System

Abstract

A system is provided.SOLUTION: A system comprising: database means for accumulating and storing past estimate data; means for receiving an input condition of a printed matter; means for generating a rough estimate of each printing company based on the condition of the printed matter; means for generating a comparison table for comparing the generated rough estimates; means for providing the generated comparison table to a user terminal; means for selecting a final printing company; and means for requesting a formal estimate to the selected printing company.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Currently, the process of repeatedly requesting quotes from multiple printing companies based on conditions such as the shape, quantity, and whether or not printing is required, and creating a comparison table for each company each time, is extremely time-consuming and labor-intensive. Furthermore, the process of comparing quotes is cumbersome, making it difficult to make quick and accurate decisions. This increases the burden on sales representatives and in-house production staff at printing companies. The present invention aims to solve these problems and provide an efficient and effective quote request process. [Means for solving the problem]

[0005] The present invention provides a system including a database for accumulating and storing past quotation data, a means for receiving input print product requirements, a means for referencing the past quotation data and generating rough quotation estimates from printing companies based on the print product requirements, a means for generating a comparison table for comparing the generated rough quotation estimates, a means for providing the generated comparison table to a user terminal, a means for selecting a final printing company, and a means for requesting a formal quotation from the selected printing company. The system further includes a means for filtering information corresponding to specific printing requirements from the past quotation data and applying a machine learning algorithm based on the relevant data, and a means for accepting print product requirements input from the user terminal via an interface and transmitting the requirements to a server in JSON format. In this way, the present invention makes it possible to easily utilize past data, efficiently generate rough quotation estimates, and select an appropriate printing company.

[0006] "Past quotation data" refers to previously obtained amounts and quotation information based on the specifications and conditions of the printed matter.

[0007] "Database means" refers to systems and software that enable the systematic management and storage of information and the rapid access to required data.

[0008] "User terminal" refers to a device operated by a user, such as a computer, smartphone, or tablet.

[0009] "Means for receiving" refers to a system or process for receiving input data or information from outside.

[0010] A "rough estimate" refers to estimate information that roughly calculates costs based on past data and standards.

[0011] A "comparison table" refers to a document or data display format that lists multiple estimates in a format that makes it easy to compare and contrast them.

[0012] "Means for providing" refers to the functions and processes for effectively presenting the generated information to users or other systems.

[0013] "Means of selection" refers to the functions and processes for selecting the most appropriate option from multiple options.

[0014] "Means for making a formal quotation request" refers to the process or system for formally requesting a quotation from the selected printing company.

[0015] A "machine learning algorithm" is a computational method that learns patterns and trends from past data and makes predictions and classifications for future data.

[0016] "JSON format" is a lightweight data exchange format that has rules for expressing data in text format.

[0017] An "interface" refers to the part that provides the screen and operating means for the user to operate the system or software. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention provides a system for streamlining the quotation request process for printed materials. This system utilizes past quotation data to efficiently generate rough quotation estimates and select appropriate printing companies. The following describes in detail the embodiments of the present invention.

[0040] Server-side processing

[0041] 1. Database Means

[0042] The server has a database that stores past quotation data. This database stores past quotation information from each printing company (printed object shape, size, quantity, whether or not printing is required, price, etc.).

[0043] 2. Estimate generation method

[0044] The server generates a rough estimate for each printing company based on the input print requirements, referencing past estimate data. To do this, the server uses a machine learning algorithm to learn each company's estimate patterns from past data. For example,

[0045] Input requirements: Brochure, A4 size, 1000 copies, full color

[0046] We search past data for quote information with similar conditions and calculate approximate quotes from each printing company.

[0047] 3. Comparison table generation means

[0048] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[0049] 4. Selection and formal quotation procedures

[0050] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[0051] Terminal side processing

[0052] 1. Condition input method

[0053] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[0054] 2. Comparison table display means

[0055] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[0056] User processing

[0057] 1. Enter conditions

[0058] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[0059] 2. Comparison and Selection

[0060] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[0061] 3. Formal Request for Quote

[0062] The user then performs an operation to request a formal quotation from the selected printing company.

[0063] Specific examples

[0064] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user uses a terminal to input these requirements. The terminal sends this information to the server, which uses the input information to search past quote data and generate rough estimates from each company. The server generates a comparison table and provides it to the terminal. The user looks at the comparison table, selects the most suitable printing company, and submits a formal quote request via the terminal. For example, if printing company A is the cheapest, the user submits a formal quote request to that company and proceeds with the printing procedure.

[0065] This allows users to quickly and efficiently select a printing company, significantly reducing the effort required to compare quotes.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] User Input

[0069] The user opens the application on the terminal and inputs the conditions for the printout (such as shape, size, quantity, whether or not it is printed). For example, they input information such as "Shape: Pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color."

[0070] Step 2:

[0071] Sending terminal

[0072] The terminal converts the information entered by the user into JSON format and sends it to the server. Example: {"Shape": "Pamphlet", "Size": "A4", "Quantity": 1000, "Printing": "Full color"}

[0073] Step 3:

[0074] Server Reception

[0075] The server receives data based on the conditions for the print received from the terminal.

[0076] Step 4:

[0077] Database Access

[0078] Based on the received information, the server accesses a database that stores past quotation data.

[0079] Step 5:

[0080] Data Filtering

[0081] The server filters past quotation data that matches the specified conditions. For example, it extracts data that is close to "shape = pamphlet," "size = A4," "print = full color," and "quantity = 1000."

[0082] Step 6:

[0083] Applying machine learning algorithms

[0084] The server uses the filtered past quotation data to apply a machine learning model that has learned the quotation patterns of each printing company, thereby calculating a rough quotation for each company.

[0085] Step 7:

[0086] Generate a rough estimate

[0087] The server generates a rough estimate for each printing company and creates a comparison table. Example: {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}

[0088] Step 8:

[0089] Return to terminal

[0090] The server returns the generated estimate comparison table to the terminal in JSON format. Example: {"Estimate Comparison Table": {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}}

[0091] Step 9:

[0092] Comparison table display

[0093] The terminal analyzes the rough estimate comparison table received from the server and displays it to the user, allowing the user to easily compare estimates from each printing company.

[0094] Step 10:

[0095] Choosing a printing company

[0096] The user checks the displayed comparison table and selects the most suitable printing company. For example, the user may determine that printing company A's quote is the cheapest.

[0097] Step 11:

[0098] Formal Request for Quote

[0099] The user then submits a formal quotation request to the selected printing company through the terminal interface.

[0100] Step 12:

[0101] Sending selection information

[0102] The device notifies the server of the user's selection in JSON format. Example: {"Selected printing company": "Printing company A"}

[0103] Step 13:

[0104] Generate a formal request for quotation

[0105] The server generates a formal request for quotation from the selected printing company based on the received information.

[0106] Step 14:

[0107] Send a quote request

[0108] The server then sends a formal request for quotation to the selected printing company via email or API.

[0109] Example 1

[0110] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0111] The conventional process of requesting quotes for printed materials has the problem of requiring a great deal of time and effort to obtain and compare quotes from multiple printing companies. Furthermore, there is no system in place to effectively utilize past quote data, making it difficult to select the most appropriate quote. Therefore, it is desirable to provide a system that can efficiently generate rough quotes and select the appropriate printing company.

[0112] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0113] In this invention, the server includes a database means for accumulating and storing past quotation data, a means for receiving input print product requirements, a means for referencing the past quotation data and generating rough quotation estimates from each printing service provider based on the print product requirements, a means for generating a comparison table for comparing the generated rough quotation estimates, a means for providing the generated comparison table to a user terminal, a means for selecting a final printing service provider, a means for requesting a formal quotation from the selected printing service provider, a means for transmitting the requirements received at the endpoint in JSON format to the server, a means for using a machine learning algorithm to learn each company's quotation patterns from past quotation data, and a means for displaying the generated comparison table in a visually easy-to-understand format, thereby enabling a user to efficiently and quickly compare and select quotation estimates from printing service providers.

[0114] The "database means" is a storage device or system for accumulating and storing past quotation data.

[0115] The "means for receiving input print product conditions" refers to a communication means or interface for receiving print product conditions input by the user.

[0116] The "means for generating a rough estimate for each printing business" refers to an algorithm or program for referencing past estimate data and calculating a rough estimate for each printing business based on the input conditions.

[0117] A "means for generating a comparison table" is a program or algorithm for creating a table for visually comparing the generated estimates.

[0118] The "means for providing a comparison table to a user terminal" refers to a communication means or program for transmitting the generated comparison table to the user terminal and displaying it.

[0119] The "means for selecting a final printing service provider" is an interface or program that allows the user to select a printing service provider based on the comparison table.

[0120] The "means for making a formal quotation request" is a communication means or program for sending a formal quotation request message to the selected print service provider.

[0121] "Means for transmitting conditions received at an endpoint to a server in JSON format" refers to a communication means or program for converting the printing conditions entered by the user into JSON format and transmitting them to a server via an endpoint.

[0122] "Means for learning each company's quotation patterns from past quotation data using a machine learning algorithm" refers to an algorithm or program that uses machine learning technology to analyze past quotation data and learn the quotation patterns of each printing company.

[0123] "Means for displaying the generated comparison table in a visually easy-to-understand format" refers to a program or interface for displaying the generated comparison table in a visually easy-to-understand format that allows the user to easily make comparisons.

[0124] The present invention provides a system for streamlining the quotation request process for printed materials. This system utilizes past quotation data to efficiently generate rough estimates and select appropriate printing businesses. Specific embodiments for implementing the present invention are described in detail below.

[0125] Server-side processing

[0126] The server includes the following main means. First, it has a database means for accumulating and storing past quotation data. This database stores past quotation information from each printing company (printed product shape, size, quantity, whether or not it is printed, price, etc.). The server receives the print product conditions entered by the user, and based on this, references past quotation data and uses a machine learning algorithm to generate a rough quotation from each printing company. For example, consider the case where the following conditions are entered:

[0127] Example of input conditions: Brochure, A4 size, 1000 copies, full color

[0128] Based on these conditions, the server searches past data for quotation data with similar conditions and uses this to calculate rough estimates from each printing company. It then generates a comparison table based on the rough estimates. This comparison table is designed to allow users to easily compare prices in a visually easy-to-understand format. The server sends this comparison table to the user's terminal and requests a formal quotation from the printing company selected by the user.

[0129] Terminal side processing

[0130] The terminal includes the following main means. First, it provides an interface where the user can input the conditions for the printed materials (shape, size, quantity, whether or not to print, etc.). The information entered through this interface is converted into JSON format and sent to the server. It also has a means for displaying to the user a rough estimate comparison table received from the server. This allows the user to easily compare estimates from various printing companies.

[0131] User processing

[0132] The user uses the device interface to input the requirements for the print job and select the most suitable printing service provider. For example, if a user wants to print 1,000 copies of an A4-sized full-color brochure, they would input the following requirements:

[0133] "Form: Brochure"

[0134] "Size: A4"

[0135] "Quantity: 1000 copies"

[0136] "Printing: Full color"

[0137] The device sends this information in JSON format to the server, which then searches past quote data based on the input information to generate a rough estimate from each company. The server then generates a comparison table and provides it to the device. The user can then review the comparison table to select the most suitable printing company and request a formal quote via the device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[0138] Specific examples

[0139] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user uses a terminal to input these requirements. The terminal sends this information to the server, which uses the input information to search past quote data and generate rough estimates from each company. The server generates a comparison table and provides it to the terminal. The user looks at the comparison table, selects the most suitable printing company, and submits a formal quote request via the terminal. For example, if printing company A is the cheapest, the user can submit a formal quote request to that company and proceed with the printing procedure.

[0140] Prompt Sentence Examples

[0141] "Describe a program that allows a user to find the cheapest printing service provider that can print 1,000 copies of an A4-sized full-color brochure."

[0142] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0143] Step 1:

[0144] The user uses the device interface to input the requirements for the printed material, such as "Form: Brochure," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color." The input data is collected through a form on the browser. The input is converted to JSON format.

[0145] Input: User-entered print conditions

[0146] Output: Condition data in JSON format

[0147] Step 2:

[0148] The terminal converts the condition data entered by the user into JSON format and sends it to the server using asynchronous communication technology (e.g., Ajax). The server receives this data at an endpoint.

[0149] Input: Condition data in JSON format

[0150] Output: Condition data sent to the server

[0151] Step 3:

[0152] The server analyzes the received condition data and searches the past quotation data in the database. The server generates a search query and extracts the past quotation data that matches each condition. During this process, the server performs filtering and data extraction.

[0153] Input: Condition data sent to the server, past quotation data

[0154] Output: Past quote data that matches the conditions

[0155] Step 4:

[0156] The server uses a machine learning algorithm to learn the quotation patterns of each printing company from past quotation data. The server then applies the learning model to generate a rough quotation for each printing company based on the input conditions. For example, it predicts the price based on the conditions "brochure, A4 size, 1000 copies, full color."

[0157] Input: Past quote data that matches the conditions

[0158] Output: Approximate estimate from each printing company

[0159] Step 5:

[0160] The server then creates a table comparing quotes from each printing company based on the estimated estimates generated. The comparison table is visually easy to understand, and is designed to show price, delivery time, and other information at a glance. The comparison table is generated in JSON format.

[0161] Input: Approximate estimates from each printing company

[0162] Output: Comparison table in JSON format

[0163] Step 6:

[0164] The server sends the generated comparison table in JSON format to the terminal. The terminal parses the received comparison table data and converts it into HTML format for display to the user. This conversion is performed on the browser, and the comparison table is visually displayed.

[0165] Input: Comparison table in JSON format

[0166] Output: Comparison table in HTML format

[0167] Step 7:

[0168] The user refers to the comparison table displayed on the terminal and selects the most cost-effective printing service provider. The user performs a selection operation and confirms the information of the selected printing service provider on the terminal. For example, the user selects the cheapest printing service provider A.

[0169] Input: HTML comparison table

[0170] Output: Information about the selected print provider

[0171] Step 8:

[0172] The server generates and sends a formal quotation request message to the print service provider selected by the user. This message includes the detailed requirements for the print product entered by the user. The server then makes a formal quotation request based on this information and notifies the print service provider.

[0173] Input: Information on the selected printing company, detailed conditions for printing entered by the user

[0174] Output: Formal RFQ message

[0175] (Application example 1)

[0176] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0177] The traditional process of requesting quotes for printed materials was time-consuming and laborious, making it difficult to proceed efficiently. Furthermore, logistics centers lacked a system for quickly requesting quotes for printing large volumes of packaging and labels. In particular, there was a need for an efficient way to compare quotes from various printing companies and make the appropriate selection. To solve these issues, a system was needed that utilizes past quote data and employs machine learning to efficiently generate and compare quotes.

[0178] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0179] In this invention, the server includes a database means for accumulating and storing past quotation data, a means for receiving input print product requirements, a means for referencing the past quotation data and generating rough quotation estimates from each printing company based on the print product requirements, a means for generating a comparison table for comparing the generated rough quotation estimates, a means for providing the generated comparison table to a user terminal, a means for selecting a final printing company, a means for making a formal quotation request to the selected printing company, a means for the user terminal to operate as an application for a smart device, a means for streamlining print product quotation requests at a logistics center, and a means for generating quotation results from each printing company from past quotation data using a machine learning algorithm. This streamlines the quotation request and comparison process, enabling a logistics center to quickly and optimally select large volumes of print products.

[0180] The "database means" is a storage device for accumulating and storing past estimate data.

[0181] The "receiving means" is a method for acquiring the input conditions for the printed matter.

[0182] The "quote generation means" refers to a method for referencing past quotation data and generating a rough quotation from each printing company based on the conditions of the printed matter.

[0183] The "comparison table generating means" is a method for creating a table for comparing the generated rough estimates.

[0184] The "means for providing" is a means for displaying the generated comparison table on the user terminal.

[0185] The "selection means" is the method for selecting the final printing company.

[0186] The "quote request means" is a method for officially requesting a quotation from the selected printing company.

[0187] A "smart device application" is a program that runs on smart devices such as smartphones and tablets.

[0188] The "means for streamlining print quotation requests at logistics centers" is a method for quickly and efficiently completing the quotation request process for package and label printing at logistics centers.

[0189] A "machine learning algorithm" is a calculation method for predicting and generating quotation results for each printing company based on past quotation data.

[0190] This invention is a system for streamlining the quotation request process for printed materials at a logistics center. Each component and its function are described below.

[0191] Server-side configuration and processing

[0192] The server has the following means:

[0193] 1. Database Means

[0194] The server has a database that stores past quotation data. This database contains information about past quotation data from each printing company (printed item shape, size, quantity, whether or not printing is required, price, etc.).

[0195] 2. Estimate generation method

[0196] The server receives the input print requirements and generates a rough estimate for each printing company by referencing past estimate data. To achieve this, the server uses a machine learning algorithm (e.g., a linear regression model) to predict each company's estimate based on past data.

[0197] 3. Comparison table generation means

[0198] Based on the estimated quotes from each printing company, the server generates a comparison table, which is generated in a visually easy-to-understand format to allow the user to easily compare printing companies.

[0199] 4. Means of provision

[0200] The server sends the generated comparison table to the user's terminal and displays it, allowing the user to compare quotes from each printing company.

[0201] 5. Selection Method

[0202] The server receives the printing company selected by the user and records the information.

[0203] 6. Methods for requesting a quote

[0204] The server then sends a message to the finally selected printing company requesting a formal quote.

[0205] User terminal configuration and processing

[0206] The user terminal performs the following processing as a smart device application.

[0207] 1. Condition input method

[0208] The user uses a smartphone app to enter the print specifications (e.g., form: pamphlet, size: A4, quantity: 1,000 copies, printing: full color) and sends this information in JSON format to the server.

[0209] 2. Comparison table display means

[0210] A rough estimate comparison table received from the server is visually displayed to the user.

[0211] Hardware and software used

[0212] Hardware: Smartphones, servers (cloud services, such as AWS and GCP)

[0213] Software: Python, Flask, scikit-learn, JSON files

[0214] Specific examples

[0215] For example, suppose a logistics center manager wants to print 1,000 A4-sized full-color brochures. The manager uses a smartphone to enter the following conditions:

[0216] Example prompt sentence:

[0217] Size: A4, Quantity: 1000, Color: Full color

[0218] This information is sent to a server, which applies machine learning algorithms based on past quote data to generate a rough estimate for each printing company. The resulting comparison table is then sent back to the user's device, allowing the administrator to review it and select the most suitable printing company. This makes the quote request and comparison process fast and efficient.

[0219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0220] Step 1:

[0221] The user inputs the requirements for the print using a smartphone app. The input information includes, for example, shape, size, quantity, and whether or not to print. The device converts the user-entered requirements into JSON format and sends it to the server.

[0222] Input: Shape, size, quantity, whether printed, etc.

[0223] Output: JSON format data

[0224] Step 2:

[0225] The server analyzes the JSON data received from the user device and extracts the print conditions. Based on these conditions, it generates a query to access past quotation data.

[0226] Input: Print conditions entered by the user (JSON format)

[0227] Output: Conditions of the printed material

[0228] Step 3:

[0229] The server searches past quotation data stored in a database and filters data that matches or is similar to the user's printing requirements. This filtered data is then input into a machine learning algorithm.

[0230] Input: Printing requirements, past quotation data in the database

[0231] Output: Filtered historical quote data

[0232] Step 4:

[0233] The server generates a rough estimate for each printing company based on the filtered estimate data using a machine learning algorithm (e.g., a linear regression model). This algorithm performs data processing (preprocessing) and applies a predictive model to calculate the rough estimate.

[0234] Input: Filtered past quote data, print conditions

[0235] Output: Approximate estimate from each printing company

[0236] Step 5:

[0237] The server creates a comparison table based on the generated rough estimates. This comparison table is formatted in a visually easy-to-understand format, allowing the user to easily compare the estimates from each printing company.

[0238] Input: Approximate estimates from each printing company

[0239] Output: Estimate comparison table

[0240] Step 6:

[0241] The server sends the created comparison table to the user's terminal. The terminal receives the comparison table and displays the data on the user interface. The user can then view the comparison table and select the most suitable printing company.

[0242] Input: Estimate comparison table

[0243] Output: Comparison table displayed on the user's device

[0244] Step 7:

[0245] The user selects the printing company that they think is best based on the comparison table, and the user terminal sends the selection information to the server.

[0246] Input: User selection information

[0247] Output: Selections sent to the server

[0248] Step 8:

[0249] The server generates and sends a formal request for quote message to the selected printing company, including the user's requirements for the printed material and the reasons for their selection.

[0250] Input: User selection information, printing conditions

[0251] Output: Formal RFQ message

[0252] Through the above processing steps, the process of requesting a quote for printed materials at the logistics center can be efficiently executed.

[0253] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0254] The present invention provides a more user-friendly system that streamlines the quote request process for printed materials and takes into account the user's emotional state. This system utilizes past quote data to efficiently generate rough quotes and select the most suitable printing company based on the user's emotional state. The following describes in detail the embodiments of the present invention.

[0255] Server-side processing

[0256] 1. Database Means

[0257] The server has a database that stores past quotation data. This database stores past quotation information from each printing company (printed item shape, size, quantity, whether or not printing is required, price, etc.).

[0258] 2. Estimate generation method

[0259] The server generates a rough estimate for each printing company based on the input print requirements, referencing past estimate data. To do this, the server uses a machine learning algorithm to learn each company's estimate patterns from past data. For example,

[0260] Input requirements: Brochure, A4 size, 1000 copies, full color

[0261] We search past data for quote information with similar conditions and calculate approximate quotes from each printing company.

[0262] 3. Emotion Engine

[0263] The server is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes the user's emotions based on facial expressions, voice, input speed, etc., and feeds the results back to the system.

[0264] 4. Comparison table generation means

[0265] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[0266] 5. Optimizing the presentation order

[0267] The server optimizes the display order of suggested printing companies and quotes based on the user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[0268] 6. Selection and Formal Request for Quotation Methods

[0269] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[0270] Terminal side processing

[0271] 1. Condition input method

[0272] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[0273] 2. Emotion recognition means

[0274] The device detects the user's facial expressions, voice, and operation speed in real time when inputting data, and sends this data to the server. The emotion engine uses this data to analyze the user's emotional state.

[0275] 3. Comparison table display means

[0276] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[0277] User processing

[0278] 1. Enter conditions

[0279] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[0280] 2. Emotion Input

[0281] The user operates the device using natural facial expressions and voice recordings via the camera and microphone provided by the device. For example, by showing signs of impatience or relaxation, the user conveys their emotional state to the system.

[0282] 3. Compare and select

[0283] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[0284] 4. Formal Request for Quotation

[0285] The user then performs an operation to request a formal quotation from the selected printing company.

[0286] Specific examples

[0287] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using their device. The device sends this information to the server, which then searches past quote data based on the input information to generate rough quotes from various companies. The emotion engine also simultaneously analyzes the user's emotional state. If the user appears stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then generates a comparison table and provides it to the device. The user then looks at the comparison table to select the most suitable printing company and submits a formal quote request via their device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[0288] This allows users to quickly and efficiently select a printing company, significantly reducing the effort required for comparing quotes. The emotion engine also takes the user's emotional state into consideration, providing a more comfortable operating environment.

[0289] The processing flow will be explained below.

[0290] Step 1:

[0291] User condition input

[0292] The user opens the application on the terminal and inputs the conditions for the printout (such as shape, size, quantity, whether or not it is printed). For example, they input information such as "Shape: Pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color."

[0293] Step 2:

[0294] User emotion input

[0295] The device uses a built-in camera and microphone to monitor the user's facial expressions, voice, and speed of operation, thereby detecting the user's current emotional state.

[0296] Step 3:

[0297] Sending terminal

[0298] The device converts the printing conditions and emotion data entered by the user into JSON format and sends it to the server. Example: {"Shape": "Pamphlet", "Size": "A4", "Quantity": 1000, "Print": "Full color", "Emotion data": "Stress"}

[0299] Step 4:

[0300] Server Reception

[0301] The server receives the conditions and emotion data for the print material received from the terminal.

[0302] Step 5:

[0303] Database Access

[0304] Based on the received information, the server accesses a database that stores past quotation data.

[0305] Step 6:

[0306] Data Filtering

[0307] The server filters past quotation data that matches the specified conditions. For example, it extracts data that is close to "shape = pamphlet," "size = A4," "print = full color," and "quantity = 1000."

[0308] Step 7:

[0309] Applying machine learning algorithms

[0310] The server uses the filtered past quotation data to apply a machine learning model that has learned the quotation patterns of each printing company, thereby calculating a rough quotation for each company.

[0311] Step 8:

[0312] Generate a rough estimate

[0313] The server generates a rough estimate for each printing company and creates a comparison table. Example: {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}

[0314] Step 9:

[0315] Emotional Data Feedback

[0316] The server analyzes the user's emotional state based on the received emotional data. For example, if the user is feeling stressed, the server displays simpler information that is easier to understand visually.

[0317] Step 10:

[0318] Return to terminal

[0319] The server returns the generated rough estimate comparison table and the optimized presentation order to the terminal in JSON format. Example: {"Estimate Comparison Table": {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}, "Optimized Order": ["Printing Company A", "Printing Company C", "Printing Company B"]}

[0320] Step 11:

[0321] Comparison table display

[0322] The terminal analyzes the estimated quote comparison table and the optimized presentation order received from the server and displays them to the user, allowing the user to easily compare quote information from each printing company.

[0323] Step 12:

[0324] Choosing a printing company

[0325] The user checks the displayed comparison table and selects the most suitable printing company. For example, the user may determine that printing company A's quote is the cheapest.

[0326] Step 13:

[0327] Formal Request for Quote

[0328] The user then submits a formal quotation request to the selected printing company through the terminal interface.

[0329] Step 14:

[0330] Sending selection information

[0331] The device notifies the server of the user's selection in JSON format. Example: {"Selected printing company": "Printing company A"}

[0332] Step 15:

[0333] Generate a formal request for quotation

[0334] The server generates a formal request for quotation from the selected printing company based on the received information.

[0335] Step 16:

[0336] Send a quote request

[0337] The server then sends a formal request for quotation to the selected printing company via email or API.

[0338] Example 2

[0339] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0340] In conventional print quotation request processes, users must obtain quotes from multiple print providers individually, compare them, and select a suitable one, which is time-consuming and labor-intensive. Furthermore, the process often leaves users feeling stressed because it does not take into account their emotional state. The present invention aims to solve these problems by providing a system that takes into account the user's emotional state while streamlining the quotation request process.

[0341] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0342] In this invention, the server includes a data storage means for accumulating and storing past quotation data, a data receiving means for receiving print requirements entered from a user terminal, a quotation generating means for referencing the past quotation data and generating rough quotation estimates from each print provider based on the print requirements, a sentiment analyzing means for recognizing and analyzing the user's emotional state, a data comparing means for generating a comparison table for comparing the generated rough quotation estimates, a display means for providing the generated comparison table to the user terminal, an order optimizing means for optimizing the display order of suggested print providers and quotation details based on the user's emotional state, a selection means for selecting a final print provider, and a requesting means for making a formal quotation request to the selected print provider. This makes it possible to improve the efficiency of the quotation request process and to propose quotation estimates that take the user's emotional state into consideration.

[0343] "Data storage means" refers to means for accumulating and storing past quotation data.

[0344] The "data receiving means" is a means for receiving the conditions for the print product input from the user terminal.

[0345] The "quote generating means" refers to a means for referencing past quotation data and generating a rough quotation from each print provider based on the conditions of the printed material.

[0346] "Emotion analysis means" refers to means for recognizing and analyzing the emotional state of a user.

[0347] The "data comparison means" is a means for generating a comparison table for comparing the generated rough estimates.

[0348] The "display means" is a means for providing the generated comparison table to a user terminal.

[0349] The "order optimization means" is a means for optimizing the display order of suggested print providers and quotation details based on the emotional state of the user.

[0350] A "selection means" is a means for selecting a final print provider.

[0351] The "request means" is a means for making a formal request for a quote to the selected print provider.

[0352] The present invention provides a system that streamlines the quote request process for print products and also takes into account the emotional state of the user. Specific embodiments are described below.

[0353] Server-side processing

[0354] 1. Data Storage Means

[0355] The server manages a database that stores past quotation data, including quotation information (shape, size, quantity, price, etc.) from each print provider.

[0356] 2. Data Receiving Method

[0357] The server receives the print conditions sent from the user terminal. The conditions include information in JSON format about the shape, size, quantity, and whether or not the print is present.

[0358] 3. Estimate Generation Method

[0359] The server generates a rough estimate from each printing service provider based on the received print requirements and past quotation data. To do this, it uses a machine learning algorithm to learn each company's quotation patterns from past data. For example, if the server receives input requirements such as "brochure, A4 size, 1000 copies, full color," it searches for quotation information with similar requirements and calculates a rough estimate.

[0360] 4. Emotion analysis method

[0361] The server receives data such as the user's facial expressions, voice, and input speed sent from the device, and uses an emotion engine to analyze the user's emotional state, thereby determining whether the user is feeling stressed or relaxed.

[0362] 5. Data Comparison Methods

[0363] The server generates a comparison table based on the estimated quotes from each print provider, and the comparison table is designed to be displayed in a visually easy-to-understand format.

[0364] 6. Display means

[0365] The server sends the generated comparison table to the user's terminal, which displays it. The user can check the comparison table and easily compare the quote information of each print provider.

[0366] 7. Ordinal Optimization Methods

[0367] The server optimizes the display order of suggested print providers and quotes based on the analyzed user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[0368] 8. Selection Method

[0369] Once the user has selected the most suitable print provider, the server receives the selection and issues a formal request for quote to the selected provider.

[0370] 9. Request Method

[0371] The server sends a formal request for quote message to the selected print provider, which can use an email API or the API of a dedicated B2B platform.

[0372] Terminal side processing

[0373] 1. Condition input method

[0374] The terminal provides an interface for users to input requirements for the printed material, such as "pamphlet, A4 size, 1000 copies, full color."

[0375] 2. Emotion recognition means

[0376] The device detects the user's facial expressions, voice, and operation speed in real time and sends this information to the server, where the server's emotion engine analyzes the data.

[0377] 3. Comparison table display means

[0378] The terminal displays the rough estimate comparison table sent from the server to the user, allowing the user to easily compare the estimate information of each print provider.

[0379] User processing

[0380] 1. Enter conditions

[0381] The user uses the terminal interface to input the conditions for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[0382] 2. Emotion Input

[0383] The user can communicate their emotional state to the system by inputting facial expressions and voice using the device. For example, they can communicate their emotional state by showing how anxious or relaxed they are.

[0384] 3. Compare and select

[0385] The user can select the most cost-effective printing provider by looking at the displayed rough estimate comparison table. For example, if the rough estimate for printing company A is 50,000 yen, the estimate for printing company B is 55,000 yen, and the estimate for printing company C is 52,000 yen, the user can select the most suitable provider.

[0386] 4. Formal Request for Quotation

[0387] The user then performs an operation to formally request a quote from the selected print provider.

[0388] Specific examples

[0389] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using a terminal. The terminal sends this information to the server, which searches past quote data to generate rough quotes from various companies. Meanwhile, if the emotion engine analyzes the user's emotional state and detects that the user is feeling stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then provides the terminal with a comparison table, which the user can use to select the most suitable printing provider. The user then requests a formal quote via the terminal. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing procedure.

[0390] Example prompts for generative AI models

[0391] "I'd like to print 1,000 full-color A4 brochures. Please generate rough quotes from the best print providers, taking into account past quote information and my current user sentiment, and display an easy-to-understand comparison table."

[0392] Based on this prompt, the generative AI model can understand the system's operation in detail and specify each processing step.

[0393] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0394] Step 1:

[0395] Conditions entered by the user

[0396] Processing content: The user uses the terminal interface to input the conditions for the print.

[0397] Specific operation: The user inputs information such as "pamphlet, A4 size, 1000 copies, full color" into the input interface.

[0398] Input: The print condition entered by the user.

[0399] Output: The print conditions are saved on the device.

[0400] Step 2:

[0401] Sending condition data to the server

[0402] Processing content: The terminal sends the input data in JSON format to the server.

[0403] Specific operation: The terminal formats the condition data entered by the user and sends it as an HTTP request.

[0404] Input: The print condition entered by the user.

[0405] Output: The condition data is sent to the server.

[0406] Step 3:

[0407] Server-based quotation data search and rough quotation generation

[0408] Processing details: The server searches the past estimate database based on the received conditions, extracts appropriate data, and generates a rough estimate.

[0409] What it does: The server uses SQL queries to retrieve applicable quote data from the database and calculates the price using machine learning models.

[0410] Input: Condition data and past quote data.

[0411] Output: Approximate quotes from each print provider.

[0412] Step 4:

[0413] Sending emotional data to the server and analyzing it

[0414] Processing details: The device collects user emotion data in real time and sends it to the server, which analyzes it.

[0415] Specific operation: The device collects data from the camera and microphone, calls the emotion analysis API, and sends the results to the server. The server then integrates the received data and analyzes the emotional state.

[0416] Input: Emotional data such as the user's facial expression, voice, and operation speed.

[0417] Output: Analysis of the user's emotional state.

[0418] Step 5:

[0419] Server-generated comparison table

[0420] Processing details: The server generates a visually easy-to-understand comparison table based on the analysis results and quotation data.

[0421] Specific operation: The server generates graphs and table-format comparison tables using HTML and JavaScript, and sends the generated HTML data to the terminal.

[0422] Input: Rough estimates from each printing provider and the analysis of the user's emotional state.

[0423] Output: A comparison table in a visually friendly format.

[0424] Step 6:

[0425] Providing comparison tables to users

[0426] Processing content: The terminal displays the received comparison table to the user.

[0427] What it does: The device browser renders the HTML data, allowing the user to view the comparison table directly.

[0428] Input: Comparison table sent from the server.

[0429] Output: A user-visible comparison table.

[0430] Step 7:

[0431] User selection of print provider

[0432] Process: The user looks at the comparison table and selects the best print provider.

[0433] Specific operations: The user selects a print provider on the terminal interface, confirms the selected information, and presses the send button.

[0434] Enter: comparison chart.

[0435] Output: Information about the selected print provider.

[0436] Step 8:

[0437] Sending selected data to the server

[0438] Processing content: The terminal transmits the selection information to the server.

[0439] Specific operation: The terminal formats the data of the selected print provider and sends it to the server in an HTTP request.

[0440] Enter: Selected print provider information.

[0441] Output: The selected data is sent to the server.

[0442] Step 9:

[0443] Formal request for quotation by the server

[0444] What happens: The server receives the selection information and sends a formal request for quote message to the appropriate print provider.

[0445] Specific operation: The server uses an email API or the API of a dedicated B2B platform to send a quote request to the selected print provider.

[0446] Input:Selection data.

[0447] Output: A formal request for quote message to your print provider.

[0448] (Application example 2)

[0449] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0450] The conventional process of requesting quotes for printed materials requires users to manually compare quotes from multiple printing companies, which is extremely time-consuming and labor-intensive. Furthermore, because the system does not take into account the user's emotional state, it is considered difficult to use for users who are stressed or anxious. There is a need for a system that solves these problems and allows users to request quotes for printed materials efficiently and comfortably.

[0451] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes database means for accumulating and storing past quotation data, means for receiving input print product requirements, means for referencing the past quotation data and generating rough quotation estimates from each printing company based on the print product requirements, means for generating a comparison table for comparing the generated rough quotation estimates, means for providing the generated comparison table to the user terminal, means for selecting a final printing company, means for requesting a formal quotation from the selected printing company, emotion engine means for analyzing the user's emotional state, and means for optimizing the display order of the quotation contents based on the analyzed emotional state. This enables the user to efficiently proceed with the quotation request process and provides a comfortable operating environment that suits the user's emotional state.

[0452] "Past quotation data" refers to data that stores quotation information for print requests that have been previously made.

[0453] "Database means" refers to a system that efficiently stores past quotation data and allows it to be searched and retrieved as needed.

[0454] The "input conditions for the printed matter" refers to information such as the specifications and quantity of the printed matter provided by the user when requesting a quote.

[0455] A "rough estimate" is a rough price estimate provided by each printing company, calculated based on the conditions of the printed material entered.

[0456] An "emotion engine" is a system that recognizes and analyzes a user's emotional state based on facial expressions, voice, operation speed, etc.

[0457] A "comparison table" is a table format that arranges multiple rough estimates in a visually easy-to-understand manner, allowing users to easily compare them.

[0458] A "formal quote request" is the official price confirmation and purchase request that the user sends to the printing company that they have ultimately selected.

[0459] The "means for optimizing the display order of quote contents" refers to a system for rearranging quote contents into an order that is most understandable to the user based on the user's emotional state.

[0460] This invention is a system that streamlines the process of requesting quotes for printed materials in brick-and-mortar stores and provides a comfortable operating environment by taking into account the emotional state of the user. This system operates in cooperation with a server, terminals, and users.

[0461] Server-side processing

[0462] Database Means

[0463] The server has a database that accumulates and stores past quotation data. The database stores past quotation information from each printing company (printed material shape, size, quantity, whether or not printing is required, price, etc.). This data is used to generate a rough quotation based on the conditions of the printed material.

[0464] Estimate generation method

[0465] The server references past quotation data based on the input printing requirements and generates a rough estimate for each printing company. To do this, the server uses a machine learning algorithm to learn each company's quotation patterns from past data. For example, it searches for past quotation information with similar conditions to the input requirements, such as "shape: pamphlet," "size: A4," "quantity: 1,000 copies," and "printing: full color," and calculates a rough estimate for each printing company.

[0466] Emotion Engine Means

[0467] The server is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes the user's emotions based on their facial expressions, voice, input speed, etc., and feeds the results back to the system. The analysis results are used in the display order optimization means described below.

[0468] Comparison table generation means

[0469] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[0470] Optimizing the presentation order

[0471] The server optimizes the display order of suggested printing companies and quotes based on the user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[0472] Formal request for quotation

[0473] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[0474] Terminal side processing

[0475] Condition input method

[0476] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[0477] emotion recognition means

[0478] The device detects the user's facial expressions, voice, and operation speed in real time when inputting data, and sends this data to the server. The emotion engine uses this data to analyze the user's emotional state.

[0479] Comparison table display means

[0480] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[0481] User processing

[0482] Condition input

[0483] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[0484] Emotion input

[0485] The user operates the device using natural facial expressions and voice recordings via the camera and microphone provided by the device. For example, by showing signs of impatience or relaxation, the user conveys their emotional state to the system.

[0486] Compare and select

[0487] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[0488] Formal Request for Quote

[0489] The user then performs an operation to request a formal quotation from the selected printing company.

[0490] Specific examples

[0491] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using their device. The device sends this information to the server, which then searches past quote data based on the input information to generate rough quotes from various companies. The emotion engine also simultaneously analyzes the user's emotional state. If the user appears stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then generates a comparison table and provides it to the device. The user then looks at the comparison table to select the most suitable printing company and submits a formal quote request via their device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[0492] Prompt Sentence Examples

[0493] Print requirements:

[0494] Format: Brochure

[0495] Size: A4

[0496] Quantity: 1000 copies

[0497] Printing: Full color

[0498] Optimizing presentation order:

[0499] Because users are stressed, we simplify the information and display the cheapest quotes first.

[0500] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0501] Step 1:

[0502] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not to print, etc.). The condition information entered by the user is converted into JSON format and sent to the server. At this stage, the input data are the conditions for the print, and the output data is the condition information in JSON format.

[0503] Step 2:

[0504] The server uses the received JSON-formatted print condition information to reference past quotation data from a database. It filters the past quotation data for data that most closely matches the relevant conditions and uses a machine learning algorithm to generate a rough quotation for each printing company. At this stage, the input data is the JSON-formatted print condition information, and the output data is the rough quotation information for each printing company.

[0505] Step 3:

[0506] The server converts the generated rough estimate information from each printing company into a comparison table. This comparison table visually lists the estimated prices from each company, allowing users to easily compare them. At this stage, the input data is the rough estimate information, and the output data is in the form of a comparison table.

[0507] Step 4:

[0508] The terminal displays the comparison table received from the server to the user. The user can compare the prices quoted by each printing company by looking at this comparison table. At this stage, the input data is data in the form of a comparison table, and the output data is visual information for the user.

[0509] Step 5:

[0510] The device detects data such as the user's facial expressions, voice, and operation speed in real time and sends this data to the server. The server then uses an emotion engine to analyze the user's emotional state based on the received data. At this stage, the input data is the user's emotional state data, and the output data is the analyzed emotional state.

[0511] Step 6:

[0512] The server optimizes the display order of the quote contents based on the analyzed emotional state of the user. For example, if the user is feeling stressed, the server adjusts the order to simplify the quote information and display it in an easy-to-understand manner. The input data at this stage is the analyzed emotional state, and the output data is the quote information in the optimized display order.

[0513] Step 7:

[0514] The user looks at the comparison table and selects the most cost-effective printing company. The information of the printing company selected by the user is sent from the terminal to the server. At this stage, the input data is the user's selection information, and the output data is the information of the selected printing company.

[0515] Step 8:

[0516] The server then sends a formal quotation request to the printing company selected by the user. The server receives the selection information and sends a formal quotation request message to the printing company. At this stage, the input data is the information of the selected printing company, and the output data is the formal quotation request message.

[0517] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0518] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0519] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0520] [Second embodiment]

[0521] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0522] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0523] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0524] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0525] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0526] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0527] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0528] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0529] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0530] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0531] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0532] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0533] The present invention provides a system for streamlining the quotation request process for printed materials. This system utilizes past quotation data to efficiently generate rough quotation estimates and select appropriate printing companies. The following describes in detail the embodiments of the present invention.

[0534] Server-side processing

[0535] 1. Database Means

[0536] The server has a database that stores past quotation data. This database stores past quotation information from each printing company (printed object shape, size, quantity, whether or not printing is required, price, etc.).

[0537] 2. Estimate generation method

[0538] The server generates a rough estimate for each printing company based on the input print requirements, referencing past estimate data. To do this, the server uses a machine learning algorithm to learn each company's estimate patterns from past data. For example,

[0539] Input requirements: Brochure, A4 size, 1000 copies, full color

[0540] We search past data for quote information with similar conditions and calculate approximate quotes from each printing company.

[0541] 3. Comparison table generation means

[0542] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[0543] 4. Selection and formal quotation procedures

[0544] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[0545] Terminal side processing

[0546] 1. Condition input method

[0547] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[0548] 2. Comparison table display means

[0549] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[0550] User processing

[0551] 1. Enter conditions

[0552] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[0553] 2. Comparison and Selection

[0554] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[0555] 3. Formal Request for Quote

[0556] The user then performs an operation to request a formal quotation from the selected printing company.

[0557] Specific examples

[0558] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user uses a terminal to input these requirements. The terminal sends this information to the server, which uses the input information to search past quote data and generate rough estimates from each company. The server generates a comparison table and provides it to the terminal. The user looks at the comparison table, selects the most suitable printing company, and submits a formal quote request via the terminal. For example, if printing company A is the cheapest, the user submits a formal quote request to that company and proceeds with the printing procedure.

[0559] This allows users to quickly and efficiently select a printing company, significantly reducing the effort required to compare quotes.

[0560] The processing flow will be explained below.

[0561] Step 1:

[0562] User Input

[0563] The user opens the application on the terminal and inputs the conditions for the printout (such as shape, size, quantity, whether or not it is printed). For example, they input information such as "Shape: Pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color."

[0564] Step 2:

[0565] Sending terminal

[0566] The terminal converts the information entered by the user into JSON format and sends it to the server. Example: {"Shape": "Pamphlet", "Size": "A4", "Quantity": 1000, "Printing": "Full color"}

[0567] Step 3:

[0568] Server Reception

[0569] The server receives data based on the conditions for the print received from the terminal.

[0570] Step 4:

[0571] Database Access

[0572] Based on the received information, the server accesses a database that stores past quotation data.

[0573] Step 5:

[0574] Data Filtering

[0575] The server filters past quotation data that matches the specified conditions. For example, it extracts data that is close to "shape = pamphlet," "size = A4," "print = full color," and "quantity = 1000."

[0576] Step 6:

[0577] Applying machine learning algorithms

[0578] The server uses the filtered past quotation data to apply a machine learning model that has learned the quotation patterns of each printing company, thereby calculating a rough quotation for each company.

[0579] Step 7:

[0580] Generate a rough estimate

[0581] The server generates a rough estimate for each printing company and creates a comparison table. Example: {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}

[0582] Step 8:

[0583] Return to terminal

[0584] The server returns the generated estimate comparison table to the terminal in JSON format. Example: {"Estimate Comparison Table": {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}}

[0585] Step 9:

[0586] Comparison table display

[0587] The terminal analyzes the rough estimate comparison table received from the server and displays it to the user, allowing the user to easily compare estimates from each printing company.

[0588] Step 10:

[0589] Choosing a printing company

[0590] The user checks the displayed comparison table and selects the most suitable printing company. For example, the user may determine that printing company A's quote is the cheapest.

[0591] Step 11:

[0592] Formal Request for Quote

[0593] The user then submits a formal quotation request to the selected printing company through the terminal interface.

[0594] Step 12:

[0595] Sending selection information

[0596] The device notifies the server of the user's selection in JSON format. Example: {"Selected printing company": "Printing company A"}

[0597] Step 13:

[0598] Generate a formal request for quotation

[0599] The server generates a formal request for quotation from the selected printing company based on the received information.

[0600] Step 14:

[0601] Send a quote request

[0602] The server then sends a formal request for quotation to the selected printing company via email or API.

[0603] Example 1

[0604] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0605] The conventional process of requesting quotes for printed materials has the problem of requiring a great deal of time and effort to obtain and compare quotes from multiple printing companies. Furthermore, there is no system in place to effectively utilize past quote data, making it difficult to select the most appropriate quote. Therefore, it is desirable to provide a system that can efficiently generate rough quotes and select the appropriate printing company.

[0606] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0607] In this invention, the server includes a database means for accumulating and storing past quotation data, a means for receiving input print product requirements, a means for referencing the past quotation data and generating rough quotation estimates from each printing service provider based on the print product requirements, a means for generating a comparison table for comparing the generated rough quotation estimates, a means for providing the generated comparison table to a user terminal, a means for selecting a final printing service provider, a means for requesting a formal quotation from the selected printing service provider, a means for transmitting the requirements received at the endpoint in JSON format to the server, a means for using a machine learning algorithm to learn each company's quotation patterns from past quotation data, and a means for displaying the generated comparison table in a visually easy-to-understand format, thereby enabling a user to efficiently and quickly compare and select quotation estimates from printing service providers.

[0608] The "database means" is a storage device or system for accumulating and storing past quotation data.

[0609] The "means for receiving input print product conditions" refers to a communication means or interface for receiving print product conditions input by the user.

[0610] The "means for generating a rough estimate for each printing business" refers to an algorithm or program for referencing past estimate data and calculating a rough estimate for each printing business based on the input conditions.

[0611] A "means for generating a comparison table" is a program or algorithm for creating a table for visually comparing the generated estimates.

[0612] The "means for providing a comparison table to a user terminal" refers to a communication means or program for transmitting the generated comparison table to the user terminal and displaying it.

[0613] The "means for selecting a final printing service provider" is an interface or program that allows the user to select a printing service provider based on the comparison table.

[0614] The "means for making a formal quotation request" is a communication means or program for sending a formal quotation request message to the selected print service provider.

[0615] "Means for transmitting conditions received at an endpoint to a server in JSON format" refers to a communication means or program for converting the printing conditions entered by the user into JSON format and transmitting them to a server via an endpoint.

[0616] "Means for learning each company's quotation patterns from past quotation data using a machine learning algorithm" refers to an algorithm or program that uses machine learning technology to analyze past quotation data and learn the quotation patterns of each printing company.

[0617] "Means for displaying the generated comparison table in a visually easy-to-understand format" refers to a program or interface for displaying the generated comparison table in a visually easy-to-understand format that allows the user to easily make comparisons.

[0618] The present invention provides a system for streamlining the quotation request process for printed materials. This system utilizes past quotation data to efficiently generate rough estimates and select appropriate printing businesses. Specific embodiments for implementing the present invention are described in detail below.

[0619] Server-side processing

[0620] The server includes the following main means. First, it has a database means for accumulating and storing past quotation data. This database stores past quotation information from each printing company (printed product shape, size, quantity, whether or not it is printed, price, etc.). The server receives the print product conditions entered by the user, and based on this, references past quotation data and uses a machine learning algorithm to generate a rough quotation from each printing company. For example, consider the case where the following conditions are entered:

[0621] Example of input conditions: Brochure, A4 size, 1000 copies, full color

[0622] Based on these conditions, the server searches past data for quotation data with similar conditions and uses this to calculate rough estimates from each printing company. It then generates a comparison table based on the rough estimates. This comparison table is designed to allow users to easily compare prices in a visually easy-to-understand format. The server sends this comparison table to the user's terminal and requests a formal quotation from the printing company selected by the user.

[0623] Terminal side processing

[0624] The terminal includes the following main means. First, it provides an interface where the user can input the conditions for the printed materials (shape, size, quantity, whether or not to print, etc.). The information entered through this interface is converted into JSON format and sent to the server. It also has a means for displaying to the user a rough estimate comparison table received from the server. This allows the user to easily compare estimates from various printing companies.

[0625] User processing

[0626] The user uses the device interface to input the requirements for the print job and select the most suitable printing service provider. For example, if a user wants to print 1,000 copies of an A4-sized full-color brochure, they would input the following requirements:

[0627] "Form: Brochure"

[0628] "Size: A4"

[0629] "Quantity: 1000 copies"

[0630] "Printing: Full color"

[0631] The device sends this information in JSON format to the server, which then searches past quote data based on the input information to generate a rough estimate from each company. The server then generates a comparison table and provides it to the device. The user can then review the comparison table to select the most suitable printing company and request a formal quote via the device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[0632] Specific examples

[0633] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user uses a terminal to input these requirements. The terminal sends this information to the server, which uses the input information to search past quote data and generate rough estimates from each company. The server generates a comparison table and provides it to the terminal. The user looks at the comparison table, selects the most suitable printing company, and submits a formal quote request via the terminal. For example, if printing company A is the cheapest, the user can submit a formal quote request to that company and proceed with the printing procedure.

[0634] Prompt Sentence Examples

[0635] "Describe a program that allows a user to find the cheapest printing service provider that can print 1,000 copies of an A4-sized full-color brochure."

[0636] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0637] Step 1:

[0638] The user uses the device interface to input the requirements for the printed material, such as "Form: Brochure," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color." The input data is collected through a form on the browser. The input is converted to JSON format.

[0639] Input: User-entered print conditions

[0640] Output: Condition data in JSON format

[0641] Step 2:

[0642] The terminal converts the condition data entered by the user into JSON format and sends it to the server using asynchronous communication technology (e.g., Ajax). The server receives this data at an endpoint.

[0643] Input: Condition data in JSON format

[0644] Output: Condition data sent to the server

[0645] Step 3:

[0646] The server analyzes the received condition data and searches the past quotation data in the database. The server generates a search query and extracts the past quotation data that matches each condition. During this process, the server performs filtering and data extraction.

[0647] Input: Condition data sent to the server, past quotation data

[0648] Output: Past quote data that matches the conditions

[0649] Step 4:

[0650] The server uses a machine learning algorithm to learn the quotation patterns of each printing company from past quotation data. The server then applies the learning model to generate a rough quotation for each printing company based on the input conditions. For example, it predicts the price based on the conditions "brochure, A4 size, 1000 copies, full color."

[0651] Input: Past quote data that matches the conditions

[0652] Output: Approximate estimate from each printing company

[0653] Step 5:

[0654] The server then creates a table comparing quotes from each printing company based on the estimated estimates generated. The comparison table is visually easy to understand, and is designed to show price, delivery time, and other information at a glance. The comparison table is generated in JSON format.

[0655] Input: Approximate estimates from each printing company

[0656] Output: Comparison table in JSON format

[0657] Step 6:

[0658] The server sends the generated comparison table in JSON format to the terminal. The terminal parses the received comparison table data and converts it into HTML format for display to the user. This conversion is performed on the browser, and the comparison table is visually displayed.

[0659] Input: Comparison table in JSON format

[0660] Output: Comparison table in HTML format

[0661] Step 7:

[0662] The user refers to the comparison table displayed on the terminal and selects the most cost-effective printing service provider. The user performs a selection operation and confirms the information of the selected printing service provider on the terminal. For example, the user selects the cheapest printing service provider A.

[0663] Input: HTML comparison table

[0664] Output: Information about the selected print provider

[0665] Step 8:

[0666] The server generates and sends a formal quotation request message to the print service provider selected by the user. This message includes the detailed requirements for the print product entered by the user. The server then makes a formal quotation request based on this information and notifies the print service provider.

[0667] Input: Information on the selected printing company, detailed conditions for printing entered by the user

[0668] Output: Formal RFQ message

[0669] (Application example 1)

[0670] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0671] The traditional process of requesting quotes for printed materials was time-consuming and laborious, making it difficult to proceed efficiently. Furthermore, logistics centers lacked a system for quickly requesting quotes for printing large volumes of packaging and labels. In particular, there was a need for an efficient way to compare quotes from various printing companies and make the appropriate selection. To solve these issues, a system was needed that utilizes past quote data and employs machine learning to efficiently generate and compare quotes.

[0672] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0673] In this invention, the server includes a database means for accumulating and storing past quotation data, a means for receiving input print product requirements, a means for referencing the past quotation data and generating rough quotation estimates from each printing company based on the print product requirements, a means for generating a comparison table for comparing the generated rough quotation estimates, a means for providing the generated comparison table to a user terminal, a means for selecting a final printing company, a means for making a formal quotation request to the selected printing company, a means for the user terminal to operate as an application for a smart device, a means for streamlining print product quotation requests at a logistics center, and a means for generating quotation results from each printing company from past quotation data using a machine learning algorithm. This streamlines the quotation request and comparison process, enabling a logistics center to quickly and optimally select large volumes of print products.

[0674] The "database means" is a storage device for accumulating and storing past estimate data.

[0675] The "receiving means" is a method for acquiring the input conditions for the printed matter.

[0676] The "quote generation means" refers to a method for referencing past quotation data and generating a rough quotation from each printing company based on the conditions of the printed matter.

[0677] The "comparison table generating means" is a method for creating a table for comparing the generated rough estimates.

[0678] The "means for providing" is a means for displaying the generated comparison table on the user terminal.

[0679] The "selection means" is the method for selecting the final printing company.

[0680] The "quote request means" is a method for officially requesting a quotation from the selected printing company.

[0681] A "smart device application" is a program that runs on smart devices such as smartphones and tablets.

[0682] The "means for streamlining print quotation requests at logistics centers" is a method for quickly and efficiently completing the quotation request process for package and label printing at logistics centers.

[0683] A "machine learning algorithm" is a calculation method for predicting and generating quotation results for each printing company based on past quotation data.

[0684] This invention is a system for streamlining the quotation request process for printed materials at a logistics center. Each component and its function are described below.

[0685] Server-side configuration and processing

[0686] The server has the following means:

[0687] 1. Database Means

[0688] The server has a database that stores past quotation data. This database contains information about past quotation data from each printing company (printed item shape, size, quantity, whether or not printing is required, price, etc.).

[0689] 2. Estimate generation method

[0690] The server receives the input print requirements and generates a rough estimate for each printing company by referencing past estimate data. To achieve this, the server uses a machine learning algorithm (e.g., a linear regression model) to predict each company's estimate based on past data.

[0691] 3. Comparison table generation means

[0692] Based on the estimated quotes from each printing company, the server generates a comparison table, which is generated in a visually easy-to-understand format to allow the user to easily compare printing companies.

[0693] 4. Means of provision

[0694] The server sends the generated comparison table to the user's terminal and displays it, allowing the user to compare quotes from each printing company.

[0695] 5. Selection Method

[0696] The server receives the printing company selected by the user and records the information.

[0697] 6. Methods for requesting a quote

[0698] The server then sends a message to the finally selected printing company requesting a formal quote.

[0699] User terminal configuration and processing

[0700] The user terminal performs the following processing as a smart device application.

[0701] 1. Condition input method

[0702] The user uses a smartphone app to enter the print specifications (e.g., form: pamphlet, size: A4, quantity: 1,000 copies, printing: full color) and sends this information in JSON format to the server.

[0703] 2. Comparison table display means

[0704] A rough estimate comparison table received from the server is visually displayed to the user.

[0705] Hardware and software used

[0706] Hardware: Smartphones, servers (cloud services, such as AWS and GCP)

[0707] Software: Python, Flask, scikit-learn, JSON files

[0708] Specific examples

[0709] For example, suppose a logistics center manager wants to print 1,000 A4-sized full-color brochures. The manager uses a smartphone to enter the following conditions:

[0710] Example prompt sentence:

[0711] Size: A4, Quantity: 1000, Color: Full color

[0712] This information is sent to a server, which applies machine learning algorithms based on past quote data to generate a rough estimate for each printing company. The resulting comparison table is then sent back to the user's device, allowing the administrator to review it and select the most suitable printing company. This makes the quote request and comparison process fast and efficient.

[0713] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0714] Step 1:

[0715] The user inputs the requirements for the print using a smartphone app. The input information includes, for example, shape, size, quantity, and whether or not to print. The device converts the user-entered requirements into JSON format and sends it to the server.

[0716] Input: Shape, size, quantity, whether printed, etc.

[0717] Output: JSON format data

[0718] Step 2:

[0719] The server analyzes the JSON data received from the user device and extracts the print conditions. Based on these conditions, it generates a query to access past quotation data.

[0720] Input: Print conditions entered by the user (JSON format)

[0721] Output: Conditions of the printed material

[0722] Step 3:

[0723] The server searches past quotation data stored in a database and filters data that matches or is similar to the user's printing requirements. This filtered data is then input into a machine learning algorithm.

[0724] Input: Printing requirements, past quotation data in the database

[0725] Output: Filtered historical quote data

[0726] Step 4:

[0727] The server generates a rough estimate for each printing company based on the filtered estimate data using a machine learning algorithm (e.g., a linear regression model). This algorithm performs data processing (preprocessing) and applies a predictive model to calculate the rough estimate.

[0728] Input: Filtered past quote data, print conditions

[0729] Output: Approximate estimate from each printing company

[0730] Step 5:

[0731] The server creates a comparison table based on the generated rough estimates. This comparison table is formatted in a visually easy-to-understand format, allowing the user to easily compare the estimates from each printing company.

[0732] Input: Approximate estimates from each printing company

[0733] Output: Estimate comparison table

[0734] Step 6:

[0735] The server sends the created comparison table to the user's terminal. The terminal receives the comparison table and displays the data on the user interface. The user can then view the comparison table and select the most suitable printing company.

[0736] Input: Estimate comparison table

[0737] Output: Comparison table displayed on the user's device

[0738] Step 7:

[0739] The user selects the printing company that they think is best based on the comparison table, and the user terminal sends the selection information to the server.

[0740] Input: User selection information

[0741] Output: Selections sent to the server

[0742] Step 8:

[0743] The server generates and sends a formal request for quote message to the selected printing company, including the user's requirements for the printed material and the reasons for their selection.

[0744] Input: User selection information, printing conditions

[0745] Output: Formal RFQ message

[0746] Through the above processing steps, the process of requesting a quote for printed materials at the logistics center can be efficiently executed.

[0747] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0748] The present invention provides a more user-friendly system that streamlines the quote request process for printed materials and takes into account the user's emotional state. This system utilizes past quote data to efficiently generate rough quotes and select the most suitable printing company based on the user's emotional state. The following describes in detail the embodiments of the present invention.

[0749] Server-side processing

[0750] 1. Database Means

[0751] The server has a database that stores past quotation data. This database stores past quotation information from each printing company (printed item shape, size, quantity, whether or not printing is required, price, etc.).

[0752] 2. Estimate generation method

[0753] The server generates a rough estimate for each printing company based on the input print requirements, referencing past estimate data. To do this, the server uses a machine learning algorithm to learn each company's estimate patterns from past data. For example,

[0754] Input requirements: Brochure, A4 size, 1000 copies, full color

[0755] We search past data for quote information with similar conditions and calculate approximate quotes from each printing company.

[0756] 3. Emotion Engine

[0757] The server is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes the user's emotions based on facial expressions, voice, input speed, etc., and feeds the results back to the system.

[0758] 4. Comparison table generation means

[0759] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[0760] 5. Optimizing the presentation order

[0761] The server optimizes the display order of suggested printing companies and quotes based on the user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[0762] 6. Selection and Formal Request for Quotation Methods

[0763] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[0764] Terminal side processing

[0765] 1. Condition input method

[0766] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[0767] 2. Emotion recognition means

[0768] The device detects the user's facial expressions, voice, and operation speed in real time when inputting data, and sends this data to the server. The emotion engine uses this data to analyze the user's emotional state.

[0769] 3. Comparison table display means

[0770] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[0771] User processing

[0772] 1. Enter conditions

[0773] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[0774] 2. Emotion Input

[0775] The user operates the device using natural facial expressions and voice recordings via the camera and microphone provided by the device. For example, by showing signs of impatience or relaxation, the user conveys their emotional state to the system.

[0776] 3. Compare and select

[0777] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[0778] 4. Formal Request for Quotation

[0779] The user then performs an operation to request a formal quotation from the selected printing company.

[0780] Specific examples

[0781] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using their device. The device sends this information to the server, which then searches past quote data based on the input information to generate rough quotes from various companies. The emotion engine also simultaneously analyzes the user's emotional state. If the user appears stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then generates a comparison table and provides it to the device. The user then looks at the comparison table to select the most suitable printing company and submits a formal quote request via their device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[0782] This allows users to quickly and efficiently select a printing company, significantly reducing the effort required for comparing quotes. The emotion engine also takes the user's emotional state into consideration, providing a more comfortable operating environment.

[0783] The processing flow will be explained below.

[0784] Step 1:

[0785] User condition input

[0786] The user opens the application on the terminal and inputs the conditions for the printout (such as shape, size, quantity, whether or not it is printed). For example, they input information such as "Shape: Pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color."

[0787] Step 2:

[0788] User emotion input

[0789] The device uses a built-in camera and microphone to monitor the user's facial expressions, voice, and speed of operation, thereby detecting the user's current emotional state.

[0790] Step 3:

[0791] Sending terminal

[0792] The device converts the printing conditions and emotion data entered by the user into JSON format and sends it to the server. Example: {"Shape": "Pamphlet", "Size": "A4", "Quantity": 1000, "Print": "Full color", "Emotion data": "Stress"}

[0793] Step 4:

[0794] Server Reception

[0795] The server receives the conditions and emotion data for the print material received from the terminal.

[0796] Step 5:

[0797] Database Access

[0798] Based on the received information, the server accesses a database that stores past quotation data.

[0799] Step 6:

[0800] Data Filtering

[0801] The server filters past quotation data that matches the specified conditions. For example, it extracts data that is close to "shape = pamphlet," "size = A4," "print = full color," and "quantity = 1000."

[0802] Step 7:

[0803] Applying machine learning algorithms

[0804] The server uses the filtered past quotation data to apply a machine learning model that has learned the quotation patterns of each printing company, thereby calculating a rough quotation for each company.

[0805] Step 8:

[0806] Generate a rough estimate

[0807] The server generates a rough estimate for each printing company and creates a comparison table. Example: {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}

[0808] Step 9:

[0809] Emotional Data Feedback

[0810] The server analyzes the user's emotional state based on the received emotional data. For example, if the user is feeling stressed, the server displays simpler information that is easier to understand visually.

[0811] Step 10:

[0812] Return to terminal

[0813] The server returns the generated rough estimate comparison table and the optimized presentation order to the terminal in JSON format. Example: {"Estimate Comparison Table": {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}, "Optimized Order": ["Printing Company A", "Printing Company C", "Printing Company B"]}

[0814] Step 11:

[0815] Comparison table display

[0816] The terminal analyzes the estimated quote comparison table and the optimized presentation order received from the server and displays them to the user, allowing the user to easily compare quote information from each printing company.

[0817] Step 12:

[0818] Choosing a printing company

[0819] The user checks the displayed comparison table and selects the most suitable printing company. For example, the user may determine that printing company A's quote is the cheapest.

[0820] Step 13:

[0821] Formal Request for Quote

[0822] The user then submits a formal quotation request to the selected printing company through the terminal interface.

[0823] Step 14:

[0824] Sending selection information

[0825] The device notifies the server of the user's selection in JSON format. Example: {"Selected printing company": "Printing company A"}

[0826] Step 15:

[0827] Generate a formal request for quotation

[0828] The server generates a formal request for quotation from the selected printing company based on the received information.

[0829] Step 16:

[0830] Send a quote request

[0831] The server then sends a formal request for quotation to the selected printing company via email or API.

[0832] Example 2

[0833] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0834] In conventional print quotation request processes, users must obtain quotes from multiple print providers individually, compare them, and select a suitable one, which is time-consuming and labor-intensive. Furthermore, the process often leaves users feeling stressed because it does not take into account their emotional state. The present invention aims to solve these problems by providing a system that takes into account the user's emotional state while streamlining the quotation request process.

[0835] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0836] In this invention, the server includes a data storage means for accumulating and storing past quotation data, a data receiving means for receiving print requirements entered from a user terminal, a quotation generating means for referencing the past quotation data and generating rough quotation estimates from each print provider based on the print requirements, a sentiment analyzing means for recognizing and analyzing the user's emotional state, a data comparing means for generating a comparison table for comparing the generated rough quotation estimates, a display means for providing the generated comparison table to the user terminal, an order optimizing means for optimizing the display order of suggested print providers and quotation details based on the user's emotional state, a selection means for selecting a final print provider, and a requesting means for making a formal quotation request to the selected print provider. This makes it possible to improve the efficiency of the quotation request process and to propose quotation estimates that take the user's emotional state into consideration.

[0837] "Data storage means" refers to means for accumulating and storing past quotation data.

[0838] The "data receiving means" is a means for receiving the conditions for the print product input from the user terminal.

[0839] The "quote generating means" refers to a means for referencing past quotation data and generating a rough quotation from each print provider based on the conditions of the printed material.

[0840] "Emotion analysis means" refers to means for recognizing and analyzing the emotional state of a user.

[0841] The "data comparison means" is a means for generating a comparison table for comparing the generated rough estimates.

[0842] The "display means" is a means for providing the generated comparison table to a user terminal.

[0843] The "order optimization means" is a means for optimizing the display order of suggested print providers and quotation details based on the emotional state of the user.

[0844] A "selection means" is a means for selecting a final print provider.

[0845] The "request means" is a means for making a formal request for a quote to the selected print provider.

[0846] The present invention provides a system that streamlines the quote request process for print products and also takes into account the emotional state of the user. Specific embodiments are described below.

[0847] Server-side processing

[0848] 1. Data Storage Means

[0849] The server manages a database that stores past quotation data, including quotation information (shape, size, quantity, price, etc.) from each print provider.

[0850] 2. Data Receiving Method

[0851] The server receives the print conditions sent from the user terminal. The conditions include information in JSON format about the shape, size, quantity, and whether or not the print is present.

[0852] 3. Estimate Generation Method

[0853] The server generates a rough estimate from each printing service provider based on the received print requirements and past quotation data. To do this, it uses a machine learning algorithm to learn each company's quotation patterns from past data. For example, if the server receives input requirements such as "brochure, A4 size, 1000 copies, full color," it searches for quotation information with similar requirements and calculates a rough estimate.

[0854] 4. Emotion analysis method

[0855] The server receives data such as the user's facial expressions, voice, and input speed sent from the device, and uses an emotion engine to analyze the user's emotional state, thereby determining whether the user is feeling stressed or relaxed.

[0856] 5. Data Comparison Methods

[0857] The server generates a comparison table based on the estimated quotes from each print provider, and the comparison table is designed to be displayed in a visually easy-to-understand format.

[0858] 6. Display means

[0859] The server sends the generated comparison table to the user's terminal, which displays it. The user can check the comparison table and easily compare the quote information of each print provider.

[0860] 7. Ordinal Optimization Methods

[0861] The server optimizes the display order of suggested print providers and quotes based on the analyzed user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[0862] 8. Selection Method

[0863] Once the user has selected the most suitable print provider, the server receives the selection and issues a formal request for quote to the selected provider.

[0864] 9. Request Method

[0865] The server sends a formal request for quote message to the selected print provider, which can use an email API or the API of a dedicated B2B platform.

[0866] Terminal side processing

[0867] 1. Condition input method

[0868] The terminal provides an interface for users to input requirements for the printed material, such as "pamphlet, A4 size, 1000 copies, full color."

[0869] 2. Emotion recognition means

[0870] The device detects the user's facial expressions, voice, and operation speed in real time and sends this information to the server, where the server's emotion engine analyzes the data.

[0871] 3. Comparison table display means

[0872] The terminal displays the rough estimate comparison table sent from the server to the user, allowing the user to easily compare the estimate information of each print provider.

[0873] User processing

[0874] 1. Enter conditions

[0875] The user uses the terminal interface to input the conditions for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[0876] 2. Emotion Input

[0877] The user can communicate their emotional state to the system by inputting facial expressions and voice using the device. For example, they can communicate their emotional state by showing how anxious or relaxed they are.

[0878] 3. Compare and select

[0879] The user can select the most cost-effective printing provider by looking at the displayed rough estimate comparison table. For example, if the rough estimate for printing company A is 50,000 yen, the estimate for printing company B is 55,000 yen, and the estimate for printing company C is 52,000 yen, the user can select the most suitable provider.

[0880] 4. Formal Request for Quotation

[0881] The user then performs an operation to formally request a quote from the selected print provider.

[0882] Specific examples

[0883] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using a terminal. The terminal sends this information to the server, which searches past quote data to generate rough quotes from various companies. Meanwhile, if the emotion engine analyzes the user's emotional state and detects that the user is feeling stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then provides the terminal with a comparison table, which the user can use to select the most suitable printing provider. The user then requests a formal quote via the terminal. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing procedure.

[0884] Example prompts for generative AI models

[0885] "I'd like to print 1,000 full-color A4 brochures. Please generate rough quotes from the best print providers, taking into account past quote information and my current user sentiment, and display an easy-to-understand comparison table."

[0886] Based on this prompt, the generative AI model can understand the system's operation in detail and specify each processing step.

[0887] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0888] Step 1:

[0889] Conditions entered by the user

[0890] Processing content: The user uses the terminal interface to input the conditions for the print.

[0891] Specific operation: The user inputs information such as "pamphlet, A4 size, 1000 copies, full color" into the input interface.

[0892] Input: The print condition entered by the user.

[0893] Output: The print conditions are saved on the device.

[0894] Step 2:

[0895] Sending condition data to the server

[0896] Processing content: The terminal sends the input data in JSON format to the server.

[0897] Specific operation: The terminal formats the condition data entered by the user and sends it as an HTTP request.

[0898] Input: The print condition entered by the user.

[0899] Output: The condition data is sent to the server.

[0900] Step 3:

[0901] Server-based quotation data search and rough quotation generation

[0902] Processing details: The server searches the past estimate database based on the received conditions, extracts appropriate data, and generates a rough estimate.

[0903] What it does: The server uses SQL queries to retrieve applicable quote data from the database and calculates the price using machine learning models.

[0904] Input: Condition data and past quote data.

[0905] Output: Approximate quotes from each print provider.

[0906] Step 4:

[0907] Sending emotional data to the server and analyzing it

[0908] Processing details: The device collects user emotion data in real time and sends it to the server, which analyzes it.

[0909] Specific operation: The device collects data from the camera and microphone, calls the emotion analysis API, and sends the results to the server. The server then integrates the received data and analyzes the emotional state.

[0910] Input: Emotional data such as the user's facial expression, voice, and operation speed.

[0911] Output: Analysis of the user's emotional state.

[0912] Step 5:

[0913] Server-generated comparison table

[0914] Processing details: The server generates a visually easy-to-understand comparison table based on the analysis results and quotation data.

[0915] Specific operation: The server generates graphs and table-format comparison tables using HTML and JavaScript, and sends the generated HTML data to the terminal.

[0916] Input: Rough estimates from each printing provider and the analysis of the user's emotional state.

[0917] Output: A comparison table in a visually friendly format.

[0918] Step 6:

[0919] Providing comparison tables to users

[0920] Processing content: The terminal displays the received comparison table to the user.

[0921] What it does: The device browser renders the HTML data, allowing the user to view the comparison table directly.

[0922] Input: Comparison table sent from the server.

[0923] Output: A user-visible comparison table.

[0924] Step 7:

[0925] User selection of print provider

[0926] Process: The user looks at the comparison table and selects the best print provider.

[0927] Specific operations: The user selects a print provider on the terminal interface, confirms the selected information, and presses the send button.

[0928] Enter: comparison chart.

[0929] Output: Information about the selected print provider.

[0930] Step 8:

[0931] Sending selected data to the server

[0932] Processing content: The terminal transmits the selection information to the server.

[0933] Specific operation: The terminal formats the data of the selected print provider and sends it to the server in an HTTP request.

[0934] Enter: Selected print provider information.

[0935] Output: The selected data is sent to the server.

[0936] Step 9:

[0937] Formal request for quotation by the server

[0938] What happens: The server receives the selection information and sends a formal request for quote message to the appropriate print provider.

[0939] Specific operation: The server uses an email API or the API of a dedicated B2B platform to send a quote request to the selected print provider.

[0940] Input:Selection data.

[0941] Output: A formal request for quote message to your print provider.

[0942] (Application example 2)

[0943] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0944] The conventional process of requesting quotes for printed materials requires users to manually compare quotes from multiple printing companies, which is extremely time-consuming and labor-intensive. Furthermore, because the system does not take into account the user's emotional state, it is considered difficult to use for users who are stressed or anxious. There is a need for a system that solves these problems and allows users to request quotes for printed materials efficiently and comfortably.

[0945] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes database means for accumulating and storing past quotation data, means for receiving input print product requirements, means for referencing the past quotation data and generating rough quotation estimates from each printing company based on the print product requirements, means for generating a comparison table for comparing the generated rough quotation estimates, means for providing the generated comparison table to the user terminal, means for selecting a final printing company, means for requesting a formal quotation from the selected printing company, emotion engine means for analyzing the user's emotional state, and means for optimizing the display order of the quotation contents based on the analyzed emotional state. This enables the user to efficiently proceed with the quotation request process and provides a comfortable operating environment that suits the user's emotional state.

[0946] "Past quotation data" refers to data that stores quotation information for print requests that have been previously made.

[0947] "Database means" refers to a system that efficiently stores past quotation data and allows it to be searched and retrieved as needed.

[0948] The "input conditions for the printed matter" refers to information such as the specifications and quantity of the printed matter provided by the user when requesting a quote.

[0949] A "rough estimate" is a rough price estimate provided by each printing company, calculated based on the conditions of the printed material entered.

[0950] An "emotion engine" is a system that recognizes and analyzes a user's emotional state based on facial expressions, voice, operation speed, etc.

[0951] A "comparison table" is a table format that arranges multiple rough estimates in a visually easy-to-understand manner, allowing users to easily compare them.

[0952] A "formal quote request" is the official price confirmation and purchase request that the user sends to the printing company that they have ultimately selected.

[0953] The "means for optimizing the display order of quote contents" refers to a system for rearranging quote contents into an order that is most understandable to the user based on the user's emotional state.

[0954] This invention is a system that streamlines the process of requesting quotes for printed materials in brick-and-mortar stores and provides a comfortable operating environment by taking into account the emotional state of the user. This system operates in cooperation with a server, terminals, and users.

[0955] Server-side processing

[0956] Database Means

[0957] The server has a database that accumulates and stores past quotation data. The database stores past quotation information from each printing company (printed material shape, size, quantity, whether or not printing is required, price, etc.). This data is used to generate a rough quotation based on the conditions of the printed material.

[0958] Estimate generation method

[0959] The server references past quotation data based on the input printing requirements and generates a rough estimate for each printing company. To do this, the server uses a machine learning algorithm to learn each company's quotation patterns from past data. For example, it searches for past quotation information with similar conditions to the input requirements, such as "shape: pamphlet," "size: A4," "quantity: 1,000 copies," and "printing: full color," and calculates a rough estimate for each printing company.

[0960] Emotion Engine Means

[0961] The server is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes the user's emotions based on their facial expressions, voice, input speed, etc., and feeds the results back to the system. The analysis results are used in the display order optimization means described below.

[0962] Comparison table generation means

[0963] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[0964] Optimizing the presentation order

[0965] The server optimizes the display order of suggested printing companies and quotes based on the user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[0966] Formal request for quotation

[0967] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[0968] Terminal side processing

[0969] Condition input method

[0970] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[0971] emotion recognition means

[0972] The device detects the user's facial expressions, voice, and operation speed in real time when inputting data, and sends this data to the server. The emotion engine uses this data to analyze the user's emotional state.

[0973] Comparison table display means

[0974] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[0975] User processing

[0976] Condition input

[0977] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[0978] Emotion input

[0979] The user operates the device using natural facial expressions and voice recordings via the camera and microphone provided by the device. For example, by showing signs of impatience or relaxation, the user conveys their emotional state to the system.

[0980] Compare and select

[0981] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[0982] Formal Request for Quote

[0983] The user then performs an operation to request a formal quotation from the selected printing company.

[0984] Specific examples

[0985] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using their device. The device sends this information to the server, which then searches past quote data based on the input information to generate rough quotes from various companies. The emotion engine also simultaneously analyzes the user's emotional state. If the user appears stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then generates a comparison table and provides it to the device. The user then looks at the comparison table to select the most suitable printing company and submits a formal quote request via their device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[0986] Prompt Sentence Examples

[0987] Print requirements:

[0988] Format: Brochure

[0989] Size: A4

[0990] Quantity: 1000 copies

[0991] Printing: Full color

[0992] Optimizing presentation order:

[0993] Because users are stressed, we simplify the information and display the cheapest quotes first.

[0994] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0995] Step 1:

[0996] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not to print, etc.). The condition information entered by the user is converted into JSON format and sent to the server. At this stage, the input data are the conditions for the print, and the output data is the condition information in JSON format.

[0997] Step 2:

[0998] The server uses the received JSON-formatted print condition information to reference past quotation data from a database. It filters the past quotation data for data that most closely matches the relevant conditions and uses a machine learning algorithm to generate a rough quotation for each printing company. At this stage, the input data is the JSON-formatted print condition information, and the output data is the rough quotation information for each printing company.

[0999] Step 3:

[1000] The server converts the generated rough estimate information from each printing company into a comparison table. This comparison table visually lists the estimated prices from each company, allowing users to easily compare them. At this stage, the input data is the rough estimate information, and the output data is in the form of a comparison table.

[1001] Step 4:

[1002] The terminal displays the comparison table received from the server to the user. The user can compare the prices quoted by each printing company by looking at this comparison table. At this stage, the input data is data in the form of a comparison table, and the output data is visual information for the user.

[1003] Step 5:

[1004] The device detects data such as the user's facial expressions, voice, and operation speed in real time and sends this data to the server. The server then uses an emotion engine to analyze the user's emotional state based on the received data. At this stage, the input data is the user's emotional state data, and the output data is the analyzed emotional state.

[1005] Step 6:

[1006] The server optimizes the display order of the quote contents based on the analyzed emotional state of the user. For example, if the user is feeling stressed, the server adjusts the order to simplify the quote information and display it in an easy-to-understand manner. The input data at this stage is the analyzed emotional state, and the output data is the quote information in the optimized display order.

[1007] Step 7:

[1008] The user looks at the comparison table and selects the most cost-effective printing company. The information of the printing company selected by the user is sent from the terminal to the server. At this stage, the input data is the user's selection information, and the output data is the information of the selected printing company.

[1009] Step 8:

[1010] The server then sends a formal quotation request to the printing company selected by the user. The server receives the selection information and sends a formal quotation request message to the printing company. At this stage, the input data is the information of the selected printing company, and the output data is the formal quotation request message.

[1011] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1012] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1013] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1014] [Third embodiment]

[1015] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1016] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1018] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1019] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1020] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1022] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1023] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1025] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1026] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1027] The present invention provides a system for streamlining the quotation request process for printed materials. This system utilizes past quotation data to efficiently generate rough quotation estimates and select appropriate printing companies. The following describes in detail the embodiments of the present invention.

[1028] Server-side processing

[1029] 1. Database Means

[1030] The server has a database that stores past quotation data. This database stores past quotation information from each printing company (printed object shape, size, quantity, whether or not printing is required, price, etc.).

[1031] 2. Estimate generation method

[1032] The server generates a rough estimate for each printing company based on the input print requirements, referencing past estimate data. To do this, the server uses a machine learning algorithm to learn each company's estimate patterns from past data. For example,

[1033] Input requirements: Brochure, A4 size, 1000 copies, full color

[1034] We search past data for quote information with similar conditions and calculate approximate quotes from each printing company.

[1035] 3. Comparison table generation means

[1036] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[1037] 4. Selection and formal quotation procedures

[1038] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[1039] Terminal side processing

[1040] 1. Condition input method

[1041] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[1042] 2. Comparison table display means

[1043] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[1044] User processing

[1045] 1. Enter conditions

[1046] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[1047] 2. Comparison and Selection

[1048] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[1049] 3. Formal Request for Quote

[1050] The user then performs an operation to request a formal quotation from the selected printing company.

[1051] Specific examples

[1052] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user uses a terminal to input these requirements. The terminal sends this information to the server, which uses the input information to search past quote data and generate rough estimates from each company. The server generates a comparison table and provides it to the terminal. The user looks at the comparison table, selects the most suitable printing company, and submits a formal quote request via the terminal. For example, if printing company A is the cheapest, the user submits a formal quote request to that company and proceeds with the printing procedure.

[1053] This allows users to quickly and efficiently select a printing company, significantly reducing the effort required to compare quotes.

[1054] The processing flow will be explained below.

[1055] Step 1:

[1056] User Input

[1057] The user opens the application on the terminal and inputs the conditions for the printout (such as shape, size, quantity, whether or not it is printed). For example, they input information such as "Shape: Pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color."

[1058] Step 2:

[1059] Sending terminal

[1060] The terminal converts the information entered by the user into JSON format and sends it to the server. Example: {"Shape": "Pamphlet", "Size": "A4", "Quantity": 1000, "Printing": "Full color"}

[1061] Step 3:

[1062] Server Reception

[1063] The server receives data based on the conditions for the print received from the terminal.

[1064] Step 4:

[1065] Database Access

[1066] Based on the received information, the server accesses a database that stores past quotation data.

[1067] Step 5:

[1068] Data Filtering

[1069] The server filters past quotation data that matches the specified conditions. For example, it extracts data that is close to "shape = pamphlet," "size = A4," "print = full color," and "quantity = 1000."

[1070] Step 6:

[1071] Applying machine learning algorithms

[1072] The server uses the filtered past quotation data to apply a machine learning model that has learned the quotation patterns of each printing company, thereby calculating a rough quotation for each company.

[1073] Step 7:

[1074] Generate a rough estimate

[1075] The server generates a rough estimate for each printing company and creates a comparison table. Example: {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}

[1076] Step 8:

[1077] Return to terminal

[1078] The server returns the generated estimate comparison table to the terminal in JSON format. Example: {"Estimate Comparison Table": {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}}

[1079] Step 9:

[1080] Comparison table display

[1081] The terminal analyzes the rough estimate comparison table received from the server and displays it to the user, allowing the user to easily compare estimates from each printing company.

[1082] Step 10:

[1083] Choosing a printing company

[1084] The user checks the displayed comparison table and selects the most suitable printing company. For example, the user may determine that printing company A's quote is the cheapest.

[1085] Step 11:

[1086] Formal Request for Quote

[1087] The user then submits a formal quotation request to the selected printing company through the terminal interface.

[1088] Step 12:

[1089] Sending selection information

[1090] The device notifies the server of the user's selection in JSON format. Example: {"Selected printing company": "Printing company A"}

[1091] Step 13:

[1092] Generate a formal request for quotation

[1093] The server generates a formal request for quotation from the selected printing company based on the received information.

[1094] Step 14:

[1095] Send a quote request

[1096] The server then sends a formal request for quotation to the selected printing company via email or API.

[1097] Example 1

[1098] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1099] The conventional process of requesting quotes for printed materials has the problem of requiring a great deal of time and effort to obtain and compare quotes from multiple printing companies. Furthermore, there is no system in place to effectively utilize past quote data, making it difficult to select the most appropriate quote. Therefore, it is desirable to provide a system that can efficiently generate rough quotes and select the appropriate printing company.

[1100] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1101] In this invention, the server includes a database means for accumulating and storing past quotation data, a means for receiving input print product requirements, a means for referencing the past quotation data and generating rough quotation estimates from each printing service provider based on the print product requirements, a means for generating a comparison table for comparing the generated rough quotation estimates, a means for providing the generated comparison table to a user terminal, a means for selecting a final printing service provider, a means for requesting a formal quotation from the selected printing service provider, a means for transmitting the requirements received at the endpoint in JSON format to the server, a means for using a machine learning algorithm to learn each company's quotation patterns from past quotation data, and a means for displaying the generated comparison table in a visually easy-to-understand format, thereby enabling a user to efficiently and quickly compare and select quotation estimates from printing service providers.

[1102] The "database means" is a storage device or system for accumulating and storing past quotation data.

[1103] The "means for receiving input print product conditions" refers to a communication means or interface for receiving print product conditions input by the user.

[1104] The "means for generating a rough estimate for each printing business" refers to an algorithm or program for referencing past estimate data and calculating a rough estimate for each printing business based on the input conditions.

[1105] A "means for generating a comparison table" is a program or algorithm for creating a table for visually comparing the generated estimates.

[1106] The "means for providing a comparison table to a user terminal" refers to a communication means or program for transmitting the generated comparison table to the user terminal and displaying it.

[1107] The "means for selecting a final printing service provider" is an interface or program that allows the user to select a printing service provider based on the comparison table.

[1108] The "means for making a formal quotation request" is a communication means or program for sending a formal quotation request message to the selected print service provider.

[1109] "Means for transmitting conditions received at an endpoint to a server in JSON format" refers to a communication means or program for converting the printing conditions entered by the user into JSON format and transmitting them to a server via an endpoint.

[1110] "Means for learning each company's quotation patterns from past quotation data using a machine learning algorithm" refers to an algorithm or program that uses machine learning technology to analyze past quotation data and learn the quotation patterns of each printing company.

[1111] "Means for displaying the generated comparison table in a visually easy-to-understand format" refers to a program or interface for displaying the generated comparison table in a visually easy-to-understand format that allows the user to easily make comparisons.

[1112] The present invention provides a system for streamlining the quotation request process for printed materials. This system utilizes past quotation data to efficiently generate rough estimates and select appropriate printing businesses. Specific embodiments for implementing the present invention are described in detail below.

[1113] Server-side processing

[1114] The server includes the following main means. First, it has a database means for accumulating and storing past quotation data. This database stores past quotation information from each printing company (printed product shape, size, quantity, whether or not it is printed, price, etc.). The server receives the print product conditions entered by the user, and based on this, references past quotation data and uses a machine learning algorithm to generate a rough quotation from each printing company. For example, consider the case where the following conditions are entered:

[1115] Example of input conditions: Brochure, A4 size, 1000 copies, full color

[1116] Based on these conditions, the server searches past data for quotation data with similar conditions and uses this to calculate rough estimates from each printing company. It then generates a comparison table based on the rough estimates. This comparison table is designed to allow users to easily compare prices in a visually easy-to-understand format. The server sends this comparison table to the user's terminal and requests a formal quotation from the printing company selected by the user.

[1117] Terminal side processing

[1118] The terminal includes the following main means. First, it provides an interface where the user can input the conditions for the printed materials (shape, size, quantity, whether or not to print, etc.). The information entered through this interface is converted into JSON format and sent to the server. It also has a means for displaying to the user a rough estimate comparison table received from the server. This allows the user to easily compare estimates from various printing companies.

[1119] User processing

[1120] The user uses the device interface to input the requirements for the print job and select the most suitable printing service provider. For example, if a user wants to print 1,000 copies of an A4-sized full-color brochure, they would input the following requirements:

[1121] "Form: Brochure"

[1122] "Size: A4"

[1123] "Quantity: 1000 copies"

[1124] "Printing: Full color"

[1125] The device sends this information in JSON format to the server, which then searches past quote data based on the input information to generate a rough estimate from each company. The server then generates a comparison table and provides it to the device. The user can then review the comparison table to select the most suitable printing company and request a formal quote via the device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[1126] Specific examples

[1127] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user uses a terminal to input these requirements. The terminal sends this information to the server, which uses the input information to search past quote data and generate rough estimates from each company. The server generates a comparison table and provides it to the terminal. The user looks at the comparison table, selects the most suitable printing company, and submits a formal quote request via the terminal. For example, if printing company A is the cheapest, the user can submit a formal quote request to that company and proceed with the printing procedure.

[1128] Prompt Sentence Examples

[1129] "Describe a program that allows a user to find the cheapest printing service provider that can print 1,000 copies of an A4-sized full-color brochure."

[1130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1131] Step 1:

[1132] The user uses the device interface to input the requirements for the printed material, such as "Form: Brochure," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color." The input data is collected through a form on the browser. The input is converted to JSON format.

[1133] Input: User-entered print conditions

[1134] Output: Condition data in JSON format

[1135] Step 2:

[1136] The terminal converts the condition data entered by the user into JSON format and sends it to the server using asynchronous communication technology (e.g., Ajax). The server receives this data at an endpoint.

[1137] Input: Condition data in JSON format

[1138] Output: Condition data sent to the server

[1139] Step 3:

[1140] The server analyzes the received condition data and searches the past quotation data in the database. The server generates a search query and extracts the past quotation data that matches each condition. During this process, the server performs filtering and data extraction.

[1141] Input: Condition data sent to the server, past quotation data

[1142] Output: Past quote data that matches the conditions

[1143] Step 4:

[1144] The server uses a machine learning algorithm to learn the quotation patterns of each printing company from past quotation data. The server then applies the learning model to generate a rough quotation for each printing company based on the input conditions. For example, it predicts the price based on the conditions "brochure, A4 size, 1000 copies, full color."

[1145] Input: Past quote data that matches the conditions

[1146] Output: Approximate estimate from each printing company

[1147] Step 5:

[1148] The server then creates a table comparing quotes from each printing company based on the estimated estimates generated. The comparison table is visually easy to understand, and is designed to show price, delivery time, and other information at a glance. The comparison table is generated in JSON format.

[1149] Input: Approximate estimates from each printing company

[1150] Output: Comparison table in JSON format

[1151] Step 6:

[1152] The server sends the generated comparison table in JSON format to the terminal. The terminal parses the received comparison table data and converts it into HTML format for display to the user. This conversion is performed on the browser, and the comparison table is visually displayed.

[1153] Input: Comparison table in JSON format

[1154] Output: Comparison table in HTML format

[1155] Step 7:

[1156] The user refers to the comparison table displayed on the terminal and selects the most cost-effective printing service provider. The user performs a selection operation and confirms the information of the selected printing service provider on the terminal. For example, the user selects the cheapest printing service provider A.

[1157] Input: HTML comparison table

[1158] Output: Information about the selected print provider

[1159] Step 8:

[1160] The server generates and sends a formal quotation request message to the print service provider selected by the user. This message includes the detailed requirements for the print product entered by the user. The server then makes a formal quotation request based on this information and notifies the print service provider.

[1161] Input: Information on the selected printing company, detailed conditions for printing entered by the user

[1162] Output: Formal RFQ message

[1163] (Application example 1)

[1164] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1165] The traditional process of requesting quotes for printed materials was time-consuming and laborious, making it difficult to proceed efficiently. Furthermore, logistics centers lacked a system for quickly requesting quotes for printing large volumes of packaging and labels. In particular, there was a need for an efficient way to compare quotes from various printing companies and make the appropriate selection. To solve these issues, a system was needed that utilizes past quote data and employs machine learning to efficiently generate and compare quotes.

[1166] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1167] In this invention, the server includes a database means for accumulating and storing past quotation data, a means for receiving input print product requirements, a means for referencing the past quotation data and generating rough quotation estimates from each printing company based on the print product requirements, a means for generating a comparison table for comparing the generated rough quotation estimates, a means for providing the generated comparison table to a user terminal, a means for selecting a final printing company, a means for making a formal quotation request to the selected printing company, a means for the user terminal to operate as an application for a smart device, a means for streamlining print product quotation requests at a logistics center, and a means for generating quotation results from each printing company from past quotation data using a machine learning algorithm. This streamlines the quotation request and comparison process, enabling a logistics center to quickly and optimally select large volumes of print products.

[1168] The "database means" is a storage device for accumulating and storing past estimate data.

[1169] The "receiving means" is a method for acquiring the input conditions for the printed matter.

[1170] The "quote generation means" refers to a method for referencing past quotation data and generating a rough quotation from each printing company based on the conditions of the printed matter.

[1171] The "comparison table generating means" is a method for creating a table for comparing the generated rough estimates.

[1172] The "means for providing" is a means for displaying the generated comparison table on the user terminal.

[1173] The "selection means" is the method for selecting the final printing company.

[1174] The "quote request means" is a method for officially requesting a quotation from the selected printing company.

[1175] A "smart device application" is a program that runs on smart devices such as smartphones and tablets.

[1176] The "means for streamlining print quotation requests at logistics centers" is a method for quickly and efficiently completing the quotation request process for package and label printing at logistics centers.

[1177] A "machine learning algorithm" is a calculation method for predicting and generating quotation results for each printing company based on past quotation data.

[1178] This invention is a system for streamlining the quotation request process for printed materials at a logistics center. Each component and its function are described below.

[1179] Server-side configuration and processing

[1180] The server has the following means:

[1181] 1. Database Means

[1182] The server has a database that stores past quotation data. This database contains information about past quotation data from each printing company (printed item shape, size, quantity, whether or not printing is required, price, etc.).

[1183] 2. Estimate generation method

[1184] The server receives the input print requirements and generates a rough estimate for each printing company by referencing past estimate data. To achieve this, the server uses a machine learning algorithm (e.g., a linear regression model) to predict each company's estimate based on past data.

[1185] 3. Comparison table generation means

[1186] Based on the estimated quotes from each printing company, the server generates a comparison table, which is generated in a visually easy-to-understand format to allow the user to easily compare printing companies.

[1187] 4. Means of provision

[1188] The server sends the generated comparison table to the user's terminal and displays it, allowing the user to compare quotes from each printing company.

[1189] 5. Selection Method

[1190] The server receives the printing company selected by the user and records the information.

[1191] 6. Methods for requesting a quote

[1192] The server then sends a message to the finally selected printing company requesting a formal quote.

[1193] User terminal configuration and processing

[1194] The user terminal performs the following processing as a smart device application.

[1195] 1. Condition input method

[1196] The user uses a smartphone app to enter the print specifications (e.g., form: pamphlet, size: A4, quantity: 1,000 copies, printing: full color) and sends this information in JSON format to the server.

[1197] 2. Comparison table display means

[1198] A rough estimate comparison table received from the server is visually displayed to the user.

[1199] Hardware and software used

[1200] Hardware: Smartphones, servers (cloud services, such as AWS and GCP)

[1201] Software: Python, Flask, scikit-learn, JSON files

[1202] Specific examples

[1203] For example, suppose a logistics center manager wants to print 1,000 A4-sized full-color brochures. The manager uses a smartphone to enter the following conditions:

[1204] Example prompt sentence:

[1205] Size: A4, Quantity: 1000, Color: Full color

[1206] This information is sent to a server, which applies machine learning algorithms based on past quote data to generate a rough estimate for each printing company. The resulting comparison table is then sent back to the user's device, allowing the administrator to review it and select the most suitable printing company. This makes the quote request and comparison process fast and efficient.

[1207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1208] Step 1:

[1209] The user inputs the requirements for the print using a smartphone app. The input information includes, for example, shape, size, quantity, and whether or not to print. The device converts the user-entered requirements into JSON format and sends it to the server.

[1210] Input: Shape, size, quantity, whether printed, etc.

[1211] Output: JSON format data

[1212] Step 2:

[1213] The server analyzes the JSON data received from the user device and extracts the print conditions. Based on these conditions, it generates a query to access past quotation data.

[1214] Input: Print conditions entered by the user (JSON format)

[1215] Output: Conditions of the printed material

[1216] Step 3:

[1217] The server searches past quotation data stored in a database and filters data that matches or is similar to the user's printing requirements. This filtered data is then input into a machine learning algorithm.

[1218] Input: Printing requirements, past quotation data in the database

[1219] Output: Filtered historical quote data

[1220] Step 4:

[1221] The server generates a rough estimate for each printing company based on the filtered estimate data using a machine learning algorithm (e.g., a linear regression model). This algorithm performs data processing (preprocessing) and applies a predictive model to calculate the rough estimate.

[1222] Input: Filtered past quote data, print conditions

[1223] Output: Approximate estimate from each printing company

[1224] Step 5:

[1225] The server creates a comparison table based on the generated rough estimates. This comparison table is formatted in a visually easy-to-understand format, allowing the user to easily compare the estimates from each printing company.

[1226] Input: Approximate estimates from each printing company

[1227] Output: Estimate comparison table

[1228] Step 6:

[1229] The server sends the created comparison table to the user's terminal. The terminal receives the comparison table and displays the data on the user interface. The user can then view the comparison table and select the most suitable printing company.

[1230] Input: Estimate comparison table

[1231] Output: Comparison table displayed on the user's device

[1232] Step 7:

[1233] The user selects the printing company that they think is best based on the comparison table, and the user terminal sends the selection information to the server.

[1234] Input: User selection information

[1235] Output: Selections sent to the server

[1236] Step 8:

[1237] The server generates and sends a formal request for quote message to the selected printing company, including the user's requirements for the printed material and the reasons for their selection.

[1238] Input: User selection information, printing conditions

[1239] Output: Formal RFQ message

[1240] Through the above processing steps, the process of requesting a quote for printed materials at the logistics center can be efficiently executed.

[1241] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1242] The present invention provides a more user-friendly system that streamlines the quote request process for printed materials and takes into account the user's emotional state. This system utilizes past quote data to efficiently generate rough quotes and select the most suitable printing company based on the user's emotional state. The following describes in detail the embodiments of the present invention.

[1243] Server-side processing

[1244] 1. Database Means

[1245] The server has a database that stores past quotation data. This database stores past quotation information from each printing company (printed item shape, size, quantity, whether or not printing is required, price, etc.).

[1246] 2. Estimate generation method

[1247] The server generates a rough estimate for each printing company based on the input print requirements, referencing past estimate data. To do this, the server uses a machine learning algorithm to learn each company's estimate patterns from past data. For example,

[1248] Input requirements: Brochure, A4 size, 1000 copies, full color

[1249] We search past data for quote information with similar conditions and calculate approximate quotes from each printing company.

[1250] 3. Emotion Engine

[1251] The server is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes the user's emotions based on facial expressions, voice, input speed, etc., and feeds the results back to the system.

[1252] 4. Comparison table generation means

[1253] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[1254] 5. Optimizing the presentation order

[1255] The server optimizes the display order of suggested printing companies and quotes based on the user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[1256] 6. Selection and Formal Request for Quotation Methods

[1257] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[1258] Terminal side processing

[1259] 1. Condition input method

[1260] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[1261] 2. Emotion recognition means

[1262] The device detects the user's facial expressions, voice, and operation speed in real time when inputting data, and sends this data to the server. The emotion engine uses this data to analyze the user's emotional state.

[1263] 3. Comparison table display means

[1264] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[1265] User processing

[1266] 1. Enter conditions

[1267] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[1268] 2. Emotion Input

[1269] The user operates the device using natural facial expressions and voice recordings via the camera and microphone provided by the device. For example, by showing signs of impatience or relaxation, the user conveys their emotional state to the system.

[1270] 3. Compare and select

[1271] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[1272] 4. Formal Request for Quotation

[1273] The user then performs an operation to request a formal quotation from the selected printing company.

[1274] Specific examples

[1275] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using their device. The device sends this information to the server, which then searches past quote data based on the input information to generate rough quotes from various companies. The emotion engine also simultaneously analyzes the user's emotional state. If the user appears stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then generates a comparison table and provides it to the device. The user then looks at the comparison table to select the most suitable printing company and submits a formal quote request via their device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[1276] This allows users to quickly and efficiently select a printing company, significantly reducing the effort required for comparing quotes. The emotion engine also takes the user's emotional state into consideration, providing a more comfortable operating environment.

[1277] The processing flow will be explained below.

[1278] Step 1:

[1279] User condition input

[1280] The user opens the application on the terminal and inputs the conditions for the printout (such as shape, size, quantity, whether or not it is printed). For example, they input information such as "Shape: Pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color."

[1281] Step 2:

[1282] User emotion input

[1283] The device uses a built-in camera and microphone to monitor the user's facial expressions, voice, and speed of operation, thereby detecting the user's current emotional state.

[1284] Step 3:

[1285] Sending terminal

[1286] The device converts the printing conditions and emotion data entered by the user into JSON format and sends it to the server. Example: {"Shape": "Pamphlet", "Size": "A4", "Quantity": 1000, "Print": "Full color", "Emotion data": "Stress"}

[1287] Step 4:

[1288] Server Reception

[1289] The server receives the conditions and emotion data for the print material received from the terminal.

[1290] Step 5:

[1291] Database Access

[1292] Based on the received information, the server accesses a database that stores past quotation data.

[1293] Step 6:

[1294] Data Filtering

[1295] The server filters past quotation data that matches the specified conditions. For example, it extracts data that is close to "shape = pamphlet," "size = A4," "print = full color," and "quantity = 1000."

[1296] Step 7:

[1297] Applying machine learning algorithms

[1298] The server uses the filtered past quotation data to apply a machine learning model that has learned the quotation patterns of each printing company, thereby calculating a rough quotation for each company.

[1299] Step 8:

[1300] Generate a rough estimate

[1301] The server generates a rough estimate for each printing company and creates a comparison table. Example: {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}

[1302] Step 9:

[1303] Emotional Data Feedback

[1304] The server analyzes the user's emotional state based on the received emotional data. For example, if the user is feeling stressed, the server displays simpler information that is easier to understand visually.

[1305] Step 10:

[1306] Return to terminal

[1307] The server returns the generated rough estimate comparison table and the optimized presentation order to the terminal in JSON format. Example: {"Estimate Comparison Table": {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}, "Optimized Order": ["Printing Company A", "Printing Company C", "Printing Company B"]}

[1308] Step 11:

[1309] Comparison table display

[1310] The terminal analyzes the estimated quote comparison table and the optimized presentation order received from the server and displays them to the user, allowing the user to easily compare quote information from each printing company.

[1311] Step 12:

[1312] Choosing a printing company

[1313] The user checks the displayed comparison table and selects the most suitable printing company. For example, the user may determine that printing company A's quote is the cheapest.

[1314] Step 13:

[1315] Formal Request for Quote

[1316] The user then submits a formal quotation request to the selected printing company through the terminal interface.

[1317] Step 14:

[1318] Sending selection information

[1319] The device notifies the server of the user's selection in JSON format. Example: {"Selected printing company": "Printing company A"}

[1320] Step 15:

[1321] Generate a formal request for quotation

[1322] The server generates a formal request for quotation from the selected printing company based on the received information.

[1323] Step 16:

[1324] Send a quote request

[1325] The server then sends a formal request for quotation to the selected printing company via email or API.

[1326] Example 2

[1327] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1328] In conventional print quotation request processes, users must obtain quotes from multiple print providers individually, compare them, and select a suitable one, which is time-consuming and labor-intensive. Furthermore, the process often leaves users feeling stressed because it does not take into account their emotional state. The present invention aims to solve these problems by providing a system that takes into account the user's emotional state while streamlining the quotation request process.

[1329] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1330] In this invention, the server includes a data storage means for accumulating and storing past quotation data, a data receiving means for receiving print requirements entered from a user terminal, a quotation generating means for referencing the past quotation data and generating rough quotation estimates from each print provider based on the print requirements, a sentiment analyzing means for recognizing and analyzing the user's emotional state, a data comparing means for generating a comparison table for comparing the generated rough quotation estimates, a display means for providing the generated comparison table to the user terminal, an order optimizing means for optimizing the display order of suggested print providers and quotation details based on the user's emotional state, a selection means for selecting a final print provider, and a requesting means for making a formal quotation request to the selected print provider. This makes it possible to improve the efficiency of the quotation request process and to propose quotation estimates that take the user's emotional state into consideration.

[1331] "Data storage means" refers to means for accumulating and storing past quotation data.

[1332] The "data receiving means" is a means for receiving the conditions for the print product input from the user terminal.

[1333] The "quote generating means" refers to a means for referencing past quotation data and generating a rough quotation from each print provider based on the conditions of the printed material.

[1334] "Emotion analysis means" refers to means for recognizing and analyzing the emotional state of a user.

[1335] The "data comparison means" is a means for generating a comparison table for comparing the generated rough estimates.

[1336] The "display means" is a means for providing the generated comparison table to a user terminal.

[1337] The "order optimization means" is a means for optimizing the display order of suggested print providers and quotation details based on the emotional state of the user.

[1338] A "selection means" is a means for selecting a final print provider.

[1339] The "request means" is a means for making a formal request for a quote to the selected print provider.

[1340] The present invention provides a system that streamlines the quote request process for print products and also takes into account the emotional state of the user. Specific embodiments are described below.

[1341] Server-side processing

[1342] 1. Data Storage Means

[1343] The server manages a database that stores past quotation data, including quotation information (shape, size, quantity, price, etc.) from each print provider.

[1344] 2. Data Receiving Method

[1345] The server receives the print conditions sent from the user terminal. The conditions include information in JSON format about the shape, size, quantity, and whether or not the print is present.

[1346] 3. Estimate Generation Method

[1347] The server generates a rough estimate from each printing service provider based on the received print requirements and past quotation data. To do this, it uses a machine learning algorithm to learn each company's quotation patterns from past data. For example, if the server receives input requirements such as "brochure, A4 size, 1000 copies, full color," it searches for quotation information with similar requirements and calculates a rough estimate.

[1348] 4. Emotion analysis method

[1349] The server receives data such as the user's facial expressions, voice, and input speed sent from the device, and uses an emotion engine to analyze the user's emotional state, thereby determining whether the user is feeling stressed or relaxed.

[1350] 5. Data Comparison Methods

[1351] The server generates a comparison table based on the estimated quotes from each print provider, and the comparison table is designed to be displayed in a visually easy-to-understand format.

[1352] 6. Display means

[1353] The server sends the generated comparison table to the user's terminal, which displays it. The user can check the comparison table and easily compare the quote information of each print provider.

[1354] 7. Ordinal Optimization Methods

[1355] The server optimizes the display order of suggested print providers and quotes based on the analyzed user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[1356] 8. Selection Method

[1357] Once the user has selected the most suitable print provider, the server receives the selection and issues a formal request for quote to the selected provider.

[1358] 9. Request Method

[1359] The server sends a formal request for quote message to the selected print provider, which can use an email API or the API of a dedicated B2B platform.

[1360] Terminal side processing

[1361] 1. Condition input method

[1362] The terminal provides an interface for users to input requirements for the printed material, such as "pamphlet, A4 size, 1000 copies, full color."

[1363] 2. Emotion recognition means

[1364] The device detects the user's facial expressions, voice, and operation speed in real time and sends this information to the server, where the server's emotion engine analyzes the data.

[1365] 3. Comparison table display means

[1366] The terminal displays the rough estimate comparison table sent from the server to the user, allowing the user to easily compare the estimate information of each print provider.

[1367] User processing

[1368] 1. Enter conditions

[1369] The user uses the terminal interface to input the conditions for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[1370] 2. Emotion Input

[1371] The user can communicate their emotional state to the system by inputting facial expressions and voice using the device. For example, they can communicate their emotional state by showing how anxious or relaxed they are.

[1372] 3. Compare and select

[1373] The user can select the most cost-effective printing provider by looking at the displayed rough estimate comparison table. For example, if the rough estimate for printing company A is 50,000 yen, the estimate for printing company B is 55,000 yen, and the estimate for printing company C is 52,000 yen, the user can select the most suitable provider.

[1374] 4. Formal Request for Quotation

[1375] The user then performs an operation to formally request a quote from the selected print provider.

[1376] Specific examples

[1377] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using a terminal. The terminal sends this information to the server, which searches past quote data to generate rough quotes from various companies. Meanwhile, if the emotion engine analyzes the user's emotional state and detects that the user is feeling stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then provides the terminal with a comparison table, which the user can use to select the most suitable printing provider. The user then requests a formal quote via the terminal. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing procedure.

[1378] Example prompts for generative AI models

[1379] "I'd like to print 1,000 full-color A4 brochures. Please generate rough quotes from the best print providers, taking into account past quote information and my current user sentiment, and display an easy-to-understand comparison table."

[1380] Based on this prompt, the generative AI model can understand the system's operation in detail and specify each processing step.

[1381] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1382] Step 1:

[1383] Conditions entered by the user

[1384] Processing content: The user uses the terminal interface to input the conditions for the print.

[1385] Specific operation: The user inputs information such as "pamphlet, A4 size, 1000 copies, full color" into the input interface.

[1386] Input: The print condition entered by the user.

[1387] Output: The print conditions are saved on the device.

[1388] Step 2:

[1389] Sending condition data to the server

[1390] Processing content: The terminal sends the input data in JSON format to the server.

[1391] Specific operation: The terminal formats the condition data entered by the user and sends it as an HTTP request.

[1392] Input: The print condition entered by the user.

[1393] Output: The condition data is sent to the server.

[1394] Step 3:

[1395] Server-based quotation data search and rough quotation generation

[1396] Processing details: The server searches the past estimate database based on the received conditions, extracts appropriate data, and generates a rough estimate.

[1397] What it does: The server uses SQL queries to retrieve applicable quote data from the database and calculates the price using machine learning models.

[1398] Input: Condition data and past quote data.

[1399] Output: Approximate quotes from each print provider.

[1400] Step 4:

[1401] Sending emotional data to the server and analyzing it

[1402] Processing details: The device collects user emotion data in real time and sends it to the server, which analyzes it.

[1403] Specific operation: The device collects data from the camera and microphone, calls the emotion analysis API, and sends the results to the server. The server then integrates the received data and analyzes the emotional state.

[1404] Input: Emotional data such as the user's facial expression, voice, and operation speed.

[1405] Output: Analysis of the user's emotional state.

[1406] Step 5:

[1407] Server-generated comparison table

[1408] Processing details: The server generates a visually easy-to-understand comparison table based on the analysis results and quotation data.

[1409] Specific operation: The server generates graphs and table-format comparison tables using HTML and JavaScript, and sends the generated HTML data to the terminal.

[1410] Input: Rough estimates from each printing provider and the analysis of the user's emotional state.

[1411] Output: A comparison table in a visually friendly format.

[1412] Step 6:

[1413] Providing comparison tables to users

[1414] Processing content: The terminal displays the received comparison table to the user.

[1415] What it does: The device browser renders the HTML data, allowing the user to view the comparison table directly.

[1416] Input: Comparison table sent from the server.

[1417] Output: A user-visible comparison table.

[1418] Step 7:

[1419] User selection of print provider

[1420] Process: The user looks at the comparison table and selects the best print provider.

[1421] Specific operations: The user selects a print provider on the terminal interface, confirms the selected information, and presses the send button.

[1422] Enter: comparison chart.

[1423] Output: Information about the selected print provider.

[1424] Step 8:

[1425] Sending selected data to the server

[1426] Processing content: The terminal transmits the selection information to the server.

[1427] Specific operation: The terminal formats the data of the selected print provider and sends it to the server in an HTTP request.

[1428] Enter: Selected print provider information.

[1429] Output: The selected data is sent to the server.

[1430] Step 9:

[1431] Formal request for quotation by the server

[1432] What happens: The server receives the selection information and sends a formal request for quote message to the appropriate print provider.

[1433] Specific operation: The server uses an email API or the API of a dedicated B2B platform to send a quote request to the selected print provider.

[1434] Input:Selection data.

[1435] Output: A formal request for quote message to your print provider.

[1436] (Application example 2)

[1437] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1438] The conventional process of requesting quotes for printed materials requires users to manually compare quotes from multiple printing companies, which is extremely time-consuming and labor-intensive. Furthermore, because the system does not take into account the user's emotional state, it is considered difficult to use for users who are stressed or anxious. There is a need for a system that solves these problems and allows users to request quotes for printed materials efficiently and comfortably.

[1439] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes database means for accumulating and storing past quotation data, means for receiving input print product requirements, means for referencing the past quotation data and generating rough quotation estimates from each printing company based on the print product requirements, means for generating a comparison table for comparing the generated rough quotation estimates, means for providing the generated comparison table to the user terminal, means for selecting a final printing company, means for requesting a formal quotation from the selected printing company, emotion engine means for analyzing the user's emotional state, and means for optimizing the display order of the quotation contents based on the analyzed emotional state. This enables the user to efficiently proceed with the quotation request process and provides a comfortable operating environment that suits the user's emotional state.

[1440] "Past quotation data" refers to data that stores quotation information for print requests that have been previously made.

[1441] "Database means" refers to a system that efficiently stores past quotation data and allows it to be searched and retrieved as needed.

[1442] The "input conditions for the printed matter" refers to information such as the specifications and quantity of the printed matter provided by the user when requesting a quote.

[1443] A "rough estimate" is a rough price estimate provided by each printing company, calculated based on the conditions of the printed material entered.

[1444] An "emotion engine" is a system that recognizes and analyzes a user's emotional state based on facial expressions, voice, operation speed, etc.

[1445] A "comparison table" is a table format that arranges multiple rough estimates in a visually easy-to-understand manner, allowing users to easily compare them.

[1446] A "formal quote request" is the official price confirmation and purchase request that the user sends to the printing company that they have ultimately selected.

[1447] The "means for optimizing the display order of quote contents" refers to a system for rearranging quote contents into an order that is most understandable to the user based on the user's emotional state.

[1448] This invention is a system that streamlines the process of requesting quotes for printed materials in brick-and-mortar stores and provides a comfortable operating environment by taking into account the emotional state of the user. This system operates in cooperation with a server, terminals, and users.

[1449] Server-side processing

[1450] Database Means

[1451] The server has a database that accumulates and stores past quotation data. The database stores past quotation information from each printing company (printed material shape, size, quantity, whether or not printing is required, price, etc.). This data is used to generate a rough quotation based on the conditions of the printed material.

[1452] Estimate generation method

[1453] The server references past quotation data based on the input printing requirements and generates a rough estimate for each printing company. To do this, the server uses a machine learning algorithm to learn each company's quotation patterns from past data. For example, it searches for past quotation information with similar conditions to the input requirements, such as "shape: pamphlet," "size: A4," "quantity: 1,000 copies," and "printing: full color," and calculates a rough estimate for each printing company.

[1454] Emotion Engine Means

[1455] The server is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes the user's emotions based on their facial expressions, voice, input speed, etc., and feeds the results back to the system. The analysis results are used in the display order optimization means described below.

[1456] Comparison table generation means

[1457] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[1458] Optimizing the presentation order

[1459] The server optimizes the display order of suggested printing companies and quotes based on the user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[1460] Formal request for quotation

[1461] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[1462] Terminal side processing

[1463] Condition input method

[1464] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[1465] emotion recognition means

[1466] The device detects the user's facial expressions, voice, and operation speed in real time when inputting data, and sends this data to the server. The emotion engine uses this data to analyze the user's emotional state.

[1467] Comparison table display means

[1468] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[1469] User processing

[1470] Condition input

[1471] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[1472] Emotion input

[1473] The user operates the device using natural facial expressions and voice recordings via the camera and microphone provided by the device. For example, by showing signs of impatience or relaxation, the user conveys their emotional state to the system.

[1474] Compare and select

[1475] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[1476] Formal Request for Quote

[1477] The user then performs an operation to request a formal quotation from the selected printing company.

[1478] Specific examples

[1479] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using their device. The device sends this information to the server, which then searches past quote data based on the input information to generate rough quotes from various companies. The emotion engine also simultaneously analyzes the user's emotional state. If the user appears stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then generates a comparison table and provides it to the device. The user then looks at the comparison table to select the most suitable printing company and submits a formal quote request via their device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[1480] Prompt Sentence Examples

[1481] Print requirements:

[1482] Format: Brochure

[1483] Size: A4

[1484] Quantity: 1000 copies

[1485] Printing: Full color

[1486] Optimizing presentation order:

[1487] Because users are stressed, we simplify the information and display the cheapest quotes first.

[1488] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1489] Step 1:

[1490] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not to print, etc.). The condition information entered by the user is converted into JSON format and sent to the server. At this stage, the input data are the conditions for the print, and the output data is the condition information in JSON format.

[1491] Step 2:

[1492] The server uses the received JSON-formatted print condition information to reference past quotation data from a database. It filters the past quotation data for data that most closely matches the relevant conditions and uses a machine learning algorithm to generate a rough quotation for each printing company. At this stage, the input data is the JSON-formatted print condition information, and the output data is the rough quotation information for each printing company.

[1493] Step 3:

[1494] The server converts the generated rough estimate information from each printing company into a comparison table. This comparison table visually lists the estimated prices from each company, allowing users to easily compare them. At this stage, the input data is the rough estimate information, and the output data is in the form of a comparison table.

[1495] Step 4:

[1496] The terminal displays the comparison table received from the server to the user. The user can compare the prices quoted by each printing company by looking at this comparison table. At this stage, the input data is data in the form of a comparison table, and the output data is visual information for the user.

[1497] Step 5:

[1498] The device detects data such as the user's facial expressions, voice, and operation speed in real time and sends this data to the server. The server then uses an emotion engine to analyze the user's emotional state based on the received data. At this stage, the input data is the user's emotional state data, and the output data is the analyzed emotional state.

[1499] Step 6:

[1500] The server optimizes the display order of the quote contents based on the analyzed emotional state of the user. For example, if the user is feeling stressed, the server adjusts the order to simplify the quote information and display it in an easy-to-understand manner. The input data at this stage is the analyzed emotional state, and the output data is the quote information in the optimized display order.

[1501] Step 7:

[1502] The user looks at the comparison table and selects the most cost-effective printing company. The information of the printing company selected by the user is sent from the terminal to the server. At this stage, the input data is the user's selection information, and the output data is the information of the selected printing company.

[1503] Step 8:

[1504] The server then sends a formal quotation request to the printing company selected by the user. The server receives the selection information and sends a formal quotation request message to the printing company. At this stage, the input data is the information of the selected printing company, and the output data is the formal quotation request message.

[1505] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1506] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1507] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1508] [Fourth embodiment]

[1509] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1510] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1511] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1512] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1513] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1514] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1515] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1516] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1517] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1518] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1519] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1520] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1521] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1522] The present invention provides a system for streamlining the quotation request process for printed materials. This system utilizes past quotation data to efficiently generate rough quotation estimates and select appropriate printing companies. The following describes in detail the embodiments of the present invention.

[1523] Server-side processing

[1524] 1. Database Means

[1525] The server has a database that stores past quotation data. This database stores past quotation information from each printing company (printed object shape, size, quantity, whether or not printing is required, price, etc.).

[1526] 2. Estimate generation method

[1527] The server generates a rough estimate for each printing company based on the input print requirements, referencing past estimate data. To do this, the server uses a machine learning algorithm to learn each company's estimate patterns from past data. For example,

[1528] Input requirements: Brochure, A4 size, 1000 copies, full color

[1529] We search past data for quote information with similar conditions and calculate approximate quotes from each printing company.

[1530] 3. Comparison table generation means

[1531] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[1532] 4. Selection and formal quotation procedures

[1533] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[1534] Terminal side processing

[1535] 1. Condition input method

[1536] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[1537] 2. Comparison table display means

[1538] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[1539] User processing

[1540] 1. Enter conditions

[1541] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[1542] 2. Comparison and Selection

[1543] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[1544] 3. Formal Request for Quote

[1545] The user then performs an operation to request a formal quotation from the selected printing company.

[1546] Specific examples

[1547] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user uses a terminal to input these requirements. The terminal sends this information to the server, which uses the input information to search past quote data and generate rough estimates from each company. The server generates a comparison table and provides it to the terminal. The user looks at the comparison table, selects the most suitable printing company, and submits a formal quote request via the terminal. For example, if printing company A is the cheapest, the user submits a formal quote request to that company and proceeds with the printing procedure.

[1548] This allows users to quickly and efficiently select a printing company, significantly reducing the effort required to compare quotes.

[1549] The processing flow will be explained below.

[1550] Step 1:

[1551] User Input

[1552] The user opens the application on the terminal and inputs the conditions for the printout (such as shape, size, quantity, whether or not it is printed). For example, they input information such as "Shape: Pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color."

[1553] Step 2:

[1554] Sending terminal

[1555] The terminal converts the information entered by the user into JSON format and sends it to the server. Example: {"Shape": "Pamphlet", "Size": "A4", "Quantity": 1000, "Printing": "Full color"}

[1556] Step 3:

[1557] Server Reception

[1558] The server receives data based on the conditions for the print received from the terminal.

[1559] Step 4:

[1560] Database Access

[1561] Based on the received information, the server accesses a database that stores past quotation data.

[1562] Step 5:

[1563] Data Filtering

[1564] The server filters past quotation data that matches the specified conditions. For example, it extracts data that is close to "shape = pamphlet," "size = A4," "print = full color," and "quantity = 1000."

[1565] Step 6:

[1566] Applying machine learning algorithms

[1567] The server uses the filtered past quotation data to apply a machine learning model that has learned the quotation patterns of each printing company, thereby calculating a rough quotation for each company.

[1568] Step 7:

[1569] Generate a rough estimate

[1570] The server generates a rough estimate for each printing company and creates a comparison table. Example: {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}

[1571] Step 8:

[1572] Return to terminal

[1573] The server returns the generated estimate comparison table to the terminal in JSON format. Example: {"Estimate Comparison Table": {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}}

[1574] Step 9:

[1575] Comparison table display

[1576] The terminal analyzes the rough estimate comparison table received from the server and displays it to the user, allowing the user to easily compare estimates from each printing company.

[1577] Step 10:

[1578] Choosing a printing company

[1579] The user checks the displayed comparison table and selects the most suitable printing company. For example, the user may determine that printing company A's quote is the cheapest.

[1580] Step 11:

[1581] Formal Request for Quote

[1582] The user then submits a formal quotation request to the selected printing company through the terminal interface.

[1583] Step 12:

[1584] Sending selection information

[1585] The device notifies the server of the user's selection in JSON format. Example: {"Selected printing company": "Printing company A"}

[1586] Step 13:

[1587] Generate a formal request for quotation

[1588] The server generates a formal request for quotation from the selected printing company based on the received information.

[1589] Step 14:

[1590] Send a quote request

[1591] The server then sends a formal request for quotation to the selected printing company via email or API.

[1592] Example 1

[1593] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1594] The conventional process of requesting quotes for printed materials has the problem of requiring a great deal of time and effort to obtain and compare quotes from multiple printing companies. Furthermore, there is no system in place to effectively utilize past quote data, making it difficult to select the most appropriate quote. Therefore, it is desirable to provide a system that can efficiently generate rough quotes and select the appropriate printing company.

[1595] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1596] In this invention, the server includes a database means for accumulating and storing past quotation data, a means for receiving input print product requirements, a means for referencing the past quotation data and generating rough quotation estimates from each printing service provider based on the print product requirements, a means for generating a comparison table for comparing the generated rough quotation estimates, a means for providing the generated comparison table to a user terminal, a means for selecting a final printing service provider, a means for requesting a formal quotation from the selected printing service provider, a means for transmitting the requirements received at the endpoint in JSON format to the server, a means for using a machine learning algorithm to learn each company's quotation patterns from past quotation data, and a means for displaying the generated comparison table in a visually easy-to-understand format, thereby enabling a user to efficiently and quickly compare and select quotation estimates from printing service providers.

[1597] The "database means" is a storage device or system for accumulating and storing past quotation data.

[1598] The "means for receiving input print product conditions" refers to a communication means or interface for receiving print product conditions input by the user.

[1599] The "means for generating a rough estimate for each printing business" refers to an algorithm or program for referencing past estimate data and calculating a rough estimate for each printing business based on the input conditions.

[1600] A "means for generating a comparison table" is a program or algorithm for creating a table for visually comparing the generated estimates.

[1601] The "means for providing a comparison table to a user terminal" refers to a communication means or program for transmitting the generated comparison table to the user terminal and displaying it.

[1602] The "means for selecting a final printing service provider" is an interface or program that allows the user to select a printing service provider based on the comparison table.

[1603] The "means for making a formal quotation request" is a communication means or program for sending a formal quotation request message to the selected print service provider.

[1604] "Means for transmitting conditions received at an endpoint to a server in JSON format" refers to a communication means or program for converting the printing conditions entered by the user into JSON format and transmitting them to a server via an endpoint.

[1605] "Means for learning each company's quotation patterns from past quotation data using a machine learning algorithm" refers to an algorithm or program that uses machine learning technology to analyze past quotation data and learn the quotation patterns of each printing company.

[1606] "Means for displaying the generated comparison table in a visually easy-to-understand format" refers to a program or interface for displaying the generated comparison table in a visually easy-to-understand format that allows the user to easily make comparisons.

[1607] The present invention provides a system for streamlining the quotation request process for printed materials. This system utilizes past quotation data to efficiently generate rough estimates and select appropriate printing businesses. Specific embodiments for implementing the present invention are described in detail below.

[1608] Server-side processing

[1609] The server includes the following main means. First, it has a database means for accumulating and storing past quotation data. This database stores past quotation information from each printing company (printed product shape, size, quantity, whether or not it is printed, price, etc.). The server receives the print product conditions entered by the user, and based on this, references past quotation data and uses a machine learning algorithm to generate a rough quotation from each printing company. For example, consider the case where the following conditions are entered:

[1610] Example of input conditions: Brochure, A4 size, 1000 copies, full color

[1611] Based on these conditions, the server searches past data for quotation data with similar conditions and uses this to calculate rough estimates from each printing company. It then generates a comparison table based on the rough estimates. This comparison table is designed to allow users to easily compare prices in a visually easy-to-understand format. The server sends this comparison table to the user's terminal and requests a formal quotation from the printing company selected by the user.

[1612] Terminal side processing

[1613] The terminal includes the following main means. First, it provides an interface where the user can input the conditions for the printed materials (shape, size, quantity, whether or not to print, etc.). The information entered through this interface is converted into JSON format and sent to the server. It also has a means for displaying to the user a rough estimate comparison table received from the server. This allows the user to easily compare estimates from various printing companies.

[1614] User processing

[1615] The user uses the device interface to input the requirements for the print job and select the most suitable printing service provider. For example, if a user wants to print 1,000 copies of an A4-sized full-color brochure, they would input the following requirements:

[1616] "Form: Brochure"

[1617] "Size: A4"

[1618] "Quantity: 1000 copies"

[1619] "Printing: Full color"

[1620] The device sends this information in JSON format to the server, which then searches past quote data based on the input information to generate a rough estimate from each company. The server then generates a comparison table and provides it to the device. The user can then review the comparison table to select the most suitable printing company and request a formal quote via the device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[1621] Specific examples

[1622] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user uses a terminal to input these requirements. The terminal sends this information to the server, which uses the input information to search past quote data and generate rough estimates from each company. The server generates a comparison table and provides it to the terminal. The user looks at the comparison table, selects the most suitable printing company, and submits a formal quote request via the terminal. For example, if printing company A is the cheapest, the user can submit a formal quote request to that company and proceed with the printing procedure.

[1623] Prompt Sentence Examples

[1624] "Describe a program that allows a user to find the cheapest printing service provider that can print 1,000 copies of an A4-sized full-color brochure."

[1625] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1626] Step 1:

[1627] The user uses the device interface to input the requirements for the printed material, such as "Form: Brochure," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color." The input data is collected through a form on the browser. The input is converted to JSON format.

[1628] Input: User-entered print conditions

[1629] Output: Condition data in JSON format

[1630] Step 2:

[1631] The terminal converts the condition data entered by the user into JSON format and sends it to the server using asynchronous communication technology (e.g., Ajax). The server receives this data at an endpoint.

[1632] Input: Condition data in JSON format

[1633] Output: Condition data sent to the server

[1634] Step 3:

[1635] The server analyzes the received condition data and searches the past quotation data in the database. The server generates a search query and extracts the past quotation data that matches each condition. During this process, the server performs filtering and data extraction.

[1636] Input: Condition data sent to the server, past quotation data

[1637] Output: Past quote data that matches the conditions

[1638] Step 4:

[1639] The server uses a machine learning algorithm to learn the quotation patterns of each printing company from past quotation data. The server then applies the learning model to generate a rough quotation for each printing company based on the input conditions. For example, it predicts the price based on the conditions "brochure, A4 size, 1000 copies, full color."

[1640] Input: Past quote data that matches the conditions

[1641] Output: Approximate estimate from each printing company

[1642] Step 5:

[1643] The server then creates a table comparing quotes from each printing company based on the estimated estimates generated. The comparison table is visually easy to understand, and is designed to show price, delivery time, and other information at a glance. The comparison table is generated in JSON format.

[1644] Input: Approximate estimates from each printing company

[1645] Output: Comparison table in JSON format

[1646] Step 6:

[1647] The server sends the generated comparison table in JSON format to the terminal. The terminal parses the received comparison table data and converts it into HTML format for display to the user. This conversion is performed on the browser, and the comparison table is visually displayed.

[1648] Input: Comparison table in JSON format

[1649] Output: Comparison table in HTML format

[1650] Step 7:

[1651] The user refers to the comparison table displayed on the terminal and selects the most cost-effective printing service provider. The user performs a selection operation and confirms the information of the selected printing service provider on the terminal. For example, the user selects the cheapest printing service provider A.

[1652] Input: HTML comparison table

[1653] Output: Information about the selected print provider

[1654] Step 8:

[1655] The server generates and sends a formal quotation request message to the print service provider selected by the user. This message includes the detailed requirements for the print product entered by the user. The server then makes a formal quotation request based on this information and notifies the print service provider.

[1656] Input: Information on the selected printing company, detailed conditions for printing entered by the user

[1657] Output: Formal RFQ message

[1658] (Application example 1)

[1659] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1660] The traditional process of requesting quotes for printed materials was time-consuming and laborious, making it difficult to proceed efficiently. Furthermore, logistics centers lacked a system for quickly requesting quotes for printing large volumes of packaging and labels. In particular, there was a need for an efficient way to compare quotes from various printing companies and make the appropriate selection. To solve these issues, a system was needed that utilizes past quote data and employs machine learning to efficiently generate and compare quotes.

[1661] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1662] In this invention, the server includes a database means for accumulating and storing past quotation data, a means for receiving input print product requirements, a means for referencing the past quotation data and generating rough quotation estimates from each printing company based on the print product requirements, a means for generating a comparison table for comparing the generated rough quotation estimates, a means for providing the generated comparison table to a user terminal, a means for selecting a final printing company, a means for making a formal quotation request to the selected printing company, a means for the user terminal to operate as an application for a smart device, a means for streamlining print product quotation requests at a logistics center, and a means for generating quotation results from each printing company from past quotation data using a machine learning algorithm. This streamlines the quotation request and comparison process, enabling a logistics center to quickly and optimally select large volumes of print products.

[1663] The "database means" is a storage device for accumulating and storing past estimate data.

[1664] The "receiving means" is a method for acquiring the input conditions for the printed matter.

[1665] The "quote generation means" refers to a method for referencing past quotation data and generating a rough quotation from each printing company based on the conditions of the printed matter.

[1666] The "comparison table generating means" is a method for creating a table for comparing the generated rough estimates.

[1667] The "means for providing" is a means for displaying the generated comparison table on the user terminal.

[1668] The "selection means" is the method for selecting the final printing company.

[1669] The "quote request means" is a method for officially requesting a quotation from the selected printing company.

[1670] A "smart device application" is a program that runs on smart devices such as smartphones and tablets.

[1671] The "means for streamlining print quotation requests at logistics centers" is a method for quickly and efficiently completing the quotation request process for package and label printing at logistics centers.

[1672] A "machine learning algorithm" is a calculation method for predicting and generating quotation results for each printing company based on past quotation data.

[1673] This invention is a system for streamlining the quotation request process for printed materials at a logistics center. Each component and its function are described below.

[1674] Server-side configuration and processing

[1675] The server has the following means:

[1676] 1. Database Means

[1677] The server has a database that stores past quotation data. This database contains information about past quotation data from each printing company (printed item shape, size, quantity, whether or not printing is required, price, etc.).

[1678] 2. Estimate generation method

[1679] The server receives the input print requirements and generates a rough estimate for each printing company by referencing past estimate data. To achieve this, the server uses a machine learning algorithm (e.g., a linear regression model) to predict each company's estimate based on past data.

[1680] 3. Comparison table generation means

[1681] Based on the estimated quotes from each printing company, the server generates a comparison table, which is generated in a visually easy-to-understand format to allow the user to easily compare printing companies.

[1682] 4. Means of provision

[1683] The server sends the generated comparison table to the user's terminal and displays it, allowing the user to compare quotes from each printing company.

[1684] 5. Selection Method

[1685] The server receives the printing company selected by the user and records the information.

[1686] 6. Methods for requesting a quote

[1687] The server then sends a message to the finally selected printing company requesting a formal quote.

[1688] User terminal configuration and processing

[1689] The user terminal performs the following processing as a smart device application.

[1690] 1. Condition input method

[1691] The user uses a smartphone app to enter the print specifications (e.g., form: pamphlet, size: A4, quantity: 1,000 copies, printing: full color) and sends this information in JSON format to the server.

[1692] 2. Comparison table display means

[1693] A rough estimate comparison table received from the server is visually displayed to the user.

[1694] Hardware and software used

[1695] Hardware: Smartphones, servers (cloud services, such as AWS and GCP)

[1696] Software: Python, Flask, scikit-learn, JSON files

[1697] Specific examples

[1698] For example, suppose a logistics center manager wants to print 1,000 A4-sized full-color brochures. The manager uses a smartphone to enter the following conditions:

[1699] Example prompt sentence:

[1700] Size: A4, Quantity: 1000, Color: Full color

[1701] This information is sent to a server, which applies machine learning algorithms based on past quote data to generate a rough estimate for each printing company. The resulting comparison table is then sent back to the user's device, allowing the administrator to review it and select the most suitable printing company. This makes the quote request and comparison process fast and efficient.

[1702] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1703] Step 1:

[1704] The user inputs the requirements for the print using a smartphone app. The input information includes, for example, shape, size, quantity, and whether or not to print. The device converts the user-entered requirements into JSON format and sends it to the server.

[1705] Input: Shape, size, quantity, whether printed, etc.

[1706] Output: JSON format data

[1707] Step 2:

[1708] The server analyzes the JSON data received from the user device and extracts the print conditions. Based on these conditions, it generates a query to access past quotation data.

[1709] Input: Print conditions entered by the user (JSON format)

[1710] Output: Conditions of the printed material

[1711] Step 3:

[1712] The server searches past quotation data stored in a database and filters data that matches or is similar to the user's printing requirements. This filtered data is then input into a machine learning algorithm.

[1713] Input: Printing requirements, past quotation data in the database

[1714] Output: Filtered historical quote data

[1715] Step 4:

[1716] The server generates a rough estimate for each printing company based on the filtered estimate data using a machine learning algorithm (e.g., a linear regression model). This algorithm performs data processing (preprocessing) and applies a predictive model to calculate the rough estimate.

[1717] Input: Filtered past quote data, print conditions

[1718] Output: Approximate estimate from each printing company

[1719] Step 5:

[1720] The server creates a comparison table based on the generated rough estimates. This comparison table is formatted in a visually easy-to-understand format, allowing the user to easily compare the estimates from each printing company.

[1721] Input: Approximate estimates from each printing company

[1722] Output: Estimate comparison table

[1723] Step 6:

[1724] The server sends the created comparison table to the user's terminal. The terminal receives the comparison table and displays the data on the user interface. The user can then view the comparison table and select the most suitable printing company.

[1725] Input: Estimate comparison table

[1726] Output: Comparison table displayed on the user's device

[1727] Step 7:

[1728] The user selects the printing company that they think is best based on the comparison table, and the user terminal sends the selection information to the server.

[1729] Input: User selection information

[1730] Output: Selections sent to the server

[1731] Step 8:

[1732] The server generates and sends a formal request for quote message to the selected printing company, including the user's requirements for the printed material and the reasons for their selection.

[1733] Input: User selection information, printing conditions

[1734] Output: Formal RFQ message

[1735] Through the above processing steps, the process of requesting a quote for printed materials at the logistics center can be efficiently executed.

[1736] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1737] The present invention provides a more user-friendly system that streamlines the quote request process for printed materials and takes into account the user's emotional state. This system utilizes past quote data to efficiently generate rough quotes and select the most suitable printing company based on the user's emotional state. The following describes in detail the embodiments of the present invention.

[1738] Server-side processing

[1739] 1. Database Means

[1740] The server has a database that stores past quotation data. This database stores past quotation information from each printing company (printed item shape, size, quantity, whether or not printing is required, price, etc.).

[1741] 2. Estimate generation method

[1742] The server generates a rough estimate for each printing company based on the input print requirements, referencing past estimate data. To do this, the server uses a machine learning algorithm to learn each company's estimate patterns from past data. For example,

[1743] Input requirements: Brochure, A4 size, 1000 copies, full color

[1744] We search past data for quote information with similar conditions and calculate approximate quotes from each printing company.

[1745] 3. Emotion Engine

[1746] The server is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes the user's emotions based on facial expressions, voice, input speed, etc., and feeds the results back to the system.

[1747] 4. Comparison table generation means

[1748] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[1749] 5. Optimizing the presentation order

[1750] The server optimizes the display order of suggested printing companies and quotes based on the user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[1751] 6. Selection and Formal Request for Quotation Methods

[1752] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[1753] Terminal side processing

[1754] 1. Condition input method

[1755] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[1756] 2. Emotion recognition means

[1757] The device detects the user's facial expressions, voice, and operation speed in real time when inputting data, and sends this data to the server. The emotion engine uses this data to analyze the user's emotional state.

[1758] 3. Comparison table display means

[1759] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[1760] User processing

[1761] 1. Enter conditions

[1762] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[1763] 2. Emotion Input

[1764] The user operates the device using natural facial expressions and voice recordings via the camera and microphone provided by the device. For example, by showing signs of impatience or relaxation, the user conveys their emotional state to the system.

[1765] 3. Compare and select

[1766] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[1767] 4. Formal Request for Quotation

[1768] The user then performs an operation to request a formal quotation from the selected printing company.

[1769] Specific examples

[1770] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using their device. The device sends this information to the server, which then searches past quote data based on the input information to generate rough quotes from various companies. The emotion engine also simultaneously analyzes the user's emotional state. If the user appears stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then generates a comparison table and provides it to the device. The user then looks at the comparison table to select the most suitable printing company and submits a formal quote request via their device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[1771] This allows users to quickly and efficiently select a printing company, significantly reducing the effort required for comparing quotes. The emotion engine also takes the user's emotional state into consideration, providing a more comfortable operating environment.

[1772] The processing flow will be explained below.

[1773] Step 1:

[1774] User condition input

[1775] The user opens the application on the terminal and inputs the conditions for the printout (such as shape, size, quantity, whether or not it is printed). For example, they input information such as "Shape: Pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: Full color."

[1776] Step 2:

[1777] User emotion input

[1778] The device uses a built-in camera and microphone to monitor the user's facial expressions, voice, and speed of operation, thereby detecting the user's current emotional state.

[1779] Step 3:

[1780] Sending terminal

[1781] The device converts the printing conditions and emotion data entered by the user into JSON format and sends it to the server. Example: {"Shape": "Pamphlet", "Size": "A4", "Quantity": 1000, "Print": "Full color", "Emotion data": "Stress"}

[1782] Step 4:

[1783] Server Reception

[1784] The server receives the conditions and emotion data for the print material received from the terminal.

[1785] Step 5:

[1786] Database Access

[1787] Based on the received information, the server accesses a database that stores past quotation data.

[1788] Step 6:

[1789] Data Filtering

[1790] The server filters past quotation data that matches the specified conditions. For example, it extracts data that is close to "shape = pamphlet," "size = A4," "print = full color," and "quantity = 1000."

[1791] Step 7:

[1792] Applying machine learning algorithms

[1793] The server uses the filtered past quotation data to apply a machine learning model that has learned the quotation patterns of each printing company, thereby calculating a rough quotation for each company.

[1794] Step 8:

[1795] Generate a rough estimate

[1796] The server generates a rough estimate for each printing company and creates a comparison table. Example: {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}

[1797] Step 9:

[1798] Emotional Data Feedback

[1799] The server analyzes the user's emotional state based on the received emotional data. For example, if the user is feeling stressed, the server displays simpler information that is easier to understand visually.

[1800] Step 10:

[1801] Return to terminal

[1802] The server returns the generated rough estimate comparison table and the optimized presentation order to the terminal in JSON format. Example: {"Estimate Comparison Table": {"Printing Company A": 50000, "Printing Company B": 55000, "Printing Company C": 52000}, "Optimized Order": ["Printing Company A", "Printing Company C", "Printing Company B"]}

[1803] Step 11:

[1804] Comparison table display

[1805] The terminal analyzes the estimated quote comparison table and the optimized presentation order received from the server and displays them to the user, allowing the user to easily compare quote information from each printing company.

[1806] Step 12:

[1807] Choosing a printing company

[1808] The user checks the displayed comparison table and selects the most suitable printing company. For example, the user may determine that printing company A's quote is the cheapest.

[1809] Step 13:

[1810] Formal Request for Quote

[1811] The user then submits a formal quotation request to the selected printing company through the terminal interface.

[1812] Step 14:

[1813] Sending selection information

[1814] The device notifies the server of the user's selection in JSON format. Example: {"Selected printing company": "Printing company A"}

[1815] Step 15:

[1816] Generate a formal request for quotation

[1817] The server generates a formal request for quotation from the selected printing company based on the received information.

[1818] Step 16:

[1819] Send a quote request

[1820] The server then sends a formal request for quotation to the selected printing company via email or API.

[1821] Example 2

[1822] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1823] In conventional print quotation request processes, users must obtain quotes from multiple print providers individually, compare them, and select a suitable one, which is time-consuming and labor-intensive. Furthermore, the process often leaves users feeling stressed because it does not take into account their emotional state. The present invention aims to solve these problems by providing a system that takes into account the user's emotional state while streamlining the quotation request process.

[1824] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1825] In this invention, the server includes a data storage means for accumulating and storing past quotation data, a data receiving means for receiving print requirements entered from a user terminal, a quotation generating means for referencing the past quotation data and generating rough quotation estimates from each print provider based on the print requirements, a sentiment analyzing means for recognizing and analyzing the user's emotional state, a data comparing means for generating a comparison table for comparing the generated rough quotation estimates, a display means for providing the generated comparison table to the user terminal, an order optimizing means for optimizing the display order of suggested print providers and quotation details based on the user's emotional state, a selection means for selecting a final print provider, and a requesting means for making a formal quotation request to the selected print provider. This makes it possible to improve the efficiency of the quotation request process and to propose quotation estimates that take the user's emotional state into consideration.

[1826] "Data storage means" refers to means for accumulating and storing past quotation data.

[1827] The "data receiving means" is a means for receiving the conditions for the print product input from the user terminal.

[1828] The "quote generating means" refers to a means for referencing past quotation data and generating a rough quotation from each print provider based on the conditions of the printed material.

[1829] "Emotion analysis means" refers to means for recognizing and analyzing the emotional state of a user.

[1830] The "data comparison means" is a means for generating a comparison table for comparing the generated rough estimates.

[1831] The "display means" is a means for providing the generated comparison table to a user terminal.

[1832] The "order optimization means" is a means for optimizing the display order of suggested print providers and quotation details based on the emotional state of the user.

[1833] A "selection means" is a means for selecting a final print provider.

[1834] The "request means" is a means for making a formal request for a quote to the selected print provider.

[1835] The present invention provides a system that streamlines the quote request process for print products and also takes into account the emotional state of the user. Specific embodiments are described below.

[1836] Server-side processing

[1837] 1. Data Storage Means

[1838] The server manages a database that stores past quotation data, including quotation information (shape, size, quantity, price, etc.) from each print provider.

[1839] 2. Data Receiving Method

[1840] The server receives the print conditions sent from the user terminal. The conditions include information in JSON format about the shape, size, quantity, and whether or not the print is present.

[1841] 3. Estimate Generation Method

[1842] The server generates a rough estimate from each printing service provider based on the received print requirements and past quotation data. To do this, it uses a machine learning algorithm to learn each company's quotation patterns from past data. For example, if the server receives input requirements such as "brochure, A4 size, 1000 copies, full color," it searches for quotation information with similar requirements and calculates a rough estimate.

[1843] 4. Emotion analysis method

[1844] The server receives data such as the user's facial expressions, voice, and input speed sent from the device, and uses an emotion engine to analyze the user's emotional state, thereby determining whether the user is feeling stressed or relaxed.

[1845] 5. Data Comparison Methods

[1846] The server generates a comparison table based on the estimated quotes from each print provider, and the comparison table is designed to be displayed in a visually easy-to-understand format.

[1847] 6. Display means

[1848] The server sends the generated comparison table to the user's terminal, which displays it. The user can check the comparison table and easily compare the quote information of each print provider.

[1849] 7. Ordinal Optimization Methods

[1850] The server optimizes the display order of suggested print providers and quotes based on the analyzed user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[1851] 8. Selection Method

[1852] Once the user has selected the most suitable print provider, the server receives the selection and issues a formal request for quote to the selected provider.

[1853] 9. Request Method

[1854] The server sends a formal request for quote message to the selected print provider, which can use an email API or the API of a dedicated B2B platform.

[1855] Terminal side processing

[1856] 1. Condition input method

[1857] The terminal provides an interface for users to input requirements for the printed material, such as "pamphlet, A4 size, 1000 copies, full color."

[1858] 2. Emotion recognition means

[1859] The device detects the user's facial expressions, voice, and operation speed in real time and sends this information to the server, where the server's emotion engine analyzes the data.

[1860] 3. Comparison table display means

[1861] The terminal displays the rough estimate comparison table sent from the server to the user, allowing the user to easily compare the estimate information of each print provider.

[1862] User processing

[1863] 1. Enter conditions

[1864] The user uses the terminal interface to input the conditions for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[1865] 2. Emotion Input

[1866] The user can communicate their emotional state to the system by inputting facial expressions and voice using the device. For example, they can communicate their emotional state by showing how anxious or relaxed they are.

[1867] 3. Compare and select

[1868] The user can select the most cost-effective printing provider by looking at the displayed rough estimate comparison table. For example, if the rough estimate for printing company A is 50,000 yen, the estimate for printing company B is 55,000 yen, and the estimate for printing company C is 52,000 yen, the user can select the most suitable provider.

[1869] 4. Formal Request for Quotation

[1870] The user then performs an operation to formally request a quote from the selected print provider.

[1871] Specific examples

[1872] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using a terminal. The terminal sends this information to the server, which searches past quote data to generate rough quotes from various companies. Meanwhile, if the emotion engine analyzes the user's emotional state and detects that the user is feeling stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then provides the terminal with a comparison table, which the user can use to select the most suitable printing provider. The user then requests a formal quote via the terminal. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing procedure.

[1873] Example prompts for generative AI models

[1874] "I'd like to print 1,000 full-color A4 brochures. Please generate rough quotes from the best print providers, taking into account past quote information and my current user sentiment, and display an easy-to-understand comparison table."

[1875] Based on this prompt, the generative AI model can understand the system's operation in detail and specify each processing step.

[1876] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1877] Step 1:

[1878] Conditions entered by the user

[1879] Processing content: The user uses the terminal interface to input the conditions for the print.

[1880] Specific operation: The user inputs information such as "pamphlet, A4 size, 1000 copies, full color" into the input interface.

[1881] Input: The print condition entered by the user.

[1882] Output: The print conditions are saved on the device.

[1883] Step 2:

[1884] Sending condition data to the server

[1885] Processing content: The terminal sends the input data in JSON format to the server.

[1886] Specific operation: The terminal formats the condition data entered by the user and sends it as an HTTP request.

[1887] Input: The print condition entered by the user.

[1888] Output: The condition data is sent to the server.

[1889] Step 3:

[1890] Server-based quotation data search and rough quotation generation

[1891] Processing details: The server searches the past estimate database based on the received conditions, extracts appropriate data, and generates a rough estimate.

[1892] What it does: The server uses SQL queries to retrieve applicable quote data from the database and calculates the price using machine learning models.

[1893] Input: Condition data and past quote data.

[1894] Output: Approximate quotes from each print provider.

[1895] Step 4:

[1896] Sending emotional data to the server and analyzing it

[1897] Processing details: The device collects user emotion data in real time and sends it to the server, which analyzes it.

[1898] Specific operation: The device collects data from the camera and microphone, calls the emotion analysis API, and sends the results to the server. The server then integrates the received data and analyzes the emotional state.

[1899] Input: Emotional data such as the user's facial expression, voice, and operation speed.

[1900] Output: Analysis of the user's emotional state.

[1901] Step 5:

[1902] Server-generated comparison table

[1903] Processing details: The server generates a visually easy-to-understand comparison table based on the analysis results and quotation data.

[1904] Specific operation: The server generates graphs and table-format comparison tables using HTML and JavaScript, and sends the generated HTML data to the terminal.

[1905] Input: Rough estimates from each printing provider and the analysis of the user's emotional state.

[1906] Output: A comparison table in a visually friendly format.

[1907] Step 6:

[1908] Providing comparison tables to users

[1909] Processing content: The terminal displays the received comparison table to the user.

[1910] What it does: The device browser renders the HTML data, allowing the user to view the comparison table directly.

[1911] Input: Comparison table sent from the server.

[1912] Output: A user-visible comparison table.

[1913] Step 7:

[1914] User selection of print provider

[1915] Process: The user looks at the comparison table and selects the best print provider.

[1916] Specific operations: The user selects a print provider on the terminal interface, confirms the selected information, and presses the send button.

[1917] Enter: comparison chart.

[1918] Output: Information about the selected print provider.

[1919] Step 8:

[1920] Sending selected data to the server

[1921] Processing content: The terminal transmits the selection information to the server.

[1922] Specific operation: The terminal formats the data of the selected print provider and sends it to the server in an HTTP request.

[1923] Enter: Selected print provider information.

[1924] Output: The selected data is sent to the server.

[1925] Step 9:

[1926] Formal request for quotation by the server

[1927] What happens: The server receives the selection information and sends a formal request for quote message to the appropriate print provider.

[1928] Specific operation: The server uses an email API or the API of a dedicated B2B platform to send a quote request to the selected print provider.

[1929] Input:Selection data.

[1930] Output: A formal request for quote message to your print provider.

[1931] (Application example 2)

[1932] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1933] The conventional process of requesting quotes for printed materials requires users to manually compare quotes from multiple printing companies, which is extremely time-consuming and labor-intensive. Furthermore, because the system does not take into account the user's emotional state, it is considered difficult to use for users who are stressed or anxious. There is a need for a system that solves these problems and allows users to request quotes for printed materials efficiently and comfortably.

[1934] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes database means for accumulating and storing past quotation data, means for receiving input print product requirements, means for referencing the past quotation data and generating rough quotation estimates from each printing company based on the print product requirements, means for generating a comparison table for comparing the generated rough quotation estimates, means for providing the generated comparison table to the user terminal, means for selecting a final printing company, means for requesting a formal quotation from the selected printing company, emotion engine means for analyzing the user's emotional state, and means for optimizing the display order of the quotation contents based on the analyzed emotional state. This enables the user to efficiently proceed with the quotation request process and provides a comfortable operating environment that suits the user's emotional state.

[1935] "Past quotation data" refers to data that stores quotation information for print requests that have been previously made.

[1936] "Database means" refers to a system that efficiently stores past quotation data and allows it to be searched and retrieved as needed.

[1937] The "input conditions for the printed matter" refers to information such as the specifications and quantity of the printed matter provided by the user when requesting a quote.

[1938] A "rough estimate" is a rough price estimate provided by each printing company, calculated based on the conditions of the printed material entered.

[1939] An "emotion engine" is a system that recognizes and analyzes a user's emotional state based on facial expressions, voice, operation speed, etc.

[1940] A "comparison table" is a table format that arranges multiple rough estimates in a visually easy-to-understand manner, allowing users to easily compare them.

[1941] A "formal quote request" is the official price confirmation and purchase request that the user sends to the printing company that they have ultimately selected.

[1942] The "means for optimizing the display order of quote contents" refers to a system for rearranging quote contents into an order that is most understandable to the user based on the user's emotional state.

[1943] This invention is a system that streamlines the process of requesting quotes for printed materials in brick-and-mortar stores and provides a comfortable operating environment by taking into account the emotional state of the user. This system operates in cooperation with a server, terminals, and users.

[1944] Server-side processing

[1945] Database Means

[1946] The server has a database that accumulates and stores past quotation data. The database stores past quotation information from each printing company (printed material shape, size, quantity, whether or not printing is required, price, etc.). This data is used to generate a rough quotation based on the conditions of the printed material.

[1947] Estimate generation method

[1948] The server references past quotation data based on the input printing requirements and generates a rough estimate for each printing company. To do this, the server uses a machine learning algorithm to learn each company's quotation patterns from past data. For example, it searches for past quotation information with similar conditions to the input requirements, such as "shape: pamphlet," "size: A4," "quantity: 1,000 copies," and "printing: full color," and calculates a rough estimate for each printing company.

[1949] Emotion Engine Means

[1950] The server is equipped with an emotion engine that recognizes the user's emotional state. The emotion engine analyzes the user's emotions based on their facial expressions, voice, input speed, etc., and feeds the results back to the system. The analysis results are used in the display order optimization means described below.

[1951] Comparison table generation means

[1952] The server generates a comparison table based on the estimated quotes from each printing company, and displays the comparison table in a visually easy-to-understand format so that the user can easily compare prices.

[1953] Optimizing the presentation order

[1954] The server optimizes the display order of suggested printing companies and quotes based on the user's emotional state. For example, if the user is feeling stressed, the server prioritizes displaying concise and easy-to-understand information.

[1955] Formal request for quotation

[1956] The server then sends a formal quotation request to the printing company that the user has selected from the comparison table. To this end, the server receives the selection information and sends a formal quotation request message to the printing company.

[1957] Terminal side processing

[1958] Condition input method

[1959] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not it is printed, etc.) The input information is sent to the server in JSON format.

[1960] emotion recognition means

[1961] The device detects the user's facial expressions, voice, and operation speed in real time when inputting data, and sends this data to the server. The emotion engine uses this data to analyze the user's emotional state.

[1962] Comparison table display means

[1963] The terminal displays the rough estimate comparison table received from the server to the user, allowing the user to easily compare estimates from different printing companies.

[1964] User processing

[1965] Condition input

[1966] The user uses the terminal interface to input the requirements for the printed material, such as "Form: pamphlet," "Size: A4," "Quantity: 1000 copies," and "Printing: full color."

[1967] Emotion input

[1968] The user operates the device using natural facial expressions and voice recordings via the camera and microphone provided by the device. For example, by showing signs of impatience or relaxation, the user conveys their emotional state to the system.

[1969] Compare and select

[1970] The user looks at a rough estimate comparison table displayed on the terminal and selects the most cost-effective printing company. For example, if the rough estimate from printing company A is 50,000 yen, the estimate from printing company B is 55,000 yen, and the estimate from printing company C is 52,000 yen, the user will select printing company A.

[1971] Formal Request for Quote

[1972] The user then performs an operation to request a formal quotation from the selected printing company.

[1973] Specific examples

[1974] For example, suppose a user wants to print 1,000 copies of an A4-sized full-color brochure. The user enters this information using their device. The device sends this information to the server, which then searches past quote data based on the input information to generate rough quotes from various companies. The emotion engine also simultaneously analyzes the user's emotional state. If the user appears stressed, the server will simplify the quote and present it in an easy-to-understand manner. The server then generates a comparison table and provides it to the device. The user then looks at the comparison table to select the most suitable printing company and submits a formal quote request via their device. For example, if printing company A is the cheapest, the user can request a formal quote from that company and proceed with the printing process.

[1975] Prompt Sentence Examples

[1976] Print requirements:

[1977] Format: Brochure

[1978] Size: A4

[1979] Quantity: 1000 copies

[1980] Printing: Full color

[1981] Optimizing presentation order:

[1982] Because users are stressed, we simplify the information and display the cheapest quotes first.

[1983] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1984] Step 1:

[1985] The terminal provides an interface where the user can input the conditions for the print (shape, size, quantity, whether or not to print, etc.). The condition information entered by the user is converted into JSON format and sent to the server. At this stage, the input data are the conditions for the print, and the output data is the condition information in JSON format.

[1986] Step 2:

[1987] The server uses the received JSON-formatted print condition information to reference past quotation data from a database. It filters the past quotation data for data that most closely matches the relevant conditions and uses a machine learning algorithm to generate a rough quotation for each printing company. At this stage, the input data is the JSON-formatted print condition information, and the output data is the rough quotation information for each printing company.

[1988] Step 3:

[1989] The server converts the generated rough estimate information from each printing company into a comparison table. This comparison table visually lists the estimated prices from each company, allowing users to easily compare them. At this stage, the input data is the rough estimate information, and the output data is in the form of a comparison table.

[1990] Step 4:

[1991] The terminal displays the comparison table received from the server to the user. The user can compare the prices quoted by each printing company by looking at this comparison table. At this stage, the input data is data in the form of a comparison table, and the output data is visual information for the user.

[1992] Step 5:

[1993] The device detects data such as the user's facial expressions, voice, and operation speed in real time and sends this data to the server. The server then uses an emotion engine to analyze the user's emotional state based on the received data. At this stage, the input data is the user's emotional state data, and the output data is the analyzed emotional state.

[1994] Step 6:

[1995] The server optimizes the display order of the quote contents based on the analyzed emotional state of the user. For example, if the user is feeling stressed, the server adjusts the order to simplify the quote information and display it in an easy-to-understand manner. The input data at this stage is the analyzed emotional state, and the output data is the quote information in the optimized display order.

[1996] Step 7:

[1997] The user looks at the comparison table and selects the most cost-effective printing company. The information of the printing company selected by the user is sent from the terminal to the server. At this stage, the input data is the user's selection information, and the output data is the information of the selected printing company.

[1998] Step 8:

[1999] The server then sends a formal quotation request to the printing company selected by the user. The server receives the selection information and sends a formal quotation request message to the printing company. At this stage, the input data is the information of the selected printing company, and the output data is the formal quotation request message.

[2000] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2001] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2002] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2003] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2004] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2005] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2006] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2007] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2008] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2009] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2010] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general inf...

Claims

1. a database means for accumulating and storing past estimate data; means for receiving input print product conditions; A means for generating a rough estimate for each printing company based on the conditions of the printed matter by referring to past estimate data; means for generating a comparison table for comparing the generated estimates; means for providing the generated comparison table to a user terminal; How to select the final printing company and A means of submitting a formal request for quotation to the selected printing company; A system including:

2. The system according to claim 1 , further comprising means for filtering information corresponding to specific printing conditions from past quotation data and applying a machine learning algorithm based on the relevant data.

3. The system according to claim 1, further comprising means for accepting input of print conditions from a user terminal through an interface and transmitting the conditions to a server in JSON format.

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