system

Abstract

We provide the system. [Solution] A means for inputting basic information about a product or service, means for transmitting the input information, A means for extracting similar data from a database based on the input information, A means for calculating a fair price based on the extracted data, A means for generating a quotation based on the calculated price information, A means for transmitting the generated quotation to a receiving terminal, Means for displaying the aforementioned quotation, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] The price setting of commercial products and services is easily affected by daily changing market trends, and it is difficult to estimate an appropriate price. In particular, for new commercial products and services, since there is insufficient past price data, it is difficult to calculate an appropriate price. As a result, sales staff may present an excessive or underestimated price when creating an estimate, which may damage the trust relationship with customers. Therefore, a system that can quickly and accurately perform appropriate price setting is required.

Means for Solving the Problems

[0005] This invention provides a means for inputting basic information about a product or service. Furthermore, it includes means for transmitting the inputted information and means for extracting similar data from a database based on the received information. It also includes means for calculating an appropriate price based on the extracted data and means for generating a quotation based on the calculated price information. The system provides means for transmitting the generated quotation to a receiving terminal and means for displaying the received quotation. This system allows sales representatives to quickly and accurately estimate appropriate prices and strengthen trust with customers. Furthermore, by configuring the means for calculating appropriate prices to include an algorithm that considers market trends and competitive situations, more accurate pricing becomes possible. In addition, by generating and transmitting quotations in PDF format, the system becomes user-friendly.

[0006] "Merchandise or services" is a general term referring to the goods or services offered to customers in the course of business activities.

[0007] "Basic information" refers to detailed information about a product or service, such as its name, specifications, desired price range, and target market.

[0008] "Means of input" refers to interfaces or devices that allow users to register basic information about a product or service in a system.

[0009] "Means of transmission" refers to the means of communication used to send information entered from the user's terminal to the server.

[0010] A "database" refers to a data storage system that stores past price data, quotation data, market price data, and other information related to products and services.

[0011] "Extraction method" refers to a function that searches for and retrieves similar data from a database based on the input information.

[0012] "Fair price" refers to the most reasonable price calculated based on the market value of the goods or services.

[0013] "Means of calculation" refers to algorithms or calculation functions used to calculate a fair price based on extracted data.

[0014] A "quotation" refers to a document that includes information such as the appropriate price and specifications of the goods or services being sold.

[0015] "Means of generation" refers to the function for creating an estimate based on information such as the calculated appropriate price.

[0016] A "receiving device" refers to a device such as a computer, tablet, or smartphone that a user uses to receive information.

[0017] "Means of display" refers to a function that allows the recipient to view the quotation on their terminal screen.

[0018] An "algorithm" refers to a set of calculation procedures or methods used to determine a fair price, taking into account market trends and competitive conditions. [Brief explanation of the drawing]

[0019] [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] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It 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.

Embodiments for Carrying out the Invention

[0020] 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.

[0021] First, the language used in the following description will be explained.

[0022] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0023] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0024] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0025] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0026] 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."

[0027] [First Embodiment]

[0028] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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".

[0040] As an example of implementing this invention, a system for quickly and accurately estimating the appropriate price of a product or service is provided. This system calculates an appropriate price based on information entered by the user into a terminal, taking into account comparisons with past databases and market trends, and generates and transmits the result as a quotation.

[0041] System Overview

[0042] The user enters basic information about a product or service into their terminal and sends that information to the server. The server searches its database based on the received information, extracts similar data, and calculates a fair price. Then, it generates a quotation based on the calculated price information and sends it to the user's terminal. The user can review the sent quotation and share it with the customer to strengthen trust.

[0043] Program processing flow

[0044] 1. Entering information

[0045] User: Enter basic information (product name, specifications, desired price range, target market, etc.) into the terminal to create a quote.

[0046] As a specific example, when a user requests a quote for a new software service, they input the name of "Software Service A," a brief description of its functions, desired price range, and target market information into the terminal.

[0047] 2. Data transmission

[0048] Terminal: Sends user-entered information to the server. This data should preferably be encrypted for security reasons.

[0049] 3. Database matching

[0050] Server: Receives the entered information and compares it with the company's and related market databases. Specifically, it searches and extracts past quotation data and market price data similar to the entered product name and specifications.

[0051] 4. Calculation of a fair price

[0052] Server: Based on the extracted data, it calculates a fair price. In this process, it uses an algorithm to consider market trends and competitive conditions.

[0053] As a concrete example, we have 100 historical data points for similar software services, and we extract important data from them to calculate an appropriate price considering current market trends.

[0054] 5. Generating the final quotation

[0055] Server: Generates a quotation based on the calculated fair price. This quotation includes details of the goods / services and their fair price.

[0056] For example, if the appropriate price for software service A is calculated to be 1 million yen, a PDF quotation will be generated that includes the price and service specifications.

[0057] 6. Sending and confirming the quotation

[0058] Server: Sends the generated quotation to the user's terminal. Email and push notifications are commonly used as notification methods.

[0059] Terminal: Displays received quotations and allows users to review them.

[0060] Specific example

[0061] For example, when a user requests a quote for a new software service, they would use the system as follows:

[0062] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[0063] 2. Data transmission: The terminal sends the input information to the server.

[0064] 3. Database matching: The server extracts similar past data from the database based on the input information.

[0065] 4. Calculation of appropriate price: The server calculates an appropriate price based on extracted data, taking into account market trends and competitive conditions.

[0066] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[0067] 6. Sending and confirming the quotation: The server sends the generated quotation to the user's terminal, and the user confirms it.

[0068] This system allows users to quickly calculate fair prices, helping to strengthen trust with customers.

[0069] The following describes the processing flow.

[0070] Step 1:

[0071] User: Enter basic information about the product or service to be quoted (name, specifications, desired price range, target market, etc.) into the terminal. Specifically, enter the necessary information according to the input form or selection options. A confirmation screen of the entered information is also provided to prevent input errors.

[0072] Step 2:

[0073] Terminal: Sends the basic information entered by the user to the server. The input data is converted to JSON or XML format, and encryption is recommended for security. After successful transmission, the user receives a notification.

[0074] Step 3:

[0075] Server: Analyzes the received basic information and generates queries for the database. The queries include keywords such as product name, specifications, and target market.

[0076] Step 4:

[0077] Server: Searches the database and extracts data on similar products and services from the past. In this process, it uses multiple databases or tables to collect the necessary information and normalizes the data as needed.

[0078] Step 5:

[0079] Server: Based on the extracted data, it executes an algorithm to calculate a fair price. Specifically, it uses weighted averages, regression analysis, machine learning models, etc., to calculate a fair price that takes into account past price data and current market trends.

[0080] Step 6:

[0081] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods or services, the basis for the calculation, and the fair price. Quotations are typically generated in PDF format.

[0082] Step 7:

[0083] Server: Sends the generated quotation to the user's terminal. Delivery methods include email, push notification, or in-system messaging service.

[0084] Step 8:

[0085] Terminal: Receives the sent quotation and notifies the user that the quotation has arrived. The user who receives the notification can then view the quotation displayed on their terminal.

[0086] Step 9:

[0087] User: Review the displayed quote, negotiate with the customer or make internal adjustments as needed, and prepare the final quote to provide to the customer.

[0088] This series of steps allows users to quickly calculate fair prices and provide reliable quotes to customers. Furthermore, the system's ease of use and accuracy streamline sales activities.

[0089] (Example 1)

[0090] 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."

[0091] Traditional pricing systems for goods and services made it difficult to quickly and accurately calculate fair prices. They failed to consider market trends and competitive landscapes, resulting in unreliable quotes and hindering the building of trust with customers.

[0092] 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.

[0093] In this invention, the server includes means for inputting basic information of a product or service, means for transmitting the inputted information, means for extracting similar data from a database based on the inputted information, means for calculating an appropriate price using a generation AI model based on the extracted data, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, and means for displaying the quotation. This makes it possible for the user to quickly and accurately calculate an appropriate price and generate and transmit the result as a quotation.

[0094] "Merchandise" refers to goods and services.

[0095] "Basic information" refers to information about a product or service, such as its name, specifications, desired price range, and target market.

[0096] "Means of input" refers to the hardware and software that users use to input basic information about a product or service.

[0097] "Means of transmission" refers to the means of communication used to send the entered basic information to the server.

[0098] A "database" refers to data storage that holds past estimate data and market price data for products and services.

[0099] "Extraction method" refers to the means of searching for and retrieving similar data from a database based on the basic information entered.

[0100] A "generative AI model" refers to a machine learning model that analyzes past data and calculates an appropriate price by considering current market trends and competitive conditions.

[0101] "Methods for calculating fair prices" refer to methods for calculating the appropriate price of a product or service from data extracted using a generative AI model.

[0102] A "quotation" refers to a document that contains detailed information about a product or service, including its fair price.

[0103] "Generating means" refers to software and algorithms for automatically creating quotations based on appropriate pricing and entered basic information.

[0104] "Means of transmission" refers to the means of communication used to send the generated quotation to the receiving terminal.

[0105] "Receiving terminal" refers to the device that a user uses to receive and view a quotation.

[0106] "Means of display" refers to the means by which a user can visually confirm the received quotation.

[0107] An "algorithm" refers to a procedure or method for calculating a fair price by taking into account market trends and competitive conditions.

[0108] "PDF format" is an abbreviation for Portable Document Format, and refers to a file format for electronically storing and sharing documents.

[0109] "Encrypted methods" refer to means of protecting data using cryptographic techniques to enhance data security.

[0110] This invention will be explained using a system for quickly and accurately estimating the appropriate price of a product or service as an example. Based on information entered by the user into a terminal, this system calculates an appropriate price by comparing it with past databases and considering market trends, and then generates and transmits the result as a quotation.

[0111] System Overview

[0112] The user enters basic information about the product or service into the terminal and sends that information to the server. Specifically, when a user requests a quote for a new software service, they would enter the name of "Software Service A," a brief description of its functions, the desired price range, and information about the target market.

[0113] The terminal receives basic information entered by the user, encrypts it, and sends it to the server. AES encryption is recommended for this process, and HTTPS is used as the communication protocol.

[0114] The server decodes the information received from the terminal and searches the database to extract similar data. For example, it searches and extracts data with similar product names and specifications from the quotation database for the past three years. At this time, a database query is generated to find data that matches or is similar to the product name and specifications.

[0115] Next, the server uses a generative AI model to calculate a fair price based on the extracted data. This generative AI model uses an algorithm that analyzes historical data and considers current market trends and competitive conditions. For example, it extracts important information from 100 data points and calculates a fair price considering current market trends.

[0116] After the appropriate price is calculated, the server automatically generates a quotation using a template engine (e.g., Apache® Velocity). This quotation includes details of the product or service and the appropriate price. For example, if the appropriate price for "Software Service A" is calculated to be 1.5 million yen, a PDF quotation will be generated that includes the price and service specifications.

[0117] Finally, the server sends the generated quotation to the user's device. The delivery method may include an email service (e.g., SendGrid) or push notification. Specifically, the quotation is sent as a PDF attachment with the subject line "Quotation: Software Service A".

[0118] The device allows users to download quotes via received emails and notifications, enabling them to review the contents. After reviewing, users can share these quotes with customers, strengthening their relationship of trust.

[0119] Specific example

[0120] For example, the specific steps a user might take to get an estimate for a new software service are as follows:

[0121] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[0122] 2. Data transmission: The terminal sends the input information to the server.

[0123] 3. Database matching: The server extracts similar past data from the database based on the input information.

[0124] 4. Calculation of appropriate price: The server calculates an appropriate price based on extracted data, taking into account market trends and competitive conditions.

[0125] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[0126] 6. Sending and confirming the quotation: The server sends the generated quotation to the user's terminal, and the user confirms it.

[0127] Example of a prompt

[0128] You can request a fair price estimate from a generative AI model using the following prompt:

[0129] "Please provide a quote for the following product: 'Software Service A', specifications: 'Cloud-based data management function', desired price range: '1 million to 2 million yen', target market: 'Small and medium-sized enterprises'."

[0130] Based on this prompt, the AI ​​model calculates a fair price and generates a quote.

[0131] This system allows users to quickly and accurately calculate fair prices and generate and send the results as quotations.

[0132] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0133] Step 1:

[0134] The user inputs basic information about the product or service into the terminal. Specifically, they input information such as the product name, specifications, desired price range, and target market. As an example of input, the user inputs the name "Software Service A," the specifications "Cloud-based data management function," the desired price range "1 million to 2 million yen," and the target market information "For small and medium-sized enterprises." This becomes the system's input information.

[0135] Step 2:

[0136] The terminal encrypts the entered basic information using the AES encryption method and sends it to the server via the HTTPS protocol. This is the input information transfer process. Specifically, when the send button is pressed, the terminal encrypts the data and sends it to the specified endpoint on the server. The output of this process is encrypted data, which is sent to the server.

[0137] Step 3:

[0138] The server receives encrypted data sent from the terminal and deserializes it back to its original format. The received and deserialized data includes information such as product name, specifications, desired price range, and target market. The server uses this as input to search its own and external market databases and extract similar data. This is the process of generating database queries and searching for entries in the database that match or are similar to the product name and specifications. Specifically, it extracts 100 data entries related to "cloud-based data management functionality" from the database over the past three years. The output is a set of similar data.

[0139] Step 4:

[0140] The server uses a generative AI model to calculate a fair price based on the extracted data. The input is similar data extracted in step 3. The server inputs this data into the generative AI model, which analyzes it using an algorithm that takes market trends and competitive conditions into account. Specifically, the model evaluates historical price information, current market demand, and competitor pricing to calculate a fair price. For example, a fair price of "1.5 million yen" is calculated based on historical data and current market trends. The output is the calculated fair price.

[0141] Step 5:

[0142] The server automatically generates a quotation based on the calculated fair price. The server uses a template engine (e.g., Apache Velocity) to insert the calculated fair price and the entered basic information into a template. Specifically, it generates a PDF quotation that includes the name "Software Service A," its specifications, and a price of "1.5 million yen." The input to the template is the basic information and the fair price, and the output is the generated PDF quotation.

[0143] Step 6:

[0144] The server sends the generated quotation to the user's terminal. The input is the generated quotation in PDF format. Email services (e.g., SendGrid) or push notifications are used as the means of transmission. Specifically, the email subject line will be "Quotation: Software Service A," and the PDF will be sent as an attachment. The output is the sent email or push notification.

[0145] Step 7:

[0146] The device downloads a quotation from an received email or notification and displays it to the user. The input is the received email or push notification, and the device uses this information to download and display the quotation. Specifically, the user opens the email, downloads the attached PDF, and can view it on the device. The output is the quotation displayed to the user.

[0147] This allows users to create estimates quickly and accurately.

[0148] (Application Example 1)

[0149] 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 device 14 will be referred to as the "terminal."

[0150] There is a need for a means to quickly and accurately estimate the fair price of goods or services, thereby strengthening the relationship of trust between users and customers. However, conventional systems have problems such as being time-consuming to calculate fair prices and failing to accurately reflect market trends and competitive situations. In addition, the quotation generation process is cumbersome and often detracts from the user experience.

[0151] 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.

[0152] In this invention, the server includes means for inputting basic information of a product or service, means for transmitting the inputted information, means for connecting to an external API to acquire market data, means for extracting similar data from a database based on the inputted information, means for calculating a fair price using a machine learning model, means for generating and processing prompt statements using a generative AI model, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, and means for displaying the quotation. This makes it possible to quickly and accurately calculate a fair price based on information entered by the user and to immediately generate and transmit a quotation.

[0153] "Basic information about a product or service" refers to information such as the name of the product or service, specifications, desired price range, and target market.

[0154] "Means of input" refers to interfaces or input devices that allow users to input basic information about a product or service.

[0155] "Means of transmission" refers to communication devices and protocols used to transmit input information to a server or external system.

[0156] A "database" is a collection of information used to store past transaction data and market data.

[0157] "Means for extracting similar data" refers to algorithms or programs used to search and extract past data from a database that is similar to the input information.

[0158] "Methods for calculating fair prices" refer to algorithms and calculation models for determining fair prices based on similar data extracted from a database and market trends.

[0159] "Means for generating quotations" refers to programs or software that automatically create quotations containing the calculated fair price and detailed information about the goods or services.

[0160] "Means of transmission to the receiving terminal" refers to communication devices and protocols used to send the generated quotation to the user's terminal (smartphone, PC, etc.).

[0161] "Means of display" refers to interfaces or display devices for displaying quotations on the receiving terminal.

[0162] "External API connection means" refers to programs or communication protocols used to connect to external APIs in order to obtain external market data and related data.

[0163] A "machine learning model" refers to a learning algorithm or predictive model used to predict prices based on data.

[0164] A "generative AI model" is an artificial intelligence model designed to generate appropriate output in response to a specific input (prompt).

[0165] A "prompt sentence" is an instruction or question that is input into a generative AI model to obtain a specific output.

[0166] As a concrete example of implementing this invention, a system for estimating appropriate product prices in a smartphone app for an e-commerce site is presented. This system is designed to quickly and accurately estimate appropriate prices when a user lists a new product. When a user enters product information using the smartphone app, that information is sent to a server, and an appropriate price is calculated considering past database comparisons and market trends. The generated estimate is then sent to the user.

[0167] First, the user enters basic information about the product or service into the smartphone app. This includes information such as the product name, specifications, desired price range, and target market. For example, if a user is estimating a fair price for a new smartwatch, they would enter specifications such as the name "SmartWatch Model X," screen size, and battery life.

[0168] Next, the terminal sends the entered information to the server. This data is preferably encrypted for security reasons. Based on the received information, the server connects to external APIs to obtain internal and external market data. It then extracts similar historical data from the database based on the acquired market data.

[0169] The server uses a machine learning model to calculate a fair price based on the extracted data. This process utilizes algorithms such as linear regression, taking into account market trends and competitive conditions. For example, it might use 100 historical price data points for similar smartwatches to calculate a fair price.

[0170] Based on the calculated price, the server generates a quotation. The quotation includes the product name, specifications, and appropriate price, and is saved in PDF format. The generated quotation is sent to the user's device, and the user is notified via push notification or email notification. The user can then view the received quotation in the app.

[0171] The following are some specific examples of prompt statements generated using a generative AI model.

[0172] The specifications for the "SmartWatch Model X" are as follows: Screen size: 1.5 inches, Battery life: 24 hours. Please estimate the market price for this product based on historical data and current trends.

[0173] As described above, this system allows users to quickly and accurately estimate fair prices, enabling reliable transactions.

[0174] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0175] Program processing flow

[0176] Step 1:

[0177] The user enters basic information about the product or service into a smartphone app. For example, the user might enter specifications such as the name, screen size, and battery life of a "SmartWatch Model X." This input generates product information.

[0178] Step 2:

[0179] The terminal sends the entered information to the server. This transmission is encrypted for security purposes. The input data (product name, specifications, etc.) arrives at the server as transmitted data.

[0180] Step 3:

[0181] Based on the information received, the server connects to its own and external APIs to retrieve market data. During this process, it calls external APIs to collect market data related to the specified product category. The retrieved market data is then stored as local data.

[0182] Step 4:

[0183] The server extracts historical data similar to the information entered from the database. This uses a search algorithm to narrow down the historical data using product specifications and target market information as keys. This data extraction generates a similarity dataset.

[0184] Step 5:

[0185] The server uses a machine learning model to calculate a fair price based on the extracted data and acquired market data. Here, a linear regression model is used, combining historical price data with current market trends in the calculation. The price predicted by the machine learning model is output as the fair price.

[0186] Step 6:

[0187] The server generates a quotation based on the calculated fair price. The quotation includes detailed product information and the calculated price, and is saved in PDF format. This generated quotation is output as a quotation file.

[0188] Step 7:

[0189] The server sends the generated quotation to the user's receiving device. Email or push notifications are used as the sending method. The sent quotation is received on the user's device.

[0190] Step 8:

[0191] The user views the received quotation on their smartphone app. The app provides the user with quotation information through a viewer that displays the received PDF quotation. This display allows the user to review the quotation content and prepare to share it with the customer.

[0192] 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.

[0193] As an example of implementing this invention, a system is provided that combines a user emotion engine to more accurately estimate the appropriate price of goods and services. In addition to a function that extracts similar information from a database based on information entered by the user into a terminal and calculates an appropriate price, this system uses an emotion engine to recognize the user's emotions and adjust subsequent responses accordingly.

[0194] System Overview

[0195] The user enters basic information about the product or service into their terminal and sends that information to the server. The server searches its database based on the received information, extracts similar data, and calculates a fair price. Then, it generates a quotation based on the calculated fair price and sends it to the user's terminal. Furthermore, an emotion engine recognizes the user's emotions and provides suggestions and advice based on that information. In this way, the quotation process is customized according to the user's psychological state.

[0196] Program processing flow

[0197] 1. Entering information

[0198] User: Enter basic information about the product or service to be quoted (name, specifications, desired price range, target market, etc.) into the terminal. This includes detailed descriptions and options.

[0199] 2. Data transmission

[0200] Terminal: Sends basic information entered by the user to the server. It is recommended that this data be encrypted for security reasons.

[0201] 3. Database matching

[0202] Server: Receives input information and generates queries to the database. It searches and extracts similar past data using keywords such as product name, specifications, and target market.

[0203] 4. Calculation of a fair price

[0204] Server: Executes an algorithm to calculate a fair price based on the extracted data. The price calculation uses weighted averages, regression analysis, machine learning models, etc., and takes market trends and competitive conditions into consideration.

[0205] 5. Generating the final quotation

[0206] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price. It is generally generated in PDF format.

[0207] 6. Sending the quotation

[0208] Server: Sends the generated quotation to the user's terminal. Email and push notifications are used as notification methods.

[0209] 7. Emotion recognition by an emotion engine

[0210] Terminal: Sends user input information and facial recognition data to the emotion engine. The emotion engine analyzes the user's emotions based on this information.

[0211] Emotion Engine: Sends analysis results to the server and generates suggestions and advice for the user based on them.

[0212] 8. Display of suggestions and advice

[0213] Terminal: Based on the analysis results of the emotion engine, it displays suggestions and advice to the user. This allows the user to receive appropriate information tailored to their psychological state.

[0214] Specific example

[0215] For example, when a user requests a quote for a new software service:

[0216] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[0217] 2. Data transmission: The terminal sends the input information to the server.

[0218] 3. Database matching: The server extracts similar past data based on the input information.

[0219] 4. Calculation of fair price: The server calculates a fair price considering market trends and competitive conditions.

[0220] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[0221] 6. Sending the quotation: The server sends the generated quotation to the user's terminal.

[0222] 7. Emotion recognition by emotion engine: The emotion engine analyzes the facial expression data when the user makes inputs and recognizes the user's emotions.

[0223] 8. Displaying suggestions and advice: Based on the analysis results, if the user is feeling stressed, suggestions to help them relax will be displayed; if they are happy, more positive suggestions will be displayed.

[0224] This system allows users to quickly calculate fair prices and receive appropriate support tailored to their emotions, helping to strengthen customer trust. Furthermore, the use of an emotion engine significantly improves the user experience.

[0225] The following describes the processing flow.

[0226] Step 1:

[0227] User: Enter basic information about the product or service to be quoted into the terminal. For example, enter the name of "Software Service A," a brief description of its functions, desired price range, and target market into the input form. Once the user has finished entering the information, they will check the details on the confirmation screen and press the submit button.

[0228] Step 2:

[0229] Terminal: Sends the basic information entered by the user to the server. The input information is converted to JSON or XML format and encrypted for security. After transmission is complete, a notification "Information sent" is displayed on the terminal.

[0230] Step 3:

[0231] Server: Analyzes the received basic information and generates a search query. The query sends a search command to the database containing keywords such as product name, specifications, and target market.

[0232] Step 4:

[0233] Server: Searches the database and extracts data on past products and services similar to the entered information. For example, it extracts data on similar software services for which quotes were previously created. This data includes past prices, specification details, and market price information.

[0234] Step 5:

[0235] Server: Calculates a fair price based on extracted data. It uses weighted averages, regression analysis, and machine learning models to calculate a price that considers historical data, current market trends, and competitive landscape. For example, it might derive the most appropriate price based on 100 historical price data points for similar services.

[0236] Step 6:

[0237] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price. The quotation is generated in PDF format and formatted using templates as needed.

[0238] Step 7:

[0239] Server: Sends the generated quote to the user's device. Once the transmission is complete, the user is notified via email or push notification.

[0240] Step 8:

[0241] Terminal: Displays received quotations to the user. The user can review the quotation on the terminal and check for any problems. If the quotation is correct, it is ready to be used for subsequent negotiations and proposals.

[0242] Step 9:

[0243] User: Review the quotation and communicate with the customer as needed. The quotation contains fair pricing and detailed information, enabling reliable negotiations.

[0244] Step 10:

[0245] Terminal: Acquires user input information and facial expression data during operation from the camera and sends it to the emotion engine. This uses a high-performance camera and emotion analysis software.

[0246] Step 11:

[0247] Server and Emotion Engine: Analyzes received facial recognition data to determine the user's emotional state. For example, it uses a facial expression analysis algorithm to determine whether the user is nervous, happy, etc. The emotion engine generates the analysis results and sends them to the server.

[0248] Step 12:

[0249] Server: Based on the analysis results of the emotion engine, it generates suggestions and advice for the user. If the user is stressed, it displays messages to help them relax; if they are happy, it displays even more positive suggestions.

[0250] Step 13:

[0251] Terminal: Displays generated suggestions and advice to the user. The user uses this information to review the quote and take appropriate action. Emotion-based advice improves the user experience.

[0252] This series of processes allows users to quickly calculate a fair price, receive appropriate support tailored to their emotions, and strengthen customer trust. By utilizing an emotion engine, the quotation process becomes more personalized, improving the user experience.

[0253] (Example 2)

[0254] 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".

[0255] In systems that calculate appropriate prices and generate quotations based on basic information about products and services, there is a problem with the user experience due to the lack of suggestions and advice that take user emotions into consideration. Furthermore, the system does not adequately consider market trends and competitive situations when calculating appropriate prices, making it difficult to provide accurate quotations.

[0256] The identification processing 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 inputting basic information of a product or service, means for transmitting the inputted information, means for extracting similar data from a database based on the inputted information, means for calculating an appropriate price based on the extracted data, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, means for recognizing emotions based on user input information and facial recognition data, means for making suggestions and advice to the user based on the recognized emotions, and means for displaying the quotation and the suggestions and advice. This makes it possible to make suggestions and advice that take the user's emotions into consideration, improving the user experience while realizing an accurate and appropriate price estimate.

[0257] "Basic information about the product or service" refers to detailed information about the product or service being quoted, such as its name, specifications, desired price range, and target market.

[0258] "Means of input" refers to an interface through which a user provides basic information about a product or service to the system, and includes, for example, a keyboard, mouse, or touchscreen.

[0259] "Means of transmission" refers to devices and protocols used to send information entered by a user to a server, including, for example, internet connections, Wi-Fi, and data encryption protocols.

[0260] "Means of extraction" refers to devices or software used to search for and extract data similar to the information entered into a database, and includes, for example, SQL queries and search algorithms.

[0261] "Means for calculating fair prices" refer to devices or software that calculate fair prices using algorithms such as weighted averages, regression analysis, and machine learning models based on extracted data.

[0262] "Means for generating quotations" refers to devices or software used to create quotations based on calculated fair price information, and includes, for example, template engines and PDF generation software.

[0263] "Means of sending to the receiving terminal" refers to devices or protocols for sending the generated quotation to the user's receiving terminal, including, for example, email servers and push notification services.

[0264] "Means of recognizing emotions" refer to devices and software that analyze user input information and facial recognition data to identify the user's emotions, and include, for example, facial expression recognition algorithms and emotion analysis APIs.

[0265] "Means of providing suggestions and advice" refers to devices or software that provide appropriate suggestions and advice to users based on emotions recognized by an emotion engine.

[0266] "Means of display" refers to an interface for visually displaying the generated estimates, proposals, and advice to the user, and includes, for example, displays and monitors.

[0267] This invention is a system for more accurately estimating the appropriate price of goods and services, and incorporates an emotion engine that recognizes the user's emotions and adjusts subsequent responses accordingly. In addition to a function that extracts similar information from a database based on information entered by the user on a terminal and calculates an appropriate price, this system can also provide suggestions and advice that are tailored to the user's emotions by using the emotion engine.

[0268] Users input basic information about their products or services (e.g., name, specifications, desired price range, target market, etc.) into a terminal and send this information to a server. The terminal includes input devices such as a keyboard, mouse, and touchscreen. The input information is in JSON or XML format and is encrypted using SSL / TLS. The server generates queries to access a database based on the received information and extracts similar historical data. SQL or NoSQL databases are used. Based on this extracted data, the server calculates an appropriate price using weighted averages, regression analysis, and machine learning models (e.g., linear regression and random forest). Market trends and competitive conditions are also considered in real time.

[0269] The server generates a quote based on a fair price. This quote includes detailed information about the goods or services, the basis for the calculation, and the fair price, and is generated in PDF format using a template engine (e.g., JasperReports or iText). The generated PDF quote is then sent to the user's device via email (using SMTP) or push notification (e.g., Firebase Cloud Messaging).

[0270] Furthermore, an emotion engine is provided that analyzes emotions based on user input information and facial expression data. The device uses its built-in camera to acquire facial recognition data, which is then analyzed, for example, through the Microsoft® Azure® Emotional Analysis API. The results of the emotion engine's analysis are sent to a server, which then generates suggestions and advice for the user based on these results. These might include instructions to promote relaxation or suggestions for additional options. The device is equipped with a display or monitor to show these suggestions and advice to the user.

[0271] As a concrete example, consider a scenario where a user requests a quote for a new software service A. The user inputs the name of software service A, its specifications (e.g., cloud-based, monthly subscription), desired price range ($500-$700), and target market (small and medium-sized enterprises) into the terminal. The terminal sends this information to the server in JSON format, and the data is encrypted using SSL / TLS. The server generates an SQL query based on the received information to extract similar historical data from the database. Next, the server calculates a fair price using a linear regression model, taking into account market trends and competitive information, and generates a quote in PDF format using a template engine. The server then sends the generated PDF to the user via email. Simultaneously, the terminal sends the user's facial expression data from the input process to an emotion engine, and based on the analysis results, displays relaxation-enhancing advice and additional option suggestions to the user.

[0272] Example of a prompt:

[0273] "I would like to request a quote for a new software service A. Please calculate a fair price and generate a quote based on the following information."

[0274] Software name: Software Service A

[0275] Specifications: Cloud-based, user interface is a web app, monthly subscription fee.

[0276] Desired price range: $500 - $700

[0277] Target market: Small and medium-sized enterprises (SMEs)

[0278] Furthermore, recognize the user's emotions when they input data and display suggestions and advice based on those emotions.

[0279] This system allows users to quickly and accurately calculate fair prices, and also provides personalized suggestions and advice based on their emotions, which is expected to improve customer satisfaction.

[0280] The flow of the specific process in Example 2 will be described with reference to FIG. 13.

[0281] Step 1: Information Input

[0282] User: Inputs basic information of the merchandise or service to be estimated into the terminal. Specifically, information such as merchandise name, specifications, desired price range, target market, etc. is input. The input information is processed as JSON-formatted data.

[0283] Input: Basic information such as merchandise name, specifications, desired price range, target market, etc.

[0284] Output: Basic information data in JSON format input to the terminal

[0285] Step 2: Data Transmission

[0286] Terminal: Transmits the input information to the server. At this time, to ensure the security of the information, the data is encrypted using the SSL / TLS protocol. The data format is usually JSON.

[0287] Input: Basic information data in JSON format

[0288] Output: Transmits encrypted information data in JSON format to the server

[0289] Step 3: Matching with the Database

[0290] Server: Based on the received information, generates a query for the database. Accesses the database using an SQL query, searches for and extracts past data similar to the input basic information.

[0291] Input: Encrypted information data in JSON format

[0292] Output: Extraction result of similar past data

[0293] Step 4: Calculating the Fair Price

[0294] Server: Calculates a fair price based on extracted historical data. This calculation uses weighted averages, regression analysis, and machine learning models (e.g., linear regression, random forest), taking market trends and competitive landscape into consideration.

[0295] Input: Extracted historical data, current market trend information, competitor data

[0296] Output: Calculated fair price

[0297] Step 5: Generating the final quote

[0298] Server: Generates a quotation based on the calculated fair price. The quotation is created in PDF format using a template engine (e.g., JasperReports, iText). The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price.

[0299] Input: Calculated fair price, detailed information about the product / service.

[0300] Output: Quotation in PDF format

[0301] Step 6: Send the quotation

[0302] Server: Sends the generated quote to the user's device. Quotes are sent via email (SMTP) or push notifications (e.g., Firebase Cloud Messaging).

[0303] Input: Quotation in PDF format

[0304] Output: Quotation in PDF format sent to the user's receiving terminal.

[0305] Step 7: Emotion recognition by the emotion engine

[0306] Terminal: Transmits the user's input information and facial expression data to the emotion engine. Uses the camera built into the terminal to obtain facial recognition data and transmits it to the emotion engine. As an example, the emotion engine includes a facial expression recognition algorithm and an emotion analysis API (e.g., Microsoft Azure Emotional Analysis API).

[0307] Input: User's facial expression data, input information

[0308] Output: User data transmitted to the emotion engine

[0309] Step 8: Display of suggestions and advice

[0310] Terminal: Based on the analysis results of the emotion engine, displays suggestions and advice for the user. For example, when the user is nervous, it displays a suggestion to relax, and when the user is happy, it displays a positive suggestion.

[0311] Input: Analysis results from the emotion engine

[0312] Output: Suggestions and advice displayed to the user

[0313] (Application Example 2)

[0314] Next, Application Example 2 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".

[0315] Conventional quotation systems for commercial products and services did not have the function of making appropriate suggestions and advice considering the user's emotions. Therefore, it was difficult to improve the user's psychological satisfaction and reliability, and the user experience was limited. Also, there was a lack of technology for more accurately calculating the appropriate price of commercial products and services by introducing emotion recognition in the quotation process.

[0316] 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.

[0317] In this invention, the server includes means for inputting basic information of a product or service, means for extracting similar data from a database based on the input information, means for calculating a fair price based on the extracted data, means for recognizing the user's emotional information when estimating the product or service, and means for generating suggestions and advice based on the analysis results of the emotional recognition means. This makes it possible to consider the user's psychological state during the estimation process and provide suggestions and advice that are more satisfying. Furthermore, by utilizing the user's emotional information, the accuracy of calculating a fair price is improved, resulting in more accurate price estimates.

[0318] "Basic information about the product or service" refers to detailed information entered by the user, such as the name of the product or service, specifications, desired price range, and target market.

[0319] "Input method" refers to an interface for users to input basic information about a product or service.

[0320] "Means of transmission" refers to a mechanism for securely sending input information to a server, such as by encrypting it.

[0321] "Means for extracting similar data from a database" refers to a function in which the server searches the database for and extracts similar past data based on the input information.

[0322] A "means for calculating a fair price" refers to an algorithm or model that calculates a fair price for a product or service based on historical data extracted from a database, taking into account the market value and competitive landscape.

[0323] "Methods for generating quotations" refers to the process of creating a quotation that includes detailed information about the goods or services and the basis for the calculation, based on the calculated fair price.

[0324] "Means of sending to the receiving terminal" refers to a system that sends the generated quotation to the user's terminal.

[0325] "Means of displaying the quotation" refers to a function that allows the quotation to be displayed on the receiving terminal so that it can be viewed.

[0326] "Emotion recognition means" refers to technology that recognizes a user's emotions by analyzing user input information and facial expression data.

[0327] "Means for generating suggestions and advice" refers to a function that creates suggestions and advice tailored to the user's emotional state based on the analysis results of emotion recognition.

[0328] This invention is a system that recognizes user emotions and provides appropriate suggestions and advice during the process of estimating the fair price of goods and services. The following describes embodiments for carrying out this invention.

[0329] System Configuration

[0330] The system of this invention consists of the following main components:

[0331] 1. Means of entering basic information about a product or service:

[0332] Users input basic information about products or services using a smartphone app. This basic information includes the name of the product or service, specifications, desired price range, and target market.

[0333] 2. Means of information transmission:

[0334] The terminal encrypts the entered basic information and sends it to the server. Security protocols such as TLS and HTTPS are used as encryption technologies.

[0335] 3. Methods for extracting similar data:

[0336] The server searches the database based on the received information and extracts similar past data. SQL or NoSQL databases are used for this process.

[0337] 4. Methods for calculating fair prices:

[0338] The server calculates a fair price based on similar data extracted from a database. Regression analysis and machine learning algorithms (such as Scikit-learn) are used for this calculation.

[0339] 5. Methods for generating quotations:

[0340] The server generates a quotation based on the calculated fair price. The quotation is output in PDF format, using a PDF generation library (such as ReportLab).

[0341] 6. Method of sending the quotation:

[0342] The generated quote will be sent to the user's device via email or push notification.

[0343] 7. Emotion recognition means:

[0344] An emotion engine is used to analyze user input information and facial recognition data. Examples of emotion engines include OpenCV and the Emotion API.

[0345] 8. Means of generating suggestions and advice:

[0346] The server generates suggestions and advice that the user finds optimal based on the results of emotion recognition. Using a generative AI model is recommended for this process.

[0347] Specific example

[0348] When a user requests a quote for new high-performance earphones:

[0349] 1. The user enters basic information about the "high-performance earphones" using a smartphone app.

[0350] 2. The device encrypts this information and sends it to the server.

[0351] 3. The server searches the database and extracts past data for similar earphones.

[0352] 4. The server calculates a fair price based on the extracted data.

[0353] 5. The quotation is generated in PDF format and sent to the user's device.

[0354] 6. The emotion engine analyzes the user's input information and facial expression data and recognizes that the emotion is positive.

[0355] 7. If the emotion is judged to be positive, more proactive suggestions will be made, such as, "Would you like to see our higher-performance products?"

[0356] Example of a prompt

[0357] A user has requested a quote for a new premium product. They have positive feedback. Calculate a fair price and output a sentiment-based suggestion.

[0358] input:

[0359] Basic information on high-performance earphones

[0360] output:

[0361] Estimated price: $300

[0362] Advice: "Would you like to see our higher-performance products?"

[0363] As described above, the present invention makes it possible to provide an estimation system that takes into account the user's psychological state and offers even greater satisfaction.

[0364] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0365] Step 1:

[0366] Users input basic information about a product or service using a smartphone app. This includes detailed information such as the name, specifications, desired price range, and target market.

[0367] Input: Basic information about the product or service

[0368] Output: Input basic information data

[0369] Step 2:

[0370] The terminal encrypts the entered basic information and sends it to the server. To ensure the security of the information, security protocols such as TLS and HTTPS are used.

[0371] Input: Encrypted basic information data

[0372] Output: Encrypted data sent to the server

[0373] Step 3:

[0374] The server decrypts the received basic information and searches the database to extract similar past data. SQL or NoSQL databases are used for the search.

[0375] Input: Decrypted basic information data

[0376] Output: Similar historical data

[0377] Step 4:

[0378] The server calculates a fair price based on similar data. Regression analysis and machine learning algorithms are used for this calculation. Libraries used include Scikit-learn.

[0379] Input: Similar historical data

[0380] Output: Fair price

[0381] Step 5:

[0382] The server generates a quotation in PDF format based on a fair price. Libraries such as ReportLab are used for PDF generation.

[0383] Input: Fair price

[0384] Output: Quotation in PDF format

[0385] Step 6:

[0386] The server sends the generated quotation to the user's device. Email or push notifications are used as the means of delivery.

[0387] Input: Quotation in PDF format

[0388] Output: Notification of quotation to user terminal

[0389] Step 7:

[0390] The device sends user input information and facial recognition data to the emotion engine. The emotion engine analyzes the user's emotions. Examples of emotion engines used include OpenCV and the Emotion API.

[0391] Input: User input information and facial recognition data

[0392] Output: Emotion analysis results

[0393] Step 8:

[0394] The server generates suggestions and advice for the user based on the sentiment analysis results. Using a generative AI model is effective in this process.

[0395] Input: Sentiment analysis results

[0396] Output: Suggestion and advice messages

[0397] Step 9:

[0398] The device displays generated suggestions and advice to the user. This allows the user to receive appropriate suggestions tailored to their emotions.

[0399] Input: Suggestion or advice message

[0400] Output: Content displayed to the user

[0401] 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.

[0402] Data generation model 58 is a type of 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.

[0403] 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.

[0404] [Second Embodiment]

[0405] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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".

[0417] As an example of implementing this invention, a system for quickly and accurately estimating the appropriate price of a product or service is provided. This system calculates an appropriate price based on information entered by the user into a terminal, taking into account comparisons with past databases and market trends, and generates and transmits the result as a quotation.

[0418] System Overview

[0419] The user enters basic information about a product or service into their terminal and sends that information to the server. The server searches its database based on the received information, extracts similar data, and calculates a fair price. Then, it generates a quotation based on the calculated price information and sends it to the user's terminal. The user can review the sent quotation and share it with the customer to strengthen trust.

[0420] Program processing flow

[0421] 1. Entering information

[0422] User: Enter basic information (product name, specifications, desired price range, target market, etc.) into the terminal to create a quote.

[0423] As a specific example, when a user requests a quote for a new software service, they input the name of "Software Service A," a brief description of its functions, desired price range, and target market information into the terminal.

[0424] 2. Data transmission

[0425] Terminal: Sends user-entered information to the server. This data should preferably be encrypted for security reasons.

[0426] 3. Database matching

[0427] Server: Receives the entered information and compares it with the company's and related market databases. Specifically, it searches and extracts past quotation data and market price data similar to the entered product name and specifications.

[0428] 4. Calculation of a fair price

[0429] Server: Based on the extracted data, it calculates a fair price. In this process, it uses an algorithm to consider market trends and competitive conditions.

[0430] As a concrete example, we have 100 historical data points for similar software services, and we extract important data from them to calculate an appropriate price considering current market trends.

[0431] 5. Generating the final quotation

[0432] Server: Generates a quotation based on the calculated fair price. This quotation includes details of the goods / services and their fair price.

[0433] For example, if the appropriate price for software service A is calculated to be 1 million yen, a PDF quotation will be generated that includes the price and service specifications.

[0434] 6. Sending and confirming the quotation

[0435] Server: Sends the generated quotation to the user's terminal. Email and push notifications are commonly used as notification methods.

[0436] Terminal: Displays received quotations and allows users to review them.

[0437] Specific example

[0438] For example, when a user requests a quote for a new software service, they would use the system as follows:

[0439] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[0440] 2. Data transmission: The terminal sends the input information to the server.

[0441] 3. Database matching: The server extracts similar past data from the database based on the input information.

[0442] 4. Calculation of appropriate price: The server calculates an appropriate price based on extracted data, taking into account market trends and competitive conditions.

[0443] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[0444] 6. Sending and confirming the quotation: The server sends the generated quotation to the user's terminal, and the user confirms it.

[0445] This system allows users to quickly calculate fair prices, helping to strengthen trust with customers.

[0446] The following describes the processing flow.

[0447] Step 1:

[0448] User: Enter basic information about the product or service to be quoted (name, specifications, desired price range, target market, etc.) into the terminal. Specifically, enter the necessary information according to the input form or selection options. A confirmation screen of the entered information is also provided to prevent input errors.

[0449] Step 2:

[0450] Terminal: Sends the basic information entered by the user to the server. The input data is converted to JSON or XML format, and encryption is recommended for security. After successful transmission, the user receives a notification.

[0451] Step 3:

[0452] Server: Analyzes the received basic information and generates queries for the database. The queries include keywords such as product name, specifications, and target market.

[0453] Step 4:

[0454] Server: Searches the database and extracts data on similar products and services from the past. In this process, it uses multiple databases or tables to collect the necessary information and normalizes the data as needed.

[0455] Step 5:

[0456] Server: Based on the extracted data, it executes an algorithm to calculate a fair price. Specifically, it uses weighted averages, regression analysis, machine learning models, etc., to calculate a fair price that takes into account past price data and current market trends.

[0457] Step 6:

[0458] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods or services, the basis for the calculation, and the fair price. Quotations are typically generated in PDF format.

[0459] Step 7:

[0460] Server: Sends the generated quotation to the user's terminal. Delivery methods include email, push notification, or in-system messaging service.

[0461] Step 8:

[0462] Terminal: Receives the sent quotation and notifies the user that the quotation has arrived. The user who receives the notification can then view the quotation displayed on their terminal.

[0463] Step 9:

[0464] User: Review the displayed quote, negotiate with the customer or make internal adjustments as needed, and prepare the final quote to provide to the customer.

[0465] This series of steps allows users to quickly calculate fair prices and provide reliable quotes to customers. Furthermore, the system's ease of use and accuracy streamline sales activities.

[0466] (Example 1)

[0467] 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."

[0468] Traditional pricing systems for goods and services made it difficult to quickly and accurately calculate fair prices. They failed to consider market trends and competitive landscapes, resulting in unreliable quotes and hindering the building of trust with customers.

[0469] 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.

[0470] In this invention, the server includes means for inputting basic information of a product or service, means for transmitting the inputted information, means for extracting similar data from a database based on the inputted information, means for calculating an appropriate price using a generation AI model based on the extracted data, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, and means for displaying the quotation. This makes it possible for the user to quickly and accurately calculate an appropriate price and generate and transmit the result as a quotation.

[0471] "Merchandise" refers to goods and services.

[0472] "Basic information" refers to information about a product or service, such as its name, specifications, desired price range, and target market.

[0473] "Means of input" refers to the hardware and software that users use to input basic information about a product or service.

[0474] "Means of transmission" refers to the means of communication used to send the entered basic information to the server.

[0475] A "database" refers to data storage that holds past estimate data and market price data for products and services.

[0476] "Extraction method" refers to the means of searching for and retrieving similar data from a database based on the basic information entered.

[0477] A "generative AI model" refers to a machine learning model that analyzes past data and calculates an appropriate price by considering current market trends and competitive conditions.

[0478] "Methods for calculating fair prices" refer to methods for calculating the appropriate price of a product or service from data extracted using a generative AI model.

[0479] A "quotation" refers to a document that contains detailed information about a product or service, including its fair price.

[0480] "Generating means" refers to software and algorithms for automatically creating quotations based on appropriate pricing and entered basic information.

[0481] "Means of transmission" refers to the means of communication used to send the generated quotation to the receiving terminal.

[0482] "Receiving terminal" refers to the device that a user uses to receive and view a quotation.

[0483] "Means of display" refers to the means by which a user can visually confirm the received quotation.

[0484] An "algorithm" refers to a procedure or method for calculating a fair price by taking into account market trends and competitive conditions.

[0485] "PDF format" is an abbreviation for Portable Document Format, and refers to a file format for electronically storing and sharing documents.

[0486] "Encrypted methods" refer to means of protecting data using cryptographic techniques to enhance data security.

[0487] This invention will be explained using a system for quickly and accurately estimating the appropriate price of a product or service as an example. Based on information entered by the user into a terminal, this system calculates an appropriate price by comparing it with past databases and considering market trends, and then generates and transmits the result as a quotation.

[0488] System Overview

[0489] The user enters basic information about the product or service into the terminal and sends that information to the server. Specifically, when a user requests a quote for a new software service, they would enter the name of "Software Service A," a brief description of its functions, the desired price range, and information about the target market.

[0490] The terminal receives basic information entered by the user, encrypts it, and sends it to the server. AES encryption is recommended for this process, and HTTPS is used as the communication protocol.

[0491] The server decodes the information received from the terminal and searches the database to extract similar data. For example, it searches and extracts data with similar product names and specifications from the quotation database for the past three years. At this time, a database query is generated to find data that matches or is similar to the product name and specifications.

[0492] Next, the server uses a generative AI model to calculate a fair price based on the extracted data. This generative AI model uses an algorithm that analyzes historical data and considers current market trends and competitive conditions. For example, it extracts important information from 100 data points and calculates a fair price considering current market trends.

[0493] After a fair price is calculated, the server automatically generates a quote using a template engine (e.g., Apache Velocity). This quote includes details of the product or service and its fair price. For example, if the fair price for "Software Service A" is calculated to be 1.5 million yen, a PDF quote will be generated that includes the price and service specifications.

[0494] Finally, the server sends the generated quotation to the user's device. The delivery method may include an email service (e.g., SendGrid) or push notification. Specifically, the quotation is sent as a PDF attachment with the subject line "Quotation: Software Service A".

[0495] The device allows users to download quotes via received emails and notifications, enabling them to review the contents. After reviewing, users can share these quotes with customers, strengthening their relationship of trust.

[0496] Specific example

[0497] For example, the specific steps a user might take to get an estimate for a new software service are as follows:

[0498] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[0499] 2. Data transmission: The terminal sends the input information to the server.

[0500] 3. Database matching: The server extracts similar past data from the database based on the input information.

[0501] 4. Calculation of appropriate price: The server calculates an appropriate price based on extracted data, taking into account market trends and competitive conditions.

[0502] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[0503] 6. Sending and confirming the quotation: The server sends the generated quotation to the user's terminal, and the user confirms it.

[0504] Example of a prompt

[0505] You can request a fair price estimate from a generative AI model using the following prompt:

[0506] "Please provide a quote for the following product: 'Software Service A', specifications: 'Cloud-based data management function', desired price range: '1 million to 2 million yen', target market: 'Small and medium-sized enterprises'."

[0507] Based on this prompt, the AI ​​model calculates a fair price and generates a quote.

[0508] This system allows users to quickly and accurately calculate fair prices and generate and send the results as quotations.

[0509] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0510] Step 1:

[0511] The user inputs basic information about the product or service into the terminal. Specifically, they input information such as the product name, specifications, desired price range, and target market. As an example of input, the user inputs the name "Software Service A," the specifications "Cloud-based data management function," the desired price range "1 million to 2 million yen," and the target market information "For small and medium-sized enterprises." This becomes the system's input information.

[0512] Step 2:

[0513] The terminal encrypts the entered basic information using the AES encryption method and sends it to the server via the HTTPS protocol. This is the input information transfer process. Specifically, when the send button is pressed, the terminal encrypts the data and sends it to the specified endpoint on the server. The output of this process is encrypted data, which is sent to the server.

[0514] Step 3:

[0515] The server receives encrypted data sent from the terminal and deserializes it back to its original format. The received and deserialized data includes information such as product name, specifications, desired price range, and target market. The server uses this as input to search its own and external market databases and extract similar data. This is the process of generating database queries and searching for entries in the database that match or are similar to the product name and specifications. Specifically, it extracts 100 data entries related to "cloud-based data management functionality" from the database over the past three years. The output is a set of similar data.

[0516] Step 4:

[0517] The server uses a generative AI model to calculate a fair price based on the extracted data. The input is similar data extracted in step 3. The server inputs this data into the generative AI model, which analyzes it using an algorithm that takes market trends and competitive conditions into account. Specifically, the model evaluates historical price information, current market demand, and competitor pricing to calculate a fair price. For example, a fair price of "1.5 million yen" is calculated based on historical data and current market trends. The output is the calculated fair price.

[0518] Step 5:

[0519] The server automatically generates a quotation based on the calculated fair price. The server uses a template engine (e.g., Apache Velocity) to insert the calculated fair price and the entered basic information into a template. Specifically, it generates a PDF quotation that includes the name "Software Service A," its specifications, and a price of "1.5 million yen." The input to the template is the basic information and the fair price, and the output is the generated PDF quotation.

[0520] Step 6:

[0521] The server sends the generated quotation to the user's terminal. The input is the generated quotation in PDF format. Email services (e.g., SendGrid) or push notifications are used as the means of transmission. Specifically, the email subject line will be "Quotation: Software Service A," and the PDF will be sent as an attachment. The output is the sent email or push notification.

[0522] Step 7:

[0523] The device downloads a quotation from an received email or notification and displays it to the user. The input is the received email or push notification, and the device uses this information to download and display the quotation. Specifically, the user opens the email, downloads the attached PDF, and can view it on the device. The output is the quotation displayed to the user.

[0524] This allows users to create estimates quickly and accurately.

[0525] (Application Example 1)

[0526] 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."

[0527] There is a need for a means to quickly and accurately estimate the fair price of goods or services, thereby strengthening the relationship of trust between users and customers. However, conventional systems have problems such as being time-consuming to calculate fair prices and failing to accurately reflect market trends and competitive situations. In addition, the quotation generation process is cumbersome and often detracts from the user experience.

[0528] 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.

[0529] In this invention, the server includes means for inputting basic information of a product or service, means for transmitting the inputted information, means for connecting to an external API to acquire market data, means for extracting similar data from a database based on the inputted information, means for calculating a fair price using a machine learning model, means for generating and processing prompt statements using a generative AI model, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, and means for displaying the quotation. This makes it possible to quickly and accurately calculate a fair price based on information entered by the user and to immediately generate and transmit a quotation.

[0530] "Basic information about a product or service" refers to information such as the name of the product or service, specifications, desired price range, and target market.

[0531] "Means of input" refers to interfaces or input devices that allow users to input basic information about a product or service.

[0532] "Means of transmission" refers to communication devices and protocols used to transmit input information to a server or external system.

[0533] A "database" is a collection of information used to store past transaction data and market data.

[0534] "Means for extracting similar data" refers to algorithms or programs used to search and extract past data from a database that is similar to the input information.

[0535] "Methods for calculating fair prices" refer to algorithms and calculation models for determining fair prices based on similar data extracted from a database and market trends.

[0536] "Means for generating quotations" refers to programs or software that automatically create quotations containing the calculated fair price and detailed information about the goods or services.

[0537] "Means of transmission to the receiving terminal" refers to communication devices and protocols used to send the generated quotation to the user's terminal (smartphone, PC, etc.).

[0538] "Means of display" refers to interfaces or display devices for displaying quotations on the receiving terminal.

[0539] "External API connection means" refers to programs or communication protocols used to connect to external APIs in order to obtain external market data and related data.

[0540] A "machine learning model" refers to a learning algorithm or predictive model used to predict prices based on data.

[0541] A "generative AI model" is an artificial intelligence model designed to generate appropriate output in response to a specific input (prompt).

[0542] A "prompt sentence" is an instruction or question that is input into a generative AI model to obtain a specific output.

[0543] As a concrete example of implementing this invention, a system for estimating appropriate product prices in a smartphone app for an e-commerce site is presented. This system is designed to quickly and accurately estimate appropriate prices when a user lists a new product. When a user enters product information using the smartphone app, that information is sent to a server, and an appropriate price is calculated considering past database comparisons and market trends. The generated estimate is then sent to the user.

[0544] First, the user enters basic information about the product or service into the smartphone app. This includes information such as the product name, specifications, desired price range, and target market. For example, if a user is estimating a fair price for a new smartwatch, they would enter specifications such as the name "SmartWatch Model X," screen size, and battery life.

[0545] Next, the terminal sends the entered information to the server. This data is preferably encrypted for security reasons. Based on the received information, the server connects to external APIs to obtain internal and external market data. It then extracts similar historical data from the database based on the acquired market data.

[0546] The server uses a machine learning model to calculate a fair price based on the extracted data. This process utilizes algorithms such as linear regression, taking into account market trends and competitive conditions. For example, it might use 100 historical price data points for similar smartwatches to calculate a fair price.

[0547] Based on the calculated price, the server generates a quotation. The quotation includes the product name, specifications, and appropriate price, and is saved in PDF format. The generated quotation is sent to the user's device, and the user is notified via push notification or email notification. The user can then view the received quotation in the app.

[0548] The following are some specific examples of prompt statements generated using a generative AI model.

[0549] The specifications for the "SmartWatch Model X" are as follows: Screen size: 1.5 inches, Battery life: 24 hours. Please estimate the market price for this product based on historical data and current trends.

[0550] As described above, this system allows users to quickly and accurately estimate fair prices, enabling reliable transactions.

[0551] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0552] Program processing flow

[0553] Step 1:

[0554] The user enters basic information about the product or service into a smartphone app. For example, the user might enter specifications such as the name, screen size, and battery life of a "SmartWatch Model X." This input generates product information.

[0555] Step 2:

[0556] The terminal sends the entered information to the server. This transmission is encrypted for security purposes. The input data (product name, specifications, etc.) arrives at the server as transmitted data.

[0557] Step 3:

[0558] Based on the information received, the server connects to its own and external APIs to retrieve market data. During this process, it calls external APIs to collect market data related to the specified product category. The retrieved market data is then stored as local data.

[0559] Step 4:

[0560] The server extracts historical data similar to the information entered from the database. This uses a search algorithm to narrow down the historical data using product specifications and target market information as keys. This data extraction generates a similarity dataset.

[0561] Step 5:

[0562] The server uses a machine learning model to calculate a fair price based on the extracted data and acquired market data. Here, a linear regression model is used, combining historical price data with current market trends in the calculation. The price predicted by the machine learning model is output as the fair price.

[0563] Step 6:

[0564] The server generates a quotation based on the calculated fair price. The quotation includes detailed product information and the calculated price, and is saved in PDF format. This generated quotation is output as a quotation file.

[0565] Step 7:

[0566] The server sends the generated quotation to the user's receiving device. Email or push notifications are used as the sending method. The sent quotation is received on the user's device.

[0567] Step 8:

[0568] The user views the received quotation on their smartphone app. The app provides the user with quotation information through a viewer that displays the received PDF quotation. This display allows the user to review the quotation content and prepare to share it with the customer.

[0569] 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.

[0570] As an example of implementing this invention, a system is provided that combines a user emotion engine to more accurately estimate the appropriate price of goods and services. In addition to a function that extracts similar information from a database based on information entered by the user into a terminal and calculates an appropriate price, this system uses an emotion engine to recognize the user's emotions and adjust subsequent responses accordingly.

[0571] System Overview

[0572] The user enters basic information about the product or service into their terminal and sends that information to the server. The server searches its database based on the received information, extracts similar data, and calculates a fair price. Then, it generates a quotation based on the calculated fair price and sends it to the user's terminal. Furthermore, an emotion engine recognizes the user's emotions and provides suggestions and advice based on that information. In this way, the quotation process is customized according to the user's psychological state.

[0573] Program processing flow

[0574] 1. Entering information

[0575] User: Enter basic information about the product or service to be quoted (name, specifications, desired price range, target market, etc.) into the terminal. This includes detailed descriptions and options.

[0576] 2. Data transmission

[0577] Terminal: Sends basic information entered by the user to the server. It is recommended that this data be encrypted for security reasons.

[0578] 3. Database matching

[0579] Server: Receives input information and generates queries to the database. It searches and extracts similar past data using keywords such as product name, specifications, and target market.

[0580] 4. Calculation of a fair price

[0581] Server: Executes an algorithm to calculate a fair price based on the extracted data. The price calculation uses weighted averages, regression analysis, machine learning models, etc., and takes market trends and competitive conditions into consideration.

[0582] 5. Generating the final quotation

[0583] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price. It is generally generated in PDF format.

[0584] 6. Sending the quotation

[0585] Server: Sends the generated quotation to the user's terminal. Email and push notifications are used as notification methods.

[0586] 7. Emotion recognition by an emotion engine

[0587] Terminal: Sends user input information and facial recognition data to the emotion engine. The emotion engine analyzes the user's emotions based on this information.

[0588] Emotion Engine: Sends analysis results to the server and generates suggestions and advice for the user based on them.

[0589] 8. Display of suggestions and advice

[0590] Terminal: Based on the analysis results of the emotion engine, it displays suggestions and advice to the user. This allows the user to receive appropriate information tailored to their psychological state.

[0591] Specific example

[0592] For example, when a user requests a quote for a new software service:

[0593] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[0594] 2. Data transmission: The terminal sends the input information to the server.

[0595] 3. Database matching: The server extracts similar past data based on the input information.

[0596] 4. Calculation of fair price: The server calculates a fair price considering market trends and competitive conditions.

[0597] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[0598] 6. Sending the quotation: The server sends the generated quotation to the user's terminal.

[0599] 7. Emotion recognition by emotion engine: The emotion engine analyzes the facial expression data when the user makes inputs and recognizes the user's emotions.

[0600] 8. Displaying suggestions and advice: Based on the analysis results, if the user is feeling stressed, suggestions to help them relax will be displayed; if they are happy, more positive suggestions will be displayed.

[0601] This system allows users to quickly calculate fair prices and receive appropriate support tailored to their emotions, helping to strengthen customer trust. Furthermore, the use of an emotion engine significantly improves the user experience.

[0602] The following describes the processing flow.

[0603] Step 1:

[0604] User: Enter basic information about the product or service to be quoted into the terminal. For example, enter the name of "Software Service A," a brief description of its functions, desired price range, and target market into the input form. Once the user has finished entering the information, they will check the details on the confirmation screen and press the submit button.

[0605] Step 2:

[0606] Terminal: Sends the basic information entered by the user to the server. The input information is converted to JSON or XML format and encrypted for security. After transmission is complete, a notification "Information sent" is displayed on the terminal.

[0607] Step 3:

[0608] Server: Analyzes the received basic information and generates a search query. The query sends a search command to the database containing keywords such as product name, specifications, and target market.

[0609] Step 4:

[0610] Server: Searches the database and extracts data on past products and services similar to the entered information. For example, it extracts data on similar software services for which quotes were previously created. This data includes past prices, specification details, and market price information.

[0611] Step 5:

[0612] Server: Calculates a fair price based on extracted data. It uses weighted averages, regression analysis, and machine learning models to calculate a price that considers historical data, current market trends, and competitive landscape. For example, it might derive the most appropriate price based on 100 historical price data points for similar services.

[0613] Step 6:

[0614] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price. The quotation is generated in PDF format and formatted using templates as needed.

[0615] Step 7:

[0616] Server: Sends the generated quote to the user's device. Once the transmission is complete, the user is notified via email or push notification.

[0617] Step 8:

[0618] Terminal: Displays received quotations to the user. The user can review the quotation on the terminal and check for any problems. If the quotation is correct, it is ready to be used for subsequent negotiations and proposals.

[0619] Step 9:

[0620] User: Review the quotation and communicate with the customer as needed. The quotation contains fair pricing and detailed information, enabling reliable negotiations.

[0621] Step 10:

[0622] Terminal: Acquires user input information and facial expression data during operation from the camera and sends it to the emotion engine. This uses a high-performance camera and emotion analysis software.

[0623] Step 11:

[0624] Server and Emotion Engine: Analyzes received facial recognition data to determine the user's emotional state. For example, it uses a facial expression analysis algorithm to determine whether the user is nervous, happy, etc. The emotion engine generates the analysis results and sends them to the server.

[0625] Step 12:

[0626] Server: Based on the analysis results of the emotion engine, it generates suggestions and advice for the user. If the user is stressed, it displays messages to help them relax; if they are happy, it displays even more positive suggestions.

[0627] Step 13:

[0628] Terminal: Displays generated suggestions and advice to the user. The user uses this information to review the quote and take appropriate action. Emotion-based advice improves the user experience.

[0629] This series of processes allows users to quickly calculate a fair price, receive appropriate support tailored to their emotions, and strengthen customer trust. By utilizing an emotion engine, the quotation process becomes more personalized, improving the user experience.

[0630] (Example 2)

[0631] 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".

[0632] In systems that calculate appropriate prices and generate quotations based on basic information about products and services, there is a problem with the user experience due to the lack of suggestions and advice that take user emotions into consideration. Furthermore, the system does not adequately consider market trends and competitive situations when calculating appropriate prices, making it difficult to provide accurate quotations.

[0633] The identification processing 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 inputting basic information of a product or service, means for transmitting the inputted information, means for extracting similar data from a database based on the inputted information, means for calculating an appropriate price based on the extracted data, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, means for recognizing emotions based on user input information and facial recognition data, means for making suggestions and advice to the user based on the recognized emotions, and means for displaying the quotation and the suggestions and advice. This makes it possible to make suggestions and advice that take the user's emotions into consideration, improving the user experience while realizing an accurate and appropriate price estimate.

[0634] "Basic information about the product or service" refers to detailed information about the product or service being quoted, such as its name, specifications, desired price range, and target market.

[0635] "Means of input" refers to an interface through which a user provides basic information about a product or service to the system, and includes, for example, a keyboard, mouse, or touchscreen.

[0636] "Means of transmission" refers to devices and protocols used to send information entered by a user to a server, including, for example, internet connections, Wi-Fi, and data encryption protocols.

[0637] "Means of extraction" refers to devices or software used to search for and extract data similar to the information entered into a database, and includes, for example, SQL queries and search algorithms.

[0638] "Means for calculating fair prices" refer to devices or software that calculate fair prices using algorithms such as weighted averages, regression analysis, and machine learning models based on extracted data.

[0639] "Means for generating quotations" refers to devices or software used to create quotations based on calculated fair price information, and includes, for example, template engines and PDF generation software.

[0640] "Means of sending to the receiving terminal" refers to devices or protocols for sending the generated quotation to the user's receiving terminal, including, for example, email servers and push notification services.

[0641] "Means of recognizing emotions" refer to devices and software that analyze user input information and facial recognition data to identify the user's emotions, and include, for example, facial expression recognition algorithms and emotion analysis APIs.

[0642] "Means of providing suggestions and advice" refers to devices or software that provide appropriate suggestions and advice to users based on emotions recognized by an emotion engine.

[0643] "Means of display" refers to an interface for visually displaying the generated estimates, proposals, and advice to the user, and includes, for example, displays and monitors.

[0644] This invention is a system for more accurately estimating the appropriate price of goods and services, and incorporates an emotion engine that recognizes the user's emotions and adjusts subsequent responses accordingly. In addition to a function that extracts similar information from a database based on information entered by the user on a terminal and calculates an appropriate price, this system can also provide suggestions and advice that are tailored to the user's emotions by using the emotion engine.

[0645] Users input basic information about their products or services (e.g., name, specifications, desired price range, target market, etc.) into a terminal and send this information to a server. The terminal includes input devices such as a keyboard, mouse, and touchscreen. The input information is in JSON or XML format and is encrypted using SSL / TLS. The server generates queries to access a database based on the received information and extracts similar historical data. SQL or NoSQL databases are used. Based on this extracted data, the server calculates an appropriate price using weighted averages, regression analysis, and machine learning models (e.g., linear regression and random forest). Market trends and competitive conditions are also considered in real time.

[0646] The server generates a quote based on a fair price. This quote includes detailed information about the goods or services, the basis for the calculation, and the fair price, and is generated in PDF format using a template engine (e.g., JasperReports or iText). The generated PDF quote is then sent to the user's device via email (using SMTP) or push notification (e.g., Firebase Cloud Messaging).

[0647] Furthermore, an emotion engine is provided that analyzes emotions based on user input information and facial expression data. It uses the device's built-in camera to acquire facial recognition data, which is then analyzed, for example, through the Microsoft Azure Emotional Analysis API. The results of the emotion engine's analysis are sent to a server, which then generates suggestions and advice for the user based on these results. These might include instructions to promote relaxation or suggestions for additional options. The device is equipped with a display or monitor to show these suggestions and advice to the user.

[0648] As a concrete example, consider a scenario where a user requests a quote for a new software service A. The user inputs the name of software service A, its specifications (e.g., cloud-based, monthly subscription), desired price range ($500-$700), and target market (small and medium-sized enterprises) into the terminal. The terminal sends this information to the server in JSON format, and the data is encrypted using SSL / TLS. The server generates an SQL query based on the received information to extract similar historical data from the database. Next, the server calculates a fair price using a linear regression model, taking into account market trends and competitive information, and generates a quote in PDF format using a template engine. The server then sends the generated PDF to the user via email. Simultaneously, the terminal sends the user's facial expression data from the input process to an emotion engine, and based on the analysis results, displays relaxation-enhancing advice and additional option suggestions to the user.

[0649] Example of a prompt:

[0650] "I would like to request a quote for a new software service A. Please calculate a fair price and generate a quote based on the following information."

[0651] Software name: Software Service A

[0652] Specifications: Cloud-based, user interface is a web app, monthly subscription fee.

[0653] Desired price range: $500 - $700

[0654] Target market: Small and medium-sized enterprises (SMEs)

[0655] Furthermore, recognize the user's emotions when they input data and display suggestions and advice based on those emotions.

[0656] This system allows users to quickly and accurately calculate fair prices, and also provides personalized suggestions and advice based on their emotions, which is expected to improve customer satisfaction.

[0657] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0658] Step 1: Enter information

[0659] User: Enter basic information about the product or service to be quoted into the terminal. Specifically, enter information such as the product name, specifications, desired price range, and target market. The entered information will be processed as JSON data.

[0660] Input: Basic information such as product name, specifications, desired price range, and target market.

[0661] Output: Basic information data in JSON format entered into the terminal.

[0662] Step 2: Data transmission

[0663] Terminal: Sends the entered information to the server. To ensure the security of the information, the data is encrypted using the SSL / TLS protocol. The data format is typically JSON.

[0664] Input: Basic information data in JSON format

[0665] Output: Encrypted JSON formatted information data is sent to the server.

[0666] Step 3: Matching with the database

[0667] Server: Based on the received information, it generates queries for the database. It accesses the database using SQL queries, searches for and extracts past data similar to the entered basic information.

[0668] Input: Encrypted JSON format information data

[0669] Output: Extraction results of similar historical data

[0670] Step 4: Calculating the Fair Price

[0671] Server: Calculates a fair price based on extracted historical data. This calculation uses weighted averages, regression analysis, and machine learning models (e.g., linear regression, random forest), taking market trends and competitive landscape into consideration.

[0672] Input: Extracted historical data, current market trend information, competitor data

[0673] Output: Calculated fair price

[0674] Step 5: Generating the final quote

[0675] Server: Generates a quotation based on the calculated fair price. The quotation is created in PDF format using a template engine (e.g., JasperReports, iText). The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price.

[0676] Input: Calculated fair price, detailed information about the product / service.

[0677] Output: Quotation in PDF format

[0678] Step 6: Send the quotation

[0679] Server: Sends the generated quote to the user's device. Quotes are sent via email (SMTP) or push notifications (e.g., Firebase Cloud Messaging).

[0680] Input: Quotation in PDF format

[0681] Output: Quotation in PDF format sent to the user's receiving terminal.

[0682] Step 7: Emotion recognition by the emotion engine

[0683] The device sends user input information and facial expression data to the emotion engine. It uses the camera built into the device to acquire facial recognition data and sends it to the emotion engine. For example, the emotion engine includes facial expression recognition algorithms and emotion analysis APIs (e.g., Microsoft Azure Emotional Analysis API).

[0684] Input: User's facial expression data, input information

[0685] Output: User data sent to the emotion engine

[0686] Step 8: Displaying suggestions and advice

[0687] Terminal: Based on the analysis results of the emotion engine, it displays suggestions and advice to the user. For example, if the user is feeling tense, it displays suggestions to help them relax, and if the user is happy, it displays positive suggestions.

[0688] Input: Analysis results from the emotion engine

[0689] Output: Suggestions and advice displayed to the user

[0690] (Application Example 2)

[0691] 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."

[0692] Traditional pricing systems for goods and services lacked the functionality to consider user emotions and provide appropriate suggestions and advice. This made it difficult to enhance user psychological satisfaction and trust, resulting in a limited user experience. Furthermore, there was a lack of technology to more accurately calculate fair prices for goods and services by incorporating emotion recognition into the pricing process.

[0693] 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.

[0694] In this invention, the server includes means for inputting basic information of a product or service, means for extracting similar data from a database based on the input information, means for calculating a fair price based on the extracted data, means for recognizing the user's emotional information when estimating the product or service, and means for generating suggestions and advice based on the analysis results of the emotional recognition means. This makes it possible to consider the user's psychological state during the estimation process and provide suggestions and advice that are more satisfying. Furthermore, by utilizing the user's emotional information, the accuracy of calculating a fair price is improved, resulting in more accurate price estimates.

[0695] "Basic information about the product or service" refers to detailed information entered by the user, such as the name of the product or service, specifications, desired price range, and target market.

[0696] "Input method" refers to an interface for users to input basic information about a product or service.

[0697] "Means of transmission" refers to a mechanism for securely sending input information to a server, such as by encrypting it.

[0698] "Means for extracting similar data from a database" refers to a function in which the server searches the database for and extracts similar past data based on the input information.

[0699] A "means for calculating a fair price" refers to an algorithm or model that calculates a fair price for a product or service based on historical data extracted from a database, taking into account the market value and competitive landscape.

[0700] "Methods for generating quotations" refers to the process of creating a quotation that includes detailed information about the goods or services and the basis for the calculation, based on the calculated fair price.

[0701] "Means of sending to the receiving terminal" refers to a system that sends the generated quotation to the user's terminal.

[0702] "Means of displaying the quotation" refers to a function that allows the quotation to be displayed on the receiving terminal so that it can be viewed.

[0703] "Emotion recognition means" refers to technology that recognizes a user's emotions by analyzing user input information and facial expression data.

[0704] "Means for generating suggestions and advice" refers to a function that creates suggestions and advice tailored to the user's emotional state based on the analysis results of emotion recognition.

[0705] This invention is a system that recognizes user emotions and provides appropriate suggestions and advice during the process of estimating the fair price of goods and services. The following describes embodiments for carrying out this invention.

[0706] System Configuration

[0707] The system of this invention consists of the following main components:

[0708] 1. Means of entering basic information about a product or service:

[0709] Users input basic information about products or services using a smartphone app. This basic information includes the name of the product or service, specifications, desired price range, and target market.

[0710] 2. Means of information transmission:

[0711] The terminal encrypts the entered basic information and sends it to the server. Security protocols such as TLS and HTTPS are used as encryption technologies.

[0712] 3. Methods for extracting similar data:

[0713] The server searches the database based on the received information and extracts similar past data. SQL or NoSQL databases are used for this process.

[0714] 4. Methods for calculating fair prices:

[0715] The server calculates a fair price based on similar data extracted from a database. Regression analysis and machine learning algorithms (such as Scikit-learn) are used for this calculation.

[0716] 5. Methods for generating quotations:

[0717] The server generates a quotation based on the calculated fair price. The quotation is output in PDF format, using a PDF generation library (such as ReportLab).

[0718] 6. Method of sending the quotation:

[0719] The generated quote will be sent to the user's device via email or push notification.

[0720] 7. Emotion recognition means:

[0721] An emotion engine is used to analyze user input information and facial recognition data. Examples of emotion engines include OpenCV and the Emotion API.

[0722] 8. Means of generating suggestions and advice:

[0723] The server generates suggestions and advice that the user finds optimal based on the results of emotion recognition. Using a generative AI model is recommended for this process.

[0724] Specific example

[0725] When a user requests a quote for new high-performance earphones:

[0726] 1. The user enters basic information about the "high-performance earphones" using a smartphone app.

[0727] 2. The device encrypts this information and sends it to the server.

[0728] 3. The server searches the database and extracts past data for similar earphones.

[0729] 4. The server calculates a fair price based on the extracted data.

[0730] 5. The quotation is generated in PDF format and sent to the user's device.

[0731] 6. The emotion engine analyzes the user's input information and facial expression data and recognizes that the emotion is positive.

[0732] 7. If the emotion is judged to be positive, more proactive suggestions will be made, such as, "Would you like to see our higher-performance products?"

[0733] Example of a prompt

[0734] A user has requested a quote for a new premium product. They have positive feedback. Calculate a fair price and output a sentiment-based suggestion.

[0735] input:

[0736] Basic information on high-performance earphones

[0737] output:

[0738] Estimated price: $300

[0739] Advice: "Would you like to see our higher-performance products?"

[0740] As described above, the present invention makes it possible to provide an estimation system that takes into account the user's psychological state and offers even greater satisfaction.

[0741] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0742] Step 1:

[0743] Users input basic information about a product or service using a smartphone app. This includes detailed information such as the name, specifications, desired price range, and target market.

[0744] Input: Basic information about the product or service

[0745] Output: Input basic information data

[0746] Step 2:

[0747] The terminal encrypts the entered basic information and sends it to the server. To ensure the security of the information, security protocols such as TLS and HTTPS are used.

[0748] Input: Encrypted basic information data

[0749] Output: Encrypted data sent to the server

[0750] Step 3:

[0751] The server decrypts the received basic information and searches the database to extract similar past data. SQL or NoSQL databases are used for the search.

[0752] Input: Decrypted basic information data

[0753] Output: Similar historical data

[0754] Step 4:

[0755] The server calculates a fair price based on similar data. Regression analysis and machine learning algorithms are used for this calculation. Libraries used include Scikit-learn.

[0756] Input: Similar historical data

[0757] Output: Fair price

[0758] Step 5:

[0759] The server generates a quotation in PDF format based on a fair price. Libraries such as ReportLab are used for PDF generation.

[0760] Input: Fair price

[0761] Output: Quotation in PDF format

[0762] Step 6:

[0763] The server sends the generated quotation to the user's device. Email or push notifications are used as the means of delivery.

[0764] Input: Quotation in PDF format

[0765] Output: Notification of quotation to user terminal

[0766] Step 7:

[0767] The device sends user input information and facial recognition data to the emotion engine. The emotion engine analyzes the user's emotions. Examples of emotion engines used include OpenCV and the Emotion API.

[0768] Input: User input information and facial recognition data

[0769] Output: Emotion analysis results

[0770] Step 8:

[0771] The server generates suggestions and advice for the user based on the sentiment analysis results. Using a generative AI model is effective in this process.

[0772] Input: Sentiment analysis results

[0773] Output: Suggestion and advice messages

[0774] Step 9:

[0775] The device displays generated suggestions and advice to the user. This allows the user to receive appropriate suggestions tailored to their emotions.

[0776] Input: Suggestion or advice message

[0777] Output: Content displayed to the user

[0778] 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.

[0779] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0780] 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.

[0781] [Third Embodiment]

[0782] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0783] 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.

[0784] 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).

[0785] 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.

[0786] 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.

[0787] 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).

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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.

[0793] 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".

[0794] As an example of implementing this invention, a system for quickly and accurately estimating the appropriate price of a product or service is provided. This system calculates an appropriate price based on information entered by the user into a terminal, taking into account comparisons with past databases and market trends, and generates and transmits the result as a quotation.

[0795] System Overview

[0796] The user enters basic information about a product or service into their terminal and sends that information to the server. The server searches its database based on the received information, extracts similar data, and calculates a fair price. Then, it generates a quotation based on the calculated price information and sends it to the user's terminal. The user can review the sent quotation and share it with the customer to strengthen trust.

[0797] Program processing flow

[0798] 1. Entering information

[0799] User: Enter basic information (product name, specifications, desired price range, target market, etc.) into the terminal to create a quote.

[0800] As a specific example, when a user requests a quote for a new software service, they input the name of "Software Service A," a brief description of its functions, desired price range, and target market information into the terminal.

[0801] 2. Data transmission

[0802] Terminal: Sends user-entered information to the server. This data should preferably be encrypted for security reasons.

[0803] 3. Database matching

[0804] Server: Receives the entered information and compares it with the company's and related market databases. Specifically, it searches and extracts past quotation data and market price data similar to the entered product name and specifications.

[0805] 4. Calculation of a fair price

[0806] Server: Based on the extracted data, it calculates a fair price. In this process, it uses an algorithm to consider market trends and competitive conditions.

[0807] As a concrete example, we have 100 historical data points for similar software services, and we extract important data from them to calculate an appropriate price considering current market trends.

[0808] 5. Generating the final quotation

[0809] Server: Generates a quotation based on the calculated fair price. This quotation includes details of the goods / services and their fair price.

[0810] For example, if the appropriate price for software service A is calculated to be 1 million yen, a PDF quotation will be generated that includes the price and service specifications.

[0811] 6. Sending and confirming the quotation

[0812] Server: Sends the generated quotation to the user's terminal. Email and push notifications are commonly used as notification methods.

[0813] Terminal: Displays received quotations and allows users to review them.

[0814] Specific example

[0815] For example, when a user requests a quote for a new software service, they would use the system as follows:

[0816] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[0817] 2. Data transmission: The terminal sends the input information to the server.

[0818] 3. Database matching: The server extracts similar past data from the database based on the input information.

[0819] 4. Calculation of appropriate price: The server calculates an appropriate price based on extracted data, taking into account market trends and competitive conditions.

[0820] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[0821] 6. Sending and confirming the quotation: The server sends the generated quotation to the user's terminal, and the user confirms it.

[0822] This system allows users to quickly calculate fair prices, helping to strengthen trust with customers.

[0823] The following describes the processing flow.

[0824] Step 1:

[0825] User: Enter basic information about the product or service to be quoted (name, specifications, desired price range, target market, etc.) into the terminal. Specifically, enter the necessary information according to the input form or selection options. A confirmation screen of the entered information is also provided to prevent input errors.

[0826] Step 2:

[0827] Terminal: Sends the basic information entered by the user to the server. The input data is converted to JSON or XML format, and encryption is recommended for security. After successful transmission, the user receives a notification.

[0828] Step 3:

[0829] Server: Analyzes the received basic information and generates queries for the database. The queries include keywords such as product name, specifications, and target market.

[0830] Step 4:

[0831] Server: Searches the database and extracts data on similar products and services from the past. In this process, it uses multiple databases or tables to collect the necessary information and normalizes the data as needed.

[0832] Step 5:

[0833] Server: Based on the extracted data, it executes an algorithm to calculate a fair price. Specifically, it uses weighted averages, regression analysis, machine learning models, etc., to calculate a fair price that takes into account past price data and current market trends.

[0834] Step 6:

[0835] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods or services, the basis for the calculation, and the fair price. Quotations are typically generated in PDF format.

[0836] Step 7:

[0837] Server: Sends the generated quotation to the user's terminal. Delivery methods include email, push notification, or in-system messaging service.

[0838] Step 8:

[0839] Terminal: Receives the sent quotation and notifies the user that the quotation has arrived. The user who receives the notification can then view the quotation displayed on their terminal.

[0840] Step 9:

[0841] User: Review the displayed quote, negotiate with the customer or make internal adjustments as needed, and prepare the final quote to provide to the customer.

[0842] This series of steps allows users to quickly calculate fair prices and provide reliable quotes to customers. Furthermore, the system's ease of use and accuracy streamline sales activities.

[0843] (Example 1)

[0844] 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."

[0845] Traditional pricing systems for goods and services made it difficult to quickly and accurately calculate fair prices. They failed to consider market trends and competitive landscapes, resulting in unreliable quotes and hindering the building of trust with customers.

[0846] 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.

[0847] In this invention, the server includes means for inputting basic information of a product or service, means for transmitting the inputted information, means for extracting similar data from a database based on the inputted information, means for calculating an appropriate price using a generation AI model based on the extracted data, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, and means for displaying the quotation. This makes it possible for the user to quickly and accurately calculate an appropriate price and generate and transmit the result as a quotation.

[0848] "Merchandise" refers to goods and services.

[0849] "Basic information" refers to information about a product or service, such as its name, specifications, desired price range, and target market.

[0850] "Means of input" refers to the hardware and software that users use to input basic information about a product or service.

[0851] "Means of transmission" refers to the means of communication used to send the entered basic information to the server.

[0852] A "database" refers to data storage that holds past estimate data and market price data for products and services.

[0853] "Extraction method" refers to the means of searching for and retrieving similar data from a database based on the basic information entered.

[0854] A "generative AI model" refers to a machine learning model that analyzes past data and calculates an appropriate price by considering current market trends and competitive conditions.

[0855] "Methods for calculating fair prices" refer to methods for calculating the appropriate price of a product or service from data extracted using a generative AI model.

[0856] A "quotation" refers to a document that contains detailed information about a product or service, including its fair price.

[0857] "Generating means" refers to software and algorithms for automatically creating quotations based on appropriate pricing and entered basic information.

[0858] "Means of transmission" refers to the means of communication used to send the generated quotation to the receiving terminal.

[0859] "Receiving terminal" refers to the device that a user uses to receive and view a quotation.

[0860] "Means of display" refers to the means by which a user can visually confirm the received quotation.

[0861] An "algorithm" refers to a procedure or method for calculating a fair price by taking into account market trends and competitive conditions.

[0862] "PDF format" is an abbreviation for Portable Document Format, and refers to a file format for electronically storing and sharing documents.

[0863] "Encrypted methods" refer to means of protecting data using cryptographic techniques to enhance data security.

[0864] This invention will be explained using a system for quickly and accurately estimating the appropriate price of a product or service as an example. Based on information entered by the user into a terminal, this system calculates an appropriate price by comparing it with past databases and considering market trends, and then generates and transmits the result as a quotation.

[0865] System Overview

[0866] The user enters basic information about the product or service into the terminal and sends that information to the server. Specifically, when a user requests a quote for a new software service, they would enter the name of "Software Service A," a brief description of its functions, the desired price range, and information about the target market.

[0867] The terminal receives basic information entered by the user, encrypts it, and sends it to the server. AES encryption is recommended for this process, and HTTPS is used as the communication protocol.

[0868] The server decodes the information received from the terminal and searches the database to extract similar data. For example, it searches and extracts data with similar product names and specifications from the quotation database for the past three years. At this time, a database query is generated to find data that matches or is similar to the product name and specifications.

[0869] Next, the server uses a generative AI model to calculate a fair price based on the extracted data. This generative AI model uses an algorithm that analyzes historical data and considers current market trends and competitive conditions. For example, it extracts important information from 100 data points and calculates a fair price considering current market trends.

[0870] After a fair price is calculated, the server automatically generates a quote using a template engine (e.g., Apache Velocity). This quote includes details of the product or service and its fair price. For example, if the fair price for "Software Service A" is calculated to be 1.5 million yen, a PDF quote will be generated that includes the price and service specifications.

[0871] Finally, the server sends the generated quotation to the user's device. The delivery method may include an email service (e.g., SendGrid) or push notification. Specifically, the quotation is sent as a PDF attachment with the subject line "Quotation: Software Service A".

[0872] The device allows users to download quotes via received emails and notifications, enabling them to review the contents. After reviewing, users can share these quotes with customers, strengthening their relationship of trust.

[0873] Specific example

[0874] For example, the specific steps a user might take to get an estimate for a new software service are as follows:

[0875] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[0876] 2. Data transmission: The terminal sends the input information to the server.

[0877] 3. Database matching: The server extracts similar past data from the database based on the input information.

[0878] 4. Calculation of appropriate price: The server calculates an appropriate price based on extracted data, taking into account market trends and competitive conditions.

[0879] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[0880] 6. Sending and confirming the quotation: The server sends the generated quotation to the user's terminal, and the user confirms it.

[0881] Example of a prompt

[0882] You can request a fair price estimate from a generative AI model using the following prompt:

[0883] "Please provide a quote for the following product: 'Software Service A', specifications: 'Cloud-based data management function', desired price range: '1 million to 2 million yen', target market: 'Small and medium-sized enterprises'."

[0884] Based on this prompt, the AI ​​model calculates a fair price and generates a quote.

[0885] This system allows users to quickly and accurately calculate fair prices and generate and send the results as quotations.

[0886] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0887] Step 1:

[0888] The user inputs basic information about the product or service into the terminal. Specifically, they input information such as the product name, specifications, desired price range, and target market. As an example of input, the user inputs the name "Software Service A," the specifications "Cloud-based data management function," the desired price range "1 million to 2 million yen," and the target market information "For small and medium-sized enterprises." This becomes the system's input information.

[0889] Step 2:

[0890] The terminal encrypts the entered basic information using the AES encryption method and sends it to the server via the HTTPS protocol. This is the input information transfer process. Specifically, when the send button is pressed, the terminal encrypts the data and sends it to the specified endpoint on the server. The output of this process is encrypted data, which is sent to the server.

[0891] Step 3:

[0892] The server receives encrypted data sent from the terminal and deserializes it back to its original format. The received and deserialized data includes information such as product name, specifications, desired price range, and target market. The server uses this as input to search its own and external market databases and extract similar data. This is the process of generating database queries and searching for entries in the database that match or are similar to the product name and specifications. Specifically, it extracts 100 data entries related to "cloud-based data management functionality" from the database over the past three years. The output is a set of similar data.

[0893] Step 4:

[0894] The server uses a generative AI model to calculate a fair price based on the extracted data. The input is similar data extracted in step 3. The server inputs this data into the generative AI model, which analyzes it using an algorithm that takes market trends and competitive conditions into account. Specifically, the model evaluates historical price information, current market demand, and competitor pricing to calculate a fair price. For example, a fair price of "1.5 million yen" is calculated based on historical data and current market trends. The output is the calculated fair price.

[0895] Step 5:

[0896] The server automatically generates a quotation based on the calculated fair price. The server uses a template engine (e.g., Apache Velocity) to insert the calculated fair price and the entered basic information into a template. Specifically, it generates a PDF quotation that includes the name "Software Service A," its specifications, and a price of "1.5 million yen." The input to the template is the basic information and the fair price, and the output is the generated PDF quotation.

[0897] Step 6:

[0898] The server sends the generated quotation to the user's terminal. The input is the generated quotation in PDF format. Email services (e.g., SendGrid) or push notifications are used as the means of transmission. Specifically, the email subject line will be "Quotation: Software Service A," and the PDF will be sent as an attachment. The output is the sent email or push notification.

[0899] Step 7:

[0900] The device downloads a quotation from an received email or notification and displays it to the user. The input is the received email or push notification, and the device uses this information to download and display the quotation. Specifically, the user opens the email, downloads the attached PDF, and can view it on the device. The output is the quotation displayed to the user.

[0901] This allows users to create estimates quickly and accurately.

[0902] (Application Example 1)

[0903] 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."

[0904] There is a need for a means to quickly and accurately estimate the fair price of goods or services, thereby strengthening the relationship of trust between users and customers. However, conventional systems have problems such as being time-consuming to calculate fair prices and failing to accurately reflect market trends and competitive situations. In addition, the quotation generation process is cumbersome and often detracts from the user experience.

[0905] 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.

[0906] In this invention, the server includes means for inputting basic information of a product or service, means for transmitting the inputted information, means for connecting to an external API to acquire market data, means for extracting similar data from a database based on the inputted information, means for calculating a fair price using a machine learning model, means for generating and processing prompt statements using a generative AI model, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, and means for displaying the quotation. This makes it possible to quickly and accurately calculate a fair price based on information entered by the user and to immediately generate and transmit a quotation.

[0907] "Basic information about a product or service" refers to information such as the name of the product or service, specifications, desired price range, and target market.

[0908] "Means of input" refers to interfaces or input devices that allow users to input basic information about a product or service.

[0909] "Means of transmission" refers to communication devices and protocols used to transmit input information to a server or external system.

[0910] A "database" is a collection of information used to store past transaction data and market data.

[0911] "Means for extracting similar data" refers to algorithms or programs used to search and extract past data from a database that is similar to the input information.

[0912] "Methods for calculating fair prices" refer to algorithms and calculation models for determining fair prices based on similar data extracted from a database and market trends.

[0913] "Means for generating quotations" refers to programs or software that automatically create quotations containing the calculated fair price and detailed information about the goods or services.

[0914] "Means of transmission to the receiving terminal" refers to communication devices and protocols used to send the generated quotation to the user's terminal (smartphone, PC, etc.).

[0915] "Means of display" refers to interfaces or display devices for displaying quotations on the receiving terminal.

[0916] "External API connection means" refers to programs or communication protocols used to connect to external APIs in order to obtain external market data and related data.

[0917] A "machine learning model" refers to a learning algorithm or predictive model used to predict prices based on data.

[0918] A "generative AI model" is an artificial intelligence model designed to generate appropriate output in response to a specific input (prompt).

[0919] A "prompt sentence" is an instruction or question that is input into a generative AI model to obtain a specific output.

[0920] As a concrete example of implementing this invention, a system for estimating appropriate product prices in a smartphone app for an e-commerce site is presented. This system is designed to quickly and accurately estimate appropriate prices when a user lists a new product. When a user enters product information using the smartphone app, that information is sent to a server, and an appropriate price is calculated considering past database comparisons and market trends. The generated estimate is then sent to the user.

[0921] First, the user enters basic information about the product or service into the smartphone app. This includes information such as the product name, specifications, desired price range, and target market. For example, if a user is estimating a fair price for a new smartwatch, they would enter specifications such as the name "SmartWatch Model X," screen size, and battery life.

[0922] Next, the terminal sends the entered information to the server. This data is preferably encrypted for security reasons. Based on the received information, the server connects to external APIs to obtain internal and external market data. It then extracts similar historical data from the database based on the acquired market data.

[0923] The server uses a machine learning model to calculate a fair price based on the extracted data. This process utilizes algorithms such as linear regression, taking into account market trends and competitive conditions. For example, it might use 100 historical price data points for similar smartwatches to calculate a fair price.

[0924] Based on the calculated price, the server generates a quotation. The quotation includes the product name, specifications, and appropriate price, and is saved in PDF format. The generated quotation is sent to the user's device, and the user is notified via push notification or email notification. The user can then view the received quotation in the app.

[0925] The following are some specific examples of prompt statements generated using a generative AI model.

[0926] The specifications for the "SmartWatch Model X" are as follows: Screen size: 1.5 inches, Battery life: 24 hours. Please estimate the market price for this product based on historical data and current trends.

[0927] As described above, this system allows users to quickly and accurately estimate fair prices, enabling reliable transactions.

[0928] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0929] Program processing flow

[0930] Step 1:

[0931] The user enters basic information about the product or service into a smartphone app. For example, the user might enter specifications such as the name, screen size, and battery life of a "SmartWatch Model X." This input generates product information.

[0932] Step 2:

[0933] The terminal sends the entered information to the server. This transmission is encrypted for security purposes. The input data (product name, specifications, etc.) arrives at the server as transmitted data.

[0934] Step 3:

[0935] Based on the information received, the server connects to its own and external APIs to retrieve market data. During this process, it calls external APIs to collect market data related to the specified product category. The retrieved market data is then stored as local data.

[0936] Step 4:

[0937] The server extracts historical data similar to the information entered from the database. This uses a search algorithm to narrow down the historical data using product specifications and target market information as keys. This data extraction generates a similarity dataset.

[0938] Step 5:

[0939] The server uses a machine learning model to calculate a fair price based on the extracted data and acquired market data. Here, a linear regression model is used, combining historical price data with current market trends in the calculation. The price predicted by the machine learning model is output as the fair price.

[0940] Step 6:

[0941] The server generates a quotation based on the calculated fair price. The quotation includes detailed product information and the calculated price, and is saved in PDF format. This generated quotation is output as a quotation file.

[0942] Step 7:

[0943] The server sends the generated quotation to the user's receiving device. Email or push notifications are used as the sending method. The sent quotation is received on the user's device.

[0944] Step 8:

[0945] The user reviews the received quotation using a smartphone app. The app provides the user with quotation information through a viewer that displays the received PDF quotation. This display allows the user to review the quotation content and prepare to share it with the customer.

[0946] 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.

[0947] As an example of implementing this invention, a system is provided that combines a user emotion engine to more accurately estimate the appropriate price of goods and services. In addition to a function that extracts similar information from a database based on information entered by the user into a terminal and calculates an appropriate price, this system uses an emotion engine to recognize the user's emotions and adjust subsequent responses accordingly.

[0948] System Overview

[0949] The user enters basic information about the product or service into their terminal and sends that information to the server. The server searches its database based on the received information, extracts similar data, and calculates a fair price. Then, it generates a quotation based on the calculated fair price and sends it to the user's terminal. Furthermore, an emotion engine recognizes the user's emotions and provides suggestions and advice based on that information. In this way, the quotation process is customized according to the user's psychological state.

[0950] Program processing flow

[0951] 1. Entering information

[0952] User: Enter basic information about the product or service to be quoted (name, specifications, desired price range, target market, etc.) into the terminal. This includes detailed descriptions and options.

[0953] 2. Data transmission

[0954] Terminal: Sends basic information entered by the user to the server. It is recommended that this data be encrypted for security reasons.

[0955] 3. Database matching

[0956] Server: Receives input information and generates queries to the database. It searches and extracts similar past data using keywords such as product name, specifications, and target market.

[0957] 4. Calculation of a fair price

[0958] Server: Executes an algorithm to calculate a fair price based on the extracted data. The price calculation uses weighted averages, regression analysis, machine learning models, etc., and takes market trends and competitive conditions into consideration.

[0959] 5. Generating the final quotation

[0960] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price. It is generally generated in PDF format.

[0961] 6. Sending the quotation

[0962] Server: Sends the generated quotation to the user's terminal. Email and push notifications are used as notification methods.

[0963] 7. Emotion recognition by an emotion engine

[0964] Terminal: Sends user input information and facial recognition data to the emotion engine. The emotion engine analyzes the user's emotions based on this information.

[0965] Emotion Engine: Sends analysis results to the server and generates suggestions and advice for the user based on them.

[0966] 8. Display of suggestions and advice

[0967] Terminal: Based on the analysis results of the emotion engine, it displays suggestions and advice to the user. This allows the user to receive appropriate information tailored to their psychological state.

[0968] Specific example

[0969] For example, when a user requests a quote for a new software service:

[0970] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[0971] 2. Data transmission: The terminal sends the input information to the server.

[0972] 3. Database matching: The server extracts similar past data based on the input information.

[0973] 4. Calculation of fair price: The server calculates a fair price considering market trends and competitive conditions.

[0974] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[0975] 6. Sending the quotation: The server sends the generated quotation to the user's terminal.

[0976] 7. Emotion recognition by emotion engine: The emotion engine analyzes the facial expression data when the user makes inputs and recognizes the user's emotions.

[0977] 8. Displaying suggestions and advice: Based on the analysis results, if the user is feeling stressed, suggestions to help them relax will be displayed; if they are feeling happy, more positive suggestions will be displayed.

[0978] This system allows users to quickly calculate fair prices and receive appropriate support tailored to their emotions, helping to strengthen customer trust. Furthermore, the use of an emotion engine significantly improves the user experience.

[0979] The following describes the processing flow.

[0980] Step 1:

[0981] User: Enter basic information about the product or service to be quoted into the terminal. For example, enter the name of "Software Service A," a brief description of its functions, desired price range, and target market into the input form. Once the user has finished entering the information, they will check the details on the confirmation screen and press the submit button.

[0982] Step 2:

[0983] Terminal: Sends the basic information entered by the user to the server. The input information is converted to JSON or XML format and encrypted for security. After transmission is complete, a notification "Information sent" is displayed on the terminal.

[0984] Step 3:

[0985] Server: Analyzes the received basic information and generates a search query. The query sends a search command to the database containing keywords such as product name, specifications, and target market.

[0986] Step 4:

[0987] Server: Searches the database and extracts data on past products and services similar to the entered information. For example, it extracts data on similar software services for which quotes were previously created. This data includes past prices, specification details, and market price information.

[0988] Step 5:

[0989] Server: Calculates a fair price based on extracted data. It uses weighted averages, regression analysis, and machine learning models to calculate a price that considers historical data, current market trends, and competitive landscape. For example, it might derive the most appropriate price based on 100 historical price data points for similar services.

[0990] Step 6:

[0991] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price. The quotation is generated in PDF format and formatted using templates as needed.

[0992] Step 7:

[0993] Server: Sends the generated quote to the user's device. Once the transmission is complete, the user is notified via email or push notification.

[0994] Step 8:

[0995] Terminal: Displays received quotations to the user. The user can review the quotation on the terminal and check for any problems. If the quotation is correct, it is ready to be used for subsequent negotiations and proposals.

[0996] Step 9:

[0997] User: Review the quotation and communicate with the customer as needed. The quotation contains fair pricing and detailed information, enabling reliable negotiations.

[0998] Step 10:

[0999] Terminal: Acquires user input information and facial expression data during operation from the camera and sends it to the emotion engine. This uses a high-performance camera and emotion analysis software.

[1000] Step 11:

[1001] Server and Emotion Engine: Analyzes received facial recognition data to determine the user's emotional state. For example, it uses a facial expression analysis algorithm to determine whether the user is nervous, happy, etc. The emotion engine generates the analysis results and sends them to the server.

[1002] Step 12:

[1003] Server: Based on the analysis results of the emotion engine, it generates suggestions and advice for the user. If the user is stressed, it displays messages to help them relax; if they are happy, it displays even more positive suggestions.

[1004] Step 13:

[1005] Terminal: Displays generated suggestions and advice to the user. The user uses this information to review the quote and take appropriate action. Emotion-based advice improves the user experience.

[1006] This series of processes allows users to quickly calculate a fair price, receive appropriate support tailored to their emotions, and strengthen customer trust. By utilizing an emotion engine, the quotation process becomes more personalized, improving the user experience.

[1007] (Example 2)

[1008] 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."

[1009] In systems that calculate appropriate prices and generate quotations based on basic information about products and services, there is a problem with the user experience due to the lack of suggestions and advice that take user emotions into consideration. Furthermore, the system does not adequately consider market trends and competitive situations when calculating appropriate prices, making it difficult to provide accurate quotations.

[1010] The identification processing 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 inputting basic information of a product or service, means for transmitting the inputted information, means for extracting similar data from a database based on the inputted information, means for calculating an appropriate price based on the extracted data, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, means for recognizing emotions based on user input information and facial recognition data, means for making suggestions and advice to the user based on the recognized emotions, and means for displaying the quotation and the suggestions and advice. This makes it possible to make suggestions and advice that take the user's emotions into consideration, improving the user experience while realizing an accurate and appropriate price estimate.

[1011] "Basic information about the product or service" refers to detailed information about the product or service being quoted, such as its name, specifications, desired price range, and target market.

[1012] "Means of input" refers to an interface through which a user provides basic information about a product or service to the system, and includes, for example, a keyboard, mouse, or touchscreen.

[1013] "Means of transmission" refers to devices and protocols used to send information entered by a user to a server, including, for example, internet connections, Wi-Fi, and data encryption protocols.

[1014] "Means of extraction" refers to devices or software used to search for and extract data similar to the information entered into a database, and includes, for example, SQL queries and search algorithms.

[1015] "Means for calculating fair prices" refer to devices or software that calculate fair prices using algorithms such as weighted averages, regression analysis, and machine learning models based on extracted data.

[1016] "Means for generating quotations" refers to devices or software used to create quotations based on calculated fair price information, and includes, for example, template engines and PDF generation software.

[1017] "Means of sending to the receiving terminal" refers to devices or protocols for sending the generated quotation to the user's receiving terminal, including, for example, email servers and push notification services.

[1018] "Means of recognizing emotions" refer to devices and software that analyze user input information and facial recognition data to identify the user's emotions, and include, for example, facial expression recognition algorithms and emotion analysis APIs.

[1019] "Means of providing suggestions and advice" refers to devices or software that provide appropriate suggestions and advice to users based on emotions recognized by an emotion engine.

[1020] "Means of display" refers to an interface for visually displaying the generated estimates, proposals, and advice to the user, and includes, for example, displays and monitors.

[1021] This invention is a system for more accurately estimating the appropriate price of goods and services, and incorporates an emotion engine that recognizes the user's emotions and adjusts subsequent responses accordingly. In addition to a function that extracts similar information from a database based on information entered by the user on a terminal and calculates an appropriate price, this system can also provide suggestions and advice that are tailored to the user's emotions by using the emotion engine.

[1022] Users input basic information about their products or services (e.g., name, specifications, desired price range, target market, etc.) into a terminal and send this information to a server. The terminal includes input devices such as a keyboard, mouse, and touchscreen. The input information is in JSON or XML format and is encrypted using SSL / TLS. The server generates queries to access a database based on the received information and extracts similar historical data. SQL or NoSQL databases are used. Based on this extracted data, the server calculates an appropriate price using weighted averages, regression analysis, and machine learning models (e.g., linear regression and random forest). Market trends and competitive conditions are also considered in real time.

[1023] The server generates a quote based on a fair price. This quote includes detailed information about the goods or services, the basis for the calculation, and the fair price, and is generated in PDF format using a template engine (e.g., JasperReports or iText). The generated PDF quote is then sent to the user's device via email (using SMTP) or push notification (e.g., Firebase Cloud Messaging).

[1024] Furthermore, an emotion engine is provided that analyzes emotions based on user input information and facial expression data. It uses the device's built-in camera to acquire facial recognition data, which is then analyzed, for example, through the Microsoft Azure Emotional Analysis API. The results of the emotion engine's analysis are sent to a server, which then generates suggestions and advice for the user based on these results. These might include instructions to promote relaxation or suggestions for additional options. The device is equipped with a display or monitor to show these suggestions and advice to the user.

[1025] As a concrete example, consider a scenario where a user requests a quote for a new software service A. The user inputs the name of software service A, its specifications (e.g., cloud-based, monthly subscription), desired price range ($500-$700), and target market (small and medium-sized enterprises) into the terminal. The terminal sends this information to the server in JSON format, and the data is encrypted using SSL / TLS. The server generates an SQL query based on the received information to extract similar historical data from the database. Next, the server calculates a fair price using a linear regression model, taking into account market trends and competitive information, and generates a quote in PDF format using a template engine. The server then sends the generated PDF to the user via email. Simultaneously, the terminal sends the user's facial expression data from the input process to an emotion engine, and based on the analysis results, displays relaxation-enhancing advice and additional option suggestions to the user.

[1026] Example of a prompt:

[1027] "I would like to request a quote for a new software service A. Please calculate a fair price and generate a quote based on the following information."

[1028] Software name: Software Service A

[1029] Specifications: Cloud-based, user interface is a web app, monthly subscription fee.

[1030] Desired price range: $500 - $700

[1031] Target market: Small and medium-sized enterprises (SMEs)

[1032] Furthermore, recognize the user's emotions when they input data and display suggestions and advice based on those emotions.

[1033] This system allows users to quickly and accurately calculate fair prices, and also provides personalized suggestions and advice based on their emotions, which is expected to improve customer satisfaction.

[1034] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1035] Step 1: Enter information

[1036] User: Enter basic information about the product or service to be quoted into the terminal. Specifically, enter information such as the product name, specifications, desired price range, and target market. The entered information will be processed as JSON data.

[1037] Input: Basic information such as product name, specifications, desired price range, and target market.

[1038] Output: Basic information data in JSON format entered into the terminal.

[1039] Step 2: Data transmission

[1040] Terminal: Sends the entered information to the server. To ensure the security of the information, the data is encrypted using the SSL / TLS protocol. The data format is typically JSON.

[1041] Input: Basic information data in JSON format

[1042] Output: Encrypted JSON formatted information data is sent to the server.

[1043] Step 3: Matching with the database

[1044] Server: Based on the received information, it generates queries for the database. It accesses the database using SQL queries, searches for and extracts past data similar to the entered basic information.

[1045] Input: Encrypted JSON format information data

[1046] Output: Extraction results of similar historical data

[1047] Step 4: Calculating the Fair Price

[1048] Server: Calculates a fair price based on extracted historical data. This calculation uses weighted averages, regression analysis, and machine learning models (e.g., linear regression, random forest), taking market trends and competitive landscape into consideration.

[1049] Input: Extracted historical data, current market trend information, competitor data

[1050] Output: Calculated fair price

[1051] Step 5: Generating the final quote

[1052] Server: Generates a quotation based on the calculated fair price. The quotation is created in PDF format using a template engine (e.g., JasperReports, iText). The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price.

[1053] Input: Calculated fair price, detailed information about the product / service.

[1054] Output: Quotation in PDF format

[1055] Step 6: Send the quotation

[1056] Server: Sends the generated quote to the user's device. Quotes are sent via email (SMTP) or push notifications (e.g., Firebase Cloud Messaging).

[1057] Input: Quotation in PDF format

[1058] Output: Quotation in PDF format sent to the user's receiving terminal.

[1059] Step 7: Emotion recognition by the emotion engine

[1060] The device sends user input information and facial expression data to the emotion engine. It uses the camera built into the device to acquire facial recognition data and sends it to the emotion engine. For example, the emotion engine includes facial expression recognition algorithms and emotion analysis APIs (e.g., Microsoft Azure Emotional Analysis API).

[1061] Input: User's facial expression data, input information

[1062] Output: User data sent to the emotion engine

[1063] Step 8: Displaying suggestions and advice

[1064] Terminal: Based on the analysis results of the emotion engine, it displays suggestions and advice to the user. For example, if the user is feeling tense, it displays suggestions to help them relax, and if the user is happy, it displays positive suggestions.

[1065] Input: Analysis results from the emotion engine

[1066] Output: Suggestions and advice displayed to the user

[1067] (Application Example 2)

[1068] 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."

[1069] Traditional pricing systems for goods and services lacked the functionality to consider user emotions and provide appropriate suggestions and advice. This made it difficult to enhance user psychological satisfaction and trust, resulting in a limited user experience. Furthermore, there was a lack of technology to more accurately calculate fair prices for goods and services by incorporating emotion recognition into the pricing process.

[1070] 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.

[1071] In this invention, the server includes means for inputting basic information of a product or service, means for extracting similar data from a database based on the input information, means for calculating a fair price based on the extracted data, means for recognizing the user's emotional information when estimating the product or service, and means for generating suggestions and advice based on the analysis results of the emotional recognition means. This makes it possible to consider the user's psychological state during the estimation process and provide suggestions and advice that are more satisfying. Furthermore, by utilizing the user's emotional information, the accuracy of calculating a fair price is improved, resulting in more accurate price estimates.

[1072] "Basic information about the product or service" refers to detailed information entered by the user, such as the name of the product or service, specifications, desired price range, and target market.

[1073] "Input method" refers to an interface for users to input basic information about a product or service.

[1074] "Means of transmission" refers to a mechanism for securely sending input information to a server, such as by encrypting it.

[1075] "Means for extracting similar data from a database" refers to a function in which the server searches the database for and extracts similar past data based on the input information.

[1076] A "means for calculating a fair price" refers to an algorithm or model that calculates a fair price for a product or service based on historical data extracted from a database, taking into account the market value and competitive landscape.

[1077] "Methods for generating quotations" refers to the process of creating a quotation that includes detailed information about the goods or services and the basis for the calculation, based on the calculated fair price.

[1078] "Means of sending to the receiving terminal" refers to a system that sends the generated quotation to the user's terminal.

[1079] "Means of displaying the quotation" refers to a function that allows the quotation to be displayed on the receiving terminal so that it can be viewed.

[1080] "Emotion recognition means" refers to technology that recognizes a user's emotions by analyzing user input information and facial expression data.

[1081] "Means for generating suggestions and advice" refers to a function that creates suggestions and advice tailored to the user's emotional state based on the analysis results of emotion recognition.

[1082] This invention is a system that recognizes user emotions and provides appropriate suggestions and advice during the process of estimating the fair price of goods and services. The following describes embodiments for carrying out this invention.

[1083] System Configuration

[1084] The system of this invention consists of the following main components:

[1085] 1. Means of entering basic information about a product or service:

[1086] Users input basic information about products or services using a smartphone app. This basic information includes the name of the product or service, specifications, desired price range, and target market.

[1087] 2. Means of information transmission:

[1088] The terminal encrypts the entered basic information and sends it to the server. Security protocols such as TLS and HTTPS are used as encryption technologies.

[1089] 3. Methods for extracting similar data:

[1090] The server searches the database based on the received information and extracts similar past data. SQL or NoSQL databases are used for this process.

[1091] 4. Methods for calculating fair prices:

[1092] The server calculates a fair price based on similar data extracted from a database. Regression analysis and machine learning algorithms (such as Scikit-learn) are used for this calculation.

[1093] 5. Methods for generating quotations:

[1094] The server generates a quotation based on the calculated fair price. The quotation is output in PDF format, using a PDF generation library (such as ReportLab).

[1095] 6. Method of sending the quotation:

[1096] The generated quote will be sent to the user's device via email or push notification.

[1097] 7. Emotion recognition means:

[1098] An emotion engine is used to analyze user input information and facial recognition data. Examples of emotion engines include OpenCV and the Emotion API.

[1099] 8. Means of generating suggestions and advice:

[1100] The server generates suggestions and advice that the user finds optimal based on the results of emotion recognition. Using a generative AI model is recommended for this process.

[1101] Specific example

[1102] When a user requests a quote for new high-performance earphones:

[1103] 1. The user enters basic information about the "high-performance earphones" using a smartphone app.

[1104] 2. The device encrypts this information and sends it to the server.

[1105] 3. The server searches the database and extracts past data for similar earphones.

[1106] 4. The server calculates a fair price based on the extracted data.

[1107] 5. The quotation is generated in PDF format and sent to the user's device.

[1108] 6. The emotion engine analyzes the user's input information and facial expression data and recognizes that the emotion is positive.

[1109] 7. If the emotion is judged to be positive, more proactive suggestions will be made, such as, "Would you like to see our higher-performance products?"

[1110] Example of a prompt

[1111] A user has requested a quote for a new premium product. They have positive feedback. Calculate a fair price and output a sentiment-based suggestion.

[1112] input:

[1113] Basic information on high-performance earphones

[1114] output:

[1115] Estimated price: $300

[1116] Advice: "Would you like to see our higher-performance products?"

[1117] As described above, the present invention makes it possible to provide an estimation system that takes into account the user's psychological state and offers even greater satisfaction.

[1118] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1119] Step 1:

[1120] Users input basic information about a product or service using a smartphone app. This includes detailed information such as the name, specifications, desired price range, and target market.

[1121] Input: Basic information about the product or service

[1122] Output: Input basic information data

[1123] Step 2:

[1124] The terminal encrypts the entered basic information and sends it to the server. To ensure the security of the information, security protocols such as TLS and HTTPS are used.

[1125] Input: Encrypted basic information data

[1126] Output: Encrypted data sent to the server

[1127] Step 3:

[1128] The server decrypts the received basic information and searches the database to extract similar past data. SQL or NoSQL databases are used for the search.

[1129] Input: Decrypted basic information data

[1130] Output: Similar historical data

[1131] Step 4:

[1132] The server calculates a fair price based on similar data. Regression analysis and machine learning algorithms are used for this calculation. Libraries used include Scikit-learn.

[1133] Input: Similar historical data

[1134] Output: Fair price

[1135] Step 5:

[1136] The server generates a quotation in PDF format based on a fair price. Libraries such as ReportLab are used for PDF generation.

[1137] Input: Fair price

[1138] Output: Quotation in PDF format

[1139] Step 6:

[1140] The server sends the generated quotation to the user's device. Email or push notifications are used as the means of delivery.

[1141] Input: Quotation in PDF format

[1142] Output: Notification of quotation to user terminal

[1143] Step 7:

[1144] The device sends user input information and facial recognition data to the emotion engine. The emotion engine analyzes the user's emotions. Examples of emotion engines used include OpenCV and the Emotion API.

[1145] Input: User input information and facial recognition data

[1146] Output: Emotion analysis results

[1147] Step 8:

[1148] The server generates suggestions and advice for the user based on the sentiment analysis results. Using a generative AI model is effective in this process.

[1149] Input: Sentiment analysis results

[1150] Output: Suggestion and advice messages

[1151] Step 9:

[1152] The device displays generated suggestions and advice to the user. This allows the user to receive appropriate suggestions tailored to their emotions.

[1153] Input: Suggestion or advice message

[1154] Output: Content displayed to the user

[1155] 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.

[1156] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[1157] 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.

[1158] [Fourth Embodiment]

[1159] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1160] 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.

[1161] 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).

[1162] 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.

[1163] 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.

[1164] 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).

[1165] 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.

[1166] 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.

[1167] 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.

[1168] 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.

[1169] 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.

[1170] 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.

[1171] 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".

[1172] As an example of implementing this invention, a system for quickly and accurately estimating the appropriate price of a product or service is provided. This system calculates an appropriate price based on information entered by the user into a terminal, taking into account comparisons with past databases and market trends, and generates and transmits the result as a quotation.

[1173] System Overview

[1174] The user enters basic information about a product or service into their terminal and sends that information to the server. The server searches its database based on the received information, extracts similar data, and calculates a fair price. Then, it generates a quotation based on the calculated price information and sends it to the user's terminal. The user can review the sent quotation and share it with the customer to strengthen trust.

[1175] Program processing flow

[1176] 1. Entering information

[1177] User: Enter basic information (product name, specifications, desired price range, target market, etc.) into the terminal to create a quote.

[1178] As a specific example, when a user requests a quote for a new software service, they input the name of "Software Service A," a brief description of its functions, desired price range, and target market information into the terminal.

[1179] 2. Data transmission

[1180] Terminal: Sends user-entered information to the server. This data should preferably be encrypted for security reasons.

[1181] 3. Database matching

[1182] Server: Receives the entered information and compares it with the company's and related market databases. Specifically, it searches and extracts past quotation data and market price data similar to the entered product name and specifications.

[1183] 4. Calculation of a fair price

[1184] Server: Based on the extracted data, it calculates a fair price. In this process, it uses an algorithm to consider market trends and competitive conditions.

[1185] As a concrete example, we have 100 historical data points for similar software services, and we extract important data from them to calculate an appropriate price considering current market trends.

[1186] 5. Generating the final quotation

[1187] Server: Generates a quotation based on the calculated fair price. This quotation includes details of the goods / services and their fair price.

[1188] For example, if the appropriate price for software service A is calculated to be 1 million yen, a PDF quotation will be generated that includes the price and service specifications.

[1189] 6. Sending and confirming the quotation

[1190] Server: Sends the generated quotation to the user's terminal. Email and push notifications are commonly used as notification methods.

[1191] Terminal: Displays received quotations and allows users to review them.

[1192] Specific example

[1193] For example, when a user requests a quote for a new software service, they would use the system as follows:

[1194] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[1195] 2. Data transmission: The terminal sends the input information to the server.

[1196] 3. Database matching: The server extracts similar past data from the database based on the input information.

[1197] 4. Calculation of appropriate price: The server calculates an appropriate price based on extracted data, taking into account market trends and competitive conditions.

[1198] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[1199] 6. Sending and confirming the quotation: The server sends the generated quotation to the user's terminal, and the user confirms it.

[1200] This system allows users to quickly calculate fair prices, helping to strengthen trust with customers.

[1201] The following describes the processing flow.

[1202] Step 1:

[1203] User: Enter basic information about the product or service to be quoted (name, specifications, desired price range, target market, etc.) into the terminal. Specifically, enter the necessary information according to the input form or selection options. A confirmation screen of the entered information is also provided to prevent input errors.

[1204] Step 2:

[1205] Terminal: Sends the basic information entered by the user to the server. The input data is converted to JSON or XML format, and encryption is recommended for security. After successful transmission, the user receives a notification.

[1206] Step 3:

[1207] Server: Analyzes the received basic information and generates queries for the database. The queries include keywords such as product name, specifications, and target market.

[1208] Step 4:

[1209] Server: Searches the database and extracts data on similar products and services from the past. In this process, it uses multiple databases or tables to collect the necessary information and normalizes the data as needed.

[1210] Step 5:

[1211] Server: Based on the extracted data, it executes an algorithm to calculate a fair price. Specifically, it uses weighted averages, regression analysis, machine learning models, etc., to calculate a fair price that takes into account past price data and current market trends.

[1212] Step 6:

[1213] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods or services, the basis for the calculation, and the fair price. Quotations are typically generated in PDF format.

[1214] Step 7:

[1215] Server: Sends the generated quotation to the user's terminal. Delivery methods include email, push notification, or in-system messaging service.

[1216] Step 8:

[1217] Terminal: Receives the sent quotation and notifies the user that the quotation has arrived. The user who receives the notification can then view the quotation displayed on their terminal.

[1218] Step 9:

[1219] User: Review the displayed quote, negotiate with the customer or make internal adjustments as needed, and prepare the final quote to provide to the customer.

[1220] This series of steps allows users to quickly calculate fair prices and provide reliable quotes to customers. Furthermore, the system's ease of use and accuracy streamline sales activities.

[1221] (Example 1)

[1222] 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".

[1223] Traditional pricing systems for goods and services made it difficult to quickly and accurately calculate fair prices. They failed to consider market trends and competitive landscapes, resulting in unreliable quotes and hindering the building of trust with customers.

[1224] 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.

[1225] In this invention, the server includes means for inputting basic information of a product or service, means for transmitting the inputted information, means for extracting similar data from a database based on the inputted information, means for calculating an appropriate price using a generation AI model based on the extracted data, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, and means for displaying the quotation. This makes it possible for the user to quickly and accurately calculate an appropriate price and generate and transmit the result as a quotation.

[1226] "Merchandise" refers to goods and services.

[1227] "Basic information" refers to information about a product or service, such as its name, specifications, desired price range, and target market.

[1228] "Means of input" refers to the hardware and software that users use to input basic information about a product or service.

[1229] "Means of transmission" refers to the means of communication used to send the entered basic information to the server.

[1230] A "database" refers to data storage that holds past estimate data and market price data for products and services.

[1231] "Extraction method" refers to the means of searching for and retrieving similar data from a database based on the basic information entered.

[1232] A "generative AI model" refers to a machine learning model that analyzes past data and calculates an appropriate price by considering current market trends and competitive conditions.

[1233] "Methods for calculating fair prices" refer to methods for calculating the appropriate price of a product or service from data extracted using a generative AI model.

[1234] A "quotation" refers to a document that contains detailed information about a product or service, including its fair price.

[1235] "Generating means" refers to software and algorithms for automatically creating quotations based on appropriate pricing and entered basic information.

[1236] "Means of transmission" refers to the means of communication used to send the generated quotation to the receiving terminal.

[1237] "Receiving terminal" refers to the device that a user uses to receive and view a quotation.

[1238] "Means of display" refers to the means by which a user can visually confirm the received quotation.

[1239] An "algorithm" refers to a procedure or method for calculating a fair price by taking into account market trends and competitive conditions.

[1240] "PDF format" is an abbreviation for Portable Document Format, and refers to a file format for electronically storing and sharing documents.

[1241] "Encrypted methods" refer to means of protecting data using cryptographic techniques to enhance data security.

[1242] This invention will be explained using a system for quickly and accurately estimating the appropriate price of a product or service as an example. Based on information entered by the user into a terminal, this system calculates an appropriate price by comparing it with past databases and considering market trends, and then generates and transmits the result as a quotation.

[1243] System Overview

[1244] The user enters basic information about the product or service into the terminal and sends that information to the server. Specifically, when a user requests a quote for a new software service, they would enter the name of "Software Service A," a brief description of its functions, the desired price range, and information about the target market.

[1245] The terminal receives basic information entered by the user, encrypts it, and sends it to the server. AES encryption is recommended for this process, and HTTPS is used as the communication protocol.

[1246] The server decodes the information received from the terminal and searches the database to extract similar data. For example, it searches and extracts data with similar product names and specifications from the quotation database for the past three years. At this time, a database query is generated to find data that matches or is similar to the product name and specifications.

[1247] Next, the server uses a generative AI model to calculate a fair price based on the extracted data. This generative AI model uses an algorithm that analyzes historical data and considers current market trends and competitive conditions. For example, it extracts important information from 100 data points and calculates a fair price considering current market trends.

[1248] After a fair price is calculated, the server automatically generates a quote using a template engine (e.g., Apache Velocity). This quote includes details of the product or service and its fair price. For example, if the fair price for "Software Service A" is calculated to be 1.5 million yen, a PDF quote will be generated that includes the price and service specifications.

[1249] Finally, the server sends the generated quotation to the user's device. The delivery method may include an email service (e.g., SendGrid) or push notification. Specifically, the quotation is sent as a PDF attachment with the subject line "Quotation: Software Service A".

[1250] The device allows users to download quotes via received emails and notifications, enabling them to review the contents. After reviewing, users can share these quotes with customers, strengthening their relationship of trust.

[1251] Specific example

[1252] For example, the specific steps a user might take to get an estimate for a new software service are as follows:

[1253] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[1254] 2. Data transmission: The terminal sends the input information to the server.

[1255] 3. Database matching: The server extracts similar past data from the database based on the input information.

[1256] 4. Calculation of appropriate price: The server calculates an appropriate price based on extracted data, taking into account market trends and competitive conditions.

[1257] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[1258] 6. Sending and confirming the quotation: The server sends the generated quotation to the user's terminal, and the user confirms it.

[1259] Example of a prompt

[1260] You can request a fair price estimate from a generative AI model using the following prompt:

[1261] "Please provide a quote for the following product: 'Software Service A', specifications: 'Cloud-based data management function', desired price range: '1 million to 2 million yen', target market: 'Small and medium-sized enterprises'."

[1262] Based on this prompt, the AI ​​model calculates a fair price and generates a quote.

[1263] This system allows users to quickly and accurately calculate fair prices and generate and send the results as quotations.

[1264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1265] Step 1:

[1266] The user inputs basic information about the product or service into the terminal. Specifically, they input information such as the product name, specifications, desired price range, and target market. As an example of input, the user inputs the name "Software Service A," the specifications "Cloud-based data management function," the desired price range "1 million to 2 million yen," and the target market information "For small and medium-sized enterprises." This becomes the system's input information.

[1267] Step 2:

[1268] The terminal encrypts the entered basic information using the AES encryption method and sends it to the server via the HTTPS protocol. This is the input information transfer process. Specifically, when the send button is pressed, the terminal encrypts the data and sends it to the specified endpoint on the server. The output of this process is encrypted data, which is sent to the server.

[1269] Step 3:

[1270] The server receives encrypted data sent from the terminal and deserializes it back to its original format. The received and deserialized data includes information such as product name, specifications, desired price range, and target market. The server uses this as input to search its own and external market databases and extract similar data. This is the process of generating database queries and searching for entries in the database that match or are similar to the product name and specifications. Specifically, it extracts 100 data entries related to "cloud-based data management functionality" from the database over the past three years. The output is a set of similar data.

[1271] Step 4:

[1272] The server uses a generative AI model to calculate a fair price based on the extracted data. The input is similar data extracted in step 3. The server inputs this data into the generative AI model, which analyzes it using an algorithm that takes market trends and competitive conditions into account. Specifically, the model evaluates historical price information, current market demand, and competitor pricing to calculate a fair price. For example, a fair price of "1.5 million yen" is calculated based on historical data and current market trends. The output is the calculated fair price.

[1273] Step 5:

[1274] The server automatically generates a quotation based on the calculated fair price. The server uses a template engine (e.g., Apache Velocity) to insert the calculated fair price and the entered basic information into a template. Specifically, it generates a PDF quotation that includes the name "Software Service A," its specifications, and a price of "1.5 million yen." The input to the template is the basic information and the fair price, and the output is the generated PDF quotation.

[1275] Step 6:

[1276] The server sends the generated quotation to the user's terminal. The input is the generated quotation in PDF format. Email services (e.g., SendGrid) or push notifications are used as the means of transmission. Specifically, the email subject line will be "Quotation: Software Service A," and the PDF will be sent as an attachment. The output is the sent email or push notification.

[1277] Step 7:

[1278] The device downloads a quotation from an received email or notification and displays it to the user. The input is the received email or push notification, and the device uses this information to download and display the quotation. Specifically, the user opens the email, downloads the attached PDF, and can view it on the device. The output is the quotation displayed to the user.

[1279] This allows users to create estimates quickly and accurately.

[1280] (Application Example 1)

[1281] 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 robot 414 as the "terminal".

[1282] There is a need for a means to quickly and accurately estimate the fair price of goods or services, thereby strengthening the relationship of trust between users and customers. However, conventional systems have problems such as being time-consuming to calculate fair prices and failing to accurately reflect market trends and competitive situations. In addition, the quotation generation process is cumbersome and often detracts from the user experience.

[1283] 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.

[1284] In this invention, the server includes means for inputting basic information of a product or service, means for transmitting the inputted information, means for connecting to an external API to acquire market data, means for extracting similar data from a database based on the inputted information, means for calculating a fair price using a machine learning model, means for generating and processing prompt statements using a generative AI model, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, and means for displaying the quotation. This makes it possible to quickly and accurately calculate a fair price based on information entered by the user and to immediately generate and transmit a quotation.

[1285] "Basic information about a product or service" refers to information such as the name of the product or service, specifications, desired price range, and target market.

[1286] "Means of input" refers to interfaces or input devices that allow users to input basic information about a product or service.

[1287] "Means of transmission" refers to communication devices and protocols used to transmit input information to a server or external system.

[1288] A "database" is a collection of information used to store past transaction data and market data.

[1289] "Means for extracting similar data" refers to algorithms or programs used to search and extract past data from a database that is similar to the input information.

[1290] "Methods for calculating fair prices" refer to algorithms and calculation models for determining fair prices based on similar data extracted from a database and market trends.

[1291] "Means for generating quotations" refers to programs or software that automatically create quotations containing the calculated fair price and detailed information about the goods or services.

[1292] "Means of transmission to the receiving terminal" refers to communication devices and protocols used to send the generated quotation to the user's terminal (smartphone, PC, etc.).

[1293] "Means of display" refers to interfaces or display devices for displaying quotations on the receiving terminal.

[1294] "External API connection means" refers to programs or communication protocols used to connect to external APIs in order to obtain external market data and related data.

[1295] A "machine learning model" refers to a learning algorithm or predictive model used to predict prices based on data.

[1296] A "generative AI model" is an artificial intelligence model designed to generate appropriate output in response to a specific input (prompt).

[1297] A "prompt sentence" is an instruction or question that is input into a generative AI model to obtain a specific output.

[1298] As a concrete example of implementing this invention, a system for estimating appropriate product prices in a smartphone app for an e-commerce site is presented. This system is designed to quickly and accurately estimate appropriate prices when a user lists a new product. When a user enters product information using the smartphone app, that information is sent to a server, and an appropriate price is calculated considering past database comparisons and market trends. The generated estimate is then sent to the user.

[1299] First, the user enters basic information about the product or service into the smartphone app. This includes information such as the product name, specifications, desired price range, and target market. For example, if a user is estimating a fair price for a new smartwatch, they would enter specifications such as the name "SmartWatch Model X," screen size, and battery life.

[1300] Next, the terminal sends the entered information to the server. This data is preferably encrypted for security reasons. Based on the received information, the server connects to external APIs to obtain internal and external market data. It then extracts similar historical data from the database based on the acquired market data.

[1301] The server uses a machine learning model to calculate a fair price based on the extracted data. This process utilizes algorithms such as linear regression, taking into account market trends and competitive conditions. For example, it might use 100 historical price data points for similar smartwatches to calculate a fair price.

[1302] Based on the calculated price, the server generates a quotation. The quotation includes the product name, specifications, and appropriate price, and is saved in PDF format. The generated quotation is sent to the user's device, and the user is notified via push notification or email notification. The user can then view the received quotation in the app.

[1303] The following are some specific examples of prompt statements generated using a generative AI model.

[1304] The specifications for the "SmartWatch Model X" are as follows: Screen size: 1.5 inches, Battery life: 24 hours. Please estimate the market price for this product based on historical data and current trends.

[1305] As described above, this system allows users to quickly and accurately estimate fair prices, enabling reliable transactions.

[1306] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1307] Program processing flow

[1308] Step 1:

[1309] The user enters basic information about the product or service into a smartphone app. For example, the user might enter specifications such as the name, screen size, and battery life of a "SmartWatch Model X." This input generates product information.

[1310] Step 2:

[1311] The terminal sends the entered information to the server. This transmission is encrypted for security purposes. The input data (product name, specifications, etc.) arrives at the server as transmitted data.

[1312] Step 3:

[1313] Based on the information received, the server connects to its own and external APIs to retrieve market data. During this process, it calls external APIs to collect market data related to the specified product category. The retrieved market data is then stored as local data.

[1314] Step 4:

[1315] The server extracts historical data similar to the information entered from the database. This uses a search algorithm to narrow down the historical data using product specifications and target market information as keys. This data extraction generates a similarity dataset.

[1316] Step 5:

[1317] The server uses a machine learning model to calculate a fair price based on the extracted data and acquired market data. Here, a linear regression model is used, combining historical price data with current market trends in the calculation. The price predicted by the machine learning model is output as the fair price.

[1318] Step 6:

[1319] The server generates a quotation based on the calculated fair price. The quotation includes detailed product information and the calculated price, and is saved in PDF format. This generated quotation is output as a quotation file.

[1320] Step 7:

[1321] The server sends the generated quotation to the user's receiving device. Email or push notifications are used as the sending method. The sent quotation is received on the user's device.

[1322] Step 8:

[1323] The user reviews the received quotation using a smartphone app. The app provides the user with quotation information through a viewer that displays the received PDF quotation. This display allows the user to review the quotation content and prepare to share it with the customer.

[1324] 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.

[1325] As an example of implementing this invention, a system is provided that combines a user emotion engine to more accurately estimate the appropriate price of goods and services. In addition to a function that extracts similar information from a database based on information entered by the user into a terminal and calculates an appropriate price, this system uses an emotion engine to recognize the user's emotions and adjust subsequent responses accordingly.

[1326] System Overview

[1327] The user enters basic information about the product or service into their terminal and sends that information to the server. The server searches its database based on the received information, extracts similar data, and calculates a fair price. Then, it generates a quotation based on the calculated fair price and sends it to the user's terminal. Furthermore, an emotion engine recognizes the user's emotions and provides suggestions and advice based on that information. In this way, the quotation process is customized according to the user's psychological state.

[1328] Program processing flow

[1329] 1. Entering information

[1330] User: Enter basic information about the product or service to be quoted (name, specifications, desired price range, target market, etc.) into the terminal. This includes detailed descriptions and options.

[1331] 2. Data transmission

[1332] Terminal: Sends basic information entered by the user to the server. It is recommended that this data be encrypted for security reasons.

[1333] 3. Database matching

[1334] Server: Receives input information and generates queries to the database. It searches and extracts similar past data using keywords such as product name, specifications, and target market.

[1335] 4. Calculation of a fair price

[1336] Server: Executes an algorithm to calculate a fair price based on the extracted data. The price calculation uses weighted averages, regression analysis, machine learning models, etc., and takes market trends and competitive conditions into consideration.

[1337] 5. Generating the final quotation

[1338] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price. It is generally generated in PDF format.

[1339] 6. Sending the quotation

[1340] Server: Sends the generated quotation to the user's terminal. Email and push notifications are used as notification methods.

[1341] 7. Emotion recognition by an emotion engine

[1342] Terminal: Sends user input information and facial recognition data to the emotion engine. The emotion engine analyzes the user's emotions based on this information.

[1343] Emotion Engine: Sends analysis results to the server and generates suggestions and advice for the user based on them.

[1344] 8. Display of suggestions and advice

[1345] Terminal: Based on the analysis results of the emotion engine, it displays suggestions and advice to the user. This allows the user to receive appropriate information tailored to their psychological state.

[1346] Specific example

[1347] For example, when a user requests a quote for a new software service:

[1348] 1. Information Input: The user enters the basic information for "Software Service A" into the terminal.

[1349] 2. Data transmission: The terminal sends the input information to the server.

[1350] 3. Database matching: The server extracts similar past data based on the input information.

[1351] 4. Calculation of fair price: The server calculates a fair price considering market trends and competitive conditions.

[1352] 5. Generating the final estimate: The server generates an estimate based on the calculated price and saves it in PDF format.

[1353] 6. Sending the quotation: The server sends the generated quotation to the user's terminal.

[1354] 7. Emotion recognition by emotion engine: The emotion engine analyzes the facial expression data when the user makes inputs and recognizes the user's emotions.

[1355] 8. Displaying suggestions and advice: Based on the analysis results, if the user is feeling stressed, suggestions to help them relax will be displayed; if they are feeling happy, more positive suggestions will be displayed.

[1356] This system allows users to quickly calculate fair prices and receive appropriate support tailored to their emotions, helping to strengthen customer trust. Furthermore, the use of an emotion engine significantly improves the user experience.

[1357] The following describes the processing flow.

[1358] Step 1:

[1359] User: Enter basic information about the product or service to be quoted into the terminal. For example, enter the name of "Software Service A," a brief description of its functions, desired price range, and target market into the input form. Once the user has finished entering the information, they will check the details on the confirmation screen and press the submit button.

[1360] Step 2:

[1361] Terminal: Sends the basic information entered by the user to the server. The input information is converted to JSON or XML format and encrypted for security. After transmission is complete, a notification "Information sent" is displayed on the terminal.

[1362] Step 3:

[1363] Server: Analyzes the received basic information and generates a search query. The query sends a search command to the database containing keywords such as product name, specifications, and target market.

[1364] Step 4:

[1365] Server: Searches the database and extracts data on past products and services similar to the entered information. For example, it extracts data on similar software services for which quotes were previously created. This data includes historical pricing, specification details, and market price information.

[1366] Step 5:

[1367] Server: Calculates a fair price based on extracted data. It uses weighted averages, regression analysis, and machine learning models to calculate a price that considers historical data, current market trends, and competitive landscape. For example, it might derive the most appropriate price based on 100 historical price data points for similar services.

[1368] Step 6:

[1369] Server: Generates a quotation based on the calculated fair price. The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price. The quotation is generated in PDF format and formatted with templates as needed.

[1370] Step 7:

[1371] Server: Sends the generated quote to the user's device. Once the transmission is complete, the user is notified via email or push notification.

[1372] Step 8:

[1373] Terminal: Displays received quotations to the user. The user can review the quotation on the terminal and check for any problems. If the quotation is correct, it is ready to be used for subsequent negotiations and proposals.

[1374] Step 9:

[1375] User: Review the quote and communicate with the customer as needed. The quote contains fair pricing and detailed information, enabling reliable negotiations.

[1376] Step 10:

[1377] Terminal: Acquires user input information and facial expression data during operation from the camera and sends it to the emotion engine. This uses a high-performance camera and emotion analysis software.

[1378] Step 11:

[1379] Server and Emotion Engine: Analyzes received facial recognition data to determine the user's emotional state. For example, it uses a facial expression analysis algorithm to determine whether the user is nervous, happy, etc. The emotion engine generates the analysis results and sends them to the server.

[1380] Step 12:

[1381] Server: Based on the analysis results of the emotion engine, it generates suggestions and advice for the user. If the user is feeling stressed, it displays messages to help them relax; if they are happy, it displays even more positive suggestions.

[1382] Step 13:

[1383] Terminal: Displays generated suggestions and advice to the user. The user uses this information to review the quote and take appropriate action. Emotion-based advice improves the user experience.

[1384] This series of processes allows users to quickly calculate a fair price, receive appropriate support tailored to their emotions, and strengthen customer trust. By utilizing an emotion engine, the quotation process becomes more personalized, improving the user experience.

[1385] (Example 2)

[1386] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1387] In systems that calculate appropriate prices and generate quotations based on basic information about products and services, there is a problem with the user experience due to the lack of suggestions and advice that take user emotions into consideration. Furthermore, the system does not adequately consider market trends and competitive situations when calculating appropriate prices, making it difficult to provide accurate quotations.

[1388] The identification processing 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 inputting basic information of a product or service, means for transmitting the inputted information, means for extracting similar data from a database based on the inputted information, means for calculating an appropriate price based on the extracted data, means for generating a quotation based on the calculated price information, means for transmitting the generated quotation to a receiving terminal, means for recognizing emotions based on user input information and facial recognition data, means for making suggestions and advice to the user based on the recognized emotions, and means for displaying the quotation and the suggestions and advice. This makes it possible to make suggestions and advice that take the user's emotions into consideration, improving the user experience while realizing an accurate and appropriate price estimate.

[1389] "Basic information about the product or service" refers to detailed information about the product or service being quoted, such as its name, specifications, desired price range, and target market.

[1390] "Means of input" refers to an interface that allows a user to provide basic information about a product or service to the system, and includes, for example, a keyboard, mouse, or touchscreen.

[1391] "Means of transmission" refers to devices and protocols used to send information entered by a user to a server, including, for example, internet connections, Wi-Fi, and data encryption protocols.

[1392] "Means of extraction" refers to devices or software used to search for and extract data similar to the information entered into a database, and includes, for example, SQL queries and search algorithms.

[1393] "Means for calculating fair prices" refer to devices or software that calculate fair prices using algorithms such as weighted averages, regression analysis, and machine learning models based on extracted data.

[1394] "Means for generating quotations" refers to devices or software used to create quotations based on calculated fair price information, and includes, for example, template engines and PDF generation software.

[1395] "Means of sending to the receiving terminal" refers to devices or protocols for sending the generated quotation to the user's receiving terminal, including, for example, email servers and push notification services.

[1396] "Means of recognizing emotions" refer to devices and software that analyze user input information and facial recognition data to identify the user's emotions, and include, for example, facial expression recognition algorithms and emotion analysis APIs.

[1397] "Means of providing suggestions and advice" refers to devices or software that provide appropriate suggestions and advice to users based on emotions recognized by an emotion engine.

[1398] "Means of display" refers to an interface for visually displaying the generated estimates, proposals, and advice to the user, and includes, for example, displays and monitors.

[1399] This invention is a system for more accurately estimating the appropriate price of goods and services, and incorporates an emotion engine that recognizes the user's emotions and adjusts subsequent responses accordingly. In addition to a function that extracts similar information from a database based on information entered by the user on a terminal and calculates an appropriate price, this system can also provide suggestions and advice that are tailored to the user's emotions by using the emotion engine.

[1400] Users input basic information about their products or services (e.g., name, specifications, desired price range, target market, etc.) into a terminal and send this information to a server. The terminal includes input devices such as a keyboard, mouse, and touchscreen. The input information is in JSON or XML format and is encrypted using SSL / TLS. The server generates queries to access a database based on the received information and extracts similar historical data. SQL or NoSQL databases are used. Based on this extracted data, the server calculates an appropriate price using weighted averages, regression analysis, and machine learning models (e.g., linear regression and random forest). Market trends and competitive conditions are also considered in real time.

[1401] The server generates a quote based on a fair price. This quote includes detailed information about the goods or services, the basis for the calculation, and the fair price, and is generated in PDF format using a template engine (e.g., JasperReports or iText). The generated PDF quote is then sent to the user's device via email (using SMTP) or push notification (e.g., Firebase Cloud Messaging).

[1402] Furthermore, an emotion engine is provided that analyzes emotions based on user input information and facial expression data. It uses the device's built-in camera to acquire facial recognition data, which is then analyzed, for example, through the Microsoft Azure Emotional Analysis API. The results of the emotion engine's analysis are sent to a server, which then generates suggestions and advice for the user based on these results. These might include instructions to promote relaxation or suggestions for additional options. The device is equipped with a display or monitor to show these suggestions and advice to the user.

[1403] As a concrete example, consider a scenario where a user requests a quote for a new software service A. The user inputs the name of software service A, its specifications (e.g., cloud-based, monthly subscription), desired price range ($500-$700), and target market (small and medium-sized enterprises) into the terminal. The terminal sends this information to the server in JSON format, and the data is encrypted using SSL / TLS. The server generates an SQL query based on the received information to extract similar historical data from the database. Next, the server calculates a fair price using a linear regression model, taking into account market trends and competitive information, and generates a quote in PDF format using a template engine. The server then sends the generated PDF to the user via email. Simultaneously, the terminal sends the user's facial expression data from the input process to an emotion engine, and based on the analysis results, displays relaxation-enhancing advice and additional option suggestions to the user.

[1404] Example of a prompt:

[1405] "I would like to request a quote for a new software service A. Please calculate a fair price and generate a quote based on the following information."

[1406] Software name: Software Service A

[1407] Specifications: Cloud-based, user interface is a web app, monthly subscription fee.

[1408] Desired price range: $500 - $700

[1409] Target market: Small and medium-sized enterprises (SMEs)

[1410] Furthermore, recognize the user's emotions when they input data and display suggestions and advice based on those emotions.

[1411] This system allows users to quickly and accurately calculate fair prices, and also provides personalized suggestions and advice based on their emotions, which is expected to improve customer satisfaction.

[1412] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1413] Step 1: Enter information

[1414] User: Enter basic information about the product or service to be quoted into the terminal. Specifically, enter information such as the product name, specifications, desired price range, and target market. The entered information will be processed as JSON data.

[1415] Input: Basic information such as product name, specifications, desired price range, and target market.

[1416] Output: Basic information data in JSON format entered into the terminal.

[1417] Step 2: Data transmission

[1418] Terminal: Sends the entered information to the server. To ensure the security of the information, the data is encrypted using the SSL / TLS protocol. The data format is typically JSON.

[1419] Input: Basic information data in JSON format

[1420] Output: Encrypted JSON formatted information data is sent to the server.

[1421] Step 3: Matching with the database

[1422] Server: Based on the received information, it generates queries for the database. It accesses the database using SQL queries, searches for and extracts past data similar to the entered basic information.

[1423] Input: Encrypted JSON format information data

[1424] Output: Extraction results of similar historical data

[1425] Step 4: Calculating the Fair Price

[1426] Server: Calculates a fair price based on extracted historical data. This calculation uses weighted averages, regression analysis, and machine learning models (e.g., linear regression, random forest), taking market trends and competitive landscape into consideration.

[1427] Input: Extracted historical data, current market trend information, competitor data

[1428] Output: Calculated fair price

[1429] Step 5: Generating the final quote

[1430] Server: Generates a quotation based on the calculated fair price. The quotation is created in PDF format using a template engine (e.g., JasperReports, iText). The quotation includes detailed information about the goods / services, the basis for the calculation, and the fair price.

[1431] Input: Calculated fair price, detailed information about the product / service.

[1432] Output: Quotation in PDF format

[1433] Step 6: Send the quotation

[1434] Server: Sends the generated quote to the user's device. Quotes are sent via email (SMTP) or push notifications (e.g., Firebase Cloud Messaging).

[1435] Input: Quotation in PDF format

[1436] Output: Quotation in PDF format sent to the user's receiving terminal.

[1437] Step 7: Emotion recognition by the emotion engine

[1438] The device sends user input information and facial expression data to the emotion engine. It uses the camera built into the device to acquire facial recognition data and sends it to the emotion engine. For example, the emotion engine includes facial expression recognition algorithms and emotion analysis APIs (e.g., Microsoft Azure Emotional Analysis API).

[1439] Input: User's facial expression data, input information

[1440] Output: User data sent to the emotion engine

[1441] Step 8: Displaying suggestions and advice

[1442] Terminal: Based on the analysis results of the emotion engine, it displays suggestions and advice to the user. For example, if the user is feeling tense, it displays suggestions to help them relax, and if the user is happy, it displays positive suggestions.

[1443] Input: Analysis results from the emotion engine

[1444] Output: Suggestions and advice displayed to the user

[1445] (Application Example 2)

[1446] 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 robot 414 as the "terminal".

[1447] Traditional pricing systems for goods and services lacked the functionality to consider user emotions and provide appropriate suggestions and advice. This made it difficult to enhance user psychological satisfaction and trust, resulting in a limited user experience. Furthermore, there was a lack of technology to more accurately calculate fair prices for goods and services by incorporating emotion recognition into the pricing process.

[1448] 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.

[1449] In this invention, the server includes means for inputting basic information of a product or service, means for extracting similar data from a database based on the input information, means for calculating a fair price based on the extracted data, means for recognizing the user's emotional information when estimating the product or service, and means for generating suggestions and advice based on the analysis results of the emotional recognition means. This makes it possible to consider the user's psychological state during the estimation process and provide suggestions and advice that are more satisfying. Furthermore, by utilizing the user's emotional information, the accuracy of calculating a fair price is improved, resulting in more accurate price estimates.

[1450] "Basic information about the product or service" refers to detailed information entered by the user, such as the name of the product or service, specifications, desired price range, and target market.

[1451] "Input method" refers to an interface for users to input basic information about a product or service.

[1452] "Means of transmission" refers to a mechanism for securely sending input information to a server, such as by encrypting it.

[1453] "Means for extracting similar data from a database" refers to a function in which the server searches the database for and extracts similar past data based on the input information.

[1454] A "means for calculating a fair price" refers to an algorithm or model that calculates a fair price for a product or service based on historical data extracted from a database, taking into account the market value and competitive situation.

[1455] "Methods for generating quotations" refers to the process of creating a quotation that includes detailed information about the goods or services and the basis for the calculation, based on the calculated fair price.

[1456] "Means of sending to the receiving terminal" refers to a system that sends the generated quotation to the user's terminal.

[1457] "Means of displaying the quotation" refers to a function that allows the quotation to be displayed on the receiving terminal so that it can be viewed.

[1458] "Emotion recognition means" refers to technology that recognizes a user's emotions by analyzing user input information and facial expression data.

[1459] "Means for generating suggestions and advice" refers to a function that creates suggestions and advice tailored to the user's emotional state based on the analysis results of emotion recognition.

[1460] This invention is a system that recognizes user emotions and provides appropriate suggestions and advice during the process of estimating the fair price of goods and services. The following describes embodiments for carrying out this invention.

[1461] System Configuration

[1462] The system of this invention consists of the following main components:

[1463] 1. Means of entering basic information about a product or service:

[1464] Users input basic information about products or services using a smartphone app. This basic information includes the name of the product or service, specifications, desired price range, and target market.

[1465] 2. Means of information transmission:

[1466] The terminal encrypts the entered basic information and sends it to the server. Security protocols such as TLS and HTTPS are used as encryption technologies.

[1467] 3. Methods for extracting similar data:

[1468] The server searches the database based on the received information and extracts similar past data. SQL or NoSQL databases are used for this process.

[1469] 4. Methods for calculating fair prices:

[1470] The server calculates a fair price based on similar data extracted from a database. Regression analysis and machine learning algorithms (such as Scikit-learn) are used for this calculation.

[1471] 5. Methods for generating quotations:

[1472] The server generates a quotation based on the calculated fair price. The quotation is output in PDF format, using a PDF generation library (such as ReportLab).

[1473] 6. Method of sending the quotation:

[1474] The generated quote will be sent to the user's device via email or push notification.

[1475] 7. Emotion recognition means:

[1476] An emotion engine is used to analyze user input information and facial recognition data. Examples of emotion engines include OpenCV and the Emotion API.

[1477] 8. Means of generating suggestions and advice:

[1478] The server generates suggestions and advice that the user finds optimal based on the results of emotion recognition. Using a generative AI model is recommended for this process.

[1479] Specific example

[1480] When a user requests a quote for new high-performance earphones:

[1481] 1. The user enters basic information about the "high-performance earphones" using a smartphone app.

[1482] 2. The device encrypts this information and sends it to the server.

[1483] 3. The server searches the database and extracts past data for similar earphones.

[1484] 4. The server calculates a fair price based on the extracted data.

[1485] 5. The quotation is generated in PDF format and sent to the user's device.

[1486] 6. The emotion engine analyzes the user's input information and facial expression data and recognizes that the emotion is positive.

[1487] 7. If the emotion is judged to be positive, more proactive suggestions will be made, such as, "Would you like to see our higher-performance products?"

[1488] Example of a prompt

[1489] A user has requested a quote for a new premium product. They have positive feedback. Calculate a fair price and output a sentiment-based suggestion.

[1490] input:

[1491] Basic information on high-performance earphones

[1492] output:

[1493] Estimated price: $300

[1494] Advice: "Would you like to see our higher-performance products?"

[1495] As described above, the present invention makes it possible to provide an estimation system that takes into account the user's psychological state and offers even greater satisfaction.

[1496] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1497] Step 1:

[1498] Users input basic information about a product or service using a smartphone app. This includes detailed information such as the name, specifications, desired price range, and target market.

[1499] Input: Basic information about the product or service

[1500] Output: Input basic information data

[1501] Step 2:

[1502] The terminal encrypts the entered basic information and sends it to the server. To ensure the security of the information, security protocols such as TLS and HTTPS are used.

[1503] Input: Encrypted basic information data

[1504] Output: Encrypted data sent to the server

[1505] Step 3:

[1506] The server decrypts the received basic information and searches the database to extract similar past data. SQL or NoSQL databases are used for the search.

[1507] Input: Decrypted basic information data

[1508] Output: Similar historical data

[1509] Step 4:

[1510] The server calculates a fair price based on similar data. Regression analysis and machine learning algorithms are used for this calculation. Libraries used include Scikit-learn.

[1511] Input: Similar historical data

[1512] Output: Fair price

[1513] Step 5:

[1514] The server generates a quotation in PDF format based on a fair price. Libraries such as ReportLab are used for PDF generation.

[1515] Input: Fair price

[1516] Output: Quotation in PDF format

[1517] Step 6:

[1518] The server sends the generated quotation to the user's terminal. Email or push notifications are used as the means of delivery.

[1519] Input: Quotation in PDF format

[1520] Output: Notification of quotation to user terminal

[1521] Step 7:

[1522] The device sends user input information and facial recognition data to the emotion engine. The emotion engine analyzes the user's emotions. Examples of emotion engines used include OpenCV and the Emotion API.

[1523] Input: User input information and facial recognition data

[1524] Output: Emotion analysis results

[1525] Step 8:

[1526] The server generates suggestions and advice for the user based on the sentiment analysis results. Using a generative AI model is effective in this process.

[1527] Input: Sentiment analysis results

[1528] Output: Suggestion and advice messages

[1529] Step 9:

[1530] The device displays generated suggestions and advice to the user. This allows the user to receive appropriate suggestions tailored to their emotions.

[1531] Input: Suggestion or advice message

[1532] Output: Content displayed to the user

[1533] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 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.

[1534] 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.

[1535] 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 robot 414.

[1536] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1537] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1538] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1539] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1540] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1541] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1542] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1543] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1544] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1545] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1546] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1547] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1548] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1549] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1550] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1551] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1552] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1553] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1554] The following is further disclosed regarding the embodiments described above.

[1555] (Claim 1)

[1556] A means of entering basic information about a product or service,

[1557] means for transmitting the input information,

[1558] A means for extracting similar data from a database based on the input information,

[1559] A means for calculating a fair price based on the extracted data,

[1560] A means for generating a quotation based on the calculated price information,

[1561] A means for transmitting the generated quotation to a receiving terminal,

[1562] Means for displaying the aforementioned quotation,

[1563] A system that includes this.

[1564] (Claim 2)

[1565] The system according to claim 1, wherein the means for calculating the appropriate price includes an algorithm that takes into account market trends and competitive conditions.

[1566] (Claim 3)

[1567] The system according to claim 1, characterized in that it generates and sends the aforementioned quotation in PDF format.

[1568] "Example 1"

[1569] (Claim 1)

[1570] A means of entering basic information about a product or service,

[1571] means for transmitting the input information,

[1572] A means for extracting similar data from a database based on the input information,

[1573] A means for calculating a fair price using a generated AI model based on the extracted data,

[1574] A means for generating a quotation based on the calculated price information,

[1575] A means for transmitting the generated quotation to a receiving terminal,

[1576] Means for displaying the aforementioned quotation,

[1577] A system that includes this.

[1578] (Claim 2)

[1579] The system according to claim 1, wherein the means for calculating the appropriate price includes an algorithm that takes into account market trends and competitive conditions.

[1580] (Claim 3)

[1581] The system according to claim 1, characterized in that it generates the aforementioned quotation in PDF format and transmits it using an encrypted method.

[1582] "Application Example 1"

[1583] (Claim 1)

[1584] A means of entering basic information about a product or service,

[1585] means for transmitting the input information,

[1586] A means for extracting similar data from a database based on the input information,

[1587] A means for calculating a fair price based on the extracted data,

[1588] A means for generating a quotation based on the calculated price information,

[1589] A means for transmitting the generated quotation to a receiving terminal,

[1590] Means for displaying the aforementioned quotation,

[1591] External API connection means for obtaining market data,

[1592] A method for calculating a fair price using a machine learning model,

[1593] A means for generating and processing prompt sentences using a generative AI model,

[1594] A system that includes this.

[1595] (Claim 2)

[1596] The system according to claim 1, wherein the means for calculating the appropriate price includes an algorithm that takes into account market trends and competitive conditions.

[1597] (Claim 3)

[1598] The system according to claim 1, characterized in that it generates and sends the aforementioned quotation in PDF format.

[1599] "Example 2 of combining an emotion engine"

[1600] (Claim 1)

[1601] A means of entering basic information about a product or service,

[1602] means for transmitting the input information,

[1603] A means for extracting similar data from a database based on the input information,

[1604] A means for calculating a fair price based on the extracted data,

[1605] A means for generating a quotation based on the calculated price information,

[1606] A means for transmitting the generated quotation to a receiving terminal,

[1607] A means of recognizing emotions based on user input information and facial recognition data,

[1608] A means of providing suggestions and advice to the user based on the recognized emotions,

[1609] The means for displaying the aforementioned estimate and proposals or advice,

[1610] A system that includes this.

[1611] (Claim 2)

[1612] The system according to claim 1, wherein the means for calculating the appropriate price includes an algorithm that takes into account market trends and competitive conditions.

[1613] (Claim 3)

[1614] The system according to claim 1, characterized in that it generates and sends the aforementioned quotation in PDF format.

[1615] "Application example 2 when combining with an emotional engine"

[1616] (Claim 1)

[1617] A means of entering basic information about a product or service,

[1618] means for transmitting the input information,

[1619] A means for extracting similar data from a database based on the input information,

[1620] A means for calculating a fair price based on the extracted data,

[1621] A means for generating a quotation based on the calculated price information,

[1622] A means for transmitting the generated quotation to a receiving terminal,

[1623] Means for displaying the aforementioned quotation,

[1624] When estimating the aforementioned goods or services, an emotion recognition means for analyzing the user's emotional information,

[1625] A means for generating suggestions and advice based on the analysis results of the aforementioned emotion recognition means,

[1626] A system that includes this.

[1627] (Claim 2)

[1628] The system according to claim 1, wherein the means for calculating the appropriate price includes an algorithm that takes into account market trends and competitive conditions.

[1629] (Claim 3)

[1630] The system according to claim 1, characterized in that it generates and sends the aforementioned quotation in PDF format. [Explanation of Symbols]

[1631] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of entering basic information about a product or service, means for transmitting the input information, A means for extracting similar data from a database based on the input information, A means for calculating a fair price based on the extracted data, A means for generating a quotation based on the calculated price information, A means for transmitting the generated quotation to a receiving terminal, Means for displaying the aforementioned quotation, A system that includes this.

2. The system according to claim 1, wherein the means for calculating the appropriate price includes an algorithm that takes into account market trends and competitive conditions.

3. The system according to claim 1, characterized in that it generates and sends the aforementioned quotation in PDF format.

Images