system

Abstract

We provide the system. [Solution] A means of providing an interface for accepting conditions provided by the user, Means for collecting the information necessary for the calculation from various databases and systems, A means of performing calculations using artificial intelligence based on collected information and user input, A method for automatically generating proposal documents based on the calculation results, A means of providing the generated materials to an information terminal, A means for generating proposal documents to suggest the optimal settings based on transaction conditions, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0006] An "interface" is a general term for software and hardware that provide a means for users to input conditions and data into a system.

[0007] A "database" is a collection of digital information that systematically stores the information necessary for calculations, making it searchable and retrievable.

[0008] Artificial intelligence is a system that mimics the intellectual processes performed by humans and automatically solves problems and makes decisions based on data.

[0009] "Calculation" is the process of performing calculations based on given conditions to derive predicted prices and other numerical results.

[0010] "Proposal materials" refer to documents and presentation files used in sales activities that are automatically generated based on the results of calculations.

[0011] A "user" is an individual or group that operates the system and inputs the necessary conditions and data. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention is a system that streamlines the calculation of estimates in sales activities and enables the rapid generation of proposal materials. Based on conditions provided by the user, this system automatically performs estimates and generates and supplies proposal materials.

[0034] First, the user inputs the calculation conditions through the interface. Specifically, they provide the system with elements such as product category, quantity, and delivery date. This transmits the basic information necessary for the calculation to the system.

[0035] Next, the server collects relevant information from internal and external databases based on the received input conditions. This includes sales history databases, inventory management systems, and data sources showing market trends. This information is used to form the basis of the data necessary for the calculations.

[0036] Subsequently, the server uses artificial intelligence to analyze the collected information and user input conditions to simulate optimal pricing and delivery times. The AI ​​utilizes historical data and algorithms to perform efficient and accurate calculations.

[0037] Once the calculations are complete, the server automatically generates proposal materials based on the results. These materials include a summary of the calculations, relevant graphs, and are formatted for presentations. This allows sales representatives to quickly obtain high-quality materials.

[0038] Finally, the generated materials are delivered from the server to the user's terminal. Users can download the materials and use them in their sales activities. This significantly streamlines the process from cost estimation to proposal, and is expected to improve competitiveness.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user accesses the interface and enters the conditions necessary for the calculation. Specifically, they enter information such as product category, quantity, and desired delivery date into the form and press the "Submit" button, at which point all the entered data is sent to the server.

[0042] Step 2:

[0043] The server activates a data aggregation module based on the input conditions received from the user. The data aggregation module accesses the sales database, inventory management system, and external market data sources to collect the necessary information. This gathers the relevant data for use in calculations.

[0044] Step 3:

[0045] The server activates the AI ​​calculation module and automatically performs calculations based on the collected information and user input conditions. The AI ​​uses machine learning algorithms to calculate optimal pricing and delivery date adjustments, and improves prediction accuracy by considering past data and market trends.

[0046] Step 4:

[0047] The server uses a results generation module to automatically generate proposal documents based on AI-generated calculations. These documents include a summary of the calculations, pricing information, and graphs related to delivery times. The visually organized information is then created in PowerPoint format for presentation purposes.

[0048] Step 5:

[0049] The server provides the generated materials to the user's terminal via the output module. The user receives a download link for the materials via the interface or receives access information for the materials via email, allowing them to download and review the materials.

[0050] (Example 1)

[0051] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0052] Preparing cost estimates for sales activities is time-consuming and labor-intensive, making it difficult to provide timely and accurate proposal materials. Traditional methods often involve manual data collection, analysis, and document creation, resulting in inefficiency. Therefore, there is a need for a system that automates these processes, enabling the rapid and accurate generation of cost estimates and proposal materials.

[0053] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0054] In this invention, the server includes information receiving means for inputting conditions provided by the user, data collection means for obtaining data necessary for calculations from external information sources and databases, and analysis means for performing calculations using a generated AI model based on the collected data and the user's input conditions. This enables the user to perform calculations efficiently and generate high-quality proposal materials quickly.

[0055] "Information receiving means" refers to a means for inputting conditions provided by the user and transmitting them to the system, and includes the user interface.

[0056] "Data collection methods" refer to means of obtaining data necessary for calculations from external sources or databases, and include technologies for collecting information using APIs and data queries.

[0057] A "generative AI model" is an artificial intelligence model that learns from past data and patterns to perform calculations and analyses based on user conditions.

[0058] "Analysis means" refers to methods for performing calculations based on collected data and user input conditions, and includes techniques for deriving optimal results using a generative AI model.

[0059] "Document generation means" refers to means for automatically creating reports based on calculation results, and includes technology for formatting documents in a visually-oriented format.

[0060] "Data provision means" refers to means for providing the generated report to the user's information device, and includes communication technology for securely and quickly transferring data.

[0061] This invention is a system for automating the creation of cost estimates in sales activities. First, the user accesses the interface using a terminal and inputs the conditions necessary for the estimate. The interface provides forms for inputting conditions such as product category, quantity, and delivery date.

[0062] The conditions entered by the user are sent from the terminal to the server. The server receives these conditions using information receiving means and uses them as the starting point for analysis. At this time, the server uses data collection means to gather the necessary data and issues queries to external information sources and multiple databases. These databases include sales history, inventory information, and market trend information.

[0063] Next, the collected data is analyzed by a generative AI model. The generative AI model learns from past data and patterns and automatically performs calculations based on the given conditions. The AI ​​utilizes specific algorithms to perform efficient and accurate pricing and delivery time simulations.

[0064] Subsequently, based on the analyzed results, the server automatically generates a proposal document using a document generation mechanism. This document includes visual charts and graphs that summarize the analysis results and related data. The document is created in formats such as PDF and PPTX, and is ready for immediate use in presentations.

[0065] Finally, the generated proposal documents are sent to the user's terminal using a data delivery method. The user downloads the documents and uses them in actual sales activities. The documents are delivered via a secure connection, ensuring the confidentiality and security of the information.

[0066] As a concrete example, when a user is planning to sell a new product, they might prompt the system with instructions such as, "Please provide data to calculate the optimal price and delivery date for the electronics sales plan. Conditions: Product category 'Electronics', Quantity '500', Delivery date '30 days'." This prompt allows the system to collect and analyze the necessary information and quickly provide accurate calculation results.

[0067] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0068] Step 1:

[0069] The user accesses the interface using a terminal and enters the conditions necessary for the calculation. Specifically, they enter the product category, quantity, and delivery date, and click the "Submit" button through the input form. This operation sends the condition information from the terminal to the server. The input consists of the user's business requirements and conditions, and the output is the input data transferred to the server.

[0070] Step 2:

[0071] The server receives data sent from the user using an information receiving device. Based on the received data, it decides which information to collect. The server uses a data collection device to obtain relevant sales history, inventory information, and market trend information. Data acquisition is performed via APIs or database queries. The input is conditional information from the user, and the output is information from the relevant database.

[0072] Step 3:

[0073] The server inputs the collected data into a generating AI model and begins the analysis. The AI ​​model uses historical data to efficiently perform calculations. The AI ​​performs calculations using multivariate analysis and predictive algorithms. The input is the collected data, and the output is the calculation results. Specifically, the AI ​​performs optimal pricing and delivery time simulations.

[0074] Step 4:

[0075] Based on the calculation results, the server automatically generates proposal materials using a document generation system. These materials include a summary of the calculation results and visually displayed graphs. The materials are generated in PDF or PPTX format and processed into a format suitable for sales activities. The input is the calculation results, and the output is a document file usable by the user.

[0076] Step 5:

[0077] The server sends the generated proposal document to the user's terminal via a data delivery system. The document is transferred via a secure connection, and the user can download and use the document on their terminal. The input is the generated document file, and the output is the document that the user can access. Specifically, the user clicks the "Download" button to obtain the document.

[0078] (Application Example 1)

[0079] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0080] In sales activities, it is necessary to quickly and accurately perform calculations based on transaction terms and create optimal proposal materials for customers based on the results. However, with conventional methods, these calculations and material creation require a great deal of time and effort, making it difficult to streamline sales activities. Furthermore, they lacked the flexibility to make proposals that could adapt to the dynamic market environment.

[0081] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0082] In this invention, the server includes means for providing an interface for accepting conditions provided by the user, means for collecting information necessary for calculations from various databases and systems, and means for performing calculations using artificial intelligence based on the collected information and user input. This enables rapid and precise calculations based on transaction conditions and automatic generation of optimal proposal materials adapted to the dynamically changing market environment.

[0083] "Means of providing an interface for accepting conditions provided by the user" refers to elements that enable users to directly provide information by providing a graphical user interface on the device for inputting transaction conditions and requirements.

[0084] "Means for collecting information necessary for estimation from various databases and systems" refers to a device that has a process for accumulating information related to transactions and markets from internal and external data storage systems and constructing the basic data for estimation.

[0085] "Methods for performing calculations using artificial intelligence based on collected information and user input" refers to methods that use artificial intelligence technology to analyze user input conditions and collected data, and systematically calculate the optimal price and service conditions.

[0086] "Method for automatically generating proposal materials" refers to a device that automatically creates sales materials based on calculation results, visualizes those materials, and structures them so that they can be presented to users.

[0087] "Means of providing generated materials to information terminals" refers to technical means that transfer the created sales proposal materials to the user's digital device, enabling direct use.

[0088] "A means of generating proposal documents to suggest the optimal settings based on transaction conditions" refers to a system that automatically constructs proposal documents that reflect the optimal commission rates and services provided by the user, according to the transaction conditions they have provided.

[0089] This invention provides a system that enables the rapid and accurate generation of calculations and proposal materials based on transaction conditions in sales activities. The entire system consists of user terminals, a central server, and a database.

[0090] Users first input transaction terms through an interface provided on a device such as a smartphone or tablet. This interface is built as a user-friendly graphical user interface (GUI) and is implemented using React Native technology. The input terms include transaction amount, contract period, industry, etc.

[0091] The entered conditions are sent via the internet to a central cloud server. The server uses Python scripts to collect relevant data from database systems (e.g., AWS® RDS or MongoDB). Based on the collected data, an AI model (using TENSORFLOW®) determines the optimal fee settings and profit forecasts for the trading conditions.

[0092] The server creates a proposal document in Markdown format based on the calculation results obtained by the generating AI model. This document will include not only the calculated figures but also graphs and charts that are easy to understand visually. Finally, the generated proposal document will be converted to HTML format and provided to the user's device for download.

[0093] As a concrete example, consider a scenario where a sales representative wants to propose annual transaction terms to a new retail customer. The representative opens the application and enters the following conditions: "Transaction amount: 5 million yen, contract period: 1 year, industry: retail." The system quickly processes these conditions and generates a proposal document with the optimal commission rate based on past data.

[0094] An example of a prompt message would be: "Please suggest the optimal fee structure based on past transaction data. The conditions are as follows: transaction amount of 5 million yen, contract period of 1 year, industry is retail."

[0095] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0096] Step 1:

[0097] The user launches the application on their device and enters transaction terms through the interface. Specifically, they enter information such as the transaction amount, contract period, and industry, which is then converted into JSON format within the application. The entered information is formatted for data transmission from the user interface and sent to the server via the network.

[0098] Step 2:

[0099] The server parses the received transaction conditions in JSON format. Based on the parsed transaction conditions, it sends queries to a database that stores the necessary historical transaction data. A Python script is used here to issue SQL and NoSQL queries to databases such as AWS RDS and MongoDB. The data extracted from the database is returned to the server.

[0100] Step 3:

[0101] The server performs data analysis using an AI model based on the return values ​​from the received database and the input transaction conditions. A generative AI model built using TensorFlow plays a major role in this process. The AI ​​model calculates the optimal fee settings and terms of service based on past transaction data and outputs the simulation results.

[0102] Step 4:

[0103] The server receives the analysis results from the AI ​​model and automatically generates proposal documents based on them. The proposal documents convert the numerical data from the generated AI model into Markdown format and add graphs and charts as visual elements as needed. This formats the information in a way that is easy for the user to understand.

[0104] Step 5:

[0105] The server converts the generated proposal document into HTML format and prepares it for transmission to the user's device. Finally, the document is provided to the user's device as a download link. The user receives a notification and can view and utilize the proposal document from their device.

[0106] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0107] This invention is a system that automates calculations based on user-provided conditions and combines these calculations with an emotion engine to enable more effective sales proposals. This system identifies the user's emotions and generates proposal materials accordingly.

[0108] First, the user enters the conditions for the calculation via the terminal interface. These conditions include product information, quantity, and desired delivery date. The interface sends the user's input to the emotion engine and simultaneously transmits the condition data to the server.

[0109] The server collects relevant information to perform calculations based on user input and past data. During this process, an emotion engine is activated to analyze the emotions and context embedded in the user's input. This analysis is then used to generate more personalized calculations.

[0110] Next, the server uses artificial intelligence to calculate a streamlined price proposal and supply conditions. Simultaneously, it adjusts the calculation results to reflect the analysis results of the emotion engine, deriving the optimal proposal.

[0111] Subsequently, the server automatically creates a proposal document based on the generated calculation results. The tone and content of the document are optimized for the user, taking into account the output of the emotion engine. The document incorporates emotion-based language and expressions that are more likely to resonate with the user.

[0112] Finally, the server provides the proposal materials to the user's terminal. These materials are provided in a downloadable format and can be immediately used in sales proposals. This system enables proposals based on deep insights that take into account the user's emotions, making it a powerful tool for strengthening customer relationships.

[0113] The following describes the processing flow.

[0114] Step 1:

[0115] The user accesses the terminal interface and enters the conditions necessary for the calculation. Specifically, they provide details such as the type of product, quantity, and desired delivery date. Along with these conditions, the interface transmits the user's facial expressions and tone of voice to the emotion engine, acquiring emotional data as well.

[0116] Step 2:

[0117] The server activates the emotion engine and analyzes the user's emotions and reactions during input. The emotion engine performs voice analysis and text mining to evaluate the user's emotions. This evaluation result is reflected in subsequent calculations.

[0118] Step 3:

[0119] The server uses a data aggregation module to collect information from various databases based on user input. This includes inventory information, past sales history, market trends, and other data, preparing it for calculations.

[0120] Step 4:

[0121] The server uses artificial intelligence to perform calculations and determine the optimal pricing and terms of service. During this process, it considers the results of an emotion engine analysis and adjusts the suggestions to match the user's emotions. For example, if a risk-averse emotion is detected, more stable suggestions will be offered.

[0122] Step 5:

[0123] The server automatically generates proposal documents based on the calculation results using a results generation module. These proposal documents reflect insights gained from sentiment analysis and present information in a format that resonates with users. In addition to graphs and charts, they include emotionally appropriate wording and recommendations.

[0124] Step 6:

[0125] The server provides the generated proposal materials to the user's terminal. The user can download the materials from their terminal and use them immediately for sales activities. At this time, the materials are customized to take emotions into consideration, enabling proposals that meet the user's expectations.

[0126] (Example 2)

[0127] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0128] Traditional sales proposal systems could perform calculations based on user conditions, but they struggled to automatically create personalized proposals that took user emotions into account. As a result, the quality of proposals tended to be uniform, and there was a problem in not being able to gain sufficient empathy to deepen relationships with customers.

[0129] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0130] In this invention, the server includes means for providing an input / output device for receiving conditions provided by the user, means for collecting data necessary for calculations from various storage devices and management devices, and means for analyzing the user's emotions using an emotion analysis device. This enables the automatic creation of proposal materials that take the user's emotions into consideration, and facilitates effective sales proposals that strengthen relationships with customers.

[0131] An "input / output device" is a device that receives conditional information from a user and transmits it to a server.

[0132] A "storage device" is a device that stores and manages the data necessary for calculations and makes that data accessible to a server.

[0133] A "management device" is a device designed to efficiently collect and manipulate data.

[0134] An "emotion analysis device" is a device that analyzes user input data to determine emotions and their context.

[0135] "Generated intelligence" refers to artificial intelligence technology used to perform calculations and create proposals based on large amounts of data.

[0136] A "proposal document" is a sales proposal document that is automatically generated based on the results of calculations and sentiment analysis.

[0137] This system's program automates calculations based on user-provided conditions and personalizes sales proposals by combining them with an emotion engine. Users access the interface using a terminal and input conditions for calculations. The terminal receives this information, sends it to the server as input, and simultaneously passes the data to the emotion analysis device.

[0138] The server collects data from storage and management devices based on the input conditions. This allows the server to efficiently gather the information necessary for calculations. Based on the collected data, the server utilizes a generative AI model to calculate reasonable pricing and terms of service. Furthermore, the server uses an emotion analysis device to analyze the user's emotions, reflects the results in the calculations, and adjusts the content of the proposal.

[0139] For example, suppose a user is considering purchasing a new computer. The user inputs their desired specifications, budget, and delivery date via a terminal. The server collects market data based on these conditions and analyzes the user's emotions (e.g., their emphasis on price). Based on these results, the server generates a proposal for a cost-effective product. The generated proposal includes emotionally resonant language and is provided to the user in an immediately downloadable format.

[0140] This system ensures that proposals take customer emotions into consideration, enabling higher-quality sales proposals. As a result, customer relationships are strengthened, and the effectiveness of sales activities is enhanced.

[0141] Example of a prompt

[0142] "Based on the user's input, please propose new PC models. If the user is concerned about price, prioritize suggesting models with high cost-performance."

[0143] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0144] Step 1:

[0145] The user inputs the conditions for the calculation through the terminal's interface. For example, product information, quantity, and desired delivery date are input items. The terminal transmits this information to the sentiment analysis device and also transfers the condition data to the server. This allows the server to receive the initial data input.

[0146] Step 2:

[0147] The server collects necessary data from relevant storage and management devices based on the received conditional data. This data is essential for performing quick and accurate calculations. As a result of the data collection, important information such as inventory status and market prices is output.

[0148] Step 3:

[0149] The server analyzes the user's emotions and intentions based on conditional data received using an emotion analysis device. A generative AI model assists this process, performing deep analysis of the user's text input and making emotional judgments such as positive or negative. The analysis results are used in the next stage of calculation.

[0150] Step 4:

[0151] The server uses a generative AI model based on collected data and sentiment analysis results to perform calculations. These calculations determine reasonable pricing and supply conditions. The output is a realistic and user-friendly price and condition proposal.

[0152] Step 5:

[0153] The server automatically generates a proposal, taking into account the calculation results and sentiment analysis. This proposal's tone is adjusted to match the user's emotions, and the document generation technology using a generation AI model incorporates expressions that resonate with the user.

[0154] Step 6:

[0155] The server transfers the completed proposal to the terminal. The terminal then provides the proposal in a format that the user can review and download. This allows the user to immediately utilize the proposal in their sales activities.

[0156] (Application Example 2)

[0157] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0158] In modern e-commerce, providing consumers with the most suitable product recommendations is crucial for attracting their interest and increasing their purchase intent. However, existing systems struggle to efficiently provide personalized recommendations that incorporate consumer emotions. Therefore, there is a need for a system that analyzes consumer input conditions and emotions in real time and provides optimized recommendation materials based on that analysis.

[0159] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0160] In this invention, the server includes means for providing an output unit for receiving criteria provided by the user, means for acquiring information necessary for calculations from various information infrastructures and systems, and means for performing calculations using machine learning based on the acquired information and user input. This makes it possible to provide personalized product suggestions in real time based on the user's emotions and past behavior.

[0161] A "user" is a specific individual or organization that uses the system to input conditions and receive suggestions.

[0162] "Criteria" refers to the conditions and requirements related to the calculations and proposals provided by the user.

[0163] The term "output section" refers to the general term for interfaces and devices that users use to input references.

[0164] "Estimate" refers to the evaluation results of prices, supply conditions, etc., calculated based on the input criteria.

[0165] "Information infrastructure" refers to all electronic databases and systems that store and provide the information necessary for calculations.

[0166] "Machine learning" is a method that learns patterns from past data and uses new data to perform inferences and predictions.

[0167] "Emotion recognition output" refers to data obtained by analyzing and identifying the user's emotions.

[0168] A "proposal document" is a collection of information presented to consumers, automatically generated based on calculation results and emotion recognition output.

[0169] "Two-dimensional and three-dimensional representations" refer to formats for representing graphical visual information included in proposal documents, and serve to visually supplement the information.

[0170] "Communication terminal" refers to all electronic devices used by users to receive proposal documents.

[0171] The system for realizing this invention is designed to analyze user input and their emotions, and to provide consumers with the most suitable product recommendations. Users input purchasing criteria through a communication terminal interface. These criteria include product category, price range, and desired delivery date. Once data is entered, the terminal transmits it to an information infrastructure and prepares to retrieve the necessary information.

[0172] The server uses machine learning models to perform calculations based on collected information and user input. Platforms such as AWS SageMaker can be used for this purpose. Furthermore, technologies such as IBM Watson® are used as emotion recognition engines to interpret user emotions and intentions.

[0173] By combining the acquired calculation results and emotion recognition output, the server automatically generates a proposal document. This document includes two-dimensional and three-dimensional visual information and is graphically represented, making it easy for users to understand. The generated document is immediately transmitted to the communication terminal and presented to the user.

[0174] As a concrete example, suppose a user is searching for "new sports shoes." Based on their past purchase history and reviews, it is analyzed that they are interested in sporty design and durability. Based on this information, the server suggests shoes that match these attributes and creates materials that include catchy phrases such as "Innovative design to make everyday running comfortable."

[0175] Examples of prompts for a generative AI model:

[0176] "The user is looking for new sports shoes. Based on their past reviews, they have a strong interest in sporty design and durability. Please generate personalized product suggestions and compelling taglines."

[0177] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0178] Step 1:

[0179] The user inputs criteria such as product category, price range, and desired delivery date through the interface of a communication terminal. The terminal's role is to transmit this information as input data to the server. The output here is the input criteria information.

[0180] Step 2:

[0181] The server retrieves relevant data from the information infrastructure based on the received reference information. This data includes product inventory, pricing information, and past transaction history. Using the retrieved data as input, the server filters the necessary information and prepares it for calculation. The output is the filtered product data.

[0182] Step 3:

[0183] The server utilizes machine learning models to perform calculations based on filtered product data and user criteria information. In this process, machine learning models (e.g., AWS SageMaker) are used to calculate pricing and optimal supply conditions. The input to the process is filtered product data, and the output is the calculation result.

[0184] Step 4:

[0185] Simultaneously, the server uses an emotion recognition engine to analyze emotions from user input. This utilizes an implementation for emotion recognition (e.g., IBM Watson). The input is the user's baseline information, and the output is the result of emotion recognition.

[0186] Step 5:

[0187] The server integrates the calculation results and emotion recognition results to automatically generate proposal materials. The materials integrate two-dimensional and three-dimensional visual information and add promotional text and taglines that resonate with the user's emotions. A generation AI model is used to create wording optimized for the user's interests and emotions. The output is the generated proposal material.

[0188] Step 6:

[0189] The server sends the generated proposal document to the communication terminal and displays it to the user. This document is provided directly to the user and used for purchasing decisions. The output is the proposal document provided to the user.

[0190] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0191] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0192] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0193] [Second Embodiment]

[0194] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0195] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0196] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0197] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0198] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0199] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0200] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0201] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0202] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0203] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0204] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0205] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0206] This invention is a system that streamlines the calculation of estimates in sales activities and enables the rapid generation of proposal materials. Based on conditions provided by the user, this system automatically performs estimates and generates and supplies proposal materials.

[0207] First, the user inputs the calculation conditions through the interface. Specifically, they provide the system with elements such as product category, quantity, and delivery date. This transmits the basic information necessary for the calculation to the system.

[0208] Next, the server collects relevant information from internal and external databases based on the received input conditions. This includes sales history databases, inventory management systems, and data sources showing market trends. This information is used to form the basis of the data necessary for the calculations.

[0209] Subsequently, the server uses artificial intelligence to analyze the collected information and user input conditions to simulate optimal pricing and delivery times. The AI ​​utilizes historical data and algorithms to perform efficient and accurate calculations.

[0210] Once the calculations are complete, the server automatically generates proposal materials based on the results. These materials include a summary of the calculations, relevant graphs, and are formatted for presentations. This allows sales representatives to quickly obtain high-quality materials.

[0211] Finally, the generated materials are delivered from the server to the user's terminal. Users can download the materials and use them in their sales activities. This significantly streamlines the process from cost estimation to proposal, and is expected to improve competitiveness.

[0212] The following describes the processing flow.

[0213] Step 1:

[0214] The user accesses the interface and enters the conditions necessary for the calculation. Specifically, they enter information such as product category, quantity, and desired delivery date into the form and press the "Submit" button, at which point all the entered data is sent to the server.

[0215] Step 2:

[0216] The server activates a data aggregation module based on the input conditions received from the user. The data aggregation module accesses the sales database, inventory management system, and external market data sources to collect the necessary information. This gathers the relevant data for use in calculations.

[0217] Step 3:

[0218] The server activates the AI ​​calculation module and automatically performs calculations based on the collected information and user input conditions. The AI ​​uses machine learning algorithms to calculate optimal pricing and delivery date adjustments, and improves prediction accuracy by considering past data and market trends.

[0219] Step 4:

[0220] The server uses a results generation module to automatically generate proposal documents based on AI-generated calculations. These documents include a summary of the calculations, pricing information, and graphs related to delivery times. The visually organized information is then created in PowerPoint format for presentation purposes.

[0221] Step 5:

[0222] The server provides the generated materials to the user's terminal via the output module. The user receives a download link for the materials via the interface or receives access information for the materials via email, allowing them to download and review the materials.

[0223] (Example 1)

[0224] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0225] Preparing cost estimates for sales activities is time-consuming and labor-intensive, making it difficult to provide timely and accurate proposal materials. Traditional methods often involve manual data collection, analysis, and document creation, resulting in inefficiency. Therefore, there is a need for a system that automates these processes, enabling the rapid and accurate generation of cost estimates and proposal materials.

[0226] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0227] In this invention, the server includes information receiving means for inputting conditions provided by the user, data collection means for obtaining data necessary for calculations from external information sources and databases, and analysis means for performing calculations using a generated AI model based on the collected data and the user's input conditions. This enables the user to perform calculations efficiently and generate high-quality proposal materials quickly.

[0228] "Information receiving means" refers to a means for inputting conditions provided by the user and transmitting them to the system, and includes the user interface.

[0229] "Data collection methods" refer to means of obtaining data necessary for calculations from external sources or databases, and include technologies for collecting information using APIs and data queries.

[0230] A "generative AI model" is an artificial intelligence model that learns from past data and patterns to perform calculations and analyses based on user conditions.

[0231] "Analysis means" refers to methods for performing calculations based on collected data and user input conditions, and includes techniques for deriving optimal results using a generative AI model.

[0232] "Document generation means" refers to means for automatically creating reports based on calculation results, and includes technology for formatting documents in a visually-oriented format.

[0233] "Data provision means" refers to means for providing the generated report to the user's information device, and includes communication technology for securely and quickly transferring data.

[0234] This invention is a system for automating the creation of cost estimates in sales activities. First, the user accesses the interface using a terminal and inputs the conditions necessary for the estimate. The interface provides forms for inputting conditions such as product category, quantity, and delivery date.

[0235] The conditions entered by the user are sent from the terminal to the server. The server receives these conditions using information receiving means and uses them as the starting point for analysis. At this time, the server uses data collection means to gather the necessary data and issues queries to external information sources and multiple databases. These databases include sales history, inventory information, and market trend information.

[0236] Next, the collected data is analyzed by a generative AI model. The generative AI model learns from past data and patterns and automatically performs calculations based on the given conditions. The AI ​​utilizes specific algorithms to perform efficient and accurate pricing and delivery time simulations.

[0237] Subsequently, based on the analyzed results, the server automatically generates a proposal document using a document generation mechanism. This document includes a summary of the analysis results and visual charts and graphs showing related data. The document is created in formats such as PDF and PPTX, and is ready for immediate use in presentations.

[0238] Finally, the generated proposal documents are sent to the user's terminal using a data delivery method. The user downloads the documents and uses them in actual sales activities. The documents are delivered via a secure connection, ensuring the confidentiality and security of the information.

[0239] As a concrete example, when a user is planning to sell a new product, they might prompt the system with instructions such as, "Please provide data to calculate the optimal price and delivery date for the electronics sales plan. Conditions: Product category 'Electronics', Quantity '500', Delivery date '30 days'." This prompt allows the system to collect and analyze the necessary information and quickly provide accurate calculation results.

[0240] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0241] Step 1:

[0242] The user accesses the interface using a terminal and enters the conditions necessary for the calculation. Specifically, they enter the product category, quantity, and delivery date, and click the "Submit" button through the input form. This operation sends the condition information from the terminal to the server. The input consists of the user's business requirements and conditions, and the output is the input data transferred to the server.

[0243] Step 2:

[0244] The server receives data sent from the user using an information receiving device. Based on the received data, it decides which information to collect. The server uses a data collection device to obtain relevant sales history, inventory information, and market trend information. Data acquisition is performed via APIs or database queries. The input is conditional information from the user, and the output is information from the relevant database.

[0245] Step 3:

[0246] The server inputs the collected data into a generating AI model and begins the analysis. The AI ​​model uses historical data to efficiently perform calculations. The AI ​​performs calculations using multivariate analysis and predictive algorithms. The input is the collected data, and the output is the calculation results. Specifically, the AI ​​performs optimal pricing and delivery time simulations.

[0247] Step 4:

[0248] Based on the calculation results, the server automatically generates proposal materials using a document generation system. These materials include a summary of the calculation results and visually displayed graphs. The materials are generated in PDF or PPTX format and processed into a format suitable for sales activities. The input is the calculation results, and the output is a document file usable by the user.

[0249] Step 5:

[0250] The server sends the generated proposal document to the user's terminal via a data delivery system. The document is transferred via a secure connection, and the user can download and use the document on their terminal. The input is the generated document file, and the output is the document that the user can access. Specifically, the user clicks the "Download" button to obtain the document.

[0251] (Application Example 1)

[0252] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0253] In sales activities, it is necessary to quickly and accurately perform calculations based on transaction terms and create optimal proposal materials for customers based on the results. However, with conventional methods, these calculations and material creation require a great deal of time and effort, making it difficult to streamline sales activities. Furthermore, they lacked the flexibility to make proposals that could adapt to the dynamic market environment.

[0254] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0255] In this invention, the server includes means for providing an interface for accepting conditions provided by the user, means for collecting information necessary for calculations from various databases and systems, and means for performing calculations using artificial intelligence based on the collected information and user input. This enables rapid and precise calculations based on transaction conditions and automatic generation of optimal proposal materials adapted to the dynamically changing market environment.

[0256] "Means of providing an interface for accepting conditions provided by the user" refers to elements that enable users to directly provide information by providing a graphical user interface on the device for inputting transaction conditions and requirements.

[0257] "Means for collecting information necessary for estimation from various databases and systems" refers to a device that has a process for accumulating information related to transactions and markets from internal and external data storage systems and constructing the basic data for estimation.

[0258] "Methods for performing calculations using artificial intelligence based on collected information and user input" refers to methods that use artificial intelligence technology to analyze user input conditions and collected data, and systematically calculate the optimal price and service conditions.

[0259] "Method for automatically generating proposal materials" refers to a device that automatically creates sales materials based on calculation results, visualizes those materials, and structures them so that they can be presented to users.

[0260] "Means of providing generated materials to information terminals" refers to technical means that transfer the created sales proposal materials to the user's digital device, enabling direct use.

[0261] "A means of generating proposal documents to suggest the optimal settings based on transaction conditions" refers to a system that automatically constructs proposal documents that reflect the optimal commission rates and services provided by the user, according to the transaction conditions they have provided.

[0262] This invention provides a system that enables the rapid and accurate generation of calculations and proposal documents based on transaction conditions in sales activities. The entire system consists of user terminals, a central server, and a database.

[0263] Users first input transaction terms through an interface provided on a device such as a smartphone or tablet. This interface is built as a user-friendly graphical user interface (GUI) and is implemented using React Native technology. The input terms include transaction amount, contract period, industry, etc.

[0264] The entered conditions are sent via the internet to a central cloud server. The server uses a Python script to collect relevant data from a database system (e.g., AWS RDS or MongoDB). Based on the collected data, an AI model (using TensorFlow) determines the optimal fee settings and profit forecasts for the trading conditions.

[0265] The server creates a proposal document in Markdown format based on the calculation results obtained by the generating AI model. This document will include not only the calculated figures but also graphs and charts that are easy to understand visually. Finally, the generated proposal document will be converted to HTML format and provided to the user's device for download.

[0266] As a concrete example, consider a scenario where a sales representative wants to propose annual transaction terms to a new retail customer. The representative opens the application and enters the following conditions: "Transaction amount: 5 million yen, contract period: 1 year, industry: retail." The system quickly processes these conditions and generates a proposal document with the optimal commission rate based on past data.

[0267] An example of a prompt message would be: "Please suggest the optimal fee structure based on past transaction data. The conditions are as follows: transaction amount of 5 million yen, contract period of 1 year, industry is retail."

[0268] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0269] Step 1:

[0270] The user launches the application on their device and enters transaction terms through the interface. Specifically, they enter information such as the transaction amount, contract period, and industry, which is then converted into JSON format within the application. The entered information is formatted for data transmission from the user interface and sent to the server via the network.

[0271] Step 2:

[0272] The server parses the received transaction conditions in JSON format. Based on the parsed transaction conditions, it sends queries to a database that stores the necessary historical transaction data. A Python script is used here to issue SQL and NoSQL queries to databases such as AWS RDS and MongoDB. The data extracted from the database is returned to the server.

[0273] Step 3:

[0274] The server performs data analysis using an AI model based on the return values ​​from the received database and the input transaction conditions. A generative AI model built using TensorFlow plays a major role in this process. The AI ​​model calculates the optimal fee settings and terms of service based on past transaction data and outputs the simulation results.

[0275] Step 4:

[0276] The server receives the analysis results from the AI ​​model and automatically generates proposal documents based on them. The proposal documents convert the numerical data from the generated AI model into Markdown format and add graphs and charts as visual elements as needed. This formats the information in a way that is easy for the user to understand.

[0277] Step 5:

[0278] The server converts the generated proposal document into HTML format and prepares it for transmission to the user's device. Finally, the document is provided to the user's device as a download link. The user receives a notification and can view and utilize the proposal document from their device.

[0279] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0280] This invention is a system that automates calculations based on user-provided conditions and combines these calculations with an emotion engine to enable more effective sales proposals. This system identifies the user's emotions and generates proposal materials accordingly.

[0281] First, the user enters the conditions for the calculation via the terminal interface. These conditions include product information, quantity, and desired delivery date. The interface sends the user's input to the emotion engine and simultaneously transmits the condition data to the server.

[0282] The server collects relevant information to perform calculations based on user input and past data. During this process, an emotion engine is activated to analyze the emotions and context embedded in the user's input. This analysis is then used to generate more personalized calculations.

[0283] Next, the server uses artificial intelligence to calculate a rationalized price proposal and supply conditions. At the same time, it adjusts the trial calculation results in a form that reflects the analysis results of the emotion engine to derive an optimal proposal.

[0284] After that, based on the generated trial calculation results, the server automatically creates proposal materials. Considering the output of the emotion engine, the tone and content of the materials are optimized for the user. The materials incorporate expressions based on emotions and expressions that are more likely to resonate with the user.

[0285] Finally, the server provides the proposal materials to the user's terminal. These materials are provided in a form that the user can download and can be immediately utilized for business proposals. This system enables proposals based on deep insights considering the user's emotions, becoming a powerful tool for strengthening the relationship with customers.

[0286] The processing flow will be described below.

[0287] Step 1:

[0288] The user accesses the terminal interface and enters the conditions required for the trial calculation. Specifically, details such as the product type, quantity, and desired delivery date are provided. Along with these conditions, the interface sends the user's expression and voice tone to the emotion engine to also obtain emotion data.

[0289] Step 2:

[0290] The server activates the emotion engine and analyzes the emotions and reactions of the user during input. The emotion engine performs voice analysis and text mining to evaluate what emotions the user has. This evaluation result is reflected in the subsequent trial calculation.

[0291] Step 3: <000092​The server uses a data aggregation module to collect information from various databases based on user input. This includes inventory information, past sales history, market trends, and other data, preparing it for calculations.

[0293] Step 4:

[0294] The server uses artificial intelligence to perform calculations and determine the optimal pricing and terms of service. During this process, it considers the results of an emotion engine analysis and adjusts the suggestions to match the user's emotions. For example, if a risk-averse emotion is detected, more stable suggestions will be offered.

[0295] Step 5:

[0296] The server automatically generates proposal documents based on the calculation results using a results generation module. These proposal documents reflect insights gained from sentiment analysis and present information in a format that resonates with users. In addition to graphs and charts, they include emotionally appropriate wording and recommendations.

[0297] Step 6:

[0298] The server provides the generated proposal materials to the user's terminal. The user can download the materials from their terminal and use them immediately for sales activities. At this time, the materials are customized to take emotions into consideration, enabling proposals that meet the user's expectations.

[0299] (Example 2)

[0300] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0301] In the conventional sales proposal system, although it was possible to perform a rough estimate based on the user's conditions, it was difficult to automatically create a personalized proposal that took into account the user's feelings. Therefore, there was a problem that the quality of the proposals tended to be uniform and it was impossible to obtain sufficient empathy to deepen the relationship with the customers.

[0302] 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.

[0303] In this invention, the server includes means for providing an input / output device for receiving the conditions provided by the user, means for collecting the data necessary for the rough estimate from various storage devices and management devices, and means for analyzing the user's feelings using a sentiment analysis device. As a result, it becomes possible to automatically create proposal materials that take into account the user's feelings, and an effective sales proposal that strengthens the relationship with the customer becomes possible.

[0304] The "input / output device" is a device for receiving condition information from the user and transmitting it to the server.

[0305] The "storage device" is a device for storing and managing the data necessary for the rough estimate and making the data accessible to the server.

[0306] The "management device" is a device designed to efficiently collect and manipulate data.

[0307] The "sentiment analysis device" is a device for analyzing the user's input data and judging the feelings and their context.

[0308] The "generated intelligence" is an artificial intelligence technology for performing a rough estimate and creating proposal materials based on a large amount of data.

[0309] The "proposal document" is a document for an automatically generated sales proposal based on the rough estimate result and the sentiment analysis result.

[0310] This system's program automates calculations based on user-provided conditions and personalizes sales proposals by combining them with an emotion engine. Users access the interface using a terminal and input conditions for calculations. The terminal receives this information, sends it to the server as input, and simultaneously passes the data to the emotion analysis device.

[0311] The server collects data from storage and management devices based on the input conditions. This allows the server to efficiently gather the information necessary for calculations. Based on the collected data, the server utilizes a generative AI model to calculate reasonable pricing and terms of service. Furthermore, the server uses an emotion analysis device to analyze the user's emotions, reflects the results in the calculations, and adjusts the content of the proposal.

[0312] For example, suppose a user is considering purchasing a new computer. The user inputs their desired specifications, budget, and delivery date via a terminal. The server collects market data based on these conditions and analyzes the user's emotions (e.g., their emphasis on price). Based on these results, the server generates a proposal for a cost-effective product. The generated proposal includes emotionally resonant language and is provided to the user in an immediately downloadable format.

[0313] This system ensures that proposals take customer emotions into consideration, enabling higher-quality sales proposals. As a result, customer relationships are strengthened, and the effectiveness of sales activities is enhanced.

[0314] Example of a prompt

[0315] "Based on the user's input, please propose new PC models. If the user is concerned about price, prioritize suggesting models with high cost-performance."

[0316] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0317] Step 1:

[0318] The user inputs the conditions for the calculation through the terminal's interface. For example, product information, quantity, and desired delivery date are input items. The terminal transmits this information to the sentiment analysis device and also transfers the condition data to the server. This allows the server to receive the initial data input.

[0319] Step 2:

[0320] The server collects necessary data from relevant storage and management devices based on the received conditional data. This data is essential for performing quick and accurate calculations. As a result of the data collection, important information such as inventory status and market prices is output.

[0321] Step 3:

[0322] The server analyzes the user's emotions and intentions based on conditional data received using an emotion analysis device. A generative AI model assists this process, performing deep analysis of the user's text input and making emotional judgments such as positive or negative. The analysis results are used in the next stage of calculation.

[0323] Step 4:

[0324] The server uses a generative AI model based on collected data and sentiment analysis results to perform calculations. These calculations determine reasonable pricing and supply conditions. The output is a realistic and user-friendly price and condition proposal.

[0325] Step 5:

[0326] The server automatically generates a proposal, taking into account the calculation results and sentiment analysis. This proposal's tone is adjusted to match the user's emotions, and the document generation technology using a generation AI model incorporates expressions that resonate with the user.

[0327] Step 6:

[0328] The server transfers the completed proposal to the terminal. The terminal then provides the proposal in a format that the user can review and download. This allows the user to immediately utilize the proposal in their sales activities.

[0329] (Application Example 2)

[0330] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0331] In modern e-commerce, providing consumers with the most suitable product recommendations is crucial for attracting their interest and increasing their purchase intent. However, existing systems struggle to efficiently provide personalized recommendations that incorporate consumer emotions. Therefore, there is a need for a system that analyzes consumer input conditions and emotions in real time and provides optimized recommendation materials based on that analysis.

[0332] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0333] In this invention, the server includes means for providing an output unit for receiving criteria provided by the user, means for acquiring information necessary for calculations from various information infrastructures and systems, and means for performing calculations using machine learning based on the acquired information and user input. This makes it possible to provide personalized product suggestions in real time based on the user's emotions and past behavior.

[0334] A "user" is a specific individual or organization that uses the system to input conditions and receive suggestions.

[0335] "Criteria" refers to the conditions and requirements related to the calculations and proposals provided by the user.

[0336] The term "output section" refers to the general term for interfaces and devices that users use to input references.

[0337] "Estimate" refers to the evaluation results of prices, supply conditions, etc., calculated based on the input criteria.

[0338] "Information infrastructure" refers to all electronic databases and systems that store and provide the information necessary for calculations.

[0339] "Machine learning" is a method that learns patterns from past data and uses new data to perform inferences and predictions.

[0340] "Emotion recognition output" refers to data obtained by analyzing and identifying the user's emotions.

[0341] A "proposal document" is a collection of information presented to consumers, automatically generated based on calculation results and emotion recognition output.

[0342] "Two-dimensional and three-dimensional representations" refer to formats for representing graphical visual information included in proposal documents, and serve to visually supplement the information.

[0343] "Communication terminal" refers to all electronic devices used by users to receive proposal documents.

[0344] The system for realizing this invention is designed to analyze user input and their emotions, and to provide consumers with the most suitable product recommendations. Users input purchasing criteria through a communication terminal interface. These criteria include product category, price range, and desired delivery date. Once data is entered, the terminal transmits it to an information infrastructure and prepares to retrieve the necessary information.

[0345] The server uses machine learning models to perform calculations based on collected information and user input. Platforms such as AWS SageMaker can be used for this purpose. Furthermore, technologies such as IBM Watson are used as emotion recognition engines to interpret user emotions and intentions.

[0346] By combining the acquired calculation results and emotion recognition output, the server automatically generates a proposal document. This document includes two-dimensional and three-dimensional visual information and is graphically represented, making it easy for users to understand. The generated document is immediately transmitted to the communication terminal and presented to the user.

[0347] As a concrete example, suppose a user is searching for "new sports shoes." Based on their past purchase history and reviews, it is analyzed that they are interested in sporty design and durability. Based on this information, the server suggests shoes that match these attributes and creates materials that include catchy phrases such as "Innovative design to make everyday running comfortable."

[0348] Examples of prompts for a generative AI model:

[0349] "The user is looking for new sports shoes. Based on their past reviews, they have a strong interest in sporty design and durability. Please generate personalized product suggestions and compelling taglines."

[0350] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0351] Step 1:

[0352] The user inputs criteria such as product category, price range, and desired delivery date through the interface of a communication terminal. The terminal's role is to transmit this information as input data to the server. The output here is the input criteria information.

[0353] Step 2:

[0354] The server retrieves relevant data from the information infrastructure based on the received reference information. This data includes product inventory, pricing information, and past transaction history. Using the retrieved data as input, the server filters the necessary information and prepares it for calculation. The output is the filtered product data.

[0355] Step 3:

[0356] The server utilizes machine learning models to perform calculations based on filtered product data and user criteria information. In this process, machine learning models (e.g., AWS SageMaker) are used to calculate pricing and optimal supply conditions. The input to the process is filtered product data, and the output is the calculation result.

[0357] Step 4:

[0358] Simultaneously, the server uses an emotion recognition engine to analyze emotions from user input. This utilizes an implementation for emotion recognition (e.g., IBM Watson). The input is the user's baseline information, and the output is the result of emotion recognition.

[0359] Step 5:

[0360] The server integrates the calculation results and emotion recognition results to automatically generate proposal materials. The materials integrate two-dimensional and three-dimensional visual information and add promotional text and taglines that resonate with the user's emotions. A generation AI model is used to create wording optimized for the user's interests and emotions. The output is the generated proposal material.

[0361] Step 6:

[0362] The server sends the generated proposal document to the communication terminal and displays it to the user. This document is provided directly to the user and used for purchasing decisions. The output is the proposal document provided to the user.

[0363] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0364] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0365] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0366] [Third Embodiment]

[0367] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0368] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0369] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0370] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0371] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0372] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0373] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0374] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0375] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0376] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0377] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0378] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0379] This invention is a system that streamlines the calculation of estimates in sales activities and enables the rapid generation of proposal materials. Based on conditions provided by the user, this system automatically performs estimates and generates and supplies proposal materials.

[0380] First, the user inputs the calculation conditions through the interface. Specifically, they provide the system with elements such as product category, quantity, and delivery date. This transmits the basic information necessary for the calculation to the system.

[0381] Next, the server collects relevant information from internal and external databases based on the received input conditions. This includes sales history databases, inventory management systems, and data sources showing market trends. This information is used to form the basis of the data necessary for the calculations.

[0382] Subsequently, the server uses artificial intelligence to analyze the collected information and user input conditions to simulate optimal pricing and delivery times. The AI ​​utilizes historical data and algorithms to perform efficient and accurate calculations.

[0383] Once the calculations are complete, the server automatically generates proposal materials based on the results. These materials include a summary of the calculations, relevant graphs, and are formatted for presentations. This allows sales representatives to quickly obtain high-quality materials.

[0384] Finally, the generated materials are delivered from the server to the user's terminal. Users can download the materials and use them in their sales activities. This significantly streamlines the process from cost estimation to proposal, and is expected to improve competitiveness.

[0385] The following describes the processing flow.

[0386] Step 1:

[0387] The user accesses the interface and enters the conditions necessary for the calculation. Specifically, they enter information such as product category, quantity, and desired delivery date into the form and press the "Submit" button, at which point all the entered data is sent to the server.

[0388] Step 2:

[0389] The server activates a data aggregation module based on the input conditions received from the user. The data aggregation module accesses the sales database, inventory management system, and external market data sources to collect the necessary information. This gathers the relevant data for use in calculations.

[0390] Step 3:

[0391] The server activates the AI ​​calculation module and automatically performs calculations based on the collected information and user input conditions. The AI ​​uses machine learning algorithms to calculate optimal pricing and delivery date adjustments, and improves prediction accuracy by considering past data and market trends.

[0392] Step 4:

[0393] The server uses a results generation module to automatically generate proposal documents based on AI-generated calculations. These documents include a summary of the calculations, pricing information, and graphs related to delivery times. The visually organized information is then created in PowerPoint format for presentation purposes.

[0394] Step 5:

[0395] The server provides the generated materials to the user's terminal via the output module. The user receives a download link for the materials via the interface or receives access information for the materials via email, allowing them to download and review the materials.

[0396] (Example 1)

[0397] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0398] Preparing cost estimates for sales activities is time-consuming and labor-intensive, making it difficult to provide timely and accurate proposal materials. Traditional methods often involve manual data collection, analysis, and document creation, resulting in inefficiency. Therefore, there is a need for a system that automates these processes, enabling the rapid and accurate generation of cost estimates and proposal materials.

[0399] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0400] In this invention, the server includes information receiving means for inputting conditions provided by the user, data collection means for obtaining data necessary for calculations from external information sources and databases, and analysis means for performing calculations using a generated AI model based on the collected data and the user's input conditions. This enables the user to perform calculations efficiently and generate high-quality proposal materials quickly.

[0401] "Information receiving means" refers to a means for inputting conditions provided by the user and transmitting them to the system, and includes the user interface.

[0402] "Data collection methods" refer to means of obtaining data necessary for calculations from external sources or databases, and include technologies for collecting information using APIs and data queries.

[0403] A "generative AI model" is an artificial intelligence model that learns from past data and patterns to perform calculations and analyses based on user conditions.

[0404] "Analysis means" refers to methods for performing calculations based on collected data and user input conditions, and includes techniques for deriving optimal results using a generative AI model.

[0405] "Document generation means" refers to means for automatically creating reports based on calculation results, and includes technology for formatting documents in a visually-oriented format.

[0406] "Data provision means" refers to means for providing the generated report to the user's information device, and includes communication technology for securely and quickly transferring data.

[0407] This invention is a system for automating the creation of cost estimates in sales activities. First, the user accesses the interface using a terminal and inputs the conditions necessary for the estimate. The interface provides forms for inputting conditions such as product category, quantity, and delivery date.

[0408] The conditions entered by the user are sent from the terminal to the server. The server receives these conditions using information receiving means and uses them as the starting point for analysis. At this time, the server uses data collection means to gather the necessary data and issues queries to external information sources and multiple databases. These databases include sales history, inventory information, and market trend information.

[0409] Next, the collected data is analyzed by a generative AI model. The generative AI model learns from past data and patterns and automatically performs calculations based on the given conditions. The AI ​​utilizes specific algorithms to perform efficient and accurate pricing and delivery time simulations.

[0410] Subsequently, based on the analyzed results, the server automatically generates a proposal document using a document generation mechanism. This document includes a summary of the analysis results and visual charts and graphs showing related data. The document is created in formats such as PDF and PPTX, and is ready for immediate use in presentations.

[0411] Finally, the generated proposal documents are sent to the user's terminal using a data delivery method. The user downloads the documents and uses them in actual sales activities. The documents are delivered via a secure connection, ensuring the confidentiality and security of the information.

[0412] As a concrete example, when a user is planning to sell a new product, they might prompt the system with instructions such as, "Please provide data to calculate the optimal price and delivery date for the electronics sales plan. Conditions: Product category 'Electronics', Quantity '500', Delivery date '30 days'." This prompt allows the system to collect and analyze the necessary information and quickly provide accurate calculation results.

[0413] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0414] Step 1:

[0415] The user accesses the interface using a terminal and enters the conditions necessary for the calculation. Specifically, they enter the product category, quantity, and delivery date, and click the "Submit" button through the input form. This operation sends the condition information from the terminal to the server. The input consists of the user's business requirements and conditions, and the output is the input data transferred to the server.

[0416] Step 2:

[0417] The server receives data sent from the user using an information receiving device. Based on the received data, it decides which information to collect. The server uses a data collection device to obtain relevant sales history, inventory information, and market trend information. Data acquisition is performed via APIs or database queries. The input is conditional information from the user, and the output is information from the relevant database.

[0418] Step 3:

[0419] The server inputs the collected data into a generating AI model and begins the analysis. The AI ​​model uses historical data to efficiently perform calculations. The AI ​​performs calculations using multivariate analysis and predictive algorithms. The input is the collected data, and the output is the calculation results. Specifically, the AI ​​performs optimal pricing and delivery time simulations.

[0420] Step 4:

[0421] Based on the calculation results, the server automatically generates proposal materials using a document generation system. These materials include a summary of the calculation results and visually displayed graphs. The materials are generated in PDF or PPTX format and processed into a format suitable for sales activities. The input is the calculation results, and the output is a document file usable by the user.

[0422] Step 5:

[0423] The server sends the generated proposal document to the user's terminal via a data delivery system. The document is transferred via a secure connection, and the user can download and use the document on their terminal. The input is the generated document file, and the output is the document that the user can access. Specifically, the user clicks the "Download" button to obtain the document.

[0424] (Application Example 1)

[0425] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0426] In sales activities, it is necessary to quickly and accurately perform calculations based on transaction terms and create optimal proposal materials for customers based on the results. However, with conventional methods, these calculations and material creation require a great deal of time and effort, making it difficult to streamline sales activities. Furthermore, they lacked the flexibility to make proposals that could adapt to the dynamic market environment.

[0427] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0428] In this invention, the server includes means for providing an interface for accepting conditions provided by the user, means for collecting information necessary for calculations from various databases and systems, and means for performing calculations using artificial intelligence based on the collected information and user input. This enables rapid and precise calculations based on transaction conditions and automatic generation of optimal proposal materials adapted to the dynamically changing market environment.

[0429] "Means of providing an interface for accepting conditions provided by the user" refers to elements that enable users to directly provide information by providing a graphical user interface on the device for inputting transaction conditions and requirements.

[0430] "Means for collecting information necessary for estimation from various databases and systems" refers to a device that has a process for accumulating information related to transactions and markets from internal and external data storage systems and constructing the basic data for estimation.

[0431] "Methods for performing calculations using artificial intelligence based on collected information and user input" refers to methods that use artificial intelligence technology to analyze user input conditions and collected data, and systematically calculate the optimal price and service conditions.

[0432] "Method for automatically generating proposal materials" refers to a device that automatically creates sales materials based on calculation results, visualizes those materials, and structures them so that they can be presented to users.

[0433] "Means of providing generated materials to information terminals" refers to technical means that transfer the created sales proposal materials to the user's digital device, enabling direct use.

[0434] "A means of generating proposal documents to suggest the optimal settings based on transaction conditions" refers to a system that automatically constructs proposal documents that reflect the optimal commission rates and services provided by the user, according to the transaction conditions they have provided.

[0435] This invention provides a system that enables the rapid and accurate generation of calculations and proposal documents based on transaction conditions in sales activities. The entire system consists of user terminals, a central server, and a database.

[0436] Users first input transaction terms through an interface provided on a device such as a smartphone or tablet. This interface is built as a user-friendly graphical user interface (GUI) and is implemented using React Native technology. The input terms include transaction amount, contract period, industry, etc.

[0437] The entered conditions are sent via the internet to a central cloud server. The server uses a Python script to collect relevant data from a database system (e.g., AWS RDS or MongoDB). Based on the collected data, an AI model (using TensorFlow) determines the optimal fee settings and profit forecasts for the trading conditions.

[0438] The server creates a proposal document in Markdown format based on the calculation results obtained by the generating AI model. This document will include not only the calculated figures but also graphs and charts that are easy to understand visually. Finally, the generated proposal document will be converted to HTML format and provided to the user's device for download.

[0439] As a concrete example, consider a scenario where a sales representative wants to propose annual transaction terms to a new retail customer. The representative opens the application and enters the following conditions: "Transaction amount: 5 million yen, contract period: 1 year, industry: retail." The system quickly processes these conditions and generates a proposal document with the optimal commission rate based on past data.

[0440] An example of a prompt message would be: "Please suggest the optimal fee structure based on past transaction data. The conditions are as follows: transaction amount of 5 million yen, contract period of 1 year, industry is retail."

[0441] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0442] Step 1:

[0443] The user launches the application on their device and enters transaction terms through the interface. Specifically, they enter information such as the transaction amount, contract period, and industry, which is then converted into JSON format within the application. The entered information is formatted for data transmission from the user interface and sent to the server via the network.

[0444] Step 2:

[0445] The server parses the received transaction conditions in JSON format. Based on the parsed transaction conditions, it sends queries to a database that stores the necessary historical transaction data. A Python script is used here to issue SQL and NoSQL queries to databases such as AWS RDS and MongoDB. The data extracted from the database is returned to the server.

[0446] Step 3:

[0447] The server performs data analysis using an AI model based on the return values ​​from the received database and the input transaction conditions. A generative AI model built using TensorFlow plays a major role in this process. The AI ​​model calculates the optimal fee settings and terms of service based on past transaction data and outputs the simulation results.

[0448] Step 4:

[0449] The server receives the analysis results from the AI ​​model and automatically generates proposal documents based on them. The proposal documents convert the numerical data from the generated AI model into Markdown format and add graphs and charts as visual elements as needed. This formats the information in a way that is easy for the user to understand.

[0450] Step 5:

[0451] The server converts the generated proposal document into HTML format and prepares it for transmission to the user's device. Finally, the document is provided to the user's device as a download link. The user receives a notification and can view and utilize the proposal document from their device.

[0452] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0453] This invention is a system that automates calculations based on user-provided conditions and combines these calculations with an emotion engine to enable more effective sales proposals. This system identifies the user's emotions and generates proposal materials accordingly.

[0454] First, the user enters the conditions for the calculation via the terminal interface. These conditions include product information, quantity, and desired delivery date. The interface sends the user's input to the emotion engine and simultaneously transmits the condition data to the server.

[0455] The server collects relevant information to perform calculations based on user input and past data. During this process, an emotion engine is activated to analyze the emotions and context embedded in the user's input. This analysis is then used to generate more personalized calculations.

[0456] Next, the server uses artificial intelligence to calculate a streamlined price proposal and supply conditions. Simultaneously, it adjusts the calculation results to reflect the analysis results of the emotion engine, deriving the optimal proposal.

[0457] Subsequently, the server automatically creates a proposal document based on the generated calculation results. The tone and content of the document are optimized for the user, taking into account the output of the emotion engine. The document incorporates emotion-based language and expressions that are more likely to resonate with the user.

[0458] Finally, the server provides the proposal materials to the user's terminal. These materials are provided in a downloadable format and can be immediately used in sales proposals. This system enables proposals based on deep insights that take into account the user's emotions, making it a powerful tool for strengthening customer relationships.

[0459] The following describes the processing flow.

[0460] Step 1:

[0461] The user accesses the terminal interface and enters the conditions necessary for the calculation. Specifically, they provide details such as the type of product, quantity, and desired delivery date. Along with these conditions, the interface transmits the user's facial expressions and tone of voice to the emotion engine, acquiring emotional data as well.

[0462] Step 2:

[0463] The server activates the emotion engine and analyzes the user's emotions and reactions during input. The emotion engine performs voice analysis and text mining to evaluate the user's emotions. This evaluation result is reflected in subsequent calculations.

[0464] Step 3:

[0465] The server uses a data aggregation module to collect information from various databases based on user input. This includes inventory information, past sales history, market trends, and other data, preparing it for calculations.

[0466] Step 4:

[0467] The server uses artificial intelligence to perform calculations and determine the optimal pricing and terms of service. During this process, it considers the results of an emotion engine analysis and adjusts the suggestions to match the user's emotions. For example, if a risk-averse emotion is detected, more stable suggestions will be offered.

[0468] Step 5:

[0469] The server automatically generates proposal documents based on the calculation results using a results generation module. These proposal documents reflect insights gained from sentiment analysis and present information in a format that resonates with users. In addition to graphs and charts, they include emotionally appropriate wording and recommendations.

[0470] Step 6:

[0471] The server provides the generated proposal materials to the user's terminal. The user can download the materials from their terminal and use them immediately for sales activities. At this time, the materials are customized to take emotions into consideration, enabling proposals that meet the user's expectations.

[0472] (Example 2)

[0473] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] Traditional sales proposal systems could perform calculations based on user conditions, but they struggled to automatically create personalized proposals that took user emotions into account. As a result, the quality of proposals tended to be uniform, and there was a problem in not being able to gain sufficient empathy to deepen relationships with customers.

[0475] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0476] In this invention, the server includes means for providing an input / output device for receiving conditions provided by the user, means for collecting data necessary for calculations from various storage devices and management devices, and means for analyzing the user's emotions using an emotion analysis device. This enables the automatic creation of proposal materials that take the user's emotions into consideration, and facilitates effective sales proposals that strengthen relationships with customers.

[0477] An "input / output device" is a device that receives conditional information from a user and transmits it to a server.

[0478] A "storage device" is a device that stores and manages the data necessary for calculations and makes that data accessible to a server.

[0479] A "management device" is a device designed to efficiently collect and manipulate data.

[0480] An "emotion analysis device" is a device that analyzes user input data to determine emotions and their context.

[0481] "Generated intelligence" refers to artificial intelligence technology used to perform calculations and create proposals based on large amounts of data.

[0482] A "proposal document" is a sales proposal document that is automatically generated based on the results of calculations and sentiment analysis.

[0483] This system's program automates calculations based on user-provided conditions and personalizes sales proposals by combining them with an emotion engine. Users access the interface using a terminal and input conditions for calculations. The terminal receives this information, sends it to the server as input, and simultaneously passes the data to the emotion analysis device.

[0484] The server collects data from storage and management devices based on the input conditions. This allows the server to efficiently gather the information necessary for calculations. Based on the collected data, the server utilizes a generative AI model to calculate reasonable pricing and terms of service. Furthermore, the server uses an emotion analysis device to analyze the user's emotions, reflects the results in the calculations, and adjusts the content of the proposal.

[0485] For example, suppose a user is considering purchasing a new computer. The user inputs their desired specifications, budget, and delivery date via a terminal. The server collects market data based on these conditions and analyzes the user's emotions (e.g., their emphasis on price). Based on these results, the server generates a proposal for a cost-effective product. The generated proposal includes emotionally resonant language and is provided to the user in an immediately downloadable format.

[0486] This system ensures that proposals take customer emotions into consideration, enabling higher-quality sales proposals. As a result, customer relationships are strengthened, and the effectiveness of sales activities is enhanced.

[0487] Example of a prompt

[0488] "Based on the user's input, please propose new PC models. If the user is concerned about price, prioritize suggesting models with high cost-performance."

[0489] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0490] Step 1:

[0491] The user inputs the conditions for the calculation through the terminal's interface. For example, product information, quantity, and desired delivery date are input items. The terminal transmits this information to the sentiment analysis device and also transfers the condition data to the server. This allows the server to receive the initial data input.

[0492] Step 2:

[0493] The server collects necessary data from relevant storage and management devices based on the received conditional data. This data is essential for performing quick and accurate calculations. As a result of the data collection, important information such as inventory status and market prices is output.

[0494] Step 3:

[0495] The server analyzes the user's emotions and intentions based on conditional data received using an emotion analysis device. A generative AI model assists this process, performing deep analysis of the user's text input and making emotional judgments such as positive or negative. The analysis results are used in the next stage of calculation.

[0496] Step 4:

[0497] The server uses a generative AI model based on collected data and sentiment analysis results to perform calculations. These calculations determine reasonable pricing and supply conditions. The output is a realistic and user-friendly price and condition proposal.

[0498] Step 5:

[0499] The server automatically generates a proposal, taking into account the calculation results and sentiment analysis. This proposal's tone is adjusted to match the user's emotions, and the document generation technology using a generation AI model incorporates expressions that resonate with the user.

[0500] Step 6:

[0501] The server transfers the completed proposal to the terminal. The terminal then provides the proposal in a format that the user can review and download. This allows the user to immediately utilize the proposal in their sales activities.

[0502] (Application Example 2)

[0503] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0504] In modern e-commerce, providing consumers with the most suitable product recommendations is crucial for attracting their interest and increasing their purchase intent. However, existing systems struggle to efficiently provide personalized recommendations that incorporate consumer emotions. Therefore, there is a need for a system that analyzes consumer input conditions and emotions in real time and provides optimized recommendation materials based on that analysis.

[0505] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0506] In this invention, the server includes means for providing an output unit for receiving criteria provided by the user, means for acquiring information necessary for calculations from various information infrastructures and systems, and means for performing calculations using machine learning based on the acquired information and user input. This makes it possible to provide personalized product suggestions in real time based on the user's emotions and past behavior.

[0507] A "user" is a specific individual or organization that uses the system to input conditions and receive suggestions.

[0508] "Criteria" refers to the conditions and requirements related to the calculations and proposals provided by the user.

[0509] The term "output section" refers to the general term for interfaces and devices that users use to input references.

[0510] "Estimate" refers to the evaluation results of prices, supply conditions, etc., calculated based on the input criteria.

[0511] "Information infrastructure" refers to all electronic databases and systems that store and provide the information necessary for calculations.

[0512] "Machine learning" is a method that learns patterns from past data and uses new data to perform inferences and predictions.

[0513] "Emotion recognition output" refers to data obtained by analyzing and identifying the user's emotions.

[0514] A "proposal document" is a collection of information presented to consumers, automatically generated based on calculation results and emotion recognition output.

[0515] "Two-dimensional and three-dimensional representations" refer to formats for representing graphical visual information included in proposal documents, and serve to visually supplement the information.

[0516] "Communication terminal" refers to all electronic devices used by users to receive proposal documents.

[0517] The system for realizing this invention is designed to analyze user input and their emotions, and to provide consumers with the most suitable product recommendations. Users input purchasing criteria through a communication terminal interface. These criteria include product category, price range, and desired delivery date. Once data is entered, the terminal transmits it to an information infrastructure and prepares to retrieve the necessary information.

[0518] The server uses machine learning models to perform calculations based on collected information and user input. Platforms such as AWS SageMaker can be used for this purpose. Furthermore, technologies such as IBM Watson are used as emotion recognition engines to interpret user emotions and intentions.

[0519] By combining the acquired calculation results and emotion recognition output, the server automatically generates a proposal document. This document includes two-dimensional and three-dimensional visual information and is graphically represented, making it easy for users to understand. The generated document is immediately transmitted to the communication terminal and presented to the user.

[0520] As a concrete example, suppose a user is searching for "new sports shoes." Based on their past purchase history and reviews, it is analyzed that they are interested in sporty design and durability. Based on this information, the server suggests shoes that match these attributes and creates materials that include catchy phrases such as "Innovative design to make everyday running comfortable."

[0521] Examples of prompts for a generative AI model:

[0522] "The user is looking for new sports shoes. Based on their past reviews, they have a strong interest in sporty design and durability. Please generate personalized product suggestions and compelling taglines."

[0523] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0524] Step 1:

[0525] The user inputs criteria such as product category, price range, and desired delivery date through the interface of a communication terminal. The terminal's role is to transmit this information as input data to the server. The output here is the input criteria information.

[0526] Step 2:

[0527] The server retrieves relevant data from the information infrastructure based on the received reference information. This data includes product inventory, pricing information, and past transaction history. Using the retrieved data as input, the server filters the necessary information and prepares it for calculation. The output is the filtered product data.

[0528] Step 3:

[0529] The server utilizes a machine learning model to perform calculations based on filtered product data and user criteria information. In this process, a machine learning model (e.g., AWS SageMaker) is used to calculate pricing and optimal supply conditions. The input to the process is filtered product data, and the output is the calculation result.

[0530] Step 4:

[0531] Simultaneously, the server uses an emotion recognition engine to analyze emotions from user input. This utilizes an implementation for emotion recognition (e.g., IBM Watson). The input is the user's baseline information, and the output is the result of emotion recognition.

[0532] Step 5:

[0533] The server integrates the calculation results and emotion recognition results to automatically generate proposal materials. The materials integrate two-dimensional and three-dimensional visual information and add promotional text and taglines that resonate with the user's emotions. Using a generation AI model, it creates wording optimized for the user's interests and emotions. The output is the generated proposal material.

[0534] Step 6:

[0535] The server sends the generated proposal document to the communication terminal and displays it to the user. This document is provided directly to the user and used for purchasing decisions. The output is the proposal document provided to the user.

[0536] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0537] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0538] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0539] [Fourth Embodiment]

[0540] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0541] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0542] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0543] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0544] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0545] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0546] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0547] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0548] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0549] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0550] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0551] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0552] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0553] This invention is a system that streamlines the calculation of estimates in sales activities and enables the rapid generation of proposal materials. Based on conditions provided by the user, this system automatically performs estimates and generates and supplies proposal materials.

[0554] First, the user inputs the calculation conditions through the interface. Specifically, they provide the system with elements such as product category, quantity, and delivery date. This transmits the basic information necessary for the calculation to the system.

[0555] Next, the server collects relevant information from internal and external databases based on the received input conditions. This includes sales history databases, inventory management systems, and data sources showing market trends. This information is used to form the basis of the data necessary for the calculations.

[0556] Subsequently, the server uses artificial intelligence to analyze the collected information and user input conditions to simulate optimal pricing and delivery times. The AI ​​utilizes historical data and algorithms to perform efficient and accurate calculations.

[0557] Once the calculations are complete, the server automatically generates proposal materials based on the results. These materials include a summary of the calculations, relevant graphs, and are formatted for presentations. This allows sales representatives to quickly obtain high-quality materials.

[0558] Finally, the generated materials are delivered from the server to the user's terminal. Users can download the materials and use them in their sales activities. This significantly streamlines the process from cost estimation to proposal, and is expected to improve competitiveness.

[0559] The following describes the processing flow.

[0560] Step 1:

[0561] The user accesses the interface and enters the conditions necessary for the calculation. Specifically, they enter information such as product category, quantity, and desired delivery date into the form and press the "Submit" button, at which point all the entered data is sent to the server.

[0562] Step 2:

[0563] The server activates a data aggregation module based on the input conditions received from the user. The data aggregation module accesses the sales database, inventory management system, and external market data sources to collect the necessary information. This gathers the relevant data for use in calculations.

[0564] Step 3:

[0565] The server activates the AI ​​calculation module and automatically performs calculations based on the collected information and user input conditions. The AI ​​uses machine learning algorithms to calculate optimal pricing and delivery date adjustments, and improves prediction accuracy by considering past data and market trends.

[0566] Step 4:

[0567] The server uses a results generation module to automatically generate proposal documents based on AI-generated calculations. These documents include a summary of the calculations, pricing information, and graphs related to delivery times. The visually organized information is then created in PowerPoint format for presentation purposes.

[0568] Step 5:

[0569] The server provides the generated materials to the user's terminal via the output module. The user receives a download link for the materials via the interface or receives access information for the materials via email, allowing them to download and review the materials.

[0570] (Example 1)

[0571] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0572] Preparing cost estimates for sales activities is time-consuming and labor-intensive, making it difficult to provide timely and accurate proposal materials. Traditional methods often involve manual data collection, analysis, and document creation, resulting in inefficiency. Therefore, there is a need for a system that automates these processes, enabling the rapid and accurate generation of cost estimates and proposal materials.

[0573] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0574] In this invention, the server includes information receiving means for inputting conditions provided by the user, data collection means for obtaining data necessary for calculations from external information sources and databases, and analysis means for performing calculations using a generated AI model based on the collected data and the user's input conditions. This enables the user to perform calculations efficiently and generate high-quality proposal materials quickly.

[0575] "Information receiving means" refers to a means for inputting conditions provided by the user and transmitting them to the system, and includes the user interface.

[0576] "Data collection methods" refer to means of obtaining data necessary for calculations from external sources or databases, and include technologies for collecting information using APIs and data queries.

[0577] A "generative AI model" is an artificial intelligence model that learns from past data and patterns to perform calculations and analyses based on user conditions.

[0578] "Analysis means" refers to methods for performing calculations based on collected data and user input conditions, and includes techniques for deriving optimal results using a generative AI model.

[0579] "Document generation means" refers to means for automatically creating reports based on calculation results, and includes technology for formatting documents in a visually-oriented format.

[0580] "Data provision means" refers to means for providing the generated report to the user's information device, and includes communication technology for securely and quickly transferring data.

[0581] This invention is a system for automating the creation of cost estimates in sales activities. First, the user accesses the interface using a terminal and inputs the conditions necessary for the estimate. The interface provides forms for inputting conditions such as product category, quantity, and delivery date.

[0582] The conditions entered by the user are sent from the terminal to the server. The server receives these conditions using information receiving means and uses them as the starting point for analysis. At this time, the server uses data collection means to gather the necessary data and issues queries to external information sources and multiple databases. These databases include sales history, inventory information, and market trend information.

[0583] Next, the collected data is analyzed by a generative AI model. The generative AI model learns from past data and patterns and automatically performs calculations based on the given conditions. The AI ​​utilizes specific algorithms to perform efficient and accurate pricing and delivery time simulations.

[0584] Subsequently, based on the analyzed results, the server automatically generates a proposal document using a document generation mechanism. This document includes a summary of the analysis results and visual charts and graphs showing related data. The document is created in formats such as PDF and PPTX, and is ready for immediate use in presentations.

[0585] Finally, the generated proposal documents are sent to the user's terminal using a data delivery method. The user downloads the documents and uses them in actual sales activities. The documents are delivered via a secure connection, ensuring the confidentiality and security of the information.

[0586] As a concrete example, when a user is planning to sell a new product, they might prompt the system with instructions such as, "Please provide data to calculate the optimal price and delivery date for the electronics sales plan. Conditions: Product category 'Electronics', Quantity '500', Delivery date '30 days'." This prompt allows the system to collect and analyze the necessary information and quickly provide accurate calculation results.

[0587] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0588] Step 1:

[0589] The user accesses the interface using a terminal and enters the conditions necessary for the calculation. Specifically, they enter the product category, quantity, and delivery date, and click the "Submit" button through the input form. This operation sends the condition information from the terminal to the server. The input consists of the user's business requirements and conditions, and the output is the input data transferred to the server.

[0590] Step 2:

[0591] The server receives data sent from the user using an information receiving device. Based on the received data, it decides which information to collect. The server uses a data collection device to obtain relevant sales history, inventory information, and market trend information. Data acquisition is performed via APIs or database queries. The input is conditional information from the user, and the output is information from the relevant database.

[0592] Step 3:

[0593] The server inputs the collected data into a generating AI model and begins the analysis. The AI ​​model uses historical data to efficiently perform calculations. The AI ​​performs calculations using multivariate analysis and predictive algorithms. The input is the collected data, and the output is the calculation results. Specifically, the AI ​​performs optimal pricing and delivery time simulations.

[0594] Step 4:

[0595] Based on the calculation results, the server automatically generates proposal materials using a document generation system. These materials include a summary of the calculation results and visually displayed graphs. The materials are generated in PDF or PPTX format and processed into a format suitable for sales activities. The input is the calculation results, and the output is a document file usable by the user.

[0596] Step 5:

[0597] The server sends the generated proposal document to the user's terminal via a data delivery system. The document is transferred via a secure connection, and the user can download and use the document on their terminal. The input is the generated document file, and the output is the document that the user can access. Specifically, the user clicks the "Download" button to obtain the document.

[0598] (Application Example 1)

[0599] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0600] In sales activities, it is necessary to quickly and accurately perform calculations based on transaction terms and create optimal proposal materials for customers based on the results. However, with conventional methods, these calculations and material creation require a great deal of time and effort, making it difficult to streamline sales activities. Furthermore, they lacked the flexibility to make proposals that could adapt to the dynamic market environment.

[0601] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0602] In this invention, the server includes means for providing an interface for accepting conditions provided by the user, means for collecting information necessary for calculations from various databases and systems, and means for performing calculations using artificial intelligence based on the collected information and user input. This enables rapid and precise calculations based on transaction conditions and automatic generation of optimal proposal materials adapted to the dynamically changing market environment.

[0603] "Means of providing an interface for accepting conditions provided by the user" refers to elements that enable users to directly provide information by providing a graphical user interface on the device for inputting transaction conditions and requirements.

[0604] "Means for collecting information necessary for estimation from various databases and systems" refers to a device that has a process for accumulating information related to transactions and markets from internal and external data storage systems and constructing the basic data for estimation.

[0605] "Methods for performing calculations using artificial intelligence based on collected information and user input" refers to methods that use artificial intelligence technology to analyze user input conditions and collected data, and systematically calculate the optimal price and service conditions.

[0606] "Method for automatically generating proposal materials" refers to a device that automatically creates sales materials based on calculation results, visualizes those materials, and structures them so that they can be presented to users.

[0607] "Means of providing generated materials to information terminals" refers to technical means that transfer the created sales proposal materials to the user's digital device, enabling direct use.

[0608] "A means of generating proposal documents to suggest the optimal settings based on transaction conditions" refers to a system that automatically constructs proposal documents that reflect the optimal commission rates and services provided by the user, according to the transaction conditions they have provided.

[0609] This invention provides a system that enables the rapid and accurate generation of calculations and proposal documents based on transaction conditions in sales activities. The entire system consists of user terminals, a central server, and a database.

[0610] Users first input transaction terms through an interface provided on a device such as a smartphone or tablet. This interface is built as a user-friendly graphical user interface (GUI) and is implemented using React Native technology. The input terms include transaction amount, contract period, industry, etc.

[0611] The entered conditions are sent via the internet to a central cloud server. The server uses a Python script to collect relevant data from a database system (e.g., AWS RDS or MongoDB). Based on the collected data, an AI model (using TensorFlow) determines the optimal fee settings and profit forecasts for the trading conditions.

[0612] The server creates a proposal document in Markdown format based on the calculation results obtained by the generating AI model. This document will include not only the calculated figures but also graphs and charts that are easy to understand visually. Finally, the generated proposal document will be converted to HTML format and provided to the user's device for download.

[0613] As a concrete example, consider a scenario where a sales representative wants to propose annual transaction terms to a new retail customer. The representative opens the application and enters the following conditions: "Transaction amount: 5 million yen, contract period: 1 year, industry: retail." The system quickly processes these conditions and generates a proposal document with the optimal commission rate based on past data.

[0614] An example of a prompt message would be: "Please suggest the optimal fee structure based on past transaction data. The conditions are as follows: transaction amount of 5 million yen, contract period of 1 year, industry is retail."

[0615] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0616] Step 1:

[0617] The user launches the application on their device and enters transaction terms through the interface. Specifically, they enter information such as the transaction amount, contract period, and industry, which is then converted into JSON format within the application. The entered information is formatted for data transmission from the user interface and sent to the server via the network.

[0618] Step 2:

[0619] The server parses the received transaction conditions in JSON format. Based on the parsed transaction conditions, it sends queries to a database that stores the necessary historical transaction data. A Python script is used here to issue SQL and NoSQL queries to databases such as AWS RDS and MongoDB. The data extracted from the database is returned to the server.

[0620] Step 3:

[0621] The server performs data analysis using an AI model based on the return values ​​from the received database and the input transaction conditions. A generative AI model built using TensorFlow plays a major role in this process. The AI ​​model calculates the optimal fee settings and terms of service based on past transaction data and outputs the simulation results.

[0622] Step 4:

[0623] The server receives the analysis results from the AI ​​model and automatically generates proposal documents based on them. The proposal documents convert the numerical data from the generated AI model into Markdown format and add graphs and charts as visual elements as needed. This formats the information in a way that is easy for the user to understand.

[0624] Step 5:

[0625] The server converts the generated proposal document into HTML format and prepares it for transmission to the user's device. Finally, the document is provided to the user's device as a download link. The user receives a notification and can view and utilize the proposal document from their device.

[0626] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0627] This invention is a system that automates calculations based on user-provided conditions and combines these calculations with an emotion engine to enable more effective sales proposals. This system identifies the user's emotions and generates proposal materials accordingly.

[0628] First, the user enters the conditions for the calculation via the terminal interface. These conditions include product information, quantity, and desired delivery date. The interface sends the user's input to the emotion engine and simultaneously transmits the condition data to the server.

[0629] The server collects relevant information to perform calculations based on user input and past data. During this process, an emotion engine is activated to analyze the emotions and context embedded in the user's input. This analysis is then used to generate more personalized calculations.

[0630] Next, the server uses artificial intelligence to calculate a streamlined price proposal and supply conditions. Simultaneously, it adjusts the calculation results to reflect the analysis results of the emotion engine, deriving the optimal proposal.

[0631] Subsequently, the server automatically creates a proposal document based on the generated calculation results. The tone and content of the document are optimized for the user, taking into account the output of the emotion engine. The document incorporates emotion-based language and expressions that are more likely to resonate with the user.

[0632] Finally, the server provides the proposal materials to the user's terminal. These materials are provided in a downloadable format and can be immediately used in sales proposals. This system enables proposals based on deep insights that take into account the user's emotions, making it a powerful tool for strengthening customer relationships.

[0633] The following describes the processing flow.

[0634] Step 1:

[0635] The user accesses the terminal interface and enters the conditions necessary for the calculation. Specifically, they provide details such as the type of product, quantity, and desired delivery date. Along with these conditions, the interface transmits the user's facial expressions and tone of voice to the emotion engine, acquiring emotional data as well.

[0636] Step 2:

[0637] The server activates the emotion engine and analyzes the user's emotions and reactions during input. The emotion engine performs voice analysis and text mining to evaluate the user's emotions. This evaluation result is reflected in subsequent calculations.

[0638] Step 3:

[0639] The server uses a data aggregation module to collect information from various databases based on user input. This includes inventory information, past sales history, market trends, and other data, preparing it for calculations.

[0640] Step 4:

[0641] The server uses artificial intelligence to perform calculations and determine the optimal pricing and terms of service. During this process, it considers the results of an emotion engine analysis and adjusts the suggestions to match the user's emotions. For example, if a risk-averse emotion is detected, more stable suggestions will be offered.

[0642] Step 5:

[0643] The server automatically generates proposal documents based on the calculation results using a results generation module. These proposal documents reflect insights gained from sentiment analysis and present information in a format that resonates with users. In addition to graphs and charts, they include emotionally appropriate wording and recommendations.

[0644] Step 6:

[0645] The server provides the generated proposal materials to the user's terminal. The user can download the materials from their terminal and use them immediately for sales activities. At this time, the materials are customized to take emotions into consideration, enabling proposals that meet the user's expectations.

[0646] (Example 2)

[0647] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0648] Traditional sales proposal systems could perform calculations based on user conditions, but they struggled to automatically create personalized proposals that took user emotions into account. As a result, the quality of proposals tended to be uniform, and there was a problem in not being able to gain sufficient empathy to deepen relationships with customers.

[0649] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0650] In this invention, the server includes means for providing an input / output device for receiving conditions provided by the user, means for collecting data necessary for calculations from various storage devices and management devices, and means for analyzing the user's emotions using an emotion analysis device. This enables the automatic creation of proposal materials that take the user's emotions into consideration, and facilitates effective sales proposals that strengthen relationships with customers.

[0651] An "input / output device" is a device that receives conditional information from a user and transmits it to a server.

[0652] A "storage device" is a device that stores and manages the data necessary for calculations and makes that data accessible to a server.

[0653] A "management device" is a device designed to efficiently collect and manipulate data.

[0654] An "emotion analysis device" is a device that analyzes user input data to determine emotions and their context.

[0655] "Generated intelligence" refers to artificial intelligence technology used to perform calculations and create proposals based on large amounts of data.

[0656] A "proposal document" is a sales proposal document that is automatically generated based on the results of calculations and sentiment analysis.

[0657] This system's program automates calculations based on user-provided conditions and personalizes sales proposals by combining them with an emotion engine. Users access the interface using a terminal and input conditions for calculations. The terminal receives this information, sends it to the server as input, and simultaneously passes the data to the emotion analysis device.

[0658] The server collects data from storage and management devices based on the input conditions. This allows the server to efficiently gather the information necessary for calculations. Based on the collected data, the server utilizes a generative AI model to calculate reasonable pricing and terms of service. Furthermore, the server uses an emotion analysis device to analyze the user's emotions, reflects the results in the calculations, and adjusts the content of the proposal.

[0659] For example, suppose a user is considering purchasing a new computer. The user inputs their desired specifications, budget, and delivery date via a terminal. The server collects market data based on these conditions and analyzes the user's emotions (e.g., their emphasis on price). Based on these results, the server generates a proposal for a cost-effective product. The generated proposal includes emotionally resonant language and is provided to the user in an immediately downloadable format.

[0660] This system ensures that proposals take customer emotions into consideration, enabling higher-quality sales proposals. As a result, customer relationships are strengthened, and the effectiveness of sales activities is enhanced.

[0661] Example of a prompt

[0662] "Based on the user's input, please propose new PC models. If the user is concerned about price, prioritize suggesting models with high cost-performance."

[0663] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0664] Step 1:

[0665] The user inputs the conditions for the calculation through the terminal's interface. For example, product information, quantity, and desired delivery date are input items. The terminal transmits this information to the sentiment analysis device and also transfers the condition data to the server. This allows the server to receive the initial data input.

[0666] Step 2:

[0667] The server collects necessary data from relevant storage and management devices based on the received conditional data. This data is essential for performing quick and accurate calculations. As a result of the data collection, important information such as inventory status and market prices is output.

[0668] Step 3:

[0669] The server analyzes the user's emotions and intentions based on conditional data received using an emotion analysis device. A generative AI model assists this process, performing deep analysis of the user's text input and making emotional judgments such as positive or negative. The analysis results are used in the next stage of calculation.

[0670] Step 4:

[0671] The server uses a generative AI model based on collected data and sentiment analysis results to perform calculations. These calculations determine reasonable pricing and supply conditions. The output is a realistic and user-friendly price and condition proposal.

[0672] Step 5:

[0673] The server automatically generates a proposal, taking into account the calculation results and sentiment analysis. This proposal's tone is adjusted to match the user's emotions, and the document generation technology using a generation AI model incorporates expressions that resonate with the user.

[0674] Step 6:

[0675] The server transfers the completed proposal to the terminal. The terminal then provides the proposal in a format that the user can review and download. This allows the user to immediately utilize the proposal in their sales activities.

[0676] (Application Example 2)

[0677] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0678] In modern e-commerce, providing consumers with the most suitable product recommendations is crucial for attracting their interest and increasing their purchase intent. However, existing systems struggle to efficiently provide personalized recommendations that incorporate consumer emotions. Therefore, there is a need for a system that analyzes consumer input conditions and emotions in real time and provides optimized recommendation materials based on that analysis.

[0679] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0680] In this invention, the server includes means for providing an output unit for receiving criteria provided by the user, means for acquiring information necessary for calculations from various information infrastructures and systems, and means for performing calculations using machine learning based on the acquired information and user input. This makes it possible to provide personalized product suggestions in real time based on the user's emotions and past behavior.

[0681] A "user" is a specific individual or organization that uses the system to input conditions and receive suggestions.

[0682] "Criteria" refers to the conditions and requirements related to the calculations and proposals provided by the user.

[0683] The term "output section" refers to the general term for interfaces and devices that users use to input references.

[0684] "Estimate" refers to the evaluation results of prices, supply conditions, etc., calculated based on the input criteria.

[0685] "Information infrastructure" refers to all electronic databases and systems that store and provide the information necessary for calculations.

[0686] "Machine learning" is a method that learns patterns from past data and uses new data to perform inferences and predictions.

[0687] "Emotion recognition output" refers to data obtained by analyzing and identifying the user's emotions.

[0688] A "proposal document" is a collection of information presented to consumers, automatically generated based on calculation results and emotion recognition output.

[0689] "Two-dimensional and three-dimensional representations" refer to formats for representing graphical visual information included in proposal documents, and serve to visually supplement the information.

[0690] "Communication terminal" refers to all electronic devices used by users to receive proposal documents.

[0691] The system for realizing this invention is designed to analyze user input and their emotions, and to provide consumers with the most suitable product recommendations. Users input purchasing criteria through a communication terminal interface. These criteria include product category, price range, and desired delivery date. Once data is entered, the terminal transmits it to an information infrastructure and prepares to retrieve the necessary information.

[0692] The server uses machine learning models to perform calculations based on collected information and user input. Platforms such as AWS SageMaker can be used for this purpose. Furthermore, technologies such as IBM Watson are used as emotion recognition engines to interpret user emotions and intentions.

[0693] By combining the acquired calculation results and emotion recognition output, the server automatically generates a proposal document. This document includes two-dimensional and three-dimensional visual information and is graphically represented, making it easy for users to understand. The generated document is immediately transmitted to the communication terminal and presented to the user.

[0694] As a concrete example, suppose a user is searching for "new sports shoes." Based on their past purchase history and reviews, it is analyzed that they are interested in sporty design and durability. Based on this information, the server suggests shoes that match these attributes and creates materials that include catchy phrases such as "Innovative design to make everyday running comfortable."

[0695] Examples of prompts for a generative AI model:

[0696] "The user is looking for new sports shoes. Based on their past reviews, they have a strong interest in sporty design and durability. Please generate personalized product suggestions and compelling taglines."

[0697] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0698] Step 1:

[0699] The user inputs criteria such as product category, price range, and desired delivery date through the interface of a communication terminal. The terminal's role is to transmit this information as input data to the server. The output here is the input criteria information.

[0700] Step 2:

[0701] The server retrieves relevant data from the information infrastructure based on the received reference information. This data includes product inventory, pricing information, and past transaction history. Using the retrieved data as input, the server filters the necessary information and prepares it for calculation. The output is the filtered product data.

[0702] Step 3:

[0703] The server utilizes a machine learning model to perform calculations based on filtered product data and user criteria information. In this process, a machine learning model (e.g., AWS SageMaker) is used to calculate pricing and optimal supply conditions. The input to the process is filtered product data, and the output is the calculation result.

[0704] Step 4:

[0705] Simultaneously, the server uses an emotion recognition engine to analyze emotions from user input. This utilizes an implementation for emotion recognition (e.g., IBM Watson). The input is the user's baseline information, and the output is the result of emotion recognition.

[0706] Step 5:

[0707] The server integrates the calculation results and emotion recognition results to automatically generate proposal materials. The materials integrate two-dimensional and three-dimensional visual information and add promotional text and taglines that resonate with the user's emotions. Using a generation AI model, it creates wording optimized for the user's interests and emotions. The output is the generated proposal material.

[0708] Step 6:

[0709] The server sends the generated proposal document to the communication terminal and displays it to the user. This document is provided directly to the user and used for purchasing decisions. The output is the proposal document provided to the user.

[0710] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0711] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0712] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0713] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0714] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0715] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0716] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0717] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0718] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0719] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0720] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0721] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0722] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0723] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0724] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0725] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0726] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0727] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0728] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0729] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0730] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0731] The following is further disclosed regarding the embodiments described above.

[0732] (Claim 1)

[0733] A means of providing an interface for accepting conditions provided by the user,

[0734] Means for collecting the information necessary for the calculation from various databases and systems,

[0735] A means of performing calculations using artificial intelligence based on collected information and user input,

[0736] A method for automatically generating proposal documents based on the calculation results,

[0737] Means for providing the generated materials to the user,

[0738] A system that includes this.

[0739] (Claim 2)

[0740] The system according to claim 1, which calculates the optimal pricing and terms of service based on conditions entered by the user.

[0741] (Claim 3)

[0742] The proposed document includes visual information, including graphs and charts, according to claim 1.

[0743] "Example 1"

[0744] (Claim 1)

[0745] A means for receiving information to input conditions provided by the user,

[0746] A data collection method for obtaining the data necessary for the calculation from external sources and databases,

[0747] An analytical means for performing calculations using a generated AI model based on collected data and user input conditions,

[0748] A document generation method for automatically generating a report based on the calculated calculation results,

[0749] A data provision means for providing the generated report to the user's information device,

[0750] A system that includes this.

[0751] (Claim 2)

[0752] The system according to claim 1, which calculates optimized pricing and time management in accordance with conditions provided by the user.

[0753] (Claim 3)

[0754] The system according to claim 1, the report includes visual elements such as charts and graphs.

[0755] "Application Example 1"

[0756] (Claim 1)

[0757] A means of providing an interface for accepting conditions provided by the user,

[0758] Means for collecting the information necessary for the calculation from various databases and systems,

[0759] A means of performing calculations using artificial intelligence based on collected information and user input,

[0760] A method for automatically generating proposal documents based on the calculation results,

[0761] A means of providing the generated materials to an information terminal,

[0762] A means for generating proposal documents to suggest the optimal settings based on transaction conditions,

[0763] A system that includes this.

[0764] (Claim 2)

[0765] The system according to claim 1, which calculates the optimal pricing and terms of service based on conditions entered by the user.

[0766] (Claim 3)

[0767] The proposed document includes visual information, including graphs and charts, according to claim 1.

[0768] "Example 2 of combining an emotion engine"

[0769] (Claim 1)

[0770] Means for providing an input / output device for accepting conditions provided by the user,

[0771] A means for collecting data necessary for calculations from various storage devices and management devices,

[0772] A means of performing calculations using intelligence generated based on collected data and user input,

[0773] A means of analyzing a user's emotions using an emotion analysis device,

[0774] A method for automatically generating a proposal based on the calculation results and the results of sentiment analysis,

[0775] A means of providing the generated proposal to the user,

[0776] A system that includes this.

[0777] (Claim 2)

[0778] The system according to claim 1, which calculates optimal pricing and service conditions based on user input and the results of sentiment analysis.

[0779] (Claim 3)

[0780] The proposed document includes wording optimized based on sentiment analysis, according to claim 1.

[0781] "Application example 2 when combining with an emotional engine"

[0782] (Claim 1)

[0783] Means for providing an output unit for accepting criteria provided by the user,

[0784] Means for obtaining the information necessary for the calculation from various information infrastructures and systems,

[0785] A means of performing calculations using machine learning based on acquired information and user input,

[0786] A means for automatically generating proposal documents that reflect the calculation results and emotion recognition output,

[0787] A means of providing the generated data to a communication terminal,

[0788] A system that includes this.

[0789] (Claim 2)

[0790] The system according to claim 1, which calculates optimal pricing and proposal conditions in accordance with the criteria and emotion recognition output entered by the user.

[0791] (Claim 3)

[0792] The system according to claim 1, wherein the proposed document includes visual information including two-dimensional and three-dimensional displays. [Explanation of Symbols]

[0793] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of providing an interface for accepting conditions provided by the user, Means for collecting the information necessary for the calculation from various databases and systems, A means of performing calculations using artificial intelligence based on collected information and user input, A method for automatically generating proposal documents based on the calculation results, A means of providing the generated materials to an information terminal, A means for generating proposal documents to suggest the optimal settings based on transaction conditions, A system that includes this.

2. The system according to claim 1, which calculates the optimal pricing and service conditions according to the conditions entered by the user.

3. The system according to claim 1, wherein the proposed document includes visual information including graphs and charts.

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