system

Abstract

We provide the system. [Solution] A means of entering project information, A means of saving case information to a database, A means for generating an estimate based on saved project information, A means for generating a preliminary proposal based on saved project information, A system that includes means for responding to customer inquiries.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In sales operations, assistant operations such as quotation creation, primary proposal creation, and response to inquiries from customers require a lot of time and effort. For this reason, there is a problem that the burden on sales staff increases and efficiency decreases. In particular, there is a current situation where means for automating and improving the efficiency of these operations are insufficient, and it is difficult to improve business efficiency without increasing the number of sales staff.

Means for Solving the Problems

[0005] To solve the above problems, the present invention provides a system that includes means for inputting case information, means for storing case information in a database, means for generating quotations based on the stored case information, means for generating initial proposals based on the stored case information, and means for responding to customer inquiries. Furthermore, by adding means for automatically generating quotations based on case information and sending them to sales representatives, and natural language processing means for analyzing inquiry content and generating appropriate automated responses, it is possible to significantly improve the work efficiency of sales representatives.

[0006] "Project information" refers to data that includes customer information related to sales activities, detailed information about the products being proposed, and information such as desired delivery dates.

[0007] A "database" is a system that systematically manages stored case information and allows for easy access as needed.

[0008] A "quotation" is a document that lists the price, quantity, delivery date, and other details of the goods or services being proposed.

[0009] A "first-round proposal" is a summary proposal to the client that includes the features, advantages, and competitive analysis of the proposed product or service.

[0010] An "inquiry" is a form of communication from a customer that consists of questions or requests for clarification.

[0011] "Natural language processing methods" refer to algorithms and technologies for analyzing text data and understanding human language.

[0012] A "sales representative" is a person whose job is to propose and sell products and services to customers. [Brief explanation of the drawing]

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

Mode for Carrying Out the Invention

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

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

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

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

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

[0019] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

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

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

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

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

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

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

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

[0034] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Its embodiments will be described in detail below.

[0035] System Overview

[0036] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries.

[0037] Program Processing Description

[0038] 1. Entering and saving project information

[0039] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into the form and clicks the submit button.

[0040] Terminal: Formats form input data into the required format and sends a request to the server.

[0041] Server: Receives case information, saves it to the database, generates a new case ID, and notifies the sales representative.

[0042] 2. Creating a quotation

[0043] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[0044] Terminal: Sends a quotation creation request to the server, including the project ID.

[0045] Server: Receives a request, retrieves the relevant project ID information from the database, and generates a quotation. The generated quotation is saved in PDF format and sent to the sales representative via email.

[0046] 3. Preparation of the initial proposal

[0047] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[0048] Terminal: Sends a proposal creation request to the server, including the project ID.

[0049] Server: Receives a request, retrieves the relevant case ID information from the database, and generates a preliminary proposal. The generated preliminary proposal is saved in PDF or Word format and sent to the sales representative via email.

[0050] 4. Handling inquiries

[0051] User: The customer enters their question into the inquiry form and clicks the submit button.

[0052] Terminal: Sends query data to the server.

[0053] Server: Analyzes the inquiry, retrieves relevant information from the database and FAQs, and generates an appropriate response using a natural language processing engine. The generated response is sent to the customer as an automated email.

[0054] Specific example

[0055] Specific examples of entering and saving project information

[0056] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being Company A, the proposed product being Product B, and the desired delivery date being December 1, 2023.

[0057] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[0058] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[0059] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[0060] Examples of creating a quotation

[0061] 1. User: A sales representative requests a quote for case ID 12345.

[0062] 2. Terminal: The request data is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[0063] 3. Server: Execute a database query to retrieve information for case ID 12345.

[0064] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[0065] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. This significantly improves the efficiency of sales activities, saving considerable time and effort.

[0066] The following describes the processing flow.

[0067] Entering and saving project information

[0068] Step 1:

[0069] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into a dedicated form and clicks the submit button.

[0070] Step 2:

[0071] Terminal: Formats user input into a data format (e.g., JSON format) and sends it to the server as an HTTP request.

[0072] Step 3:

[0073] Server: The server analyzes the received data and performs validation as case information (e.g., checking required fields, verifying data types).

[0074] Step 4:

[0075] Server: Saves case information that has successfully been validated to the database. Generates a new case ID at the same time as saving.

[0076] Step 5:

[0077] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[0078] Step 6:

[0079] Terminal: Displays the case ID and a message confirming that the case has been saved to the sales representative.

[0080] Request and generation of a quotation

[0081] Step 1:

[0082] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[0083] Step 2:

[0084] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[0085] Step 3:

[0086] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[0087] Step 4:

[0088] Server: Embeds the acquired data into a quotation template and generates a quotation in PDF format.

[0089] Step 5:

[0090] Server: Saves the generated quotation to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[0091] Step 6:

[0092] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[0093] Request and generation of the initial proposal.

[0094] Step 1:

[0095] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[0096] Step 2:

[0097] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[0098] Step 3:

[0099] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[0100] Step 4:

[0101] Server: Embeds the acquired data into a proposal template and generates a preliminary proposal in Word or PDF format.

[0102] Step 5:

[0103] Server: Saves the generated initial proposal to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[0104] Step 6:

[0105] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[0106] Inquiry handling process

[0107] Step 1:

[0108] User: The customer enters their question into the inquiry form and clicks the submit button.

[0109] Step 2:

[0110] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[0111] Step 3:

[0112] Server: Passes the received query content to the natural language processing engine for analysis.

[0113] Step 4:

[0114] Server: Executes database queries based on the inquiry content and generates appropriate answers from relevant information and FAQs.

[0115] Step 5:

[0116] Server: The generated response is sent to the customer's email address as an automated reply email.

[0117] Step 6:

[0118] Terminal: Notifies the customer that a response to their inquiry has been sent.

[0119] These processing steps streamline key tasks related to sales activities, enabling them to be executed quickly and accurately.

[0120] (Example 1)

[0121] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0122] In sales operations, a significant amount of time and effort is spent on managing project information, creating quotations and proposals, and responding to customer inquiries. Performing these tasks manually is particularly burdensome and reduces operational efficiency. Therefore, the present invention aims to provide a system that streamlines these sales operations and saves time and effort.

[0123] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0124] In this invention, the server includes means for formatting case information into a data format and transmitting it to the server; means for the server to store the received case information in a database and generate a new case ID; means for generating a quotation based on the case information and sending it to a sales representative; and means for generating a preliminary proposal based on the case information and sending it to a sales representative. This enables more efficient sales operations and faster customer response.

[0125] "Project information" refers to all information related to sales activities, such as customer information, proposed product information, and desired delivery dates.

[0126] A "data format" is a standardized format used for sending, receiving, and storing data, and JSON format is one example.

[0127] A "server" refers to a computer system used to process, store, and transmit data over a network.

[0128] A "database" refers to a system for efficiently storing and retrieving structured data.

[0129] "Case ID" refers to an identifier generated to uniquely identify each case.

[0130] A "quotation" refers to a document that clearly specifies the price and conditions of the goods or services offered to a customer.

[0131] A "first proposal" is a document that outlines the content of the initial proposal and serves to show the client the proposed content and conditions.

[0132] "Inquiry details" refers to information that contains questions or requests from customers.

[0133] A "natural language processing engine" refers to computer technology used to understand and generate human language.

[0134] An "automated response email" refers to an email that is automatically generated and sent by a system based on specific content.

[0135] A "template engine" refers to a software tool that embeds data into a template to automatically generate the final document or file.

[0136] A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to learn patterns from data and generate new information.

[0137] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries.

[0138] System Overview

[0139] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries.

[0140] Entering and saving project information

[0141] 1. User: The sales representative enters customer information (e.g., customer name, contact information), proposed products (e.g., product name, quantity), desired delivery date, and other case information into a form on the terminal.

[0142] 2. Terminal: Formats the input data into a specified format (e.g., JSON format).

[0143] 3. Terminal: Sends the formatted data to the server.

[0144] 4. Server: Saves the received data to the database and generates a new case ID.

[0145] 5. Server: Notifies the user's terminal of the case ID and a message indicating that saving is complete.

[0146] Estimate creation

[0147] 1. User: Enter the project ID on the quotation creation request screen and click the "Create Quotation" button.

[0148] 2. Terminal: Sends a quotation creation request to the server, including the project ID.

[0149] 3. Server: Retrieves information for the relevant case ID from the database.

[0150] 4. Server: Generates a quotation based on the acquired information. The generated quotation is saved in PDF format.

[0151] 5. Server: Send the generated PDF file to the sales representative via email.

[0152] Preparation of the initial proposal

[0153] 1. User: Enter the project ID on the initial proposal creation request screen and click the "Create Proposal" button.

[0154] 2. Terminal: Send a proposal creation request to the server, including the project ID.

[0155] 3. Server: Retrieves information for the relevant case ID from the database.

[0156] 4. Server: Generates a preliminary proposal based on the acquired information. The generated proposal is saved in PDF or Word format.

[0157] 5. Server: Send the generated files to the sales representative via email.

[0158] Inquiry response

[0159] 1. User: The customer enters their question into the inquiry form and clicks the submit button.

[0160] 2. Terminal: Sends the inquiry data to the server.

[0161] 3. Server: Analyzes the inquiry content and retrieves relevant information from the database and FAQs.

[0162] 4. Server: Generates appropriate answers using a natural language processing engine (e.g., a generative AI model).

[0163] 5. Server: Sends the generated response to the customer as an automated reply email.

[0164] Hardware and software to be used

[0165] Server: A computer system that performs data processing and storage.

[0166] Database: A system for efficiently storing and retrieving project information (e.g., MySQL®, PostgreSQL)

[0167] Template engine: Software used to embed data into templates and generate documents (e.g., JasperReports, Apache® POI)

[0168] Natural Language Processing Engine: Software that uses generative AI models to analyze dialogues and generate responses (e.g., GPT-3®).

[0169] Specific example

[0170] Specific examples of entering and saving project information

[0171] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being Company A, the proposed product being Product B, and the desired delivery date being December 1, 2023.

[0172] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[0173] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[0174] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[0175] Examples of creating a quotation

[0176] 1. User: A sales representative requests a quote for case ID 12345.

[0177] 2. Terminal: The request data is sent to the server with a prompt message such as "Please enter the information required to create the quotation."

[0178] 3. Server: Execute a database query to retrieve information for case ID 12345.

[0179] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[0180] Example of a prompt

[0181] "Please enter the information required to create the quotation."

[0182] "As an example of how to handle inquiries, please generate a list of frequently asked questions and their answers."

[0183] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. It significantly improves the efficiency of sales activities, saving considerable time and effort.

[0184] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0185] Entering and saving project information

[0186] Step 1:

[0187] The user enters project information (customer information, proposed products, desired delivery date, etc.) into a form on their device.

[0188] Input: Customer information, proposed products, desired delivery date, and other project information.

[0189] Specific operation: The user enters information into each field of the form using the device's keyboard and clicks the submit button.

[0190] Output: Input case information data

[0191] Step 2:

[0192] The terminal formats the entered case information data into a specified format (e.g., JSON format).

[0193] Input: Project information data entered by the user

[0194] Specific operation: The terminal automatically converts the input data into JSON format.

[0195] Output: Formatted case information data

[0196] Step 3:

[0197] The terminal sends the formatted case information data to the server.

[0198] Input: Project information data formatted in JSON format

[0199] Specific operation: The terminal sends an HTTP POST request and sends data to the server.

[0200] Output: Data transmission to server complete.

[0201] Step 4:

[0202] The server receives the case information data, saves it to the database, and generates a new case ID.

[0203] Input: Case information data sent from the terminal

[0204] Specific operation: The server executes an INSERT query into the database and saves the case information. A new case ID is generated.

[0205] Output: New case ID generated and saved to database.

[0206] Step 5:

[0207] The server notifies the user's terminal of the case ID and a message indicating that saving is complete.

[0208] Input: New case ID, Saved status

[0209] Specific operation: The server returns the case ID and message to the terminal as an HTTP response. The terminal displays the notification message.

[0210] Output: Notification display of case ID and save completion message.

[0211] Estimate creation

[0212] Step 1:

[0213] The user enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[0214] Input: Case ID

[0215] Specific action: The sales representative enters the deal ID and clicks the button.

[0216] Output: Request to create a quotation

[0217] Step 2:

[0218] The terminal sends a request to the server to create a quotation, including the project ID.

[0219] Input: Request to create a quote

[0220] Specific operation: The terminal sends an HTTP POST request and sends the request data to the server.

[0221] Output: Request sent to server successfully

[0222] Step 3:

[0223] The server retrieves the relevant case ID information from the database.

[0224] Input: Case ID

[0225] Specific operation: The server executes a SELECT query and retrieves data corresponding to the case ID.

[0226] Output: Case information retrieved from the database

[0227] Step 4:

[0228] The server generates a quotation based on the information it has acquired. The generated quotation is saved in PDF format.

[0229] Input: Case information retrieved from the database

[0230] Specific operation: The server uses a template engine (e.g., JasperReports) to generate a quotation and create a PDF file.

[0231] Output: Generated PDF quotation

[0232] Step 5:

[0233] The server sends the generated PDF file to the sales representative via email.

[0234] Input: Quotation in PDF format

[0235] Specific operation: The server sends the generated PDF via SMTP server as an email attachment.

[0236] Output: Email sent to sales representative completed.

[0237] Preparation of the initial proposal

[0238] Step 1:

[0239] The user enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[0240] Input: Case ID

[0241] Specific action: The sales representative enters the deal ID and clicks the button.

[0242] Output: Proposal creation request

[0243] Step 2:

[0244] The terminal sends a proposal creation request to the server, including the project ID.

[0245] Input: Proposal creation request

[0246] Specific operation: The terminal sends an HTTP POST request and sends the request data to the server.

[0247] Output: Request sent to server successfully

[0248] Step 3:

[0249] The server retrieves the relevant case ID information from the database.

[0250] Input: Case ID

[0251] Specific operation: The server executes a SELECT query and retrieves data corresponding to the case ID.

[0252] Output: Case information retrieved from the database

[0253] Step 4:

[0254] The server generates a preliminary proposal based on the information it acquires. The generated proposal is saved in PDF or Word format.

[0255] Input: Case information retrieved from the database

[0256] Specific operation: The server uses a template engine (e.g., Apache POI) to generate a proposal and create a PDF or Word file.

[0257] Output: Proposal in generated PDF or Word format

[0258] Step 5:

[0259] The server sends the generated files to the sales representative via email.

[0260] Input: Proposal in PDF or Word format

[0261] Specific operation: The server sends the file generated via the SMTP server as an email attachment.

[0262] Output: Email sent to sales representative completed.

[0263] Inquiry response

[0264] Step 1:

[0265] The user enters a question into the inquiry form and clicks the submit button.

[0266] Input: Question content

[0267] Specific action: The customer enters a question and clicks the submit button.

[0268] Output: Input query content

[0269] Step 2:

[0270] The terminal sends the inquiry data to the server.

[0271] Input: Inquiry details entered

[0272] Specific operation: The terminal sends an HTTP POST request and sends the query data to the server.

[0273] Output: Data transmission to server complete.

[0274] Step 3:

[0275] The server analyzes the query and retrieves relevant information from the database and FAQs.

[0276] Input: Inquiry content sent from the device

[0277] Specific operation: The server searches the database and FAQs based on the content of the inquiry it receives and retrieves relevant information.

[0278] Output: Retrieve relevant query information

[0279] Step 4:

[0280] The server uses a natural language processing engine (e.g., a generative AI model) to generate an appropriate response.

[0281] Input: Related inquiry information

[0282] Specific operation: The generative AI model generates a response based on the information it has acquired.

[0283] Output: Generated response text

[0284] Step 5:

[0285] The server sends the generated response to the customer as an auto - response email.

[0286] Input: Generated response text

[0287] Specific operation: The server sends an email containing the response content to the customer through the SMTP server.

[0288] Output: Completion of sending auto - response email to the customer

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0291] Conventional sales support systems individually generate estimates and proposals and handle customer inquiries, which imposes a heavy burden on sales staff and makes real - time response difficult. Also, it is difficult to immediately provide appropriate responses in customer interactions, and improving customer satisfaction has been an issue.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0293] In this invention, the server includes means for inputting case information, means for storing case information in a database, means for generating quotations based on the stored case information, means for generating initial proposals based on the stored case information, means for responding to customer inquiries, means for analyzing the input inquiry content, generating appropriate responses, and transmitting them to a display device, means for generating quotations and initial proposals in real time and displaying them on a display device, and means for recording and automatically analyzing customer interactions. This enables sales representatives to quickly generate quotations and proposals and provide immediate and appropriate responses to inquiries while interacting with customers in a virtual store. This is expected to improve the work efficiency of sales representatives and enhance customer satisfaction.

[0294] "Project information" refers to detailed information related to transactions with customers, including customer information, details of proposed products, and desired delivery dates.

[0295] A "database" is an electronic information storage system used to store and efficiently manage project information, quotations, proposals, and inquiry details.

[0296] A "quotation" is an official document that lists the price, quantity, delivery date, and other details of the goods or services to be provided in a transaction.

[0297] A "first proposal" is a document that outlines the initial proposal to a customer, primarily intended to explain the features and benefits of the proposed product.

[0298] "Handling inquiries" refers to the act of providing appropriate answers and information to customer questions and requests.

[0299] A "display device" is an electronic device used to visually display information, such as smart glasses or mobile terminals.

[0300] "Input means" refers to the methods and devices for inputting case information, inquiry content, etc. into the system, including keyboards, voice input, etc.

[0301] "Storage means" refers to the methods and devices for storing the input case information, etc. in a database.

[0302] "Generation means" refers to the methods and devices for automatically creating estimates and proposals based on the stored case information.

[0303] "Natural language processing" is an artificial intelligence technology for analyzing natural language data such as input text and voice, and generating appropriate responses.

[0304] "Real-time" refers to a state where processing and responses are performed immediately without delay.

[0305] "Interaction" refers to the act of sales staff and customers interacting with each other, including conversations, question-and-answer sessions, etc.

[0306] "Automatic response" is a function in which the system automatically generates responses to customer inquiries using natural language processing technology.

[0307] The present invention is a system for streamlining case information management and sales support. In particular, it improves the operational efficiency of sales by automating the creation of estimates, primary proposals, and responses to customer inquiries in real-time. The embodiments for implementing this invention will be described in detail below.

[0308] Overview of the system

[0309] This system consists of a server, terminals, and users (sales staff and customers). The sales staff wears smart glasses to handle customer interactions. The terminals function as smart glasses, mobile terminals, and display devices, and communicate with the server. The server stores case information in a database and executes various processes.

[0310] Program Processing Description

[0311] Hardware and Software

[0312] Hardware: Smart glasses, mobile devices, servers.

[0313] Software: Python scripts, NaturalLanguageProcessing library, EstimateGenerator, ProposalGenerator, SmartGlass® API, ServerAPI.

[0314] Data processing and calculation

[0315] The main processes are described below.

[0316] 1. Inquiry handling:

[0317] The user (customer) voice-inputs a question to the smart glasses. For example, they might ask, "What is the price of this product?"

[0318] The device (smart glasses) converts voice input into text data and sends it to the server.

[0319] The server's natural language processing engine analyzes the input text and generates an appropriate response from the relevant database. For example, "The price of this product is XX yen."

[0320] The generated response is displayed on the device's (smart glasses) screen and provided to the user immediately.

[0321] 2. Prepare a quotation:

[0322] The user (sales representative) enters the case ID and sends a request to create a quote to the server via smart glasses.

[0323] The device (smart glasses) sends the request to the server in a predetermined format.

[0324] The server retrieves the relevant project ID information from the database and generates an estimate in PDF format using the EstimateGenerator engine.

[0325] The generated quotation is displayed on the terminal (smart glasses) and provided to the user.

[0326] 3. Preparation of the initial proposal:

[0327] The user (sales representative) enters the case ID and sends a request to create a preliminary proposal to the server via smart glasses.

[0328] The device (smart glasses) sends the request to the server in a predetermined format.

[0329] The server retrieves the relevant project ID information from the database and uses the ProposalGenerator engine to generate a preliminary proposal in PDF or Word format.

[0330] The generated initial proposal is displayed on the device (smart glasses) and provided to the user.

[0331] Specific example

[0332] Specific examples are given below.

[0333] Suppose a sales representative contacts customer A in a virtual store via smart glasses and receives the question, "What is the price of this product?" In this case, the natural language processing engine immediately provides an answer, and the smart glasses display shows, "The price of this product is XX yen."

[0334] If customer A requests a quote on the spot, the sales representative enters the case ID and sends a quote creation request to the server. The server generates the quote and displays it on the smart glasses.

[0335] Furthermore, if a customer requests a detailed proposal, the sales representative can submit a request for a preliminary proposal, the server will generate the proposal, and this will also be displayed on the smart glasses.

[0336] Example of a prompt

[0337] customer_query = "What is the price of this product?"

[0338] response = NaturalLanguageProcessing.generate_response(customer_query)

[0339] print(response)

[0340] This system will enable sales representatives to handle customer inquiries more efficiently in virtual stores, generating quotes and proposals and providing real-time responses to inquiries. This will significantly improve the efficiency of sales activities and is expected to increase customer satisfaction.

[0341] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0342] Step 1:

[0343] The user (customer) inputs a question to the smart glasses using voice.

[0344] Input: Customer voice question: "What is the price of this product?"

[0345] Output: Audio data

[0346] Specific operation: The microphone in the smart glasses captures the sound and processes it as audio data.

[0347] Step 2:

[0348] The device (smart glasses) converts the audio data into text data and sends it to the server.

[0349] Input: Audio data

[0350] Output: Text data "What is the price of this product?"

[0351] Specific operation: Use speech recognition software to convert speech to text and send the text data to the server.

[0352] Step 3:

[0353] The server's natural language processing engine analyzes text data and generates appropriate responses from relevant databases.

[0354] Input: Text data "What is the price of this product?"

[0355] Output: Response text "The price of this product is XX yen."

[0356] Specific operation: A natural language processing engine analyzes the text, retrieves price information from a database, and generates the response text.

[0357] Step 4:

[0358] The server sends the generated response text to the device (smart glasses).

[0359] Input: Response text "The price of this product is XX yen."

[0360] Output: Response text data

[0361] Specific action: Send the generated text data to the smart glasses.

[0362] Step 5:

[0363] The device (smart glasses) displays the response text on the display device.

[0364] Input: Response text data

[0365] Output: Display on screen: "The price of this product is ¥XX."

[0366] Specific operation: Visually display the response text data on the screen.

[0367] Step 6:

[0368] The user (sales representative) enters the case ID and sends a request for quotation creation to the server via smart glasses.

[0369] Input: Case ID "12345"

[0370] Output: Quotation creation request data

[0371] Specific operation: Use the smart glasses' input device to enter the case ID and send a request to the server.

[0372] Step 7:

[0373] The server retrieves project information from the database based on the project ID and generates a PDF-formatted estimate using the EstimateGenerator engine.

[0374] Input: Case ID "12345"

[0375] Output: Quotation in PDF format "Quotation_12345.pdf"

[0376] Specific operation: Execute a database query to retrieve project information, embed that information into a template, and generate a quotation.

[0377] Step 8:

[0378] The server sends the generated quote to the smart glasses.

[0379] Input: PDF quotation "Quotation_12345.pdf"

[0380] Output: Quotation data

[0381] Specific action: Send the generated PDF data to the smart glasses.

[0382] Step 9:

[0383] The terminal (smart glasses) displays the generated quotation on the display device.

[0384] Input: Quotation data

[0385] Output: Estimate displayed on the screen

[0386] Specific operation: Performs rendering processing to display PDF data on the screen.

[0387] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0388] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Furthermore, by incorporating an emotion engine that recognizes user emotions, it aims to improve the quality of inquiry responses and enhance the user experience. Its embodiments will be described in detail below.

[0389] System Overview

[0390] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries. Furthermore, an emotion engine analyzes the user's emotions and generates appropriate responses.

[0391] Program Processing Description

[0392] 1. Entering and saving project information

[0393] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into the form and clicks the submit button.

[0394] Terminal: Formats user input into a data format (e.g., JSON format) and sends it to the server as an HTTP request.

[0395] Server: The server analyzes the received data and validates it as case information. Successfully validated case information is saved to the database, and a new case ID is generated.

[0396] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[0397] Terminal: Displays the case ID and a message confirming that the case has been saved to the sales representative.

[0398] 2. Creating a quotation

[0399] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[0400] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[0401] Server: Receives a request, executes a database query based on the project ID, and retrieves relevant project information. Embeds the retrieved data into a quotation template and generates a quotation in PDF format.

[0402] Server: Saves the generated quotation to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[0403] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[0404] 3. Preparation of the initial proposal

[0405] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[0406] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[0407] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information. Embeds the retrieved data into a proposal template and generates a preliminary proposal in Word or PDF format.

[0408] Server: Saves the generated initial proposal to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[0409] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[0410] 4. Handling inquiries

[0411] User: The customer enters their question into the inquiry form and clicks the submit button.

[0412] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[0413] Server: Passes the received query content to the natural language processing engine for analysis. Executes database queries based on the query content and generates appropriate answers from relevant information and FAQs.

[0414] Emotional Engine: Analyzes the customer's emotions when they make an inquiry and adjusts the response based on those emotions.

[0415] Server: The generated response is sent to the customer's email address as an automated reply email.

[0416] Terminal: Notifies the customer that a response to their inquiry has been sent.

[0417] Specific example

[0418] Specific examples of entering and saving project information

[0419] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being company A, the proposed product being product B, and the desired delivery date being December 1, 2023.

[0420] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[0421] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[0422] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[0423] Examples of creating a quotation

[0424] 1. User: A sales representative requests a quote for case ID 12345.

[0425] 2. Terminal: The request data is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[0426] 3. Server: Execute a database query to retrieve information for case ID 12345.

[0427] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[0428] Examples of how to handle inquiries

[0429] 1. User: The customer asks, "When is the delivery date?"

[0430] 2. Terminal: Sends the inquiry content to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[0431] 3. Server: Analyzes the inquiry content using a natural language processing engine and retrieves relevant information from the database and FAQs.

[0432] 4. Emotional Engine: Analyzes the customer's emotions and determines if they are experiencing "anxiety."

[0433] 5. Server: Based on the results of the emotion engine, the server adjusts and generates the response email to have a more reassuring tone.

[0434] 6. Server: Sends the generated response email to the customer's email address.

[0435] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. Furthermore, the introduction of an emotion engine allows for appropriate responses tailored to customer emotions, thereby improving customer satisfaction.

[0436] The following describes the processing flow.

[0437] Entering and saving project information

[0438] Step 1:

[0439] User: The sales representative enters the project information (customer name, contact information, proposed products, desired delivery date, etc.) into a dedicated form and clicks the submit button.

[0440] Step 2:

[0441] Terminal: Converts case information into a specified format (e.g., JSON format) and sends it as an HTTP request.

[0442] Step 3:

[0443] Server: Analyzes the received data and validates the case information (checking required fields, data type checks, etc.).

[0444] Step 4:

[0445] Server: Saves case information that has successfully been validated to the database. Generates a new case ID at the same time as saving.

[0446] Step 5:

[0447] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[0448] Step 6:

[0449] Terminal: The sales representative's screen will display the case ID and a message indicating that the case has been saved.

[0450] Request and generation of a quotation

[0451] Step 1:

[0452] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[0453] Step 2:

[0454] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[0455] Step 3:

[0456] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[0457] Step 4:

[0458] Server: Embeds the acquired data into a quotation template and generates a quotation in PDF format.

[0459] Step 5:

[0460] Server: Saves the generated quotation to the specified directory and sends it as an attachment to the sales representative's email address.

[0461] Step 6:

[0462] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[0463] Request and generation of the initial proposal.

[0464] Step 1:

[0465] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[0466] Step 2:

[0467] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[0468] Step 3:

[0469] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[0470] Step 4:

[0471] Server: Embeds the acquired data into a proposal template and generates a preliminary proposal in Word or PDF format.

[0472] Step 5:

[0473] Server: Saves the generated initial proposal to the specified directory and sends it as an attachment to the sales representative's email address.

[0474] Step 6:

[0475] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[0476] Inquiry handling process and use of emotion engine

[0477] Step 1:

[0478] User: The customer enters their question into the inquiry form and clicks the submit button.

[0479] Step 2:

[0480] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[0481] Step 3:

[0482] Server: Passes the received query content to the natural language processing engine for analysis.

[0483] Step 4:

[0484] Server: Executes database queries based on the inquiry content and generates appropriate answers from relevant information and FAQs.

[0485] Step 5:

[0486] Emotion Engine: Analyzes customer inquiries to understand their emotions and obtain emotional data (e.g., "anxiety" is perceived).

[0487] Step 6:

[0488] Server: Based on the results of the emotion engine, the server adjusts and generates the content of the response email. In this case, it adjusts the content to be reassuring in response to the emotion of "anxiety."

[0489] Step 7:

[0490] Server: Automatically sends the generated response email to the customer's email address.

[0491] Step 8:

[0492] Terminal: Notifies the customer that a response to their inquiry has been sent.

[0493] Specific example

[0494] Specific examples of entering and saving project information

[0495] Step 1:

[0496] User: The sales representative enters new project information (Customer name: Company A, Proposed product: Product B, Desired delivery date: December 1, 2023) and clicks the submit button.

[0497] Step 2:

[0498] Terminal: Converts the entered information into JSON format and sends it to the server as an HTTP request.

[0499] Step 3:

[0500] Server: Receives case information and performs validation (e.g., checks input fields, checks data types).

[0501] Step 4:

[0502] Server: Saves case information that passed validation to the database and generates case ID 12345.

[0503] Step 5:

[0504] Server: Sends case ID 12345 and a save completion notification to the sales representative's terminal.

[0505] Step 6:

[0506] Terminal: Displays case ID 12345 and a message indicating that saving is complete to the sales representative.

[0507] Examples of creating a quotation

[0508] Step 1:

[0509] User: A sales representative requests a quote for case ID 12345.

[0510] Step 2:

[0511] The terminal sends request data to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[0512] Step 3:

[0513] Server: Executes a database query to retrieve information for case ID 12345.

[0514] Step 4:

[0515] Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format.

[0516] Step 5:

[0517] Server: Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[0518] Step 6:

[0519] Terminal: Notifies the sales representative that a quotation has been generated and sent via email.

[0520] Examples of how to handle inquiries

[0521] Step 1:

[0522] User: A customer asks, "When is the delivery date?"

[0523] Step 2:

[0524] Terminal: Sends the inquiry to the server in the format {"action": "inquiry", "message": "What is the delivery date?"}.

[0525] Step 3:

[0526] Server: Analyzes the inquiry content using a natural language processing engine and retrieves relevant information from the database and FAQs.

[0527] Step 4:

[0528] Emotional Engine: Analyzes customer inquiries to determine if they are experiencing "anxiety" or "anxiety."

[0529] Step 5:

[0530] Server: Based on the results of the emotion engine, it adjusts and generates a response email with reassuring content.

[0531] Step 6:

[0532] Server: Sends the generated response email to the customer's email address.

[0533] Step 7:

[0534] Terminal: Notifies the customer that a response to their inquiry has been sent.

[0535] These processing steps improve the efficiency of sales activities and enable appropriate responses that respond to customer emotions. This leads to increased customer satisfaction and a significant improvement in the quality and efficiency of operations.

[0536] (Example 2)

[0537] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0538] To streamline project information management and sales support, it is necessary to automate a series of tasks, from inputting project information to generating quotations and proposals, and even responding to customer inquiries. Furthermore, in handling inquiries, it is crucial to respond in a way that considers the customer's feelings, thereby improving customer satisfaction. However, conventional systems do not integrate these functions and require manual operation, which limits the efficiency of operations and the improvement of customer satisfaction.

[0539] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for organizing case information into a data format and transmitting it to the server; means for storing case information in a database and generating a new case ID; means for generating quotations and initial proposals based on the stored case information and sending them to terminals and via email; and means for analyzing customer inquiries, generating appropriate automated responses using an emotion engine, and transmitting them. This enables efficient management of case information, automatic generation of quotations and proposals, and inquiry handling that takes user emotions into consideration.

[0540] "Project information" refers to various data related to a project, such as customer information, proposed products, and desired delivery dates.

[0541] A "data format" is a format that represents information in a structured way, and examples include JSON and XML.

[0542] A "server" is a computer system that processes data and provides services in response to requests from clients.

[0543] A "database" is a system for efficiently managing and manipulating stored data, and it is a place where project information and other related data are stored.

[0544] A "New Case ID" is an identifier used to uniquely identify case information newly saved in the database.

[0545] A "quotation" is a document that lists the prices and conditions of goods related to a project, and is intended to be presented to the customer.

[0546] A "first-round proposal" is a document that summarizes the basic proposal content and conditions, and is used when submitting an initial proposal.

[0547] A "terminal" is a device used by users to input information or view output, and includes personal computers and smartphones.

[0548] "Email" refers to electronic messages sent and received via the internet, and it is possible to attach documents and files to them.

[0549] "Inquiry" refers to questions or requests that customers submit through the system.

[0550] An "emotion engine" is a system that analyzes a user's emotions and generates the optimal response based on those emotions.

[0551] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[0552] An "automated response" is a response message that a system generates and sends to a user based on pre-programmed rules or algorithms.

[0553] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Furthermore, by incorporating an emotion engine that recognizes user emotions, it aims to improve the quality of inquiry responses and enhance the user experience. Its embodiments will be described in detail below.

[0554] System Overview

[0555] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries. Furthermore, an emotion engine analyzes the user's emotions and generates appropriate responses.

[0556] Entering and saving project information

[0557] Specific example

[0558] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being company A, the proposed product being product B, and the desired delivery date being December 1, 2023.

[0559] 2. Terminal: The terminal formats the entered information into a predetermined data format such as JSON and sends it to the server as an HTTP request.

[0560] 3. Server: Analyzes the received data and validates the input content. Successfully validated case information is saved to the database, and a new case ID (e.g., Case ID 12345) is generated.

[0561] 4. Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[0562] 5. Terminal: Display the case ID 12345 and a message indicating that the case has been saved to the sales representative.

[0563] Estimate creation

[0564] Specific example

[0565] 1. User: A sales representative requests a quote for case ID 12345.

[0566] 2. Terminal: A request to create an estimate is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[0567] 3. Server: Execute a database query to retrieve information for case ID 12345.

[0568] 4. Server: The server embeds the acquired information into the quotation template and generates a quotation in PDF format (e.g., "Quotation_12345.pdf").

[0569] 5. Server: Saves the generated quotation to the specified directory in the file system and sends it as an attachment to the sales representative's email address.

[0570] 6. Terminal: Notify the sales representative that the quotation has been created and sent via email.

[0571] Preparation of the initial proposal

[0572] Specific example

[0573] 1. User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[0574] 2. Terminal: Send the proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[0575] 3. Server: Receives the request, executes a database query based on the case ID, and retrieves the relevant case information.

[0576] 4. Server: The server embeds the acquired data into the proposal template and generates a preliminary proposal in Word or PDF format (e.g., "proposal_12345.docx" or "proposal_12345.pdf").

[0577] 5. Server: Saves the generated initial proposal to the specified directory and sends it as an attachment to the sales representative's email address.

[0578] 6. Terminal: Notify the sales representative that the initial proposal has been completed and sent via email.

[0579] Inquiry response

[0580] Specific example

[0581] 1. User: The customer asks, "When is the delivery date?"

[0582] 2. Terminal: Sends the inquiry content to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[0583] 3. Server: Analyzes the inquiry content using a natural language processing engine and retrieves relevant information from the database and FAQs.

[0584] 4. Emotion Engine: Analyzes the customer's emotions from the inquiry and adjusts the response based on those emotions. For example, if "anxiety" is perceived, the tone will be adjusted to "Please rest assured. The delivery date is December 1st, 2023."

[0585] 5. Server: Generates a tailored response as an automated reply email and sends it to the customer's email address.

[0586] 6. Terminal: Notifies the customer that a response to their inquiry has been sent.

[0587] Examples of prompts for generative AI models

[0588] "Please enter the project information: Customer name, proposed product, desired delivery date."

[0589] "Please create a quotation for project ID 12345."

[0590] "Please create a preliminary proposal for project ID 12345."

[0591] "What is the deadline?"

[0592] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. Furthermore, the introduction of an emotion engine allows for appropriate responses tailored to customer emotions, leading to improved customer satisfaction.

[0593] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0594] Entering and saving project information

[0595] Step 1:

[0596] User: The sales representative enters the case information into the web form on their terminal and clicks the submit button.

[0597] Input: Customer name (e.g., Company A), Proposed product (e.g., Product B), Desired delivery date (e.g., 2023-12-01)

[0598] Specific action: The sales representative enters the required case information into the web form and clicks the "Submit" button.

[0599] Output: The input data is sent to the terminal.

[0600] Step 2:

[0601] Terminal: Converts input data to JSON format and sends it to the server as an HTTP request.

[0602] Input: Project information entered by the user

[0603] Specific operation: The terminal converts the input data into a JSON format {"customer": "Company A", "product": "Product B", "delivery_date": "2023-12-01"} and sends a POST request to the server.

[0604] Output: Data in JSON format is sent to the server.

[0605] Step 3:

[0606] Server: Analyzes incoming data, performs validation, and saves it to the database.

[0607] Input: Project information data in JSON format

[0608] Specific operation: The server analyzes the received data and validates the input. After successful validation, it saves the data to the database and generates a new case ID (e.g., case ID 12345).

[0609] Output: A new case ID and save status are generated.

[0610] Step 4:

[0611] Server: Generates a response including the case ID and a save completion notification, and sends it to the terminal.

[0612] Input: Case ID 12345 and saved status

[0613] Specific action: The server generates a response {"case_id": "12345", "status": "Save complete"} and sends it to the terminal.

[0614] Output: Response data is sent to the terminal.

[0615] Step 5:

[0616] Terminal: Displays the case ID and a save completion message to the user.

[0617] Input: Response data from the server

[0618] Specific action: The terminal displays the message "Case ID 12345 has been saved" to the user.

[0619] Output: The message is displayed to the user.

[0620] Estimate creation

[0621] Step 1:

[0622] User: The sales representative enters the project ID and requests the creation of a quote.

[0623] Input: Case ID (Example: 12345)

[0624] Specific action: The sales representative enters the project ID and clicks the "Create Quote" button.

[0625] Output: The request data is sent to the terminal.

[0626] Step 2:

[0627] Terminal: Converts the quotation creation request data into JSON format and sends it to the server.

[0628] Input: Request data from sales representative

[0629] Specific action: The terminal sends a JSON request to the server with the format {"action": "create_estimate", "case_id": "12345"}.

[0630] Output: Request data in JSON format is sent to the server.

[0631] Step 3:

[0632] Server: Retrieves project information from the database and generates a quotation.

[0633] Input: Case ID (Example: 12345)

[0634] Specific operation: The server executes a database query to retrieve information for case ID 12345, embeds the retrieved data into the estimate template, and generates "Estimate_12345.pdf".

[0635] Output: Generated PDF quotation

[0636] Step 4:

[0637] Server: Save the quotation to the file system and send it via email.

[0638] Input: Generated PDF quotation

[0639] Specific operation: The server saves "quote_12345.pdf" to the specified directory and sends it as an attachment to the sales representative's email address.

[0640] Output: The quotation will be sent via email.

[0641] Step 5:

[0642] Terminal: Displays a notification to the user that the quotation has been created and sent via email.

[0643] Input: Processing completion notification from the server

[0644] Specific action: The terminal displays the message "A quotation has been created and an email has been sent" to the user.

[0645] Output: The message is displayed to the user.

[0646] Preparation of the initial proposal

[0647] Step 1:

[0648] User: The sales representative enters the project ID and submits a request to create a preliminary proposal.

[0649] Input: Case ID (Example: 12345)

[0650] Specific action: The sales representative enters the project ID and clicks the "Create Initial Proposal" button.

[0651] Output: The request data is sent to the terminal.

[0652] Step 2:

[0653] Terminal: Converts the initial proposal creation request data into JSON format and sends it to the server.

[0654] Input: Request data from sales representative

[0655] Specific action: The terminal sends a JSON request to the server with the format {"action": "create_proposal", "case_id": "12345"}.

[0656] Output: Request data in JSON format is sent to the server.

[0657] Step 3:

[0658] Server: Retrieves project information from the database and generates a preliminary proposal.

[0659] Input: Case ID (Example: 12345)

[0660] Specific operation: The server executes a database query to retrieve information for case ID 12345, embeds the retrieved data into the proposal template, and generates "proposal_12345.docx" or "proposal_12345.pdf".

[0661] Output: Generated initial proposal

[0662] Step 4:

[0663] Server: Save the initial proposal to the file system and send it via email.

[0664] Input: Generated initial proposal

[0665] Specific operation: The server saves "proposal_12345.docx" or "proposal_12345.pdf" to the specified directory and sends it as an attachment to the sales representative's email address.

[0666] Output: The proposal will be sent via email.

[0667] Step 5:

[0668] Terminal: Displays a notification to the user that the initial proposal has been completed and sent via email.

[0669] Input: Processing completion notification from the server

[0670] Specific action: The device displays the message "The initial proposal has been created and sent via email" to the user.

[0671] Output: The message is displayed to the user.

[0672] Inquiry response

[0673] Step 1:

[0674] User: The customer enters their question through the inquiry form and submits it.

[0675] Input: Question (Example: "What is the delivery date?")

[0676] Specific action: The customer enters a question into the inquiry form and clicks the "Submit" button.

[0677] Output: The input data is sent to the terminal.

[0678] Step 2:

[0679] Terminal: Converts the query data into JSON format and sends it to the server.

[0680] Input: Question entered by the customer

[0681] Specific action: The terminal generates data in JSON format with the format {"action": "inquiry", "message": "What is the delivery date?"} and sends a POST request to the server.

[0682] Output: Data in JSON format is sent to the server.

[0683] Step 3:

[0684] Server: Analyzes the inquiry and generates an appropriate response.

[0685] Input: Inquiry content in JSON format

[0686] Specific operation: The server analyzes the query using a natural language processing engine and retrieves relevant information from the database and FAQs.

[0687] Output: Analysis results and response text

[0688] Step 4:

[0689] Emotion Engine: Analyzes the emotion behind the inquiry and adjusts the response accordingly.

[0690] Input: Analysis results and query text

[0691] Specific operation: The emotion engine analyzes the inquiry, determines the emotion, and adjusts the response accordingly. For example, if there is a sense of urgency, it will adjust the tone to one that provides reassurance, such as, "Please rest assured. The deadline is December 1st, 2023."

[0692] Output: Adjusted response

[0693] Step 5:

[0694] Server: Generates a tailored response email and sends it to the customer.

[0695] Input: Adjusted response

[0696] Specific operation: The server converts the generated response into an email format and sends it to the customer's email address.

[0697] Output: A response email is sent to the customer.

[0698] Step 6:

[0699] Terminal: Notifies the user that a response to their inquiry has been sent.

[0700] Input: Processing completion notification from the server

[0701] Specific action: The device displays the message "An email has been sent" to the user.

[0702] Output: The message is displayed to the user.

[0703] (Application Example 2)

[0704] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0705] While conventional sales support systems have proven somewhat effective in managing project information and generating quotations and proposals, they have a problem in that they struggle to manage customer information in real time at physical stores and to provide appropriate responses that reflect customer emotions. In particular, there is a lack of technical support for sales representatives at physical stores to respond quickly based on customer emotions. There is also a need for a means to properly manage and immediately send generated quotations, proposals, and responses to inquiries.

[0706] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0707] In this invention, the server includes means for inputting case information, means for storing case information in a database, means for generating quotations based on the stored case information, means for generating initial proposals based on the stored case information, means for responding to customer inquiries, means for sales representatives in physical stores to manage customer information in real time using terminals, sentiment analysis means for analyzing customer emotions through terminals and guiding appropriate responses, and means for transmitting generated quotations, proposals, and responses to inquiries to terminals. This streamlines sales activities in physical stores and enables appropriate responses based on customer emotions.

[0708] "Project information" refers to a series of data necessary for sales activities, such as information about the customer, customer requests, details of the products to be sold, and desired delivery dates.

[0709] A "database" is a system for organizing, storing, and managing information, allowing for quick retrieval and extraction of data as needed.

[0710] A "quotation" is a document that outlines the price and terms of a product or service offered to a customer.

[0711] A "primary proposal" is a document that summarizes the proposed content and plan presented to a customer in the initial stages of business negotiations.

[0712] An "inquiry" refers to a question or request from a customer, and it is necessary to provide an appropriate response to it.

[0713] A "system" is a collection of components in which multiple means or elements interact with each other to achieve a specific function.

[0714] A "physical store" refers to a physical commercial facility where customers can actually visit, view, and purchase products.

[0715] A "terminal" refers to an electronic device used by sales representatives or customers that is capable of data input, information display, and communication.

[0716] "Emotional analysis" refers to the technology of reading and analyzing a customer's emotions from their speech, written statements, and actions.

[0717] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate language that humans use on a daily basis.

[0718] This invention is a system that supports sales activities in physical stores and guides appropriate responses to customer emotions. This system is configured to streamline the management of case information, the generation of quotations and initial proposals, and the handling of inquiries.

[0719] System Overview

[0720] 1. Entering and saving project information

[0721] User: The sales representative uses a device (e.g., a tablet) to enter new customer information and clicks the submit button.

[0722] Terminal: The terminal formats the entered information into JSON format and sends it to the server as an HTTP request.

[0723] Server: The server parses the received data, saves it to a database (MySQL or PostgreSQL), and generates a new customer ID. It then sends the generated customer ID and a notification that the data has been saved to the terminal.

[0724] Terminal: Displays the customer ID and a message confirming that the data has been saved to the sales representative.

[0725] 2. Creating a quotation

[0726] User: The sales representative uses a terminal to enter the customer ID and submits a request for a quote.

[0727] Terminal: The terminal sends a quotation creation request to the server in a specified format (e.g., {"action": "create_estimate", "customer_id": "C12345"}).

[0728] Server: The server executes a database query based on the customer ID, embeds the retrieved information into a quotation template, and generates a quotation in PDF format. The generated quotation is saved to a specified directory and sent to the sales representative's email address.

[0729] Terminal: Notifies the sales representative that a quotation has been generated and sent via email.

[0730] 3. Preparation of the initial proposal

[0731] User: The sales representative uses a terminal to enter the customer ID and submits a request for the creation of an initial proposal.

[0732] Terminal: The terminal sends a proposal creation request to the server in a specified format (e.g., {"action": "create_proposal", "customer_id": "C12345"}).

[0733] Server: The server executes a database query based on the customer ID, embeds the retrieved information into a proposal template, and generates a preliminary proposal in Word or PDF format. The generated proposal is saved to a specified directory and sent to the sales representative's email address.

[0734] Terminal: Notifies the sales representative that the proposal has been generated and sent via email.

[0735] 4. Handling inquiries

[0736] User: The customer provides inquiry information to the store staff. The staff enters the inquiry via a terminal and clicks the send button.

[0737] Terminal: The terminal sends inquiry data to the server in a specified format (e.g., {"action": "inquiry", "message": "When will this product be in stock?"}).

[0738] Server: The server uses the Google® Cloud Natural Language API to process the received inquiry using natural language processing, and retrieves relevant information from the database and FAQs based on the analyzed data. Next, it uses the IBM Watson® Tone Analyzer API to analyze the customer's emotions and adjusts the tone of the response based on the results.

[0739] Server: Sends the generated response email to the customer's email address and also notifies the staff.

[0740] Hardware and software

[0741] Hardware:

[0742] Device: iOS or Android® tablet

[0743] Server: Standard web server (e.g., Apache, Nginx)

[0744] Database: MySQL or PostgreSQL

[0745] software:

[0746] Natural Language Processing Engine: Google Cloud Natural Language API

[0747] Sentiment analysis engine: IBM Watson Tone Analyzer API

[0748] Programming language: Python3

[0749] Web framework: Flask

[0750] Specific example

[0751] Entering and saving customer information

[0752] 1. A sales representative enters new customer information and sends it to the server. For example, if the customer's name is "Taro Yamada" and their product of interest is "smartphone".

[0753] 2. The terminal sends the input information to the server in JSON format, and a customer ID is generated.

[0754] 3. The terminal displays "Customer ID: C12345" and notifies the user that the information has been saved.

[0755] Estimate creation

[0756] 1. The sales representative requests the generation of a quotation using "Customer ID: C12345".

[0757] 2. A quotation will be generated and sent to the specified email address.

[0758] Examples of how to handle inquiries

[0759] 1. A customer asks, "When will this product be in stock?"

[0760] 2. The server analyzes the inquiry and retrieves relevant information from the FAQ.

[0761] 3. The emotion analysis engine determines that there is a feeling of "anxiety" and generates a response with an adjusted tone.

[0762] 4. The generated response email is sent to the customer.

[0763] Example of a prompt

[0764] "Please tell me when this product will be back in stock."

[0765] "Please explain the contents of the quotation in detail."

[0766] With the above configuration, the present invention is expected to streamline sales activities in physical stores, enable appropriate responses that respond to customer emotions, and improve customer satisfaction.

[0767] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0768] Step 1:

[0769] The user (sales representative) enters new customer information into the terminal and clicks the submit button. The input includes the customer's name, contact information, and products of interest. This information is formatted in JSON format.

[0770] Input: Customer's name, contact information, items of interest

[0771] Output: Customer information in JSON format (Example: {"name": "Taro Yamada", "contact": "xxx@example.com", "interest": "Smartphone"})

[0772] Step 2:

[0773] Customer information in JSON format sent from the terminal is sent to the server as an HTTP request. The server receives this request and parses the data.

[0774] Input: HTTP request (customer information in JSON format)

[0775] Output: Analyzed customer information

[0776] Step 3:

[0777] The server saves the analyzed customer information to a MySQL or PostgreSQL database. A new customer ID is generated and saved to the database.

[0778] Input: Analyzed customer information

[0779] Output: New customer ID (Example: C12345)

[0780] Step 4:

[0781] The server generates a new customer ID and a save completion notification, and sends it to the terminal. This notification includes the customer ID and a save completion message.

[0782] Input: New Customer ID

[0783] Output: Customer ID and save completion notification (JSON format)

[0784] Step 5:

[0785] The device displays notifications received by the sales representative, including the customer ID. The sales representative can then verify this customer ID.

[0786] Input: Customer ID and save completion notification

[0787] Output: Customer ID and save completion message displayed on the terminal screen

[0788] Step 6:

[0789] The user (sales representative) uses a terminal to enter the customer ID and submits a request to create a quotation. This request is formatted in JSON format and sent to the server as an HTTP request.

[0790] Input: Customer ID

[0791] Output: Estimate creation request (Example: {"action": "create_estimate", "customer_id": "C12345"})

[0792] Step 7:

[0793] The server receives the quotation creation request sent from the terminal and executes a database query to retrieve information based on the customer ID.

[0794] Input: Request to create a quote

[0795] Output: Information based on customer ID

[0796] Step 8:

[0797] The server embeds the acquired information into a quotation template and generates a quotation in PDF format. The generated quotation is then saved to the specified directory.

[0798] Input: Information based on customer ID

[0799] Output: Generated quotation (PDF format)

[0800] Step 9:

[0801] The server sends the generated quotation to the sales representative's email address. It also notifies the terminal that the quotation has been generated and sent.

[0802] Input: Generated quote

[0803] Output: Sent emails and notifications

[0804] Step 10:

[0805] The user (sales representative) uses a terminal to enter the customer ID and submits a request to create a preliminary proposal. This request is also formatted in JSON and sent to the server as an HTTP request.

[0806] Input: Customer ID

[0807] Output: Request to create a first proposal (Example: {"action": "create_proposal", "customer_id": "C12345"})

[0808] Step 11:

[0809] The server receives the initial proposal creation request sent from the terminal and executes a database query to retrieve information based on the customer ID.

[0810] Input: Request to create initial proposal

[0811] Output: Information based on customer ID

[0812] Step 12:

[0813] The server embeds the acquired information into a proposal template and generates a preliminary proposal in Word or PDF format. The generated proposal is then saved to the specified directory.

[0814] Input: Information based on customer ID

[0815] Output: Generated initial proposal (Word or PDF format)

[0816] Step 13:

[0817] The server sends the generated initial proposal to the sales representative's email address and notifies the terminal that the proposal has been generated and sent.

[0818] Input: Generated initial proposal

[0819] Output: Sent emails and notifications

[0820] Step 14:

[0821] The user (customer) provides inquiry information to the store staff. The staff member enters the inquiry via a terminal and clicks the send button. The inquiry data is formatted in JSON format and sent to the server.

[0822] Input: Inquiry Information

[0823] Output: Inquiry data in JSON format (Example: {"action": "inquiry", "message": "When will this product be in stock?"})

[0824] Step 15:

[0825] The server receives the inquiry data sent from the terminal and performs natural language processing using the Google Cloud Natural Language API. Based on the analyzed data, the server retrieves relevant information from the database and FAQs.

[0826] Input: Query data in JSON format

[0827] Output: Analyzed data and related information

[0828] Step 16:

[0829] The server uses the IBM Watson Tone Analyzer API to analyze customer sentiment and generates a response email with an adjusted tone based on the results.

[0830] Input: Analyzed data and related information

[0831] Output: Tone-adjusted response email

[0832] Step 17:

[0833] The server sends the generated response email to the customer's email address and also notifies the store staff.

[0834] Input: Tone-adjusted response email

[0835] Output: Sent emails and notifications

[0836] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0837] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0838] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0839] [Second Embodiment]

[0840] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0841] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0842] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0843] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0844] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0845] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0846] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0847] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0848] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0849] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0850] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0851] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0852] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Its embodiments will be described in detail below.

[0853] System Overview

[0854] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries.

[0855] Program Processing Description

[0856] 1. Entering and saving project information

[0857] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into the form and clicks the submit button.

[0858] Terminal: Formats form input data into the required format and sends a request to the server.

[0859] Server: Receives case information, saves it to the database, generates a new case ID, and notifies the sales representative.

[0860] 2. Creating a quotation

[0861] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[0862] Terminal: Sends a quotation creation request to the server, including the project ID.

[0863] Server: Receives a request, retrieves the relevant project ID information from the database, and generates a quotation. The generated quotation is saved in PDF format and sent to the sales representative via email.

[0864] 3. Preparation of the initial proposal

[0865] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[0866] Terminal: Sends a proposal creation request to the server, including the project ID.

[0867] Server: Receives a request, retrieves the relevant case ID information from the database, and generates a preliminary proposal. The generated preliminary proposal is saved in PDF or Word format and sent to the sales representative via email.

[0868] 4. Handling inquiries

[0869] User: The customer enters their question into the inquiry form and clicks the submit button.

[0870] Terminal: Sends query data to the server.

[0871] Server: Analyzes the inquiry, retrieves relevant information from the database and FAQs, and generates an appropriate response using a natural language processing engine. The generated response is sent to the customer as an automated email.

[0872] Specific example

[0873] Specific examples of entering and saving project information

[0874] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being Company A, the proposed product being Product B, and the desired delivery date being December 1, 2023.

[0875] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[0876] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[0877] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[0878] Examples of creating a quotation

[0879] 1. User: A sales representative requests a quote for case ID 12345.

[0880] 2. Terminal: The request data is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[0881] 3. Server: Execute a database query to retrieve information for case ID 12345.

[0882] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[0883] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. This significantly improves the efficiency of sales activities, saving considerable time and effort.

[0884] The following describes the processing flow.

[0885] Entering and saving project information

[0886] Step 1:

[0887] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into a dedicated form and clicks the submit button.

[0888] Step 2:

[0889] Terminal: Formats user input into a data format (e.g., JSON format) and sends it to the server as an HTTP request.

[0890] Step 3:

[0891] Server: The server analyzes the received data and performs validation as case information (e.g., checking required fields, verifying data types).

[0892] Step 4:

[0893] Server: Saves case information that has successfully been validated to the database. Generates a new case ID at the same time as saving.

[0894] Step 5:

[0895] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[0896] Step 6:

[0897] Terminal: Displays the case ID and a message confirming that the case has been saved to the sales representative.

[0898] Request and generation of a quotation

[0899] Step 1:

[0900] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[0901] Step 2:

[0902] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[0903] Step 3:

[0904] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[0905] Step 4:

[0906] Server: Embeds the acquired data into a quotation template and generates a quotation in PDF format.

[0907] Step 5:

[0908] Server: Saves the generated quotation to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[0909] Step 6:

[0910] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[0911] Request and generation of the initial proposal.

[0912] Step 1:

[0913] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[0914] Step 2:

[0915] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[0916] Step 3:

[0917] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[0918] Step 4:

[0919] Server: Embeds the acquired data into a proposal template and generates a preliminary proposal in Word or PDF format.

[0920] Step 5:

[0921] Server: Saves the generated initial proposal to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[0922] Step 6:

[0923] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[0924] Inquiry handling process

[0925] Step 1:

[0926] User: The customer enters their question into the inquiry form and clicks the submit button.

[0927] Step 2:

[0928] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[0929] Step 3:

[0930] Server: Passes the received query content to the natural language processing engine for analysis.

[0931] Step 4:

[0932] Server: Executes database queries based on the inquiry content and generates appropriate answers from relevant information and FAQs.

[0933] Step 5:

[0934] Server: The generated response is sent to the customer's email address as an automated reply email.

[0935] Step 6:

[0936] Terminal: Notifies the customer that a response to their inquiry has been sent.

[0937] These processing steps streamline key tasks related to sales activities, enabling them to be executed quickly and accurately.

[0938] (Example 1)

[0939] Next, we will describe Example 1. 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."

[0940] In sales operations, a significant amount of time and effort is spent on managing project information, creating quotations and proposals, and responding to customer inquiries. Performing these tasks manually is particularly burdensome and reduces operational efficiency. Therefore, the present invention aims to provide a system that streamlines these sales operations and saves time and effort.

[0941] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0942] In this invention, the server includes means for formatting case information into a data format and transmitting it to the server; means for the server to store the received case information in a database and generate a new case ID; means for generating a quotation based on the case information and sending it to a sales representative; and means for generating a preliminary proposal based on the case information and sending it to a sales representative. This enables more efficient sales operations and faster customer response.

[0943] "Project information" refers to all information related to sales activities, such as customer information, proposed product information, and desired delivery dates.

[0944] A "data format" is a standardized format used for sending, receiving, and storing data, and JSON format is one example.

[0945] A "server" refers to a computer system used to process, store, and transmit data over a network.

[0946] A "database" refers to a system for efficiently storing and retrieving structured data.

[0947] "Case ID" refers to an identifier generated to uniquely identify each case.

[0948] A "quotation" refers to a document that clearly specifies the price and conditions of the goods or services offered to a customer.

[0949] A "first proposal" is a document that outlines the content of the initial proposal and serves to show the client the proposed content and conditions.

[0950] "Inquiry details" refers to information that contains questions or requests from customers.

[0951] A "natural language processing engine" refers to computer technology used to understand and generate human language.

[0952] An "automated response email" refers to an email that is automatically generated and sent by a system based on specific content.

[0953] A "template engine" refers to a software tool that embeds data into a template to automatically generate the final document or file.

[0954] A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to learn patterns from data and generate new information.

[0955] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries.

[0956] System Overview

[0957] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries.

[0958] Entering and saving project information

[0959] 1. User: The sales representative enters customer information (e.g., customer name, contact information), proposed products (e.g., product name, quantity), desired delivery date, and other case information into a form on the terminal.

[0960] 2. Terminal: Formats the input data into a specified format (e.g., JSON format).

[0961] 3. Terminal: Sends the formatted data to the server.

[0962] 4. Server: Saves the received data to the database and generates a new case ID.

[0963] 5. Server: Notifies the user's terminal of the case ID and a message indicating that saving is complete.

[0964] Estimate creation

[0965] 1. User: Enter the project ID on the quotation creation request screen and click the "Create Quotation" button.

[0966] 2. Terminal: Sends a quotation creation request to the server, including the project ID.

[0967] 3. Server: Retrieves information for the relevant case ID from the database.

[0968] 4. Server: Generates a quotation based on the acquired information. The generated quotation is saved in PDF format.

[0969] 5. Server: Send the generated PDF file to the sales representative via email.

[0970] Preparation of the initial proposal

[0971] 1. User: Enter the project ID on the initial proposal creation request screen and click the "Create Proposal" button.

[0972] 2. Terminal: Send a proposal creation request to the server, including the project ID.

[0973] 3. Server: Retrieves information for the relevant case ID from the database.

[0974] 4. Server: Generates a preliminary proposal based on the acquired information. The generated proposal is saved in PDF or Word format.

[0975] 5. Server: Send the generated files to the sales representative via email.

[0976] Inquiry response

[0977] 1. User: The customer enters their question into the inquiry form and clicks the submit button.

[0978] 2. Terminal: Sends the inquiry data to the server.

[0979] 3. Server: Analyzes the inquiry content and retrieves relevant information from the database and FAQs.

[0980] 4. Server: Generates appropriate answers using a natural language processing engine (e.g., a generative AI model).

[0981] 5. Server: Sends the generated response to the customer as an automated reply email.

[0982] Hardware and software to be used

[0983] Server: A computer system that performs data processing and storage.

[0984] Database: A system for efficiently storing and retrieving project information (e.g., MySQL, PostgreSQL)

[0985] Template engine: Software used to embed data into templates and generate documents (e.g., JasperReports, Apache POI)

[0986] Natural Language Processing Engine: Software that uses generative AI models to analyze dialogues and generate responses (e.g., GPT-3)

[0987] Specific example

[0988] Specific examples of entering and saving project information

[0989] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being Company A, the proposed product being Product B, and the desired delivery date being December 1, 2023.

[0990] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[0991] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[0992] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[0993] Examples of creating a quotation

[0994] 1. User: A sales representative requests a quote for case ID 12345.

[0995] 2. Terminal: The request data is sent to the server with a prompt message such as "Please enter the information required to create the quotation."

[0996] 3. Server: Execute a database query to retrieve information for case ID 12345.

[0997] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[0998] Example of a prompt

[0999] "Please enter the information required to create the quotation."

[1000] "As an example of how to handle inquiries, please generate a list of frequently asked questions and their answers."

[1001] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. It significantly improves the efficiency of sales activities, saving considerable time and effort.

[1002] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1003] Entering and saving project information

[1004] Step 1:

[1005] The user enters project information (customer information, proposed products, desired delivery date, etc.) into a form on their device.

[1006] Input: Customer information, proposed products, desired delivery date, and other project information.

[1007] Specific operation: The user enters information into each field of the form using the device's keyboard and clicks the submit button.

[1008] Output: Input case information data

[1009] Step 2:

[1010] The terminal formats the entered case information data into a specified format (e.g., JSON format).

[1011] Input: Project information data entered by the user

[1012] Specific operation: The terminal automatically converts the input data into JSON format.

[1013] Output: Formatted case information data

[1014] Step 3:

[1015] The terminal sends the formatted case information data to the server.

[1016] Input: Project information data formatted in JSON format

[1017] Specific operation: The terminal sends an HTTP POST request and sends data to the server.

[1018] Output: Data transmission to server complete.

[1019] Step 4:

[1020] The server receives the case information data, saves it to the database, and generates a new case ID.

[1021] Input: Case information data sent from the terminal

[1022] Specific operation: The server executes an INSERT query into the database and saves the case information. A new case ID is generated.

[1023] Output: New case ID generated and saved to database.

[1024] Step 5:

[1025] The server notifies the user's terminal of the case ID and a message indicating that saving is complete.

[1026] Input: New case ID, Saved status

[1027] Specific operation: The server returns the case ID and message to the terminal as an HTTP response. The terminal displays the notification message.

[1028] Output: Notification display of case ID and save completion message.

[1029] Estimate creation

[1030] Step 1:

[1031] The user enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[1032] Input: Case ID

[1033] Specific action: The sales representative enters the deal ID and clicks the button.

[1034] Output: Request to create a quotation

[1035] Step 2:

[1036] The terminal sends a request to the server to create a quotation, including the project ID.

[1037] Input: Request to create a quote

[1038] Specific operation: The terminal sends an HTTP POST request and sends the request data to the server.

[1039] Output: Request sent to server successfully

[1040] Step 3:

[1041] The server retrieves the relevant case ID information from the database.

[1042] Input: Case ID

[1043] Specific operation: The server executes a SELECT query and retrieves data corresponding to the case ID.

[1044] Output: Case information retrieved from the database

[1045] Step 4:

[1046] The server generates a quotation based on the information it has acquired. The generated quotation is saved in PDF format.

[1047] Input: Case information retrieved from the database

[1048] Specific operation: The server uses a template engine (e.g., JasperReports) to generate a quotation and create a PDF file.

[1049] Output: Generated PDF quotation

[1050] Step 5:

[1051] The server sends the generated PDF file to the sales representative via email.

[1052] Input: Quotation in PDF format

[1053] Specific operation: The server sends the generated PDF via SMTP server as an email attachment.

[1054] Output: Email sent to sales representative completed.

[1055] Preparation of the initial proposal

[1056] Step 1:

[1057] The user enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[1058] Input: Case ID

[1059] Specific action: The sales representative enters the deal ID and clicks the button.

[1060] Output: Proposal creation request

[1061] Step 2:

[1062] The terminal sends a proposal creation request to the server, including the project ID.

[1063] Input: Proposal creation request

[1064] Specific operation: The terminal sends an HTTP POST request and sends the request data to the server.

[1065] Output: Request sent to server successfully

[1066] Step 3:

[1067] The server retrieves the relevant case ID information from the database.

[1068] Input: Case ID

[1069] Specific operation: The server executes a SELECT query and retrieves data corresponding to the case ID.

[1070] Output: Case information retrieved from the database

[1071] Step 4:

[1072] The server generates a preliminary proposal based on the information it acquires. The generated proposal is saved in PDF or Word format.

[1073] Input: Case information retrieved from the database

[1074] Specific operation: The server uses a template engine (e.g., Apache POI) to generate a proposal and create a PDF or Word file.

[1075] Output: Proposal in generated PDF or Word format

[1076] Step 5:

[1077] The server sends the generated files to the sales representative via email.

[1078] Input: Proposal in PDF or Word format

[1079] Specific operation: The server sends the file generated via the SMTP server as an email attachment.

[1080] Output: Email sent to sales representative completed.

[1081] Inquiry response

[1082] Step 1:

[1083] The user enters a question into the inquiry form and clicks the submit button.

[1084] Input: Question content

[1085] Specific action: The customer enters a question and clicks the submit button.

[1086] Output: Input query content

[1087] Step 2:

[1088] The terminal sends the inquiry data to the server.

[1089] Input: Inquiry details entered

[1090] Specific operation: The terminal sends an HTTP POST request and sends the query data to the server.

[1091] Output: Data transmission to server complete.

[1092] Step 3:

[1093] The server analyzes the query and retrieves relevant information from the database and FAQs.

[1094] Input: Inquiry content sent from the device

[1095] Specific operation: The server searches the database and FAQs based on the content of the inquiry it receives and retrieves relevant information.

[1096] Output: Retrieve relevant query information

[1097] Step 4:

[1098] The server uses a natural language processing engine (e.g., a generative AI model) to generate an appropriate response.

[1099] Input: Related inquiry information

[1100] Specific operation: The generative AI model generates a response based on the information it has acquired.

[1101] Output: Generated response

[1102] Step 5:

[1103] The server will send the generated response to the customer as an automated email.

[1104] Input: Generated response

[1105] Specific operation: The server sends an email containing the response to the customer via the SMTP server.

[1106] Output: Automated response email sent to customer successfully.

[1107] (Application Example 1)

[1108] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1109] Traditional sales support systems required sales representatives to individually generate quotations and proposals and handle customer inquiries, resulting in a heavy burden on them and making real-time responses difficult. Furthermore, providing appropriate and immediate responses during customer interactions was challenging, hindering improvements in customer satisfaction.

[1110] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1111] In this invention, the server includes means for inputting case information, means for storing case information in a database, means for generating quotations based on the stored case information, means for generating initial proposals based on the stored case information, means for responding to customer inquiries, means for analyzing the input inquiry content, generating appropriate responses, and transmitting them to a display device, means for generating quotations and initial proposals in real time and displaying them on a display device, and means for recording and automatically analyzing customer interactions. This enables sales representatives to quickly generate quotations and proposals and provide immediate and appropriate responses to inquiries while interacting with customers in a virtual store. This is expected to improve the work efficiency of sales representatives and enhance customer satisfaction.

[1112] "Project information" refers to detailed information related to transactions with customers, including customer information, details of proposed products, and desired delivery dates.

[1113] A "database" is an electronic information storage system used to store and efficiently manage project information, quotations, proposals, and inquiry details.

[1114] A "quotation" is an official document that lists the price, quantity, delivery date, and other details of the goods or services to be provided in a transaction.

[1115] A "first proposal" is a document that outlines the initial proposal to a customer, primarily intended to explain the features and benefits of the proposed product.

[1116] "Handling inquiries" refers to the act of providing appropriate answers and information to customer questions and requests.

[1117] A "display device" is an electronic device used to visually display information, such as smart glasses or mobile terminals.

[1118] "Input method" refers to the method or device for inputting case information, inquiry details, etc., into the system, and includes keyboards, voice input, etc.

[1119] "Storage means" refers to methods or devices for saving entered case information and other data to a database.

[1120] "Generation means" refers to methods or devices that automatically create estimates and proposals based on stored project information.

[1121] "Natural language processing" is an artificial intelligence technology that analyzes natural language data, such as input text and speech, and generates appropriate responses.

[1122] "Real-time" refers to a state where processing and responses occur instantly without delay.

[1123] "Interaction" refers to the mutual interaction between a sales representative and a customer, and includes dialogue, question-and-answer sessions, and so on.

[1124] "Automated response" refers to a function in which a system automatically generates answers to customer inquiries using natural language processing technology.

[1125] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries in real time. The embodiments for carrying out this invention are described in detail below.

[1126] System Overview

[1127] This system consists of a server, terminals, and users (sales representatives and customers). Sales representatives wear smart glasses to interact with customers. Terminals function as smart glasses, mobile devices, and display devices, and communicate with the server. The server stores case information in a database and performs various processes.

[1128] Program Processing Description

[1129] Hardware and Software

[1130] Hardware: Smart glasses, mobile devices, servers.

[1131] Software: Python scripts, NaturalLanguageProcessing library, EstimateGenerator, ProposalGenerator, SmartGlassesAPI, ServerAPI.

[1132] Data processing and calculation

[1133] The main processes are described below.

[1134] 1. Inquiry handling:

[1135] The user (customer) voice-inputs a question to the smart glasses. For example, they might ask, "What is the price of this product?"

[1136] The device (smart glasses) converts voice input into text data and sends it to the server.

[1137] The server's natural language processing engine analyzes the input text and generates an appropriate response from the relevant database. For example, "The price of this product is XX yen."

[1138] The generated response is displayed on the device's (smart glasses) screen and provided to the user immediately.

[1139] 2. Prepare a quotation:

[1140] The user (sales representative) enters the case ID and sends a request to create a quote to the server via smart glasses.

[1141] The device (smart glasses) sends the request to the server in a predetermined format.

[1142] The server retrieves the relevant project ID information from the database and generates an estimate in PDF format using the EstimateGenerator engine.

[1143] The generated quotation is displayed on the terminal (smart glasses) and provided to the user.

[1144] 3. Preparation of the initial proposal:

[1145] The user (sales representative) enters the case ID and sends a request to create a preliminary proposal to the server via smart glasses.

[1146] The device (smart glasses) sends the request to the server in a predetermined format.

[1147] The server retrieves the relevant project ID information from the database and uses the ProposalGenerator engine to generate a preliminary proposal in PDF or Word format.

[1148] The generated initial proposal is displayed on the device (smart glasses) and provided to the user.

[1149] Specific example

[1150] Specific examples are given below.

[1151] Suppose a sales representative contacts customer A in a virtual store via smart glasses and receives the question, "What is the price of this product?" In this case, the natural language processing engine immediately provides an answer, and the smart glasses display shows, "The price of this product is XX yen."

[1152] If customer A requests a quote on the spot, the sales representative enters the case ID and sends a quote creation request to the server. The server generates the quote and displays it on the smart glasses.

[1153] Furthermore, if a customer requests a detailed proposal, the sales representative can submit a request for a preliminary proposal, the server will generate the proposal, and this will also be displayed on the smart glasses.

[1154] Example of a prompt

[1155] customer_query = "What is the price of this product?"

[1156] response = NaturalLanguageProcessing.generate_response(customer_query)

[1157] print(response)

[1158] This system will enable sales representatives to handle customer inquiries more efficiently in virtual stores, generating quotes and proposals and providing real-time responses to inquiries. This will significantly improve the efficiency of sales activities and is expected to increase customer satisfaction.

[1159] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1160] Step 1:

[1161] The user (customer) inputs a question to the smart glasses using voice.

[1162] Input: Customer voice question: "What is the price of this product?"

[1163] Output: Audio data

[1164] Specific operation: The microphone in the smart glasses captures the sound and processes it as audio data.

[1165] Step 2:

[1166] The device (smart glasses) converts the audio data into text data and sends it to the server.

[1167] Input: Audio data

[1168] Output: Text data "What is the price of this product?"

[1169] Specific operation: Use speech recognition software to convert speech to text and send the text data to the server.

[1170] Step 3:

[1171] The server's natural language processing engine analyzes text data and generates appropriate responses from relevant databases.

[1172] Input: Text data "What is the price of this product?"

[1173] Output: Response text "The price of this product is XX yen."

[1174] Specific operation: A natural language processing engine analyzes the text, retrieves price information from a database, and generates the response text.

[1175] Step 4:

[1176] The server sends the generated response text to the device (smart glasses).

[1177] Input: Response text "The price of this product is XX yen."

[1178] Output: Response text data

[1179] Specific action: Send the generated text data to the smart glasses.

[1180] Step 5:

[1181] The device (smart glasses) displays the response text on the display device.

[1182] Input: Response text data

[1183] Output: Display on screen: "The price of this product is ¥XX."

[1184] Specific operation: Visually display the response text data on the screen.

[1185] Step 6:

[1186] The user (sales representative) enters the case ID and sends a request for quotation creation to the server via smart glasses.

[1187] Input: Case ID "12345"

[1188] Output: Quotation creation request data

[1189] Specific operation: Use the smart glasses' input device to enter the case ID and send a request to the server.

[1190] Step 7:

[1191] The server retrieves project information from the database based on the project ID and generates a PDF-formatted estimate using the EstimateGenerator engine.

[1192] Input: Case ID "12345"

[1193] Output: Quotation in PDF format "Quotation_12345.pdf"

[1194] Specific operation: Execute a database query to retrieve project information, embed that information into a template, and generate a quotation.

[1195] Step 8:

[1196] The server sends the generated quote to the smart glasses.

[1197] Input: PDF quotation "Quotation_12345.pdf"

[1198] Output: Quotation data

[1199] Specific action: Send the generated PDF data to the smart glasses.

[1200] Step 9:

[1201] The terminal (smart glasses) displays the generated quotation on the display device.

[1202] Input: Quotation data

[1203] Output: Estimate displayed on the screen

[1204] Specific operation: Performs rendering processing to display PDF data on the screen.

[1205] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1206] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Furthermore, by incorporating an emotion engine that recognizes user emotions, it aims to improve the quality of inquiry responses and enhance the user experience. Its embodiments will be described in detail below.

[1207] System Overview

[1208] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries. Furthermore, an emotion engine analyzes the user's emotions and generates appropriate responses.

[1209] Program Processing Description

[1210] 1. Entering and saving project information

[1211] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into the form and clicks the submit button.

[1212] Terminal: Formats user input into a data format (e.g., JSON format) and sends it to the server as an HTTP request.

[1213] Server: The server analyzes the received data and validates it as case information. Successfully validated case information is saved to the database, and a new case ID is generated.

[1214] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[1215] Terminal: Displays the case ID and a message confirming that the case has been saved to the sales representative.

[1216] 2. Creating a quotation

[1217] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[1218] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[1219] Server: Receives a request, executes a database query based on the project ID, and retrieves relevant project information. Embeds the retrieved data into a quotation template and generates a quotation in PDF format.

[1220] Server: Saves the generated quotation to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[1221] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[1222] 3. Preparation of the initial proposal

[1223] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[1224] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[1225] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information. Embeds the retrieved data into a proposal template and generates a preliminary proposal in Word or PDF format.

[1226] Server: Saves the generated initial proposal to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[1227] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[1228] 4. Handling inquiries

[1229] User: The customer enters their question into the inquiry form and clicks the submit button.

[1230] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[1231] Server: Passes the received query content to the natural language processing engine for analysis. Executes database queries based on the query content and generates appropriate answers from relevant information and FAQs.

[1232] Emotional Engine: Analyzes the customer's emotions when they make an inquiry and adjusts the response based on those emotions.

[1233] Server: The generated response is sent to the customer's email address as an automated reply email.

[1234] Terminal: Notifies the customer that a response to their inquiry has been sent.

[1235] Specific example

[1236] Specific examples of entering and saving project information

[1237] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being company A, the proposed product being product B, and the desired delivery date being December 1, 2023.

[1238] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[1239] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[1240] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[1241] Examples of creating a quotation

[1242] 1. User: A sales representative requests a quote for case ID 12345.

[1243] 2. Terminal: The request data is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[1244] 3. Server: Execute a database query to retrieve information for case ID 12345.

[1245] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[1246] Examples of how to handle inquiries

[1247] 1. User: The customer asks, "When is the delivery date?"

[1248] 2. Terminal: Sends the inquiry content to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[1249] 3. Server: Analyzes the inquiry content using a natural language processing engine and retrieves relevant information from the database and FAQs.

[1250] 4. Emotional Engine: Analyzes the customer's emotions and determines if they are experiencing "anxiety."

[1251] 5. Server: Based on the results of the emotion engine, the server adjusts and generates the response email to have a more reassuring tone.

[1252] 6. Server: Sends the generated response email to the customer's email address.

[1253] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. Furthermore, the introduction of an emotion engine allows for appropriate responses tailored to customer emotions, thereby improving customer satisfaction.

[1254] The following describes the processing flow.

[1255] Entering and saving project information

[1256] Step 1:

[1257] User: The sales representative enters the project information (customer name, contact information, proposed products, desired delivery date, etc.) into a dedicated form and clicks the submit button.

[1258] Step 2:

[1259] Terminal: Converts case information into a specified format (e.g., JSON format) and sends it as an HTTP request.

[1260] Step 3:

[1261] Server: Analyzes the received data and validates the case information (checking required fields, data type checks, etc.).

[1262] Step 4:

[1263] Server: Saves case information that has successfully been validated to the database. Generates a new case ID at the same time as saving.

[1264] Step 5:

[1265] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[1266] Step 6:

[1267] Terminal: The sales representative's screen will display the case ID and a message indicating that the case has been saved.

[1268] Request and generation of a quotation

[1269] Step 1:

[1270] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[1271] Step 2:

[1272] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[1273] Step 3:

[1274] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[1275] Step 4:

[1276] Server: Embeds the acquired data into a quotation template and generates a quotation in PDF format.

[1277] Step 5:

[1278] Server: Saves the generated quotation to the specified directory and sends it as an attachment to the sales representative's email address.

[1279] Step 6:

[1280] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[1281] Request and generation of the initial proposal.

[1282] Step 1:

[1283] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[1284] Step 2:

[1285] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[1286] Step 3:

[1287] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[1288] Step 4:

[1289] Server: Embeds the acquired data into a proposal template and generates a preliminary proposal in Word or PDF format.

[1290] Step 5:

[1291] Server: Saves the generated initial proposal to the specified directory and sends it as an attachment to the sales representative's email address.

[1292] Step 6:

[1293] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[1294] Inquiry handling process and use of emotion engine

[1295] Step 1:

[1296] User: The customer enters their question into the inquiry form and clicks the submit button.

[1297] Step 2:

[1298] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[1299] Step 3:

[1300] Server: Passes the received query content to the natural language processing engine for analysis.

[1301] Step 4:

[1302] Server: Executes database queries based on the inquiry content and generates appropriate answers from relevant information and FAQs.

[1303] Step 5:

[1304] Emotion Engine: Analyzes customer inquiries to understand their emotions and obtain emotional data (e.g., "anxiety" is perceived).

[1305] Step 6:

[1306] Server: Based on the results of the emotion engine, the server adjusts and generates the content of the response email. In this case, it adjusts the content to be reassuring in response to the emotion of "anxiety."

[1307] Step 7:

[1308] Server: Automatically sends the generated response email to the customer's email address.

[1309] Step 8:

[1310] Terminal: Notifies the customer that a response to their inquiry has been sent.

[1311] Specific example

[1312] Specific examples of entering and saving project information

[1313] Step 1:

[1314] User: The sales representative enters new project information (Customer name: Company A, Proposed product: Product B, Desired delivery date: December 1, 2023) and clicks the submit button.

[1315] Step 2:

[1316] Terminal: Converts the entered information into JSON format and sends it to the server as an HTTP request.

[1317] Step 3:

[1318] Server: Receives case information and performs validation (e.g., checks input fields, checks data types).

[1319] Step 4:

[1320] Server: Saves case information that passed validation to the database and generates case ID 12345.

[1321] Step 5:

[1322] Server: Sends case ID 12345 and a save completion notification to the sales representative's terminal.

[1323] Step 6:

[1324] Terminal: Displays case ID 12345 and a message indicating that saving is complete to the sales representative.

[1325] Examples of creating a quotation

[1326] Step 1:

[1327] User: A sales representative requests a quote for case ID 12345.

[1328] Step 2:

[1329] The terminal sends request data to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[1330] Step 3:

[1331] Server: Executes a database query to retrieve information for case ID 12345.

[1332] Step 4:

[1333] Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format.

[1334] Step 5:

[1335] Server: Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[1336] Step 6:

[1337] Terminal: Notifies the sales representative that a quotation has been generated and sent via email.

[1338] Examples of how to handle inquiries

[1339] Step 1:

[1340] User: A customer asks, "When is the delivery date?"

[1341] Step 2:

[1342] Terminal: Sends the inquiry to the server in the format {"action": "inquiry", "message": "What is the delivery date?"}.

[1343] Step 3:

[1344] Server: Analyzes the inquiry content using a natural language processing engine and retrieves relevant information from the database and FAQs.

[1345] Step 4:

[1346] Emotional Engine: Analyzes customer inquiries to determine if they are experiencing "anxiety" or "anxiety."

[1347] Step 5:

[1348] Server: Based on the results of the emotion engine, it adjusts and generates a response email with reassuring content.

[1349] Step 6:

[1350] Server: Sends the generated response email to the customer's email address.

[1351] Step 7:

[1352] Terminal: Notifies the customer that a response to their inquiry has been sent.

[1353] These processing steps improve the efficiency of sales activities and enable appropriate responses that respond to customer emotions. This leads to increased customer satisfaction and a significant improvement in the quality and efficiency of operations.

[1354] (Example 2)

[1355] Next, we will describe Example 2. 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".

[1356] To streamline project information management and sales support, it is necessary to automate a series of tasks, from inputting project information to generating quotations and proposals, and even responding to customer inquiries. Furthermore, in handling inquiries, it is crucial to respond in a way that considers the customer's feelings, thereby improving customer satisfaction. However, conventional systems do not integrate these functions and require manual operation, which limits the efficiency of operations and the improvement of customer satisfaction.

[1357] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for organizing case information into a data format and transmitting it to the server; means for storing case information in a database and generating a new case ID; means for generating quotations and initial proposals based on the stored case information and sending them to terminals and via email; and means for analyzing customer inquiries, generating appropriate automated responses using an emotion engine, and transmitting them. This enables efficient management of case information, automatic generation of quotations and proposals, and inquiry handling that takes user emotions into consideration.

[1358] "Project information" refers to various data related to a project, such as customer information, proposed products, and desired delivery dates.

[1359] A "data format" is a format that represents information in a structured way, and examples include JSON and XML.

[1360] A "server" is a computer system that processes data and provides services in response to requests from clients.

[1361] A "database" is a system for efficiently managing and manipulating stored data, and it is a place where project information and other related data are stored.

[1362] A "New Case ID" is an identifier used to uniquely identify case information newly saved in the database.

[1363] A "quotation" is a document that lists the prices and conditions of goods related to a project, and is intended to be presented to the customer.

[1364] A "first-round proposal" is a document that summarizes the basic proposal content and conditions, and is used when submitting an initial proposal.

[1365] A "terminal" is a device used by users to input information or view output, and includes personal computers and smartphones.

[1366] "Email" refers to electronic messages sent and received via the internet, and it is possible to attach documents and files to them.

[1367] "Inquiry" refers to questions or requests that customers submit through the system.

[1368] An "emotion engine" is a system that analyzes a user's emotions and generates the optimal response based on those emotions.

[1369] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[1370] An "automated response" is a response message that a system generates and sends to a user based on pre-programmed rules or algorithms.

[1371] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Furthermore, by incorporating an emotion engine that recognizes user emotions, it aims to improve the quality of inquiry responses and enhance the user experience. Its embodiments will be described in detail below.

[1372] System Overview

[1373] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries. Furthermore, an emotion engine analyzes the user's emotions and generates appropriate responses.

[1374] Entering and saving project information

[1375] Specific example

[1376] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being company A, the proposed product being product B, and the desired delivery date being December 1, 2023.

[1377] 2. Terminal: The terminal formats the entered information into a predetermined data format such as JSON and sends it to the server as an HTTP request.

[1378] 3. Server: Analyzes the received data and validates the input content. Successfully validated case information is saved to the database, and a new case ID (e.g., Case ID 12345) is generated.

[1379] 4. Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[1380] 5. Terminal: Display the case ID 12345 and a message indicating that the case has been saved to the sales representative.

[1381] Estimate creation

[1382] Specific example

[1383] 1. User: A sales representative requests a quote for case ID 12345.

[1384] 2. Terminal: A request to create an estimate is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[1385] 3. Server: Execute a database query to retrieve information for case ID 12345.

[1386] 4. Server: The server embeds the acquired information into the quotation template and generates a quotation in PDF format (e.g., "Quotation_12345.pdf").

[1387] 5. Server: Saves the generated quotation to the specified directory in the file system and sends it as an attachment to the sales representative's email address.

[1388] 6. Terminal: Notify the sales representative that the quotation has been created and sent via email.

[1389] Preparation of the initial proposal

[1390] Specific example

[1391] 1. User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[1392] 2. Terminal: Send the proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[1393] 3. Server: Receives the request, executes a database query based on the case ID, and retrieves the relevant case information.

[1394] 4. Server: The server embeds the acquired data into the proposal template and generates a preliminary proposal in Word or PDF format (e.g., "proposal_12345.docx" or "proposal_12345.pdf").

[1395] 5. Server: Saves the generated initial proposal to the specified directory and sends it as an attachment to the sales representative's email address.

[1396] 6. Terminal: Notify the sales representative that the initial proposal has been completed and sent via email.

[1397] Inquiry response

[1398] Specific example

[1399] 1. User: The customer asks, "When is the delivery date?"

[1400] 2. Terminal: Sends the inquiry content to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[1401] 3. Server: Analyzes the inquiry content using a natural language processing engine and retrieves relevant information from the database and FAQs.

[1402] 4. Emotion Engine: Analyzes the customer's emotions from the inquiry and adjusts the response based on those emotions. For example, if "anxiety" is perceived, the tone will be adjusted to "Please rest assured. The delivery date is December 1st, 2023."

[1403] 5. Server: Generates a tailored response as an automated reply email and sends it to the customer's email address.

[1404] 6. Terminal: Notifies the customer that a response to their inquiry has been sent.

[1405] Examples of prompts for generative AI models

[1406] "Please enter the project information: Customer name, proposed product, desired delivery date."

[1407] "Please create a quotation for project ID 12345."

[1408] "Please create a preliminary proposal for project ID 12345."

[1409] "What is the deadline?"

[1410] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. Furthermore, the introduction of an emotion engine allows for appropriate responses tailored to customer emotions, leading to improved customer satisfaction.

[1411] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1412] Entering and saving project information

[1413] Step 1:

[1414] User: The sales representative enters the case information into the web form on their terminal and clicks the submit button.

[1415] Input: Customer name (e.g., Company A), Proposed product (e.g., Product B), Desired delivery date (e.g., 2023-12-01)

[1416] Specific action: The sales representative enters the required case information into the web form and clicks the "Submit" button.

[1417] Output: The input data is sent to the terminal.

[1418] Step 2:

[1419] Terminal: Converts input data to JSON format and sends it to the server as an HTTP request.

[1420] Input: Project information entered by the user

[1421] Specific operation: The terminal converts the input data into a JSON format {"customer": "Company A", "product": "Product B", "delivery_date": "2023-12-01"} and sends a POST request to the server.

[1422] Output: Data in JSON format is sent to the server.

[1423] Step 3:

[1424] Server: Analyzes incoming data, performs validation, and saves it to the database.

[1425] Input: Project information data in JSON format

[1426] Specific operation: The server analyzes the received data and validates the input. After successful validation, it saves the data to the database and generates a new case ID (e.g., case ID 12345).

[1427] Output: A new case ID and save status are generated.

[1428] Step 4:

[1429] Server: Generates a response including the case ID and a save completion notification, and sends it to the terminal.

[1430] Input: Case ID 12345 and saved status

[1431] Specific action: The server generates a response {"case_id": "12345", "status": "Save complete"} and sends it to the terminal.

[1432] Output: Response data is sent to the terminal.

[1433] Step 5:

[1434] Terminal: Displays the case ID and a save completion message to the user.

[1435] Input: Response data from the server

[1436] Specific action: The terminal displays the message "Case ID 12345 has been saved" to the user.

[1437] Output: The message is displayed to the user.

[1438] Estimate creation

[1439] Step 1:

[1440] User: The sales representative enters the project ID and requests the creation of a quote.

[1441] Input: Case ID (Example: 12345)

[1442] Specific action: The sales representative enters the project ID and clicks the "Create Quote" button.

[1443] Output: The request data is sent to the terminal.

[1444] Step 2:

[1445] Terminal: Converts the quotation creation request data into JSON format and sends it to the server.

[1446] Input: Request data from sales representative

[1447] Specific action: The terminal sends a JSON request to the server with the format {"action": "create_estimate", "case_id": "12345"}.

[1448] Output: Request data in JSON format is sent to the server.

[1449] Step 3:

[1450] Server: Retrieves project information from the database and generates a quotation.

[1451] Input: Case ID (Example: 12345)

[1452] Specific operation: The server executes a database query to retrieve information for case ID 12345, embeds the retrieved data into the estimate template, and generates "Estimate_12345.pdf".

[1453] Output: Generated PDF quotation

[1454] Step 4:

[1455] Server: Save the quotation to the file system and send it via email.

[1456] Input: Generated PDF quotation

[1457] Specific operation: The server saves "quote_12345.pdf" to the specified directory and sends it as an attachment to the sales representative's email address.

[1458] Output: The quotation will be sent via email.

[1459] Step 5:

[1460] Terminal: Displays a notification to the user that the quotation has been created and sent via email.

[1461] Input: Processing completion notification from the server

[1462] Specific action: The terminal displays the message "A quotation has been created and an email has been sent" to the user.

[1463] Output: The message is displayed to the user.

[1464] Preparation of the initial proposal

[1465] Step 1:

[1466] User: The sales representative enters the project ID and submits a request to create a preliminary proposal.

[1467] Input: Case ID (Example: 12345)

[1468] Specific action: The sales representative enters the project ID and clicks the "Create Initial Proposal" button.

[1469] Output: The request data is sent to the terminal.

[1470] Step 2:

[1471] Terminal: Converts the initial proposal creation request data into JSON format and sends it to the server.

[1472] Input: Request data from sales representative

[1473] Specific action: The terminal sends a JSON request to the server with the format {"action": "create_proposal", "case_id": "12345"}.

[1474] Output: Request data in JSON format is sent to the server.

[1475] Step 3:

[1476] Server: Retrieves project information from the database and generates a preliminary proposal.

[1477] Input: Case ID (Example: 12345)

[1478] Specific operation: The server executes a database query to retrieve information for case ID 12345, embeds the retrieved data into the proposal template, and generates "proposal_12345.docx" or "proposal_12345.pdf".

[1479] Output: Generated initial proposal

[1480] Step 4:

[1481] Server: Save the initial proposal to the file system and send it via email.

[1482] Input: Generated initial proposal

[1483] Specific operation: The server saves "proposal_12345.docx" or "proposal_12345.pdf" to the specified directory and sends it as an attachment to the sales representative's email address.

[1484] Output: The proposal will be sent via email.

[1485] Step 5:

[1486] Terminal: Displays a notification to the user that the initial proposal has been completed and sent via email.

[1487] Input: Processing completion notification from the server

[1488] Specific action: The device displays the message "The initial proposal has been created and sent via email" to the user.

[1489] Output: The message is displayed to the user.

[1490] Inquiry response

[1491] Step 1:

[1492] User: The customer enters their question through the inquiry form and submits it.

[1493] Input: Question (Example: "What is the delivery date?")

[1494] Specific action: The customer enters a question into the inquiry form and clicks the "Submit" button.

[1495] Output: The input data is sent to the terminal.

[1496] Step 2:

[1497] Terminal: Converts the query data into JSON format and sends it to the server.

[1498] Input: Question entered by the customer

[1499] Specific action: The terminal generates data in JSON format with the format {"action": "inquiry", "message": "What is the delivery date?"} and sends a POST request to the server.

[1500] Output: Data in JSON format is sent to the server.

[1501] Step 3:

[1502] Server: Analyzes the inquiry and generates an appropriate response.

[1503] Input: Inquiry content in JSON format

[1504] Specific operation: The server analyzes the query using a natural language processing engine and retrieves relevant information from the database and FAQs.

[1505] Output: Analysis results and response text

[1506] Step 4:

[1507] Emotion Engine: Analyzes the emotion behind the inquiry and adjusts the response accordingly.

[1508] Input: Analysis results and query text

[1509] Specific operation: The emotion engine analyzes the inquiry, determines the emotion, and adjusts the response accordingly. For example, if there is a sense of urgency, it will adjust the tone to one that provides reassurance, such as, "Please rest assured. The deadline is December 1st, 2023."

[1510] Output: Adjusted response

[1511] Step 5:

[1512] Server: Generates a tailored response email and sends it to the customer.

[1513] Input: Adjusted response

[1514] Specific operation: The server converts the generated response into an email format and sends it to the customer's email address.

[1515] Output: A response email is sent to the customer.

[1516] Step 6:

[1517] Terminal: Notifies the user that a response to their inquiry has been sent.

[1518] Input: Processing completion notification from the server

[1519] Specific action: The device displays the message "An email has been sent" to the user.

[1520] Output: The message is displayed to the user.

[1521] (Application Example 2)

[1522] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[1523] While conventional sales support systems have proven somewhat effective in managing project information and generating quotations and proposals, they have a problem in that they struggle to manage customer information in real time at physical stores and to provide appropriate responses that reflect customer emotions. In particular, there is a lack of technical support for sales representatives at physical stores to respond quickly based on customer emotions. There is also a need for a means to properly manage and immediately send generated quotations, proposals, and responses to inquiries.

[1524] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1525] In this invention, the server includes means for inputting case information, means for storing case information in a database, means for generating quotations based on the stored case information, means for generating initial proposals based on the stored case information, means for responding to customer inquiries, means for sales representatives in physical stores to manage customer information in real time using terminals, sentiment analysis means for analyzing customer emotions through terminals and guiding appropriate responses, and means for transmitting generated quotations, proposals, and responses to inquiries to terminals. This streamlines sales activities in physical stores and enables appropriate responses based on customer emotions.

[1526] "Project information" refers to a series of data necessary for sales activities, such as information about the customer, customer requests, details of the products to be sold, and desired delivery dates.

[1527] A "database" is a system for organizing, storing, and managing information, allowing for quick retrieval and extraction of data as needed.

[1528] A "quotation" is a document that outlines the price and terms of a product or service offered to a customer.

[1529] A "primary proposal" is a document that summarizes the proposed content and plan presented to a customer in the initial stages of business negotiations.

[1530] An "inquiry" refers to a question or request from a customer, and it is necessary to provide an appropriate response to it.

[1531] A "system" is a collection of components in which multiple means or elements interact with each other to achieve a specific function.

[1532] A "physical store" refers to a physical commercial facility where customers can actually visit, view, and purchase products.

[1533] A "terminal" refers to an electronic device used by sales representatives or customers that is capable of data input, information display, and communication.

[1534] "Emotional analysis" refers to the technology of reading and analyzing a customer's emotions from their speech, written statements, and actions.

[1535] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate language that humans use on a daily basis.

[1536] This invention is a system that supports sales activities in physical stores and guides appropriate responses to customer emotions. This system is configured to streamline the management of case information, the generation of quotations and initial proposals, and the handling of inquiries.

[1537] System Overview

[1538] 1. Entering and saving project information

[1539] User: The sales representative uses a device (e.g., a tablet) to enter new customer information and clicks the submit button.

[1540] Terminal: The terminal formats the entered information into JSON format and sends it to the server as an HTTP request.

[1541] Server: The server parses the received data, saves it to a database (MySQL or PostgreSQL), and generates a new customer ID. It then sends the generated customer ID and a notification that the data has been saved to the terminal.

[1542] Terminal: Displays the customer ID and a message confirming that the data has been saved to the sales representative.

[1543] 2. Creating a quotation

[1544] User: The sales representative uses a terminal to enter the customer ID and submits a request for a quote.

[1545] Terminal: The terminal sends a quotation creation request to the server in a specified format (e.g., {"action": "create_estimate", "customer_id": "C12345"}).

[1546] Server: The server executes a database query based on the customer ID, embeds the retrieved information into a quotation template, and generates a quotation in PDF format. The generated quotation is saved to a specified directory and sent to the sales representative's email address.

[1547] Terminal: Notifies the sales representative that a quotation has been generated and sent via email.

[1548] 3. Preparation of the initial proposal

[1549] User: The sales representative uses a terminal to enter the customer ID and submits a request for the creation of an initial proposal.

[1550] Terminal: The terminal sends a proposal creation request to the server in a specified format (e.g., {"action": "create_proposal", "customer_id": "C12345"}).

[1551] Server: The server executes a database query based on the customer ID, embeds the retrieved information into a proposal template, and generates a preliminary proposal in Word or PDF format. The generated proposal is saved to a specified directory and sent to the sales representative's email address.

[1552] Terminal: Notifies the sales representative that the proposal has been generated and sent via email.

[1553] 4. Handling inquiries

[1554] User: The customer provides inquiry information to the store staff. The staff enters the inquiry via a terminal and clicks the send button.

[1555] Terminal: The terminal sends inquiry data to the server in a specified format (e.g., {"action": "inquiry", "message": "When will this product be in stock?"}).

[1556] Server: The server uses the Google Cloud Natural Language API to process the received inquiry using natural language processing, and retrieves relevant information from the database and FAQs based on the analyzed data. Next, it uses the IBM Watson Tone Analyzer API to analyze the customer's sentiment and adjusts the tone of the response based on the results.

[1557] Server: Sends the generated response email to the customer's email address and also notifies the staff.

[1558] Hardware and software

[1559] Hardware:

[1560] Device: iOS or Android tablet

[1561] Server: Standard web server (e.g., Apache, Nginx)

[1562] Database: MySQL or PostgreSQL

[1563] software:

[1564] Natural Language Processing Engine: Google Cloud Natural Language API

[1565] Sentiment analysis engine: IBM Watson Tone Analyzer API

[1566] Programming language: Python3

[1567] Web framework: Flask

[1568] Specific example

[1569] Entering and saving customer information

[1570] 1. A sales representative enters new customer information and sends it to the server. For example, if the customer's name is "Taro Yamada" and their product of interest is "smartphone".

[1571] 2. The terminal sends the input information to the server in JSON format, and a customer ID is generated.

[1572] 3. The terminal displays "Customer ID: C12345" and notifies the user that the information has been saved.

[1573] Estimate creation

[1574] 1. The sales representative requests the generation of a quotation using "Customer ID: C12345".

[1575] 2. A quotation will be generated and sent to the specified email address.

[1576] Examples of how to handle inquiries

[1577] 1. A customer asks, "When will this product be in stock?"

[1578] 2. The server analyzes the inquiry and retrieves relevant information from the FAQ.

[1579] 3. The emotion analysis engine determines that there is a feeling of "anxiety" and generates a response with an adjusted tone.

[1580] 4. The generated response email is sent to the customer.

[1581] Example of a prompt

[1582] "Please tell me when this product will be back in stock."

[1583] "Please explain the contents of the quotation in detail."

[1584] With the above configuration, the present invention is expected to streamline sales activities in physical stores, enable appropriate responses that respond to customer emotions, and improve customer satisfaction.

[1585] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1586] Step 1:

[1587] The user (sales representative) enters new customer information into the terminal and clicks the submit button. The input includes the customer's name, contact information, and products of interest. This information is formatted in JSON format.

[1588] Input: Customer's name, contact information, items of interest

[1589] Output: Customer information in JSON format (Example: {"name": "Taro Yamada", "contact": "xxx@example.com", "interest": "Smartphone"})

[1590] Step 2:

[1591] Customer information in JSON format sent from the terminal is sent to the server as an HTTP request. The server receives this request and parses the data.

[1592] Input: HTTP request (customer information in JSON format)

[1593] Output: Analyzed customer information

[1594] Step 3:

[1595] The server saves the analyzed customer information to a MySQL or PostgreSQL database. A new customer ID is generated and saved to the database.

[1596] Input: Analyzed customer information

[1597] Output: New customer ID (Example: C12345)

[1598] Step 4:

[1599] The server generates a new customer ID and a save completion notification, and sends it to the terminal. This notification includes the customer ID and a save completion message.

[1600] Input: New Customer ID

[1601] Output: Customer ID and save completion notification (JSON format)

[1602] Step 5:

[1603] The device displays notifications received by the sales representative, including the customer ID. The sales representative can then verify this customer ID.

[1604] Input: Customer ID and save completion notification

[1605] Output: Customer ID and save completion message displayed on the terminal screen

[1606] Step 6:

[1607] The user (sales representative) uses a terminal to enter the customer ID and submits a request to create a quotation. This request is formatted in JSON format and sent to the server as an HTTP request.

[1608] Input: Customer ID

[1609] Output: Estimate creation request (Example: {"action": "create_estimate", "customer_id": "C12345"})

[1610] Step 7:

[1611] The server receives the quotation creation request sent from the terminal and executes a database query to retrieve information based on the customer ID.

[1612] Input: Request to create a quote

[1613] Output: Information based on customer ID

[1614] Step 8:

[1615] The server embeds the acquired information into a quotation template and generates a quotation in PDF format. The generated quotation is then saved to the specified directory.

[1616] Input: Information based on customer ID

[1617] Output: Generated quotation (PDF format)

[1618] Step 9:

[1619] The server sends the generated quotation to the sales representative's email address. It also notifies the terminal that the quotation has been generated and sent.

[1620] Input: Generated quote

[1621] Output: Sent emails and notifications

[1622] Step 10:

[1623] The user (sales representative) uses a terminal to enter the customer ID and submits a request to create a preliminary proposal. This request is also formatted in JSON and sent to the server as an HTTP request.

[1624] Input: Customer ID

[1625] Output: Request to create a first proposal (Example: {"action": "create_proposal", "customer_id": "C12345"})

[1626] Step 11:

[1627] The server receives the initial proposal creation request sent from the terminal and executes a database query to retrieve information based on the customer ID.

[1628] Input: Request to create initial proposal

[1629] Output: Information based on customer ID

[1630] Step 12:

[1631] The server embeds the acquired information into a proposal template and generates a preliminary proposal in Word or PDF format. The generated proposal is then saved to the specified directory.

[1632] Input: Information based on customer ID

[1633] Output: Generated initial proposal (Word or PDF format)

[1634] Step 13:

[1635] The server sends the generated initial proposal to the sales representative's email address and notifies the terminal that the proposal has been generated and sent.

[1636] Input: Generated initial proposal

[1637] Output: Sent emails and notifications

[1638] Step 14:

[1639] The user (customer) provides inquiry information to the store staff. The staff member enters the inquiry via a terminal and clicks the send button. The inquiry data is formatted in JSON format and sent to the server.

[1640] Input: Inquiry Information

[1641] Output: Inquiry data in JSON format (Example: {"action": "inquiry", "message": "When will this product be in stock?"})

[1642] Step 15:

[1643] The server receives the inquiry data sent from the terminal and performs natural language processing using the Google Cloud Natural Language API. Based on the analyzed data, the server retrieves relevant information from the database and FAQs.

[1644] Input: Query data in JSON format

[1645] Output: Analyzed data and related information

[1646] Step 16:

[1647] The server uses the IBM Watson Tone Analyzer API to analyze customer sentiment and generates a response email with an adjusted tone based on the results.

[1648] Input: Analyzed data and related information

[1649] Output: Tone-adjusted response email

[1650] Step 17:

[1651] The server sends the generated response email to the customer's email address and also notifies the store staff.

[1652] Input: Tone-adjusted response email

[1653] Output: Sent emails and notifications

[1654] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1655] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1656] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[1657] [Third Embodiment]

[1658] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[1659] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1660] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1661] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[1662] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1663] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1664] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1665] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1666] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[1667] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1668] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1669] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[1670] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Its embodiments will be described in detail below.

[1671] System Overview

[1672] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries.

[1673] Program Processing Description

[1674] 1. Entering and saving project information

[1675] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into the form and clicks the submit button.

[1676] Terminal: Formats form input data into the required format and sends a request to the server.

[1677] Server: Receives case information, saves it to the database, generates a new case ID, and notifies the sales representative.

[1678] 2. Creating a quotation

[1679] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[1680] Terminal: Sends a quotation creation request to the server, including the project ID.

[1681] Server: Receives a request, retrieves the relevant project ID information from the database, and generates a quotation. The generated quotation is saved in PDF format and sent to the sales representative via email.

[1682] 3. Preparation of the initial proposal

[1683] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[1684] Terminal: Sends a proposal creation request to the server, including the project ID.

[1685] Server: Receives a request, retrieves the relevant case ID information from the database, and generates a preliminary proposal. The generated preliminary proposal is saved in PDF or Word format and sent to the sales representative via email.

[1686] 4. Handling inquiries

[1687] User: The customer enters their question into the inquiry form and clicks the submit button.

[1688] Terminal: Sends query data to the server.

[1689] Server: Analyzes the inquiry, retrieves relevant information from the database and FAQs, and generates an appropriate response using a natural language processing engine. The generated response is sent to the customer as an automated email.

[1690] Specific example

[1691] Specific examples of entering and saving project information

[1692] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being Company A, the proposed product being Product B, and the desired delivery date being December 1, 2023.

[1693] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[1694] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[1695] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[1696] Examples of creating a quotation

[1697] 1. User: A sales representative requests a quote for case ID 12345.

[1698] 2. Terminal: The request data is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[1699] 3. Server: Execute a database query to retrieve information for case ID 12345.

[1700] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[1701] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. This significantly improves the efficiency of sales activities, saving considerable time and effort.

[1702] The following describes the processing flow.

[1703] Entering and saving project information

[1704] Step 1:

[1705] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into a dedicated form and clicks the submit button.

[1706] Step 2:

[1707] Terminal: Formats user input into a data format (e.g., JSON format) and sends it to the server as an HTTP request.

[1708] Step 3:

[1709] Server: The server analyzes the received data and performs validation as case information (e.g., checking required fields, verifying data types).

[1710] Step 4:

[1711] Server: Saves case information that has successfully been validated to the database. Generates a new case ID at the same time as saving.

[1712] Step 5:

[1713] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[1714] Step 6:

[1715] Terminal: Displays the case ID and a message confirming that the case has been saved to the sales representative.

[1716] Request and generation of a quotation

[1717] Step 1:

[1718] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[1719] Step 2:

[1720] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[1721] Step 3:

[1722] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[1723] Step 4:

[1724] Server: Embeds the acquired data into a quotation template and generates a quotation in PDF format.

[1725] Step 5:

[1726] Server: Saves the generated quotation to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[1727] Step 6:

[1728] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[1729] Request and generation of the initial proposal.

[1730] Step 1:

[1731] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[1732] Step 2:

[1733] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[1734] Step 3:

[1735] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[1736] Step 4:

[1737] Server: Embeds the acquired data into a proposal template and generates a preliminary proposal in Word or PDF format.

[1738] Step 5:

[1739] Server: Saves the generated initial proposal to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[1740] Step 6:

[1741] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[1742] Inquiry handling process

[1743] Step 1:

[1744] User: The customer enters their question into the inquiry form and clicks the submit button.

[1745] Step 2:

[1746] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[1747] Step 3:

[1748] Server: Passes the received query content to the natural language processing engine for analysis.

[1749] Step 4:

[1750] Server: Executes database queries based on the inquiry content and generates appropriate answers from relevant information and FAQs.

[1751] Step 5:

[1752] Server: The generated response is sent to the customer's email address as an automated reply email.

[1753] Step 6:

[1754] Terminal: Notifies the customer that a response to their inquiry has been sent.

[1755] These processing steps streamline key tasks related to sales activities, enabling them to be executed quickly and accurately.

[1756] (Example 1)

[1757] Next, we will describe Example 1. 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."

[1758] In sales operations, a significant amount of time and effort is spent on managing project information, creating quotations and proposals, and responding to customer inquiries. Performing these tasks manually is particularly burdensome and reduces operational efficiency. Therefore, the present invention aims to provide a system that streamlines these sales operations and saves time and effort.

[1759] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1760] In this invention, the server includes means for formatting case information into a data format and transmitting it to the server; means for the server to store the received case information in a database and generate a new case ID; means for generating a quotation based on the case information and sending it to a sales representative; and means for generating a preliminary proposal based on the case information and sending it to a sales representative. This enables more efficient sales operations and faster customer response.

[1761] "Project information" refers to all information related to sales activities, such as customer information, proposed product information, and desired delivery dates.

[1762] A "data format" is a standardized format used for sending, receiving, and storing data, and JSON format is one example.

[1763] A "server" refers to a computer system used to process, store, and transmit data over a network.

[1764] A "database" refers to a system for efficiently storing and retrieving structured data.

[1765] "Case ID" refers to an identifier generated to uniquely identify each case.

[1766] A "quotation" refers to a document that clearly specifies the price and conditions of the goods or services offered to a customer.

[1767] A "first proposal" is a document that outlines the content of the initial proposal and serves to show the client the proposed content and conditions.

[1768] "Inquiry details" refers to information that contains questions or requests from customers.

[1769] A "natural language processing engine" refers to computer technology used to understand and generate human language.

[1770] An "automated response email" refers to an email that is automatically generated and sent by a system based on specific content.

[1771] A "template engine" refers to a software tool that embeds data into a template to automatically generate the final document or file.

[1772] A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to learn patterns from data and generate new information.

[1773] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries.

[1774] System Overview

[1775] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries.

[1776] Entering and saving project information

[1777] 1. User: The sales representative enters customer information (e.g., customer name, contact information), proposed products (e.g., product name, quantity), desired delivery date, and other case information into a form on the terminal.

[1778] 2. Terminal: Formats the input data into a specified format (e.g., JSON format).

[1779] 3. Terminal: Sends the formatted data to the server.

[1780] 4. Server: Saves the received data to the database and generates a new case ID.

[1781] 5. Server: Notifies the user's terminal of the case ID and a message indicating that saving is complete.

[1782] Estimate creation

[1783] 1. User: Enter the project ID on the quotation creation request screen and click the "Create Quotation" button.

[1784] 2. Terminal: Sends a quotation creation request to the server, including the project ID.

[1785] 3. Server: Retrieves information for the relevant case ID from the database.

[1786] 4. Server: Generates a quotation based on the acquired information. The generated quotation is saved in PDF format.

[1787] 5. Server: Send the generated PDF file to the sales representative via email.

[1788] Preparation of the initial proposal

[1789] 1. User: Enter the project ID on the initial proposal creation request screen and click the "Create Proposal" button.

[1790] 2. Terminal: Send a proposal creation request to the server, including the project ID.

[1791] 3. Server: Retrieves information for the relevant case ID from the database.

[1792] 4. Server: Generates a preliminary proposal based on the acquired information. The generated proposal is saved in PDF or Word format.

[1793] 5. Server: Send the generated files to the sales representative via email.

[1794] Inquiry response

[1795] 1. User: The customer enters their question into the inquiry form and clicks the submit button.

[1796] 2. Terminal: Sends the inquiry data to the server.

[1797] 3. Server: Analyzes the inquiry content and retrieves relevant information from the database and FAQs.

[1798] 4. Server: Generates appropriate answers using a natural language processing engine (e.g., a generative AI model).

[1799] 5. Server: Sends the generated response to the customer as an automated reply email.

[1800] Hardware and software to be used

[1801] Server: A computer system that performs data processing and storage.

[1802] Database: A system for efficiently storing and retrieving project information (e.g., MySQL, PostgreSQL)

[1803] Template engine: Software used to embed data into templates and generate documents (e.g., JasperReports, Apache POI)

[1804] Natural Language Processing Engine: Software that uses generative AI models to analyze dialogues and generate responses (e.g., GPT-3)

[1805] Specific example

[1806] Specific examples of entering and saving project information

[1807] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being Company A, the proposed product being Product B, and the desired delivery date being December 1, 2023.

[1808] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[1809] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[1810] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[1811] Examples of creating a quotation

[1812] 1. User: A sales representative requests a quote for case ID 12345.

[1813] 2. Terminal: The request data is sent to the server with a prompt message such as "Please enter the information required to create the quotation."

[1814] 3. Server: Execute a database query to retrieve information for case ID 12345.

[1815] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[1816] Example of a prompt

[1817] "Please enter the information required to create the quotation."

[1818] "As an example of how to handle inquiries, please generate a list of frequently asked questions and their answers."

[1819] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. It significantly improves the efficiency of sales activities, saving considerable time and effort.

[1820] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1821] Entering and saving project information

[1822] Step 1:

[1823] The user enters project information (customer information, proposed products, desired delivery date, etc.) into a form on their device.

[1824] Input: Customer information, proposed products, desired delivery date, and other project information.

[1825] Specific operation: The user enters information into each field of the form using the device's keyboard and clicks the submit button.

[1826] Output: Input case information data

[1827] Step 2:

[1828] The terminal formats the entered case information data into a specified format (e.g., JSON format).

[1829] Input: Project information data entered by the user

[1830] Specific operation: The terminal automatically converts the input data into JSON format.

[1831] Output: Formatted case information data

[1832] Step 3:

[1833] The terminal sends the formatted case information data to the server.

[1834] Input: Project information data formatted in JSON format

[1835] Specific operation: The terminal sends an HTTP POST request and sends data to the server.

[1836] Output: Data transmission to server complete.

[1837] Step 4:

[1838] The server receives the case information data, saves it to the database, and generates a new case ID.

[1839] Input: Case information data sent from the terminal

[1840] Specific operation: The server executes an INSERT query into the database and saves the case information. A new case ID is generated.

[1841] Output: New case ID generated and saved to database.

[1842] Step 5:

[1843] The server notifies the user's terminal of the case ID and a message indicating that saving is complete.

[1844] Input: New case ID, Saved status

[1845] Specific operation: The server returns the case ID and message to the terminal as an HTTP response. The terminal displays the notification message.

[1846] Output: Notification display of case ID and save completion message.

[1847] Estimate creation

[1848] Step 1:

[1849] The user enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[1850] Input: Case ID

[1851] Specific action: The sales representative enters the deal ID and clicks the button.

[1852] Output: Request to create a quotation

[1853] Step 2:

[1854] The terminal sends a request to the server to create a quotation, including the project ID.

[1855] Input: Request to create a quote

[1856] Specific operation: The terminal sends an HTTP POST request and sends the request data to the server.

[1857] Output: Request sent to server successfully

[1858] Step 3:

[1859] The server retrieves the relevant case ID information from the database.

[1860] Input: Case ID

[1861] Specific operation: The server executes a SELECT query and retrieves data corresponding to the case ID.

[1862] Output: Case information retrieved from the database

[1863] Step 4:

[1864] The server generates a quotation based on the information it has acquired. The generated quotation is saved in PDF format.

[1865] Input: Case information retrieved from the database

[1866] Specific operation: The server uses a template engine (e.g., JasperReports) to generate a quotation and create a PDF file.

[1867] Output: Generated PDF quotation

[1868] Step 5:

[1869] The server sends the generated PDF file to the sales representative via email.

[1870] Input: Quotation in PDF format

[1871] Specific operation: The server sends the generated PDF via SMTP server as an email attachment.

[1872] Output: Email sent to sales representative completed.

[1873] Preparation of the initial proposal

[1874] Step 1:

[1875] The user enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[1876] Input: Case ID

[1877] Specific action: The sales representative enters the deal ID and clicks the button.

[1878] Output: Proposal creation request

[1879] Step 2:

[1880] The terminal sends a proposal creation request to the server, including the project ID.

[1881] Input: Proposal creation request

[1882] Specific operation: The terminal sends an HTTP POST request and sends the request data to the server.

[1883] Output: Request sent to server successfully

[1884] Step 3:

[1885] The server retrieves the relevant case ID information from the database.

[1886] Input: Case ID

[1887] Specific operation: The server executes a SELECT query and retrieves data corresponding to the case ID.

[1888] Output: Case information retrieved from the database

[1889] Step 4:

[1890] The server generates a preliminary proposal based on the information it acquires. The generated proposal is saved in PDF or Word format.

[1891] Input: Case information retrieved from the database

[1892] Specific operation: The server uses a template engine (e.g., Apache POI) to generate a proposal and create a PDF or Word file.

[1893] Output: Proposal in generated PDF or Word format

[1894] Step 5:

[1895] The server sends the generated files to the sales representative via email.

[1896] Input: Proposal in PDF or Word format

[1897] Specific operation: The server sends the file generated via the SMTP server as an email attachment.

[1898] Output: Email sent to sales representative completed.

[1899] Inquiry response

[1900] Step 1:

[1901] The user enters a question into the inquiry form and clicks the submit button.

[1902] Input: Question content

[1903] Specific action: The customer enters a question and clicks the submit button.

[1904] Output: Input query content

[1905] Step 2:

[1906] The terminal sends the inquiry data to the server.

[1907] Input: Inquiry details entered

[1908] Specific operation: The terminal sends an HTTP POST request and sends the query data to the server.

[1909] Output: Data transmission to server complete.

[1910] Step 3:

[1911] The server analyzes the query and retrieves relevant information from the database and FAQs.

[1912] Input: Inquiry content sent from the device

[1913] Specific operation: The server searches the database and FAQs based on the content of the inquiry it receives and retrieves relevant information.

[1914] Output: Retrieve relevant query information

[1915] Step 4:

[1916] The server uses a natural language processing engine (e.g., a generative AI model) to generate an appropriate response.

[1917] Input: Related inquiry information

[1918] Specific operation: The generative AI model generates a response based on the information it has acquired.

[1919] Output: Generated response

[1920] Step 5:

[1921] The server will send the generated response to the customer as an automated email.

[1922] Input: Generated response

[1923] Specific operation: The server sends an email containing the response to the customer via the SMTP server.

[1924] Output: Automated response email sent to customer successfully.

[1925] (Application Example 1)

[1926] Next, we will explain Application Example 1. In the following explanation, 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."

[1927] Traditional sales support systems required sales representatives to individually generate quotations and proposals and handle customer inquiries, resulting in a heavy burden on them and making real-time responses difficult. Furthermore, providing appropriate and immediate responses during customer interactions was challenging, hindering improvements in customer satisfaction.

[1928] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1929] In this invention, the server includes means for inputting case information, means for storing case information in a database, means for generating quotations based on the stored case information, means for generating initial proposals based on the stored case information, means for responding to customer inquiries, means for analyzing the input inquiry content, generating appropriate responses, and transmitting them to a display device, means for generating quotations and initial proposals in real time and displaying them on a display device, and means for recording and automatically analyzing customer interactions. This enables sales representatives to quickly generate quotations and proposals and provide immediate and appropriate responses to inquiries while interacting with customers in a virtual store. This is expected to improve the work efficiency of sales representatives and enhance customer satisfaction.

[1930] "Project information" refers to detailed information related to transactions with customers, including customer information, details of proposed products, and desired delivery dates.

[1931] A "database" is an electronic information storage system used to store and efficiently manage project information, quotations, proposals, and inquiry details.

[1932] A "quotation" is an official document that lists the price, quantity, delivery date, and other details of the goods or services to be provided in a transaction.

[1933] A "first proposal" is a document that outlines the initial proposal to a customer, primarily intended to explain the features and benefits of the proposed product.

[1934] "Handling inquiries" refers to the act of providing appropriate answers and information to customer questions and requests.

[1935] A "display device" is an electronic device used to visually display information, such as smart glasses or mobile terminals.

[1936] "Input method" refers to the method or device for inputting case information, inquiry details, etc., into the system, and includes keyboards, voice input, etc.

[1937] "Storage means" refers to methods or devices for saving entered case information and other data to a database.

[1938] "Generation means" refers to methods or devices that automatically create estimates and proposals based on stored project information.

[1939] "Natural language processing" is an artificial intelligence technology that analyzes natural language data, such as input text and speech, and generates appropriate responses.

[1940] "Real-time" refers to a state where processing and responses occur instantly without delay.

[1941] "Interaction" refers to the mutual interaction between a sales representative and a customer, and includes dialogue, question-and-answer sessions, and so on.

[1942] "Automated response" refers to a function in which a system automatically generates answers to customer inquiries using natural language processing technology.

[1943] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries in real time. The embodiments for carrying out this invention are described in detail below.

[1944] System Overview

[1945] This system consists of a server, terminals, and users (sales representatives and customers). Sales representatives wear smart glasses to interact with customers. Terminals function as smart glasses, mobile devices, and display devices, and communicate with the server. The server stores case information in a database and performs various processes.

[1946] Program Processing Description

[1947] Hardware and Software

[1948] Hardware: Smart glasses, mobile devices, servers.

[1949] Software: Python scripts, NaturalLanguageProcessing library, EstimateGenerator, ProposalGenerator, SmartGlassesAPI, ServerAPI.

[1950] Data processing and calculation

[1951] The main processes are described below.

[1952] 1. Inquiry handling:

[1953] The user (customer) voice-inputs a question to the smart glasses. For example, they might ask, "What is the price of this product?"

[1954] The device (smart glasses) converts voice input into text data and sends it to the server.

[1955] The server's natural language processing engine analyzes the input text and generates an appropriate response from the relevant database. For example, "The price of this product is XX yen."

[1956] The generated response is displayed on the device's (smart glasses) screen and provided to the user immediately.

[1957] 2. Prepare a quotation:

[1958] The user (sales representative) enters the case ID and sends a request to create a quote to the server via smart glasses.

[1959] The device (smart glasses) sends the request to the server in a predetermined format.

[1960] The server retrieves the relevant project ID information from the database and generates an estimate in PDF format using the EstimateGenerator engine.

[1961] The generated quotation is displayed on the terminal (smart glasses) and provided to the user.

[1962] 3. Preparation of the initial proposal:

[1963] The user (sales representative) enters the case ID and sends a request to create a preliminary proposal to the server via smart glasses.

[1964] The device (smart glasses) sends the request to the server in a predetermined format.

[1965] The server retrieves the relevant project ID information from the database and uses the ProposalGenerator engine to generate a preliminary proposal in PDF or Word format.

[1966] The generated initial proposal is displayed on the device (smart glasses) and provided to the user.

[1967] Specific example

[1968] Specific examples are given below.

[1969] Suppose a sales representative contacts customer A in a virtual store via smart glasses and receives the question, "What is the price of this product?" In this case, the natural language processing engine immediately responds, and the smart glasses display shows, "The price of this product is XX yen."

[1970] If customer A requests a quote on the spot, the sales representative enters the case ID and sends a quote creation request to the server. The server generates the quote and displays it on the smart glasses.

[1971] Furthermore, if a customer requests a detailed proposal, the sales representative can submit a request for a preliminary proposal, the server will generate the proposal, and this will also be displayed on the smart glasses.

[1972] Example of a prompt

[1973] customer_query = "What is the price of this product?"

[1974] response = NaturalLanguageProcessing.generate_response(customer_query)

[1975] print(response)

[1976] This system will enable sales representatives to handle customer inquiries more efficiently in virtual stores, generating quotes and proposals and providing real-time responses to inquiries. This will significantly improve the efficiency of sales activities and is expected to increase customer satisfaction.

[1977] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1978] Step 1:

[1979] The user (customer) inputs a question to the smart glasses using voice.

[1980] Input: Customer voice question: "What is the price of this product?"

[1981] Output: Audio data

[1982] Specific operation: The microphone in the smart glasses captures the sound and processes it as audio data.

[1983] Step 2:

[1984] The device (smart glasses) converts the audio data into text data and sends it to the server.

[1985] Input: Audio data

[1986] Output: Text data "What is the price of this product?"

[1987] Specific operation: Use speech recognition software to convert speech to text and send the text data to the server.

[1988] Step 3:

[1989] The server's natural language processing engine analyzes text data and generates appropriate responses from relevant databases.

[1990] Input: Text data "What is the price of this product?"

[1991] Output: Response text "The price of this product is XX yen."

[1992] Specific operation: A natural language processing engine analyzes the text, retrieves price information from a database, and generates the response text.

[1993] Step 4:

[1994] The server sends the generated response text to the device (smart glasses).

[1995] Input: Response text "The price of this product is XX yen."

[1996] Output: Response text data

[1997] Specific action: Send the generated text data to the smart glasses.

[1998] Step 5:

[1999] The device (smart glasses) displays the response text on the display device.

[2000] Input: Response text data

[2001] Output: Display on screen: "The price of this product is ¥XX."

[2002] Specific operation: Visually display the response text data on the screen.

[2003] Step 6:

[2004] The user (sales representative) enters the case ID and sends a request for quotation creation to the server via smart glasses.

[2005] Input: Case ID "12345"

[2006] Output: Quotation creation request data

[2007] Specific operation: Use the smart glasses' input device to enter the case ID and send a request to the server.

[2008] Step 7:

[2009] The server retrieves project information from the database based on the project ID and generates a PDF-formatted estimate using the EstimateGenerator engine.

[2010] Input: Case ID "12345"

[2011] Output: Quotation in PDF format "Quotation_12345.pdf"

[2012] Specific operation: Execute a database query to retrieve project information, embed that information into a template, and generate a quotation.

[2013] Step 8:

[2014] The server sends the generated quote to the smart glasses.

[2015] Input: PDF quotation "Quotation_12345.pdf"

[2016] Output: Quotation data

[2017] Specific action: Send the generated PDF data to the smart glasses.

[2018] Step 9:

[2019] The terminal (smart glasses) displays the generated quotation on the display device.

[2020] Input: Quotation data

[2021] Output: Estimate displayed on the screen

[2022] Specific operation: Performs rendering processing to display PDF data on the screen.

[2023] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[2024] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Furthermore, by incorporating an emotion engine that recognizes user emotions, it aims to improve the quality of inquiry responses and enhance the user experience. Its embodiments will be described in detail below.

[2025] System Overview

[2026] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries. Furthermore, an emotion engine analyzes the user's emotions and generates appropriate responses.

[2027] Program Processing Description

[2028] 1. Entering and saving project information

[2029] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into the form and clicks the submit button.

[2030] Terminal: Formats user input into a data format (e.g., JSON format) and sends it to the server as an HTTP request.

[2031] Server: The server analyzes the received data and validates it as case information. Successfully validated case information is saved to the database, and a new case ID is generated.

[2032] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[2033] Terminal: Displays the case ID and a message confirming that the case has been saved to the sales representative.

[2034] 2. Creating a quotation

[2035] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[2036] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[2037] Server: Receives a request, executes a database query based on the project ID, and retrieves relevant project information. Embeds the retrieved data into a quotation template and generates a quotation in PDF format.

[2038] Server: Saves the generated quotation to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[2039] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[2040] 3. Preparation of the initial proposal

[2041] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[2042] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[2043] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information. Embeds the retrieved data into a proposal template and generates a preliminary proposal in Word or PDF format.

[2044] Server: Saves the generated initial proposal to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[2045] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[2046] 4. Handling inquiries

[2047] User: The customer enters their question into the inquiry form and clicks the submit button.

[2048] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[2049] Server: Passes the received query content to the natural language processing engine for analysis. Executes database queries based on the query content and generates appropriate answers from relevant information and FAQs.

[2050] Emotional Engine: Analyzes the customer's emotions when they make an inquiry and adjusts the response based on those emotions.

[2051] Server: The generated response is sent to the customer's email address as an automated reply email.

[2052] Terminal: Notifies the customer that a response to their inquiry has been sent.

[2053] Specific example

[2054] Specific examples of entering and saving project information

[2055] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being company A, the proposed product being product B, and the desired delivery date being December 1, 2023.

[2056] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[2057] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[2058] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[2059] Examples of creating a quotation

[2060] 1. User: A sales representative requests a quote for case ID 12345.

[2061] 2. Terminal: The request data is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[2062] 3. Server: Execute a database query to retrieve information for case ID 12345.

[2063] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[2064] Examples of how to handle inquiries

[2065] 1. User: The customer asks, "When is the delivery date?"

[2066] 2. Terminal: Sends the inquiry content to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[2067] 3. Server: Analyzes the inquiry content using a natural language processing engine and retrieves relevant information from the database and FAQs.

[2068] 4. Emotional Engine: Analyzes the customer's emotions and determines if they are experiencing "anxiety."

[2069] 5. Server: Based on the results of the emotion engine, the server adjusts and generates the response email to have a more reassuring tone.

[2070] 6. Server: Sends the generated response email to the customer's email address.

[2071] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. Furthermore, the introduction of an emotion engine allows for appropriate responses tailored to customer emotions, thereby improving customer satisfaction.

[2072] The following describes the processing flow.

[2073] Entering and saving project information

[2074] Step 1:

[2075] User: The sales representative enters the project information (customer name, contact information, proposed products, desired delivery date, etc.) into a dedicated form and clicks the submit button.

[2076] Step 2:

[2077] Terminal: Converts case information into a specified format (e.g., JSON format) and sends it as an HTTP request.

[2078] Step 3:

[2079] Server: Analyzes the received data and validates the case information (checking required fields, data type checks, etc.).

[2080] Step 4:

[2081] Server: Saves case information that has successfully been validated to the database. Generates a new case ID at the same time as saving.

[2082] Step 5:

[2083] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[2084] Step 6:

[2085] Terminal: The sales representative's screen will display the case ID and a message indicating that the case has been saved.

[2086] Request and generation of a quotation

[2087] Step 1:

[2088] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[2089] Step 2:

[2090] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[2091] Step 3:

[2092] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[2093] Step 4:

[2094] Server: Embeds the acquired data into a quotation template and generates a quotation in PDF format.

[2095] Step 5:

[2096] Server: Saves the generated quotation to the specified directory and sends it as an attachment to the sales representative's email address.

[2097] Step 6:

[2098] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[2099] Request and generation of the initial proposal.

[2100] Step 1:

[2101] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[2102] Step 2:

[2103] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[2104] Step 3:

[2105] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[2106] Step 4:

[2107] Server: Embeds the acquired data into a proposal template and generates a preliminary proposal in Word or PDF format.

[2108] Step 5:

[2109] Server: Saves the generated initial proposal to the specified directory and sends it as an attachment to the sales representative's email address.

[2110] Step 6:

[2111] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[2112] Inquiry handling process and use of emotion engine

[2113] Step 1:

[2114] User: The customer enters their question into the inquiry form and clicks the submit button.

[2115] Step 2:

[2116] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[2117] Step 3:

[2118] Server: Passes the received query content to the natural language processing engine for analysis.

[2119] Step 4:

[2120] Server: Executes database queries based on the inquiry content and generates appropriate answers from relevant information and FAQs.

[2121] Step 5:

[2122] Emotion Engine: Analyzes customer inquiries to understand their emotions and obtain emotional data (e.g., "anxiety" is perceived).

[2123] Step 6:

[2124] Server: Based on the results of the emotion engine, the server adjusts and generates the content of the response email. In this case, it adjusts the content to be reassuring in response to the emotion of "anxiety."

[2125] Step 7:

[2126] Server: Automatically sends the generated response email to the customer's email address.

[2127] Step 8:

[2128] Terminal: Notifies the customer that a response to their inquiry has been sent.

[2129] Specific example

[2130] Specific examples of entering and saving project information

[2131] Step 1:

[2132] User: The sales representative enters new project information (Customer name: Company A, Proposed product: Product B, Desired delivery date: December 1, 2023) and clicks the submit button.

[2133] Step 2:

[2134] Terminal: Converts the entered information into JSON format and sends it to the server as an HTTP request.

[2135] Step 3:

[2136] Server: Receives case information and performs validation (e.g., checks input fields, checks data types).

[2137] Step 4:

[2138] Server: Saves case information that passed validation to the database and generates case ID 12345.

[2139] Step 5:

[2140] Server: Sends case ID 12345 and a save completion notification to the sales representative's terminal.

[2141] Step 6:

[2142] Terminal: Displays case ID 12345 and a message indicating that saving is complete to the sales representative.

[2143] Examples of creating a quotation

[2144] Step 1:

[2145] User: A sales representative requests a quote for case ID 12345.

[2146] Step 2:

[2147] The terminal sends request data to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[2148] Step 3:

[2149] Server: Executes a database query to retrieve information for case ID 12345.

[2150] Step 4:

[2151] Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format.

[2152] Step 5:

[2153] Server: Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[2154] Step 6:

[2155] Terminal: Notifies the sales representative that a quotation has been generated and sent via email.

[2156] Examples of how to handle inquiries

[2157] Step 1:

[2158] User: A customer asks, "When is the delivery date?"

[2159] Step 2:

[2160] Terminal: Sends the inquiry to the server in the format {"action": "inquiry", "message": "What is the delivery date?"}.

[2161] Step 3:

[2162] Server: Analyzes the inquiry content using a natural language processing engine and retrieves relevant information from the database and FAQs.

[2163] Step 4:

[2164] Emotional Engine: Analyzes customer inquiries to determine if they are experiencing "anxiety" or "anxiety."

[2165] Step 5:

[2166] Server: Based on the results of the emotion engine, it adjusts and generates a response email with reassuring content.

[2167] Step 6:

[2168] Server: Sends the generated response email to the customer's email address.

[2169] Step 7:

[2170] Terminal: Notifies the customer that a response to their inquiry has been sent.

[2171] These processing steps improve the efficiency of sales activities and enable appropriate responses that respond to customer emotions. This leads to increased customer satisfaction and a significant improvement in the quality and efficiency of operations.

[2172] (Example 2)

[2173] Next, we will describe Example 2. 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."

[2174] To streamline project information management and sales support, it is necessary to automate a series of tasks, from inputting project information to generating quotations and proposals, and even responding to customer inquiries. Furthermore, in handling inquiries, it is crucial to respond in a way that considers the customer's feelings, thereby improving customer satisfaction. However, conventional systems do not integrate these functions and require manual operation, which limits the efficiency of operations and the improvement of customer satisfaction.

[2175] The identification processing performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for organizing case information into a data format and transmitting it to the server; means for storing case information in a database and generating a new case ID; means for generating quotations and initial proposals based on the stored case information and sending them to terminals and via email; and means for analyzing customer inquiries, generating appropriate automated responses using an emotion engine, and transmitting them. This enables efficient management of case information, automatic generation of quotations and proposals, and inquiry handling that takes user emotions into consideration.

[2176] "Project information" refers to various data related to a project, such as customer information, proposed products, and desired delivery dates.

[2177] A "data format" is a format that represents information in a structured way, and examples include JSON and XML.

[2178] A "server" is a computer system that processes data and provides services in response to requests from clients.

[2179] A "database" is a system for efficiently managing and manipulating stored data, and it is a place where project information and other related data are stored.

[2180] A "New Case ID" is an identifier used to uniquely identify case information newly saved in the database.

[2181] A "quotation" is a document that lists the prices and conditions of goods related to a project, and is intended to be presented to the customer.

[2182] A "first-round proposal" is a document that summarizes the basic proposal content and conditions, and is used when submitting an initial proposal.

[2183] A "terminal" is a device used by users to input information or view output, and includes personal computers and smartphones.

[2184] "Email" refers to electronic messages sent and received via the internet, and it is possible to attach documents and files to them.

[2185] "Inquiry" refers to questions or requests that customers submit through the system.

[2186] An "emotion engine" is a system that analyzes a user's emotions and generates the optimal response based on those emotions.

[2187] "Natural language processing" is a technology that enables computers to understand, analyze, and generate human language.

[2188] An "automated response" is a response message that a system generates and sends to a user based on pre-programmed rules or algorithms.

[2189] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Furthermore, by incorporating an emotion engine that recognizes user emotions, it aims to improve the quality of inquiry responses and enhance the user experience. Its embodiments will be described in detail below.

[2190] System Overview

[2191] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries. Furthermore, an emotion engine analyzes the user's emotions and generates appropriate responses.

[2192] Entering and saving project information

[2193] Specific example

[2194] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being company A, the proposed product being product B, and the desired delivery date being December 1, 2023.

[2195] 2. Terminal: The terminal formats the entered information into a predetermined data format such as JSON and sends it to the server as an HTTP request.

[2196] 3. Server: Analyzes the received data and validates the input content. Successfully validated case information is saved to the database, and a new case ID (e.g., Case ID 12345) is generated.

[2197] 4. Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[2198] 5. Terminal: Display the case ID 12345 and a message indicating that the case has been saved to the sales representative.

[2199] Estimate creation

[2200] Specific example

[2201] 1. User: A sales representative requests a quote for case ID 12345.

[2202] 2. Terminal: A request to create an estimate is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[2203] 3. Server: Execute a database query to retrieve information for case ID 12345.

[2204] 4. Server: The server embeds the acquired information into the quotation template and generates a quotation in PDF format (e.g., "Quotation_12345.pdf").

[2205] 5. Server: Saves the generated quotation to the specified directory in the file system and sends it as an attachment to the sales representative's email address.

[2206] 6. Terminal: Notify the sales representative that the quotation has been created and sent via email.

[2207] Preparation of the initial proposal

[2208] Specific example

[2209] 1. User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[2210] 2. Terminal: Send the proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[2211] 3. Server: Receives the request, executes a database query based on the case ID, and retrieves the relevant case information.

[2212] 4. Server: The server embeds the acquired data into the proposal template and generates a preliminary proposal in Word or PDF format (e.g., "proposal_12345.docx" or "proposal_12345.pdf").

[2213] 5. Server: Saves the generated initial proposal to the specified directory and sends it as an attachment to the sales representative's email address.

[2214] 6. Terminal: Notify the sales representative that the initial proposal has been completed and sent via email.

[2215] Inquiry response

[2216] Specific example

[2217] 1. User: The customer asks, "When is the delivery date?"

[2218] 2. Terminal: Sends the inquiry content to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[2219] 3. Server: Analyzes the inquiry content using a natural language processing engine and retrieves relevant information from the database and FAQs.

[2220] 4. Emotion Engine: Analyzes the customer's emotions from the inquiry and adjusts the response based on those emotions. For example, if "anxiety" is perceived, the tone will be adjusted to "Please rest assured. The delivery date is December 1st, 2023."

[2221] 5. Server: Generates a tailored response as an automated reply email and sends it to the customer's email address.

[2222] 6. Terminal: Notifies the customer that a response to their inquiry has been sent.

[2223] Examples of prompts for generative AI models

[2224] "Please enter the project information: Customer name, proposed product, desired delivery date."

[2225] "Please create a quotation for project ID 12345."

[2226] "Please create a preliminary proposal for project ID 12345."

[2227] "What is the deadline?"

[2228] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. Furthermore, the introduction of an emotion engine allows for appropriate responses tailored to customer emotions, leading to improved customer satisfaction.

[2229] The flow of the specific processing in Example 2 will be explained using Figure 13.

[2230] Entering and saving project information

[2231] Step 1:

[2232] User: The sales representative enters the case information into the web form on their terminal and clicks the submit button.

[2233] Input: Customer name (e.g., Company A), Proposed product (e.g., Product B), Desired delivery date (e.g., 2023-12-01)

[2234] Specific action: The sales representative enters the required case information into the web form and clicks the "Submit" button.

[2235] Output: The input data is sent to the terminal.

[2236] Step 2:

[2237] Terminal: Converts input data to JSON format and sends it to the server as an HTTP request.

[2238] Input: Project information entered by the user

[2239] Specific operation: The terminal converts the input data into a JSON format {"customer": "Company A", "product": "Product B", "delivery_date": "2023-12-01"} and sends a POST request to the server.

[2240] Output: Data in JSON format is sent to the server.

[2241] Step 3:

[2242] Server: Analyzes incoming data, performs validation, and saves it to the database.

[2243] Input: Project information data in JSON format

[2244] Specific operation: The server analyzes the received data and validates the input. After successful validation, it saves the data to the database and generates a new case ID (e.g., case ID 12345).

[2245] Output: A new case ID and save status are generated.

[2246] Step 4:

[2247] Server: Generates a response including the case ID and a save completion notification, and sends it to the terminal.

[2248] Input: Case ID 12345 and saved status

[2249] Specific action: The server generates a response {"case_id": "12345", "status": "Save complete"} and sends it to the terminal.

[2250] Output: Response data is sent to the terminal.

[2251] Step 5:

[2252] Terminal: Displays the case ID and a save completion message to the user.

[2253] Input: Response data from the server

[2254] Specific action: The terminal displays the message "Case ID 12345 has been saved" to the user.

[2255] Output: The message is displayed to the user.

[2256] Estimate creation

[2257] Step 1:

[2258] User: The sales representative enters the project ID and requests the creation of a quote.

[2259] Input: Case ID (Example: 12345)

[2260] Specific action: The sales representative enters the project ID and clicks the "Create Quote" button.

[2261] Output: The request data is sent to the terminal.

[2262] Step 2:

[2263] Terminal: Converts the quotation creation request data into JSON format and sends it to the server.

[2264] Input: Request data from sales representative

[2265] Specific action: The terminal sends a JSON request to the server with the format {"action": "create_estimate", "case_id": "12345"}.

[2266] Output: Request data in JSON format is sent to the server.

[2267] Step 3:

[2268] Server: Retrieves project information from the database and generates a quotation.

[2269] Input: Case ID (Example: 12345)

[2270] Specific operation: The server executes a database query to retrieve information for case ID 12345, embeds the retrieved data into the estimate template, and generates "Estimate_12345.pdf".

[2271] Output: Generated PDF quotation

[2272] Step 4:

[2273] Server: Save the quotation to the file system and send it via email.

[2274] Input: Generated PDF quotation

[2275] Specific operation: The server saves "quote_12345.pdf" to the specified directory and sends it as an attachment to the sales representative's email address.

[2276] Output: The quotation will be sent via email.

[2277] Step 5:

[2278] Terminal: Displays a notification to the user that the quotation has been created and sent via email.

[2279] Input: Processing completion notification from the server

[2280] Specific action: The terminal displays the message "A quotation has been created and an email has been sent" to the user.

[2281] Output: The message is displayed to the user.

[2282] Preparation of the initial proposal

[2283] Step 1:

[2284] User: The sales representative enters the project ID and submits a request to create a preliminary proposal.

[2285] Input: Case ID (Example: 12345)

[2286] Specific action: The sales representative enters the project ID and clicks the "Create Initial Proposal" button.

[2287] Output: The request data is sent to the terminal.

[2288] Step 2:

[2289] Terminal: Converts the initial proposal creation request data into JSON format and sends it to the server.

[2290] Input: Request data from sales representative

[2291] Specific action: The terminal sends a JSON request to the server with the format {"action": "create_proposal", "case_id": "12345"}.

[2292] Output: Request data in JSON format is sent to the server.

[2293] Step 3:

[2294] Server: Retrieves project information from the database and generates a preliminary proposal.

[2295] Input: Case ID (Example: 12345)

[2296] Specific operation: The server executes a database query to retrieve information for case ID 12345, embeds the retrieved data into the proposal template, and generates "proposal_12345.docx" or "proposal_12345.pdf".

[2297] Output: Generated initial proposal

[2298] Step 4:

[2299] Server: Save the initial proposal to the file system and send it via email.

[2300] Input: Generated initial proposal

[2301] Specific operation: The server saves "proposal_12345.docx" or "proposal_12345.pdf" to the specified directory and sends it as an attachment to the sales representative's email address.

[2302] Output: The proposal will be sent via email.

[2303] Step 5:

[2304] Terminal: Displays a notification to the user that the initial proposal has been completed and sent via email.

[2305] Input: Processing completion notification from the server

[2306] Specific action: The device displays the message "The initial proposal has been created and sent via email" to the user.

[2307] Output: The message is displayed to the user.

[2308] Inquiry response

[2309] Step 1:

[2310] User: The customer enters their question through the inquiry form and submits it.

[2311] Input: Question (Example: "What is the delivery date?")

[2312] Specific action: The customer enters a question into the inquiry form and clicks the "Submit" button.

[2313] Output: The input data is sent to the terminal.

[2314] Step 2:

[2315] Terminal: Converts the query data into JSON format and sends it to the server.

[2316] Input: Question entered by the customer

[2317] Specific action: The terminal generates data in JSON format with the format {"action": "inquiry", "message": "What is the delivery date?"} and sends a POST request to the server.

[2318] Output: Data in JSON format is sent to the server.

[2319] Step 3:

[2320] Server: Analyzes the inquiry and generates an appropriate response.

[2321] Input: Inquiry content in JSON format

[2322] Specific operation: The server analyzes the query using a natural language processing engine and retrieves relevant information from the database and FAQs.

[2323] Output: Analysis results and response text

[2324] Step 4:

[2325] Emotion Engine: Analyzes the emotion behind the inquiry and adjusts the response accordingly.

[2326] Input: Analysis results and query text

[2327] Specific operation: The emotion engine analyzes the inquiry, determines the emotion, and adjusts the response accordingly. For example, if there is a sense of urgency, it will adjust the tone to one that provides reassurance, such as, "Please rest assured. The deadline is December 1st, 2023."

[2328] Output: Adjusted response

[2329] Step 5:

[2330] Server: Generates a tailored response email and sends it to the customer.

[2331] Input: Adjusted response

[2332] Specific operation: The server converts the generated response into an email format and sends it to the customer's email address.

[2333] Output: A response email is sent to the customer.

[2334] Step 6:

[2335] Terminal: Notifies the user that a response to their inquiry has been sent.

[2336] Input: Processing completion notification from the server

[2337] Specific action: The device displays the message "An email has been sent" to the user.

[2338] Output: The message is displayed to the user.

[2339] (Application Example 2)

[2340] Next, we will explain application example 2. In the following explanation, 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."

[2341] While conventional sales support systems have proven somewhat effective in managing project information and generating quotations and proposals, they have a problem in that they struggle to manage customer information in real time at physical stores and to provide appropriate responses that reflect customer emotions. In particular, there is a lack of technical support for sales representatives at physical stores to respond quickly based on customer emotions. There is also a need for a means to properly manage and immediately send generated quotations, proposals, and responses to inquiries.

[2342] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[2343] In this invention, the server includes means for inputting case information, means for storing case information in a database, means for generating quotations based on the stored case information, means for generating initial proposals based on the stored case information, means for responding to customer inquiries, means for sales representatives in physical stores to manage customer information in real time using terminals, sentiment analysis means for analyzing customer emotions through terminals and guiding appropriate responses, and means for transmitting generated quotations, proposals, and responses to inquiries to terminals. This streamlines sales activities in physical stores and enables appropriate responses based on customer emotions.

[2344] "Project information" refers to a series of data necessary for sales activities, such as information about the customer, customer requests, details of the products to be sold, and desired delivery dates.

[2345] A "database" is a system for organizing, storing, and managing information, allowing for quick retrieval and extraction of data as needed.

[2346] A "quotation" is a document that outlines the price and terms of a product or service offered to a customer.

[2347] A "primary proposal" is a document that summarizes the proposed content and plan presented to a customer in the initial stages of business negotiations.

[2348] An "inquiry" refers to a question or request from a customer, and it is necessary to provide an appropriate response to it.

[2349] A "system" is a collection of components in which multiple means or elements interact with each other to achieve a specific function.

[2350] A "physical store" refers to a physical commercial facility where customers can actually visit, view, and purchase products.

[2351] A "terminal" refers to an electronic device used by sales representatives or customers that is capable of data input, information display, and communication.

[2352] "Emotional analysis" refers to the technology of reading and analyzing a customer's emotions from their speech, written statements, and actions.

[2353] "Natural language processing" refers to the technology that enables computers to understand, analyze, and generate language that humans use on a daily basis.

[2354] This invention is a system that supports sales activities in physical stores and guides appropriate responses to customer emotions. This system is configured to streamline the management of case information, the generation of quotations and initial proposals, and the handling of inquiries.

[2355] System Overview

[2356] 1. Entering and saving project information

[2357] User: The sales representative uses a device (e.g., a tablet) to enter new customer information and clicks the submit button.

[2358] Terminal: The terminal formats the entered information into JSON format and sends it to the server as an HTTP request.

[2359] Server: The server parses the received data, saves it to a database (MySQL or PostgreSQL), and generates a new customer ID. It then sends the generated customer ID and a notification that the data has been saved to the terminal.

[2360] Terminal: Displays the customer ID and a message confirming that the data has been saved to the sales representative.

[2361] 2. Creating a quotation

[2362] User: The sales representative uses a terminal to enter the customer ID and submits a request for a quote.

[2363] Terminal: The terminal sends a quotation creation request to the server in a specified format (e.g., {"action": "create_estimate", "customer_id": "C12345"}).

[2364] Server: The server executes a database query based on the customer ID, embeds the retrieved information into a quotation template, and generates a quotation in PDF format. The generated quotation is saved to a specified directory and sent to the sales representative's email address.

[2365] Terminal: Notifies the sales representative that a quotation has been generated and sent via email.

[2366] 3. Preparation of the initial proposal

[2367] User: The sales representative uses a terminal to enter the customer ID and submits a request for the creation of an initial proposal.

[2368] Terminal: The terminal sends a proposal creation request to the server in a specified format (e.g., {"action": "create_proposal", "customer_id": "C12345"}).

[2369] Server: The server executes a database query based on the customer ID, embeds the retrieved information into a proposal template, and generates a preliminary proposal in Word or PDF format. The generated proposal is saved to a specified directory and sent to the sales representative's email address.

[2370] Terminal: Notifies the sales representative that the proposal has been generated and sent via email.

[2371] 4. Handling inquiries

[2372] User: The customer provides inquiry information to the store staff. The staff enters the inquiry via a terminal and clicks the send button.

[2373] Terminal: The terminal sends inquiry data to the server in a specified format (e.g., {"action": "inquiry", "message": "When will this product be in stock?"}).

[2374] Server: The server uses the Google Cloud Natural Language API to process the received inquiry using natural language processing, and retrieves relevant information from the database and FAQs based on the analyzed data. Next, it uses the IBM Watson Tone Analyzer API to analyze the customer's sentiment and adjusts the tone of the response based on the results.

[2375] Server: Sends the generated response email to the customer's email address and also notifies the staff.

[2376] Hardware and software

[2377] Hardware:

[2378] Device: iOS or Android tablet

[2379] Server: Standard web server (e.g., Apache, Nginx)

[2380] Database: MySQL or PostgreSQL

[2381] software:

[2382] Natural Language Processing Engine: Google Cloud Natural Language API

[2383] Sentiment analysis engine: IBM Watson Tone Analyzer API

[2384] Programming language: Python3

[2385] Web framework: Flask

[2386] Specific example

[2387] Entering and saving customer information

[2388] 1. A sales representative enters new customer information and sends it to the server. For example, if the customer's name is "Taro Yamada" and their product of interest is "smartphone".

[2389] 2. The terminal sends the input information to the server in JSON format, and a customer ID is generated.

[2390] 3. The terminal displays "Customer ID: C12345" and notifies the user that the information has been saved.

[2391] Estimate creation

[2392] 1. The sales representative requests the generation of a quotation using "Customer ID: C12345".

[2393] 2. A quotation will be generated and sent to the specified email address.

[2394] Examples of how to handle inquiries

[2395] 1. A customer asks, "When will this product be in stock?"

[2396] 2. The server analyzes the inquiry and retrieves relevant information from the FAQ.

[2397] 3. The emotion analysis engine determines that there is a feeling of "anxiety" and generates a response with an adjusted tone.

[2398] 4. The generated response email is sent to the customer.

[2399] Example of a prompt

[2400] "Please tell me when this product will be back in stock."

[2401] "Please explain the contents of the quotation in detail."

[2402] With the above configuration, the present invention is expected to streamline sales activities in physical stores, enable appropriate responses that respond to customer emotions, and improve customer satisfaction.

[2403] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[2404] Step 1:

[2405] The user (sales representative) enters new customer information into the terminal and clicks the submit button. The input includes the customer's name, contact information, and products of interest. This information is formatted in JSON format.

[2406] Input: Customer's name, contact information, items of interest

[2407] Output: Customer information in JSON format (Example: {"name": "Taro Yamada", "contact": "xxx@example.com", "interest": "Smartphone"})

[2408] Step 2:

[2409] Customer information in JSON format sent from the terminal is sent to the server as an HTTP request. The server receives this request and parses the data.

[2410] Input: HTTP request (customer information in JSON format)

[2411] Output: Analyzed customer information

[2412] Step 3:

[2413] The server saves the analyzed customer information to a MySQL or PostgreSQL database. A new customer ID is generated and saved to the database.

[2414] Input: Analyzed customer information

[2415] Output: New customer ID (Example: C12345)

[2416] Step 4:

[2417] The server generates a new customer ID and a save completion notification, and sends it to the terminal. This notification includes the customer ID and a save completion message.

[2418] Input: New Customer ID

[2419] Output: Customer ID and save completion notification (JSON format)

[2420] Step 5:

[2421] The device displays notifications received by the sales representative, including the customer ID. The sales representative can then verify this customer ID.

[2422] Input: Customer ID and save completion notification

[2423] Output: Customer ID and save completion message displayed on the terminal screen

[2424] Step 6:

[2425] The user (sales representative) uses a terminal to enter the customer ID and submits a request to create a quotation. This request is formatted in JSON format and sent to the server as an HTTP request.

[2426] Input: Customer ID

[2427] Output: Estimate creation request (Example: {"action": "create_estimate", "customer_id": "C12345"})

[2428] Step 7:

[2429] The server receives the quotation creation request sent from the terminal and executes a database query to retrieve information based on the customer ID.

[2430] Input: Request to create a quote

[2431] Output: Information based on customer ID

[2432] Step 8:

[2433] The server embeds the acquired information into a quotation template and generates a quotation in PDF format. The generated quotation is then saved to the specified directory.

[2434] Input: Information based on customer ID

[2435] Output: Generated quotation (PDF format)

[2436] Step 9:

[2437] The server sends the generated quotation to the sales representative's email address. It also notifies the terminal that the quotation has been generated and sent.

[2438] Input: Generated quote

[2439] Output: Sent emails and notifications

[2440] Step 10:

[2441] The user (sales representative) uses a terminal to enter the customer ID and submits a request to create a preliminary proposal. This request is also formatted in JSON and sent to the server as an HTTP request.

[2442] Input: Customer ID

[2443] Output: Request to create a first proposal (Example: {"action": "create_proposal", "customer_id": "C12345"})

[2444] Step 11:

[2445] The server receives the initial proposal creation request sent from the terminal and executes a database query to retrieve information based on the customer ID.

[2446] Input: Request to create initial proposal

[2447] Output: Information based on customer ID

[2448] Step 12:

[2449] The server embeds the acquired information into a proposal template and generates a preliminary proposal in Word or PDF format. The generated proposal is then saved to the specified directory.

[2450] Input: Information based on customer ID

[2451] Output: Generated initial proposal (Word or PDF format)

[2452] Step 13:

[2453] The server sends the generated initial proposal to the sales representative's email address and notifies the terminal that the proposal has been generated and sent.

[2454] Input: Generated initial proposal

[2455] Output: Sent emails and notifications

[2456] Step 14:

[2457] The user (customer) provides inquiry information to the store staff. The staff member enters the inquiry via a terminal and clicks the send button. The inquiry data is formatted in JSON format and sent to the server.

[2458] Input: Inquiry Information

[2459] Output: Inquiry data in JSON format (Example: {"action": "inquiry", "message": "When will this product be in stock?"})

[2460] Step 15:

[2461] The server receives the inquiry data sent from the terminal and performs natural language processing using the Google Cloud Natural Language API. Based on the analyzed data, the server retrieves relevant information from the database and FAQs.

[2462] Input: Query data in JSON format

[2463] Output: Analyzed data and related information

[2464] Step 16:

[2465] The server uses the IBM Watson Tone Analyzer API to analyze customer sentiment and generates a response email with an adjusted tone based on the results.

[2466] Input: Analyzed data and related information

[2467] Output: Tone-adjusted response email

[2468] Step 17:

[2469] The server sends the generated response email to the customer's email address and also notifies the store staff.

[2470] Input: Tone-adjusted response email

[2471] Output: Sent emails and notifications

[2472] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[2473] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2474] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[2475] [Fourth Embodiment]

[2476] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[2477] As shown in Figure 7, the 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.

[2478] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[2479] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[2480] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[2481] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[2482] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[2483] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[2484] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[2485] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[2486] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[2487] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[2488] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2489] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries. Its embodiments will be described in detail below.

[2490] System Overview

[2491] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries.

[2492] Program Processing Description

[2493] 1. Entering and saving project information

[2494] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into the form and clicks the submit button.

[2495] Terminal: Formats form input data into the required format and sends a request to the server.

[2496] Server: Receives case information, saves it to the database, generates a new case ID, and notifies the sales representative.

[2497] 2. Creating a quotation

[2498] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[2499] Terminal: Sends a quotation creation request to the server, including the project ID.

[2500] Server: Receives a request, retrieves the relevant project ID information from the database, and generates a quotation. The generated quotation is saved in PDF format and sent to the sales representative via email.

[2501] 3. Preparation of the initial proposal

[2502] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[2503] Terminal: Sends a proposal creation request to the server, including the project ID.

[2504] Server: Receives a request, retrieves the relevant case ID information from the database, and generates a preliminary proposal. The generated preliminary proposal is saved in PDF or Word format and sent to the sales representative via email.

[2505] 4. Handling inquiries

[2506] User: The customer enters their question into the inquiry form and clicks the submit button.

[2507] Terminal: Sends query data to the server.

[2508] Server: Analyzes the inquiry, retrieves relevant information from the database and FAQs, and generates an appropriate response using a natural language processing engine. The generated response is sent to the customer as an automated email.

[2509] Specific example

[2510] Specific examples of entering and saving project information

[2511] 1. User: The sales representative enters and submits new deal information. For example, they enter information such as the customer being Company A, the proposed product being Product B, and the desired delivery date being December 1, 2023.

[2512] 2. Terminal: The terminal processes the entered information into a predetermined data format such as JSON and sends it to the server.

[2513] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it to the database.

[2514] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[2515] Examples of creating a quotation

[2516] 1. User: A sales representative requests a quote for case ID 12345.

[2517] 2. Terminal: The request data is sent to the server in the format {"action": "create_estimate", "case_id": "12345"}.

[2518] 3. Server: Execute a database query to retrieve information for case ID 12345.

[2519] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[2520] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. This significantly improves the efficiency of sales activities, saving considerable time and effort.

[2521] The following describes the processing flow.

[2522] Entering and saving project information

[2523] Step 1:

[2524] User: The sales representative enters the project information (customer information, proposed products, desired delivery date, etc.) into a dedicated form and clicks the submit button.

[2525] Step 2:

[2526] Terminal: Formats user input into a data format (e.g., JSON format) and sends it to the server as an HTTP request.

[2527] Step 3:

[2528] Server: The server analyzes the received data and performs validation as case information (e.g., checking required fields, verifying data types).

[2529] Step 4:

[2530] Server: Saves case information that has successfully been validated to the database. Generates a new case ID at the same time as saving.

[2531] Step 5:

[2532] Server: Generates a response containing the new case ID and a notification that saving is complete, and sends it to the terminal.

[2533] Step 6:

[2534] Terminal: Displays the case ID and a message confirming that the case has been saved to the sales representative.

[2535] Request and generation of a quotation

[2536] Step 1:

[2537] User: The sales representative enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[2538] Step 2:

[2539] Terminal: Sends a quotation creation request to the server in the specified format (e.g., {"action": "create_estimate", "case_id": "12345"}).

[2540] Step 3:

[2541] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[2542] Step 4:

[2543] Server: Embeds the acquired data into a quotation template and generates a quotation in PDF format.

[2544] Step 5:

[2545] Server: Saves the generated quotation to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[2546] Step 6:

[2547] Terminal: Notifies the sales representative that the quotation has been created and sent via email.

[2548] Request and generation of the initial proposal.

[2549] Step 1:

[2550] User: The sales representative enters the project ID on the initial proposal creation request screen and clicks the "Create Proposal" button.

[2551] Step 2:

[2552] Terminal: Sends a proposal creation request to the server in the specified format (e.g., {"action": "create_proposal", "case_id": "12345"}).

[2553] Step 3:

[2554] Server: Receives a request, executes a database query based on the case ID, and retrieves relevant case information.

[2555] Step 4:

[2556] Server: Embeds the acquired data into a proposal template and generates a preliminary proposal in Word or PDF format.

[2557] Step 5:

[2558] Server: Saves the generated initial proposal to a specified directory in the file system and sends it as an attachment to the sales representative's email address.

[2559] Step 6:

[2560] Terminal: Notifies the sales representative that the initial proposal has been completed and sent via email.

[2561] Inquiry handling process

[2562] Step 1:

[2563] User: The customer enters their question into the inquiry form and clicks the submit button.

[2564] Step 2:

[2565] Terminal: Sends inquiry data to the server in the specified format (e.g., {"action": "inquiry", "message": "What is the delivery date?"}).

[2566] Step 3:

[2567] Server: Passes the received query content to the natural language processing engine for analysis.

[2568] Step 4:

[2569] Server: Executes database queries based on the inquiry content and generates appropriate answers from relevant information and FAQs.

[2570] Step 5:

[2571] Server: The generated response is sent to the customer's email address as an automated reply email.

[2572] Step 6:

[2573] Terminal: Notifies the customer that a response to their inquiry has been sent.

[2574] These processing steps streamline key tasks related to sales activities, enabling them to be executed quickly and accurately.

[2575] (Example 1)

[2576] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[2577] In sales operations, a significant amount of time and effort is spent on managing project information, creating quotations and proposals, and responding to customer inquiries. Performing these tasks manually is particularly burdensome and reduces operational efficiency. Therefore, the present invention aims to provide a system that streamlines these sales operations and saves time and effort.

[2578] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[2579] In this invention, the server includes means for formatting case information into a data format and transmitting it to the server; means for the server to store the received case information in a database and generate a new case ID; means for generating a quotation based on the case information and sending it to a sales representative; and means for generating a preliminary proposal based on the case information and sending it to a sales representative. This enables more efficient sales operations and faster customer response.

[2580] "Project information" refers to all information related to sales activities, such as customer information, proposed product information, and desired delivery dates.

[2581] A "data format" is a standardized format used for sending, receiving, and storing data, and JSON format is one example.

[2582] A "server" refers to a computer system used to process, store, and transmit data over a network.

[2583] A "database" refers to a system for efficiently storing and retrieving structured data.

[2584] "Case ID" refers to an identifier generated to uniquely identify each case.

[2585] A "quotation" refers to a document that clearly specifies the price and conditions of the goods or services offered to a customer.

[2586] A "first proposal" is a document that outlines the content of the initial proposal and serves to show the client the proposed content and conditions.

[2587] "Inquiry details" refers to information that contains questions or requests from customers.

[2588] A "natural language processing engine" refers to computer technology used to understand and generate human language.

[2589] An "automated response email" refers to an email that is automatically generated and sent by a system based on specific content.

[2590] A "template engine" refers to a software tool that embeds data into a template to automatically generate the final document or file.

[2591] A "generative AI model" refers to an artificial intelligence model that uses machine learning techniques to learn patterns from data and generate new information.

[2592] This invention is a system that streamlines project information management and sales support, and in particular improves the efficiency of sales operations by automating the creation of quotations, initial proposals, and customer inquiries.

[2593] System Overview

[2594] This system primarily consists of servers, terminals, and users. Sales representatives (users) access the system via terminals to input project information, request quotes, request initial proposals, and handle inquiries.

[2595] Entering and saving project information

[2596] 1. User: The sales representative enters customer information (e.g., customer name, contact information), proposed products (e.g., product name, quantity), desired delivery date, and other case information into a form on the terminal.

[2597] 2. Terminal: Formats the input data into a specified format (e.g., JSON format).

[2598] 3. Terminal: Sends the formatted data to the server.

[2599] 4. Server: Saves the received data to the database and generates a new case ID.

[2600] 5. Server: Notifies the user's terminal of the case ID and a message indicating that saving is complete.

[2601] Estimate creation

[2602] 1. User: Enter the project ID on the quotation creation request screen and click the "Create Quotation" button.

[2603] 2. Terminal: Sends a quotation creation request to the server, including the project ID.

[2604] 3. Server: Retrieves information for the relevant case ID from the database.

[2605] 4. Server: Generates a quotation based on the acquired information. The generated quotation is saved in PDF format.

[2606] 5. Server: Send the generated PDF file to the sales representative via email.

[2607] Preparation of the initial proposal

[2608] 1. User: Enter the project ID on the initial proposal creation request screen and click the "Create Proposal" button.

[2609] 2. Terminal: Send a proposal creation request to the server, including the project ID.

[2610] 3. Server: Retrieves information for the relevant case ID from the database.

[2611] 4. Server: Generates a preliminary proposal based on the acquired information. The generated proposal is saved in PDF or Word format.

[2612] 5. Server: Send the generated files to the sales representative via email.

[2613] Inquiry response

[2614] 1. User: The customer enters their question into the inquiry form and clicks the submit button.

[2615] 2. Terminal: Sends the inquiry data to the server.

[2616] 3. Server: Analyzes the inquiry content and retrieves relevant information from the database and FAQs.

[2617] 4. Server: Generate appropriate responses using a natural language processing engine (e.g., a generative AI model).

[2618] 5. Server: Send the generated response to the customer as an automated response email.

[2619] Hardware and Software to be Used

[2620] Server: A computer system for data processing and storage

[2621] Database: A system for efficiently storing and retrieving case information (e.g., MySQL, PostgreSQL)

[2622] Template Engine: Software for embedding data into templates and generating documents (e.g., JasperReports, Apache POI)

[2623] Natural Language Processing Engine: Software for analyzing conversations and generating responses using a generative AI model (e.g., GPT-3)

[2624] Specific Examples

[2625] Specific Examples of Input and Saving of Case Information

[2626] 1. User: A salesperson inputs and sends new case information. For example, information such as the customer is Company A, the proposed product is Product B, and the desired delivery date is December 1, 2023 is input.

[2627] 2. Terminal: Organize the input information into a predetermined data format such as JSON format and send it to the server.

[2628] 3. Server: Generate a new case ID (e.g., case ID 12345) and save it in the database.

[2629] 4. Server: Notifies the sales representative's terminal with case ID 12345 and a message indicating that saving is complete.

[2630] Examples of creating a quotation

[2631] 1. User: A sales representative requests a quote for case ID 12345.

[2632] 2. Terminal: The request data is sent to the server with a prompt message such as "Please enter the information required to create the quotation."

[2633] 3. Server: Execute a database query to retrieve information for case ID 12345.

[2634] 4. Server: Embeds the acquired information into a quotation template and generates a quotation in PDF format. Saves the generated quotation (quote_12345.pdf) to the specified directory and sends it to the sales representative via email.

[2635] Example of a prompt

[2636] "Please enter the information required to create the quotation."

[2637] "As an example of how to handle inquiries, please generate a list of frequently asked questions and their answers."

[2638] This system enables sales representatives to efficiently manage project information, generate quotations and proposals, and respond quickly and effectively to customer inquiries. It significantly improves the efficiency of sales activities, saving considerable time and effort.

[2639] The flow of the specific processing in Example 1 will be explained using Figure 11.

[2640] Entering and saving project information

[2641] Step 1:

[2642] The user enters project information (customer information, proposed products, desired delivery date, etc.) into a form on their device.

[2643] Input: Customer information, proposed products, desired delivery date, and other project information.

[2644] Specific operation: The user enters information into each field of the form using the device's keyboard and clicks the submit button.

[2645] Output: Input case information data

[2646] Step 2:

[2647] The terminal formats the entered case information data into a specified format (e.g., JSON format).

[2648] Input: Project information data entered by the user

[2649] Specific operation: The terminal automatically converts the input data into JSON format.

[2650] Output: Formatted case information data

[2651] Step 3:

[2652] The terminal sends the formatted case information data to the server.

[2653] Input: Project information data formatted in JSON format

[2654] Specific operation: The terminal sends an HTTP POST request and sends data to the server.

[2655] Output: Data transmission to server complete.

[2656] Step 4:

[2657] The server receives the case information data, saves it to the database, and generates a new case ID.

[2658] Input: Case information data sent from the terminal

[2659] Specific operation: The server executes an INSERT query into the database and saves the case information. A new case ID is generated.

[2660] Output: New case ID generated and saved to database.

[2661] Step 5:

[2662] The server notifies the user's terminal of the case ID and a message indicating that saving is complete.

[2663] Input: New case ID, Saved status

[2664] Specific operation: The server returns the case ID and message to the terminal as an HTTP response. The terminal displays the notification message.

[2665] Output: Notification display of case ID and save completion message.

[2666] Estimate creation

[2667] Step 1:

[2668] The user enters the project ID on the quote creation request screen and clicks the "Create Quote" button.

[2669] Input: Case ID

[2670] Specific action: The sales representative enters the deal ID and clicks the button.

[2671] Output: Request to create a quotation

[2672] Step 2:

[2673] The terminal sends a request to the server to create a quotation, including the project ID.

[2674] Input: Request to create a quote

[2675] Specific operation: The terminal sends an HTTP POST request and sends the request data to the server.

[2676] Output: Request sent to server successfully

[2677] Step 3:

[2678] The server retrieves the relevant case ID information from the...

Claims

1. A means of entering project information, A means of saving case information to a database, A means for generating an estimate based on saved project information, A means for generating a preliminary proposal based on saved project information, A system that includes means for responding to customer inquiries.

2. The system according to claim 1, further comprising means for automatically generating a quotation based on project information and sending it to a sales representative.

3. The system according to claim 1, further comprising natural language processing means for analyzing the content of an inquiry and generating an appropriate automated response.

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