system

Abstract

We provide the system. [Solution] Information analysis means for analyzing electronic information received from a data transmission device, A message generation means that automatically generates the content of an electronic communication message based on the information analyzed by the aforementioned information analysis means, Before sending the generated message, a means for verifying the validity of the recipient information is provided, A case reference means that optimizes the operation of the message generation means by referring to a database of similar past cases, A synchronization means that, after sending the aforementioned message, links with the sales support automation system, 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Mistransmission in electronic communication has a high risk of causing information leakage and misunderstanding, which has become a major issue in corporate activities. In addition, analyzing information and generating emails takes a lot of time and effort, which is a factor reducing business efficiency. Therefore, there is a need for a method to prevent mistransmission and at the same time improve business efficiency.

Means for Solving the Problems

[0005] The present invention includes information analysis means for analyzing electronic information received from a data transmission device. Furthermore, it includes message generation means for automatically generating electronic communication messages based on the analyzed information, and destination verification means for confirming the validity of destination information before transmission. It also includes case reference means for optimizing by referring to similar past cases, and means for linking with an automated sales support system through synchronization processing. This configuration enables efficient business execution while reducing the risk of erroneous transmission.

[0006] A "data transmission device" is a device that has the function of transmitting electronic information to another device or system.

[0007] "Electronic information" refers to data and messages expressed in digital format that are transmitted and received through communication means.

[0008] "Information analysis means" refers to a system element that includes processes and technologies for analyzing received electronic information and interpreting its content.

[0009] A "message generation means" refers to a function or device that automatically generates messages for use in electronic communication based on analyzed information.

[0010] A "destination confirmation means" is a system element that performs processing to verify the accuracy and validity of destination information before sending an electronic communication message.

[0011] A "case reference method" is a function that references data from similar past cases to extract the optimal processing method and information, thereby improving or optimizing the system's operation.

[0012] A "synchronization mechanism" is a function or process that performs coordination or temporal adjustment to maintain the consistency of data and information between different systems or devices.

[0013] A "sales support automation system" refers to any automated information system designed to streamline and effectively support sales activities. [Brief explanation of the drawing]

[0014] [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]

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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".

[0035] The system according to the present invention consists of a server, a terminal, and a user interface. The server functions as a central control unit and is responsible for analyzing electronic information, generating messages, confirming destinations, and synchronizing with the sales support automation system. The terminal is a device for user access and provides an interface to assist with information input, message preview, and confirmation operations.

[0036] Specifically, the user inputs or uploads electronic information using a terminal. The terminal organizes the received information and sends it to the server. The server analyzes the data using advanced information analysis tools and extracts the necessary information. Subsequently, the server selects the optimal message template using past similar case data via a case reference tool, and generates a specific electronic communication message using a message generation tool.

[0037] Once message generation is complete, the server verifies the validity of the destination information using a destination verification mechanism. This verification is performed by comparing it with a database of past transmission history. After confirming that the destination is correct, the server sends an intent confirmation to the terminal for final approval.

[0038] Once the user approves the transmission, the server sends the message and synchronizes it with the sales support automation system. This automatically updates the sales record and presents the user with the best course of action to take next.

[0039] For example, if a user wants to submit a proposal for a new project, they enter the proposal data into their terminal. This data is then analyzed on the server, and a message is generated based on similar past projects. After recipient verification, the proposal is sent to the appropriate recipient with the user's approval. This entire process prevents misdelivery and improves the efficiency of business processes.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user inputs or uploads electronic information using a terminal. The terminal receives this information, temporarily stores it in a local database, and checks the data format.

[0043] Step 2:

[0044] The terminal transmits electronic information to the server. The server confirms receipt of the data and prepares to analyze it.

[0045] Step 3:

[0046] The server analyzes electronic information using information analysis tools and extracts important data points. This involves using natural language processing techniques and text mining.

[0047] Step 4:

[0048] The server uses a case reference mechanism to search a database of similar past cases and selects the most suitable message template based on the analyzed information.

[0049] Step 5:

[0050] The server uses a message generation mechanism to automatically generate specific electronic communication messages based on a selected template. The generated content corresponds to customer attributes.

[0051] Step 6:

[0052] The generated message is sent from the server to the terminal, which then displays it to the user as a preview. The user reviews the displayed message and edits it as needed.

[0053] Step 7:

[0054] When a user approves a message, the device sends approval information back to the server. The server uses a recipient verification method to confirm the validity of the recipient information by comparing it with past transmission history.

[0055] Step 8:

[0056] Once recipient verification is complete, the server executes the transmission of the electronic communication message. It monitors whether the message transmission was successful and records the result.

[0057] Step 9:

[0058] After the transmission is complete, the server synchronizes with the sales support automation system. This updates customer information and deal status, and recommends the next action to take on the user's device.

[0059] Step 10:

[0060] Users can view recommended actions from the sales support automation system via their terminals and continue their work processes.

[0061] (Example 1)

[0062] 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."

[0063] In electronic communications, while the automation of message generation processes is progressing, improving accuracy and efficiency requires leveraging historical data, preventing misdeliveries, and quickly providing optimized content to each user. However, current technologies still have limitations in meeting these needs. In particular, there are many instances where manual verification is required in the processes of information analysis, automatic message generation, and recipient verification, which leads to a decrease in overall operational efficiency.

[0064] 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.

[0065] In this invention, the server includes means for analyzing received electronic data using a natural language processing algorithm, means for selecting the optimal message template by referring to a database of past cases, and means for automatically creating electronic communication messages based on prompt sentences via a generation AI model. This automates the process from generating to sending electronic communication messages, enabling users to send optimal messages quickly and accurately.

[0066] A "terminal" is a device used by users to input or upload electronic data and communicate with a server.

[0067] A "server" is a key component of a system that functions as a central control unit, performing tasks such as analyzing received data, generating messages, verifying recipients, and synchronizing with automated sales support systems.

[0068] A "natural language processing algorithm" is a computer program or method for analyzing received text data and automatically extracting necessary information.

[0069] A "case database" is a data store that stores data on similar cases that have been processed in the past, and by referring to that data when processing new cases, it enables the provision of efficient and accurate services.

[0070] A "generative AI model" is a type of artificial intelligence that automatically generates the most appropriate sentence in response to an input prompt, based on a pre-trained algorithm.

[0071] A "prompt statement" is an input statement used to specify necessary information and conditions for a generative AI model, prompting it to generate an appropriate response.

[0072] An "electronic communication message" is a message containing text or data transmitted by electronic means, intended for the exchange of information between users or systems.

[0073] The system according to the present invention is operated through a server, a terminal, and a user interface.

[0074] Users input or upload electronic data using a terminal. The terminal implements a browser-based web application where users input project information and proposals into data fields. This terminal can be a standard personal computer or smartphone.

[0075] The terminal organizes the input electronic information and sends it to the server. A REST API is used for transmission, and standard data formats such as JSON are used for the data format.

[0076] The server uses natural language processing algorithms on the received data. Specifically, it uses a Python program to utilize natural language processing libraries such as "spaCy" and "NLTK" to analyze the data. The server extracts the necessary information from the analysis results and uses it in the next step.

[0077] The case database stores information on similar past cases. The server queries this database to find the most suitable message template. MySQL® and PostgreSQL are often used for database management.

[0078] The server utilizes a generative AI model to automatically generate messages based on the prompt text. This generation uses open-source generative AI models (e.g., the GPT series). An example of an actual prompt text might be: "Based on the new project proposal, please generate the optimal proposal message, referencing past examples."

[0079] The generated message undergoes a recipient verification process by the server and is sent only after final user approval. Recipient verification involves matching the message against a database of previously sent messages.

[0080] This system enables users to conduct electronic communications efficiently and reduces the risk of accidental transmissions. Furthermore, data synchronization with the sales support automation system further optimizes business processes.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] Users input or upload electronic data through a terminal. This allows users to collect project-related information and proposal data, and provide it in the terminal's input fields. The entered data is temporarily stored and organized on the terminal. At this stage, the user's input data is converted into structured data such as JSON format.

[0084] Step 2:

[0085] The terminal sends organized electronic data to the server. The terminal uses a REST API to securely send data to the server. During this process, the terminal verifies the integrity of the data being sent and validates the data fields as needed. The specific output is a formal data request to the server.

[0086] Step 3:

[0087] The server analyzes the received electronic data using natural language processing algorithms. This analysis utilizes Python libraries such as "spaCy" and "NLTK" to extract important keywords and structures from the data. The input is data sent from the terminal, and the output is a data structure containing the analyzed information.

[0088] Step 4:

[0089] The server queries the case database based on the analysis results and selects templates related to similar past cases. Here, a database management system (e.g., MySQL) is used, and relevant template data is retrieved via SQL queries. The input is the analysis results data, and the output is the corresponding message template.

[0090] Step 5:

[0091] The server generates messages based on prompt text using a generative AI model. The server inputs prompt text into a selected template, and the generated AI model (e.g., GPT series) outputs the optimal message. The input is a template and prompt text, and the output is a completed electronic communication message.

[0092] Step 6:

[0093] The server verifies the generated message using a destination verification mechanism. By comparing it with past transmission history and confirming the accuracy of the destination information, it prevents erroneous transmissions. The input is the message's destination data, and the output is the result of the destination verification.

[0094] Step 7:

[0095] To allow the user to approve the final message content and recipient, the server sends confirmation information to the terminal, prompting the user for approval. Specifically, a message confirmation screen is displayed on the user interface, presenting the user with the option to approve or modify. User input is an approval instruction, and the output indicates that the message has been finalized and approved.

[0096] Step 8:

[0097] The server sends approved messages to the specified destination and synchronizes with the sales support automation system. The SMTP protocol is used for transmission, and sales records are automatically updated via the API. All inputs are approved message information, and the output is successful message transmission and updated sales records.

[0098] (Application Example 1)

[0099] 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."

[0100] Traditional electronic payment systems require users to select the optimal payment method themselves, which carries the risk of incorrect selection and fraudulent transactions. Furthermore, they lack sufficient optimization using past transaction history, making efficient payment suggestions difficult.

[0101] 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.

[0102] In this invention, the server includes information analysis means for analyzing electronic information received from a data collection device, support means for automatically proposing the optimal payment method, and settlement processing means for securely processing transactions based on the proposed payment method. This prevents user errors and enables efficient and secure transaction processing.

[0103] A "data acquisition device" is a device that receives electronic information and provides it to information analysis means for analysis.

[0104] An "information analysis means" is a component that has the function of analyzing received electronic information and extracting usable data.

[0105] A "support mechanism" is a component that has the function of suggesting the most suitable payment method to the user based on the analyzed information.

[0106] A "payment processing device" is a component that has the function of securely executing a transaction based on the proposed payment method.

[0107] A "history reference means" is a component that has the function of referencing past transaction details and contributing to the optimization of proposed support measures.

[0108] A "synchronization mechanism" is a component that has the function of exchanging information with an automated settlement management system after transaction processing.

[0109] To implement this invention, the server utilizes a data collection device, information analysis means, support means, payment processing means, history reference means, and synchronization means.

[0110] The program of this system performs the following processes: First, the server receives electronic information via a data collection device. The received information is analyzed by an information analysis device, and relevant data such as the user's transaction history is extracted. Based on this analysis, a support device proposes the optimal payment method to the user.

[0111] Once the user approves the payment according to the proposal, the payment processing system securely executes the transaction. This completes the payment and reduces the risk of fraudulent transactions. After the transaction is completed, the history retrieval system records the transaction in the database, and the synchronization system works with the automated payment management system to maintain the latest transaction status.

[0112] A concrete example of implementation is when a user purchases a book from an online bookstore. Purchase information is sent to the server, and the most suitable payment method is suggested from options such as credit cards and electronic money. For example, a prompt such as "Purchase amount: 5000 yen, Store: Bookstore. Please suggest the best payment method based on past purchase history" is used. This prompt is input into a generating AI model, and an optimized suggestion is provided. Through this process, the user can easily select the best payment method, resulting in safe and efficient payment.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The server receives purchase information transmitted from the terminal. This input data includes product name, price, and supplier information. The server receives this data and passes it to the information analysis system. The information analysis system analyzes this raw data and prepares to extract the necessary transaction information.

[0116] Step 2:

[0117] The server's information analysis mechanism analyzes received purchase information in detail and extracts necessary information from the relevant user's past transaction history database. Here, data calculations are performed to process the data and determine the user's consumption patterns and the priority of available payment methods. The output of this step serves as the basis for suggesting optimized payment methods.

[0118] Step 3:

[0119] The server's support system proposes an appropriate payment method to the user based on the analysis results. The support system utilizes a generative AI model to generate prompt messages based on the input past transaction data. These generated prompt messages output suggestions to the user, including the optimal payment method. For example, a message such as "Purchase amount 5000 yen, store is a bookstore. Please suggest the optimal payment method based on past purchase history." might be output.

[0120] Step 4:

[0121] The user reviews and selects a payment method from the options presented on the terminal. The user's selection is sent back to the server as the payment method, and this decision becomes the input. Next, the server's payment processing system receives this information and executes the transaction using the selected payment method. The payment process is secured by using an encrypted channel.

[0122] Step 5:

[0123] After the transaction is completed, the server's history reference system records the transaction in the database. The transaction information is stored in the history database and made available for reference in future payment proposals. The output of this step is the updated transaction history data.

[0124] Step 6:

[0125] Finally, the server's synchronization mechanism synchronizes information with the automated payment management system. This ensures that transaction information remains up-to-date throughout the entire system. The synchronization mechanism also shares information with external payment management systems, supporting the maintenance of an integrated payment process. Upon completion of this step, the state of the entire system is updated, enabling optimization for the next user.

[0126] 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.

[0127] The system according to the present invention includes a server, a terminal, a user interface, and an emotion engine. The server functions as a central control and is responsible for data analysis, message generation, recipient confirmation, emotion recognition, and synchronization with the sales support automation system. The terminal is a device for user interaction and assists with information input, message preview, and confirmation operations.

[0128] In this system, users input or upload electronic information using a terminal. The terminal sends this information to a server. The server manages the process of analyzing the electronic information using information analysis tools and extracting specific data. The analyzed information is then evaluated by an emotion engine, which assesses the user's emotional state and adjusts the tone of messages accordingly.

[0129] The emotion engine learns emotional tendencies from the user's past communication data. It also has the ability to evaluate the user's current emotions by analyzing voice or text data in real time. Based on this information, the message generation system automatically generates messages that take into account the analyzed data and the user's emotional state.

[0130] The generated message is sent to the terminal, where it is previewed by the user. If the user approves the message content, the terminal notifies the server of the approval. The server uses a recipient verification mechanism to check the recipient information and refers to past databases. Once all verifications are complete, the server sends the message and synchronizes the data with the sales support automation system.

[0131] For example, if a user wants to send product update information to a customer, they input the information on their device, and as the server generates a message from that information, an emotion engine detects the user's current emotions and adjusts the message to an appropriate tone for the customer. By utilizing this system, more effective communication with customers is achieved, and the risk of sending messages to the wrong recipient is reduced.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user inputs or uploads electronic information using a terminal. The terminal receives this information, verifies the data format, and then sends it to the server.

[0135] Step 2:

[0136] The server receives electronic information and analyzes the data using information analysis tools. Through this analysis, the information is classified and important data points are extracted.

[0137] Step 3:

[0138] The server activates an emotion engine, analyzing the user's past and real-time data to evaluate their emotional state. This allows the server to understand what the user is feeling.

[0139] Step 4:

[0140] The server uses a case reference mechanism to search a database of similar past cases and selects the most suitable message template. The selected template is then adjusted to reflect the sentiment information obtained by the sentiment engine.

[0141] Step 5:

[0142] The server automatically generates specific electronic communication messages using message generation means based on pre-configured templates. The generated messages are optimized for the user's emotional state.

[0143] Step 6:

[0144] After the message is generated, the server sends it to the terminal and displays it on a preview screen. The user checks the message and edits it as needed.

[0145] Step 7:

[0146] Once the user approves the message, the device sends that information back to the server. The server uses a recipient verification mechanism to confirm that the recipient information is correct.

[0147] Step 8:

[0148] After confirming the recipient, the server sends the message to the specified recipient. During this process, the server records the transmission results and monitors for any erroneous transmissions.

[0149] Step 9:

[0150] Finally, the server synchronizes with the sales support automation system to update customer information and deal status. This information is presented to the user via their terminal and used for future sales activities.

[0151] (Example 2)

[0152] 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".

[0153] In today's business environment, it is essential to quickly send effective and appropriate electronic messages to customers. However, when composing messages manually, it is difficult to accurately reflect the user's emotional state, and there is also the risk of sending messages to the wrong recipient. This can lead to a decline in the quality of customer communication and the loss of business opportunities.

[0154] 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.

[0155] In this invention, the server includes data analysis means, emotion recognition means, and message generation means. This makes it possible to quickly generate effective messages that take into account the user's emotional state, reduce the risk of sending messages to the wrong recipient, and improve the quality of communication with customers.

[0156] "Data analysis means" refers to a device or method that has the function of analyzing electronic information received from a data transmission device and extracting necessary information.

[0157] "Emotion recognition means" refers to a mechanism or method for identifying a user's emotions by using analyzed information to evaluate the user's emotional state, taking into account the user's past data and real-time data.

[0158] "Message generation means" refers to a mechanism or technology for automatically generating electronic communication messages, taking into account the emotional state obtained by the emotion recognition means.

[0159] A "destination verification method" is a technique or technology used to verify the validity of destination information before sending a generated message, thereby preventing errors in the recipient's address.

[0160] An "information referencing means" is a mechanism for optimizing the operation of the message generation means by referring to a database of similar past information.

[0161] "Synchronization means" refers to a technology or method that integrates information with an automated business support system after a message has been sent, thereby improving processing efficiency.

[0162] This system includes a server, terminals, a user interface, data analysis means, emotion recognition means, message generation means, destination confirmation means, information referencing means, and synchronization means.

[0163] The user first uses a device to input or upload the information they want to send as a file. The device then sends the input information to the server. In this process, general-purpose digital devices such as personal computers and smartphones are used as devices.

[0164] The server operates as a computer device with advanced processing capabilities and analyzes electronic information received from terminals using data analysis tools. This analysis utilizes natural language processing (NLP) techniques to extract keywords and understand context.

[0165] Next, the emotion recognition system evaluates the user's emotional state based on the analyzed information. This includes a function that utilizes past communication data and real-time data to analyze the user's emotional state.

[0166] In the message generation system, a generation AI model automatically generates an electronic communication message with an appropriate tone, taking into account the analyzed data and emotional state. In this process, a prompt such as "Create a message to deliver new product update information to customers in a respectful tone" may be provided.

[0167] Subsequently, a recipient verification mechanism confirms the validity of the recipient information before the generated message is sent. At this stage, a database of past transmission history is referenced to minimize the risk of sending messages to the wrong recipient.

[0168] Finally, the server actually sends the message and integrates it with the business support automation system. This integration synchronizes the data, ensuring that the transmitted information is seamlessly integrated into other business processes. This streamlines information utilization across the entire organization.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user uses a terminal to input or upload information they wish to send as a file. The terminal sends the input information to the server. This input is in the form of text data or a document file, and the transmitted information is received as input. In this step, the terminal acts as an interface for properly capturing the user's information and transferring it to the server.

[0172] Step 2:

[0173] The server analyzes the electronic information received from the terminal using data analysis tools. This input data is in text format, and the server uses natural language processing technology to extract keywords and process the data to understand the context. The output of the analysis is structured data with the necessary information identified. In this step, the server plays a role in efficiently extracting appropriate information from a large amount of data.

[0174] Step 3:

[0175] The server uses the analyzed information to evaluate the user's emotional state through its emotion recognition system. The input consists of analyzed data, including past communication history and real-time data. An emotion analysis algorithm is used for data processing, and its output serves as an indicator of the user's emotional state. This evaluation allows the server to accurately understand the user's intentions and emotions.

[0176] Step 4:

[0177] Based on the evaluation of the emotional state, the server uses a generative AI model to automatically generate a message with an appropriate tone. The prompt "Create a message to inform customers about new product updates in a respectful tone" is the input, and the generated message is obtained as output. The AI ​​model generates natural-sounding sentences that are appropriate to the situation based on the received prompt.

[0178] Step 5:

[0179] The generated message is sent from the server to the terminal, where the user previews and confirms it. If the user confirms and approves the message, the terminal notifies the server. The input for this step is the generated message, and the output is the user's approval status. The terminal is responsible for providing the confirmed message to the user and receiving feedback.

[0180] Step 6:

[0181] The server verifies the validity of the destination information using a destination verification mechanism. A database of past transmission history is referenced, and the message's destination information is provided as input. The output is a list of verified destinations. In this step, the server minimizes the risk of sending messages to the wrong recipient.

[0182] Step 7:

[0183] The server actually sends the message after confirmation and further synchronizes the data with the business support automation system. Inputs include approved messages and recipient information, and outputs include the message delivery status and synchronized business data. In this step, the server performs information transmission and data integration within the organization.

[0184] (Application Example 2)

[0185] 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".

[0186] Conventional electronic communication systems suffer from a decline in communication quality because message content cannot adequately address the emotional state of the user or the characteristics of individual recipients. Furthermore, they lack sufficient mechanisms to prevent problems caused by erroneous transmissions. Solving these problems is the objective of this invention.

[0187] 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.

[0188] In this invention, the server includes data analysis means, a message generation unit, and emotion adjustment means. This enables message adjustment that takes into account the user's emotional state and communication optimized for each recipient.

[0189] A "data analysis means" is a device that has the function of analyzing electronic information received from a data transmission unit and extracting the necessary data.

[0190] A "message generation unit" is a device that has the function of automatically generating electronic communication messages based on analyzed information.

[0191] A "destination confirmation unit" is a device that has the function of verifying the validity of the destination information before sending a generated message, thereby preventing misdelivery.

[0192] A "reference means" is a device that has the function of referencing data storage of similar past cases and optimizing the operation of the message generation unit.

[0193] A "synchronization unit" is a device that, after a message is sent, works in conjunction with the automated sales support system to synchronize information.

[0194] An "emotion adjustment device" is a device that uses an emotion analysis engine to evaluate the user's emotional state and adjusts the tone and content of messages based on that state.

[0195] A "natural language model" is a machine learning model that processes human language and generates text optimized for each recipient.

[0196] As a form for carrying out the invention, the present invention provides a system that enables message transmission that takes into account the user's emotions, particularly in electronic payment services. This system includes a server, a terminal, a user interface, and an emotion analysis engine.

[0197] The server has data analysis capabilities and analyzes electronic information transmitted from the terminal. It uses Google Cloud's "Natural Language API" and IBM's "Watson® Tone Analyzer" to analyze the user's emotional state. The analyzed emotional data is used by a message generation unit that uses natural language models such as OpenAI's "GPT-3®" to generate emotion-based messages. This allows the user to receive messages in a tone that best suits their emotional state at that time.

[0198] Furthermore, the recipient verification unit checks the validity of the recipient by referring to a database of past transmission records before sending, preventing misdeliveries. After transmission, the server synchronizes information with the sales support automation system through the synchronization unit to maintain data consistency.

[0199] For example, when a user is in a hurry to purchase an e-ticket while traveling, the system's emotion analysis engine can detect if the user is feeling stressed. Based on this, it can generate a purchase completion message in a calming tone to provide reassurance. An example of the prompt text the system might use is as follows: "Generate a transaction completion message in a pleasant tone to send when the user is feeling stressed. However, the content should be related to relaxation."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The terminal receives electronic information, and the user enters purchase information. The entered information is sent to the server. The input includes specific data about the product or service, which forms the basis for processing within the system.

[0203] Step 2:

[0204] The server uses data analysis tools to analyze the received electronic information. In this step, Google Cloud's "Natural Language API" is used to analyze the user's text data to determine their emotional state and evaluate their current emotions. The input is the user's text data, and the output is the analyzed emotional data.

[0205] Step 3:

[0206] Based on the sentiment analysis output, the server activates a message generation unit using OpenAI's "GPT-3" to generate a message that corresponds to the user's emotional state. Here, the generation AI model follows the prompt and outputs a message optimized for the user. A concrete example of this operation is "generating a transaction completion message in a relaxing tone."

[0207] Step 4:

[0208] The generated message is processed by a destination verification unit, which compares it with a database of past transmission records to confirm the validity of the destination. The input is the generated message and destination information, and the output is the verification result that the destination is valid. This step reduces the risk of sending messages to the wrong recipient.

[0209] Step 5:

[0210] The server sends a confirmed message to the terminal and displays a preview to the user. The user reviews the content and approves or modifies it. The input is the confirmed message, and the output is the message approved or modified by the user.

[0211] Step 6:

[0212] After final approval, the server sends a message and synchronizes the information with the sales support automation system using a synchronization unit. This enables data consistency and efficient information management. The input is an approved message, and the output is a transmission completion notification.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] 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.

[0219] 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).

[0220] 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.

[0221] 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.

[0222] 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).

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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".

[0229] The system according to the present invention consists of a server, a terminal, and a user interface. The server functions as a central control unit and is responsible for analyzing electronic information, generating messages, confirming destinations, and synchronizing with the sales support automation system. The terminal is a device for user access and provides an interface to assist with information input, message preview, and confirmation operations.

[0230] Specifically, the user inputs or uploads electronic information using a terminal. The terminal organizes the received information and sends it to the server. The server analyzes the data using advanced information analysis tools and extracts the necessary information. Subsequently, the server selects the optimal message template using past similar case data via a case reference tool, and generates a specific electronic communication message using a message generation tool.

[0231] Once message generation is complete, the server verifies the validity of the destination information using a destination verification mechanism. This verification is performed by comparing it with a database of past transmission history. After confirming that the destination is correct, the server sends an intent confirmation to the terminal for final approval.

[0232] Once the user approves the transmission, the server sends the message and synchronizes it with the sales support automation system. This automatically updates the sales record and presents the user with the best course of action to take next.

[0233] For example, if a user wants to submit a proposal for a new project, they enter the proposal data into their terminal. This data is then analyzed on the server, and a message is generated based on similar past projects. After recipient verification, the proposal is sent to the appropriate recipient with the user's approval. This entire process prevents misdelivery and improves the efficiency of business processes.

[0234] The following describes the processing flow.

[0235] Step 1:

[0236] The user inputs or uploads electronic information using a terminal. The terminal receives this information, temporarily stores it in a local database, and checks the data format.

[0237] Step 2:

[0238] The terminal transmits electronic information to the server. The server confirms receipt of the data and prepares to analyze it.

[0239] Step 3:

[0240] The server analyzes electronic information using information analysis tools and extracts important data points. This involves using natural language processing techniques and text mining.

[0241] Step 4:

[0242] The server uses a case reference mechanism to search a database of similar past cases and selects the most suitable message template based on the analyzed information.

[0243] Step 5:

[0244] The server uses a message generation mechanism to automatically generate specific electronic communication messages based on a selected template. The generated content corresponds to customer attributes.

[0245] Step 6:

[0246] The generated message is sent from the server to the terminal, which then displays it to the user as a preview. The user reviews the displayed message and edits it as needed.

[0247] Step 7:

[0248] When a user approves a message, the device sends approval information back to the server. The server uses a recipient verification method to confirm the validity of the recipient information by comparing it with past transmission history.

[0249] Step 8:

[0250] Once recipient verification is complete, the server executes the transmission of the electronic communication message. It monitors whether the message transmission was successful and records the result.

[0251] Step 9:

[0252] After the transmission is complete, the server synchronizes with the sales support automation system. This updates customer information and deal status, and recommends the next action to take on the user's device.

[0253] Step 10:

[0254] Users can view recommended actions from the sales support automation system via their terminals and continue their work processes.

[0255] (Example 1)

[0256] 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."

[0257] In electronic communications, while the automation of message generation processes is progressing, improving accuracy and efficiency requires leveraging historical data, preventing misdeliveries, and quickly providing optimized content to each user. However, current technologies still have limitations in meeting these needs. In particular, there are many instances where manual verification is required in the processes of information analysis, automatic message generation, and recipient verification, which leads to a decrease in overall operational efficiency.

[0258] 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.

[0259] In this invention, the server includes means for analyzing received electronic data using a natural language processing algorithm, means for selecting the optimal message template by referring to a database of past cases, and means for automatically creating electronic communication messages based on prompt sentences via a generation AI model. This automates the process from generating to sending electronic communication messages, enabling users to send optimal messages quickly and accurately.

[0260] A "terminal" is a device used by users to input or upload electronic data and communicate with a server.

[0261] A "server" is a key component of a system that functions as a central control unit, performing tasks such as analyzing received data, generating messages, verifying recipients, and synchronizing with automated sales support systems.

[0262] A "natural language processing algorithm" is a computer program or method for analyzing received text data and automatically extracting necessary information.

[0263] A "case database" is a data store that stores data on similar cases that have been processed in the past, and by referring to that data when processing new cases, it enables the provision of efficient and accurate services.

[0264] A "generative AI model" is a type of artificial intelligence that automatically generates the most appropriate sentence in response to an input prompt, based on a pre-trained algorithm.

[0265] A "prompt statement" is an input statement used to specify necessary information and conditions for a generative AI model, prompting it to generate an appropriate response.

[0266] An "electronic communication message" is a message containing text or data transmitted by electronic means, intended for the exchange of information between users or systems.

[0267] The system according to the present invention is operated through a server, a terminal, and a user interface.

[0268] Users input or upload electronic data using a terminal. The terminal implements a browser-based web application where users input project information and proposals into data fields. This terminal can be a standard personal computer or smartphone.

[0269] The terminal organizes the input electronic information and sends it to the server. A REST API is used for transmission, and standard data formats such as JSON are used for the data format.

[0270] The server uses natural language processing algorithms on the received data. Specifically, it uses a Python program to utilize natural language processing libraries such as "spaCy" and "NLTK" to analyze the data. The server extracts the necessary information from the analysis results and uses it in the next step.

[0271] The case database stores information on similar past cases. The server queries this database to find the most suitable message template. MySQL or PostgreSQL are often used for database management.

[0272] The server utilizes a generative AI model to automatically generate messages based on the prompt text. This generation uses open-source generative AI models (e.g., the GPT series). An example of an actual prompt text might be: "Based on the new project proposal, please generate the optimal proposal message, referencing past examples."

[0273] The generated message undergoes a recipient verification process by the server and is sent only after final user approval. Recipient verification involves matching the message against a database of previously sent messages.

[0274] This system enables users to conduct electronic communications efficiently and reduces the risk of accidental transmissions. Furthermore, data synchronization with the sales support automation system further optimizes business processes.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] Users input or upload electronic data through a terminal. This allows users to collect project-related information and proposal data, and provide it in the terminal's input fields. The entered data is temporarily stored and organized on the terminal. At this stage, the user's input data is converted into structured data such as JSON format.

[0278] Step 2:

[0279] The terminal sends organized electronic data to the server. The terminal uses a REST API to securely send data to the server. During this process, the terminal verifies the integrity of the data being sent and validates the data fields as needed. The specific output is a formal data request to the server.

[0280] Step 3:

[0281] The server analyzes the received electronic data using natural language processing algorithms. This analysis utilizes Python libraries such as "spaCy" and "NLTK" to extract important keywords and structures from the data. The input is data sent from the terminal, and the output is a data structure containing the analyzed information.

[0282] Step 4:

[0283] The server queries the case database based on the analysis results and selects templates related to past similar cases. Here, a database management system (e.g., MySQL) is utilized, and relevant template data is obtained through SQL queries. The input is the data of the analysis results, and the output is the corresponding message template.

[0284] Step 5:

[0285] The server uses the generative AI model to generate a message based on the prompt text. The server inputs the prompt text into the selected template, and the generated AI model (e.g., GPT series) outputs the optimal message. The input is the template and the prompt text, and the output is the completed electronic communication message.

[0286] Step 6:

[0287] The server verifies the generated message by the destination verification means. By comparing with the past sending history and confirming the accuracy of the destination information, mis-sending is prevented. The input is the destination data of the message, and the output is the result of the destination validity confirmation.

[0288] Step 7:

[0289] In order for the user to approve the content and destination of the final message, the server sends confirmation information to the terminal to prompt the user for approval. Specifically, a message confirmation screen is displayed on the user interface, presenting the user with options to approve or modify. The input from the user is the approval instruction, and the output is the state of final approval.

[0290] Step 8:

[0291] The server sends approved messages to the specified destination and synchronizes with the sales support automation system. The SMTP protocol is used for transmission, and sales records are automatically updated via the API. All inputs are approved message information, and the output is successful message transmission and updated sales records.

[0292] (Application Example 1)

[0293] 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."

[0294] Traditional electronic payment systems require users to select the optimal payment method themselves, which carries the risk of incorrect selection and fraudulent transactions. Furthermore, they lack sufficient optimization using past transaction history, making efficient payment suggestions difficult.

[0295] 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.

[0296] In this invention, the server includes information analysis means for analyzing electronic information received from a data collection device, support means for automatically proposing the optimal payment method, and settlement processing means for securely processing transactions based on the proposed payment method. This prevents user errors and enables efficient and secure transaction processing.

[0297] A "data acquisition device" is a device that receives electronic information and provides it to information analysis means for analysis.

[0298] An "information analysis means" is a component that has the function of analyzing received electronic information and extracting usable data.

[0299] A "support mechanism" is a component that has the function of suggesting the most suitable payment method to the user based on the analyzed information.

[0300] A "payment processing device" is a component that has the function of securely executing a transaction based on the proposed payment method.

[0301] A "history reference means" is a component that has the function of referencing past transaction details and contributing to the optimization of proposed support measures.

[0302] A "synchronization mechanism" is a component that has the function of exchanging information with an automated settlement management system after transaction processing.

[0303] To implement this invention, the server utilizes a data collection device, information analysis means, support means, payment processing means, history reference means, and synchronization means.

[0304] The program of this system performs the following processes: First, the server receives electronic information via a data collection device. The received information is analyzed by an information analysis device, and relevant data such as the user's transaction history is extracted. Based on this analysis, a support device proposes the optimal payment method to the user.

[0305] Once the user approves the payment according to the proposal, the payment processing system securely executes the transaction. This completes the payment and reduces the risk of fraudulent transactions. After the transaction is completed, the history retrieval system records the transaction in the database, and the synchronization system works with the automated payment management system to maintain the latest transaction status.

[0306] As a specific example of implementation, when a user purchases a book at an online bookstore, the purchase information is sent to the server, and a payment method that seems to be the most suitable among options such as credit cards and electronic money is proposed. For example, a prompt sentence like "The purchase amount is 5,000 yen, and the store is a bookstore. Please propose the most optimal payment method based on the past purchase history." is used. This prompt sentence is input into the generation AI model, and an optimized proposal is provided. Through this series of processes, the user can easily select the most optimal payment method, and secure and efficient settlement is achieved.

[0307] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0308] Step 1:

[0309] The server receives the purchase information sent from the terminal. This input data includes the product name, amount, destination information, etc. The server receives this data and passes it to the information analysis means. The information analysis means prepares to analyze this raw data and extract the necessary transaction information.

[0310] Step 2:

[0311] The information analysis means of the server analyzes the received purchase information in detail and extracts the necessary information from the relevant user's past transaction history database. Here, data calculations are performed, and data processing for determining the user's consumption pattern and the priority order of available payment methods is carried out. The output of this step is the basic data for proposing the optimized payment method.

[0312] Step 3:

[0313] The server's support system proposes an appropriate payment method to the user based on the analysis results. The support system utilizes a generative AI model to generate prompt messages based on the input past transaction data. These generated prompt messages output suggestions to the user, including the optimal payment method. For example, a message such as "Purchase amount 5000 yen, store is a bookstore. Please suggest the optimal payment method based on past purchase history." might be output.

[0314] Step 4:

[0315] The user reviews and selects a payment method from the options presented on the terminal. The user's selection is sent back to the server as the payment method, and this decision becomes the input. Next, the server's payment processing system receives this information and executes the transaction using the selected payment method. The payment process is secured by using an encrypted channel.

[0316] Step 5:

[0317] After the transaction is completed, the server's history reference system records the transaction in the database. The transaction information is stored in the history database and made available for reference in future payment proposals. The output of this step is the updated transaction history data.

[0318] Step 6:

[0319] Finally, the server's synchronization mechanism synchronizes information with the automated payment management system. This ensures that transaction information remains up-to-date throughout the entire system. The synchronization mechanism also shares information with external payment management systems, supporting the maintenance of an integrated payment process. Upon completion of this step, the state of the entire system is updated, enabling optimization for the next user.

[0320] 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.

[0321] The system according to the present invention includes a server, a terminal, a user interface, and an emotion engine. The server functions as a central control and is responsible for data analysis, message generation, recipient confirmation, emotion recognition, and synchronization with the sales support automation system. The terminal is a device for user interaction and assists with information input, message preview, and confirmation operations.

[0322] In this system, users input or upload electronic information using a terminal. The terminal sends this information to a server. The server manages the process of analyzing the electronic information using information analysis tools and extracting specific data. The analyzed information is then evaluated by an emotion engine, which assesses the user's emotional state and adjusts the tone of messages accordingly.

[0323] The emotion engine learns emotional tendencies from the user's past communication data. It also has the ability to evaluate the user's current emotions by analyzing voice or text data in real time. Based on this information, the message generation system automatically generates messages that take into account the analyzed data and the user's emotional state.

[0324] The generated message is sent to the terminal, where it is previewed by the user. If the user approves the message content, the terminal notifies the server of the approval. The server uses a recipient verification mechanism to check the recipient information and refers to past databases. Once all verifications are complete, the server sends the message and synchronizes the data with the sales support automation system.

[0325] For example, if a user wants to send product update information to a customer, they input the information on their device, and as the server generates a message from that information, an emotion engine detects the user's current emotions and adjusts the message to an appropriate tone for the customer. By utilizing this system, more effective communication with customers is achieved, and the risk of sending messages to the wrong recipient is reduced.

[0326] The following describes the processing flow.

[0327] Step 1:

[0328] The user inputs or uploads electronic information using a terminal. The terminal receives this information, verifies the data format, and then sends it to the server.

[0329] Step 2:

[0330] The server receives electronic information and analyzes the data using information analysis tools. Through this analysis, the information is classified and important data points are extracted.

[0331] Step 3:

[0332] The server activates an emotion engine, analyzing the user's past and real-time data to evaluate their emotional state. This allows the server to understand what the user is feeling.

[0333] Step 4:

[0334] The server uses a case reference mechanism to search a database of similar past cases and selects the most suitable message template. The selected template is then adjusted to reflect the sentiment information obtained by the sentiment engine.

[0335] Step 5:

[0336] The server automatically generates specific electronic communication messages using message generation means based on pre-configured templates. The generated messages are optimized for the user's emotional state.

[0337] Step 6:

[0338] After the message is generated, the server sends it to the terminal and displays it on a preview screen. The user checks the message and edits it as needed.

[0339] Step 7:

[0340] Once the user approves the message, the device sends that information back to the server. The server uses a recipient verification mechanism to confirm that the recipient information is correct.

[0341] Step 8:

[0342] After confirming the recipient, the server sends the message to the specified recipient. During this process, the server records the transmission results and monitors for any erroneous transmissions.

[0343] Step 9:

[0344] Finally, the server synchronizes with the sales support automation system to update customer information and deal status. This information is presented to the user via their terminal and used for future sales activities.

[0345] (Example 2)

[0346] 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".

[0347] In today's business environment, it is essential to quickly send effective and appropriate electronic messages to customers. However, when composing messages manually, it is difficult to accurately reflect the user's emotional state, and there is also the risk of sending messages to the wrong recipient. This can lead to a decline in the quality of customer communication and the loss of business opportunities.

[0348] 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.

[0349] In this invention, the server includes data analysis means, emotion recognition means, and message generation means. This makes it possible to quickly generate effective messages that take into account the user's emotional state, reduce the risk of sending messages to the wrong recipient, and improve the quality of communication with customers.

[0350] "Data analysis means" refers to a device or method that has the function of analyzing electronic information received from a data transmission device and extracting necessary information.

[0351] "Emotion recognition means" refers to a mechanism or method for identifying a user's emotions by using analyzed information to evaluate the user's emotional state, taking into account the user's past data and real-time data.

[0352] "Message generation means" refers to a mechanism or technology for automatically generating electronic communication messages, taking into account the emotional state obtained by the emotion recognition means.

[0353] A "destination verification method" is a technique or technology used to verify the validity of destination information before sending a generated message, thereby preventing errors in the recipient's address.

[0354] An "information referencing means" is a mechanism for optimizing the operation of the message generation means by referring to a database of similar past information.

[0355] "Synchronization means" refers to a technology or method that integrates information with an automated business support system after a message has been sent, thereby improving processing efficiency.

[0356] This system includes a server, terminals, a user interface, data analysis means, emotion recognition means, message generation means, destination confirmation means, information referencing means, and synchronization means.

[0357] The user first uses a device to input or upload the information they want to send as a file. The device then sends the input information to the server. In this process, general-purpose digital devices such as personal computers and smartphones are used as devices.

[0358] The server operates as a computer device with advanced processing capabilities and analyzes electronic information received from terminals using data analysis tools. This analysis utilizes natural language processing (NLP) techniques to extract keywords and understand context.

[0359] Next, the emotion recognition system evaluates the user's emotional state based on the analyzed information. This includes a function that utilizes past communication data and real-time data to analyze the user's emotional state.

[0360] In the message generation system, a generation AI model automatically generates an electronic communication message with an appropriate tone, taking into account the analyzed data and emotional state. In this process, a prompt such as "Create a message to deliver new product update information to customers in a respectful tone" may be provided.

[0361] Subsequently, a recipient verification mechanism confirms the validity of the recipient information before the generated message is sent. At this stage, a database of past transmission history is referenced to minimize the risk of sending messages to the wrong recipient.

[0362] Finally, the server actually sends the message and integrates it with the business support automation system. This integration synchronizes the data, ensuring that the transmitted information is seamlessly integrated into other business processes. This streamlines information utilization across the entire organization.

[0363] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0364] Step 1:

[0365] The user uses a terminal to input or upload information they wish to send as a file. The terminal sends the input information to the server. This input is in the form of text data or a document file, and the transmitted information is received as input. In this step, the terminal acts as an interface for properly capturing the user's information and transferring it to the server.

[0366] Step 2:

[0367] The server analyzes the electronic information received from the terminal using data analysis tools. This input data is in text format, and the server uses natural language processing technology to extract keywords and process the data to understand the context. The output of the analysis is structured data with the necessary information identified. In this step, the server plays a role in efficiently extracting appropriate information from a large amount of data.

[0368] Step 3:

[0369] The server uses the analyzed information to evaluate the user's emotional state through its emotion recognition system. The input consists of analyzed data, including past communication history and real-time data. An emotion analysis algorithm is used for data processing, and its output serves as an indicator of the user's emotional state. This evaluation allows the server to accurately understand the user's intentions and emotions.

[0370] Step 4:

[0371] Based on the evaluation of the emotional state, the server uses a generative AI model to automatically generate a message with an appropriate tone. The prompt "Create a message to inform customers about new product updates in a respectful tone" is the input, and the generated message is obtained as output. The AI ​​model generates natural-sounding sentences that are appropriate to the situation based on the received prompt.

[0372] Step 5:

[0373] The generated message is sent from the server to the terminal, where the user previews and confirms it. If the user confirms and approves the message, the terminal notifies the server. The input for this step is the generated message, and the output is the user's approval status. The terminal is responsible for providing the confirmed message to the user and receiving feedback.

[0374] Step 6:

[0375] The server verifies the validity of the destination information using a destination verification mechanism. A database of past transmission history is referenced, and the message's destination information is provided as input. The output is a list of verified destinations. In this step, the server minimizes the risk of sending messages to the wrong recipient.

[0376] Step 7:

[0377] The server actually sends the message after confirmation and further synchronizes the data with the business support automation system. Inputs include approved messages and recipient information, and outputs include the message delivery status and synchronized business data. In this step, the server performs information transmission and data integration within the organization.

[0378] (Application Example 2)

[0379] 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."

[0380] Conventional electronic communication systems suffer from a decline in communication quality because message content cannot adequately address the emotional state of the user or the characteristics of individual recipients. Furthermore, they lack sufficient mechanisms to prevent problems caused by erroneous transmissions. Solving these problems is the objective of this invention.

[0381] 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.

[0382] In this invention, the server includes data analysis means, a message generation unit, and emotion adjustment means. This enables message adjustment that takes into account the user's emotional state and communication optimized for each recipient.

[0383] A "data analysis means" is a device that has the function of analyzing electronic information received from a data transmission unit and extracting the necessary data.

[0384] A "message generation unit" is a device that has the function of automatically generating electronic communication messages based on analyzed information.

[0385] A "destination confirmation unit" is a device that has the function of verifying the validity of the destination information before sending a generated message, thereby preventing misdelivery.

[0386] A "reference means" is a device that has the function of referencing data storage of similar past cases and optimizing the operation of the message generation unit.

[0387] A "synchronization unit" is a device that, after a message is sent, works in conjunction with the automated sales support system to synchronize information.

[0388] An "emotion adjustment device" is a device that uses an emotion analysis engine to evaluate the user's emotional state and adjusts the tone and content of messages based on that state.

[0389] A "natural language model" is a machine learning model that processes human language and generates text optimized for each recipient.

[0390] As a form for carrying out the invention, the present invention provides a system that enables message transmission that takes into account the user's emotions, particularly in electronic payment services. This system includes a server, a terminal, a user interface, and an emotion analysis engine.

[0391] The server has data analysis capabilities and analyzes electronic information transmitted from the terminal. It uses Google Cloud's "Natural Language API" and IBM's "Watson Tone Analyzer" to analyze the user's emotional state. The analyzed emotional data is used by a message generation unit that uses natural language models such as OpenAI's "GPT-3" to generate emotion-based messages. This allows the user to receive messages in a tone that best suits their current emotional state.

[0392] Furthermore, the recipient verification unit checks the validity of the recipient by referring to a database of past transmission records before sending, preventing misdeliveries. After transmission, the server synchronizes information with the sales support automation system through the synchronization unit to maintain data consistency.

[0393] For example, when a user is in a hurry to purchase an e-ticket while traveling, the system's emotion analysis engine can detect if the user is feeling stressed. Based on this, it can generate a purchase completion message in a calming tone to provide reassurance. An example of the prompt text the system might use is as follows: "Generate a transaction completion message in a pleasant tone to send when the user is feeling stressed. However, the content should be related to relaxation."

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The terminal receives electronic information, and the user enters purchase information. The entered information is sent to the server. The input includes specific data about the product or service, which forms the basis for processing within the system.

[0397] Step 2:

[0398] The server uses data analysis tools to analyze the received electronic information. In this step, Google Cloud's "Natural Language API" is used to analyze the user's text data to determine their emotional state and evaluate their current emotions. The input is the user's text data, and the output is the analyzed emotional data.

[0399] Step 3:

[0400] Based on the sentiment analysis output, the server activates a message generation unit using OpenAI's "GPT-3" to generate a message that corresponds to the user's emotional state. Here, the generation AI model follows the prompt and outputs a message optimized for the user. A concrete example of this operation is "generating a transaction completion message in a relaxing tone."

[0401] Step 4:

[0402] The generated message is processed by a destination verification unit, which compares it with a database of past transmission records to confirm the validity of the destination. The input is the generated message and destination information, and the output is the verification result that the destination is valid. This step reduces the risk of sending messages to the wrong recipient.

[0403] Step 5:

[0404] The server sends a confirmed message to the terminal and displays a preview to the user. The user reviews the content and approves or modifies it. The input is the confirmed message, and the output is the message approved or modified by the user.

[0405] Step 6:

[0406] After final approval, the server sends a message and synchronizes the information with the sales support automation system using a synchronization unit. This enables data consistency and efficient information management. The input is an approved message, and the output is a transmission completion notification.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] The system according to the present invention consists of a server, a terminal, and a user interface. The server functions as a central control unit and is responsible for analyzing electronic information, generating messages, confirming destinations, and synchronizing with the sales support automation system. The terminal is a device for user access and provides an interface to assist with information input, message preview, and confirmation operations.

[0424] Specifically, the user inputs or uploads electronic information using a terminal. The terminal organizes the received information and sends it to the server. The server analyzes the data using advanced information analysis tools and extracts the necessary information. Subsequently, the server selects the optimal message template using past similar case data via a case reference tool, and generates a specific electronic communication message using a message generation tool.

[0425] Once message generation is complete, the server verifies the validity of the destination information using a destination verification mechanism. This verification is performed by comparing it with a database of past transmission history. After confirming that the destination is correct, the server sends an intent confirmation to the terminal for final approval.

[0426] Once the user approves the transmission, the server sends the message and synchronizes it with the sales support automation system. This automatically updates the sales record and presents the user with the best course of action to take next.

[0427] For example, if a user wants to submit a proposal for a new project, they enter the proposal data into their terminal. This data is then analyzed on the server, and a message is generated based on similar past projects. After recipient verification, the proposal is sent to the appropriate recipient with the user's approval. This entire process prevents misdelivery and improves the efficiency of business processes.

[0428] The following describes the processing flow.

[0429] Step 1:

[0430] The user inputs or uploads electronic information using a terminal. The terminal receives this information, temporarily stores it in a local database, and checks the data format.

[0431] Step 2:

[0432] The terminal transmits electronic information to the server. The server confirms receipt of the data and prepares to analyze it.

[0433] Step 3:

[0434] The server analyzes electronic information using information analysis tools and extracts important data points. This involves using natural language processing techniques and text mining.

[0435] Step 4:

[0436] The server uses a case reference mechanism to search a database of similar past cases and selects the most suitable message template based on the analyzed information.

[0437] Step 5:

[0438] The server uses a message generation mechanism to automatically generate specific electronic communication messages based on a selected template. The generated content corresponds to customer attributes.

[0439] Step 6:

[0440] The generated message is sent from the server to the terminal, which then displays it to the user as a preview. The user reviews the displayed message and edits it as needed.

[0441] Step 7:

[0442] When a user approves a message, the device sends approval information back to the server. The server uses a recipient verification method to confirm the validity of the recipient information by comparing it with past transmission history.

[0443] Step 8:

[0444] Once recipient verification is complete, the server executes the transmission of the electronic communication message. It monitors whether the message transmission was successful and records the result.

[0445] Step 9:

[0446] After the transmission is complete, the server synchronizes with the sales support automation system. This updates customer information and deal status, and recommends the next action to take on the user's device.

[0447] Step 10:

[0448] Users can view recommended actions from the sales support automation system via their terminals and continue their work processes.

[0449] (Example 1)

[0450] 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."

[0451] In electronic communications, while the automation of message generation processes is progressing, improving accuracy and efficiency requires leveraging historical data, preventing misdeliveries, and quickly providing optimized content to each user. However, current technologies still have limitations in meeting these needs. In particular, there are many instances where manual verification is required in the processes of information analysis, automatic message generation, and recipient verification, which leads to a decrease in overall operational efficiency.

[0452] 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.

[0453] In this invention, the server includes means for analyzing received electronic data using a natural language processing algorithm, means for selecting the optimal message template by referring to a database of past cases, and means for automatically creating electronic communication messages based on prompt sentences via a generation AI model. This automates the process from generating to sending electronic communication messages, enabling users to send optimal messages quickly and accurately.

[0454] A "terminal" is a device used by users to input or upload electronic data and communicate with a server.

[0455] A "server" is a key component of a system that functions as a central control unit, performing tasks such as analyzing received data, generating messages, verifying recipients, and synchronizing with automated sales support systems.

[0456] A "natural language processing algorithm" is a computer program or method for analyzing received text data and automatically extracting necessary information.

[0457] A "case database" is a data store that stores data on similar cases that have been processed in the past, and by referring to that data when processing new cases, it enables the provision of efficient and accurate services.

[0458] A "generative AI model" is a type of artificial intelligence that automatically generates the most appropriate sentence in response to an input prompt, based on a pre-trained algorithm.

[0459] A "prompt statement" is an input statement used to specify necessary information and conditions for a generative AI model, prompting it to generate an appropriate response.

[0460] An "electronic communication message" is a message containing text or data transmitted by electronic means, intended for the exchange of information between users or systems.

[0461] The system according to the present invention is operated through a server, a terminal, and a user interface.

[0462] Users input or upload electronic data using a terminal. The terminal implements a browser-based web application where users input project information and proposals into data fields. This terminal can be a standard personal computer or smartphone.

[0463] The terminal organizes the input electronic information and sends it to the server. A REST API is used for transmission, and standard data formats such as JSON are used for the data format.

[0464] The server uses natural language processing algorithms on the received data. Specifically, it uses a Python program to utilize natural language processing libraries such as "spaCy" and "NLTK" to analyze the data. The server extracts the necessary information from the analysis results and uses it in the next step.

[0465] The case database stores information on similar past cases. The server queries this database to find the most suitable message template. MySQL or PostgreSQL are often used for database management.

[0466] The server utilizes a generative AI model to automatically generate messages based on the prompt text. This generation uses open-source generative AI models (e.g., the GPT series). An example of an actual prompt text might be: "Based on the new project proposal, please generate the optimal proposal message, referencing past examples."

[0467] The generated message undergoes a recipient verification process by the server and is sent only after final user approval. Recipient verification involves matching the message against a database of previously sent messages.

[0468] This system enables users to conduct electronic communications efficiently and reduces the risk of accidental transmissions. Furthermore, data synchronization with the sales support automation system further optimizes business processes.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] Users input or upload electronic data through a terminal. This allows users to collect project-related information and proposal data, and provide it in the terminal's input fields. The entered data is temporarily stored and organized on the terminal. At this stage, the user's input data is converted into structured data such as JSON format.

[0472] Step 2:

[0473] The terminal sends organized electronic data to the server. The terminal uses a REST API to securely send data to the server. During this process, the terminal verifies the integrity of the data being sent and validates the data fields as needed. The specific output is a formal data request to the server.

[0474] Step 3:

[0475] The server analyzes the received electronic data using natural language processing algorithms. This analysis utilizes Python libraries such as "spaCy" and "NLTK" to extract important keywords and structures from the data. The input is data sent from the terminal, and the output is a data structure containing the analyzed information.

[0476] Step 4:

[0477] The server queries the case database based on the analysis results and selects templates related to similar past cases. Here, a database management system (e.g., MySQL) is used, and relevant template data is retrieved via SQL queries. The input is the analysis results data, and the output is the corresponding message template.

[0478] Step 5:

[0479] The server generates messages based on prompt text using a generative AI model. The server inputs prompt text into a selected template, and the generated AI model (e.g., GPT series) outputs the optimal message. The input is a template and prompt text, and the output is a completed electronic communication message.

[0480] Step 6:

[0481] The server verifies the generated message using a destination verification mechanism. By comparing it with past transmission history and confirming the accuracy of the destination information, it prevents erroneous transmissions. The input is the message's destination data, and the output is the result of the destination verification.

[0482] Step 7:

[0483] To allow the user to approve the final message content and recipient, the server sends confirmation information to the terminal, prompting the user for approval. Specifically, a message confirmation screen is displayed on the user interface, presenting the user with the option to approve or modify. User input is an approval instruction, and the output indicates that the message has been finalized and approved.

[0484] Step 8:

[0485] The server sends approved messages to the specified destination and synchronizes with the sales support automation system. The SMTP protocol is used for transmission, and sales records are automatically updated via the API. All inputs are approved message information, and the output is successful message transmission and updated sales records.

[0486] (Application Example 1)

[0487] 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."

[0488] Traditional electronic payment systems require users to select the optimal payment method themselves, which carries the risk of incorrect selection and fraudulent transactions. Furthermore, they lack sufficient optimization using past transaction history, making efficient payment suggestions difficult.

[0489] 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.

[0490] In this invention, the server includes information analysis means for analyzing electronic information received from a data collection device, support means for automatically proposing the optimal payment method, and settlement processing means for securely processing transactions based on the proposed payment method. This prevents user errors and enables efficient and secure transaction processing.

[0491] A "data acquisition device" is a device that receives electronic information and provides it to information analysis means for analysis.

[0492] An "information analysis means" is a component that has the function of analyzing received electronic information and extracting usable data.

[0493] A "support mechanism" is a component that has the function of suggesting the most suitable payment method to the user based on the analyzed information.

[0494] A "payment processing device" is a component that has the function of securely executing a transaction based on the proposed payment method.

[0495] A "history reference means" is a component that has the function of referencing past transaction details and contributing to the optimization of proposed support measures.

[0496] A "synchronization mechanism" is a component that has the function of exchanging information with an automated settlement management system after transaction processing.

[0497] To implement this invention, the server utilizes a data collection device, information analysis means, support means, payment processing means, history reference means, and synchronization means.

[0498] The program of this system performs the following processes: First, the server receives electronic information via a data collection device. The received information is analyzed by an information analysis device, and relevant data such as the user's transaction history is extracted. Based on this analysis, a support device proposes the optimal payment method to the user.

[0499] Once the user approves the payment according to the proposal, the payment processing system securely executes the transaction. This completes the payment and reduces the risk of fraudulent transactions. After the transaction is completed, the history retrieval system records the transaction in the database, and the synchronization system works with the automated payment management system to maintain the latest transaction status.

[0500] A concrete example of implementation is when a user purchases a book from an online bookstore. Purchase information is sent to the server, and the most suitable payment method is suggested from options such as credit cards and electronic money. For example, a prompt such as "Purchase amount: 5000 yen, Store: Bookstore. Please suggest the best payment method based on past purchase history" is used. This prompt is input into a generating AI model, and an optimized suggestion is provided. Through this process, the user can easily select the best payment method, resulting in safe and efficient payment.

[0501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0502] Step 1:

[0503] The server receives purchase information transmitted from the terminal. This input data includes product name, price, and supplier information. The server receives this data and passes it to the information analysis system. The information analysis system analyzes this raw data and prepares to extract the necessary transaction information.

[0504] Step 2:

[0505] The server's information analysis mechanism analyzes received purchase information in detail and extracts necessary information from the relevant user's past transaction history database. Here, data calculations are performed to process the data and determine the user's consumption patterns and the priority of available payment methods. The output of this step serves as the basis for suggesting optimized payment methods.

[0506] Step 3:

[0507] The server's support system proposes an appropriate payment method to the user based on the analysis results. The support system utilizes a generative AI model to generate prompt messages based on the input past transaction data. These generated prompt messages output suggestions to the user, including the optimal payment method. For example, a message such as "Purchase amount 5000 yen, store is a bookstore. Please suggest the optimal payment method based on past purchase history." might be output.

[0508] Step 4:

[0509] The user reviews and selects a payment method from the options presented on the terminal. The user's selection is sent back to the server as the payment method, and this decision becomes the input. Next, the server's payment processing system receives this information and executes the transaction using the selected payment method. The payment process is secured by using an encrypted channel.

[0510] Step 5:

[0511] After the transaction is completed, the server's history reference system records the transaction in the database. The transaction information is stored in the history database and made available for reference in future payment proposals. The output of this step is the updated transaction history data.

[0512] Step 6:

[0513] Finally, the server's synchronization mechanism synchronizes information with the automated payment management system. This ensures that transaction information remains up-to-date throughout the entire system. The synchronization mechanism also shares information with external payment management systems, supporting the maintenance of an integrated payment process. Upon completion of this step, the state of the entire system is updated, enabling optimization for the next user.

[0514] 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.

[0515] The system according to the present invention includes a server, a terminal, a user interface, and an emotion engine. The server functions as a central control and is responsible for data analysis, message generation, recipient confirmation, emotion recognition, and synchronization with the sales support automation system. The terminal is a device for user interaction and assists with information input, message preview, and confirmation operations.

[0516] In this system, users input or upload electronic information using a terminal. The terminal sends this information to a server. The server manages the process of analyzing the electronic information using information analysis tools and extracting specific data. The analyzed information is then evaluated by an emotion engine, which assesses the user's emotional state and adjusts the tone of messages accordingly.

[0517] The emotion engine learns emotional tendencies from the user's past communication data. It also has the ability to evaluate the user's current emotions by analyzing voice or text data in real time. Based on this information, the message generation system automatically generates messages that take into account the analyzed data and the user's emotional state.

[0518] The generated message is sent to the terminal, where it is previewed by the user. If the user approves the message content, the terminal notifies the server of the approval. The server uses a recipient verification mechanism to check the recipient information and refers to past databases. Once all verifications are complete, the server sends the message and synchronizes the data with the sales support automation system.

[0519] For example, if a user wants to send product update information to a customer, they input the information on their device, and as the server generates a message from that information, an emotion engine detects the user's current emotions and adjusts the message to an appropriate tone for the customer. By utilizing this system, more effective communication with customers is achieved, and the risk of sending messages to the wrong recipient is reduced.

[0520] The following describes the processing flow.

[0521] Step 1:

[0522] The user inputs or uploads electronic information using a terminal. The terminal receives this information, verifies the data format, and then sends it to the server.

[0523] Step 2:

[0524] The server receives electronic information and analyzes the data using information analysis tools. Through this analysis, the information is classified and important data points are extracted.

[0525] Step 3:

[0526] The server activates an emotion engine, analyzing the user's past and real-time data to evaluate their emotional state. This allows the server to understand what the user is feeling.

[0527] Step 4:

[0528] The server uses a case reference mechanism to search a database of similar past cases and selects the most suitable message template. The selected template is then adjusted to reflect the sentiment information obtained by the sentiment engine.

[0529] Step 5:

[0530] The server automatically generates specific electronic communication messages using message generation means based on pre-configured templates. The generated messages are optimized for the user's emotional state.

[0531] Step 6:

[0532] After the message is generated, the server sends it to the terminal and displays it on a preview screen. The user checks the message and edits it as needed.

[0533] Step 7:

[0534] Once the user approves the message, the device sends that information back to the server. The server uses a recipient verification mechanism to confirm that the recipient information is correct.

[0535] Step 8:

[0536] After confirming the recipient, the server sends the message to the specified recipient. During this process, the server records the transmission results and monitors for any erroneous transmissions.

[0537] Step 9:

[0538] Finally, the server synchronizes with the sales support automation system to update customer information and deal status. This information is presented to the user via their terminal and used for future sales activities.

[0539] (Example 2)

[0540] 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."

[0541] In today's business environment, it is essential to quickly send effective and appropriate electronic messages to customers. However, when composing messages manually, it is difficult to accurately reflect the user's emotional state, and there is also the risk of sending messages to the wrong recipient. This can lead to a decline in the quality of customer communication and the loss of business opportunities.

[0542] 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.

[0543] In this invention, the server includes data analysis means, emotion recognition means, and message generation means. This makes it possible to quickly generate effective messages that take into account the user's emotional state, reduce the risk of sending messages to the wrong recipient, and improve the quality of communication with customers.

[0544] "Data analysis means" refers to a device or method that has the function of analyzing electronic information received from a data transmission device and extracting necessary information.

[0545] "Emotion recognition means" refers to a mechanism or method for identifying a user's emotions by using analyzed information to evaluate the user's emotional state, taking into account the user's past data and real-time data.

[0546] "Message generation means" refers to a mechanism or technology for automatically generating electronic communication messages, taking into account the emotional state obtained by the emotion recognition means.

[0547] A "destination verification method" is a technique or technology used to verify the validity of destination information before sending a generated message, thereby preventing errors in the recipient's address.

[0548] An "information referencing means" is a mechanism for optimizing the operation of the message generation means by referring to a database of similar past information.

[0549] "Synchronization means" refers to a technology or method that integrates information with an automated business support system after a message has been sent, thereby improving processing efficiency.

[0550] This system includes a server, terminals, a user interface, data analysis means, emotion recognition means, message generation means, destination confirmation means, information referencing means, and synchronization means.

[0551] The user first uses a device to input or upload the information they want to send as a file. The device then sends the input information to the server. In this process, general-purpose digital devices such as personal computers and smartphones are used as devices.

[0552] The server operates as a computer device with advanced processing capabilities and analyzes electronic information received from terminals using data analysis tools. This analysis utilizes natural language processing (NLP) techniques to extract keywords and understand context.

[0553] Next, the emotion recognition system evaluates the user's emotional state based on the analyzed information. This includes a function that utilizes past communication data and real-time data to analyze the user's emotional state.

[0554] In the message generation system, a generation AI model automatically generates an electronic communication message with an appropriate tone, taking into account the analyzed data and emotional state. In this process, a prompt such as "Create a message to deliver new product update information to customers in a respectful tone" may be provided.

[0555] Subsequently, a recipient verification mechanism confirms the validity of the recipient information before the generated message is sent. At this stage, a database of past transmission history is referenced to minimize the risk of sending messages to the wrong recipient.

[0556] Finally, the server actually sends the message and integrates it with the business support automation system. This integration synchronizes the data, ensuring that the transmitted information is seamlessly integrated into other business processes. This streamlines information utilization across the entire organization.

[0557] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0558] Step 1:

[0559] The user uses a terminal to input or upload information they wish to send as a file. The terminal sends the input information to the server. This input is in the form of text data or a document file, and the transmitted information is received as input. In this step, the terminal acts as an interface for properly capturing the user's information and transferring it to the server.

[0560] Step 2:

[0561] The server analyzes the electronic information received from the terminal using data analysis tools. This input data is in text format, and the server uses natural language processing technology to extract keywords and process the data to understand the context. The output of the analysis is structured data with the necessary information identified. In this step, the server plays a role in efficiently extracting appropriate information from a large amount of data.

[0562] Step 3:

[0563] The server uses the analyzed information to evaluate the user's emotional state through its emotion recognition system. The input consists of analyzed data, including past communication history and real-time data. An emotion analysis algorithm is used for data processing, and its output serves as an indicator of the user's emotional state. This evaluation allows the server to accurately understand the user's intentions and emotions.

[0564] Step 4:

[0565] Based on the evaluation of the emotional state, the server uses a generative AI model to automatically generate a message with an appropriate tone. The prompt "Create a message to inform customers about new product updates in a respectful tone" is the input, and the generated message is obtained as output. The AI ​​model generates natural-sounding sentences that are appropriate to the situation based on the received prompt.

[0566] Step 5:

[0567] The generated message is sent from the server to the terminal, where the user previews and confirms it. If the user confirms and approves the message, the terminal notifies the server. The input for this step is the generated message, and the output is the user's approval status. The terminal is responsible for providing the confirmed message to the user and receiving feedback.

[0568] Step 6:

[0569] The server verifies the validity of the destination information using a destination verification mechanism. A database of past transmission history is referenced, and the message's destination information is provided as input. The output is a list of verified destinations. In this step, the server minimizes the risk of sending messages to the wrong recipient.

[0570] Step 7:

[0571] The server actually sends the message after confirmation and further synchronizes the data with the business support automation system. Inputs include approved messages and recipient information, and outputs include the message delivery status and synchronized business data. In this step, the server performs information transmission and data integration within the organization.

[0572] (Application Example 2)

[0573] 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."

[0574] Conventional electronic communication systems suffer from a decline in communication quality because message content cannot adequately address the emotional state of the user or the characteristics of individual recipients. Furthermore, they lack sufficient mechanisms to prevent problems caused by erroneous transmissions. Solving these problems is the objective of this invention.

[0575] 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.

[0576] In this invention, the server includes data analysis means, a message generation unit, and emotion adjustment means. This enables message adjustment that takes into account the user's emotional state and communication optimized for each recipient.

[0577] A "data analysis means" is a device that has the function of analyzing electronic information received from a data transmission unit and extracting the necessary data.

[0578] A "message generation unit" is a device that has the function of automatically generating electronic communication messages based on analyzed information.

[0579] A "destination confirmation unit" is a device that has the function of verifying the validity of the destination information before sending a generated message, thereby preventing misdelivery.

[0580] A "reference means" is a device that has the function of referencing data storage of similar past cases and optimizing the operation of the message generation unit.

[0581] A "synchronization unit" is a device that, after a message is sent, works in conjunction with the automated sales support system to synchronize information.

[0582] An "emotion adjustment device" is a device that uses an emotion analysis engine to evaluate the user's emotional state and adjusts the tone and content of messages based on that state.

[0583] A "natural language model" is a machine learning model that processes human language and generates text optimized for each recipient.

[0584] As a form for carrying out the invention, the present invention provides a system that enables message transmission that takes into account the user's emotions, particularly in electronic payment services. This system includes a server, a terminal, a user interface, and an emotion analysis engine.

[0585] The server has data analysis capabilities and analyzes electronic information transmitted from the terminal. It uses Google Cloud's "Natural Language API" and IBM's "Watson Tone Analyzer" to analyze the user's emotional state. The analyzed emotional data is used by a message generation unit that uses natural language models such as OpenAI's "GPT-3" to generate emotion-based messages. This allows the user to receive messages in a tone that best suits their current emotional state.

[0586] Furthermore, the recipient verification unit checks the validity of the recipient by referring to a database of past transmission records before sending, preventing misdeliveries. After transmission, the server synchronizes information with the sales support automation system through the synchronization unit to maintain data consistency.

[0587] For example, when a user is in a hurry to purchase an e-ticket while traveling, the system's emotion analysis engine can detect if the user is feeling stressed. Based on this, it can generate a purchase completion message in a calming tone to provide reassurance. An example of the prompt text the system might use is as follows: "Generate a transaction completion message in a pleasant tone to send when the user is feeling stressed. However, the content should be related to relaxation."

[0588] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0589] Step 1:

[0590] The terminal receives electronic information, and the user enters purchase information. The entered information is sent to the server. The input includes specific data about the product or service, which forms the basis for processing within the system.

[0591] Step 2:

[0592] The server uses data analysis tools to analyze the received electronic information. In this step, Google Cloud's "Natural Language API" is used to analyze the user's text data to determine their emotional state and evaluate their current emotions. The input is the user's text data, and the output is the analyzed emotional data.

[0593] Step 3:

[0594] Based on the sentiment analysis output, the server activates a message generation unit using OpenAI's "GPT-3" to generate a message that corresponds to the user's emotional state. Here, the generation AI model follows the prompt and outputs a message optimized for the user. A concrete example of this operation is "generating a transaction completion message in a relaxing tone."

[0595] Step 4:

[0596] The generated message is processed by a destination verification unit, which compares it with a database of past transmission records to confirm the validity of the destination. The input is the generated message and destination information, and the output is the verification result that the destination is valid. This step reduces the risk of sending messages to the wrong recipient.

[0597] Step 5:

[0598] The server sends a confirmed message to the terminal and displays a preview to the user. The user reviews the content and approves or modifies it. The input is the confirmed message, and the output is the message approved or modified by the user.

[0599] Step 6:

[0600] After final approval, the server sends a message and synchronizes the information with the sales support automation system using a synchronization unit. This enables data consistency and efficient information management. The input is an approved message, and the output is a transmission completion notification.

[0601] 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.

[0602] 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.

[0603] 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.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] 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.

[0607] 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).

[0608] 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.

[0609] 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.

[0610] 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).

[0611] 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.

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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".

[0618] The system according to the present invention consists of a server, a terminal, and a user interface. The server functions as a central control unit and is responsible for analyzing electronic information, generating messages, confirming destinations, and synchronizing with the sales support automation system. The terminal is a device for user access and provides an interface to assist with information input, message preview, and confirmation operations.

[0619] Specifically, the user inputs or uploads electronic information using a terminal. The terminal organizes the received information and sends it to the server. The server analyzes the data using advanced information analysis tools and extracts the necessary information. Subsequently, the server selects the optimal message template using past similar case data via a case reference tool, and generates a specific electronic communication message using a message generation tool.

[0620] Once message generation is complete, the server verifies the validity of the destination information using a destination verification mechanism. This verification is performed by comparing it with a database of past transmission history. After confirming that the destination is correct, the server sends an intent confirmation to the terminal for final approval.

[0621] Once the user approves the transmission, the server sends the message and synchronizes it with the sales support automation system. This automatically updates the sales record and presents the user with the best course of action to take next.

[0622] For example, if a user wants to submit a proposal for a new project, they enter the proposal data into their terminal. This data is then analyzed on the server, and a message is generated based on similar past projects. After recipient verification, the proposal is sent to the appropriate recipient with the user's approval. This entire process prevents misdelivery and improves the efficiency of business processes.

[0623] The following describes the processing flow.

[0624] Step 1:

[0625] The user inputs or uploads electronic information using a terminal. The terminal receives this information, temporarily stores it in a local database, and checks the data format.

[0626] Step 2:

[0627] The terminal transmits electronic information to the server. The server confirms receipt of the data and prepares to analyze it.

[0628] Step 3:

[0629] The server analyzes electronic information using information analysis tools and extracts important data points. This involves using natural language processing techniques and text mining.

[0630] Step 4:

[0631] The server uses a case reference mechanism to search a database of similar past cases and selects the most suitable message template based on the analyzed information.

[0632] Step 5:

[0633] The server uses a message generation mechanism to automatically generate specific electronic communication messages based on a selected template. The generated content corresponds to customer attributes.

[0634] Step 6:

[0635] The generated message is sent from the server to the terminal, which then displays it to the user as a preview. The user reviews the displayed message and edits it as needed.

[0636] Step 7:

[0637] When a user approves a message, the device sends approval information back to the server. The server uses a recipient verification method to confirm the validity of the recipient information by comparing it with past transmission history.

[0638] Step 8:

[0639] Once recipient verification is complete, the server executes the transmission of the electronic communication message. It monitors whether the message transmission was successful and records the result.

[0640] Step 9:

[0641] After the transmission is complete, the server synchronizes with the sales support automation system. This updates customer information and deal status, and recommends the next action to take on the user's device.

[0642] Step 10:

[0643] Users can view recommended actions from the sales support automation system via their terminals and continue their work processes.

[0644] (Example 1)

[0645] 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".

[0646] In electronic communications, while the automation of message generation processes is progressing, improving accuracy and efficiency requires leveraging historical data, preventing misdeliveries, and quickly providing optimized content to each user. However, current technologies still have limitations in meeting these needs. In particular, there are many instances where manual verification is required in the processes of information analysis, automatic message generation, and recipient verification, which leads to a decrease in overall operational efficiency.

[0647] 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.

[0648] In this invention, the server includes means for analyzing received electronic data using a natural language processing algorithm, means for selecting the optimal message template by referring to a database of past cases, and means for automatically creating electronic communication messages based on prompt sentences via a generation AI model. This automates the process from generating to sending electronic communication messages, enabling users to send optimal messages quickly and accurately.

[0649] A "terminal" is a device used by users to input or upload electronic data and communicate with a server.

[0650] A "server" is a key component of a system that functions as a central control unit, performing tasks such as analyzing received data, generating messages, verifying recipients, and synchronizing with automated sales support systems.

[0651] A "natural language processing algorithm" is a computer program or method for analyzing received text data and automatically extracting necessary information.

[0652] A "case database" is a data store that stores data on similar cases that have been processed in the past, and by referring to that data when processing new cases, it enables the provision of efficient and accurate services.

[0653] A "generative AI model" is a type of artificial intelligence that automatically generates the most appropriate sentence in response to an input prompt, based on a pre-trained algorithm.

[0654] A "prompt statement" is an input statement used to specify necessary information and conditions for a generative AI model, prompting it to generate an appropriate response.

[0655] An "electronic communication message" is a message containing text or data transmitted by electronic means, intended for the exchange of information between users or systems.

[0656] The system according to the present invention is operated through a server, a terminal, and a user interface.

[0657] Users input or upload electronic data using a terminal. The terminal implements a browser-based web application where users input project information and proposals into data fields. This terminal can be a standard personal computer or smartphone.

[0658] The terminal organizes the input electronic information and sends it to the server. A REST API is used for transmission, and standard data formats such as JSON are used for the data format.

[0659] The server uses natural language processing algorithms on the received data. Specifically, it uses a Python program to utilize natural language processing libraries such as "spaCy" and "NLTK" to analyze the data. The server extracts the necessary information from the analysis results and uses it in the next step.

[0660] The case database stores information on similar past cases. The server queries this database to find the most suitable message template. MySQL or PostgreSQL are often used for database management.

[0661] The server utilizes a generative AI model to automatically generate messages based on the prompt text. This generation uses open-source generative AI models (e.g., the GPT series). An example of an actual prompt text might be: "Based on the new project proposal, please generate the optimal proposal message, referencing past examples."

[0662] The generated message undergoes a recipient verification process by the server and is sent only after final user approval. Recipient verification involves matching the message against a database of previously sent messages.

[0663] This system enables users to conduct electronic communications efficiently and reduces the risk of accidental transmissions. Furthermore, data synchronization with the sales support automation system further optimizes business processes.

[0664] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0665] Step 1:

[0666] Users input or upload electronic data through a terminal. This allows users to collect project-related information and proposal data, and provide it in the terminal's input fields. The entered data is temporarily stored and organized on the terminal. At this stage, the user's input data is converted into structured data such as JSON format.

[0667] Step 2:

[0668] The terminal sends organized electronic data to the server. The terminal uses a REST API to securely send data to the server. During this process, the terminal verifies the integrity of the data being sent and validates the data fields as needed. The specific output is a formal data request to the server.

[0669] Step 3:

[0670] The server analyzes the received electronic data using natural language processing algorithms. This analysis utilizes Python libraries such as "spaCy" and "NLTK" to extract important keywords and structures from the data. The input is data sent from the terminal, and the output is a data structure containing the analyzed information.

[0671] Step 4:

[0672] The server queries the case database based on the analysis results and selects templates related to similar past cases. Here, a database management system (e.g., MySQL) is used, and relevant template data is retrieved via SQL queries. The input is the analysis results data, and the output is the corresponding message template.

[0673] Step 5:

[0674] The server generates messages based on prompt text using a generative AI model. The server inputs prompt text into a selected template, and the generated AI model (e.g., GPT series) outputs the optimal message. The input is a template and prompt text, and the output is a completed electronic communication message.

[0675] Step 6:

[0676] The server verifies the generated message using a destination verification mechanism. By comparing it with past transmission history and confirming the accuracy of the destination information, it prevents erroneous transmissions. The input is the message's destination data, and the output is the result of the destination verification.

[0677] Step 7:

[0678] To allow the user to approve the final message content and recipient, the server sends confirmation information to the terminal, prompting the user for approval. Specifically, a message confirmation screen is displayed on the user interface, presenting the user with the option to approve or modify. User input is an approval instruction, and the output indicates that the message has been finalized and approved.

[0679] Step 8:

[0680] The server sends approved messages to the specified destination and synchronizes with the sales support automation system. The SMTP protocol is used for transmission, and sales records are automatically updated via the API. All inputs are approved message information, and the output is successful message transmission and updated sales records.

[0681] (Application Example 1)

[0682] 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".

[0683] Traditional electronic payment systems require users to select the optimal payment method themselves, which carries the risk of incorrect selection and fraudulent transactions. Furthermore, they lack sufficient optimization using past transaction history, making efficient payment suggestions difficult.

[0684] 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.

[0685] In this invention, the server includes information analysis means for analyzing electronic information received from a data collection device, support means for automatically proposing the optimal payment method, and settlement processing means for securely processing transactions based on the proposed payment method. This prevents user errors and enables efficient and secure transaction processing.

[0686] A "data acquisition device" is a device that receives electronic information and provides it to information analysis means for analysis.

[0687] An "information analysis means" is a component that has the function of analyzing received electronic information and extracting usable data.

[0688] A "support mechanism" is a component that has the function of suggesting the most suitable payment method to the user based on the analyzed information.

[0689] A "payment processing device" is a component that has the function of securely executing a transaction based on the proposed payment method.

[0690] A "history reference means" is a component that has the function of referencing past transaction details and contributing to the optimization of proposed support measures.

[0691] A "synchronization mechanism" is a component that has the function of exchanging information with an automated settlement management system after transaction processing.

[0692] To implement this invention, the server utilizes a data collection device, information analysis means, support means, payment processing means, history reference means, and synchronization means.

[0693] The program of this system performs the following processes: First, the server receives electronic information via a data collection device. The received information is analyzed by an information analysis device, and relevant data such as the user's transaction history is extracted. Based on this analysis, a support device proposes the optimal payment method to the user.

[0694] Once the user approves the payment according to the proposal, the payment processing system securely executes the transaction. This completes the payment and reduces the risk of fraudulent transactions. After the transaction is completed, the history retrieval system records the transaction in the database, and the synchronization system works with the automated payment management system to maintain the latest transaction status.

[0695] A concrete example of implementation is when a user purchases a book from an online bookstore. Purchase information is sent to the server, and the most suitable payment method is suggested from options such as credit cards and electronic money. For example, a prompt such as "Purchase amount: 5000 yen, Store: Bookstore. Please suggest the best payment method based on past purchase history" is used. This prompt is input into a generating AI model, and an optimized suggestion is provided. Through this process, the user can easily select the best payment method, resulting in safe and efficient payment.

[0696] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0697] Step 1:

[0698] The server receives purchase information transmitted from the terminal. This input data includes product name, price, and supplier information. The server receives this data and passes it to the information analysis system. The information analysis system analyzes this raw data and prepares to extract the necessary transaction information.

[0699] Step 2:

[0700] The server's information analysis mechanism analyzes received purchase information in detail and extracts necessary information from the relevant user's past transaction history database. Here, data calculations are performed to process the data and determine the user's consumption patterns and the priority of available payment methods. The output of this step serves as the basis for suggesting optimized payment methods.

[0701] Step 3:

[0702] The server's support system proposes an appropriate payment method to the user based on the analysis results. The support system utilizes a generative AI model to generate prompt messages based on the input past transaction data. These generated prompt messages output suggestions to the user, including the optimal payment method. For example, a message such as "Purchase amount 5000 yen, store is a bookstore. Please suggest the optimal payment method based on past purchase history." might be output.

[0703] Step 4:

[0704] The user reviews and selects a payment method from the options presented on the terminal. The user's selection is sent back to the server as the payment method, and this decision becomes the input. Next, the server's payment processing system receives this information and executes the transaction using the selected payment method. The payment process is secured by using an encrypted channel.

[0705] Step 5:

[0706] After the transaction is completed, the server's history reference system records the transaction in the database. The transaction information is stored in the history database and made available for reference in future payment proposals. The output of this step is the updated transaction history data.

[0707] Step 6:

[0708] Finally, the server's synchronization mechanism synchronizes information with the automated payment management system. This ensures that transaction information remains up-to-date throughout the entire system. The synchronization mechanism also shares information with external payment management systems, supporting the maintenance of an integrated payment process. Upon completion of this step, the state of the entire system is updated, enabling optimization for the next user.

[0709] 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.

[0710] The system according to the present invention includes a server, a terminal, a user interface, and an emotion engine. The server functions as a central control and is responsible for data analysis, message generation, recipient confirmation, emotion recognition, and synchronization with the sales support automation system. The terminal is a device for user interaction and assists with information input, message preview, and confirmation operations.

[0711] In this system, users input or upload electronic information using a terminal. The terminal sends this information to a server. The server manages the process of analyzing the electronic information using information analysis tools and extracting specific data. The analyzed information is then evaluated by an emotion engine, which assesses the user's emotional state and adjusts the tone of messages accordingly.

[0712] The emotion engine learns emotional tendencies from the user's past communication data. It also has the ability to evaluate the user's current emotions by analyzing voice or text data in real time. Based on this information, the message generation system automatically generates messages that take into account the analyzed data and the user's emotional state.

[0713] The generated message is sent to the terminal, where it is previewed by the user. If the user approves the message content, the terminal notifies the server of the approval. The server uses a recipient verification mechanism to check the recipient information and refers to past databases. Once all verifications are complete, the server sends the message and synchronizes the data with the sales support automation system.

[0714] For example, if a user wants to send product update information to a customer, they input the information on their device, and as the server generates a message from that information, an emotion engine detects the user's current emotions and adjusts the message to an appropriate tone for the customer. By utilizing this system, more effective communication with customers is achieved, and the risk of sending messages to the wrong recipient is reduced.

[0715] The following describes the processing flow.

[0716] Step 1:

[0717] The user inputs or uploads electronic information using a terminal. The terminal receives this information, verifies the data format, and then sends it to the server.

[0718] Step 2:

[0719] The server receives electronic information and analyzes the data using information analysis tools. Through this analysis, the information is classified and important data points are extracted.

[0720] Step 3:

[0721] The server activates an emotion engine, analyzing the user's past and real-time data to evaluate their emotional state. This allows the server to understand what the user is feeling.

[0722] Step 4:

[0723] The server uses a case reference mechanism to search a database of similar past cases and selects the most suitable message template. The selected template is then adjusted to reflect the sentiment information obtained by the sentiment engine.

[0724] Step 5:

[0725] The server automatically generates specific electronic communication messages using message generation means based on pre-configured templates. The generated messages are optimized for the user's emotional state.

[0726] Step 6:

[0727] After the message is generated, the server sends it to the terminal and displays it on a preview screen. The user checks the message and edits it as needed.

[0728] Step 7:

[0729] Once the user approves the message, the device sends that information back to the server. The server uses a recipient verification mechanism to confirm that the recipient information is correct.

[0730] Step 8:

[0731] After confirming the recipient, the server sends the message to the specified recipient. During this process, the server records the transmission results and monitors for any erroneous transmissions.

[0732] Step 9:

[0733] Finally, the server synchronizes with the sales support automation system to update customer information and deal status. This information is presented to the user via their terminal and used for future sales activities.

[0734] (Example 2)

[0735] 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".

[0736] In today's business environment, it is essential to quickly send effective and appropriate electronic messages to customers. However, when composing messages manually, it is difficult to accurately reflect the user's emotional state, and there is also the risk of sending messages to the wrong recipient. This can lead to a decline in the quality of customer communication and the loss of business opportunities.

[0737] 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.

[0738] In this invention, the server includes data analysis means, emotion recognition means, and message generation means. This makes it possible to quickly generate effective messages that take into account the user's emotional state, reduce the risk of sending messages to the wrong recipient, and improve the quality of communication with customers.

[0739] "Data analysis means" refers to a device or method that has the function of analyzing electronic information received from a data transmission device and extracting necessary information.

[0740] "Emotion recognition means" refers to a mechanism or method for identifying a user's emotions by using analyzed information to evaluate the user's emotional state, taking into account the user's past data and real-time data.

[0741] "Message generation means" refers to a mechanism or technology for automatically generating electronic communication messages, taking into account the emotional state obtained by the emotion recognition means.

[0742] A "destination verification method" is a technique or technology used to verify the validity of destination information before sending a generated message, thereby preventing errors in the recipient's address.

[0743] An "information referencing means" is a mechanism for optimizing the operation of the message generation means by referring to a database of similar past information.

[0744] "Synchronization means" refers to a technology or method that integrates information with an automated business support system after a message has been sent, thereby improving processing efficiency.

[0745] This system includes a server, terminals, a user interface, data analysis means, emotion recognition means, message generation means, destination confirmation means, information referencing means, and synchronization means.

[0746] The user first uses a device to input or upload the information they want to send as a file. The device then sends the input information to the server. In this process, general-purpose digital devices such as personal computers and smartphones are used as devices.

[0747] The server operates as a computer device with advanced processing capabilities and analyzes electronic information received from terminals using data analysis tools. This analysis utilizes natural language processing (NLP) techniques to extract keywords and understand context.

[0748] Next, the emotion recognition system evaluates the user's emotional state based on the analyzed information. This includes a function that utilizes past communication data and real-time data to analyze the user's emotional state.

[0749] In the message generation system, a generation AI model automatically generates an electronic communication message with an appropriate tone, taking into account the analyzed data and emotional state. In this process, a prompt such as "Create a message to deliver new product update information to customers in a respectful tone" may be provided.

[0750] Subsequently, a recipient verification mechanism confirms the validity of the recipient information before the generated message is sent. At this stage, a database of past transmission history is referenced to minimize the risk of sending messages to the wrong recipient.

[0751] Finally, the server actually sends the message and integrates it with the business support automation system. This integration synchronizes the data, ensuring that the transmitted information is seamlessly integrated into other business processes. This streamlines information utilization across the entire organization.

[0752] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0753] Step 1:

[0754] The user uses a terminal to input or upload information they wish to send as a file. The terminal sends the input information to the server. This input is in the form of text data or a document file, and the transmitted information is received as input. In this step, the terminal acts as an interface for properly capturing the user's information and transferring it to the server.

[0755] Step 2:

[0756] The server analyzes the electronic information received from the terminal using data analysis tools. This input data is in text format, and the server uses natural language processing technology to extract keywords and process the data to understand the context. The output of the analysis is structured data with the necessary information identified. In this step, the server plays a role in efficiently extracting appropriate information from a large amount of data.

[0757] Step 3:

[0758] The server uses the analyzed information to evaluate the user's emotional state through its emotion recognition system. The input consists of analyzed data, including past communication history and real-time data. An emotion analysis algorithm is used for data processing, and its output serves as an indicator of the user's emotional state. This evaluation allows the server to accurately understand the user's intentions and emotions.

[0759] Step 4:

[0760] Based on the evaluation of the emotional state, the server uses a generative AI model to automatically generate a message with an appropriate tone. The prompt "Create a message to inform customers about new product updates in a respectful tone" is the input, and the generated message is obtained as output. The AI ​​model generates natural-sounding sentences that are appropriate to the situation based on the received prompt.

[0761] Step 5:

[0762] The generated message is sent from the server to the terminal, where the user previews and confirms it. If the user confirms and approves the message, the terminal notifies the server. The input for this step is the generated message, and the output is the user's approval status. The terminal is responsible for providing the confirmed message to the user and receiving feedback.

[0763] Step 6:

[0764] The server verifies the validity of the destination information using a destination verification mechanism. A database of past transmission history is referenced, and the message's destination information is provided as input. The output is a list of verified destinations. In this step, the server minimizes the risk of sending messages to the wrong recipient.

[0765] Step 7:

[0766] The server actually sends the message after confirmation and further synchronizes the data with the business support automation system. Inputs include approved messages and recipient information, and outputs include the message delivery status and synchronized business data. In this step, the server performs information transmission and data integration within the organization.

[0767] (Application Example 2)

[0768] 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".

[0769] Conventional electronic communication systems suffer from a decline in communication quality because message content cannot adequately address the emotional state of the user or the characteristics of individual recipients. Furthermore, they lack sufficient mechanisms to prevent problems caused by erroneous transmissions. Solving these problems is the objective of this invention.

[0770] 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.

[0771] In this invention, the server includes data analysis means, a message generation unit, and emotion adjustment means. This enables message adjustment that takes into account the user's emotional state and communication optimized for each recipient.

[0772] A "data analysis means" is a device that has the function of analyzing electronic information received from a data transmission unit and extracting the necessary data.

[0773] A "message generation unit" is a device that has the function of automatically generating electronic communication messages based on analyzed information.

[0774] A "destination confirmation unit" is a device that has the function of verifying the validity of the destination information before sending a generated message, thereby preventing misdelivery.

[0775] A "reference means" is a device that has the function of referencing data storage of similar past cases and optimizing the operation of the message generation unit.

[0776] A "synchronization unit" is a device that, after a message is sent, works in conjunction with the automated sales support system to synchronize information.

[0777] An "emotion adjustment device" is a device that uses an emotion analysis engine to evaluate the user's emotional state and adjusts the tone and content of messages based on that state.

[0778] A "natural language model" is a machine learning model that processes human language and generates text optimized for each recipient.

[0779] As a form for carrying out the invention, the present invention provides a system that enables message transmission that takes into account the user's emotions, particularly in electronic payment services. This system includes a server, a terminal, a user interface, and an emotion analysis engine.

[0780] The server has data analysis capabilities and analyzes electronic information transmitted from the terminal. It uses Google Cloud's "Natural Language API" and IBM's "Watson Tone Analyzer" to analyze the user's emotional state. The analyzed emotional data is used by a message generation unit that uses natural language models such as OpenAI's "GPT-3" to generate emotion-based messages. This allows the user to receive messages in a tone that best suits their current emotional state.

[0781] Furthermore, the recipient verification unit checks the validity of the recipient by referring to a database of past transmission records before sending, preventing misdeliveries. After transmission, the server synchronizes information with the sales support automation system through the synchronization unit to maintain data consistency.

[0782] For example, when a user is in a hurry to purchase an e-ticket while traveling, the system's emotion analysis engine can detect if the user is feeling stressed. Based on this, it can generate a purchase completion message in a calming tone to provide reassurance. An example of the prompt text the system might use is as follows: "Generate a transaction completion message in a pleasant tone to send when the user is feeling stressed. However, the content should be related to relaxation."

[0783] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0784] Step 1:

[0785] The terminal receives electronic information, and the user enters purchase information. The entered information is sent to the server. The input includes specific data about the product or service, which forms the basis for processing within the system.

[0786] Step 2:

[0787] The server uses data analysis tools to analyze the received electronic information. In this step, Google Cloud's "Natural Language API" is used to analyze the user's text data to determine their emotional state and evaluate their current emotions. The input is the user's text data, and the output is the analyzed emotional data.

[0788] Step 3:

[0789] Based on the sentiment analysis output, the server activates a message generation unit using OpenAI's "GPT-3" to generate a message that corresponds to the user's emotional state. Here, the generation AI model follows the prompt and outputs a message optimized for the user. A concrete example of this operation is "generating a transaction completion message in a relaxing tone."

[0790] Step 4:

[0791] The generated message is processed by a destination verification unit, which compares it with a database of past transmission records to confirm the validity of the destination. The input is the generated message and destination information, and the output is the verification result that the destination is valid. This step reduces the risk of sending messages to the wrong recipient.

[0792] Step 5:

[0793] The server sends a confirmed message to the terminal and displays a preview to the user. The user reviews the content and approves or modifies it. The input is the confirmed message, and the output is the message approved or modified by the user.

[0794] Step 6:

[0795] After final approval, the server sends a message and synchronizes the information with the sales support automation system using a synchronization unit. This enables data consistency and efficient information management. The input is an approved message, and the output is a transmission completion notification.

[0796] 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.

[0797] 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.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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."

[0805] 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.

[0806] 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.

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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 to be incorporated by reference.

[0817] The following is further disclosed regarding the embodiments described above.

[0818] (Claim 1)

[0819] Information analysis means for analyzing electronic information received from a data transmission device,

[0820] A message generation means that automatically generates the content of an electronic communication message based on the information analyzed by the aforementioned information analysis means,

[0821] Before sending the generated message, a means for verifying the validity of the recipient information is provided,

[0822] A case reference means that optimizes the operation of the message generation means by referring to a database of similar past cases,

[0823] A synchronization means that, after sending the aforementioned message, links with the sales support automation system,

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, wherein the recipient verification means has a function to compare the recipient list with a past transmission history database and detect the possibility of erroneous transmission.

[0827] (Claim 3)

[0828] The system according to claim 1, wherein the message generation means uses a language model to construct a message optimized for each customer.

[0829] "Example 1"

[0830] (Claim 1)

[0831] A means of organizing electronic data entered or uploaded by users via a terminal and sending it to a server,

[0832] A means of analyzing electronic data received by a server using a natural language processing algorithm and extracting important information,

[0833] A means of referring to the case database and selecting the optimal message template based on past data,

[0834] A means for automatically constructing electronic communication messages based on prompt text using a generative AI model,

[0835] Before sending the generated message, a means is provided to verify the validity of the recipient information by comparing it with past transmission history records.

[0836] A means of delivering a message after final approval and synchronizing data with the sales support automation system,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, wherein the destination confirmation means has a function to detect the risk of erroneous transmission by comparing destination information with past communication history.

[0840] (Claim 3)

[0841] The system according to claim 1, wherein the message generation means utilizes an artificial language model to design a message customized for each user.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] Information analysis means for analyzing electronic information received from a data acquisition device,

[0845] A support means that automatically proposes the optimal payment method based on the information analyzed by the aforementioned information analysis means,

[0846] A payment processing mechanism that securely processes transactions based on the proposed payment method,

[0847] A history reference means that references a database of past transaction history and optimizes the operation of the support means,

[0848] A synchronization means that performs linkage with the settlement management automation system after the transaction processing,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, wherein the payment processing means has a function to compare the payment recipient's information with a past transaction history database and detect the possibility of fraudulent transactions.

[0852] (Claim 3)

[0853] The system according to claim 1, wherein the support means makes suggestions optimized for each user using a generated AI model.

[0854] "Example 2 of combining an emotion engine"

[0855] (Claim 1)

[0856] A data analysis means for analyzing electronic information received from a data transmission device,

[0857] Based on the information analyzed by the aforementioned data analysis means, an emotion recognition means evaluates the user's emotional state.

[0858] A message generation means that automatically generates the content of an electronic communication message, taking into consideration the emotional state evaluated by the aforementioned emotion recognition means,

[0859] Before sending the generated message, a means for verifying the validity of the recipient information is provided,

[0860] Information referencing means that references a database of similar past information to optimize the operation of the message generation means,

[0861] A synchronization means that performs linkage with the business support automation system after sending the aforementioned message,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, wherein the recipient confirmation means has a function to compare the recipient list with a past communication history database and detect the possibility of erroneous transmission.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the message generation means uses a natural language model to construct a message optimized for each user.

[0867] "Application example 2 when combining with an emotional engine"

[0868] (Claim 1)

[0869] A data analysis means for analyzing electronic information received from a data transmission unit,

[0870] A message generation unit that automatically generates the content of an electronic communication message based on the information analyzed by the data analysis means,

[0871] Before sending the generated message, a destination verification unit verifies the validity of the destination information,

[0872] A reference means that optimizes the operation of the message generation unit by referring to a storage of past similar case data,

[0873] A synchronization unit that works in conjunction with the sales support automation system after the aforementioned message is sent, and

[0874] An emotion adjustment means that uses an emotion analysis engine to adjust messages based on the user's emotional state,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, wherein the destination confirmation unit has a function to compare the destination list with a past transmission record database and detect the possibility of erroneous transmission.

[0878] (Claim 3)

[0879] The system according to claim 1, wherein the message generation unit uses a natural language model to construct a message optimized for each recipient and provides a message adjusted by an emotion analysis engine. [Explanation of symbols]

[0880] 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. Information analysis means for analyzing electronic information received from a data transmission device, A message generation means that automatically generates the content of an electronic communication message based on the information analyzed by the aforementioned information analysis means, Before sending the generated message, a means for verifying the validity of the recipient information is provided, A case reference means that optimizes the operation of the message generation means by referring to a database of similar past cases, A synchronization means that, after sending the aforementioned message, links with the sales support automation system, A system that includes this.

2. The system according to claim 1, wherein the recipient verification means has a function to compare the recipient list with a past transmission history database and detect the possibility of erroneous transmission.

3. The system according to claim 1, wherein the message generation means uses a language model to construct a message optimized for each customer.

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