for a new marketing strategy, the server collects and analyzes market data and past campaign information, and uses a generative AI model to generate a proposal document like this:

[1350] "This new marketing strategy is designed based on the latest trends in our target market. Specifically, we aim to attract the attention of younger demographics by strengthening social media advertising and leveraging influencer marketing."

[1351] If the user starts to lose interest as they read through the suggestions, the emotion engine detects this change, and the server uses a generative AI model to generate new suggestions to bring the user back into the loop:

[1352] "Furthermore, by implementing the latest data analysis tools, we can measure the effectiveness of campaigns in real time and quickly adjust our strategies."

[1353] In this way, the system enables efficient and effective proposal work through collaboration between the server, terminals, and users.

[1354] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1355] Step 1:

[1356] Collecting input from the user

[1357] The user inputs the basic information and past data required for the proposal through the terminal. Specifically, the user provides market data and past campaign information.

[1358] Input: Market data, past campaign information

[1359] Output: Collected data

[1360] Specific operation: The user enters the necessary information into the input form on the terminal and presses the send button. The terminal then sends the entered data to the server.

[1361] Step 2:

[1362] Data analysis and information extraction

[1363] The server receives the data provided by the user and analyzes it using natural language processing technology. Specifically, the server performs the following processes:

[1364] Data preprocessing: noise removal and data normalization.

[1365] Information extraction: Extract important keywords and phrases.

[1366] Contextual understanding: Understand the context of the data and identify relevant information.

[1367] Input: Collected data

[1368] Output: Analysis results (important keywords, phrases, contextual information)

[1369] How it works: The server feeds the collected data into an analysis algorithm to remove noise and normalize it, then uses natural language processing techniques to extract key information and understand the context.

[1370] Step 3:

[1371] Proposal and document generation

[1372] The server generates proposal documents and materials using a generative AI model (e.g., GPT-4) based on the analysis results. The specific operations are as follows:

[1373] Prompt Generation: Create a prompt based on the suggestions.

[1374] Sentence generation: A prompt sentence is input into the generative AI model to generate natural-sounding sentences.

[1375] Document creation: Proposal materials (e.g., presentation slides) are created based on the generated text.

[1376] Input: Analysis results (important keywords, phrases, contextual information)

[1377] Output: Proposal documents, proposal materials

[1378] Specific operation: The server generates a prompt sentence based on the analysis results and inputs it into the generative AI model. A proposal document is created based on the generated sentence.

[1379] Step 4:

[1380] Emotion Tracking and Dynamic Adjustment

[1381] The device uses a camera and microphone to track the user's emotions in real time. The emotion engine analyzes the user's facial expressions and voice tone to detect changes in emotions. Specifically, it works as follows:

[1382] Facial expression analysis: Analyzes camera footage and estimates emotions from the user's facial expressions.

[1383] Voice analysis: Analyzes voice data collected by a microphone and estimates emotions from the tone and speed of the voice.

[1384] Dynamic Adjustment: When the emotion engine detects a change in emotion, the server uses a generative AI model to generate new suggestions to recapture the user's interest.

[1385] Input: Camera video, audio data

[1386] Output: Sentiment analysis results, new proposed document

[1387] Specific operation: The device uses a camera and microphone to collect the user's facial expressions and voice, and sends them to the emotion engine. The emotion engine then sends the analysis results to the server, which then generates a new proposal document.

[1388] Step 5:

[1389] Submitting suggestions and providing feedback

[1390] The server sends the generated proposal document and materials to the terminal and presents them to the user. The user checks the proposal and provides feedback. The specific operations are as follows:

[1391] Proposal presentation: The terminal displays the proposal document and materials.

[1392] Feedback collection: Users input their opinions and requests regarding the proposal.

[1393] Feedback Analysis: The server analyzes the user's feedback and readjusts the suggestions if necessary.

[1394] Input: Proposal documents, proposal materials, user feedback

[1395] Output: Reworked proposal document, proposal materials

[1396] Specific operation: The terminal displays the proposal document and materials, and the user inputs feedback. The server analyzes the feedback and adjusts the proposal content as necessary.

[1397] (Application example 2)

[1398] Next, a description will be given of Application Example 2 of Form 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."

[1399] Conventional recommendation systems have difficulty responding to changes in users' emotions and interests, making it difficult for the content of their recommendations to continue to attract the user's attention. Creating proposal documents and materials also requires a lot of time and effort, making it difficult to make efficient recommendations. Furthermore, because they are unable to utilize users' purchase and search histories to make optimal product recommendations, they are unable to maximize the user's purchasing motivation.

[1400] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1401] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful and increased sales, means for proposing optimal plans and service combinations tailored to the type of industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., means for tracking user emotions in real time and dynamically adjusting the proposal content in response to changes in those emotions, means for generating optimal product proposals based on the user's purchase history and search history, means for automatically generating proposal documents using a generative AI model, and means for capturing camera footage and performing emotion analysis. This enables efficient and effective proposals that respond to changes in the user's emotions and interests.

[1402] "Product service information" refers to detailed information about the products and services being offered.

[1403] "Example proposals for previously accepted projects" refers to examples of proposals created based on previously accepted projects.

[1404] "Means for recording whether a proposal in combination with a service is successful and sales are increasing" refers to a method or device for recording which service combinations are successful and contribute to sales.

[1405] "Means for proposing optimal plans and service combinations tailored to specific industries and customer needs" refers to methods and devices for proposing optimal plans and service combinations tailored to specific industries and customer requests.

[1406] "Natural language processing technology" refers to the technology that allows computers to understand and process human language.

[1407] "Means for proposing proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner" refers to a method or device for quickly preparing and proposing the documents, materials, and fees required for a proposal all at once.

[1408] "Means for tracking a user's emotions in real time and dynamically adjusting the content of suggestions in response to those changes" refers to a method or device for monitoring changes in a user's emotions in real time and appropriately changing the content of suggestions in response to those changes.

[1409] "Means for generating optimal product suggestions based on a user's purchase history and search history" refers to a method or device for analyzing a user's past purchase history and search history and suggesting optimal products based on that.

[1410] A "generative AI model" refers to a model that uses artificial intelligence to generate new information and suggestions from data.

[1411] "Means for capturing camera footage and performing emotion analysis" refers to a method or device for capturing footage using a camera and analyzing user emotions from the footage.

[1412] A system for carrying out this invention has the following configuration: The server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry type and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., at the time of proposal, means for tracking user emotions in real time and dynamically adjusting the proposal content in response to changes in those emotions, means for generating optimal product proposals based on the user's purchase history and search history, means for automatically generating proposal documents using a generative AI model, and means for capturing camera footage and performing sentiment analysis.

[1413] Hardware and software used

[1414] Hardware: Smartphone camera, smartphone display

[1415] Software: OpenAI GPT-3 API, camera footage capture and processing

[1416] Data processing and calculation

[1417] 1. Camera image capture: Capture the user's image in real time using the smartphone camera. Library X is used to acquire the image and process it frame by frame.

[1418] 2. Emotion analysis: Library Y is used to analyze the user's emotions from the captured video. Specifically, it analyzes the user's facial expressions and voice and tracks changes in emotions in real time.

[1419] 3. Proposal Document Generation: Using the OpenAI GPT-3 API, optimal product proposals are generated based on the user's purchase and search history. The generated proposal documents and materials are dynamically adjusted according to the user's emotions.

[1420] 4. Dynamic Adjustment: Adapting recommendations in real time based on sentiment analysis, for example, suggesting new products or services if the user is losing interest.

[1421] Specific examples

[1422] If the system detects a loss of interest in a user's facial expression while browsing products through a smartphone camera, it will suggest new products. For example, if a user searches for "smartphone cases" but loses interest, it will suggest related products such as "smartwatches" and "wireless earphones."

[1423] Prompt Sentence Examples

[1424] User data: {'Purchase history': ['Smartphone case', 'Charger'], 'Search history': ['Smartwatch', 'Wireless earphones']}

[1425] Generate optimal product suggestions.

[1426] In this way, it is possible to respond to changes in the user's emotions and interests and make efficient and effective suggestions.

[1427] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1428] Step 1:

[1429] The user activates the smartphone camera and views the product.

[1430] Input: User video

[1431] Output: Captured video frames

[1432] Specific operation: The smartphone camera captures the user's video in real time and obtains video frames.

[1433] Step 2:

[1434] The device processes the captured video frames and prepares the data for sentiment analysis.

[1435] Input: Captured video frames

[1436] Output: Data for sentiment analysis

[1437] Specific operation: Library X is used to analyze the video frame and extract the user's face.

[1438] Step 3:

[1439] The device uses library Y to analyze the user's emotions from the extracted facial parts.

[1440] Input: Data for emotion analysis (face area)

[1441] Output: Sentiment analysis results (e.g., interest, indifference, joy, etc.)

[1442] Specific operation: Using the emotion analysis model of library Y, analyze the user's emotion from the extracted facial parts in real time.

[1443] Step 4:

[1444] The server uses the OpenAI GPT-3 API to generate optimal product suggestions based on the user's purchase history and search history.

[1445] Input: User's purchase history, search history

[1446] Output: Proposal document

[1447] Specific operation: The server sends the user's purchase history and search history as prompts to the GPT-3 API, which generates optimal product suggestions.

[1448] Step 5:

[1449] The server dynamically adjusts the generated suggested documents based on the sentiment analysis results.

[1450] Input: Sentiment analysis results, proposal document

[1451] Output: Adjusted proposal document

[1452] Specific operation: The server evaluates the sentiment analysis results and dynamically adjusts the proposal document, such as adding new product proposals if the user is losing interest.

[1453] Step 6:

[1454] The terminal displays the adjusted proposal document to the user.

[1455] Input: Adjusted proposal document

[1456] Output: The proposal document that is displayed to the user

[1457] Specific operation: The adjusted proposal document is displayed on the smartphone display and provided to the user.

[1458] In this way, it is possible to respond to changes in the user's emotions and interests and make efficient and effective suggestions.

[1459] Example 3

[1460] Next, a description will be given of Example 3 of Form Example 3. 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."

[1461] With conventional systems, it was difficult not only to record product service information and past proposal examples, but also to compare them with competitors' services and learn user emotional patterns to predict future changes in emotion. It was also difficult to use natural language processing technology to create proposal documents and materials in a timely manner at the time of proposal. This led to the issue of being unable to make efficient and effective proposals when proposing optimal plans and service combinations to customers.

[1462] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1463] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for storing competitor service information in a database and comparing it with the company's own services, and means for learning user emotional patterns and predicting future emotional changes based on those patterns. This enables efficient and effective proposals to be made to customers.

[1464] "Product service information" is detailed data about the products and services offered.

[1465] "Examples of proposals for previously accepted orders" are specific proposals that have been previously submitted to clients and have been successfully accepted.

[1466] "A means of recording which services were successfully combined with the proposal and resulted in increased sales" refers to a method of saving successful cases where multiple services were combined and proposed, along with the sales data.

[1467] "Means of proposing optimal plans and service combinations tailored to the industry and customer needs" refers to a method of selecting and proposing the most appropriate plans and service combinations based on the customer's industry and specific needs.

[1468] "A means of using natural language processing technology to provide proposal documents, materials, fees, etc. all at once in a timely manner at the time of proposal" is a method of using natural language processing technology to quickly generate and provide the documents, materials, and fee information required for a proposal.

[1469] "Method of storing information about competitors' services in a database and comparing it with one's own service" refers to a method of storing information about competitors' services in a database and using that information to compare one's own service with another's.

[1470] "Means for learning a user's emotional patterns and predicting future emotional changes based on those patterns" refers to a method for analyzing a user's past emotional responses and predicting future emotional changes based on that data.

[1471] MODE FOR CARRYING OUT THE INVENTION

[1472] This invention is a system that includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales; means for proposing optimal plans and service combinations according to industry and customer needs; means for using natural language processing technology to provide proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner when making a proposal; means for storing competitor service information in a database and comparing it with one's own services; and means for learning user emotional patterns and predicting future emotional changes based on those patterns.

[1473] Server Roles

[1474] The server uses a database management system (e.g., MySQL) to record product service information and proposal examples for past orders. The server uses a web scraping tool to collect competitor service information and store it in a database. In addition, the server uses a machine learning algorithm (e.g., TENSORFLOW) to learn users' emotional patterns and predict future emotional changes.

[1475] Device Role

[1476] The terminal provides an interface for users to compare their company's services with those of competitors. The terminal uses front-end technologies such as JavaScript to display data in a user-friendly format. When a user requests a comparison, the terminal sends a request to the server to retrieve the required data.

[1477] User Roles

[1478] Users can compare their company's services with those of competitors through their devices. By checking the comparison results, users can understand the advantages of their own services. In addition, when making a proposal, users can use natural language processing technology to generate proposal documents, proposal materials, and proposed prices all at once in a timely manner.

[1479] Specific examples

[1480] For example, if a user wants to compare their company's cloud storage service with a competitor's, they can input the following prompt into the generative AI model:

[1481] Example prompt sentence:

[1482] "Compare your cloud storage service with your competitors' services."

[1483] When the user enters this prompt, the system sends a request to the server to retrieve the necessary information from the database. The terminal visually displays the retrieved information, demonstrating the advantages of the company's services to the user.

[1484] Also, if the model learns a pattern of users expressing anger toward a particular suggestion, it can input prompt sentences like the following into the generative AI model:

[1485] Example prompt sentence:

[1486] "Please predict how User A will react to the new pricing proposal."

[1487] When this prompt is entered, the emotion engine predicts User A's emotions based on past data, and the system adjusts to change the way it makes suggestions or avoid making suggestions.

[1488] The above is an embodiment of the present invention. This system makes it possible to provide efficient and effective proposals to customers. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1489] Step 1:

[1490] The server collects service information of competitors and stores it in a database.

[1491] Input: Competitor's website URL

[1492] What it does: The server uses a web scraping tool to collect service information from competitors' websites.

[1493] Data processing: Organize the collected data and store it in a MySQL database.

[1494] Output: Updated database

[1495] Step 2:

[1496] The user sends a request via the terminal to compare the company's services with those of competitors.

[1497] Input: The type of service you want to compare (e.g., cloud storage services)

[1498] Specific actions: The user selects a service type on the device interface and clicks the "Compare" button.

[1499] Data processing: The device sends this request in JSON format to the server.

[1500] Output: Request to server

[1501] Step 3:

[1502] The server retrieves the necessary information from the database and returns it to the terminal.

[1503] Input: User request (JSON format)

[1504] What happens: The server parses the incoming request and queries the MySQL database for the corresponding service information.

[1505] Data processing: Convert the acquired data into JSON format.

[1506] Output: Response to the terminal (JSON format)

[1507] Step 4:

[1508] The terminal visually displays the acquired data.

[1509] Input: Response from the server (JSON format)

[1510] Specific behavior: The device parses the received JSON data and uses a front-end framework to display it in a user-friendly format.

[1511] Data processing: Convert JSON data into tabular or graph format.

[1512] Output: The comparison results that are displayed to the user

[1513] Step 5:

[1514] The emotion engine learns the user's emotional patterns.

[1515] Input: User's past reaction data (e.g., anger, joy, sadness)

[1516] What it does: The emotion engine uses machine learning algorithms (e.g., TENSORFLOW) to learn the user's emotional patterns.

[1517] Data processing: The collected emotion data is input into the neural network model and the model is trained.

[1518] Output: A trained emotion prediction model

[1519] Step 6:

[1520] The emotion engine predicts future emotional changes of the user.

[1521] Input: Trained emotion prediction model, new user suggestion data

[1522] Specific behavior: The emotion engine predicts how a user will feel about a particular suggestion based on learned emotion patterns.

[1523] Data processing: New proposed data is input into the model to predict sentiment changes.

[1524] Output: Emotion prediction result

[1525] The above are the specific processing steps of the program of this system.

[1526] (Application example 3)

[1527] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1528] Conventional online shopping sites suggest products without considering the user's emotions, making it difficult to maximize the user's purchasing motivation. Furthermore, they are unable to demonstrate the superiority of their products compared to similar products from competitors, making it difficult to make effective suggestions to users. Furthermore, they are unable to learn the user's emotional patterns and predict future emotional changes, making it difficult to make appropriate product suggestions to users.

[1529] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1530] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for analyzing user emotions and predicting future emotional changes based on emotional patterns, and means for comparing the company's products with similar products from competitors and demonstrating the superiority of the company's products. This enables effective product proposals that take user emotions into consideration, and enables the company to demonstrate the superiority of its products through comparisons with competitors.

[1531] "Product service information" is detailed information about the products and services offered.

[1532] "Examples of proposals for previously accepted projects" are examples of specific plans and services proposed for previously accepted projects.

[1533] "Means for recording which services have been successfully proposed in combination with other services and have resulted in increased sales" refers to a method or device for recording successful cases where a particular service has been proposed in combination with other services and the resulting increase in sales.

[1534] "Means for proposing optimal plans and service combinations tailored to industry and customer needs" refers to a method or device for proposing the most appropriate plans and service combinations based on specific industry and customer requirements.

[1535] "A means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. at the time of proposal" refers to a method or device for quickly generating and presenting proposal documents, materials, fees, etc. at the time of proposal using natural language processing technology.

[1536] "Means for analyzing a user's emotions and predicting future changes in emotions based on emotional patterns" refers to a method or device for analyzing a user's emotions and predicting future changes in emotions based on the analysis results.

[1537] "Means for demonstrating the superiority of one's own products in comparison with similar products offered by competitors" refers to methods or devices for comparing one's own products with similar products offered by competitors and clearly demonstrating the superiority of one's own products.

[1538] A system for carrying out this invention has the following configuration: The server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making a proposal, means for analyzing user emotions and predicting future emotional changes based on emotion patterns, and means for comparing the company's products with similar products from competitors and demonstrating their superiority.

[1539] Hardware and software used

[1540] Hardware: Smartphone

[1541] software:

[1542] Library E: A library for analyzing user sentiment

[1543] Library F: A library for comparing products with competitors' equivalent products

[1544] Library G: An HTTP request library for retrieving user emotion data from a database.

[1545] Data processing and calculation

[1546] The server first loads the user's emotional data from the database. Then, it receives the user's input and analyzes the emotion using Library E. Based on the analysis results, it suggests appropriate products. The suggested products are compared with similar products from competitors using Library F to demonstrate the superiority of the company's products.

[1547] Specific examples

[1548] For example, if a user types "I'm looking for a new smartphone," Library E analyzes the user's emotions and suggests alternative products if it detects anger. Otherwise, it compares the company's smartphone with competitors' smartphones to demonstrate their superiority.

[1549] Prompt Sentence Examples

[1550] If a user types in "I'm looking for a new smartphone," the emotion engine analyzes the user's emotions and suggests alternative products if anger is detected, otherwise it compares the company's smartphone with competitors' smartphones to demonstrate their superiority.

[1551] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1552] Step 1:

[1553] The server loads the user's emotion data from the database. Specifically, it uses the HTTP request library (library G) to retrieve the emotion data based on the user ID. The input is the user ID, and the output is the user's past emotion data.

[1554] Step 2:

[1555] The terminal receives user input. Specifically, the user inputs questions or requests about products through a smartphone application. The input is the user's text input, and the output is that text data.

[1556] Step 3:

[1557] The server uses library E to analyze emotions from the user's input text. Specifically, it passes the text data to library E and performs emotion analysis. The input is the user's text data, and the output is the analyzed emotion data.

[1558] Step 4:

[1559] The server updates the user's emotional patterns based on the analyzed emotional data. Specifically, it integrates the new emotional data with past emotional data to learn the emotional patterns. The input is the new emotional data and past emotional data, and the output is the updated emotional patterns.

[1560] Step 5:

[1561] The server predicts future emotional changes based on the emotional patterns. Specifically, it uses the learned emotional patterns to predict how the user will feel about a particular suggestion. The input is the updated emotional patterns, and the output is the predicted emotional changes.

[1562] Step 6:

[1563] The server suggests appropriate products based on the predicted user emotion change. Specifically, if the predicted emotion is negative, it suggests a different product, but if it is positive, it continues to suggest the same product. The input is the predicted emotion change, and the output is a list of suggested products.

[1564] Step 7:

[1565] The server uses Library F to compare its own products with similar products from competitors. Specifically, it passes the list of products to be proposed to Library F, which compares them with the competitors' products. The input is the list of products to be proposed, and the output is the comparison result.

[1566] Step 8:

[1567] The server generates information showing the superiority of the company's products based on the comparison results. Specifically, it analyzes the comparison results and generates information emphasizing the superiority of the company's products. The input is the comparison results, and the output is information showing the superiority of the company's products.

[1568] Step 9:

[1569] The terminal presents the generated information to the user. Specifically, it displays information on the smartphone screen that shows the superiority of the company's products. The input is information showing the superiority of the company's products, and the output is what is displayed to the user.

[1570] 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.

[1571] 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> ) 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.

[1572] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1573] 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.

[1574] [Third embodiment]

[1575] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1576] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1577] 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).

[1578] 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.

[1579] 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.

[1580] 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).

[1581] 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.

[1582] 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.

[1583] 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.

[1584] 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.

[1585] 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.

[1586] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1587] "Example 1"

[1588] The system of the present invention is equipped with a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales. This database provides information for proposing optimal plans and service combinations tailored to the type of industry and customer needs. Specifically, it has the function of analyzing past success stories and sales growth, and proposing optimal plans and service combinations based on that.

[1589] "Example 2"

[1590] Furthermore, the system of the present invention has the function of using natural language processing technology to create proposal documents, proposal materials, proposed fees, etc. all at once and in a timely manner when making a proposal. Specifically, the system uses natural language processing technology to automatically generate the information necessary for the proposal and reflect it in the proposal documents and proposal materials. This improves the efficiency of the proposal process.

[1591] "Example 3"

[1592] The system of the present invention also allows comparison with similar services offered by competitors. Specifically, it has the function of storing information on other companies' services in a database and comparing it with your own service based on that information. This makes it possible to clearly demonstrate to customers the superiority of your company's services.

[1593] The processing flow of each embodiment will be described below.

[1594] "Example 1"

[1595] Step 1: The system references a database that records service information for the product, proposal examples from past orders, and which service combinations have been successful and led to increased sales. Step 2: Next, the system retrieves information from the database to propose optimal plans and service combinations tailored to the type of industry and customer needs.

[1596] Step 3: Finally, the system will suggest the optimal plan and combination of services based on the information obtained.

[1597] "Example 2"

[1598] Step 1: When making a proposal, the system uses natural language processing technology to create proposal documents, proposal materials, proposed fees, etc. in a timely manner all at once.

[1599] Step 2: Specifically, the system uses natural language processing technology to automatically generate the information necessary for the proposal.

[1600] Step 3: Finally, the system reflects the generated information in proposal documents and proposal materials.

[1601] "Example 3"

[1602] Step 1: The system retrieves service information from a database of competitors to compare with similar services offered by competitors.

[1603] Step 2: Next, the system compares the acquired service information of other companies with the service information of the company.

[1604] Step 3: Finally, the system presents the comparison results to the customer.

[1605] Example 1

[1606] Next, a description will be given of Example 1 of Form 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."

[1607] With the conventional proposal system, it was difficult to effectively utilize product service information and past success stories, making it difficult to quickly propose plans and service combinations that best fit customer needs. Furthermore, proposal documents and materials could not be generated in a timely manner in bulk, reducing the efficiency of proposals. Furthermore, it was difficult to compare services with equivalent competitors' services, or to present the differences in quotes and features to customers in a single visit.

[1608] 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.

[1609] In this invention, the server includes: means for recording product service information, proposal examples for past orders, and which service combinations have been successful and increased sales; means for proposing optimal plans and service combinations tailored to industry and customer needs; means for using natural language processing technology to quickly and collectively provide proposal documents, proposal materials, and proposed fees; means for a user to log in to the system and input information for creating a proposal; means for the server to receive the input information and retrieve related data from a database; means for the server to analyze the retrieved data and generate optimal plans and service combinations; means for the server to display the generated proposal to the user; means for the user to review the proposal and modify it as necessary; and means for the user to finalize the proposal and send it to the customer. This enables the server to quickly propose plans and service combinations that best suit customer needs, improving proposal efficiency. It also enables the server to compare services with similar services from competitors and propose differences in quotes and features to customers in a single visit.

[1610] "Product service information" is detailed information about the products and services offered.

[1611] "Examples of proposals for previously accepted projects" are records of specific plans and combinations of services proposed for previously accepted projects.

[1612] "A means of recording which combinations of services have been successfully proposed and have resulted in increased sales" is a function that records in a database the combinations of services that have been successfully proposed and the resulting increase in sales.

[1613] "A means of proposing optimal plans and service combinations tailored to the industry and customer needs" is a function that automatically generates and proposes optimal service combinations based on the customer's industry and specific needs.

[1614] "A means of using natural language processing technology to provide proposal documents, materials, pricing, etc. in a timely manner at the time of proposal" is a function that uses natural language processing technology to quickly generate the documents, materials, and pricing information required for a proposal and provide them all at once.

[1615] The "means for a user to log in to the system and input information for creating a proposal" refers to an interface that allows a user to access the system and input information necessary to create a proposal.

[1616] "Means for the server to receive the input information and retrieve related data from the database" refers to the function by which the server searches for and retrieves related data from the database based on the information entered by the user.

[1617] "Means for analyzing data acquired by the server and generating optimal plans and service combinations" refers to a function that analyzes data acquired by the server and automatically generates optimal service combinations.

[1618] The "means for displaying the proposal generated by the server to the user" is a function for transmitting the content of the proposal generated by the server to the user's terminal and displaying it.

[1619] The "means for the user to check the proposal and make corrections as necessary" is an interface that allows the user to check the displayed proposal content and make corrections as necessary.

[1620] The "means for the user to finalize the proposal and transmit it to the client" is a function that allows the user to finalize the content of the proposal and transmit the proposal to the client.

[1621] This system is equipped with a database that records product service information, proposal examples from past orders, and which service combinations have been successful and led to increased sales. This system has the function of proposing optimal plans and service combinations tailored to the type of industry and customer needs. Furthermore, when making proposals, natural language processing technology is used to provide timely proposals that include proposal documents, proposal materials, and proposed fees all at once.

[1622] Hardware and software used

[1623] Hardware

[1624] Database server (e.g. MySQL server)

[1625] Application server (e.g. Apache)

[1626] User device (e.g. PC, tablet)

[1627] software

[1628] Database Management System (DBMS)

[1629] Data Analysis Tools

[1630] Natural Language Processing Tools

[1631] Specific operation of the system

[1632] A user logs into the system

[1633] The user accesses the system's login screen from their terminal and enters their user ID and password to log in. The server checks the entered authentication information against the database, and if authentication is successful, transitions the user to the dashboard screen.

[1634] A user enters information to create a new suggestion

[1635] The user clicks the "Create a new proposal" button on the dashboard screen to move to the proposal creation screen, where they enter information such as the customer's industry, needs, and budget.

[1636] The server receives the entered information and retrieves the relevant data from the database.

[1637] The server takes the information entered by the user and queries a database, which returns data on past successes and sales growth.

[1638] The server analyzes the acquired data and generates the optimal plan and service combination.

[1639] The server analyzes the acquired data using publicly known libraries, specifically generating a combination of plans and services that are best suited to the customer's industry and needs based on past success stories and sales growth.

[1640] Displaying server-generated suggestions to the user

[1641] The server sends the generated plan and service combination to the user's device, which displays the proposed content on its screen.

[1642] The user reviews the proposal and makes any necessary corrections.

[1643] The user checks the displayed suggestions and makes corrections as necessary. Once corrections are complete, the user clicks the "Confirm Proposal" button.

[1644] The user finalizes the proposal and sends it to the customer

[1645] Once the user confirms the proposal, the server saves it in a database and sends it to the customer, who can receive it via email or a dedicated portal.

[1646] Examples of concrete examples and prompts

[1647] As a concrete example, consider the following scenario:

[1648] Suppose a user wants to propose the optimal service plan for a new customer. The user inputs the customer's industry and needs into the system. The server retrieves data on past successes and sales growth from the database and generates the optimal plan and combination of services. The generated plan is then proposed to the user.

[1649] Example prompt sentence:

[1650] "We would like to propose the optimal service plan for a new client. The client's industry is manufacturing, and their needs are cost reduction and efficiency. Please propose the optimal plan and combination of services based on past successes and sales growth."

[1651] By inputting this prompt into a generative AI model, the system can suggest the optimal plan and combination of services.

[1652] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1653] Step 1:

[1654] A user logs in to the system.

[1655] Input: User ID and password

[1656] Output: Authentication result (success or failure)

[1657] Specific operation: The user accesses the system's login screen from their terminal and enters their user ID and password. The server compares the entered authentication information with the database, and if authentication is successful, the user is transferred to the dashboard screen. If authentication fails, an error message is displayed.

[1658] Step 2:

[1659] A user enters information to create a new proposal.

[1660] Input: Information about the customer's industry, needs, budget, etc.

[1661] Output: Confirmation screen of the entered information

[1662] Specific operation: The user clicks the "Create a new proposal" button on the dashboard screen to move to the proposal creation screen. On the proposal creation screen, the user enters information such as the customer's industry, needs, and budget, and clicks the "Next" button. The server temporarily saves the entered information and displays a confirmation screen.

[1663] Step 3:

[1664] The server receives the entered information and retrieves relevant data from a database.

[1665] Input: Customer information entered by the user

[1666] Output: Relevant data (past success stories, sales data, etc.)

[1667] What it does: The server queries the database based on the information the user enters. The database returns data about past successes and sales growth. The server temporarily stores the retrieved data.

[1668] Step 4:

[1669] The server analyzes the data it acquires and generates the optimal plan and combination of services.

[1670] Input: Relevant data (past success stories, sales data, etc.)

[1671] Output: Best combination of plans and services

[1672] Specific operation: The server analyzes the acquired data using publicly known libraries. Specifically, it generates a combination of plans and services that are best suited to the customer's industry and needs based on past successes and sales growth. The generated plans are saved on the server.

[1673] Step 5:

[1674] The server generates suggestions and displays them to the user.

[1675] Input: Best plan and service combination

[1676] Output: Proposal display screen

[1677] Specific operation: The server sends the generated plan and service combination to the user's device, which then displays the received proposal on its screen.

[1678] Step 6:

[1679] The user reviews the proposal and corrects it if necessary.

[1680] Input: User modifications

[1681] Output: Revised proposal

[1682] Specific operation: The user checks the displayed proposal and makes any necessary corrections. Once the corrections are complete, the user clicks the "Confirm Proposal" button. The server temporarily saves the corrections and generates the final proposal.

[1683] Step 7:

[1684] The user finalizes the proposal and sends it to the customer.

[1685] Input: Final proposal

[1686] Output: Proposal sent to customer

[1687] Specific operation: When the user clicks the "Confirm Proposal" button, the server saves the proposal in the database and sends it to the customer, who can receive it via email or a dedicated portal.

[1688] (Application example 1)

[1689] Next, a description will be given of Application Example 1 of Form 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."

[1690] Conventional product proposal systems had difficulty proposing optimal plans and service combinations based on past success stories and sales growth. Furthermore, they were unable to generate proposal documents and materials in a timely manner using natural language processing technology, preventing quick and effective proposals to customers. Furthermore, they lacked the functionality to suggest optimal product and service combinations based on past purchase history and success stories of other users, making it difficult to improve customer satisfaction.

[1691] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1692] In this invention, the server includes means for recording product service information, proposal examples for past orders, and which service combinations have been successful in proposals and increased sales, means for proposing optimal plans and service combinations according to industry and customer needs, means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc., when making proposals, means for proposing optimal product and service combinations based on past purchase history and success stories of other users, and means for displaying optimal services and proposal contents. This enables prompt and effective proposals to customers, improving customer satisfaction.

[1693] "Product service information" is detailed information about the products and services offered.

[1694] "Example proposals for projects accepted in the past" are examples of proposals created based on projects accepted in the past.

[1695] "A means of recording which service combinations have been successful in proposing and resulting in increased sales" is a means of recording successful service combinations and the resulting increase in sales.

[1696] "Means for proposing optimal plans and service combinations tailored to industry and customer needs" refers to means for proposing optimal plans and service combinations based on specific industry and customer needs.

[1697] "A means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc. at the time of proposal" refers to a means for rapidly generating and proposing proposal documents, proposal materials, proposed fees, etc. at once using natural language processing technology.

[1698] "A means for proposing optimal combinations of products and services based on past purchase history and success stories of other users" refers to a means for proposing optimal combinations of products and services by referring to past purchase history and success stories of other users.

[1699] The "means for displaying the most suitable service and its proposed content" is a means for displaying the most suitable service and its proposed content to the user.

[1700] The system for implementing this invention has a database that records service information for commercial products, proposal examples for past orders, and which service combinations have been successful and increased sales. Based on this information, the server proposes optimal plans and service combinations tailored to the type of industry and customer needs.

[1701] The server uses natural language processing technology to generate proposal documents, proposal materials, proposed fees, etc. in a timely manner, and provides users with prompt and effective proposals. Specifically, the server uses publicly known software to manage the database, and uses a generative AI model for natural language processing.

[1702] The server also suggests optimal combinations of products and services based on past purchase history and success stories of other users, allowing users to easily find the products and services that best suit their needs. Furthermore, the system also has a function to display the optimal services and suggestions on the user's device.

[1703] For example, if a user is searching for a product in the "Logistics" category, the server will suggest a service called "Premium Delivery" and display the suggestion, "Premium Delivery is highly recommended for faster shipping." In this way, it is possible to suggest the optimal combination of products and services to the user.

[1704] An example of a prompt is as follows:

[1705] "When a user searches for a product in the 'Logistics' category, show them the best services and suggestions."

[1706] This system allows users to quickly and effectively receive recommendations for optimal products and services, which is expected to improve customer satisfaction.

[1707] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1708] Step 1:

[1709] The server records in a database service information for the merchandise, examples of proposals for past orders, and which services were used in combination with the proposals to increase sales.

[1710] Input: Product service information, proposal examples for past orders, successful service combinations and sales data

[1711] Output: Information recorded in the database

[1712] Specific operation: The server receives service information for the product, past proposal examples, successful service combinations and sales data, and inserts this information into the database.

[1713] Step 2:

[1714] The server will propose the optimal combination of plans and services based on the industry and customer needs.

[1715] Input: Industry information, customer needs

[1716] Output: Best combination of plans and services

[1717] Specific operation: The server extracts data related to industry information and customer needs from the database and generates optimal plans and service combinations based on past success stories.

[1718] Step 3:

[1719] The server uses natural language processing technology to generate proposal documents, proposal materials, proposed prices, etc. in a timely manner all at once.

[1720] Input: Best plan and service combination

[1721] Output: Proposal document, proposal materials, proposal fee

[1722] Specific operation: The server uses the generative AI model to generate proposal documents, proposal materials, and proposed prices based on the optimal plan and service combination.

[1723] Step 4:

[1724] The server suggests optimal combinations of products and services based on past purchase history and success stories of other users.

[1725] Input: past purchase history, success stories of other users

[1726] Output: Optimal product and service combinations

[1727] Specific operation: The server extracts past purchase history and success stories of other users from the database and generates the optimal combination of products and services based on this data.

[1728] Step 5:

[1729] The server displays the optimal services and suggestions on the user's device.

[1730] Input: Best service and proposal

[1731] Output: The suggestions displayed on the user's device

[1732] Specific operation: The server sends the generated optimal service and proposal content to the user's terminal, which displays it.

[1733] Step 6:

[1734] The user checks the displayed proposals and makes selections or purchases as necessary.

[1735] Input: User choices and purchase intentions

[1736] Output: Selected products and services, purchase information

[1737] Specific operation: The user checks the offers displayed on the terminal and makes a selection or purchase. The server receives the user's selection and purchase information and records it in the database.

[1738] Example 2

[1739] Next, a description will be given of Example 2 of Form 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."

[1740] Traditional proposal work required a lot of time and effort to create proposal documents and materials, resulting in inefficiency. It was also difficult to maintain a consistent quality in the proposal, which could lead to a lower success rate. Furthermore, it was difficult to compare proposals with competitors and propose optimal plans tailored to customer needs.

[1741] The specific processing by the specific 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 recording service information of merchandise, proposal examples of past orders, and which service combinations have been successful in proposals and increased sales; means for proposing optimal plans and service combinations tailored to the type of industry and customer needs; means for using natural language processing technology to collectively and timely propose proposal documents, proposal materials, proposed fees, etc.; means for the user to input information required for the proposal; means for the server to receive the input information and analyze it using natural language processing technology; means for the server to send prompt sentences to the generative AI model based on the analysis results; means for the generative AI model to generate proposal documents and materials; means for the server to send the generated documents and materials to the user's terminal; and means for the user to review and edit the documents and materials. This enables the efficiency and quality of proposal work to be improved.

[1742] "Product service information" is detailed information about the products and services offered.

[1743] "Examples of proposals for previously accepted projects" are examples of proposals based on previously accepted projects.

[1744] The "means for recording whether proposals are successful and sales are increasing" refers to a method or device for recording the success rate of proposals and increases in sales.

[1745] "Means for proposing optimal plans and service combinations tailored to the needs of each industry and customer" refers to a method or device for proposing optimal plans and service combinations tailored to specific industry types and customer requests.

[1746] "Natural language processing technology" is a technology that uses computers to understand and analyze human language.

[1747] "Means for proposing proposal documents, proposal materials, proposed fees, etc. all at once in a timely manner" refers to a method or device for quickly providing documents, materials, and fee information required for a proposal all at once.

[1748] The "means for the user to input information necessary for the proposal" refers to a method or device that allows the user to input information necessary for creating a proposal.

[1749] "Means for the server to receive input information and analyze it using natural language processing technology" refers to a method or device for the server to receive input information from a user and analyze that information using natural language processing technology.

[1750] "Means for the server to send a prompt sentence to the generative AI model based on the analysis results" refers to a method or device for the server to send a prompt sentence to the generative AI model based on the analysis results.

[1751] "Means for a generative AI model to generate proposal documents or materials" refe...

Claims

[Claim 1] a first means for a user to input basic information about a project, such as its name, purpose, budget, and duration; a second means for receiving the basic information and analyzing it using natural language processing technology; a third means for generating a first prompt sentence for outputting a report showing the advantages of the company's services based on service information of the company's products and service information of competitors recorded in a database in the context of the proposed content of the project identified by the result of the analysis of the basic information; a fourth means for inputting the first prompt sentence into a generative AI model to generate the report; a fifth means for transmitting the generated report to the user's terminal; a sixth means for accepting editing of the report by the user; a camera and a microphone provided on the user's terminal; a seventh means for analyzing the user's emotions from the tone of voice, facial expression, and choice of words of the user using an emotion engine based on the video data acquired from the camera and the audio data acquired from the microphone; Including, the third means generates a second prompt sentence for adjusting the report suggestion content based on the user's emotion; The fourth means inputs the generated second prompt sentence into a generative AI model to generate the report with adjusted suggestion content. system.

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