System

Abstract

A system is provided.SOLUTION: This system is provided with a means for inputting estimation contents, a means for receiving the inputted estimation contents, a means for transmitting the received estimation contents to a generation system AI, and for requesting analysis, a means for receiving an analysis result from the generation system AI, and for evaluating whether it is proper / improper / insufficient information, and a means for automatically generating and outputting a discount negotiation sentence or an urging sentence based on the evaluation results.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] When requesting a quote from a construction company to build a communications network, it is necessary to verify the accuracy of the submitted quote and negotiate a discount if necessary. However, this requires specialized knowledge and experience, and the quality of negotiations varies depending on the person in charge, which is a problem. Furthermore, the process of verifying quote details and negotiating is time-consuming and labor-intensive, making it difficult to proceed efficiently. The objective of this invention is to provide a system that solves these problems and enables quick and consistent verification of the accuracy of construction quotes and price negotiations. [Means for solving the problem]

[0005] The present invention provides a system including a means for inputting estimate details, a means for receiving the input estimate details, a means for sending the received estimate details to a generative AI and requesting analysis, a means for receiving the analysis results from the generative AI and evaluating whether the estimate is appropriate, inappropriate, or lacks information, and a means for automatically generating and outputting a price negotiation or request message based on the evaluation results. Specifically, the system includes a means for generating a message indicating that the estimate is appropriate if it is determined to be appropriate based on the evaluation results, a means for generating a price negotiation message including specific reasons if it is determined to be inappropriate, and a means for generating a request message requesting the provision of additional information if it is determined to be insufficient information. By using this system, construction estimates related to the construction of telecommunications networks can be confirmed and price negotiations can be carried out efficiently and professionally.

[0006] "Estimate contents" refers to information provided by the construction company, such as details of the construction work, period, and costs (labor costs, material costs, and other costs).

[0007] "Input means" refers to the interface or device through which the user inputs the quote details, such as a keyboard or form.

[0008] The "receiving means" refers to the function of the terminal to send the input quotation details to the server, and the function of the server to receive the quotation details from the terminal.

[0009] "Generative AI" is a type of artificial intelligence model that has the ability to generate appropriate output for specific input data. In this case, it evaluates quote details and generates negotiation documents.

[0010] "Analysis means" refers to the function for sending the received quotation details to the generation system AI and analyzing the results.

[0011] "Evaluation means" refers to the function that evaluates the appropriateness of the estimate content based on the analysis results received from the generative AI.

[0012] The "generation means" refers to a function that automatically generates a price negotiation message or a promotion message based on the evaluation results.

[0013] "Output means" refers to a function that sends the generated message (adequate notice, discount negotiation message, urging message) to the terminal and displays it to the user.

[0014] A "price negotiation document" is a document used to request a construction company to reconsider the costs when the estimate is deemed inappropriate.

[0015] A "demand letter" refers to a document requesting the construction company to provide additional information when there is insufficient information when confirming the estimate.

[0016] "Appropriateness notification" refers to a message that notifies the user when the estimate content is determined to be appropriate as a result of analysis. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] The present invention relates to a system for automating confirmation of the appropriateness of construction estimates and price negotiations. Specific embodiments of this system will be described below.

[0039] System Overview

[0040] This system allows users to input the details of the estimate they received from the construction company and uses a generative AI (ChatGPT) to check the appropriateness of the estimate. If the estimate is inappropriate or if additional information is required, the system automatically generates a price negotiation or request letter and provides it to the user.

[0041] Program processing explanation

[0042] 1. Enter and submit quote details

[0043] The user inputs the estimate details (work details, period, labor costs, material costs, other costs, etc.) received from the construction company into the form on the terminal.

[0044] The terminal sends the entered quotation details to the server as structured data (e.g., JSON format).

[0045] 2. Receiving and saving quote details

[0046] The server receives the quotation data sent from the terminal.

[0047] Temporarily store the received quotation data in a database.

[0048] 3. Request for analysis of quotation details

[0049] The server sends the saved quotation data to the generative AI (ChatGPT) and requests analysis and evaluation.

[0050] 4. Appropriateness Assessment

[0051] ChatGPT analyzes the received estimate data, comparing it with the unit prices of other projects, the general price level, and the appropriate man-hours required for construction, and evaluates the appropriateness of each item (labor costs, material costs, and other expenses).

[0052] ChatGPT will indicate the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and will return the result and reason to the server.

[0053] 5. Processing of evaluation results

[0054] The server analyzes the evaluation results received from ChatGPT and takes appropriate action.

[0055] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[0056] If it is judged to be inappropriate: The server instructs ChatGPT to generate a discount negotiation message. It generates a discount negotiation message including the specific reason and sends it to the terminal.

[0057] If the server determines that there is insufficient information, it instructs ChatGPT to generate a message requesting additional information. The generated message is sent to the device.

[0058] 6. Displaying Messages

[0059] The terminal displays the messages received from the server to the user.

[0060] The user checks the displayed messages (notification of suitability, price negotiation message, urging message) and takes the necessary action.

[0061] Specific examples

[0062] Example input

[0063] The user enters the following quote details into the terminal:

[0064] Work: Installation of a communications network

[0065] Construction period: 5 days

[0066] Labor costs: 100,000 yen

[0067] Material cost: 200,000 yen

[0068] Other: 50,000 yen

[0069] Processing example

[0070] 1. The user enters the quotation details into the terminal, and the terminal sends the details to the server.

[0071] 2. The server receives the quotation details, stores them in a database, and requests the generative AI to analyze them.

[0072] 3. ChatGPT evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and returns the result to the server, stating that "the material cost is higher than the market price."

[0073] 4. Based on the evaluation result that "material costs are high," the server instructs ChatGPT to generate a discount negotiation message and sends the generated discount negotiation message to the terminal.

[0074] 5. The terminal displays the following bargaining message to the user:

[0075] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0076] 6. The user checks the displayed negotiation text and negotiates the price with the construction company.

[0077] This makes it possible to quickly and consistently check the accuracy of construction estimates and negotiate discounts, resulting in high-quality negotiations that are not dependent on the experience of the person in charge.

[0078] The processing flow will be explained below.

[0079] Step 1:

[0080] The user inputs the estimate received from the construction company into the input form on the terminal. For example, the user might input the following information:

[0081] Work: Installation of a communications network

[0082] Construction period: 5 days

[0083] Labor costs: 100,000 yen

[0084] Material cost: 200,000 yen

[0085] Other expenses: 50,000 yen

[0086] Once the input is complete, the user clicks the send button.

[0087] Step 2:

[0088] The terminal converts the quote information entered by the user into a structured data format (e.g., JSON format), and then sends this data to the server via an HTTP request.

[0089] Step 3:

[0090] The server receives the quotation data sent from the terminal, temporarily stores the received data in a database, and prepares it for the next process.

[0091] Step 4:

[0092] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT). It sends a request to the generative AI to ask for analysis and evaluation.

[0093] Step 5:

[0094] The generative AI (ChatGPT) analyzes the estimate data sent from the server. For example, it compares labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices, and evaluates whether they are appropriate.

[0095] Step 6:

[0096] The generative AI (ChatGPT) returns the estimate evaluation results to the server. The evaluation results are either "appropriate," "inappropriate," or "insufficient information," and include any necessary reasons or comments.

[0097] Step 7:

[0098] The server analyzes the evaluation results received from the generative AI and determines the next course of action based on the results.

[0099] If it is judged to be appropriate: The server generates a message stating that the quotation is appropriate and sends it to the terminal.

[0100] If it is determined to be inappropriate: The server instructs the generation AI to generate a discount negotiation text and sends the generated negotiation text to the terminal.

[0101] If it is determined that there is insufficient information: The server instructs the generative AI to generate a prompt message requesting the provision of additional information, and sends the generated prompt message to the terminal.

[0102] Step 8:

[0103] The terminal displays the messages (eligibility notification, discount negotiation message, urging message) received from the server to the user.

[0104] Step 9:

[0105] The user checks the message displayed on the terminal. For example, if the request is deemed inappropriate and a discount negotiation message is displayed, the user contacts the construction company based on the message and negotiates the discount.

[0106] Example 1

[0107] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] In the past, the process of checking the accuracy of construction estimates and negotiating discounts as necessary was time-consuming and depended on the experience and skills of the person in charge, which could lead to a lack of consistency and reliability in the results. Furthermore, analyzing the estimates and determining their accuracy required specialized knowledge, making it difficult for average users to solve the problem. This often meant that appropriate measures could not be taken against inappropriate estimates.

[0109] 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.

[0110] In this invention, the server includes a means for a user to input estimate details, a terminal that receives the input estimate details, a means for sending the received estimate details to a generation AI model and requesting analysis, a means for receiving the analysis results from the generation AI model and evaluating whether the estimate is appropriate, inappropriate, or lacking information, and a means for automatically generating and outputting a price negotiation or request message based on the evaluation results. This allows the appropriateness of the construction estimate to be quickly and efficiently confirmed, enabling price negotiations or requests for additional information as needed. It also ensures consistent, high-quality service regardless of the skills or experience of individual staff members.

[0111] A "user" is an entity that uses the system to check the appropriateness of construction estimates and negotiate discounts.

[0112] "Estimate details" is data including detailed information such as the construction details, period, labor costs, material costs, and other expenses.

[0113] "Input means" refers to an interface or device that a user uses to input quotation details into the system.

[0114] A "terminal" is a device such as a computer or smartphone that allows a user to input information and communicate with a server.

[0115] The "server" is a central computer system that receives and stores quotes and sends analysis requests to the generative AI model.

[0116] A "generative AI model" is an artificial intelligence model that analyzes received quotation data and evaluates its appropriateness.

[0117] The "means of requesting analysis" is the process by which the server sends the quotation details to the generating AI model and has it perform the analysis.

[0118] "Means of evaluation" refers to the process of determining the appropriateness of the quotation based on the analysis results received from the generative AI model.

[0119] A "price negotiation document" is a document automatically generated for price negotiation in response to an inappropriate quote.

[0120] A "reminder" is an automatically generated document requesting additional information regarding the quote.

[0121] The "means for automatically generating and outputting" is a process for automatically generating a price negotiation message or a promotion message based on the evaluation results and transmitting it to the terminal.

[0122] "When deemed appropriate" means that the quotation is evaluated as appropriate in light of general market prices and standards.

[0123] "When judged to be inappropriate" means that the quotation is evaluated as inappropriate in light of general market prices and standards.

[0124] "When it is judged that there is insufficient information" refers to a situation where it is judged that there is insufficient information to evaluate the appropriateness of the quotation content.

[0125] The present invention relates to a system for automating the confirmation of the appropriateness of construction estimates and price negotiations. How to implement this system will be specifically described below.

[0126] This system consists of a user, a terminal, a server, and a generative AI model (e.g., ChatGPT). The user inputs the estimate received from the construction company, and the system evaluates the appropriateness of the estimate and provides countermeasures based on the results.

[0127] The user inputs the details of the estimate, such as the work content, construction period, labor costs, material costs, and other expenses, into a dedicated form on the terminal. The terminal converts the input estimate details into structured data in JSON format and sends it to the server. The server analyzes the received estimate data and saves it in a database.

[0128] The server sends the saved quotation data to the generative AI model and requests its analysis. At this time, the server generates a prompt for the analysis request. For example, it generates the following prompt:

[0129] Please rate the appropriateness of the following estimates: Work: laying a communications network, Work period: 5 days, Labor costs: 100,000 yen, Material costs: 200,000 yen, Other: 50,000 yen

[0130] The generative AI model (e.g., ChatGPT) analyzes the received quotation data and returns the evaluation result to the server. The evaluation result is expressed as either "appropriate," "inappropriate," or "insufficient information," along with the reason for the evaluation.

[0131] The server analyzes the results of this evaluation and takes the following actions:

[0132] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[0133] If it is determined to be inappropriate: The server requests the generative AI model to generate a price negotiation statement, which includes specific reasons and sends it to the terminal.

[0134] If it is determined that there is insufficient information: The server requests the generative AI model to generate a prompt message requesting the provision of additional information, and sends the generated prompt message to the terminal.

[0135] The terminal displays the messages received from the server (e.g., notification of suitability, price negotiation message, and reminder message) to the user. The user checks the displayed messages and takes the necessary action.

[0136] As a concrete example, consider the following quotation input:

[0137] Work: Installation of a communications network

[0138] Construction period: 5 days

[0139] Labor costs: 100,000 yen

[0140] Material cost: 200,000 yen

[0141] Other: 50,000 yen

[0142] This quotation is sent to the server, which then sends a prompt to the generative AI model to request analysis. The generative AI model returns the evaluation result that "the material cost is higher than the market price." Based on this evaluation result, the server requests the generative AI model to generate a price negotiation message, and displays the following message to the user:

[0143] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0144] This allows for quick and consistent confirmation of quote accuracy and price negotiations, ensuring high-quality service that is not dependent on the experience of the person in charge.

[0145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0146] Step 1:

[0147] Enter and submit quote details

[0148] The user enters the details of the estimate received from the construction company (such as construction details, duration, labor costs, material costs, and other expenses) into a dedicated form on the terminal.

[0149] Input: The estimate details entered by the user in the form. Example: "Work details: laying a communications network", "Work period: 5 days", "Labor costs: 100,000 yen", "Material costs: 200,000 yen", "Other: 50,000 yen"

[0150] The terminal converts the entered quotation details into structured data in JSON format.

[0151] Data processing: Form input -> JSON format conversion.

[0152] Output: Estimate data in JSON format. "{Work details: 'Installation of communication network', Work duration: '5 days', Labor costs: '100,000 yen', Material costs: '200,000 yen', Other: '50,000 yen'}"

[0153] The terminal adjusts the converted JSON data and sends it to the server.

[0154] Step 2:

[0155] Receiving and saving quotes

[0156] The server receives the quotation data in JSON format sent from the terminal.

[0157] Input: Quote data in JSON format sent by the terminal.

[0158] The server stores the received quotation data in a database.

[0159] Data processing: JSON format -> Insert into database.

[0160] Output: The quote stored in the database.

[0161] Specific operation: Use an SQL query to insert quotation data into the "Quotation Data" table in the database. Example: "INSERT INTO Quotation Data (Work Details, Work Period, Labor Costs, Material Costs, Other) VALUES ('Communication Network Installation', '5 Days', '100,000 Yen', '200,000 Yen', '50,000 Yen');"

[0162] Step 3:

[0163] Request for analysis of quotation details

[0164] The server sends the saved quotation data to the generative AI model and requests its analysis.

[0165] Input: Quote details stored in the database.

[0166] The server generates a prompt. For example, "Please rate the appropriateness of the following estimates: Work: laying a communications network, Work period: 5 days, Labor cost: 100,000 yen, Material cost: 200,000 yen, Other: 50,000 yen."

[0167] Data processing: Estimate content obtained from the database -> Generated prompt text.

[0168] Output: The generated prompt statement.

[0169] The server sends the prompt sentence to the generative AI model.

[0170] Step 4:

[0171] Assessment of suitability

[0172] The generative AI model analyzes the received quotation data, expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns it to the server along with the reason for the evaluation.

[0173] Input: The prompt sent to the generative AI model.

[0174] The generative AI model analyzes the quotation data and makes an assessment, for example, that "material costs are higher than market prices."

[0175] Data computation: The analytical process using generative AI models.

[0176] Output: Evaluation result. "{Evaluation result: 'Inappropriate', Reason: 'The cost of materials is higher than the market price. The market price is approximately 150,000 yen.'}"

[0177] The generative AI model sends the evaluation results back to the server.

[0178] Step 5:

[0179] Processing of evaluation results

[0180] The server analyzes the evaluation results received from the generative AI model and takes appropriate action based on the results.

[0181] Input: Evaluation results returned by the generative AI model.

[0182] If the estimate is determined to be appropriate, the server generates a message indicating that the estimate is appropriate and transmits it to the terminal.

[0183] Output: A message stating that the quote is correct.

[0184] If the server determines that the information is inappropriate, it requests the AI ​​model to generate a price negotiation message and sends the message to the terminal.If the server determines that the information is insufficient, it requests the AI ​​model to generate a urging message and sends the message to the terminal.

[0185] Data calculation: Analysis of evaluation results and message generation.

[0186] Output: Price negotiation or request. Example: "Regarding your quote, I believe the material cost is higher than the market price, so I would like to ask you to reconsider. The market price is about 150,000 yen."

[0187] Step 6:

[0188] Displaying messages

[0189] The terminal displays the messages received from the server to the user.

[0190] Input: Message sent by the server (eligibility notice, price negotiation message, reminder message).

[0191] Specific behavior: Display a message on the device screen.

[0192] Output: The message displayed to the user.

[0193] The user checks the displayed message and takes the necessary action.

[0194] (Application example 1)

[0195] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0196] In conventional construction estimate systems, the confirmation of estimate appropriateness and price negotiation depended on the experience and judgment of the person in charge, resulting in a lack of consistency and efficiency. Furthermore, on digital platforms such as virtual stores, it was difficult to streamline the estimate confirmation process and set appropriate prices. To solve these problems, it was necessary to provide a system that automates the confirmation of estimate appropriateness and price negotiation, allowing users to obtain appropriate estimate information without any hassle.

[0197] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0198] In this invention, the server includes means for inputting quotation details, means for receiving the input quotation details, means for sending the received quotation details to a generative AI model and requesting analysis, means for receiving the analysis results from the generative AI model and evaluating whether the quotation is appropriate, inappropriate, or lacking information, means for automatically generating and outputting a price negotiation or request message based on the evaluation results, and means for installing the system on a smartphone, smart glasses, head-mounted display, or robot terminal. This enables efficient and consistent confirmation of quotation appropriateness and price negotiation even in virtual stores.

[0199] "Means for inputting estimate details" refers to an interface that allows the user to input the estimate information received from the construction company into the terminal.

[0200] "Means for receiving input estimate details" refers to a device or software that has the function of receiving estimate information sent from the terminal to the server.

[0201] "Means of sending the received quotation details to the generative AI model and requesting analysis" refers to the process in which the server transfers the received quotation information to the generative AI and requests its analysis.

[0202] "Means for receiving analysis results from a generative AI model and evaluating whether they are appropriate, inappropriate, or lack information" refers to algorithms or devices for determining whether the quotation contents are appropriate, inappropriate, or lack information based on the analysis results sent from the generative AI.

[0203] "Means for automatically generating and outputting a price negotiation letter or request letter based on the evaluation results" refers to a device or software that has the function of automatically generating a negotiation letter or a document requesting additional information with appropriate content based on the evaluation results and outputting it.

[0204] A "smartphone" refers to a mobile information terminal with multiple functions, such as making calls, sending messages, and connecting to the Internet.

[0205] "Smart glasses" refers to a wearable device in the form of glasses that has the function of displaying information in the user's field of vision.

[0206] A "head-mounted display" refers to a display device that is worn on the user's head and displays images across the entire field of vision.

[0207] "Robot" refers to a mechanical device that automatically performs programmed tasks.

[0208] The present invention relates to a system for efficiently and consistently confirming quotes and negotiating prices in a virtual store, which is installed on a smartphone, smart glasses, a head-mounted display, or a robot terminal.

[0209] The system comprises the following means:

[0210] 1. How to enter quote details

[0211] The interface allows users to input the details of the estimate received from the construction company using a terminal. The interface includes fields for the work content, construction period, labor costs, material costs, and other costs.

[0212] 2. How to receive the entered quotation details

[0213] The terminal receives the quote information entered by the user and sends the data to the server in a structured format (e.g., JSON format).

[0214] 3. A method for sending the received quotation details to a generative AI model and requesting analysis

[0215] The server sends the received quotation details to the generative AI model, requesting analysis and evaluation. OpenAI's API is used as the generative AI model.

[0216] 4. A means of receiving analysis results from generative AI models and evaluating whether they are appropriate, inappropriate, or lack information.

[0217] The server receives the analysis results from the generative AI model and uses them to determine whether the estimate is appropriate, inappropriate, or lacks sufficient information. This evaluation uses data such as unit prices of other projects and market prices.

[0218] 5. A means for automatically generating and outputting price negotiation or promotional messages based on the evaluation results

[0219] Depending on the evaluation results, the server uses a generative AI model such as ChatGPT to automatically generate a price negotiation message or a message requesting additional information, which is then sent to the terminal and displayed to the user.

[0220] As a specific example, consider the case where the user inputs the following quotation details:

[0221] Example prompt sentence:

[0222] Work: Installation of a communications network

[0223] Construction period: 5 days

[0224] Labor costs: 100,000 yen

[0225] Material cost: 200,000 yen

[0226] Other: 50,000 yen

[0227] 1. The user enters the quote details using a smartphone or smart glasses.

[0228] 2. The terminal sends the entered data to the server.

[0229] 3. The server uses OpenAI's API to send the received data to the generative AI model and request analysis.

[0230] 4. The generative AI model analyzes the quote and returns an evaluation result indicating that the material cost is higher than the market price.

[0231] 5. The server generates a price negotiation message based on the evaluation results and sends it to the terminal.

[0232] 6. The user receives the following negotiation document and submits it to the construction company.

[0233] Example of generated negotiation text:

[0234] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0235] As a result, the system of the present invention automates the process of checking the appropriateness of construction estimates and negotiating discounts in the virtual store, thereby reducing the burden on users.

[0236] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0237] Step 1:

[0238] The user inputs the details of the estimate received from the construction company into the terminal interface. The input fields include the details of the work, construction period, labor costs, material costs, and other costs.

[0239] Input: The quote entered by the user

[0240] Output: Structured data (e.g., JSON format)

[0241] Step 2:

[0242] The terminal converts the quote information entered by the user into JSON format and sends it to the server. Through this process, the quote information entered by the user arrives at the server in a structured format.

[0243] Input: Structured quote content (JSON format)

[0244] Output: Quote sent to the server

[0245] Step 3:

[0246] The server sends the quote received from the device to a generative AI model such as OpenAI and requests analysis. In this process, the server makes a request to determine whether the quote is appropriate.

[0247] Input: Quote received by the server

[0248] Output: Analysis request sent to the generative AI model

[0249] Step 4:

[0250] The generative AI model analyzes the received quote and sends the results to the server. During the analysis, it compares the quote with data such as the unit price of other projects and market price to evaluate the appropriateness of the quote.

[0251] Input: The quote sent to the generative AI model

[0252] Output: Analysis result (either correct, incorrect, or insufficient information)

[0253] Step 5:

[0254] Based on the analysis results received from the generative AI model, the server determines whether the estimate is appropriate, inappropriate, or lacks information, and then decides the next action to take.

[0255] Input: Analysis results from the generative AI model

[0256] Output: Evaluation decision: Good, bad, or insufficient information

[0257] Step 6:

[0258] The server automatically generates a price negotiation or promotion message based on the evaluation results using a generative AI model (such as ChatGPT) and sends it to the device. If the message is deemed appropriate, a message indicating this is correct is generated.

[0259] Input: Evaluation result

[0260] Output: Generated discount negotiation, reminder or eligibility notification message

[0261] Step 7:

[0262] The terminal displays the messages received from the server to the user, who then communicates with the construction company through the displayed negotiation and urging messages.

[0263] Input: Message from the server

[0264] Output: The message displayed to the user

[0265] Through this entire process, estimate confirmation and price negotiation in the virtual store can be automated, reducing the burden on users.

[0266] 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.

[0267] The present invention improves the user experience by combining a system that automates the confirmation of the appropriateness of construction estimates and price negotiations with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0268] System Overview

[0269] This system allows users to input the details of the estimate they received from the construction company and uses generative AI (ChatGPT) to check its appropriateness. If it is inappropriate or if additional information is required, it automatically generates a price negotiation or request message and provides it to the user. In addition, the emotion engine recognizes the user's emotions and responds accordingly, enabling more effective communication.

[0270] Program processing explanation

[0271] 1. Enter and submit quote details

[0272] The user inputs the estimate details (work details, period, labor costs, material costs, other costs, etc.) received from the construction company into the form on the terminal.

[0273] The terminal sends the entered quotation details to the server as structured data (e.g., JSON format).

[0274] 2. Receiving and saving quote details

[0275] The server receives the quotation data sent from the terminal.

[0276] The received data is temporarily stored in a database.

[0277] 3. Request for analysis of quotation details

[0278] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT).

[0279] Send a request to the generative AI for analysis and evaluation.

[0280] 4. Appropriateness Assessment

[0281] The generative AI (ChatGPT) analyzes the received estimate data, specifically comparing labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices to assess whether they are appropriate.

[0282] The generative AI (ChatGPT) expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns the result and reason to the server.

[0283] 5. Operation of the Emotion Engine

[0284] The emotion engine estimates the user's emotions from the user's input, operation history, and other sensor information.

[0285] If the emotion engine determines that the user is feeling dissatisfied or anxious, it provides that information to the server.

[0286] 6. Processing of Evaluation Results

[0287] The server analyzes the evaluation results received from the generative AI and information from the emotion engine, and decides on a response based on the results.

[0288] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[0289] If the request is deemed inappropriate, the server instructs the AI ​​to generate a price negotiation message. The AI ​​reflects the information from the emotion engine and generates a negotiation message using a gentler tone as necessary.

[0290] If it is determined that there is insufficient information, the server instructs the generative AI to generate a prompt to request additional information. The prompt is generated in an appropriate tone as necessary, reflecting the information from the emotion engine.

[0291] 7. Displaying Messages

[0292] The terminal displays the messages (eligibility notification, discount negotiation message, urging message) received from the server to the user.

[0293] 8. User Support

[0294] The user checks the message displayed on the terminal and takes necessary action. For example, if a price negotiation message is displayed in case of inappropriateness, the user can contact the construction company based on the message and negotiate a price reduction.

[0295] Specific examples

[0296] Example input

[0297] The user enters the following quote details into the terminal:

[0298] Work: Installation of a communications network

[0299] Construction period: 5 days

[0300] Labor costs: 100,000 yen

[0301] Material cost: 200,000 yen

[0302] Other expenses: 50,000 yen

[0303] Processing example

[0304] 1. The user enters the quotation details into the terminal, and the terminal sends the details to the server.

[0305] 2. The server receives the quotation details, stores them in a database, and requests the generative AI to analyze them.

[0306] 3. The generative AI (ChatGPT) evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and returns the result to the server, stating that "the cost of materials is higher than the market price."

[0307] 4. The emotion engine detects when the user is feeling dissatisfied or anxious while typing and provides that information to the server.

[0308] 5. Based on the evaluation result that "material costs are high," the server instructs the generative AI to generate a price negotiation statement that reflects information from the emotion engine. The generated negotiation statement is sent to the terminal.

[0309] 6. The terminal displays the following bargaining message to the user:

[0310] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0311] 7. The user checks the displayed negotiation text and negotiates the price with the construction company.

[0312] This will enable quick and consistent confirmation of construction estimates and price negotiations, as well as a more personalized response that takes into account the user's emotions, improving the user experience.

[0313] The processing flow will be explained below.

[0314] Step 1:

[0315] The user inputs the estimate received from the construction company into the input form on the terminal. For example, the user might input the following information:

[0316] Work: Installation of a communications network

[0317] Construction period: 5 days

[0318] Labor costs: 100,000 yen

[0319] Material cost: 200,000 yen

[0320] Other expenses: 50,000 yen

[0321] Once the input is complete, the user clicks the send button.

[0322] Step 2:

[0323] The terminal converts the quote information entered by the user into a structured data format (e.g., JSON format), and then sends this data to the server via an HTTP request.

[0324] Step 3:

[0325] The server receives the quotation data sent from the terminal, temporarily stores the received data in a database, and prepares it for the next process.

[0326] Step 4:

[0327] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT). It sends a request to the generative AI to ask for analysis and evaluation.

[0328] Step 5:

[0329] The generative AI (ChatGPT) analyzes the estimate data sent from the server. For example, it compares labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices, and evaluates whether they are appropriate.

[0330] Step 6:

[0331] The generative AI (ChatGPT) expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns the result and reason to the server.

[0332] Step 7:

[0333] The emotion engine estimates the user's emotions from the user's input, operation history, and other sensor information. For example, it can determine the user's stress level from their typing speed and pattern.

[0334] Step 8:

[0335] The emotion engine provides information to the server when the user is feeling dissatisfied or anxious, for example by sending back a comment such as "The user is showing high stress levels."

[0336] Step 9:

[0337] The server analyzes the evaluation results received from the generative AI and the information received from the emotion engine, and determines the next response based on the results.

[0338] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[0339] If the request is deemed inappropriate, the server instructs the AI ​​to generate a price negotiation message. The AI ​​reflects the information from the emotion engine and generates a negotiation message using a gentler tone as necessary.

[0340] If it is determined that there is insufficient information, the server instructs the generative AI to generate a prompt message requesting the provision of additional information. The message is generated in an appropriate tone, reflecting the information from the emotion engine.

[0341] Step 10:

[0342] The terminal displays the message (notification of appropriateness, discount negotiation message, reminder message) received from the server to the user. For example, if the transaction is determined to be inappropriate and a discount negotiation message is displayed, the user can confirm the content.

[0343] Step 11:

[0344] Based on the message displayed on the terminal, the user contacts the construction company and takes the necessary action, for example, by copying the price negotiation statement and sending it to the construction company by email.

[0345] Example 2

[0346] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0347] Checking the accuracy of construction estimates and negotiating discounts has traditionally been a time-consuming and laborious task, placing a burden on users. Furthermore, it can be difficult to negotiate discounts or request information in appropriate language that reflects the user's feelings, which can sometimes hinder smooth communication. Therefore, a system is needed that can accurately evaluate construction estimates and automate responses that take the user's feelings into consideration.

[0348] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting estimate details, a means for receiving the input estimate details, a means for sending the received estimate details to the generation AI model and requesting analysis, a means for receiving the analysis results from the generation AI model and evaluating the appropriateness, a means for automatically generating and outputting a price negotiation message or a request message based on the evaluation results, a means for estimating the user's emotions, and a means for generating a message with an appropriate tone using the user's emotion information. This enables the appropriateness of the construction estimate and price negotiation to be confirmed quickly and consistently, and also enables personalized responses that take the user's emotions into consideration.

[0349] "Estimate details" refers to data that includes detailed information such as the costs, duration, materials, and labor costs associated with construction or services.

[0350] The "means for inputting" refers to a device or program that provides an interface for the user to input the details of the estimate.

[0351] "Means for receiving" refers to a device or program that has the function of receiving data transmitted from outside.

[0352] A "generative AI model" is a software system that uses artificial intelligence to analyze data and perform specific tasks based on the results.

[0353] A "means for requesting analysis" is a device or program that has the function of sending received data to a generative AI model and requesting analysis.

[0354] A "means for assessing appropriateness" is a device or program that has the function of determining whether the quotation contents are appropriate based on the analysis results returned from the generative AI model.

[0355] A "price negotiation letter" is a letter that requests cost reductions or changes to conditions regarding quotes that are deemed inappropriate.

[0356] A "demand letter" is a letter requesting additional information regarding the quotation content that is deemed to be insufficient.

[0357] "User emotions" refers to the psychological state, such as dissatisfaction, anxiety, or satisfaction, that the user feels while inputting or operating the estimate contents.

[0358] "Estimation means" refers to a device or program that has the function of inferring emotions based on the user's behavior, operation history, sensor information, etc.

[0359] The "means for generating sentences with an appropriate tone" refers to a device or program that has the function of generating sentences that reflect the user's emotional information and are designed to avoid making the user feel uncomfortable.

[0360] The present invention is a system that automates the confirmation of the appropriateness of construction estimates and price negotiations, and improves the user experience by recognizing the user's emotions. This system analyzes the estimate contents entered by the user and automatically takes appropriate action based on the results. Specific embodiments of this system are described below.

[0361] System Overview

[0362] This system mainly uses the following hardware and software:

[0363] Devices that accept user information input (e.g., PCs, smartphones)

[0364] A server that analyzes and stores received data

[0365] Analysis function using generative AI models (e.g. ChatGPT)

[0366] Emotion engine that estimates user emotions

[0367] Hardware Configuration

[0368] The terminal is a device that allows the user to input the details of the estimate received from the construction company. The terminal is provided with an interface (form) for inputting the details of the estimate. When the user enters the details of the estimate into the form, the terminal converts them into a structured data format (e.g., JSON) and sends it to the server.

[0369] The server is equipped with software to analyze the received data, generate and send API requests to operate both the generative AI model and the emotion engine, and store the received data in a database for further analysis and evaluation as needed.

[0370] Software Configuration

[0371] Generative AI models (e.g., ChatGPT) are used to analyze the appropriateness of quotes. Based on the received quote, they compare labor costs, material costs, and other expenses with the unit prices of other projects and the general market price to evaluate whether the quote is reasonable.

[0372] The emotion engine uses the user's input, operation history, and other sensor information to estimate the user's emotions. For example, if the user makes many corrections while entering the quote, it is determined that the user is likely to be dissatisfied. The emotion engine sends this information to the server and reflects it in the generated message.

[0373] Specific examples

[0374] The following are specific examples based on the present invention.

[0375] Example input

[0376] The user inputs the details of the work and its cost into the terminal. At this time, the data is input in the following text format:

[0377] Work details: Floor renovation

[0378] Construction period: 10 days

[0379] Labor costs: 150,000 yen

[0380] Material cost: 250,000 yen

[0381] Other expenses: 80,000 yen

[0382] Processing example

[0383] 1. When the user finishes entering data, the device automatically generates JSON format data and sends it to the server.

[0384] 2. The server stores the data received via web communication in a database and sends an API request to the generative AI model for analysis.

[0385] 3. The generative AI model analyzes the estimate and returns the evaluation results to the server along with the conclusion that "material costs are higher than the market price."

[0386] 4. The emotion engine infers that the user is feeling anxious or dissatisfied while inputting and provides that emotional information to the server.

[0387] 5. The server integrates the evaluation results and emotional information, generates a price negotiation message in an appropriate tone, and sends it to the terminal.

[0388] 6. The terminal displays the following bargaining message to the user:

[0389] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0390] 7. The user can contact the construction company based on this negotiation statement and negotiate a discount.

[0391] As described above, the system of the present invention automatically evaluates the appropriateness of construction estimates and generates messages that reflect the user's emotions, thereby realizing efficient and personalized responses.

[0392] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0393] Step 1:

[0394] The user inputs the details of the estimate received from the construction company (construction details, period, labor costs, material costs, and other expenses) into the terminal form by manually entering specific values ​​and details into the text fields.

[0395] Input: Construction details, construction period, labor costs, material costs, other costs

[0396] Output: Quote details entered in the form

[0397] Specific behavior: The user fills in all the fields in the input form and clicks the submit button.

[0398] Step 2:

[0399] The terminal converts the quote information entered by the user into JSON format, which includes mapping the values ​​of each form field to a key-value format.

[0400] Input: Quote details entered in the form

[0401] Output: Quote data in JSON format

[0402] What happens: When a form submit event occurs, JavaScript or other client script converts the data into JSON format.

[0403] Step 3:

[0404] The terminal sends the converted JSON data to the server, where it is encrypted using the HTTPS protocol.

[0405] Input: Quote data in JSON format

[0406] Output: Request to send data to the server

[0407] What it does: Sends a POST request containing JSON data over HTTPS.

[0408] Step 4:

[0409] The server receives the quotation data sent from the device using a RESTful API endpoint.

[0410] Input: Quote data in JSON format

[0411] Output: Status code of the receipt confirmation (e.g. 200 OK)

[0412] What happens: A server-side API endpoint receives the request and processes the data.

[0413] Step 5:

[0414] The server saves the received quote data in a database, storing each item in a corresponding table column according to the database schema.

[0415] Input: Quote data in JSON format

[0416] Output: Save result to database (e.g. success / failure status)

[0417] What it does: Uses a database connectivity library to execute an SQL query to insert data.

[0418] Step 6:

[0419] The server prepares an API request to the generative AI model for analysis, which includes attaching an API key and forming the request body.

[0420] Input: Saved quote data

[0421] Output: Prepared data for the analysis request

[0422] Specific behavior: Generates a request to an API endpoint that includes authentication information and analytics data.

[0423] Step 7:

[0424] The server sends an analysis request to the generative AI model.

[0425] Input: Prepared data for the analysis request

[0426] Output: Data sent to the generative AI model

[0427] What it does: Sends a request to the API endpoint of the generative AI model using the HTTPS protocol.

[0428] Step 8:

[0429] The generative AI model analyzes the received quotation data and evaluates its appropriateness. The evaluation result is returned as either "appropriate," "inappropriate," or "insufficient information."

[0430] Input: Analysis request data

[0431] Output: Analysis result (appropriate / inappropriate / insufficient information) and the reason

[0432] How it works: The generative AI model uses its internal algorithms to compare data with other cases and make an evaluation.

[0433] Step 9:

[0434] The generative AI model sends the analysis results back to the server.

[0435] Input: Analysis results and reasons

[0436] Output: Data sent back to the server

[0437] Specific operation: Analysis result data is sent to the server using the HTTPS protocol.

[0438] Step 10:

[0439] The server analyzes the evaluation results received from the generative AI model and simultaneously determines the necessary response using the user's emotional information.

[0440] Input: Analysis results from the generative AI model and user emotion information

[0441] Output: Required actions (e.g., notification of appropriateness, price negotiation, reminder)

[0442] Specific operation: The server logic integrates the evaluation results and emotional information to generate an appropriate response message.

[0443] Step 11:

[0444] The server instructs the generative AI model to generate price negotiation and demand messages, incorporating information from the emotion engine to generate sentences with an appropriate tone.

[0445] Input: Required response and emotional information

[0446] Output: A request to the generative AI model

[0447] What it does: Sends a request to an API endpoint to generate a sentence with the appropriate tone.

[0448] Step 12:

[0449] The generative AI model generates price negotiation and urging messages and sends the results back to the server.

[0450] Input: A request to generate text

[0451] Output: The generated price negotiation or reminder

[0452] How it works: The generative AI model uses its internal natural language processing algorithm to generate documents in the specified tone.

[0453] Step 13:

[0454] The server transmits the generated message (eligibility notification, discount negotiation message, urging message) to the terminal.

[0455] Input: The message returned by the generative AI model

[0456] Output: Send message to terminal

[0457] Specific operation: Calls an API to send a response containing a message back to the device.

[0458] Step 14:

[0459] The terminal displays the received message to the user.

[0460] Input: Message sent from the server

[0461] Output: The message that is displayed to the user

[0462] What it does: Uses a GUI to visually display incoming messages to the user.

[0463] Step 15:

[0464] The user checks the message displayed on the terminal and takes necessary action. For example, if a discount negotiation message is displayed, the user contacts the construction company based on the message and negotiates for a discount.

[0465] Input: Message displayed on terminal

[0466] Output: Results of contact with construction company and price negotiation

[0467] Specific actions: Contact the construction company via email or phone and negotiate a discount on the proposed price.

[0468] The specific operations, inputs, and outputs in each processing step of the system have been described above.

[0469] (Application example 2)

[0470] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0471] Conventional quotation systems require manual evaluation to confirm the appropriateness of quotation details, and also require manual negotiation of discounts and prompting for additional information. Furthermore, these systems often lack consideration for the user's feelings, resulting in a poor user experience. The present invention aims to solve these problems and enable efficient and user-friendly confirmation of quotation details and negotiation of discounts.

[0472] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting quotation details, means for receiving the input quotation details, means for sending the received quotation details to a generative AI and requesting analysis, means for receiving the analysis results from the generative AI and evaluating whether the quotation details are appropriate, inappropriate, or lacking information, means for automatically generating and outputting a price negotiation message or a request message based on the evaluation results, and means including an emotion engine that recognizes the user's emotions and adjusting the tone of the negotiation message or the request message depending on the emotions. This automates the process of confirming the appropriateness of the quotation details and negotiating a price discount, enabling personalized responses that take the user's emotions into consideration.

[0473] "Estimate details" refers to data that includes details such as the work content, cost, and duration of a user-specified construction or manufacturing project.

[0474] "Means for input" refers to technology that provides a form or interface for users to input estimate details into a terminal.

[0475] "Means for receiving" refers to the technology for receiving the input quotation details on a server or the like.

[0476] "Generative AI" refers to artificial intelligence that uses natural language processing to analyze received data and generate information.

[0477] "Means for requesting analysis" refers to the technology of sending a request to the generative AI to evaluate the appropriateness of the estimate contents.

[0478] The "means of evaluation" refers to a technology that evaluates the appropriateness of the estimate content as "appropriate," "inappropriate," or "insufficient information" based on the analysis results from generative AI.

[0479] A "price negotiation letter" is a letter used to request a discount on an inappropriate quote.

[0480] A "demand letter" is a letter requesting additional information when there are deficiencies in the quotation.

[0481] An "emotion engine" is a technology that detects emotions from user input and operation history and adjusts responses based on those emotions.

[0482] "Tone" refers to the style of language and expression used in writing or dialogue.

[0483] "System" refers to the collection of hardware and software required to automatically verify the accuracy of quote details and negotiate discounts.

[0484] The present invention is a system for efficiently confirming the appropriateness of quotation details and negotiating discounts, intended for use in factories and manufacturing fields. This system includes a means for inputting quotation details, a means for receiving the input quotation details, a means for sending the received quotation details to a generative AI and requesting analysis, a means for receiving the analysis results from the generative AI and evaluating whether the quotation is appropriate, inappropriate, or lacks information, a means for automatically generating and outputting a price negotiation or request message based on the evaluation results, and a means including an emotion engine that recognizes the user's emotions and adjusting the tone of the negotiation or request message depending on the emotion.

[0485] Example of a system

[0486] 1. Enter a quote

[0487] The user is a factory estimator and uses a smartphone application to input estimate details. For example, the user might enter the following estimate information: "Work details: installation of an automated line," "Work duration: 10 days," "Labor costs: 300,000 yen," "Material costs: 500,000 yen," and "Other costs: 200,000 yen."

[0488] 2. Receiving the quotation

[0489] The terminal receives the estimate content entered by the user and transmits it to the server.

[0490] 3. Use of generative AI

[0491] The server saves the received quotation data as structured data (JSON format) and sends an API request to a generative AI (such as ChatGPT) to request analysis.

[0492] 4. Appropriateness Assessment

[0493] The generative AI evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and determines whether it is appropriate. The result is returned to the server as either "appropriate," "inappropriate," or "insufficient information."

[0494] 5. Operation of the Emotion Engine

[0495] The server uses an emotion engine (e.g., a Sentiment Analysis tool) to estimate the user's emotions from their input and operation history. Based on this information, the server determines whether the user is feeling dissatisfied or anxious.

[0496] 6. Message Creation

[0497] The server generates an appropriate message based on the evaluation results from the generative AI and the analytical information from the emotion engine. For example, it adjusts the content according to the emotion, such as "The estimated price is higher than the market price. Could you please give us a discount?"

[0498] 7. Displaying Messages

[0499] The terminal displays the message received from the server to the user, who then negotiates a discount with the construction company based on the displayed negotiation text.

[0500] Specific examples

[0501] A specific example of quotation data entered by the user is as follows:

[0502] Quotation Data

[0503] Construction details: Installation of automated production line

[0504] Construction period: 10 days

[0505] Labor costs: 300,000 yen

[0506] Material cost: 500,000 yen

[0507] Other expenses: 200,000 yen

[0508] Emotional expression during user input

[0509] "This quote seems a little high..."

[0510] Example prompts for generative AI models

[0511] Please rate the appropriateness of the following estimates.

[0512] Construction details: Installation of automated production line

[0513] Construction period: 10 days

[0514] Labor costs: 300,000 yen

[0515] Material cost: 500,000 yen

[0516] Other expenses: 200,000 yen

[0517] In this way, the present invention automates the process of checking the accuracy of quotation details and negotiating discounts, and realizes personalized responses that take into account the user's feelings, thereby improving the efficiency of quotation work and the user experience.

[0518] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0519] Step 1:

[0520] The user is a factory estimator and uses a smartphone application to input estimate details. The input estimate details include the work content, construction period, labor costs, material costs, and other expenses. The input data is temporarily saved on the device.

[0521] Step 2:

[0522] The terminal sends the quote information entered by the user to the server. At this time, the data is sent as structured data (e.g., JSON format). The server receives this structured data and stores it in a database.

[0523] Step 3:

[0524] The server sends the received quotation data to a generative AI (such as ChatGPT) and prepares an API request to request analysis. This request includes the quotation details entered by the user. The server then generates and sends a prompt message to the generative AI requesting an appropriateness assessment.

[0525] Step 4:

[0526] The generative AI analyzes the received quotation data. Specifically, it compares it with the unit prices of other projects and the general public's prices to evaluate the appropriateness of the quotation. It then expresses the results of this evaluation as "appropriate," "inappropriate," or "insufficient information" and sends them back to the server.

[0527] Step 5:

[0528] The server receives the evaluation results from the generative AI. At the same time, it uses an emotion engine (e.g., a Sentiment Analysis tool) to estimate the user's emotions based on their input and operation history. The emotion engine analyzes whether the user is feeling dissatisfied or anxious, and provides the results to the server.

[0529] Step 6:

[0530] The server decides how to respond based on the evaluation results from the generative AI and information from the emotion engine. For example, if the evaluation result is "inappropriate" and the emotion engine detects the user's dissatisfaction, the server generates a price negotiation message in a gentle tone. This negotiation message is sent to the generative AI and automatically generated.

[0531] Step 7:

[0532] The server sends the generated negotiation text and appropriate notification message to the terminal, which receives the message and displays it to the user.

[0533] Step 8:

[0534] The user checks the message displayed on the terminal and takes the necessary action. For example, if the quote is deemed inappropriate, the user can negotiate a discount with the construction company based on the discount negotiation text. In this way, this system automates the confirmation of the appropriateness of the quote content and the discount negotiation process, enabling responses that take the user's feelings into consideration.

[0535] The above processing steps can improve the efficiency of the quotation process and the user experience.

[0536] 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.

[0537] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0538] 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.

[0539] [Second embodiment]

[0540] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0541] 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.

[0542] 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).

[0543] 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.

[0544] 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.

[0545] 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).

[0546] 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.

[0547] 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.

[0548] 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.

[0549] 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.

[0550] 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.

[0551] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0552] The present invention relates to a system for automating confirmation of the appropriateness of construction estimates and price negotiations. Specific embodiments of this system will be described below.

[0553] System Overview

[0554] This system allows users to input the details of the estimate they received from the construction company and uses a generative AI (ChatGPT) to check the appropriateness of the estimate. If the estimate is inappropriate or if additional information is required, the system automatically generates a price negotiation or request letter and provides it to the user.

[0555] Program processing explanation

[0556] 1. Enter and submit quote details

[0557] The user inputs the estimate details (work details, period, labor costs, material costs, other costs, etc.) received from the construction company into the form on the terminal.

[0558] The terminal sends the entered quotation details to the server as structured data (e.g., JSON format).

[0559] 2. Receiving and saving quote details

[0560] The server receives the quotation data sent from the terminal.

[0561] Temporarily store the received quotation data in a database.

[0562] 3. Request for analysis of quotation details

[0563] The server sends the saved quotation data to the generative AI (ChatGPT) and requests analysis and evaluation.

[0564] 4. Appropriateness Assessment

[0565] ChatGPT analyzes the received estimate data, comparing it with the unit prices of other projects, the general price level, and the appropriate man-hours required for construction, and evaluates the appropriateness of each item (labor costs, material costs, and other expenses).

[0566] ChatGPT will indicate the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and will return the result and reason to the server.

[0567] 5. Processing of evaluation results

[0568] The server analyzes the evaluation results received from ChatGPT and takes appropriate action.

[0569] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[0570] If it is judged to be inappropriate: The server instructs ChatGPT to generate a discount negotiation message. It generates a discount negotiation message including the specific reason and sends it to the terminal.

[0571] If the server determines that there is insufficient information, it instructs ChatGPT to generate a message requesting additional information. The generated message is sent to the device.

[0572] 6. Displaying Messages

[0573] The terminal displays the messages received from the server to the user.

[0574] The user checks the displayed messages (notification of suitability, price negotiation message, urging message) and takes the necessary action.

[0575] Specific examples

[0576] Example input

[0577] The user enters the following quote details into the terminal:

[0578] Work: Installation of a communications network

[0579] Construction period: 5 days

[0580] Labor costs: 100,000 yen

[0581] Material cost: 200,000 yen

[0582] Other: 50,000 yen

[0583] Processing example

[0584] 1. The user enters the quotation details into the terminal, and the terminal sends the details to the server.

[0585] 2. The server receives the quotation details, stores them in a database, and requests the generative AI to analyze them.

[0586] 3. ChatGPT evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and returns the result to the server, stating that "the material cost is higher than the market price."

[0587] 4. Based on the evaluation result that "material costs are high," the server instructs ChatGPT to generate a discount negotiation message and sends the generated discount negotiation message to the terminal.

[0588] 5. The terminal displays the following bargaining message to the user:

[0589] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0590] 6. The user checks the displayed negotiation text and negotiates the price with the construction company.

[0591] This makes it possible to quickly and consistently check the accuracy of construction estimates and negotiate discounts, resulting in high-quality negotiations that are not dependent on the experience of the person in charge.

[0592] The processing flow will be explained below.

[0593] Step 1:

[0594] The user inputs the estimate received from the construction company into the input form on the terminal. For example, the user might input the following information:

[0595] Work: Installation of a communications network

[0596] Construction period: 5 days

[0597] Labor costs: 100,000 yen

[0598] Material cost: 200,000 yen

[0599] Other expenses: 50,000 yen

[0600] Once the input is complete, the user clicks the send button.

[0601] Step 2:

[0602] The terminal converts the quote information entered by the user into a structured data format (e.g., JSON format), and then sends this data to the server via an HTTP request.

[0603] Step 3:

[0604] The server receives the quotation data sent from the terminal, temporarily stores the received data in a database, and prepares it for the next process.

[0605] Step 4:

[0606] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT). It sends a request to the generative AI to ask for analysis and evaluation.

[0607] Step 5:

[0608] The generative AI (ChatGPT) analyzes the estimate data sent from the server. For example, it compares labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices, and evaluates whether they are appropriate.

[0609] Step 6:

[0610] The generative AI (ChatGPT) returns the estimate evaluation results to the server. The evaluation results are either "appropriate," "inappropriate," or "insufficient information," and include any necessary reasons or comments.

[0611] Step 7:

[0612] The server analyzes the evaluation results received from the generative AI and determines the next course of action based on the results.

[0613] If it is judged to be appropriate: The server generates a message stating that the quotation is appropriate and sends it to the terminal.

[0614] If it is determined to be inappropriate: The server instructs the generation AI to generate a discount negotiation text and sends the generated negotiation text to the terminal.

[0615] If it is determined that there is insufficient information: The server instructs the generative AI to generate a prompt message requesting the provision of additional information, and sends the generated prompt message to the terminal.

[0616] Step 8:

[0617] The terminal displays the messages (eligibility notification, discount negotiation message, urging message) received from the server to the user.

[0618] Step 9:

[0619] The user checks the message displayed on the terminal. For example, if the request is deemed inappropriate and a discount negotiation message is displayed, the user contacts the construction company based on the message and negotiates the discount.

[0620] Example 1

[0621] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0622] In the past, the process of checking the accuracy of construction estimates and negotiating discounts as necessary was time-consuming and depended on the experience and skills of the person in charge, which could lead to a lack of consistency and reliability in the results. Furthermore, analyzing the estimates and determining their accuracy required specialized knowledge, making it difficult for average users to solve the problem. This often meant that appropriate measures could not be taken against inappropriate estimates.

[0623] 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.

[0624] In this invention, the server includes a means for a user to input estimate details, a terminal that receives the input estimate details, a means for sending the received estimate details to a generation AI model and requesting analysis, a means for receiving the analysis results from the generation AI model and evaluating whether the estimate is appropriate, inappropriate, or lacking information, and a means for automatically generating and outputting a price negotiation or request message based on the evaluation results. This allows the appropriateness of the construction estimate to be quickly and efficiently confirmed, enabling price negotiations or requests for additional information as needed. It also ensures consistent, high-quality service regardless of the skills or experience of individual staff members.

[0625] A "user" is an entity that uses the system to check the appropriateness of construction estimates and negotiate discounts.

[0626] "Estimate details" is data including detailed information such as the construction details, period, labor costs, material costs, and other expenses.

[0627] "Input means" refers to an interface or device that a user uses to input quotation details into the system.

[0628] A "terminal" is a device such as a computer or smartphone that allows a user to input information and communicate with a server.

[0629] The "server" is a central computer system that receives and stores quotes and sends analysis requests to the generative AI model.

[0630] A "generative AI model" is an artificial intelligence model that analyzes received quotation data and evaluates its appropriateness.

[0631] The "means of requesting analysis" is the process by which the server sends the quotation details to the generating AI model and has it perform the analysis.

[0632] "Means of evaluation" refers to the process of determining the appropriateness of the quotation based on the analysis results received from the generative AI model.

[0633] A "price negotiation document" is a document automatically generated for price negotiation in response to an inappropriate quote.

[0634] A "reminder" is an automatically generated document requesting additional information regarding the quote.

[0635] The "means for automatically generating and outputting" is a process for automatically generating a price negotiation message or a promotion message based on the evaluation results and transmitting it to the terminal.

[0636] "When deemed appropriate" means that the quotation is evaluated as appropriate in light of general market prices and standards.

[0637] "When judged to be inappropriate" means that the quotation is evaluated as inappropriate in light of general market prices and standards.

[0638] "When it is judged that there is insufficient information" refers to a situation where it is judged that there is insufficient information to evaluate the appropriateness of the quotation content.

[0639] The present invention relates to a system for automating the confirmation of the appropriateness of construction estimates and price negotiations. How to implement this system will be specifically described below.

[0640] This system consists of a user, a terminal, a server, and a generative AI model (e.g., ChatGPT). The user inputs the estimate received from the construction company, and the system evaluates the appropriateness of the estimate and provides countermeasures based on the results.

[0641] The user inputs the details of the estimate, such as the work content, construction period, labor costs, material costs, and other expenses, into a dedicated form on the terminal. The terminal converts the input estimate details into structured data in JSON format and sends it to the server. The server analyzes the received estimate data and saves it in a database.

[0642] The server sends the saved quotation data to the generative AI model and requests its analysis. At this time, the server generates a prompt for the analysis request. For example, it generates the following prompt:

[0643] Please rate the appropriateness of the following estimates: Work: laying a communications network, Work period: 5 days, Labor costs: 100,000 yen, Material costs: 200,000 yen, Other: 50,000 yen

[0644] The generative AI model (e.g., ChatGPT) analyzes the received quotation data and returns the evaluation result to the server. The evaluation result is expressed as either "appropriate," "inappropriate," or "insufficient information," along with the reason for the evaluation.

[0645] The server analyzes the results of this evaluation and takes the following actions:

[0646] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[0647] If it is determined to be inappropriate: The server requests the generative AI model to generate a price negotiation statement, which includes specific reasons and sends it to the terminal.

[0648] If it is determined that there is insufficient information: The server requests the generative AI model to generate a prompt message requesting the provision of additional information, and sends the generated prompt message to the terminal.

[0649] The terminal displays the messages received from the server (e.g., notification of suitability, price negotiation message, and reminder message) to the user. The user checks the displayed messages and takes the necessary action.

[0650] As a concrete example, consider the following quotation input:

[0651] Work: Installation of a communications network

[0652] Construction period: 5 days

[0653] Labor costs: 100,000 yen

[0654] Material cost: 200,000 yen

[0655] Other: 50,000 yen

[0656] This quotation is sent to the server, which then sends a prompt to the generative AI model to request analysis. The generative AI model returns the evaluation result that "the material cost is higher than the market price." Based on this evaluation result, the server requests the generative AI model to generate a price negotiation message, and displays the following message to the user:

[0657] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0658] This allows for quick and consistent confirmation of quote accuracy and price negotiations, ensuring high-quality service that is not dependent on the experience of the person in charge.

[0659] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0660] Step 1:

[0661] Enter and submit quote details

[0662] The user enters the details of the estimate received from the construction company (such as construction details, duration, labor costs, material costs, and other expenses) into a dedicated form on the terminal.

[0663] Input: The estimate details entered by the user in the form. Example: "Work details: laying a communications network", "Work period: 5 days", "Labor costs: 100,000 yen", "Material costs: 200,000 yen", "Other: 50,000 yen"

[0664] The terminal converts the entered quotation details into structured data in JSON format.

[0665] Data processing: Form input -> JSON format conversion.

[0666] Output: Estimate data in JSON format. "{Work details: 'Installation of communication network', Work duration: '5 days', Labor costs: '100,000 yen', Material costs: '200,000 yen', Other: '50,000 yen'}"

[0667] The terminal adjusts the converted JSON data and sends it to the server.

[0668] Step 2:

[0669] Receiving and saving quotes

[0670] The server receives the quotation data in JSON format sent from the terminal.

[0671] Input: Quote data in JSON format sent by the terminal.

[0672] The server stores the received quotation data in a database.

[0673] Data processing: JSON format -> Insert into database.

[0674] Output: The quote stored in the database.

[0675] Specific operation: Use an SQL query to insert quotation data into the "Quotation Data" table in the database. Example: "INSERT INTO Quotation Data (Work Details, Work Period, Labor Costs, Material Costs, Other) VALUES ('Communication Network Installation', '5 Days', '100,000 Yen', '200,000 Yen', '50,000 Yen');"

[0676] Step 3:

[0677] Request for analysis of quotation details

[0678] The server sends the saved quotation data to the generative AI model and requests its analysis.

[0679] Input: Quote details stored in the database.

[0680] The server generates a prompt. For example, "Please rate the appropriateness of the following estimates: Work: laying a communications network, Work period: 5 days, Labor cost: 100,000 yen, Material cost: 200,000 yen, Other: 50,000 yen."

[0681] Data processing: Estimate content obtained from the database -> Generated prompt text.

[0682] Output: The generated prompt statement.

[0683] The server sends the prompt sentence to the generative AI model.

[0684] Step 4:

[0685] Assessment of suitability

[0686] The generative AI model analyzes the received quotation data, expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns it to the server along with the reason for the evaluation.

[0687] Input: The prompt sent to the generative AI model.

[0688] The generative AI model analyzes the quotation data and makes an assessment, for example, that "material costs are higher than market prices."

[0689] Data computation: The analytical process using generative AI models.

[0690] Output: Evaluation result. "{Evaluation result: 'Inappropriate', Reason: 'The cost of materials is higher than the market price. The market price is approximately 150,000 yen.'}"

[0691] The generative AI model sends the evaluation results back to the server.

[0692] Step 5:

[0693] Processing of evaluation results

[0694] The server analyzes the evaluation results received from the generative AI model and takes appropriate action based on the results.

[0695] Input: Evaluation results returned by the generative AI model.

[0696] If the estimate is determined to be appropriate, the server generates a message indicating that the estimate is appropriate and transmits it to the terminal.

[0697] Output: A message stating that the quote is correct.

[0698] If the server determines that the information is inappropriate, it requests the AI ​​model to generate a price negotiation message and sends the message to the terminal.If the server determines that the information is insufficient, it requests the AI ​​model to generate a urging message and sends the message to the terminal.

[0699] Data calculation: Analysis of evaluation results and message generation.

[0700] Output: Price negotiation or request. Example: "Regarding your quote, I believe the material cost is higher than the market price, so I would like to ask you to reconsider. The market price is about 150,000 yen."

[0701] Step 6:

[0702] Displaying messages

[0703] The terminal displays the messages received from the server to the user.

[0704] Input: Message sent by the server (eligibility notice, price negotiation message, reminder message).

[0705] Specific behavior: Display a message on the device screen.

[0706] Output: The message displayed to the user.

[0707] The user checks the displayed message and takes the necessary action.

[0708] (Application example 1)

[0709] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0710] In conventional construction estimate systems, the confirmation of estimate appropriateness and price negotiation depended on the experience and judgment of the person in charge, resulting in a lack of consistency and efficiency. Furthermore, on digital platforms such as virtual stores, it was difficult to streamline the estimate confirmation process and set appropriate prices. To solve these problems, it was necessary to provide a system that automates the confirmation of estimate appropriateness and price negotiation, allowing users to obtain appropriate estimate information without any hassle.

[0711] 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.

[0712] In this invention, the server includes means for inputting quotation details, means for receiving the input quotation details, means for sending the received quotation details to a generative AI model and requesting analysis, means for receiving the analysis results from the generative AI model and evaluating whether the quotation is appropriate, inappropriate, or lacking information, means for automatically generating and outputting a price negotiation or request message based on the evaluation results, and means for installing the system on a smartphone, smart glasses, head-mounted display, or robot terminal. This enables efficient and consistent confirmation of quotation appropriateness and price negotiation even in virtual stores.

[0713] "Means for inputting estimate details" refers to an interface that allows the user to input the estimate information received from the construction company into the terminal.

[0714] "Means for receiving input estimate details" refers to a device or software that has the function of receiving estimate information sent from the terminal to the server.

[0715] "Means of sending the received quotation details to the generative AI model and requesting analysis" refers to the process in which the server transfers the received quotation information to the generative AI and requests its analysis.

[0716] "Means for receiving analysis results from a generative AI model and evaluating whether they are appropriate, inappropriate, or lack information" refers to algorithms or devices for determining whether the quotation contents are appropriate, inappropriate, or lack information based on the analysis results sent from the generative AI.

[0717] "Means for automatically generating and outputting a price negotiation letter or request letter based on the evaluation results" refers to a device or software that has the function of automatically generating a negotiation letter or a document requesting additional information with appropriate content based on the evaluation results and outputting it.

[0718] A "smartphone" refers to a mobile information terminal with multiple functions, such as making calls, sending messages, and connecting to the Internet.

[0719] "Smart glasses" refers to a wearable device in the form of glasses that has the function of displaying information in the user's field of vision.

[0720] A "head-mounted display" refers to a display device that is worn on the user's head and displays images across the entire field of vision.

[0721] "Robot" refers to a mechanical device that automatically performs programmed tasks.

[0722] The present invention relates to a system for efficiently and consistently confirming quotes and negotiating prices in a virtual store, which is installed on a smartphone, smart glasses, a head-mounted display, or a robot terminal.

[0723] The system comprises the following means:

[0724] 1. How to enter quote details

[0725] The interface allows users to input the details of the estimate received from the construction company using a terminal. The interface includes fields for the work content, construction period, labor costs, material costs, and other costs.

[0726] 2. How to receive the entered quotation details

[0727] The terminal receives the quote information entered by the user and sends the data to the server in a structured format (e.g., JSON format).

[0728] 3. A method for sending the received quotation details to a generative AI model and requesting analysis

[0729] The server sends the received quotation details to the generative AI model, requesting analysis and evaluation. OpenAI's API is used as the generative AI model.

[0730] 4. A means of receiving analysis results from generative AI models and evaluating whether they are appropriate, inappropriate, or lack information.

[0731] The server receives the analysis results from the generative AI model and uses them to determine whether the estimate is appropriate, inappropriate, or lacks sufficient information. This evaluation uses data such as unit prices of other projects and market prices.

[0732] 5. A means for automatically generating and outputting price negotiation or promotional messages based on the evaluation results

[0733] Depending on the evaluation results, the server uses a generative AI model such as ChatGPT to automatically generate a price negotiation message or a message requesting additional information, which is then sent to the terminal and displayed to the user.

[0734] As a specific example, consider the case where the user inputs the following quotation details:

[0735] Example prompt sentence:

[0736] Work: Installation of a communications network

[0737] Construction period: 5 days

[0738] Labor costs: 100,000 yen

[0739] Material cost: 200,000 yen

[0740] Other: 50,000 yen

[0741] 1. The user enters the quote details using a smartphone or smart glasses.

[0742] 2. The terminal sends the entered data to the server.

[0743] 3. The server uses OpenAI's API to send the received data to the generative AI model and request analysis.

[0744] 4. The generative AI model analyzes the quote and returns an evaluation result indicating that the material cost is higher than the market price.

[0745] 5. The server generates a price negotiation message based on the evaluation results and sends it to the terminal.

[0746] 6. The user receives the following negotiation document and submits it to the construction company.

[0747] Example of generated negotiation text:

[0748] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0749] As a result, the system of the present invention automates the process of checking the appropriateness of construction estimates and negotiating discounts in the virtual store, thereby reducing the burden on users.

[0750] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0751] Step 1:

[0752] The user inputs the details of the estimate received from the construction company into the terminal interface. The input fields include the details of the work, construction period, labor costs, material costs, and other costs.

[0753] Input: The quote entered by the user

[0754] Output: Structured data (e.g., JSON format)

[0755] Step 2:

[0756] The terminal converts the quote information entered by the user into JSON format and sends it to the server. Through this process, the quote information entered by the user arrives at the server in a structured format.

[0757] Input: Structured quote content (JSON format)

[0758] Output: Quote sent to the server

[0759] Step 3:

[0760] The server sends the quote received from the device to a generative AI model such as OpenAI and requests analysis. In this process, the server makes a request to determine whether the quote is appropriate.

[0761] Input: Quote received by the server

[0762] Output: Analysis request sent to the generative AI model

[0763] Step 4:

[0764] The generative AI model analyzes the received quote and sends the results to the server. During the analysis, it compares the quote with data such as the unit price of other projects and market price to evaluate the appropriateness of the quote.

[0765] Input: The quote sent to the generative AI model

[0766] Output: Analysis result (either correct, incorrect, or insufficient information)

[0767] Step 5:

[0768] Based on the analysis results received from the generative AI model, the server determines whether the estimate is appropriate, inappropriate, or lacks information, and then decides the next action to take.

[0769] Input: Analysis results from the generative AI model

[0770] Output: Evaluation decision: Good, bad, or insufficient information

[0771] Step 6:

[0772] The server automatically generates a price negotiation or promotion message based on the evaluation results using a generative AI model (such as ChatGPT) and sends it to the device. If the message is deemed appropriate, a message indicating this is correct is generated.

[0773] Input: Evaluation result

[0774] Output: Generated discount negotiation, reminder or eligibility notification message

[0775] Step 7:

[0776] The terminal displays the messages received from the server to the user, who then communicates with the construction company through the displayed negotiation and urging messages.

[0777] Input: Message from the server

[0778] Output: The message displayed to the user

[0779] Through this entire process, estimate confirmation and price negotiation in the virtual store can be automated, reducing the burden on users.

[0780] 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.

[0781] The present invention improves the user experience by combining a system that automates the confirmation of the appropriateness of construction estimates and price negotiations with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0782] System Overview

[0783] This system allows users to input the details of the estimate they received from the construction company and uses generative AI (ChatGPT) to check its appropriateness. If it is inappropriate or if additional information is required, it automatically generates a price negotiation or request message and provides it to the user. In addition, the emotion engine recognizes the user's emotions and responds accordingly, enabling more effective communication.

[0784] Program processing explanation

[0785] 1. Enter and submit quote details

[0786] The user inputs the estimate details (work details, period, labor costs, material costs, other costs, etc.) received from the construction company into the form on the terminal.

[0787] The terminal sends the entered quotation details to the server as structured data (e.g., JSON format).

[0788] 2. Receiving and saving quote details

[0789] The server receives the quotation data sent from the terminal.

[0790] The received data is temporarily stored in a database.

[0791] 3. Request for analysis of quotation details

[0792] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT).

[0793] Send a request to the generative AI for analysis and evaluation.

[0794] 4. Appropriateness Assessment

[0795] The generative AI (ChatGPT) analyzes the received estimate data, specifically comparing labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices to assess whether they are appropriate.

[0796] The generative AI (ChatGPT) expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns the result and reason to the server.

[0797] 5. Operation of the Emotion Engine

[0798] The emotion engine estimates the user's emotions from the user's input, operation history, and other sensor information.

[0799] If the emotion engine determines that the user is feeling dissatisfied or anxious, it provides that information to the server.

[0800] 6. Processing of Evaluation Results

[0801] The server analyzes the evaluation results received from the generative AI and information from the emotion engine, and decides on a response based on the results.

[0802] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[0803] If the request is deemed inappropriate, the server instructs the AI ​​to generate a price negotiation message. The AI ​​reflects the information from the emotion engine and generates a negotiation message using a gentler tone as necessary.

[0804] If it is determined that there is insufficient information, the server instructs the generative AI to generate a prompt to request additional information. The prompt is generated in an appropriate tone as necessary, reflecting the information from the emotion engine.

[0805] 7. Displaying Messages

[0806] The terminal displays the messages (eligibility notification, discount negotiation message, urging message) received from the server to the user.

[0807] 8. User Support

[0808] The user checks the message displayed on the terminal and takes necessary action. For example, if a price negotiation message is displayed in case of inappropriateness, the user can contact the construction company based on the message and negotiate a price reduction.

[0809] Specific examples

[0810] Example input

[0811] The user enters the following quote details into the terminal:

[0812] Work: Installation of a communications network

[0813] Construction period: 5 days

[0814] Labor costs: 100,000 yen

[0815] Material cost: 200,000 yen

[0816] Other expenses: 50,000 yen

[0817] Processing example

[0818] 1. The user enters the quotation details into the terminal, and the terminal sends the details to the server.

[0819] 2. The server receives the quotation details, stores them in a database, and requests the generative AI to analyze them.

[0820] 3. The generative AI (ChatGPT) evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and returns the result to the server, stating that "the cost of materials is higher than the market price."

[0821] 4. The emotion engine detects when the user is feeling dissatisfied or anxious while typing and provides that information to the server.

[0822] 5. Based on the evaluation result that "material costs are high," the server instructs the generative AI to generate a price negotiation statement that reflects information from the emotion engine. The generated negotiation statement is sent to the terminal.

[0823] 6. The terminal displays the following bargaining message to the user:

[0824] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0825] 7. The user checks the displayed negotiation text and negotiates the price with the construction company.

[0826] This will enable quick and consistent confirmation of construction estimates and price negotiations, as well as a more personalized response that takes into account the user's emotions, improving the user experience.

[0827] The processing flow will be explained below.

[0828] Step 1:

[0829] The user inputs the estimate received from the construction company into the input form on the terminal. For example, the user might input the following information:

[0830] Work: Installation of a communications network

[0831] Construction period: 5 days

[0832] Labor costs: 100,000 yen

[0833] Material cost: 200,000 yen

[0834] Other expenses: 50,000 yen

[0835] Once the input is complete, the user clicks the send button.

[0836] Step 2:

[0837] The terminal converts the quote information entered by the user into a structured data format (e.g., JSON format), and then sends this data to the server via an HTTP request.

[0838] Step 3:

[0839] The server receives the quotation data sent from the terminal, temporarily stores the received data in a database, and prepares it for the next process.

[0840] Step 4:

[0841] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT). It sends a request to the generative AI to ask for analysis and evaluation.

[0842] Step 5:

[0843] The generative AI (ChatGPT) analyzes the estimate data sent from the server. For example, it compares labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices, and evaluates whether they are appropriate.

[0844] Step 6:

[0845] The generative AI (ChatGPT) expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns the result and reason to the server.

[0846] Step 7:

[0847] The emotion engine estimates the user's emotions from the user's input, operation history, and other sensor information. For example, it can determine the user's stress level from their typing speed and pattern.

[0848] Step 8:

[0849] The emotion engine provides information to the server when the user is feeling dissatisfied or anxious, for example by sending back a comment such as "The user is showing high stress levels."

[0850] Step 9:

[0851] The server analyzes the evaluation results received from the generative AI and the information received from the emotion engine, and determines the next response based on the results.

[0852] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[0853] If the request is deemed inappropriate, the server instructs the AI ​​to generate a price negotiation message. The AI ​​reflects the information from the emotion engine and generates a negotiation message using a gentler tone as necessary.

[0854] If it is determined that there is insufficient information, the server instructs the generative AI to generate a prompt message requesting the provision of additional information. The message is generated in an appropriate tone, reflecting the information from the emotion engine.

[0855] Step 10:

[0856] The terminal displays the message (notification of appropriateness, discount negotiation message, reminder message) received from the server to the user. For example, if the transaction is determined to be inappropriate and a discount negotiation message is displayed, the user can confirm the content.

[0857] Step 11:

[0858] Based on the message displayed on the terminal, the user contacts the construction company and takes the necessary action, for example, by copying the price negotiation statement and sending it to the construction company by email.

[0859] Example 2

[0860] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0861] Checking the accuracy of construction estimates and negotiating discounts has traditionally been a time-consuming and laborious task, placing a burden on users. Furthermore, it can be difficult to negotiate discounts or request information in appropriate language that reflects the user's feelings, which can sometimes hinder smooth communication. Therefore, a system is needed that can accurately evaluate construction estimates and automate responses that take the user's feelings into consideration.

[0862] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting estimate details, a means for receiving the input estimate details, a means for sending the received estimate details to the generation AI model and requesting analysis, a means for receiving the analysis results from the generation AI model and evaluating the appropriateness, a means for automatically generating and outputting a price negotiation message or a request message based on the evaluation results, a means for estimating the user's emotions, and a means for generating a message with an appropriate tone using the user's emotion information. This enables the appropriateness of the construction estimate and price negotiation to be confirmed quickly and consistently, and also enables personalized responses that take the user's emotions into consideration.

[0863] "Estimate details" refers to data that includes detailed information such as the costs, duration, materials, and labor costs associated with construction or services.

[0864] The "means for inputting" refers to a device or program that provides an interface for the user to input the details of the estimate.

[0865] "Means for receiving" refers to a device or program that has the function of receiving data transmitted from outside.

[0866] A "generative AI model" is a software system that uses artificial intelligence to analyze data and perform specific tasks based on the results.

[0867] A "means for requesting analysis" is a device or program that has the function of sending received data to a generative AI model and requesting analysis.

[0868] A "means for assessing appropriateness" is a device or program that has the function of determining whether the quotation contents are appropriate based on the analysis results returned from the generative AI model.

[0869] A "price negotiation letter" is a letter that requests cost reductions or changes to conditions regarding quotes that are deemed inappropriate.

[0870] A "demand letter" is a letter requesting additional information regarding the quotation content that is deemed to be insufficient.

[0871] "User emotions" refers to the psychological state, such as dissatisfaction, anxiety, or satisfaction, that the user feels while inputting or operating the estimate contents.

[0872] "Estimation means" refers to a device or program that has the function of inferring emotions based on the user's behavior, operation history, sensor information, etc.

[0873] The "means for generating sentences with an appropriate tone" refers to a device or program that has the function of generating sentences that reflect the user's emotional information and are designed to avoid making the user feel uncomfortable.

[0874] The present invention is a system that automates the confirmation of the appropriateness of construction estimates and price negotiations, and improves the user experience by recognizing the user's emotions. This system analyzes the estimate contents entered by the user and automatically takes appropriate action based on the results. Specific embodiments of this system are described below.

[0875] System Overview

[0876] This system mainly uses the following hardware and software:

[0877] Devices that accept user information input (e.g., PCs, smartphones)

[0878] A server that analyzes and stores received data

[0879] Analysis function using generative AI models (e.g. ChatGPT)

[0880] Emotion engine that estimates user emotions

[0881] Hardware Configuration

[0882] The terminal is a device that allows the user to input the details of the estimate received from the construction company. The terminal is provided with an interface (form) for inputting the details of the estimate. When the user enters the details of the estimate into the form, the terminal converts them into a structured data format (e.g., JSON) and sends it to the server.

[0883] The server is equipped with software to analyze the received data, generate and send API requests to operate both the generative AI model and the emotion engine, and store the received data in a database for further analysis and evaluation as needed.

[0884] Software Configuration

[0885] Generative AI models (e.g., ChatGPT) are used to analyze the appropriateness of quotes. Based on the received quote, they compare labor costs, material costs, and other expenses with the unit prices of other projects and the general market price to evaluate whether the quote is reasonable.

[0886] The emotion engine uses the user's input, operation history, and other sensor information to estimate the user's emotions. For example, if the user makes many corrections while entering the quote, it is determined that the user is likely to be dissatisfied. The emotion engine sends this information to the server and reflects it in the generated message.

[0887] Specific examples

[0888] The following are specific examples based on the present invention.

[0889] Example input

[0890] The user inputs the details of the work and its cost into the terminal. At this time, the data is input in the following text format:

[0891] Work details: Floor renovation

[0892] Construction period: 10 days

[0893] Labor costs: 150,000 yen

[0894] Material cost: 250,000 yen

[0895] Other expenses: 80,000 yen

[0896] Processing example

[0897] 1. When the user finishes entering data, the device automatically generates JSON format data and sends it to the server.

[0898] 2. The server stores the data received via web communication in a database and sends an API request to the generative AI model for analysis.

[0899] 3. The generative AI model analyzes the estimate and returns the evaluation results to the server along with the conclusion that "material costs are higher than the market price."

[0900] 4. The emotion engine infers that the user is feeling anxious or dissatisfied while inputting and provides that emotional information to the server.

[0901] 5. The server integrates the evaluation results and emotional information, generates a price negotiation message in an appropriate tone, and sends it to the terminal.

[0902] 6. The terminal displays the following bargaining message to the user:

[0903] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[0904] 7. The user can contact the construction company based on this negotiation statement and negotiate a discount.

[0905] As described above, the system of the present invention automatically evaluates the appropriateness of construction estimates and generates messages that reflect the user's emotions, thereby realizing efficient and personalized responses.

[0906] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0907] Step 1:

[0908] The user inputs the details of the estimate received from the construction company (construction details, period, labor costs, material costs, and other expenses) into the terminal form by manually entering specific values ​​and details into the text fields.

[0909] Input: Construction details, construction period, labor costs, material costs, other costs

[0910] Output: Quote details entered in the form

[0911] Specific behavior: The user fills in all the fields in the input form and clicks the submit button.

[0912] Step 2:

[0913] The terminal converts the quote information entered by the user into JSON format, which includes mapping the values ​​of each form field to a key-value format.

[0914] Input: Quote details entered in the form

[0915] Output: Quote data in JSON format

[0916] What happens: When a form submit event occurs, JavaScript or other client script converts the data into JSON format.

[0917] Step 3:

[0918] The terminal sends the converted JSON data to the server, where it is encrypted using the HTTPS protocol.

[0919] Input: Quote data in JSON format

[0920] Output: Request to send data to the server

[0921] What it does: Sends a POST request containing JSON data over HTTPS.

[0922] Step 4:

[0923] The server receives the quotation data sent from the device using a RESTful API endpoint.

[0924] Input: Quote data in JSON format

[0925] Output: Status code of the receipt confirmation (e.g. 200 OK)

[0926] What happens: A server-side API endpoint receives the request and processes the data.

[0927] Step 5:

[0928] The server saves the received quote data in a database, storing each item in a corresponding table column according to the database schema.

[0929] Input: Quote data in JSON format

[0930] Output: Save result to database (e.g. success / failure status)

[0931] What it does: Uses a database connectivity library to execute an SQL query to insert data.

[0932] Step 6:

[0933] The server prepares an API request to the generative AI model for analysis, which includes attaching an API key and forming the request body.

[0934] Input: Saved quote data

[0935] Output: Prepared data for the analysis request

[0936] Specific behavior: Generates a request to an API endpoint that includes authentication information and analytics data.

[0937] Step 7:

[0938] The server sends an analysis request to the generative AI model.

[0939] Input: Prepared data for the analysis request

[0940] Output: Data sent to the generative AI model

[0941] What it does: Sends a request to the API endpoint of the generative AI model using the HTTPS protocol.

[0942] Step 8:

[0943] The generative AI model analyzes the received quotation data and evaluates its appropriateness. The evaluation result is returned as either "appropriate," "inappropriate," or "insufficient information."

[0944] Input: Analysis request data

[0945] Output: Analysis result (appropriate / inappropriate / insufficient information) and the reason

[0946] How it works: The generative AI model uses its internal algorithms to compare data with other cases and make an evaluation.

[0947] Step 9:

[0948] The generative AI model sends the analysis results back to the server.

[0949] Input: Analysis results and reasons

[0950] Output: Data sent back to the server

[0951] Specific operation: Analysis result data is sent to the server using the HTTPS protocol.

[0952] Step 10:

[0953] The server analyzes the evaluation results received from the generative AI model and simultaneously determines the necessary response using the user's emotional information.

[0954] Input: Analysis results from the generative AI model and user emotion information

[0955] Output: Required actions (e.g., notification of appropriateness, price negotiation, reminder)

[0956] Specific operation: The server logic integrates the evaluation results and emotional information to generate an appropriate response message.

[0957] Step 11:

[0958] The server instructs the generative AI model to generate price negotiation and demand messages, incorporating information from the emotion engine to generate sentences with an appropriate tone.

[0959] Input: Required response and emotional information

[0960] Output: A request to the generative AI model

[0961] What it does: Sends a request to an API endpoint to generate a sentence with the appropriate tone.

[0962] Step 12:

[0963] The generative AI model generates price negotiation and urging messages and sends the results back to the server.

[0964] Input: A request to generate text

[0965] Output: The generated price negotiation or reminder

[0966] How it works: The generative AI model uses its internal natural language processing algorithm to generate documents in the specified tone.

[0967] Step 13:

[0968] The server transmits the generated message (eligibility notification, discount negotiation message, urging message) to the terminal.

[0969] Input: The message returned by the generative AI model

[0970] Output: Send message to terminal

[0971] Specific operation: Calls an API to send a response containing a message back to the device.

[0972] Step 14:

[0973] The terminal displays the received message to the user.

[0974] Input: Message sent from the server

[0975] Output: The message that is displayed to the user

[0976] What it does: Uses a GUI to visually display incoming messages to the user.

[0977] Step 15:

[0978] The user checks the message displayed on the terminal and takes necessary action. For example, if a discount negotiation message is displayed, the user contacts the construction company based on the message and negotiates for a discount.

[0979] Input: Message displayed on terminal

[0980] Output: Results of contact with construction company and price negotiation

[0981] Specific actions: Contact the construction company via email or phone and negotiate a discount on the proposed price.

[0982] The specific operations, inputs, and outputs in each processing step of the system have been described above.

[0983] (Application example 2)

[0984] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0985] Conventional quotation systems require manual evaluation to confirm the appropriateness of quotation details, and also require manual negotiation of discounts and prompting for additional information. Furthermore, these systems often lack consideration for the user's feelings, resulting in a poor user experience. The present invention aims to solve these problems and enable efficient and user-friendly confirmation of quotation details and negotiation of discounts.

[0986] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting quotation details, means for receiving the input quotation details, means for sending the received quotation details to a generative AI and requesting analysis, means for receiving the analysis results from the generative AI and evaluating whether the quotation details are appropriate, inappropriate, or lacking information, means for automatically generating and outputting a price negotiation message or a request message based on the evaluation results, and means including an emotion engine that recognizes the user's emotions and adjusting the tone of the negotiation message or the request message depending on the emotions. This automates the process of confirming the appropriateness of the quotation details and negotiating a price discount, enabling personalized responses that take the user's emotions into consideration.

[0987] "Estimate details" refers to data that includes details such as the work content, cost, and duration of a user-specified construction or manufacturing project.

[0988] "Means for input" refers to technology that provides a form or interface for users to input estimate details into a terminal.

[0989] "Means for receiving" refers to the technology for receiving the input quotation details on a server or the like.

[0990] "Generative AI" refers to artificial intelligence that uses natural language processing to analyze received data and generate information.

[0991] "Means for requesting analysis" refers to the technology of sending a request to the generative AI to evaluate the appropriateness of the estimate contents.

[0992] The "means of evaluation" refers to a technology that evaluates the appropriateness of the estimate content as "appropriate," "inappropriate," or "insufficient information" based on the analysis results from generative AI.

[0993] A "price negotiation letter" is a letter used to request a discount on an inappropriate quote.

[0994] A "demand letter" is a letter requesting additional information when there are deficiencies in the quotation.

[0995] An "emotion engine" is a technology that detects emotions from user input and operation history and adjusts responses based on those emotions.

[0996] "Tone" refers to the style of language and expression used in writing or dialogue.

[0997] "System" refers to the collection of hardware and software required to automatically verify the accuracy of quote details and negotiate discounts.

[0998] The present invention is a system for efficiently confirming the appropriateness of quotation details and negotiating discounts, intended for use in factories and manufacturing fields. This system includes a means for inputting quotation details, a means for receiving the input quotation details, a means for sending the received quotation details to a generative AI and requesting analysis, a means for receiving the analysis results from the generative AI and evaluating whether the quotation is appropriate, inappropriate, or lacks information, a means for automatically generating and outputting a price negotiation or request message based on the evaluation results, and a means including an emotion engine that recognizes the user's emotions and adjusting the tone of the negotiation or request message depending on the emotion.

[0999] Example of a system

[1000] 1. Enter a quote

[1001] The user is a factory estimator and uses a smartphone application to input estimate details. For example, the user might enter the following estimate information: "Work details: installation of an automated line," "Work duration: 10 days," "Labor costs: 300,000 yen," "Material costs: 500,000 yen," and "Other costs: 200,000 yen."

[1002] 2. Receiving the quotation

[1003] The terminal receives the estimate content entered by the user and transmits it to the server.

[1004] 3. Use of generative AI

[1005] The server saves the received quotation data as structured data (JSON format) and sends an API request to a generative AI (such as ChatGPT) to request analysis.

[1006] 4. Appropriateness Assessment

[1007] The generative AI evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and determines whether it is appropriate. The result is returned to the server as either "appropriate," "inappropriate," or "insufficient information."

[1008] 5. Operation of the Emotion Engine

[1009] The server uses an emotion engine (e.g., a Sentiment Analysis tool) to estimate the user's emotions from their input and operation history. Based on this information, the server determines whether the user is feeling dissatisfied or anxious.

[1010] 6. Message Creation

[1011] The server generates an appropriate message based on the evaluation results from the generative AI and the analytical information from the emotion engine. For example, it adjusts the content according to the emotion, such as "The estimated price is higher than the market price. Could you please give us a discount?"

[1012] 7. Displaying Messages

[1013] The terminal displays the message received from the server to the user, who then negotiates a discount with the construction company based on the displayed negotiation text.

[1014] Specific examples

[1015] A specific example of quotation data entered by the user is as follows:

[1016] Quotation Data

[1017] Construction details: Installation of automated production line

[1018] Construction period: 10 days

[1019] Labor costs: 300,000 yen

[1020] Material cost: 500,000 yen

[1021] Other expenses: 200,000 yen

[1022] Emotional expression during user input

[1023] "This quote seems a little high..."

[1024] Example prompts for generative AI models

[1025] Please rate the appropriateness of the following estimates.

[1026] Construction details: Installation of automated production line

[1027] Construction period: 10 days

[1028] Labor costs: 300,000 yen

[1029] Material cost: 500,000 yen

[1030] Other expenses: 200,000 yen

[1031] In this way, the present invention automates the process of checking the accuracy of quotation details and negotiating discounts, and realizes personalized responses that take into account the user's feelings, thereby improving the efficiency of quotation work and the user experience.

[1032] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1033] Step 1:

[1034] The user is a factory estimator and uses a smartphone application to input estimate details. The input estimate details include the work content, construction period, labor costs, material costs, and other expenses. The input data is temporarily saved on the device.

[1035] Step 2:

[1036] The terminal sends the quote information entered by the user to the server. At this time, the data is sent as structured data (e.g., JSON format). The server receives this structured data and stores it in a database.

[1037] Step 3:

[1038] The server sends the received quotation data to a generative AI (such as ChatGPT) and prepares an API request to request analysis. This request includes the quotation details entered by the user. The server then generates and sends a prompt message to the generative AI requesting an appropriateness assessment.

[1039] Step 4:

[1040] The generative AI analyzes the received quotation data. Specifically, it compares it with the unit prices of other projects and the general public's prices to evaluate the appropriateness of the quotation. It then expresses the results of this evaluation as "appropriate," "inappropriate," or "insufficient information" and sends them back to the server.

[1041] Step 5:

[1042] The server receives the evaluation results from the generative AI. At the same time, it uses an emotion engine (e.g., a Sentiment Analysis tool) to estimate the user's emotions based on their input and operation history. The emotion engine analyzes whether the user is feeling dissatisfied or anxious, and provides the results to the server.

[1043] Step 6:

[1044] The server decides how to respond based on the evaluation results from the generative AI and information from the emotion engine. For example, if the evaluation result is "inappropriate" and the emotion engine detects the user's dissatisfaction, the server generates a price negotiation message in a gentle tone. This negotiation message is sent to the generative AI and automatically generated.

[1045] Step 7:

[1046] The server sends the generated negotiation text and appropriate notification message to the terminal, which receives the message and displays it to the user.

[1047] Step 8:

[1048] The user checks the message displayed on the terminal and takes the necessary action. For example, if the quote is deemed inappropriate, the user can negotiate a discount with the construction company based on the discount negotiation text. In this way, this system automates the confirmation of the appropriateness of the quote content and the discount negotiation process, enabling responses that take the user's feelings into consideration.

[1049] The above processing steps can improve the efficiency of the quotation process and the user experience.

[1050] 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.

[1051] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1052] 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.

[1053] [Third embodiment]

[1054] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1055] 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.

[1056] 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).

[1057] 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.

[1058] 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.

[1059] 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).

[1060] 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.

[1061] 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.

[1062] 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.

[1063] 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.

[1064] 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.

[1065] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1066] The present invention relates to a system for automating confirmation of the appropriateness of construction estimates and price negotiations. Specific embodiments of this system will be described below.

[1067] System Overview

[1068] This system allows users to input the details of the estimate they received from the construction company and uses a generative AI (ChatGPT) to check the appropriateness of the estimate. If the estimate is inappropriate or if additional information is required, the system automatically generates a price negotiation or request letter and provides it to the user.

[1069] Program processing explanation

[1070] 1. Enter and submit quote details

[1071] The user inputs the estimate details (work details, period, labor costs, material costs, other costs, etc.) received from the construction company into the form on the terminal.

[1072] The terminal sends the entered quotation details to the server as structured data (e.g., JSON format).

[1073] 2. Receiving and saving quote details

[1074] The server receives the quotation data sent from the terminal.

[1075] Temporarily store the received quotation data in a database.

[1076] 3. Request for analysis of quotation details

[1077] The server sends the saved quotation data to the generative AI (ChatGPT) and requests analysis and evaluation.

[1078] 4. Appropriateness Assessment

[1079] ChatGPT analyzes the received estimate data, comparing it with the unit prices of other projects, the general price level, and the appropriate man-hours required for construction, and evaluates the appropriateness of each item (labor costs, material costs, and other expenses).

[1080] ChatGPT will indicate the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and will return the result and reason to the server.

[1081] 5. Processing of evaluation results

[1082] The server analyzes the evaluation results received from ChatGPT and takes appropriate action.

[1083] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[1084] If it is judged to be inappropriate: The server instructs ChatGPT to generate a discount negotiation message. It generates a discount negotiation message including the specific reason and sends it to the terminal.

[1085] If the server determines that there is insufficient information, it instructs ChatGPT to generate a message requesting additional information. The generated message is sent to the device.

[1086] 6. Displaying Messages

[1087] The terminal displays the messages received from the server to the user.

[1088] The user checks the displayed messages (notification of suitability, price negotiation message, urging message) and takes the necessary action.

[1089] Specific examples

[1090] Example input

[1091] The user enters the following quote details into the terminal:

[1092] Work: Installation of a communications network

[1093] Construction period: 5 days

[1094] Labor costs: 100,000 yen

[1095] Material cost: 200,000 yen

[1096] Other: 50,000 yen

[1097] Processing example

[1098] 1. The user enters the quotation details into the terminal, and the terminal sends the details to the server.

[1099] 2. The server receives the quotation details, stores them in a database, and requests the generative AI to analyze them.

[1100] 3. ChatGPT evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and returns the result to the server, stating that "the material cost is higher than the market price."

[1101] 4. Based on the evaluation result that "material costs are high," the server instructs ChatGPT to generate a discount negotiation message and sends the generated discount negotiation message to the terminal.

[1102] 5. The terminal displays the following bargaining message to the user:

[1103] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1104] 6. The user checks the displayed negotiation text and negotiates the price with the construction company.

[1105] This makes it possible to quickly and consistently check the accuracy of construction estimates and negotiate discounts, resulting in high-quality negotiations that are not dependent on the experience of the person in charge.

[1106] The processing flow will be explained below.

[1107] Step 1:

[1108] The user inputs the estimate received from the construction company into the input form on the terminal. For example, the user might input the following information:

[1109] Work: Installation of a communications network

[1110] Construction period: 5 days

[1111] Labor costs: 100,000 yen

[1112] Material cost: 200,000 yen

[1113] Other expenses: 50,000 yen

[1114] Once the input is complete, the user clicks the send button.

[1115] Step 2:

[1116] The terminal converts the quote information entered by the user into a structured data format (e.g., JSON format), and then sends this data to the server via an HTTP request.

[1117] Step 3:

[1118] The server receives the quotation data sent from the terminal, temporarily stores the received data in a database, and prepares it for the next process.

[1119] Step 4:

[1120] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT). It sends a request to the generative AI to ask for analysis and evaluation.

[1121] Step 5:

[1122] The generative AI (ChatGPT) analyzes the estimate data sent from the server. For example, it compares labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices, and evaluates whether they are appropriate.

[1123] Step 6:

[1124] The generative AI (ChatGPT) returns the estimate evaluation results to the server. The evaluation results are either "appropriate," "inappropriate," or "insufficient information," and include any necessary reasons or comments.

[1125] Step 7:

[1126] The server analyzes the evaluation results received from the generative AI and determines the next course of action based on the results.

[1127] If it is judged to be appropriate: The server generates a message stating that the quotation is appropriate and sends it to the terminal.

[1128] If it is determined to be inappropriate: The server instructs the generation AI to generate a discount negotiation text and sends the generated negotiation text to the terminal.

[1129] If it is determined that there is insufficient information: The server instructs the generative AI to generate a prompt message requesting the provision of additional information, and sends the generated prompt message to the terminal.

[1130] Step 8:

[1131] The terminal displays the messages (eligibility notification, discount negotiation message, urging message) received from the server to the user.

[1132] Step 9:

[1133] The user checks the message displayed on the terminal. For example, if the request is deemed inappropriate and a discount negotiation message is displayed, the user contacts the construction company based on the message and negotiates the discount.

[1134] Example 1

[1135] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1136] In the past, the process of checking the accuracy of construction estimates and negotiating discounts as necessary was time-consuming and depended on the experience and skills of the person in charge, which could lead to a lack of consistency and reliability in the results. Furthermore, analyzing the estimates and determining their accuracy required specialized knowledge, making it difficult for average users to solve the problem. This often meant that appropriate measures could not be taken against inappropriate estimates.

[1137] 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.

[1138] In this invention, the server includes a means for a user to input estimate details, a terminal that receives the input estimate details, a means for sending the received estimate details to a generation AI model and requesting analysis, a means for receiving the analysis results from the generation AI model and evaluating whether the estimate is appropriate, inappropriate, or lacking information, and a means for automatically generating and outputting a price negotiation or request message based on the evaluation results. This allows the appropriateness of the construction estimate to be quickly and efficiently confirmed, enabling price negotiations or requests for additional information as needed. It also ensures consistent, high-quality service regardless of the skills or experience of individual staff members.

[1139] A "user" is an entity that uses the system to check the appropriateness of construction estimates and negotiate discounts.

[1140] "Estimate details" is data including detailed information such as the construction details, period, labor costs, material costs, and other expenses.

[1141] "Input means" refers to an interface or device that a user uses to input quotation details into the system.

[1142] A "terminal" is a device such as a computer or smartphone that allows a user to input information and communicate with a server.

[1143] The "server" is a central computer system that receives and stores quotes and sends analysis requests to the generative AI model.

[1144] A "generative AI model" is an artificial intelligence model that analyzes received quotation data and evaluates its appropriateness.

[1145] The "means of requesting analysis" is the process by which the server sends the quotation details to the generating AI model and has it perform the analysis.

[1146] "Means of evaluation" refers to the process of determining the appropriateness of the quotation based on the analysis results received from the generative AI model.

[1147] A "price negotiation document" is a document automatically generated for price negotiation in response to an inappropriate quote.

[1148] A "reminder" is an automatically generated document requesting additional information regarding the quote.

[1149] The "means for automatically generating and outputting" is a process for automatically generating a price negotiation message or a promotion message based on the evaluation results and transmitting it to the terminal.

[1150] "When deemed appropriate" means that the quotation is evaluated as appropriate in light of general market prices and standards.

[1151] "When judged to be inappropriate" means that the quotation is evaluated as inappropriate in light of general market prices and standards.

[1152] "When it is judged that there is insufficient information" refers to a situation where it is judged that there is insufficient information to evaluate the appropriateness of the quotation content.

[1153] The present invention relates to a system for automating the confirmation of the appropriateness of construction estimates and price negotiations. How to implement this system will be specifically described below.

[1154] This system consists of a user, a terminal, a server, and a generative AI model (e.g., ChatGPT). The user inputs the estimate received from the construction company, and the system evaluates the appropriateness of the estimate and provides countermeasures based on the results.

[1155] The user inputs the details of the estimate, such as the work content, construction period, labor costs, material costs, and other expenses, into a dedicated form on the terminal. The terminal converts the input estimate details into structured data in JSON format and sends it to the server. The server analyzes the received estimate data and saves it in a database.

[1156] The server sends the saved quotation data to the generative AI model and requests its analysis. At this time, the server generates a prompt for the analysis request. For example, it generates the following prompt:

[1157] Please rate the appropriateness of the following estimates: Work: laying a communications network, Work period: 5 days, Labor costs: 100,000 yen, Material costs: 200,000 yen, Other: 50,000 yen

[1158] The generative AI model (e.g., ChatGPT) analyzes the received quotation data and returns the evaluation result to the server. The evaluation result is expressed as either "appropriate," "inappropriate," or "insufficient information," along with the reason for the evaluation.

[1159] The server analyzes the results of this evaluation and takes the following actions:

[1160] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[1161] If it is determined to be inappropriate: The server requests the generative AI model to generate a price negotiation statement, which includes specific reasons and sends it to the terminal.

[1162] If it is determined that there is insufficient information: The server requests the generative AI model to generate a prompt message requesting the provision of additional information, and sends the generated prompt message to the terminal.

[1163] The terminal displays the messages received from the server (e.g., notification of suitability, price negotiation message, and reminder message) to the user. The user checks the displayed messages and takes the necessary action.

[1164] As a concrete example, consider the following quotation input:

[1165] Work: Installation of a communications network

[1166] Construction period: 5 days

[1167] Labor costs: 100,000 yen

[1168] Material cost: 200,000 yen

[1169] Other: 50,000 yen

[1170] This quotation is sent to the server, which then sends a prompt to the generative AI model to request analysis. The generative AI model returns the evaluation result that "the material cost is higher than the market price." Based on this evaluation result, the server requests the generative AI model to generate a price negotiation message, and displays the following message to the user:

[1171] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1172] This allows for quick and consistent confirmation of quote accuracy and price negotiations, ensuring high-quality service that is not dependent on the experience of the person in charge.

[1173] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1174] Step 1:

[1175] Enter and submit quote details

[1176] The user enters the details of the estimate received from the construction company (such as construction details, duration, labor costs, material costs, and other expenses) into a dedicated form on the terminal.

[1177] Input: The estimate details entered by the user in the form. Example: "Work details: laying a communications network", "Work period: 5 days", "Labor costs: 100,000 yen", "Material costs: 200,000 yen", "Other: 50,000 yen"

[1178] The terminal converts the entered quotation details into structured data in JSON format.

[1179] Data processing: Form input -> JSON format conversion.

[1180] Output: Estimate data in JSON format. "{Work details: 'Installation of communication network', Work duration: '5 days', Labor costs: '100,000 yen', Material costs: '200,000 yen', Other: '50,000 yen'}"

[1181] The terminal adjusts the converted JSON data and sends it to the server.

[1182] Step 2:

[1183] Receiving and saving quotes

[1184] The server receives the quotation data in JSON format sent from the terminal.

[1185] Input: Quote data in JSON format sent by the terminal.

[1186] The server stores the received quotation data in a database.

[1187] Data processing: JSON format -> Insert into database.

[1188] Output: The quote stored in the database.

[1189] Specific operation: Use an SQL query to insert quotation data into the "Quotation Data" table in the database. Example: "INSERT INTO Quotation Data (Work Details, Work Period, Labor Costs, Material Costs, Other) VALUES ('Communication Network Installation', '5 Days', '100,000 Yen', '200,000 Yen', '50,000 Yen');"

[1190] Step 3:

[1191] Request for analysis of quotation details

[1192] The server sends the saved quotation data to the generative AI model and requests its analysis.

[1193] Input: Quote details stored in the database.

[1194] The server generates a prompt. For example, "Please rate the appropriateness of the following estimates: Work: laying a communications network, Work period: 5 days, Labor cost: 100,000 yen, Material cost: 200,000 yen, Other: 50,000 yen."

[1195] Data processing: Estimate content obtained from the database -> Generated prompt text.

[1196] Output: The generated prompt statement.

[1197] The server sends the prompt sentence to the generative AI model.

[1198] Step 4:

[1199] Assessment of suitability

[1200] The generative AI model analyzes the received quotation data, expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns it to the server along with the reason for the evaluation.

[1201] Input: The prompt sent to the generative AI model.

[1202] The generative AI model analyzes the quotation data and makes an assessment, for example, that "material costs are higher than market prices."

[1203] Data computation: The analytical process using generative AI models.

[1204] Output: Evaluation result. "{Evaluation result: 'Inappropriate', Reason: 'The cost of materials is higher than the market price. The market price is approximately 150,000 yen.'}"

[1205] The generative AI model sends the evaluation results back to the server.

[1206] Step 5:

[1207] Processing of evaluation results

[1208] The server analyzes the evaluation results received from the generative AI model and takes appropriate action based on the results.

[1209] Input: Evaluation results returned by the generative AI model.

[1210] If the estimate is determined to be appropriate, the server generates a message indicating that the estimate is appropriate and transmits it to the terminal.

[1211] Output: A message stating that the quote is correct.

[1212] If the server determines that the information is inappropriate, it requests the AI ​​model to generate a price negotiation message and sends the message to the terminal.If the server determines that the information is insufficient, it requests the AI ​​model to generate a urging message and sends the message to the terminal.

[1213] Data calculation: Analysis of evaluation results and message generation.

[1214] Output: Price negotiation or request. Example: "Regarding your quote, I believe the material cost is higher than the market price, so I would like to ask you to reconsider. The market price is about 150,000 yen."

[1215] Step 6:

[1216] Displaying messages

[1217] The terminal displays the messages received from the server to the user.

[1218] Input: Message sent by the server (eligibility notice, price negotiation message, reminder message).

[1219] Specific behavior: Display a message on the device screen.

[1220] Output: The message displayed to the user.

[1221] The user checks the displayed message and takes the necessary action.

[1222] (Application example 1)

[1223] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1224] In conventional construction estimate systems, the confirmation of estimate appropriateness and price negotiation depended on the experience and judgment of the person in charge, resulting in a lack of consistency and efficiency. Furthermore, on digital platforms such as virtual stores, it was difficult to streamline the estimate confirmation process and set appropriate prices. To solve these problems, it was necessary to provide a system that automates the confirmation of estimate appropriateness and price negotiation, allowing users to obtain appropriate estimate information without any hassle.

[1225] 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.

[1226] In this invention, the server includes means for inputting quotation details, means for receiving the input quotation details, means for sending the received quotation details to a generative AI model and requesting analysis, means for receiving the analysis results from the generative AI model and evaluating whether the quotation is appropriate, inappropriate, or lacking information, means for automatically generating and outputting a price negotiation or request message based on the evaluation results, and means for installing the system on a smartphone, smart glasses, head-mounted display, or robot terminal. This enables efficient and consistent confirmation of quotation appropriateness and price negotiation even in virtual stores.

[1227] "Means for inputting estimate details" refers to an interface that allows the user to input the estimate information received from the construction company into the terminal.

[1228] "Means for receiving input estimate details" refers to a device or software that has the function of receiving estimate information sent from the terminal to the server.

[1229] "Means of sending the received quotation details to the generative AI model and requesting analysis" refers to the process in which the server transfers the received quotation information to the generative AI and requests its analysis.

[1230] "Means for receiving analysis results from a generative AI model and evaluating whether they are appropriate, inappropriate, or lack information" refers to algorithms or devices for determining whether the quotation contents are appropriate, inappropriate, or lack information based on the analysis results sent from the generative AI.

[1231] "Means for automatically generating and outputting a price negotiation letter or request letter based on the evaluation results" refers to a device or software that has the function of automatically generating a negotiation letter or a document requesting additional information with appropriate content based on the evaluation results and outputting it.

[1232] A "smartphone" refers to a mobile information terminal with multiple functions, such as making calls, sending messages, and connecting to the Internet.

[1233] "Smart glasses" refers to a wearable device in the form of glasses that has the function of displaying information in the user's field of vision.

[1234] A "head-mounted display" refers to a display device that is worn on the user's head and displays images across the entire field of vision.

[1235] "Robot" refers to a mechanical device that automatically performs programmed tasks.

[1236] The present invention relates to a system for efficiently and consistently confirming quotes and negotiating prices in a virtual store, which is installed on a smartphone, smart glasses, a head-mounted display, or a robot terminal.

[1237] The system comprises the following means:

[1238] 1. How to enter quote details

[1239] The interface allows users to input the details of the estimate received from the construction company using a terminal. The interface includes fields for the work content, construction period, labor costs, material costs, and other costs.

[1240] 2. How to receive the entered quotation details

[1241] The terminal receives the quote information entered by the user and sends the data to the server in a structured format (e.g., JSON format).

[1242] 3. A method for sending the received quotation details to a generative AI model and requesting analysis

[1243] The server sends the received quotation details to the generative AI model, requesting analysis and evaluation. OpenAI's API is used as the generative AI model.

[1244] 4. A means of receiving analysis results from generative AI models and evaluating whether they are appropriate, inappropriate, or lack information.

[1245] The server receives the analysis results from the generative AI model and uses them to determine whether the estimate is appropriate, inappropriate, or lacks sufficient information. This evaluation uses data such as unit prices of other projects and market prices.

[1246] 5. A means for automatically generating and outputting price negotiation or promotional messages based on the evaluation results

[1247] Depending on the evaluation results, the server uses a generative AI model such as ChatGPT to automatically generate a price negotiation message or a message requesting additional information, which is then sent to the terminal and displayed to the user.

[1248] As a specific example, consider the case where the user inputs the following quotation details:

[1249] Example prompt sentence:

[1250] Work: Installation of a communications network

[1251] Construction period: 5 days

[1252] Labor costs: 100,000 yen

[1253] Material cost: 200,000 yen

[1254] Other: 50,000 yen

[1255] 1. The user enters the quote details using a smartphone or smart glasses.

[1256] 2. The terminal sends the entered data to the server.

[1257] 3. The server uses OpenAI's API to send the received data to the generative AI model and request analysis.

[1258] 4. The generative AI model analyzes the quote and returns an evaluation result indicating that the material cost is higher than the market price.

[1259] 5. The server generates a price negotiation message based on the evaluation results and sends it to the terminal.

[1260] 6. The user receives the following negotiation document and submits it to the construction company.

[1261] Example of generated negotiation text:

[1262] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1263] As a result, the system of the present invention automates the process of checking the appropriateness of construction estimates and negotiating discounts in the virtual store, thereby reducing the burden on users.

[1264] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1265] Step 1:

[1266] The user inputs the details of the estimate received from the construction company into the terminal interface. The input fields include the details of the work, construction period, labor costs, material costs, and other costs.

[1267] Input: The quote entered by the user

[1268] Output: Structured data (e.g., JSON format)

[1269] Step 2:

[1270] The terminal converts the quote information entered by the user into JSON format and sends it to the server. Through this process, the quote information entered by the user arrives at the server in a structured format.

[1271] Input: Structured quote content (JSON format)

[1272] Output: Quote sent to the server

[1273] Step 3:

[1274] The server sends the quote received from the device to a generative AI model such as OpenAI and requests analysis. In this process, the server makes a request to determine whether the quote is appropriate.

[1275] Input: Quote received by the server

[1276] Output: Analysis request sent to the generative AI model

[1277] Step 4:

[1278] The generative AI model analyzes the received quote and sends the results to the server. During the analysis, it compares the quote with data such as the unit price of other projects and market price to evaluate the appropriateness of the quote.

[1279] Input: The quote sent to the generative AI model

[1280] Output: Analysis result (either correct, incorrect, or insufficient information)

[1281] Step 5:

[1282] Based on the analysis results received from the generative AI model, the server determines whether the estimate is appropriate, inappropriate, or lacks information, and then decides the next action to take.

[1283] Input: Analysis results from the generative AI model

[1284] Output: Evaluation decision: Good, bad, or insufficient information

[1285] Step 6:

[1286] The server automatically generates a price negotiation or promotion message based on the evaluation results using a generative AI model (such as ChatGPT) and sends it to the device. If the message is deemed appropriate, a message indicating this is correct is generated.

[1287] Input: Evaluation result

[1288] Output: Generated discount negotiation, reminder or eligibility notification message

[1289] Step 7:

[1290] The terminal displays the messages received from the server to the user, who then communicates with the construction company through the displayed negotiation and urging messages.

[1291] Input: Message from the server

[1292] Output: The message displayed to the user

[1293] Through this entire process, estimate confirmation and price negotiation in the virtual store can be automated, reducing the burden on users.

[1294] 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.

[1295] The present invention improves the user experience by combining a system that automates the confirmation of the appropriateness of construction estimates and price negotiations with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1296] System Overview

[1297] This system allows users to input the details of the estimate they received from the construction company and uses generative AI (ChatGPT) to check its appropriateness. If it is inappropriate or if additional information is required, it automatically generates a price negotiation or request message and provides it to the user. In addition, the emotion engine recognizes the user's emotions and responds accordingly, enabling more effective communication.

[1298] Program processing explanation

[1299] 1. Enter and submit quote details

[1300] The user inputs the estimate details (work details, period, labor costs, material costs, other costs, etc.) received from the construction company into the form on the terminal.

[1301] The terminal sends the entered quotation details to the server as structured data (e.g., JSON format).

[1302] 2. Receiving and saving quote details

[1303] The server receives the quotation data sent from the terminal.

[1304] The received data is temporarily stored in a database.

[1305] 3. Request for analysis of quotation details

[1306] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT).

[1307] Send a request to the generative AI for analysis and evaluation.

[1308] 4. Appropriateness Assessment

[1309] The generative AI (ChatGPT) analyzes the received estimate data, specifically comparing labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices to assess whether they are appropriate.

[1310] The generative AI (ChatGPT) expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns the result and reason to the server.

[1311] 5. Operation of the Emotion Engine

[1312] The emotion engine estimates the user's emotions from the user's input, operation history, and other sensor information.

[1313] If the emotion engine determines that the user is feeling dissatisfied or anxious, it provides that information to the server.

[1314] 6. Processing of Evaluation Results

[1315] The server analyzes the evaluation results received from the generative AI and information from the emotion engine, and decides on a response based on the results.

[1316] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[1317] If the request is deemed inappropriate, the server instructs the AI ​​to generate a price negotiation message. The AI ​​reflects the information from the emotion engine and generates a negotiation message using a gentler tone as necessary.

[1318] If it is determined that there is insufficient information, the server instructs the generative AI to generate a prompt to request additional information. The prompt is generated in an appropriate tone as necessary, reflecting the information from the emotion engine.

[1319] 7. Displaying Messages

[1320] The terminal displays the messages (eligibility notification, discount negotiation message, urging message) received from the server to the user.

[1321] 8. User Support

[1322] The user checks the message displayed on the terminal and takes necessary action. For example, if a price negotiation message is displayed in case of inappropriateness, the user can contact the construction company based on the message and negotiate a price reduction.

[1323] Specific examples

[1324] Example input

[1325] The user enters the following quote details into the terminal:

[1326] Work: Installation of a communications network

[1327] Construction period: 5 days

[1328] Labor costs: 100,000 yen

[1329] Material cost: 200,000 yen

[1330] Other expenses: 50,000 yen

[1331] Processing example

[1332] 1. The user enters the quotation details into the terminal, and the terminal sends the details to the server.

[1333] 2. The server receives the quotation details, stores them in a database, and requests the generative AI to analyze them.

[1334] 3. The generative AI (ChatGPT) evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and returns the result to the server, stating that "the cost of materials is higher than the market price."

[1335] 4. The emotion engine detects when the user is feeling dissatisfied or anxious while typing and provides that information to the server.

[1336] 5. Based on the evaluation result that "material costs are high," the server instructs the generative AI to generate a price negotiation statement that reflects information from the emotion engine. The generated negotiation statement is sent to the terminal.

[1337] 6. The terminal displays the following bargaining message to the user:

[1338] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1339] 7. The user checks the displayed negotiation text and negotiates the price with the construction company.

[1340] This will enable quick and consistent confirmation of construction estimates and price negotiations, as well as a more personalized response that takes into account the user's emotions, improving the user experience.

[1341] The processing flow will be explained below.

[1342] Step 1:

[1343] The user inputs the estimate received from the construction company into the input form on the terminal. For example, the user might input the following information:

[1344] Work: Installation of a communications network

[1345] Construction period: 5 days

[1346] Labor costs: 100,000 yen

[1347] Material cost: 200,000 yen

[1348] Other expenses: 50,000 yen

[1349] Once the input is complete, the user clicks the send button.

[1350] Step 2:

[1351] The terminal converts the quote information entered by the user into a structured data format (e.g., JSON format), and then sends this data to the server via an HTTP request.

[1352] Step 3:

[1353] The server receives the quotation data sent from the terminal, temporarily stores the received data in a database, and prepares it for the next process.

[1354] Step 4:

[1355] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT). It sends a request to the generative AI to ask for analysis and evaluation.

[1356] Step 5:

[1357] The generative AI (ChatGPT) analyzes the estimate data sent from the server. For example, it compares labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices, and evaluates whether they are appropriate.

[1358] Step 6:

[1359] The generative AI (ChatGPT) expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns the result and reason to the server.

[1360] Step 7:

[1361] The emotion engine estimates the user's emotions from the user's input, operation history, and other sensor information. For example, it can determine the user's stress level from their typing speed and pattern.

[1362] Step 8:

[1363] The emotion engine provides information to the server when the user is feeling dissatisfied or anxious, for example by sending back a comment such as "The user is showing high stress levels."

[1364] Step 9:

[1365] The server analyzes the evaluation results received from the generative AI and the information received from the emotion engine, and determines the next response based on the results.

[1366] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[1367] If the request is deemed inappropriate, the server instructs the AI ​​to generate a price negotiation message. The AI ​​reflects the information from the emotion engine and generates a negotiation message using a gentler tone as necessary.

[1368] If it is determined that there is insufficient information, the server instructs the generative AI to generate a prompt message requesting the provision of additional information. The message is generated in an appropriate tone, reflecting the information from the emotion engine.

[1369] Step 10:

[1370] The terminal displays the message (notification of appropriateness, discount negotiation message, reminder message) received from the server to the user. For example, if the transaction is determined to be inappropriate and a discount negotiation message is displayed, the user can confirm the content.

[1371] Step 11:

[1372] Based on the message displayed on the terminal, the user contacts the construction company and takes the necessary action, for example, by copying the price negotiation statement and sending it to the construction company by email.

[1373] Example 2

[1374] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1375] Checking the accuracy of construction estimates and negotiating discounts has traditionally been a time-consuming and laborious task, placing a burden on users. Furthermore, it can be difficult to negotiate discounts or request information in appropriate language that reflects the user's feelings, which can sometimes hinder smooth communication. Therefore, a system is needed that can accurately evaluate construction estimates and automate responses that take the user's feelings into consideration.

[1376] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting estimate details, a means for receiving the input estimate details, a means for sending the received estimate details to the generation AI model and requesting analysis, a means for receiving the analysis results from the generation AI model and evaluating the appropriateness, a means for automatically generating and outputting a price negotiation message or a request message based on the evaluation results, a means for estimating the user's emotions, and a means for generating a message with an appropriate tone using the user's emotion information. This enables the appropriateness of the construction estimate and price negotiation to be confirmed quickly and consistently, and also enables personalized responses that take the user's emotions into consideration.

[1377] "Estimate details" refers to data that includes detailed information such as the costs, duration, materials, and labor costs associated with construction or services.

[1378] The "means for inputting" refers to a device or program that provides an interface for the user to input the details of the estimate.

[1379] "Means for receiving" refers to a device or program that has the function of receiving data transmitted from outside.

[1380] A "generative AI model" is a software system that uses artificial intelligence to analyze data and perform specific tasks based on the results.

[1381] A "means for requesting analysis" is a device or program that has the function of sending received data to a generative AI model and requesting analysis.

[1382] A "means for assessing appropriateness" is a device or program that has the function of determining whether the quotation contents are appropriate based on the analysis results returned from the generative AI model.

[1383] A "price negotiation letter" is a letter that requests cost reductions or changes to conditions regarding quotes that are deemed inappropriate.

[1384] A "demand letter" is a letter requesting additional information regarding the quotation content that is deemed to be insufficient.

[1385] "User emotions" refers to the psychological state, such as dissatisfaction, anxiety, or satisfaction, that the user feels while inputting or operating the estimate contents.

[1386] "Estimation means" refers to a device or program that has the function of inferring emotions based on the user's behavior, operation history, sensor information, etc.

[1387] The "means for generating sentences with an appropriate tone" refers to a device or program that has the function of generating sentences that reflect the user's emotional information and are designed to avoid making the user feel uncomfortable.

[1388] The present invention is a system that automates the confirmation of the appropriateness of construction estimates and price negotiations, and improves the user experience by recognizing the user's emotions. This system analyzes the estimate contents entered by the user and automatically takes appropriate action based on the results. Specific embodiments of this system are described below.

[1389] System Overview

[1390] This system mainly uses the following hardware and software:

[1391] Devices that accept user information input (e.g., PCs, smartphones)

[1392] A server that analyzes and stores received data

[1393] Analysis function using generative AI models (e.g. ChatGPT)

[1394] Emotion engine that estimates user emotions

[1395] Hardware Configuration

[1396] The terminal is a device that allows the user to input the details of the estimate received from the construction company. The terminal is provided with an interface (form) for inputting the details of the estimate. When the user enters the details of the estimate into the form, the terminal converts them into a structured data format (e.g., JSON) and sends it to the server.

[1397] The server is equipped with software to analyze the received data, generate and send API requests to operate both the generative AI model and the emotion engine, and store the received data in a database for further analysis and evaluation as needed.

[1398] Software Configuration

[1399] Generative AI models (e.g., ChatGPT) are used to analyze the appropriateness of quotes. Based on the received quote, they compare labor costs, material costs, and other expenses with the unit prices of other projects and the general market price to evaluate whether the quote is reasonable.

[1400] The emotion engine uses the user's input, operation history, and other sensor information to estimate the user's emotions. For example, if the user makes many corrections while entering the quote, it is determined that the user is likely to be dissatisfied. The emotion engine sends this information to the server and reflects it in the generated message.

[1401] Specific examples

[1402] The following are specific examples based on the present invention.

[1403] Example input

[1404] The user inputs the details of the work and its cost into the terminal. At this time, the data is input in the following text format:

[1405] Work details: Floor renovation

[1406] Construction period: 10 days

[1407] Labor costs: 150,000 yen

[1408] Material cost: 250,000 yen

[1409] Other expenses: 80,000 yen

[1410] Processing example

[1411] 1. When the user finishes entering data, the device automatically generates JSON format data and sends it to the server.

[1412] 2. The server stores the data received via web communication in a database and sends an API request to the generative AI model for analysis.

[1413] 3. The generative AI model analyzes the estimate and returns the evaluation results to the server along with the conclusion that "material costs are higher than the market price."

[1414] 4. The emotion engine infers that the user is feeling anxious or dissatisfied while inputting and provides that emotional information to the server.

[1415] 5. The server integrates the evaluation results and emotional information, generates a price negotiation message in an appropriate tone, and sends it to the terminal.

[1416] 6. The terminal displays the following bargaining message to the user:

[1417] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1418] 7. The user can contact the construction company based on this negotiation statement and negotiate a discount.

[1419] As described above, the system of the present invention automatically evaluates the appropriateness of construction estimates and generates messages that reflect the user's emotions, thereby realizing efficient and personalized responses.

[1420] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1421] Step 1:

[1422] The user inputs the details of the estimate received from the construction company (construction details, period, labor costs, material costs, and other expenses) into the terminal form by manually entering specific values ​​and details into the text fields.

[1423] Input: Construction details, construction period, labor costs, material costs, other costs

[1424] Output: Quote details entered in the form

[1425] Specific behavior: The user fills in all the fields in the input form and clicks the submit button.

[1426] Step 2:

[1427] The terminal converts the quote information entered by the user into JSON format, which includes mapping the values ​​of each form field to a key-value format.

[1428] Input: Quote details entered in the form

[1429] Output: Quote data in JSON format

[1430] What happens: When a form submit event occurs, JavaScript or other client script converts the data into JSON format.

[1431] Step 3:

[1432] The terminal sends the converted JSON data to the server, where it is encrypted using the HTTPS protocol.

[1433] Input: Quote data in JSON format

[1434] Output: Request to send data to the server

[1435] What it does: Sends a POST request containing JSON data over HTTPS.

[1436] Step 4:

[1437] The server receives the quotation data sent from the device using a RESTful API endpoint.

[1438] Input: Quote data in JSON format

[1439] Output: Status code of the receipt confirmation (e.g. 200 OK)

[1440] What happens: A server-side API endpoint receives the request and processes the data.

[1441] Step 5:

[1442] The server saves the received quote data in a database, storing each item in a corresponding table column according to the database schema.

[1443] Input: Quote data in JSON format

[1444] Output: Save result to database (e.g. success / failure status)

[1445] What it does: Uses a database connectivity library to execute an SQL query to insert data.

[1446] Step 6:

[1447] The server prepares an API request to the generative AI model for analysis, which includes attaching an API key and forming the request body.

[1448] Input: Saved quote data

[1449] Output: Prepared data for the analysis request

[1450] Specific behavior: Generates a request to an API endpoint that includes authentication information and analytics data.

[1451] Step 7:

[1452] The server sends an analysis request to the generative AI model.

[1453] Input: Prepared data for the analysis request

[1454] Output: Data sent to the generative AI model

[1455] What it does: Sends a request to the API endpoint of the generative AI model using the HTTPS protocol.

[1456] Step 8:

[1457] The generative AI model analyzes the received quotation data and evaluates its appropriateness. The evaluation result is returned as either "appropriate," "inappropriate," or "insufficient information."

[1458] Input: Analysis request data

[1459] Output: Analysis result (appropriate / inappropriate / insufficient information) and the reason

[1460] How it works: The generative AI model uses its internal algorithms to compare data with other cases and make an evaluation.

[1461] Step 9:

[1462] The generative AI model sends the analysis results back to the server.

[1463] Input: Analysis results and reasons

[1464] Output: Data sent back to the server

[1465] Specific operation: Analysis result data is sent to the server using the HTTPS protocol.

[1466] Step 10:

[1467] The server analyzes the evaluation results received from the generative AI model and simultaneously determines the necessary response using the user's emotional information.

[1468] Input: Analysis results from the generative AI model and user emotion information

[1469] Output: Required actions (e.g., notification of appropriateness, price negotiation, reminder)

[1470] Specific operation: The server logic integrates the evaluation results and emotional information to generate an appropriate response message.

[1471] Step 11:

[1472] The server instructs the generative AI model to generate price negotiation and demand messages, incorporating information from the emotion engine to generate sentences with an appropriate tone.

[1473] Input: Required response and emotional information

[1474] Output: A request to the generative AI model

[1475] What it does: Sends a request to an API endpoint to generate a sentence with the appropriate tone.

[1476] Step 12:

[1477] The generative AI model generates price negotiation and urging messages and sends the results back to the server.

[1478] Input: A request to generate text

[1479] Output: The generated price negotiation or reminder

[1480] How it works: The generative AI model uses its internal natural language processing algorithm to generate documents in the specified tone.

[1481] Step 13:

[1482] The server transmits the generated message (eligibility notification, discount negotiation message, urging message) to the terminal.

[1483] Input: The message returned by the generative AI model

[1484] Output: Send message to terminal

[1485] Specific operation: Calls an API to send a response containing a message back to the device.

[1486] Step 14:

[1487] The terminal displays the received message to the user.

[1488] Input: Message sent from the server

[1489] Output: The message that is displayed to the user

[1490] What it does: Uses a GUI to visually display incoming messages to the user.

[1491] Step 15:

[1492] The user checks the message displayed on the terminal and takes necessary action. For example, if a discount negotiation message is displayed, the user contacts the construction company based on the message and negotiates for a discount.

[1493] Input: Message displayed on terminal

[1494] Output: Results of contact with construction company and price negotiation

[1495] Specific actions: Contact the construction company via email or phone and negotiate a discount on the proposed price.

[1496] The specific operations, inputs, and outputs in each processing step of the system have been described above.

[1497] (Application example 2)

[1498] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1499] Conventional quotation systems require manual evaluation to confirm the appropriateness of quotation details, and also require manual negotiation of discounts and prompting for additional information. Furthermore, these systems often lack consideration for the user's feelings, resulting in a poor user experience. The present invention aims to solve these problems and enable efficient and user-friendly confirmation of quotation details and negotiation of discounts.

[1500] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting quotation details, means for receiving the input quotation details, means for sending the received quotation details to a generative AI and requesting analysis, means for receiving the analysis results from the generative AI and evaluating whether the quotation details are appropriate, inappropriate, or lacking information, means for automatically generating and outputting a price negotiation message or a request message based on the evaluation results, and means including an emotion engine that recognizes the user's emotions and adjusting the tone of the negotiation message or the request message depending on the emotions. This automates the process of confirming the appropriateness of the quotation details and negotiating a price discount, enabling personalized responses that take the user's emotions into consideration.

[1501] "Estimate details" refers to data that includes details such as the work content, cost, and duration of a user-specified construction or manufacturing project.

[1502] "Means for input" refers to technology that provides a form or interface for users to input estimate details into a terminal.

[1503] "Means for receiving" refers to the technology for receiving the input quotation details on a server or the like.

[1504] "Generative AI" refers to artificial intelligence that uses natural language processing to analyze received data and generate information.

[1505] "Means for requesting analysis" refers to the technology of sending a request to the generative AI to evaluate the appropriateness of the estimate contents.

[1506] The "means of evaluation" refers to a technology that evaluates the appropriateness of the estimate content as "appropriate," "inappropriate," or "insufficient information" based on the analysis results from generative AI.

[1507] A "price negotiation letter" is a letter used to request a discount on an inappropriate quote.

[1508] A "demand letter" is a letter requesting additional information when there are deficiencies in the quotation.

[1509] An "emotion engine" is a technology that detects emotions from user input and operation history and adjusts responses based on those emotions.

[1510] "Tone" refers to the style of language and expression used in writing or dialogue.

[1511] "System" refers to the collection of hardware and software required to automatically verify the accuracy of quote details and negotiate discounts.

[1512] The present invention is a system for efficiently confirming the appropriateness of quotation details and negotiating discounts, intended for use in factories and manufacturing fields. This system includes a means for inputting quotation details, a means for receiving the input quotation details, a means for sending the received quotation details to a generative AI and requesting analysis, a means for receiving the analysis results from the generative AI and evaluating whether the quotation is appropriate, inappropriate, or lacks information, a means for automatically generating and outputting a price negotiation or request message based on the evaluation results, and a means including an emotion engine that recognizes the user's emotions and adjusting the tone of the negotiation or request message depending on the emotion.

[1513] Example of a system

[1514] 1. Enter a quote

[1515] The user is a factory estimator and uses a smartphone application to input estimate details. For example, the user might enter the following estimate information: "Work details: installation of an automated line," "Work duration: 10 days," "Labor costs: 300,000 yen," "Material costs: 500,000 yen," and "Other costs: 200,000 yen."

[1516] 2. Receiving the quotation

[1517] The terminal receives the estimate content entered by the user and transmits it to the server.

[1518] 3. Use of generative AI

[1519] The server saves the received quotation data as structured data (JSON format) and sends an API request to a generative AI (such as ChatGPT) to request analysis.

[1520] 4. Appropriateness Assessment

[1521] The generative AI evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and determines whether it is appropriate. The result is returned to the server as either "appropriate," "inappropriate," or "insufficient information."

[1522] 5. Operation of the Emotion Engine

[1523] The server uses an emotion engine (e.g., a Sentiment Analysis tool) to estimate the user's emotions from their input and operation history. Based on this information, the server determines whether the user is feeling dissatisfied or anxious.

[1524] 6. Message Creation

[1525] The server generates an appropriate message based on the evaluation results from the generative AI and the analytical information from the emotion engine. For example, it adjusts the content according to the emotion, such as "The estimated price is higher than the market price. Could you please give us a discount?"

[1526] 7. Displaying Messages

[1527] The terminal displays the message received from the server to the user, who then negotiates a discount with the construction company based on the displayed negotiation text.

[1528] Specific examples

[1529] A specific example of quotation data entered by the user is as follows:

[1530] Quotation Data

[1531] Construction details: Installation of automated production line

[1532] Construction period: 10 days

[1533] Labor costs: 300,000 yen

[1534] Material cost: 500,000 yen

[1535] Other expenses: 200,000 yen

[1536] Emotional expression during user input

[1537] "This quote seems a little high..."

[1538] Example prompts for generative AI models

[1539] Please rate the appropriateness of the following estimates.

[1540] Construction details: Installation of automated production line

[1541] Construction period: 10 days

[1542] Labor costs: 300,000 yen

[1543] Material cost: 500,000 yen

[1544] Other expenses: 200,000 yen

[1545] In this way, the present invention automates the process of checking the accuracy of quotation details and negotiating discounts, and realizes personalized responses that take into account the user's feelings, thereby improving the efficiency of quotation work and the user experience.

[1546] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1547] Step 1:

[1548] The user is a factory estimator and uses a smartphone application to input estimate details. The input estimate details include the work content, construction period, labor costs, material costs, and other expenses. The input data is temporarily saved on the device.

[1549] Step 2:

[1550] The terminal sends the quote information entered by the user to the server. At this time, the data is sent as structured data (e.g., JSON format). The server receives this structured data and stores it in a database.

[1551] Step 3:

[1552] The server sends the received quotation data to a generative AI (such as ChatGPT) and prepares an API request to request analysis. This request includes the quotation details entered by the user. The server then generates and sends a prompt message to the generative AI requesting an appropriateness assessment.

[1553] Step 4:

[1554] The generative AI analyzes the received quotation data. Specifically, it compares it with the unit prices of other projects and the general public's prices to evaluate the appropriateness of the quotation. It then expresses the results of this evaluation as "appropriate," "inappropriate," or "insufficient information" and sends them back to the server.

[1555] Step 5:

[1556] The server receives the evaluation results from the generative AI. At the same time, it uses an emotion engine (e.g., a Sentiment Analysis tool) to estimate the user's emotions based on their input and operation history. The emotion engine analyzes whether the user is feeling dissatisfied or anxious, and provides the results to the server.

[1557] Step 6:

[1558] The server decides how to respond based on the evaluation results from the generative AI and information from the emotion engine. For example, if the evaluation result is "inappropriate" and the emotion engine detects the user's dissatisfaction, the server generates a price negotiation message in a gentle tone. This negotiation message is sent to the generative AI and automatically generated.

[1559] Step 7:

[1560] The server sends the generated negotiation text and appropriate notification message to the terminal, which receives the message and displays it to the user.

[1561] Step 8:

[1562] The user checks the message displayed on the terminal and takes the necessary action. For example, if the quote is deemed inappropriate, the user can negotiate a discount with the construction company based on the discount negotiation text. In this way, this system automates the confirmation of the appropriateness of the quote content and the discount negotiation process, enabling responses that take the user's feelings into consideration.

[1563] The above processing steps can improve the efficiency of the quotation process and the user experience.

[1564] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1565] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1566] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1567] [Fourth embodiment]

[1568] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1569] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1570] 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).

[1571] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1572] 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.

[1573] 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).

[1574] 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.

[1575] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1576] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1577] 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.

[1578] 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.

[1579] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1580] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1581] The present invention relates to a system for automating confirmation of the appropriateness of construction estimates and price negotiations. Specific embodiments of this system will be described below.

[1582] System Overview

[1583] This system allows users to input the details of the estimate they received from the construction company and uses a generative AI (ChatGPT) to check the appropriateness of the estimate. If the estimate is inappropriate or if additional information is required, the system automatically generates a price negotiation or request letter and provides it to the user.

[1584] Program processing explanation

[1585] 1. Enter and submit quote details

[1586] The user inputs the estimate details (work details, period, labor costs, material costs, other costs, etc.) received from the construction company into the form on the terminal.

[1587] The terminal sends the entered quotation details to the server as structured data (e.g., JSON format).

[1588] 2. Receiving and saving quote details

[1589] The server receives the quotation data sent from the terminal.

[1590] Temporarily store the received quotation data in a database.

[1591] 3. Request for analysis of quotation details

[1592] The server sends the saved quotation data to the generative AI (ChatGPT) and requests analysis and evaluation.

[1593] 4. Appropriateness Assessment

[1594] ChatGPT analyzes the received estimate data, comparing it with the unit prices of other projects, the general price level, and the appropriate man-hours required for construction, and evaluates the appropriateness of each item (labor costs, material costs, and other expenses).

[1595] ChatGPT will indicate the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and will return the result and reason to the server.

[1596] 5. Processing of evaluation results

[1597] The server analyzes the evaluation results received from ChatGPT and takes appropriate action.

[1598] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[1599] If it is judged to be inappropriate: The server instructs ChatGPT to generate a discount negotiation message. It generates a discount negotiation message including the specific reason and sends it to the terminal.

[1600] If the server determines that there is insufficient information, it instructs ChatGPT to generate a message requesting additional information. The generated message is sent to the device.

[1601] 6. Displaying Messages

[1602] The terminal displays the messages received from the server to the user.

[1603] The user checks the displayed messages (notification of suitability, price negotiation message, urging message) and takes the necessary action.

[1604] Specific examples

[1605] Example input

[1606] The user enters the following quote details into the terminal:

[1607] Work: Installation of a communications network

[1608] Construction period: 5 days

[1609] Labor costs: 100,000 yen

[1610] Material cost: 200,000 yen

[1611] Other: 50,000 yen

[1612] Processing example

[1613] 1. The user enters the quotation details into the terminal, and the terminal sends the details to the server.

[1614] 2. The server receives the quotation details, stores them in a database, and requests the generative AI to analyze them.

[1615] 3. ChatGPT evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and returns the result to the server, stating that "the material cost is higher than the market price."

[1616] 4. Based on the evaluation result that "material costs are high," the server instructs ChatGPT to generate a discount negotiation message and sends the generated discount negotiation message to the terminal.

[1617] 5. The terminal displays the following bargaining message to the user:

[1618] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1619] 6. The user checks the displayed negotiation text and negotiates the price with the construction company.

[1620] This makes it possible to quickly and consistently check the accuracy of construction estimates and negotiate discounts, resulting in high-quality negotiations that are not dependent on the experience of the person in charge.

[1621] The processing flow will be explained below.

[1622] Step 1:

[1623] The user inputs the estimate received from the construction company into the input form on the terminal. For example, the user might input the following information:

[1624] Work: Installation of a communications network

[1625] Construction period: 5 days

[1626] Labor costs: 100,000 yen

[1627] Material cost: 200,000 yen

[1628] Other expenses: 50,000 yen

[1629] Once the input is complete, the user clicks the send button.

[1630] Step 2:

[1631] The terminal converts the quote information entered by the user into a structured data format (e.g., JSON format), and then sends this data to the server via an HTTP request.

[1632] Step 3:

[1633] The server receives the quotation data sent from the terminal, temporarily stores the received data in a database, and prepares it for the next process.

[1634] Step 4:

[1635] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT). It sends a request to the generative AI to ask for analysis and evaluation.

[1636] Step 5:

[1637] The generative AI (ChatGPT) analyzes the estimate data sent from the server. For example, it compares labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices, and evaluates whether they are appropriate.

[1638] Step 6:

[1639] The generative AI (ChatGPT) returns the estimate evaluation results to the server. The evaluation results are either "appropriate," "inappropriate," or "insufficient information," and include any necessary reasons or comments.

[1640] Step 7:

[1641] The server analyzes the evaluation results received from the generative AI and determines the next course of action based on the results.

[1642] If it is judged to be appropriate: The server generates a message stating that the quotation is appropriate and sends it to the terminal.

[1643] If it is determined to be inappropriate: The server instructs the generation AI to generate a discount negotiation text and sends the generated negotiation text to the terminal.

[1644] If it is determined that there is insufficient information: The server instructs the generative AI to generate a prompt message requesting the provision of additional information, and sends the generated prompt message to the terminal.

[1645] Step 8:

[1646] The terminal displays the messages (eligibility notification, discount negotiation message, urging message) received from the server to the user.

[1647] Step 9:

[1648] The user checks the message displayed on the terminal. For example, if the request is deemed inappropriate and a discount negotiation message is displayed, the user contacts the construction company based on the message and negotiates the discount.

[1649] Example 1

[1650] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1651] In the past, the process of checking the accuracy of construction estimates and negotiating discounts as necessary was time-consuming and depended on the experience and skills of the person in charge, which could lead to a lack of consistency and reliability in the results. Furthermore, analyzing the estimates and determining their accuracy required specialized knowledge, making it difficult for average users to solve the problem. This often meant that appropriate measures could not be taken against inappropriate estimates.

[1652] 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.

[1653] In this invention, the server includes a means for a user to input estimate details, a terminal that receives the input estimate details, a means for sending the received estimate details to a generation AI model and requesting analysis, a means for receiving the analysis results from the generation AI model and evaluating whether the estimate is appropriate, inappropriate, or lacking information, and a means for automatically generating and outputting a price negotiation or request message based on the evaluation results. This allows the appropriateness of the construction estimate to be quickly and efficiently confirmed, enabling price negotiations or requests for additional information as needed. It also ensures consistent, high-quality service regardless of the skills or experience of individual staff members.

[1654] A "user" is an entity that uses the system to check the appropriateness of construction estimates and negotiate discounts.

[1655] "Estimate details" is data including detailed information such as the construction details, period, labor costs, material costs, and other expenses.

[1656] "Input means" refers to an interface or device that a user uses to input quotation details into the system.

[1657] A "terminal" is a device such as a computer or smartphone that allows a user to input information and communicate with a server.

[1658] The "server" is a central computer system that receives and stores quotes and sends analysis requests to the generative AI model.

[1659] A "generative AI model" is an artificial intelligence model that analyzes received quotation data and evaluates its appropriateness.

[1660] The "means of requesting analysis" is the process by which the server sends the quotation details to the generating AI model and has it perform the analysis.

[1661] "Means of evaluation" refers to the process of determining the appropriateness of the quotation based on the analysis results received from the generative AI model.

[1662] A "price negotiation document" is a document automatically generated for price negotiation in response to an inappropriate quote.

[1663] A "reminder" is an automatically generated document requesting additional information regarding the quote.

[1664] The "means for automatically generating and outputting" is a process for automatically generating a price negotiation message or a promotion message based on the evaluation results and transmitting it to the terminal.

[1665] "When deemed appropriate" means that the quotation is evaluated as appropriate in light of general market prices and standards.

[1666] "When judged to be inappropriate" means that the quotation is evaluated as inappropriate in light of general market prices and standards.

[1667] "When it is judged that there is insufficient information" refers to a situation where it is judged that there is insufficient information to evaluate the appropriateness of the quotation content.

[1668] The present invention relates to a system for automating the confirmation of the appropriateness of construction estimates and price negotiations. How to implement this system will be specifically described below.

[1669] This system consists of a user, a terminal, a server, and a generative AI model (e.g., ChatGPT). The user inputs the estimate received from the construction company, and the system evaluates the appropriateness of the estimate and provides countermeasures based on the results.

[1670] The user inputs the details of the estimate, such as the work content, construction period, labor costs, material costs, and other expenses, into a dedicated form on the terminal. The terminal converts the input estimate details into structured data in JSON format and sends it to the server. The server analyzes the received estimate data and saves it in a database.

[1671] The server sends the saved quotation data to the generative AI model and requests its analysis. At this time, the server generates a prompt for the analysis request. For example, it generates the following prompt:

[1672] Please rate the appropriateness of the following estimates: Work: laying a communications network, Work period: 5 days, Labor costs: 100,000 yen, Material costs: 200,000 yen, Other: 50,000 yen

[1673] The generative AI model (e.g., ChatGPT) analyzes the received quotation data and returns the evaluation result to the server. The evaluation result is expressed as either "appropriate," "inappropriate," or "insufficient information," along with the reason for the evaluation.

[1674] The server analyzes the results of this evaluation and takes the following actions:

[1675] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[1676] If it is determined to be inappropriate: The server requests the generative AI model to generate a price negotiation statement, which includes specific reasons and sends it to the terminal.

[1677] If it is determined that there is insufficient information: The server requests the generative AI model to generate a prompt message requesting the provision of additional information, and sends the generated prompt message to the terminal.

[1678] The terminal displays the messages received from the server (e.g., notification of suitability, price negotiation message, and reminder message) to the user. The user checks the displayed messages and takes the necessary action.

[1679] As a concrete example, consider the following quotation input:

[1680] Work: Installation of a communications network

[1681] Construction period: 5 days

[1682] Labor costs: 100,000 yen

[1683] Material cost: 200,000 yen

[1684] Other: 50,000 yen

[1685] This quotation is sent to the server, which then sends a prompt to the generative AI model to request analysis. The generative AI model returns the evaluation result that "the material cost is higher than the market price." Based on this evaluation result, the server requests the generative AI model to generate a price negotiation message, and displays the following message to the user:

[1686] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1687] This allows for quick and consistent confirmation of quote accuracy and price negotiations, ensuring high-quality service that is not dependent on the experience of the person in charge.

[1688] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1689] Step 1:

[1690] Enter and submit quote details

[1691] The user enters the details of the estimate received from the construction company (such as construction details, duration, labor costs, material costs, and other expenses) into a dedicated form on the terminal.

[1692] Input: The estimate details entered by the user in the form. Example: "Work details: laying a communications network", "Work period: 5 days", "Labor costs: 100,000 yen", "Material costs: 200,000 yen", "Other: 50,000 yen"

[1693] The terminal converts the entered quotation details into structured data in JSON format.

[1694] Data processing: Form input -> JSON format conversion.

[1695] Output: Estimate data in JSON format. "{Work details: 'Installation of communication network', Work duration: '5 days', Labor costs: '100,000 yen', Material costs: '200,000 yen', Other: '50,000 yen'}"

[1696] The terminal adjusts the converted JSON data and sends it to the server.

[1697] Step 2:

[1698] Receiving and saving quotes

[1699] The server receives the quotation data in JSON format sent from the terminal.

[1700] Input: Quote data in JSON format sent by the terminal.

[1701] The server stores the received quotation data in a database.

[1702] Data processing: JSON format -> Insert into database.

[1703] Output: The quote stored in the database.

[1704] Specific operation: Use an SQL query to insert quotation data into the "Quotation Data" table in the database. Example: "INSERT INTO Quotation Data (Work Details, Work Period, Labor Costs, Material Costs, Other) VALUES ('Communication Network Installation', '5 Days', '100,000 Yen', '200,000 Yen', '50,000 Yen');"

[1705] Step 3:

[1706] Request for analysis of quotation details

[1707] The server sends the saved quotation data to the generative AI model and requests its analysis.

[1708] Input: Quote details stored in the database.

[1709] The server generates a prompt. For example, "Please rate the appropriateness of the following estimates: Work: laying a communications network, Work period: 5 days, Labor cost: 100,000 yen, Material cost: 200,000 yen, Other: 50,000 yen."

[1710] Data processing: Estimate content obtained from the database -> Generated prompt text.

[1711] Output: The generated prompt statement.

[1712] The server sends the prompt sentence to the generative AI model.

[1713] Step 4:

[1714] Assessment of suitability

[1715] The generative AI model analyzes the received quotation data, expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns it to the server along with the reason for the evaluation.

[1716] Input: The prompt sent to the generative AI model.

[1717] The generative AI model analyzes the quotation data and makes an assessment, for example, that "material costs are higher than market prices."

[1718] Data computation: The analytical process using generative AI models.

[1719] Output: Evaluation result. "{Evaluation result: 'Inappropriate', Reason: 'The cost of materials is higher than the market price. The market price is approximately 150,000 yen.'}"

[1720] The generative AI model sends the evaluation results back to the server.

[1721] Step 5:

[1722] Processing of evaluation results

[1723] The server analyzes the evaluation results received from the generative AI model and takes appropriate action based on the results.

[1724] Input: Evaluation results returned by the generative AI model.

[1725] If the estimate is determined to be appropriate, the server generates a message indicating that the estimate is appropriate and transmits it to the terminal.

[1726] Output: A message stating that the quote is correct.

[1727] If the server determines that the information is inappropriate, it requests the AI ​​model to generate a price negotiation message and sends the message to the terminal.If the server determines that the information is insufficient, it requests the AI ​​model to generate a urging message and sends the message to the terminal.

[1728] Data calculation: Analysis of evaluation results and message generation.

[1729] Output: Price negotiation or request. Example: "Regarding your quote, I believe the material cost is higher than the market price, so I would like to ask you to reconsider. The market price is about 150,000 yen."

[1730] Step 6:

[1731] Displaying messages

[1732] The terminal displays the messages received from the server to the user.

[1733] Input: Message sent by the server (eligibility notice, price negotiation message, reminder message).

[1734] Specific behavior: Display a message on the device screen.

[1735] Output: The message displayed to the user.

[1736] The user checks the displayed message and takes the necessary action.

[1737] (Application example 1)

[1738] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1739] In conventional construction estimate systems, the confirmation of estimate appropriateness and price negotiation depended on the experience and judgment of the person in charge, resulting in a lack of consistency and efficiency. Furthermore, on digital platforms such as virtual stores, it was difficult to streamline the estimate confirmation process and set appropriate prices. To solve these problems, it was necessary to provide a system that automates the confirmation of estimate appropriateness and price negotiation, allowing users to obtain appropriate estimate information without any hassle.

[1740] 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.

[1741] In this invention, the server includes means for inputting quotation details, means for receiving the input quotation details, means for sending the received quotation details to a generative AI model and requesting analysis, means for receiving the analysis results from the generative AI model and evaluating whether the quotation is appropriate, inappropriate, or lacking information, means for automatically generating and outputting a price negotiation or request message based on the evaluation results, and means for installing the system on a smartphone, smart glasses, head-mounted display, or robot terminal. This enables efficient and consistent confirmation of quotation appropriateness and price negotiation even in virtual stores.

[1742] "Means for inputting estimate details" refers to an interface that allows the user to input the estimate information received from the construction company into the terminal.

[1743] "Means for receiving input estimate details" refers to a device or software that has the function of receiving estimate information sent from the terminal to the server.

[1744] "Means of sending the received quotation details to the generative AI model and requesting analysis" refers to the process in which the server transfers the received quotation information to the generative AI and requests its analysis.

[1745] "Means for receiving analysis results from a generative AI model and evaluating whether they are appropriate, inappropriate, or lack information" refers to algorithms or devices for determining whether the quotation contents are appropriate, inappropriate, or lack information based on the analysis results sent from the generative AI.

[1746] "Means for automatically generating and outputting a price negotiation letter or request letter based on the evaluation results" refers to a device or software that has the function of automatically generating a negotiation letter or a document requesting additional information with appropriate content based on the evaluation results and outputting it.

[1747] A "smartphone" refers to a mobile information terminal with multiple functions, such as making calls, sending messages, and connecting to the Internet.

[1748] "Smart glasses" refers to a wearable device in the form of glasses that has the function of displaying information in the user's field of vision.

[1749] A "head-mounted display" refers to a display device that is worn on the user's head and displays images across the entire field of vision.

[1750] "Robot" refers to a mechanical device that automatically performs programmed tasks.

[1751] The present invention relates to a system for efficiently and consistently confirming quotes and negotiating prices in a virtual store, which is installed on a smartphone, smart glasses, a head-mounted display, or a robot terminal.

[1752] The system comprises the following means:

[1753] 1. How to enter quote details

[1754] The interface allows users to input the details of the estimate received from the construction company using a terminal. The interface includes fields for the work content, construction period, labor costs, material costs, and other costs.

[1755] 2. How to receive the entered quotation details

[1756] The terminal receives the quote information entered by the user and sends the data to the server in a structured format (e.g., JSON format).

[1757] 3. A method for sending the received quotation details to a generative AI model and requesting analysis

[1758] The server sends the received quotation details to the generative AI model, requesting analysis and evaluation. OpenAI's API is used as the generative AI model.

[1759] 4. A means of receiving analysis results from generative AI models and evaluating whether they are appropriate, inappropriate, or lack information.

[1760] The server receives the analysis results from the generative AI model and uses them to determine whether the estimate is appropriate, inappropriate, or lacks sufficient information. This evaluation uses data such as unit prices of other projects and market prices.

[1761] 5. A means for automatically generating and outputting price negotiation or promotional messages based on the evaluation results

[1762] Depending on the evaluation results, the server uses a generative AI model such as ChatGPT to automatically generate a price negotiation message or a message requesting additional information, which is then sent to the terminal and displayed to the user.

[1763] As a specific example, consider the case where the user inputs the following quotation details:

[1764] Example prompt sentence:

[1765] Work: Installation of a communications network

[1766] Construction period: 5 days

[1767] Labor costs: 100,000 yen

[1768] Material cost: 200,000 yen

[1769] Other: 50,000 yen

[1770] 1. The user enters the quote details using a smartphone or smart glasses.

[1771] 2. The terminal sends the entered data to the server.

[1772] 3. The server uses OpenAI's API to send the received data to the generative AI model and request analysis.

[1773] 4. The generative AI model analyzes the quote and returns an evaluation result indicating that the material cost is higher than the market price.

[1774] 5. The server generates a price negotiation message based on the evaluation results and sends it to the terminal.

[1775] 6. The user receives the following negotiation document and submits it to the construction company.

[1776] Example of generated negotiation text:

[1777] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1778] As a result, the system of the present invention automates the process of checking the appropriateness of construction estimates and negotiating discounts in the virtual store, thereby reducing the burden on users.

[1779] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1780] Step 1:

[1781] The user inputs the details of the estimate received from the construction company into the terminal interface. The input fields include the details of the work, construction period, labor costs, material costs, and other costs.

[1782] Input: The quote entered by the user

[1783] Output: Structured data (e.g., JSON format)

[1784] Step 2:

[1785] The terminal converts the quote information entered by the user into JSON format and sends it to the server. Through this process, the quote information entered by the user arrives at the server in a structured format.

[1786] Input: Structured quote content (JSON format)

[1787] Output: Quote sent to the server

[1788] Step 3:

[1789] The server sends the quote received from the device to a generative AI model such as OpenAI and requests analysis. In this process, the server makes a request to determine whether the quote is appropriate.

[1790] Input: Quote received by the server

[1791] Output: Analysis request sent to the generative AI model

[1792] Step 4:

[1793] The generative AI model analyzes the received quote and sends the results to the server. During the analysis, it compares the quote with data such as the unit price of other projects and market price to evaluate the appropriateness of the quote.

[1794] Input: The quote sent to the generative AI model

[1795] Output: Analysis result (either correct, incorrect, or insufficient information)

[1796] Step 5:

[1797] Based on the analysis results received from the generative AI model, the server determines whether the estimate is appropriate, inappropriate, or lacks information, and then decides the next action to take.

[1798] Input: Analysis results from the generative AI model

[1799] Output: Evaluation decision: Good, bad, or insufficient information

[1800] Step 6:

[1801] The server automatically generates a price negotiation or promotion message based on the evaluation results using a generative AI model (such as ChatGPT) and sends it to the device. If the message is deemed appropriate, a message indicating this is correct is generated.

[1802] Input: Evaluation result

[1803] Output: Generated discount negotiation, reminder or eligibility notification message

[1804] Step 7:

[1805] The terminal displays the messages received from the server to the user, who then communicates with the construction company through the displayed negotiation and urging messages.

[1806] Input: Message from the server

[1807] Output: The message displayed to the user

[1808] Through this entire process, estimate confirmation and price negotiation in the virtual store can be automated, reducing the burden on users.

[1809] 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.

[1810] The present invention improves the user experience by combining a system that automates the confirmation of the appropriateness of construction estimates and price negotiations with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1811] System Overview

[1812] This system allows users to input the details of the estimate they received from the construction company and uses generative AI (ChatGPT) to check its appropriateness. If it is inappropriate or if additional information is required, it automatically generates a price negotiation or request message and provides it to the user. In addition, the emotion engine recognizes the user's emotions and responds accordingly, enabling more effective communication.

[1813] Program processing explanation

[1814] 1. Enter and submit quote details

[1815] The user inputs the estimate details (work details, period, labor costs, material costs, other costs, etc.) received from the construction company into the form on the terminal.

[1816] The terminal sends the entered quotation details to the server as structured data (e.g., JSON format).

[1817] 2. Receiving and saving quote details

[1818] The server receives the quotation data sent from the terminal.

[1819] The received data is temporarily stored in a database.

[1820] 3. Request for analysis of quotation details

[1821] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT).

[1822] Send a request to the generative AI for analysis and evaluation.

[1823] 4. Appropriateness Assessment

[1824] The generative AI (ChatGPT) analyzes the received estimate data, specifically comparing labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices to assess whether they are appropriate.

[1825] The generative AI (ChatGPT) expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns the result and reason to the server.

[1826] 5. Operation of the Emotion Engine

[1827] The emotion engine estimates the user's emotions from the user's input, operation history, and other sensor information.

[1828] If the emotion engine determines that the user is feeling dissatisfied or anxious, it provides that information to the server.

[1829] 6. Processing of Evaluation Results

[1830] The server analyzes the evaluation results received from the generative AI and information from the emotion engine, and decides on a response based on the results.

[1831] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[1832] If the request is deemed inappropriate, the server instructs the AI ​​to generate a price negotiation message. The AI ​​reflects the information from the emotion engine and generates a negotiation message using a gentler tone as necessary.

[1833] If it is determined that there is insufficient information, the server instructs the generative AI to generate a prompt to request additional information. The prompt is generated in an appropriate tone as necessary, reflecting the information from the emotion engine.

[1834] 7. Displaying Messages

[1835] The terminal displays the messages (eligibility notification, discount negotiation message, urging message) received from the server to the user.

[1836] 8. User Support

[1837] The user checks the message displayed on the terminal and takes necessary action. For example, if a price negotiation message is displayed in case of inappropriateness, the user can contact the construction company based on the message and negotiate a price reduction.

[1838] Specific examples

[1839] Example input

[1840] The user enters the following quote details into the terminal:

[1841] Work: Installation of a communications network

[1842] Construction period: 5 days

[1843] Labor costs: 100,000 yen

[1844] Material cost: 200,000 yen

[1845] Other expenses: 50,000 yen

[1846] Processing example

[1847] 1. The user enters the quotation details into the terminal, and the terminal sends the details to the server.

[1848] 2. The server receives the quotation details, stores them in a database, and requests the generative AI to analyze them.

[1849] 3. The generative AI (ChatGPT) evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and returns the result to the server, stating that "the cost of materials is higher than the market price."

[1850] 4. The emotion engine detects when the user is feeling dissatisfied or anxious while typing and provides that information to the server.

[1851] 5. Based on the evaluation result that "material costs are high," the server instructs the generative AI to generate a price negotiation statement that reflects information from the emotion engine. The generated negotiation statement is sent to the terminal.

[1852] 6. The terminal displays the following bargaining message to the user:

[1853] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1854] 7. The user checks the displayed negotiation text and negotiates the price with the construction company.

[1855] This will enable quick and consistent confirmation of construction estimates and price negotiations, as well as a more personalized response that takes into account the user's emotions, improving the user experience.

[1856] The processing flow will be explained below.

[1857] Step 1:

[1858] The user inputs the estimate received from the construction company into the input form on the terminal. For example, the user might input the following information:

[1859] Work: Installation of a communications network

[1860] Construction period: 5 days

[1861] Labor costs: 100,000 yen

[1862] Material cost: 200,000 yen

[1863] Other expenses: 50,000 yen

[1864] Once the input is complete, the user clicks the send button.

[1865] Step 2:

[1866] The terminal converts the quote information entered by the user into a structured data format (e.g., JSON format), and then sends this data to the server via an HTTP request.

[1867] Step 3:

[1868] The server receives the quotation data sent from the terminal, temporarily stores the received data in a database, and prepares it for the next process.

[1869] Step 4:

[1870] The server prepares an API request to send the saved quotation data to the generative AI (ChatGPT). It sends a request to the generative AI to ask for analysis and evaluation.

[1871] Step 5:

[1872] The generative AI (ChatGPT) analyzes the estimate data sent from the server. For example, it compares labor costs, material costs, and other expenses with the unit prices of other projects and the general public's prices, and evaluates whether they are appropriate.

[1873] Step 6:

[1874] The generative AI (ChatGPT) expresses the evaluation result as either "appropriate," "inappropriate," or "insufficient information," and returns the result and reason to the server.

[1875] Step 7:

[1876] The emotion engine estimates the user's emotions from the user's input, operation history, and other sensor information. For example, it can determine the user's stress level from their typing speed and pattern.

[1877] Step 8:

[1878] The emotion engine provides information to the server when the user is feeling dissatisfied or anxious, for example by sending back a comment such as "The user is showing high stress levels."

[1879] Step 9:

[1880] The server analyzes the evaluation results received from the generative AI and the information received from the emotion engine, and determines the next response based on the results.

[1881] If it is determined to be valid: The server generates a message indicating that the quote is valid and sends it to the terminal.

[1882] If the request is deemed inappropriate, the server instructs the AI ​​to generate a price negotiation message. The AI ​​reflects the information from the emotion engine and generates a negotiation message using a gentler tone as necessary.

[1883] If it is determined that there is insufficient information, the server instructs the generative AI to generate a prompt message requesting the provision of additional information. The message is generated in an appropriate tone, reflecting the information from the emotion engine.

[1884] Step 10:

[1885] The terminal displays the message (notification of appropriateness, discount negotiation message, reminder message) received from the server to the user. For example, if the transaction is determined to be inappropriate and a discount negotiation message is displayed, the user can confirm the content.

[1886] Step 11:

[1887] Based on the message displayed on the terminal, the user contacts the construction company and takes the necessary action, for example, by copying the price negotiation statement and sending it to the construction company by email.

[1888] Example 2

[1889] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1890] Checking the accuracy of construction estimates and negotiating discounts has traditionally been a time-consuming and laborious task, placing a burden on users. Furthermore, it can be difficult to negotiate discounts or request information in appropriate language that reflects the user's feelings, which can sometimes hinder smooth communication. Therefore, a system is needed that can accurately evaluate construction estimates and automate responses that take the user's feelings into consideration.

[1891] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for inputting estimate details, a means for receiving the input estimate details, a means for sending the received estimate details to the generation AI model and requesting analysis, a means for receiving the analysis results from the generation AI model and evaluating the appropriateness, a means for automatically generating and outputting a price negotiation message or a request message based on the evaluation results, a means for estimating the user's emotions, and a means for generating a message with an appropriate tone using the user's emotion information. This enables the appropriateness of the construction estimate and price negotiation to be confirmed quickly and consistently, and also enables personalized responses that take the user's emotions into consideration.

[1892] "Estimate details" refers to data that includes detailed information such as the costs, duration, materials, and labor costs associated with construction or services.

[1893] The "means for inputting" refers to a device or program that provides an interface for the user to input the details of the estimate.

[1894] "Means for receiving" refers to a device or program that has the function of receiving data transmitted from outside.

[1895] A "generative AI model" is a software system that uses artificial intelligence to analyze data and perform specific tasks based on the results.

[1896] A "means for requesting analysis" is a device or program that has the function of sending received data to a generative AI model and requesting analysis.

[1897] A "means for assessing appropriateness" is a device or program that has the function of determining whether the quotation contents are appropriate based on the analysis results returned from the generative AI model.

[1898] A "price negotiation letter" is a letter that requests cost reductions or changes to conditions regarding quotes that are deemed inappropriate.

[1899] A "demand letter" is a letter requesting additional information regarding the quotation content that is deemed to be insufficient.

[1900] "User emotions" refers to the psychological state, such as dissatisfaction, anxiety, or satisfaction, that the user feels while inputting or operating the estimate contents.

[1901] "Estimation means" refers to a device or program that has the function of inferring emotions based on the user's behavior, operation history, sensor information, etc.

[1902] The "means for generating sentences with an appropriate tone" refers to a device or program that has the function of generating sentences that reflect the user's emotional information and are designed to avoid making the user feel uncomfortable.

[1903] The present invention is a system that automates the confirmation of the appropriateness of construction estimates and price negotiations, and improves the user experience by recognizing the user's emotions. This system analyzes the estimate contents entered by the user and automatically takes appropriate action based on the results. Specific embodiments of this system are described below.

[1904] System Overview

[1905] This system mainly uses the following hardware and software:

[1906] Devices that accept user information input (e.g., PCs, smartphones)

[1907] A server that analyzes and stores received data

[1908] Analysis function using generative AI models (e.g. ChatGPT)

[1909] Emotion engine that estimates user emotions

[1910] Hardware Configuration

[1911] The terminal is a device that allows the user to input the details of the estimate received from the construction company. The terminal is provided with an interface (form) for inputting the details of the estimate. When the user enters the details of the estimate into the form, the terminal converts them into a structured data format (e.g., JSON) and sends it to the server.

[1912] The server is equipped with software to analyze the received data, generate and send API requests to operate both the generative AI model and the emotion engine, and store the received data in a database for further analysis and evaluation as needed.

[1913] Software Configuration

[1914] Generative AI models (e.g., ChatGPT) are used to analyze the appropriateness of quotes. Based on the received quote, they compare labor costs, material costs, and other expenses with the unit prices of other projects and the general market price to evaluate whether the quote is reasonable.

[1915] The emotion engine uses the user's input, operation history, and other sensor information to estimate the user's emotions. For example, if the user makes many corrections while entering the quote, it is determined that the user is likely to be dissatisfied. The emotion engine sends this information to the server and reflects it in the generated message.

[1916] Specific examples

[1917] The following are specific examples based on the present invention.

[1918] Example input

[1919] The user inputs the details of the work and its cost into the terminal. At this time, the data is input in the following text format:

[1920] Work details: Floor renovation

[1921] Construction period: 10 days

[1922] Labor costs: 150,000 yen

[1923] Material cost: 250,000 yen

[1924] Other expenses: 80,000 yen

[1925] Processing example

[1926] 1. When the user finishes entering data, the device automatically generates JSON format data and sends it to the server.

[1927] 2. The server stores the data received via web communication in a database and sends an API request to the generative AI model for analysis.

[1928] 3. The generative AI model analyzes the estimate and returns the evaluation results to the server along with the conclusion that "material costs are higher than the market price."

[1929] 4. The emotion engine infers that the user is feeling anxious or dissatisfied while inputting and provides that emotional information to the server.

[1930] 5. The server integrates the evaluation results and emotional information, generates a price negotiation message in an appropriate tone, and sends it to the terminal.

[1931] 6. The terminal displays the following bargaining message to the user:

[1932] Regarding your estimate, I believe the material costs are higher than the market price, so I would like to ask you to reconsider.

[1933] 7. The user can contact the construction company based on this negotiation statement and negotiate a discount.

[1934] As described above, the system of the present invention automatically evaluates the appropriateness of construction estimates and generates messages that reflect the user's emotions, thereby realizing efficient and personalized responses.

[1935] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1936] Step 1:

[1937] The user inputs the details of the estimate received from the construction company (construction details, period, labor costs, material costs, and other expenses) into the terminal form by manually entering specific values ​​and details into the text fields.

[1938] Input: Construction details, construction period, labor costs, material costs, other costs

[1939] Output: Quote details entered in the form

[1940] Specific behavior: The user fills in all the fields in the input form and clicks the submit button.

[1941] Step 2:

[1942] The terminal converts the quote information entered by the user into JSON format, which includes mapping the values ​​of each form field to a key-value format.

[1943] Input: Quote details entered in the form

[1944] Output: Quote data in JSON format

[1945] What happens: When a form submit event occurs, JavaScript or other client script converts the data into JSON format.

[1946] Step 3:

[1947] The terminal sends the converted JSON data to the server, where it is encrypted using the HTTPS protocol.

[1948] Input: Quote data in JSON format

[1949] Output: Request to send data to the server

[1950] What it does: Sends a POST request containing JSON data over HTTPS.

[1951] Step 4:

[1952] The server receives the quotation data sent from the device using a RESTful API endpoint.

[1953] Input: Quote data in JSON format

[1954] Output: Status code of the receipt confirmation (e.g. 200 OK)

[1955] What happens: A server-side API endpoint receives the request and processes the data.

[1956] Step 5:

[1957] The server saves the received quote data in a database, storing each item in a corresponding table column according to the database schema.

[1958] Input: Quote data in JSON format

[1959] Output: Save result to database (e.g. success / failure status)

[1960] What it does: Uses a database connectivity library to execute an SQL query to insert data.

[1961] Step 6:

[1962] The server prepares an API request to the generative AI model for analysis, which includes attaching an API key and forming the request body.

[1963] Input: Saved quote data

[1964] Output: Prepared data for the analysis request

[1965] Specific behavior: Generates a request to an API endpoint that includes authentication information and analytics data.

[1966] Step 7:

[1967] The server sends an analysis request to the generative AI model.

[1968] Input: Prepared data for the analysis request

[1969] Output: Data sent to the generative AI model

[1970] What it does: Sends a request to the API endpoint of the generative AI model using the HTTPS protocol.

[1971] Step 8:

[1972] The generative AI model analyzes the received quotation data and evaluates its appropriateness. The evaluation result is returned as either "appropriate," "inappropriate," or "insufficient information."

[1973] Input: Analysis request data

[1974] Output: Analysis result (appropriate / inappropriate / insufficient information) and the reason

[1975] How it works: The generative AI model uses its internal algorithms to compare data with other cases and make an evaluation.

[1976] Step 9:

[1977] The generative AI model sends the analysis results back to the server.

[1978] Input: Analysis results and reasons

[1979] Output: Data sent back to the server

[1980] Specific operation: Analysis result data is sent to the server using the HTTPS protocol.

[1981] Step 10:

[1982] The server analyzes the evaluation results received from the generative AI model and simultaneously determines the necessary response using the user's emotional information.

[1983] Input: Analysis results from the generative AI model and user emotion information

[1984] Output: Required actions (e.g., notification of appropriateness, price negotiation, reminder)

[1985] Specific operation: The server logic integrates the evaluation results and emotional information to generate an appropriate response message.

[1986] Step 11:

[1987] The server instructs the generative AI model to generate price negotiation and demand messages, incorporating information from the emotion engine to generate sentences with an appropriate tone.

[1988] Input: Required response and emotional information

[1989] Output: A request to the generative AI model

[1990] What it does: Sends a request to an API endpoint to generate a sentence with the appropriate tone.

[1991] Step 12:

[1992] The generative AI model generates price negotiation and urging messages and sends the results back to the server.

[1993] Input: A request to generate text

[1994] Output: The generated price negotiation or reminder

[1995] How it works: The generative AI model uses its internal natural language processing algorithm to generate documents in the specified tone.

[1996] Step 13:

[1997] The server transmits the generated message (eligibility notification, discount negotiation message, urging message) to the terminal.

[1998] Input: The message returned by the generative AI model

[1999] Output: Send message to terminal

[2000] Specific operation: Calls an API to send a response containing a message back to the device.

[2001] Step 14:

[2002] The terminal displays the received message to the user.

[2003] Input: Message sent from the server

[2004] Output: The message that is displayed to the user

[2005] What it does: Uses a GUI to visually display incoming messages to the user.

[2006] Step 15:

[2007] The user checks the message displayed on the terminal and takes necessary action. For example, if a discount negotiation message is displayed, the user contacts the construction company based on the message and negotiates for a discount.

[2008] Input: Message displayed on terminal

[2009] Output: Results of contact with construction company and price negotiation

[2010] Specific actions: Contact the construction company via email or phone and negotiate a discount on the proposed price.

[2011] The specific operations, inputs, and outputs in each processing step of the system have been described above.

[2012] (Application example 2)

[2013] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2014] Conventional quotation systems require manual evaluation to confirm the appropriateness of quotation details, and also require manual negotiation of discounts and prompting for additional information. Furthermore, these systems often lack consideration for the user's feelings, resulting in a poor user experience. The present invention aims to solve these problems and enable efficient and user-friendly confirmation of quotation details and negotiation of discounts.

[2015] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting quotation details, means for receiving the input quotation details, means for sending the received quotation details to a generative AI and requesting analysis, means for receiving the analysis results from the generative AI and evaluating whether the quotation details are appropriate, inappropriate, or lacking information, means for automatically generating and outputting a price negotiation message or a request message based on the evaluation results, and means including an emotion engine that recognizes the user's emotions and adjusting the tone of the negotiation message or the request message depending on the emotions. This automates the process of confirming the appropriateness of the quotation details and negotiating a price discount, enabling personalized responses that take the user's emotions into consideration.

[2016] "Estimate details" refers to data that includes details such as the work content, cost, and duration of a user-specified construction or manufacturing project.

[2017] "Means for input" refers to technology that provides a form or interface for users to input estimate details into a terminal.

[2018] "Means for receiving" refers to the technology for receiving the input quotation details on a server or the like.

[2019] "Generative AI" refers to artificial intelligence that uses natural language processing to analyze received data and generate information.

[2020] "Means for requesting analysis" refers to the technology of sending a request to the generative AI to evaluate the appropriateness of the estimate contents.

[2021] The "means of evaluation" refers to a technology that evaluates the appropriateness of the estimate content as "appropriate," "inappropriate," or "insufficient information" based on the analysis results from generative AI.

[2022] A "price negotiation letter" is a letter used to request a discount on an inappropriate quote.

[2023] A "demand letter" is a letter requesting additional information when there are deficiencies in the quotation.

[2024] An "emotion engine" is a technology that detects emotions from user input and operation history and adjusts responses based on those emotions.

[2025] "Tone" refers to the style of language and expression used in writing or dialogue.

[2026] "System" refers to the collection of hardware and software required to automatically verify the accuracy of quote details and negotiate discounts.

[2027] The present invention is a system for efficiently confirming the appropriateness of quotation details and negotiating discounts, intended for use in factories and manufacturing fields. This system includes a means for inputting quotation details, a means for receiving the input quotation details, a means for sending the received quotation details to a generative AI and requesting analysis, a means for receiving the analysis results from the generative AI and evaluating whether the quotation is appropriate, inappropriate, or lacks information, a means for automatically generating and outputting a price negotiation or request message based on the evaluation results, and a means including an emotion engine that recognizes the user's emotions and adjusting the tone of the negotiation or request message depending on the emotion.

[2028] Example of a system

[2029] 1. Enter a quote

[2030] The user is a factory estimator and uses a smartphone application to input estimate details. For example, the user might enter the following estimate information: "Work details: installation of an automated line," "Work duration: 10 days," "Labor costs: 300,000 yen," "Material costs: 500,000 yen," and "Other costs: 200,000 yen."

[2031] 2. Receiving the quotation

[2032] The terminal receives the estimate content entered by the user and transmits it to the server.

[2033] 3. Use of generative AI

[2034] The server saves the received quotation data as structured data (JSON format) and sends an API request to a generative AI (such as ChatGPT) to request analysis.

[2035] 4. Appropriateness Assessment

[2036] The generative AI evaluates the estimate by comparing it with the unit prices of other projects and the general public's prices, and determines whether it is appropriate. The result is returned to the server as either "appropriate," "inappropriate," or "insufficient information."

[2037] 5. Operation of the Emotion Engine

[2038] The server uses an emotion engine (e.g., a Sentiment Analysis tool) to estimate the user's emotions from their input and operation history. Based on this information, the server determines whether the user is feeling dissatisfied or anxious.

[2039] 6. Message Creation

[2040] The server generates an appropriate message based on the evaluation results from the generative AI and the analytical information from the emotion engine. For example, it adjusts the content according to the emotion, such as "The estimated price is higher than the market price. Could you please give us a discount?"

[2041] 7. Displaying Messages

[2042] The terminal displays the message received from the server to the user, who then negotiates a discount with the construction company based on the displayed negotiation text.

[2043] Specific examples

[2044] A specific example of quotation data entered by the user is as follows:

[2045] Quotation Data

[2046] Construction details: Installation of automated production line

[2047] Construction period: 10 days

[2048] Labor costs: 300,000 yen

[2049] Material cost: 500,000 yen

[2050] Other expenses: 200,000 yen

[2051] Emotional expression during user input

[2052] "This quote seems a little high..."

[2053] Example prompts for generative AI models

[2054] Please rate the appropriateness of the following estimates.

[2055] Construction details: Installation of automated production line

[2056] Construction period: 10 days

[2057] Labor costs: 300,000 yen

[2058] Material cost: 500,000 yen

[2059] Other expenses: 200,000 yen

[2060] In this way, the present invention automates the process of checking the accuracy of quotation details and negotiating discounts, and realizes personalized responses that take into account the user's feelings, thereby improving the efficiency of quotation work and the user experience.

[2061] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2062] Step 1:

[2063] The user is a factory estimator and uses a smartphone application to input estimate details. The input estimate details include the work content, construction period, labor costs, material costs, and other expenses. The input data is temporarily saved on the device.

[2064] Step 2:

[2065] The terminal sends the quote information entered by the user to the server. At this time, the data is sent as structured data (e.g., JSON format). The server receives this structured data and stores it in a database.

[2066] Step 3:

[2067] The server sends the received quotation data to a generative AI (such as ChatGPT) and prepares an API request to request analysis. This request includes the quotation details entered by the user. The server then generates and sends a prompt message to the generative AI requesting an appropriateness assessment.

[2068] Step 4:

[2069] The generative AI analyzes the received quotation data. Specifically, it compares it with the unit prices of other projects and the general public's prices to evaluate the appropriateness of the quotation. It then expresses the results of this evaluation as "appropriate," "inappropriate," or "insufficient information" and sends them back to the server.

[2070] Step 5:

[2071] The server receives the evaluation results from the generative AI. At the same time, it uses an emotion engine (e.g., a Sentiment Analysis tool) to estimate the user's emotions based on their input and operation history. The emotion engine analyzes whether the user is feeling dissatisfied or anxious, and provides the results to the server.

[2072] Step 6:

[2073] The server decides how to respond based on the evaluation results from the generative AI and information from the emotion engine. For example, if the evaluation result is "inappropriate" and the emotion engine detects the user's dissatisfaction, the server generates a price negotiation message in a gentle tone. This negotiation message is sent to the generative AI and automatically generated.

[2074] Step 7:

[2075] The server sends the generated negotiation text and appropriate notification message to the terminal, which receives the message and displays it to the user.

[2076] Step 8:

[2077] The user checks the message displayed on the terminal and takes the necessary action. For example, if the quote is deemed inappropriate, the user can negotiate a discount with the construction company based on the discount negotiation text. In this way, this system automates the confirmation of the appropriateness of the quote content and the discount negotiation process, enabling responses that take the user's feelings into consideration.

[2078] The above processing steps can improve the efficiency of the quotation process and the user experience.

[2079] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2081] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2082] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2083] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2084] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2085] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2086] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2087] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2088] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion ...

Claims

1. A means for inputting quotation details; A means for receiving the input quotation details; A means to send the received quotation details to the generative AI and request analysis, A means of receiving the analysis results from the generative AI and evaluating whether they are appropriate, inappropriate, or lack information; means for automatically generating and outputting a price negotiation message or a request message based on the evaluation results; A system including:

2. 2. The system according to claim 1, further comprising means for generating a message indicating that the estimate is appropriate when the estimate is judged to be appropriate based on the evaluation result.

3. 2. The system according to claim 1, further comprising means for generating a price reduction negotiation statement including specific reasons for the inappropriateness of the price based on the evaluation results.

4. 2. The system according to claim 1, further comprising means for generating a message requesting the provision of additional information when it is determined that the information is insufficient based on the evaluation result.

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