system

Abstract

Provide a system. 【Solution means】 A means for a computing device to receive data via a communication network and store the data; A means for analyzing similar cases based on the data and selecting an optimal data processing method; A means for automatically generating a draft of an email; A means for preventing mistransmission by collating the destination information included in the draft with a customer database; A means for providing content confirmation of the draft to a user device and obtaining approval; A means for sending the approved email to a predetermined destination; A means for analyzing past advertising data and customer response data to optimize an advertising campaign and proposing an optimal advertising strategy to a user; A system including the above.

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] The mistransmission of emails within a company is a serious problem that can lead to the leakage of confidential information and the loss of credibility. In the conventional email transmission process, mistransmission due to human error cannot be completely eliminated, and there is a problem that accurate transmission of information cannot be guaranteed. In addition, the reuse of similar cases in email transmission work is insufficient, which does not contribute to the improvement of work efficiency.

Means for Solving the Problems

[0005] This invention is a system in which a computing device receives and stores diverse data, analyzes similar cases based on that data, and selects the optimal data processing method. Furthermore, after the computing device automatically generates an email draft, it prevents misdelivery by comparing the recipient information with a customer database. It also presents the draft content to the user device to ensure approval, and finally sends the email to the designated recipient. This system eliminates human error in email transmission and improves business efficiency by analyzing past customer responses and using that information for future business activities.

[0006] A "computer" is a device that has the function of processing information and performs data reception, storage, analysis, and communication.

[0007] A "communication network" is a connection path that enables the transmission and reception of data between computing devices, and is a system composed of wired or wireless communication technologies.

[0008] "Data" refers to elements used to represent information, and is a collection of numbers, strings, or symbols that are processed by a computing device.

[0009] A "similar case" refers to a past process or event that has similar conditions or context to the current situation.

[0010] A "data processing method" is a procedure or technique for collecting, organizing, transforming, or filtering data to make it suitable for a particular purpose.

[0011] A "mail draft" is a draft of an unsent message, compiled before sending, including the email body and associated metadata.

[0012] A "customer database" is a system that systematically collects, stores, and enables searching and analysis of customer information.

[0013] A "user device" is an electronic information terminal, such as a mobile phone, computer, or tablet, used to interact with a computing device.

[0014] "Customer response" refers to the replies and feedback received from customers in response to emails or messages that have been sent. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Mode for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] 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.

[0019] 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.

[0020] 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, etc.

[0021] 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).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] To implement this invention, first, a server acting as a computing device is installed within the company and connected via a communication network. Users access a portal through a terminal and upload various business-related data to the server. This data includes product information, customer lists, transaction history, etc.

[0037] The server stores the uploaded data in a database, which the AI ​​agent accesses to identify similar past cases. Based on these similar cases, the AI ​​agent selects the optimal data processing method and processes the data in a way that conforms to the business objectives set by the user.

[0038] Next, the server automatically generates a draft email to send to the customer. The email draft includes product details, promotional information, and customer suggestions, designed to capture the customer's interest. The server, through an AI agent, verifies that the draft is accurate and appropriate, and that the correct recipients have been selected. At this stage, a check against the customer database is performed to prevent accidental sending.

[0039] Sales representatives, who are users with terminals, receive notifications from the server and review the email draft. After making any necessary revisions, they approve sending the email. Upon receiving this approval, the server sends the email to the customer. After sending, the server automatically receives the customer's response, which is then analyzed by an AI agent. This process organizes the customer's reaction and helps improve future sales activities.

[0040] As a concrete example, consider sending a campaign email targeting a particular product. A product planner inputs product promotion information from their terminal into a server, and the server uses AI to analyze the relevant data and create a draft. A sales representative reviews this draft, verifies the appropriate customer list, and after approval, the email is sent quickly and accurately.

[0041] In this way, the invention is incorporated into actual business operations, reducing the risk of sending emails to the wrong recipient and improving operational efficiency.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users (project planners) upload project-related data to the system using their terminals. This data includes product information and market analysis results.

[0045] Step 2:

[0046] The server receives the uploaded data and stores it in its internal database. Here, it automatically checks for data integrity and proper formatting.

[0047] Step 3:

[0048] The server passes the data to the AI ​​agent, which analyzes similar past cases. Based on the data, the AI ​​agent selects the optimal processing strategy and determines the processing procedure.

[0049] Step 4:

[0050] The server processes data based on instructions from the AI ​​agent. This includes tasks such as filtering, aggregation, and formatting.

[0051] Step 5:

[0052] The server automatically generates an email draft using the processed data. This draft clearly indicates the message to be conveyed to the customer, and the proposal is created based on the template.

[0053] Step 6:

[0054] The server compares the generated email draft with the customer database. Here, it automatically verifies that the correct recipients are specified.

[0055] Step 7:

[0056] The terminal (sales representative) receives a request to review an email draft sent from the server. The representative reviews the draft and makes revisions as needed.

[0057] Step 8:

[0058] The terminal (sales representative) approves the revised draft and instructs the server to send the email. At this time, a confirmation prompt will be displayed in the message content.

[0059] Step 9:

[0060] The server sends the approved email to the designated customer. After sending, it updates the record as a basis for timely follow-up with the customer.

[0061] Step 10:

[0062] The server automatically receives customer responses, which are then analyzed by an AI agent. The analysis results are presented to sales representatives to help them create future emails and improve customer service.

[0063] (Example 1)

[0064] 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."

[0065] In today's business environment, efficient and highly accurate data processing and communication are essential. However, traditional systems require significant human effort and time for data management, analysis of similar cases, and the creation and transmission of electronic messages, and also carry the risk of sending messages to the wrong recipient. This hinders operational efficiency and prevents effective customer engagement.

[0066] 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.

[0067] In this invention, the server includes means for receiving and storing data, means for identifying similar cases and selecting the optimal data processing method, and means for automatically generating drafts of electronic messages. This enables efficient data management, fast and accurate email transmission, and prevention of erroneous transmissions.

[0068] A "computer" is a device that has the function of processing and recording data via a communication network.

[0069] A "communication network" is a network system that enables the exchange of information.

[0070] "Data" refers to information that is collected and analyzed for a specific purpose.

[0071] "Storage" refers to the safe and long-term retention of data.

[0072] "Similar cases" refer to similar incidents or activities that have been handled in the past.

[0073] "Identification" refers to finding objects that are related.

[0074] A "data processing method" is a technique for analyzing, transforming, or manipulating data.

[0075] An "electronic message" is information that is sent and received using electronic means.

[0076] A "draft" refers to a preliminary version or draft before it becomes the final version.

[0077] "Misdirected transmission" refers to information being sent to an unintended recipient.

[0078] A "customer information database" is a structured collection of data in which information about customers is gathered and recorded.

[0079] "Contrast" refers to comparing different data or pieces of information.

[0080] A "user device" is a computer or electronic device used by an end user to input or output information.

[0081] "Approval" refers to acknowledging or acknowledging the content or actions of something.

[0082] "Target recipient" refers to the person to whom specific information should be sent.

[0083] "Analysis" refers to the process of breaking down complex information and then understanding or interpreting it.

[0084] To implement this invention, first, a server, acting as a computing device, is installed within the company and connected to terminals and users via a communication network. Users access the company's portal using their terminals and upload business-related data such as product information, customer lists, and transaction history to the server.

[0085] The server stores the received data in a database. This database efficiently stores structured data and allows for quick access when needed. Based on the data stored in the database, the server activates a generative AI model to identify similar cases. Analysis by this generative AI model extracts past successful cases and relevant examples.

[0086] Next, the server uses an AI agent to select the optimal data processing method based on the identified similar cases and processes the data. This process often includes data cleaning and summarization.

[0087] The server then automatically generates a draft of an electronic message to send to the customer through an AI agent. For example, it might use a prompt such as, "Explain the features of product A to the customer and state its benefits," to create the email content. This draft includes product details, promotional information, and suggestions for the customer.

[0088] To prevent accidental sending, the generated email draft is cross-referenced by the server with a customer information database to verify the recipient information. Users review the draft content via their terminal, make any necessary corrections, and approve it only after confirming its accuracy.

[0089] After the email is approved, the server sends an electronic message to the designated recipient. The server then automatically receives the customer's response, and an AI agent analyzes its content. The analysis results are provided to the user as information useful for future sales activities.

[0090] In this way, the invention is integrated into business operations, enabling efficient and effective communication, reducing the risk of sending emails to the wrong recipient, and improving overall work efficiency.

[0091] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0092] Step 1:

[0093] Users access the company's portal using their devices and upload business-related data such as product information, customer lists, and transaction history. Input is in the form of a CSV file or similar format, and output is data sent to the server via the network. Users submit the data by clicking the "Upload" button on the portal.

[0094] Step 2:

[0095] The server receives data sent from users via the communication network and stores it in a database. The input is raw data sent by the user, and the output is structured database entries. The server parses the data format and uses SQL to save the data to the database.

[0096] Step 3:

[0097] The server accesses the database, activates a generative AI model, and identifies similar cases based on past data. The input is the stored data and the generative AI model, and the output is a list of similar cases. The server utilizes natural language processing and machine learning techniques to extract highly relevant cases from past data.

[0098] Step 4:

[0099] The AI ​​agent selects the optimal data processing method based on identified similar cases and processes the data. The input is the identified similar cases, and the output is the processed data. The AI ​​agent organizes the necessary information by performing tasks such as data cleaning and summarization.

[0100] Step 5:

[0101] The server automatically generates drafts of electronic messages to be sent to customers via an AI agent. The input is processed data and prompt text, and the output is a draft of the electronic message. For example, the server might use the prompt text "Explain the features of product A to the customer and state its benefits" to generate an email.

[0102] Step 6:

[0103] The server verifies the recipient information in the draft against the customer information database to prevent misdeliveries. The input is the recipient information in the draft, and the output is the verified recipient information after verification. The server automatically checks whether the recipient is correct.

[0104] Step 7:

[0105] The user reviews the draft on their device, makes any necessary corrections, and then approves it. The input is the draft content, and the output is the corrected, approved draft. The user edits on the device interface and clicks the "Approve" button.

[0106] Step 8:

[0107] The server sends the approved electronic message to the designated recipient. The input is the approved draft, and the output is the sent electronic message. The server sends the email using the SMTP protocol.

[0108] Step 9:

[0109] The server automatically receives responses from customers, and an AI agent analyzes their content. The input is the customer response message, and the output is the analyzed customer feedback information. The server receives responses using the POP3 or IMAP protocol, and the AI ​​agent performs semantic analysis.

[0110] (Application Example 1)

[0111] 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."

[0112] Traditional business processes require significant time and effort to optimize advertising campaigns, and there are challenges in effectively utilizing historical data. Furthermore, misdirected emails to customers and insufficient targeting accuracy for advertising messages are also problems. These issues lead to decreased operational efficiency and reduced customer satisfaction, thus necessitating a new system to address these challenges.

[0113] 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.

[0114] In this invention, the server includes means for receiving and storing data, means for analyzing similar cases and selecting the optimal data processing method, means for automatically generating email drafts, and means for analyzing past advertising data and customer response data to optimize advertising campaigns and propose the optimal advertising strategy to the user. This enables effective optimization of advertising campaigns and improves the accuracy of delivering targeted messages to customers.

[0115] A "computing device" is a device designed to collect, store, process, and analyze data.

[0116] A "communication network" is an infrastructure for sending and receiving data between different devices.

[0117] "Means for receiving and storing data" refers to a function for taking in data from external sources and saving it.

[0118] "A means of analyzing similar cases and selecting the optimal data processing method" refers to a function that analyzes past data and determines an appropriate data processing method based on that analysis.

[0119] "Methods for automatically generating email drafts" refers to a function that uses AI technology to automatically create a draft of an email that is scheduled to be sent.

[0120] "A means of analyzing past advertising data and customer response data to optimize advertising campaigns and propose the optimal advertising strategy to users" refers to a process of evaluating past advertising performance and customer responses to derive effective advertising methods.

[0121] This invention is primarily implemented through a system using a server and user terminals. The server is located within the company and connected to the user terminals via a communication network. Since this system performs a series of processes from data collection and analysis to proposals and notifications, it is implemented in the following specific forms.

[0122] The server utilizes a database management system to receive and store data. Specific software options for this include MySQL® and PostgreSQL. For analyzing similar cases and selecting the optimal data processing method, Python-based data processing libraries such as Pandas and the machine learning library Scikit-learn are used.

[0123] Generative AI models using natural language processing technology are used to generate email drafts. Specifically, generative models such as OpenAI's GPT series can be used. The generated email draft is notified to the user's terminal, where the user can review and revise it.

[0124] Furthermore, the server analyzes historical advertising data and customer response data to optimize advertising campaigns. This process includes data cluster analysis and predictive modeling. In this case, using Python's Pandas or Scikit-learn is also suitable.

[0125] For example, when planning a promotional campaign for a new product, data from similar past products can be used to suggest the optimal targeting method and advertising content. Users can input prompts such as, "Generate an effective advertising message that appeals to the target audience. The target audience is women in their 20s, and the product is a new cosmetic product," into the AI ​​model to generate the advertising message. This entire process leads to the execution of a more effective advertising strategy.

[0126] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0127] Step 1:

[0128] The server receives advertising data and customer response data from the user's terminal. Input data includes past advertising campaign information and customer behavior history. The server uses a database management system such as MySQL to store this data in a database. The output is structured data stored in the database.

[0129] Step 2:

[0130] The server retrieves advertising data stored in the database and analyzes similar campaigns. In this process, it preprocesses the data using Python's Pandas library, and then uses Scikit-learn to create a predictive model based on past successful campaigns. The input is advertising data retrieved from the database, and the output is advertising strategy suggestions generated by the predictive model.

[0131] Step 3:

[0132] The server uses a generative AI model (e.g., GPT) to generate email drafts suitable for the target audience. By providing the generative AI model with prompts based on user-specified conditions, the system generates the optimal advertising message. The input consists of advertising strategy suggestions from the predictive model and user-specified prompts, while the output is the generated advertising message.

[0133] Step 4:

[0134] The generated advertising message is sent from the server to the user's device. The user reviews it on their device and makes any necessary modifications. They then return the modified message to the server as approval. The input to this process is the generated advertising message, and the output is the final, modified advertising message.

[0135] Step 5:

[0136] The server compares the revised advertising message against the customer database to prevent accidental sending. It compares the customer information in the database with the message recipients to check for any discrepancies. The input for this step is the revised advertising message, and the output is the final recipient list.

[0137] Step 6:

[0138] The server sends the advertising message, whose final destination has been confirmed, to the designated recipient. After delivery is complete, it automatically receives customer responses and analyzes them to help optimize future campaigns. The input for this step is customer response data after delivery is complete, and the output is optimization suggestions for future campaigns based on that data.

[0139] 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.

[0140] This invention is a communication system using a computing device that incorporates an emotion recognition engine to analyze the user's emotional state and optimize the email sending process. In an embodiment of the invention, a server is first installed and connected to the user's terminal via a communication network. The user uploads business data and related information using the terminal, and processing by the computing device begins.

[0141] The server stores the data provided by the user in a database and then uses an emotion engine to infer the user's emotional state from their conversations and text input. This emotional information is reflected in the creation of email drafts, and the content is adjusted accordingly; for example, if the user is feeling stressed, a softer tone is suggested to alleviate the situation.

[0142] Furthermore, the server uses an AI agent to analyze similar cases in the data and select the most effective data processing method. Based on the processed information, an email draft is automatically generated, and a system is in place to prevent accidental sending by cross-referencing it with the customer database.

[0143] Users can review the draft via their device and make revisions in line with the communication strategy suggested by the sentiment engine. Once approved, the server sends an email and analyzes the customer's response immediately upon receipt. The sentiment engine also analyzes the customer's reaction and provides the user with suggestions for the next communication activity.

[0144] As a concrete example, consider a case where a user sends an email introducing a new product to a customer. The server analyzes the raw data entered by the user and, if it determines that the user is excited, creates an email that appropriately conveys that enthusiasm. If the user is highly anxious, the server suggests a message structure that provides reassurance to alleviate that anxiety. Through this process, communication with customers becomes more effective and personalized, improving operational efficiency.

[0145] This system offers a new form of automated email management that takes user emotions into account, significantly reducing misdeliveries and communication breakdowns.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] Users log in to the system using their terminals and input or upload necessary business data. This data includes product information, related documents, customer lists, and more.

[0149] Step 2:

[0150] The server receives the uploaded data and stores it in the database. Simultaneously, it performs integrity checks to ensure the data is stored correctly.

[0151] Step 3:

[0152] The server activates the emotion engine and analyzes the user's current emotional state based on the context and keywords obtained from the user's input. This analysis identifies states such as tension, anxiety, and joy.

[0153] Step 4:

[0154] The server uses an AI agent to research similar cases and determine the optimal processing method. The processed information is then enhanced by referencing past success stories.

[0155] Step 5:

[0156] The server automatically generates an email draft. This draft is adjusted based on the user's emotional state, incorporating a softer tone and highlighting key points.

[0157] Step 6:

[0158] The server compares the generated email draft with the customer database to verify that the correct recipients have been selected.

[0159] Step 7:

[0160] The terminal displays a request for review of an email draft sent from the server. The user reviews the draft and makes necessary revisions based on suggestions from the sentiment engine.

[0161] Step 8:

[0162] The user approves the final revised draft and sends a command to the server from their terminal.

[0163] Step 9:

[0164] The server, upon receiving user approval, sends the email to the designated customer. The sending result is recorded and saved as part of the correspondence history.

[0165] Step 10:

[0166] The server automatically aggregates customer responses received after emails are sent and analyzes them through an emotion engine. The analysis results are then provided to the user as strategic proposals for future sales activities.

[0167] (Example 2)

[0168] 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".

[0169] In recent years, with the advancement of information and communication technology, communication using email has increased, but problems such as misunderstandings of emotions, communication failures, and accidental sending of emails have become more pronounced. There is a need for methods to solve these problems and achieve efficient and effective communication.

[0170] 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.

[0171] In this invention, the server includes means for receiving and storing data via a communication network, means for inferring the user's emotional state using an emotion analysis mechanism, and means for automatically generating the content of the communication message based on the emotional state. This enables communication that is more appropriate to the user's emotions, prevents accidental transmissions, and facilitates the building of good relationships with customers.

[0172] A "computing device" is a hardware or software device used to process, store, analyze, and output data.

[0173] A "communication network" refers to an information transmission path that allows for the sending and receiving of data, and constitutes a broad network infrastructure that includes the internet and corporate networks.

[0174] "Data storage means" refers to a device or process for temporarily or permanently recording received data and keeping it in a state where it can be retrieved later.

[0175] An "emotion analysis mechanism" is a technology that analyzes user input information, such as text data, and uses that information to infer the user's emotional state.

[0176] "Automatic communication message generation means" refers to a method or device for automatically generating appropriate communication messages based on user input or analyzed sentiment information.

[0177] "Similar case analysis methods" refer to analytical processes that extract similar cases based on past data and select the optimal processing method.

[0178] A "recipient verification means" is a means for verifying the correct recipient by comparing the recipient information contained in the message with a data storage device.

[0179] A "display device" is a device that can visually present information to a user and provides an interface for the user to check and edit that information.

[0180] A "received response processing means" is a means for automatically receiving responses to transmitted messages, analyzing their content, and using that information to determine the next action.

[0181] This invention is an advanced communication system using a computing device that analyzes the user's emotional state to optimize email transmission. The server functions as a processing unit for receiving and storing business data and related information from the user's terminal via a communication network. The emotion analysis mechanism implemented in the server is used to infer emotions from text provided by the user, applying natural language processing technology.

[0182] As a concrete example, a user creates an email introducing a new product using their device. Based on the data uploaded to the server, the emotion analysis mechanism analyzes the user's input, and if, for example, the user is excited, the AI ​​model for generating communication text automatically generates content that reflects that passion. A concrete example of a prompt would be an instruction such as, "Please generate an email to excitedly inform customers about the new product."

[0183] The server further analyzes similar cases using an AI agent and selects the optimal data processing method. The automatically generated email draft includes recipient information and is cross-referenced with the data storage device to reduce the risk of sending emails to the wrong recipient. Finally, the user can review the generated email draft on their device and make revisions based on sentiment analysis as needed.

[0184] This system enables users to engage in emotionally-driven communication, improving work efficiency and the quality of customer service.

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] Users input business data and related information using a terminal. This includes email drafts and past communication history. The entered data is sent to the server via the communication network. The server receives the data and stores it in a database. This stored data becomes the foundational information used in subsequent processing.

[0188] Step 2:

[0189] The server analyzes the received data using an emotion analysis mechanism. It analyzes the received text data using natural language processing techniques to infer the user's emotional state. For example, if the word "happy" is used frequently, the user's emotion is classified as "excited." The output is the result of the emotion analysis.

[0190] Step 3:

[0191] The server uses a generative AI model to automatically generate email drafts based on sentiment analysis results and referencing prompt text. Inputs include sentiment states and prompt text (e.g., "Generate an email to excitedly inform customers about a new product"). The output is an email draft tailored to the sentiment.

[0192] Step 4:

[0193] The server uses an AI agent to extract similar past cases from the database and select the optimal data processing method. The input is data from similar past cases, and the output is the optimized data processing method. This process further refines the generated email.

[0194] Step 5:

[0195] The user reviews the generated email draft using their device. This draft includes content based on sentiment analysis and can be manually adjusted. Input includes user feedback and correction instructions, and output is the finalized email draft.

[0196] Step 6:

[0197] The server checks for misdeliveries by comparing the confirmed email recipient information with customer information in the database. The input is the recipient information, and the output is a verified recipient list. After obtaining user approval, the email is sent.

[0198] Step 7:

[0199] After an email is sent, the server automatically receives the customer's response and analyzes it using an emotion analysis mechanism. The input is the customer's response, and the output is the analyzed emotional response of the customer. This is used to suggest future communication strategies.

[0200] (Application Example 2)

[0201] 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".

[0202] Traditional email systems create messages uniformly without considering the user's emotional state, making personalized communication difficult. Furthermore, the lack of campaign and incentive offers tailored to user emotions makes maximizing customer satisfaction a challenge.

[0203] 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.

[0204] In this invention, the server includes a data processing device that receives information via a communication network and stores the information, analyzes similar cases based on the information and selects the optimal information processing method, and automatically generates a draft message. This makes it possible to analyze the user's emotional state, create personalized messages that correspond to that emotion, and propose optimal campaigns and special offers.

[0205] A "data processing device" is a device that has the function of receiving information via a communication network, and storing and processing that information.

[0206] "Analysis of similar cases" is the process of finding the optimal processing method by comparing past cases with current data and extracting patterns.

[0207] An "information processing method" is a technique for converting received information into a format suitable for a specific purpose, enabling its efficient use.

[0208] A "message draft" is a preliminary version of a message created before sending, and is provided in a state where it can be reviewed and edited.

[0209] "Preventing accidental sending" means implementing a checking mechanism to avoid sending messages to unintended recipients.

[0210] "Analyzing emotional state" is the process of inferring the user's emotions and psychological state at a given time based on data obtained from the user.

[0211] "Suggesting campaigns and special offers" refers to the act of selecting and presenting appropriate campaigns and special offers based on the user's current status and past behavior.

[0212] To realize this application, the server operates as a system integrating multiple functions. The server receives information from users via a communication network using a data processing unit and stores that information in a database. It is equipped with a function to analyze the user's emotional state using an emotion recognition engine. Based on the analyzed data, the server proposes optimal campaigns and special offers. A natural language processing engine (for example, Google® Cloud Natural Language) is used for the analysis.

[0213] The user's device receives and displays this campaign and promotional information to the user. It also continuously updates the database by sending user feedback back to the server.

[0214] A concrete example is a case where, when a user makes an electronic payment, heart rate sensors and voice input devices are used to collect real-time emotional data, and appropriate promotions are suggested based on that emotional state. This enables personalized and effective communication.

[0215] Examples of prompt statements to input into a generative AI model are as follows:

[0216] "How can we recognize a user's emotions based on their current heart rate and voice tone, and suggest an appropriate relaxation campaign if we determine they are experiencing stress?"

[0217] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0218] Step 1:

[0219] The server receives data from the user's terminal device via a communication network. Inputs include the user's past payment history and real-time sensor information (e.g., heart rate, voice input). This information is stored in a database, forming the foundation for subsequent processing.

[0220] Step 2:

[0221] The server uses an emotion recognition engine to analyze the user's emotional state based on the stored data. Sensor data is used as input, and a natural language processing engine performs emotion analysis, obtaining the results as output. Specifically, the analysis determines stress levels and agitation levels.

[0222] Step 3:

[0223] The server selects the most suitable campaigns and reward information based on the analysis results. The input involves matching the sentiment analysis results with the user's past preference data and retrieving appropriate promotional information from the commercial database. As a result, personalized campaign information is output.

[0224] Step 4:

[0225] The server sends the selected campaign information to the user's device. The device displays the received information on its screen for the user to review. The campaign information details the specific benefits offered.

[0226] Step 5:

[0227] Users make selections based on the suggested campaigns and offers. These selections are then sent back to the server from the device as feedback data. This allows feedback information to be accumulated in a database, contributing to improvements in system accuracy.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] [Second Embodiment]

[0232] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0233] 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.

[0234] 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).

[0235] 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.

[0236] 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.

[0237] 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).

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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".

[0244] To implement this invention, first, a server acting as a computing device is installed within the company and connected via a communication network. Users access a portal through a terminal and upload various business-related data to the server. This data includes product information, customer lists, transaction history, etc.

[0245] The server stores the uploaded data in a database, which the AI ​​agent accesses to identify similar past cases. Based on these similar cases, the AI ​​agent selects the optimal data processing method and processes the data in a way that conforms to the business objectives set by the user.

[0246] Next, the server automatically generates a draft email to send to the customer. The email draft includes product details, promotional information, and customer suggestions, designed to capture the customer's interest. The server, through an AI agent, verifies that the draft is accurate and appropriate, and that the correct recipients have been selected. At this stage, a check against the customer database is performed to prevent accidental sending.

[0247] Sales representatives, who are users with terminals, receive notifications from the server and review the email draft. After making any necessary revisions, they approve sending the email. Upon receiving this approval, the server sends the email to the customer. After sending, the server automatically receives the customer's response, which is then analyzed by an AI agent. This process organizes the customer's reaction and helps improve future sales activities.

[0248] As a concrete example, consider sending a campaign email targeting a particular product. A product planner inputs product promotion information from their terminal into a server, and the server uses AI to analyze the relevant data and create a draft. A sales representative reviews this draft, verifies the appropriate customer list, and after approval, the email is sent quickly and accurately.

[0249] In this way, the invention is incorporated into actual business operations, reducing the risk of sending emails to the wrong recipient and improving operational efficiency.

[0250] The following describes the processing flow.

[0251] Step 1:

[0252] Users (project planners) upload project-related data to the system using their terminals. This data includes product information and market analysis results.

[0253] Step 2:

[0254] The server receives the uploaded data and stores it in its internal database. Here, it automatically checks for data integrity and proper formatting.

[0255] Step 3:

[0256] The server passes the data to the AI ​​agent, which analyzes similar past cases. Based on the data, the AI ​​agent selects the optimal processing strategy and determines the processing procedure.

[0257] Step 4:

[0258] The server processes data based on instructions from the AI ​​agent. This includes tasks such as filtering, aggregation, and formatting.

[0259] Step 5:

[0260] The server automatically generates an email draft using the processed data. This draft clearly indicates the message to be conveyed to the customer, and the proposal is created based on the template.

[0261] Step 6:

[0262] The server compares the generated email draft with the customer database. Here, it automatically verifies that the correct recipients are specified.

[0263] Step 7:

[0264] The terminal (sales representative) receives a request to review an email draft sent from the server. The representative reviews the draft and makes revisions as needed.

[0265] Step 8:

[0266] The terminal (sales representative) approves the revised draft and instructs the server to send the email. At this time, a confirmation prompt will be displayed in the message content.

[0267] Step 9:

[0268] The server sends the approved email to the designated customer. After sending, it updates the record as a basis for timely follow-up with the customer.

[0269] Step 10:

[0270] The server automatically receives customer responses, which are then analyzed by an AI agent. The analysis results are presented to sales representatives to help them create future emails and improve customer service.

[0271] (Example 1)

[0272] 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."

[0273] In today's business environment, efficient and highly accurate data processing and communication are essential. However, traditional systems require significant human effort and time for data management, analysis of similar cases, and the creation and transmission of electronic messages, and also carry the risk of sending messages to the wrong recipient. This hinders operational efficiency and prevents effective customer engagement.

[0274] 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.

[0275] In this invention, the server includes means for receiving and storing data, means for identifying similar cases and selecting the optimal data processing method, and means for automatically generating drafts of electronic messages. This enables efficient data management, fast and accurate email transmission, and prevention of erroneous transmissions.

[0276] A "computing device" is a device that has the function of processing and recording data via a communication network.

[0277] A "communication network" is a network system capable of information exchange.

[0278] "Data" is information collected and analyzed for a specific purpose.

[0279] "Storage" refers to holding data securely and over a long period.

[0280] "Similar cases" refer to similar cases and activities handled in the past.

[0281] "Identification" refers to finding relevant objects.

[0282] A "data processing method" is a technique for analyzing, converting, or manipulating data.

[0283] An "electronic message" is information transmitted and received using electronic means.

[0284] A "draft" refers to a preliminary draft or a rough copy before the final version.

[0285] "Mistransmission" refers to information being sent to an unintended recipient.

[0286] A "customer information database" is a structured collection of data in which information about customers is collected and recorded.

[0287] "Comparison" refers to comparing different data and information with each other.

[0288] A "user device" is a computer or an electronic device used by an end-user to input or output information.

[0289] "Approval" refers to acknowledging or acknowledging the content or actions of something.

[0290] "Target recipient" refers to the person to whom specific information should be sent.

[0291] "Analysis" refers to the process of breaking down complex information and then understanding or interpreting it.

[0292] To implement this invention, first, a server, acting as a computing device, is installed within the company and connected to terminals and users via a communication network. Users access the company's portal using their terminals and upload business-related data such as product information, customer lists, and transaction history to the server.

[0293] The server stores the received data in a database. This database efficiently stores structured data and allows for quick access when needed. Based on the data stored in the database, the server activates a generative AI model to identify similar cases. Analysis by this generative AI model extracts past successful cases and relevant examples.

[0294] Next, the server uses an AI agent to select the optimal data processing method based on the identified similar cases and processes the data. This process often includes data cleaning and summarization.

[0295] The server then automatically generates a draft of an electronic message to send to the customer through an AI agent. For example, it might use a prompt such as, "Explain the features of product A to the customer and state its benefits," to create the email content. This draft includes product details, promotional information, and suggestions for the customer.

[0296] To prevent accidental sending, the generated email draft is cross-referenced by the server with a customer information database to verify the recipient information. Users review the draft content via their terminal, make any necessary corrections, and approve it only after confirming its accuracy.

[0297] After the email is approved, the server sends an electronic message to the designated recipient. The server then automatically receives the customer's response, and an AI agent analyzes its content. The analysis results are provided to the user as information useful for future sales activities.

[0298] In this way, the invention is integrated into business operations, enabling efficient and effective communication, reducing the risk of sending emails to the wrong recipient, and improving overall work efficiency.

[0299] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0300] Step 1:

[0301] Users access the company's portal using their devices and upload business-related data such as product information, customer lists, and transaction history. Input is in the form of a CSV file or similar format, and output is data sent to the server via the network. Users submit the data by clicking the "Upload" button on the portal.

[0302] Step 2:

[0303] The server receives data sent from users via the communication network and stores it in a database. The input is raw data sent by the user, and the output is structured database entries. The server parses the data format and uses SQL to save the data to the database.

[0304] Step 3:

[0305] The server accesses the database, activates a generative AI model, and identifies similar cases based on past data. The input is the stored data and the generative AI model, and the output is a list of similar cases. The server utilizes natural language processing and machine learning techniques to extract highly relevant cases from past data.

[0306] Step 4:

[0307] The AI agent selects an optimal data processing method based on the identified similar cases and processes the data. The input is the identified similar cases, and the output is the processed data. The AI agent performs operations such as data cleaning and summarization to organize the necessary information.

[0308] Step 5:

[0309] The server automatically generates a draft of the electronic message to be sent to the customer through the AI agent. The input is the processed data and the prompt text, and the output is the draft of the electronic message. The server uses a prompt text such as "Please explain the features of Product A to the customer and state its advantages" to generate the email.

[0310] Step 6:

[0311] The server collates the recipient information in the draft with the customer information database to prevent mis-sending. The input is the recipient information of the draft, and the output is the confirmed recipient information after collation. The server automatically checks whether the recipient is appropriate.

[0312] Step 7:

[0313] The user checks the content of the draft on the terminal, makes corrections if necessary, and then approves it. The input is the draft content, and the output is the corrected and approved draft. The user edits on the interface of the terminal and clicks the "Approve" button.

[0314] Step 8:

[0315] The server sends the approved electronic message to the predetermined destination. The input is the approved draft, and the output is the sent electronic message. The server uses the SMTP protocol to send the email.

[0316] Step 9:

[0317] The server automatically receives responses from customers, and an AI agent analyzes their content. The input is the customer response message, and the output is the analyzed customer feedback information. The server receives responses using the POP3 or IMAP protocol, and the AI ​​agent performs semantic analysis.

[0318] (Application Example 1)

[0319] 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."

[0320] Traditional business processes require significant time and effort to optimize advertising campaigns, and there are challenges in effectively utilizing historical data. Furthermore, misdirected emails to customers and insufficient targeting accuracy for advertising messages are also problems. These issues lead to decreased operational efficiency and reduced customer satisfaction, thus necessitating a new system to address these challenges.

[0321] 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.

[0322] In this invention, the server includes means for receiving and storing data, means for analyzing similar cases and selecting the optimal data processing method, means for automatically generating email drafts, and means for analyzing past advertising data and customer response data to optimize advertising campaigns and propose the optimal advertising strategy to the user. This enables effective optimization of advertising campaigns and improves the accuracy of delivering targeted messages to customers.

[0323] A "computing device" is a device designed to collect, store, process, and analyze data.

[0324] A "communication network" is an infrastructure for sending and receiving data between different devices.

[0325] "Means for receiving and storing data" refers to a function for taking in data from external sources and saving it.

[0326] "A means of analyzing similar cases and selecting the optimal data processing method" refers to a function that analyzes past data and determines an appropriate data processing method based on that analysis.

[0327] "Methods for automatically generating email drafts" refers to a function that uses AI technology to automatically create a draft of an email that is scheduled to be sent.

[0328] "A means of analyzing past advertising data and customer response data to optimize advertising campaigns and propose the optimal advertising strategy to users" refers to a process of evaluating past advertising performance and customer responses to derive effective advertising methods.

[0329] This invention is primarily implemented through a system using a server and user terminals. The server is located within the company and connected to the user terminals via a communication network. Since this system performs a series of processes from data collection and analysis to proposals and notifications, it is implemented in the following specific forms.

[0330] The server utilizes a database management system to receive and store data. Specific software options for this include MySQL and PostgreSQL. For analyzing similar cases and selecting the optimal data processing method, Python-based data processing libraries such as Pandas and the machine learning library Scikit-learn are used.

[0331] Generative AI models using natural language processing technology are used to generate email drafts. Specifically, generative models such as OpenAI's GPT series can be used. The generated email draft is notified to the user's terminal, where the user can review and revise it.

[0332] Furthermore, the server analyzes historical advertising data and customer response data to optimize advertising campaigns. This process includes data cluster analysis and predictive modeling. In this case, using Python's Pandas or Scikit-learn is also suitable.

[0333] For example, when planning a promotional campaign for a new product, data from similar past products can be used to suggest the optimal targeting method and advertising content. Users can input prompts such as, "Generate an effective advertising message that appeals to the target audience. The target audience is women in their 20s, and the product is a new cosmetic product," into the AI ​​model to generate the advertising message. This entire process leads to the execution of a more effective advertising strategy.

[0334] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0335] Step 1:

[0336] The server receives advertising data and customer response data from the user's terminal. Input data includes past advertising campaign information and customer behavior history. The server uses a database management system such as MySQL to store this data in a database. The output is structured data stored in the database.

[0337] Step 2:

[0338] The server retrieves advertising data stored in the database and analyzes similar campaigns. In this process, it preprocesses the data using Python's Pandas library, and then uses Scikit-learn to create a predictive model based on past successful campaigns. The input is advertising data retrieved from the database, and the output is advertising strategy suggestions generated by the predictive model.

[0339] Step 3:

[0340] The server uses a generative AI model (e.g., GPT) to generate email drafts suitable for the target audience. By providing the generative AI model with prompts based on user-specified conditions, the system generates the optimal advertising message. The input consists of advertising strategy suggestions from the predictive model and user-specified prompts, while the output is the generated advertising message.

[0341] Step 4:

[0342] The generated advertising message is sent from the server to the user's device. The user reviews it on their device and makes any necessary modifications. They then return the modified message to the server as approval. The input to this process is the generated advertising message, and the output is the final, modified advertising message.

[0343] Step 5:

[0344] The server compares the revised advertising message against the customer database to prevent accidental sending. It compares the customer information in the database with the message recipients to check for any discrepancies. The input for this step is the revised advertising message, and the output is the final recipient list.

[0345] Step 6:

[0346] The server sends the advertising message, whose final destination has been confirmed, to the designated recipient. After delivery is complete, it automatically receives customer responses and analyzes them to help optimize future campaigns. The input for this step is customer response data after delivery is complete, and the output is optimization suggestions for future campaigns based on that data.

[0347] 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.

[0348] This invention is a communication system using a computing device that incorporates an emotion recognition engine to analyze the user's emotional state and optimize the email sending process. In an embodiment of the invention, a server is first installed and connected to the user's terminal via a communication network. The user uploads business data and related information using the terminal, and processing by the computing device begins.

[0349] The server stores the data provided by the user in a database and then uses an emotion engine to infer the user's emotional state from their conversations and text input. This emotional information is reflected in the creation of email drafts, and the content is adjusted accordingly; for example, if the user is feeling stressed, a softer tone is suggested to alleviate the situation.

[0350] Furthermore, the server uses an AI agent to analyze similar cases in the data and select the most effective data processing method. Based on the processed information, an email draft is automatically generated, and a system is in place to prevent accidental sending by cross-referencing it with the customer database.

[0351] Users can review the draft via their device and make revisions in line with the communication strategy suggested by the sentiment engine. Once approved, the server sends an email and analyzes the customer's response immediately upon receipt. The sentiment engine also analyzes the customer's reaction and provides the user with suggestions for the next communication activity.

[0352] As a concrete example, consider a case where a user sends an email introducing a new product to a customer. The server analyzes the raw data entered by the user and, if it determines that the user is excited, creates an email that appropriately conveys that enthusiasm. If the user is highly anxious, the server suggests a message structure that provides reassurance to alleviate that anxiety. Through this process, communication with customers becomes more effective and personalized, improving operational efficiency.

[0353] This system offers a new form of automated email management that takes user emotions into account, significantly reducing misdeliveries and communication breakdowns.

[0354] The following describes the processing flow.

[0355] Step 1:

[0356] Users log in to the system using their terminals and input or upload necessary business data. This data includes product information, related documents, customer lists, and more.

[0357] Step 2:

[0358] The server receives the uploaded data and stores it in the database. Simultaneously, it performs integrity checks to ensure the data is stored correctly.

[0359] Step 3:

[0360] The server activates the emotion engine and analyzes the user's current emotional state based on the context and keywords obtained from the user's input. This analysis identifies states such as tension, anxiety, and joy.

[0361] Step 4:

[0362] The server uses an AI agent to research similar cases and determine the optimal processing method. The processed information is then enhanced by referencing past success stories.

[0363] Step 5:

[0364] The server automatically generates an email draft. This draft is adjusted based on the user's emotional state, incorporating a softer tone and highlighting key points.

[0365] Step 6:

[0366] The server compares the generated email draft with the customer database to verify that the correct recipients have been selected.

[0367] Step 7:

[0368] The terminal displays a request for review of an email draft sent from the server. The user reviews the draft and makes necessary revisions based on suggestions from the sentiment engine.

[0369] Step 8:

[0370] The user approves the final revised draft and sends a command to the server from their terminal.

[0371] Step 9:

[0372] The server, upon receiving user approval, sends the email to the designated customer. The sending result is recorded and saved as part of the correspondence history.

[0373] Step 10:

[0374] The server automatically aggregates customer responses received after emails are sent and analyzes them through an emotion engine. The analysis results are then provided to the user as strategic proposals for future sales activities.

[0375] (Example 2)

[0376] 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".

[0377] In recent years, with the advancement of information and communication technology, communication using email has increased, but problems such as misunderstandings of emotions, communication failures, and accidental sending of emails have become more pronounced. There is a need for methods to solve these problems and achieve efficient and effective communication.

[0378] 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.

[0379] In this invention, the server includes means for receiving and storing data via a communication network, means for inferring the user's emotional state using an emotion analysis mechanism, and means for automatically generating the content of the communication message based on the emotional state. This enables communication that is more appropriate to the user's emotions, prevents accidental transmissions, and facilitates the building of good relationships with customers.

[0380] A "computing device" is a hardware or software device used to process, store, analyze, and output data.

[0381] A "communication network" refers to an information transmission path that allows for the sending and receiving of data, and constitutes a broad network infrastructure that includes the internet and corporate networks.

[0382] "Data storage means" refers to a device or process for temporarily or permanently recording received data and keeping it in a state where it can be retrieved later.

[0383] An "emotion analysis mechanism" is a technology that analyzes user input information, such as text data, and uses that information to infer the user's emotional state.

[0384] "Automatic communication message generation means" refers to a method or device for automatically generating appropriate communication messages based on user input or analyzed sentiment information.

[0385] "Similar case analysis methods" refer to analytical processes that extract similar cases based on past data and select the optimal processing method.

[0386] A "recipient verification means" is a means for verifying the correct recipient by comparing the recipient information contained in the message with a data storage device.

[0387] A "display device" is a device that can visually present information to a user and provides an interface for the user to check and edit that information.

[0388] A "received response processing means" is a means for automatically receiving responses to transmitted messages, analyzing their content, and using that information to determine the next action.

[0389] This invention is an advanced communication system using a computing device that analyzes the user's emotional state to optimize email transmission. The server functions as a processing unit for receiving and storing business data and related information from the user's terminal via a communication network. The emotion analysis mechanism implemented in the server is used to infer emotions from text provided by the user, applying natural language processing technology.

[0390] As a concrete example, a user creates an email introducing a new product using their device. Based on the data uploaded to the server, the emotion analysis mechanism analyzes the user's input, and if, for example, the user is excited, the AI ​​model for generating communication text automatically generates content that reflects that passion. A concrete example of a prompt would be an instruction such as, "Please generate an email to excitedly inform customers about the new product."

[0391] The server further analyzes similar cases using an AI agent and selects the optimal data processing method. The automatically generated email draft includes recipient information and is cross-referenced with the data storage device to reduce the risk of sending emails to the wrong recipient. Finally, the user can review the generated email draft on their device and make revisions based on sentiment analysis as needed.

[0392] This system enables users to engage in emotionally-driven communication, improving work efficiency and the quality of customer service.

[0393] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0394] Step 1:

[0395] Users input business data and related information using a terminal. This includes email drafts and past communication history. The entered data is sent to the server via the communication network. The server receives the data and stores it in a database. This stored data becomes the foundational information used in subsequent processing.

[0396] Step 2:

[0397] The server analyzes the received data using an emotion analysis mechanism. It analyzes the received text data using natural language processing techniques to infer the user's emotional state. For example, if the word "happy" is used frequently, the user's emotion is classified as "excited." The output is the result of the emotion analysis.

[0398] Step 3:

[0399] The server uses a generative AI model to automatically generate email drafts based on sentiment analysis results and referencing prompt text. Inputs include sentiment states and prompt text (e.g., "Generate an email to excitedly inform customers about a new product"). The output is an email draft tailored to the sentiment.

[0400] Step 4:

[0401] The server uses an AI agent to extract similar past cases from the database and select the optimal data processing method. The input is data from similar past cases, and the output is the optimized data processing method. This process further refines the generated email.

[0402] Step 5:

[0403] The user reviews the generated email draft using their device. This draft includes content based on sentiment analysis and can be manually adjusted. Input includes user feedback and correction instructions, and output is the finalized email draft.

[0404] Step 6:

[0405] The server checks for misdeliveries by comparing the confirmed email recipient information with customer information in the database. The input is the recipient information, and the output is a verified recipient list. After obtaining user approval, the email is sent.

[0406] Step 7:

[0407] After an email is sent, the server automatically receives the customer's response and analyzes it using an emotion analysis mechanism. The input is the customer's response, and the output is the analyzed emotional response of the customer. This is used to suggest future communication strategies.

[0408] (Application Example 2)

[0409] 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."

[0410] Traditional email systems create messages uniformly without considering the user's emotional state, making personalized communication difficult. Furthermore, the lack of campaign and incentive offers tailored to user emotions makes maximizing customer satisfaction a challenge.

[0411] 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.

[0412] In this invention, the server includes a data processing device that receives information via a communication network and stores the information, analyzes similar cases based on the information and selects the optimal information processing method, and automatically generates a draft message. This makes it possible to analyze the user's emotional state, create personalized messages that correspond to that emotion, and propose optimal campaigns and special offers.

[0413] A "data processing device" is a device that has the function of receiving information via a communication network, and storing and processing that information.

[0414] "Analysis of similar cases" is the process of finding the optimal processing method by comparing past cases with current data and extracting patterns.

[0415] An "information processing method" is a technique for converting received information into a format suitable for a specific purpose, enabling its efficient use.

[0416] A "message draft" is a preliminary version of a message created before sending, and is provided in a state where it can be reviewed and edited.

[0417] "Preventing accidental sending" means implementing a checking mechanism to avoid sending messages to unintended recipients.

[0418] "Analyzing emotional state" is the process of inferring the user's emotions and psychological state at a given time based on data obtained from the user.

[0419] "Suggesting campaigns and special offers" refers to the act of selecting and presenting appropriate campaigns and special offers based on the user's current status and past behavior.

[0420] To realize this application, the server operates as a system integrating multiple functions. The server receives information from users via a communication network using a data processing unit and stores that information in a database. It is equipped with a function to analyze the user's emotional state using an emotion recognition engine. Based on the analyzed data, the server proposes optimal campaigns and reward information. A natural language processing engine (such as Google Cloud Natural Language) is used for the analysis.

[0421] The user's device receives and displays this campaign and promotional information to the user. It also continuously updates the database by sending user feedback back to the server.

[0422] A concrete example is a case where, when a user makes an electronic payment, heart rate sensors and voice input devices are used to collect real-time emotional data, and appropriate promotions are suggested based on that emotional state. This enables personalized and effective communication.

[0423] Examples of prompt statements to input into a generative AI model are as follows:

[0424] "How can we recognize a user's emotions based on their current heart rate and voice tone, and suggest an appropriate relaxation campaign if we determine they are experiencing stress?"

[0425] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0426] Step 1:

[0427] The server receives data from the user's terminal device via a communication network. Inputs include the user's past payment history and real-time sensor information (e.g., heart rate, voice input). This information is stored in a database, forming the foundation for subsequent processing.

[0428] Step 2:

[0429] The server uses an emotion recognition engine to analyze the user's emotional state based on the stored data. Sensor data is used as input, and a natural language processing engine performs emotion analysis, obtaining the results as output. Specifically, the analysis determines stress levels and agitation levels.

[0430] Step 3:

[0431] The server selects the most suitable campaigns and reward information based on the analysis results. The input involves matching the sentiment analysis results with the user's past preference data and retrieving appropriate promotional information from the commercial database. As a result, personalized campaign information is output.

[0432] Step 4:

[0433] The server sends the selected campaign information to the user's device. The device displays the received information on its screen for the user to review. The campaign information details the specific benefits offered.

[0434] Step 5:

[0435] Users make selections based on the suggested campaigns and offers. These selections are then sent back to the server from the device as feedback data. This allows feedback information to be accumulated in a database, contributing to improvements in system accuracy.

[0436] 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.

[0437] 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.

[0438] 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.

[0439] [Third Embodiment]

[0440] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0441] 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.

[0442] 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).

[0443] 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.

[0444] 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.

[0445] 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).

[0446] 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.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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".

[0452] To implement this invention, first, a server acting as a computing device is installed within the company and connected via a communication network. Users access a portal through a terminal and upload various business-related data to the server. This data includes product information, customer lists, transaction history, etc.

[0453] The server stores the uploaded data in a database, which the AI ​​agent accesses to identify similar past cases. Based on these similar cases, the AI ​​agent selects the optimal data processing method and processes the data in a way that conforms to the business objectives set by the user.

[0454] Next, the server automatically generates a draft email to send to the customer. The email draft includes product details, promotional information, and customer suggestions, designed to capture the customer's interest. The server, through an AI agent, verifies that the draft is accurate and appropriate, and that the correct recipients have been selected. At this stage, a check against the customer database is performed to prevent accidental sending.

[0455] Sales representatives, who are users with terminals, receive notifications from the server and review the email draft. After making any necessary revisions, they approve sending the email. Upon receiving this approval, the server sends the email to the customer. After sending, the server automatically receives the customer's response, which is then analyzed by an AI agent. This process organizes the customer's reaction and helps improve future sales activities.

[0456] As a concrete example, consider sending a campaign email targeting a particular product. A product planner inputs product promotion information from their terminal into a server, and the server uses AI to analyze the relevant data and create a draft. A sales representative reviews this draft, verifies the appropriate customer list, and after approval, the email is sent quickly and accurately.

[0457] In this way, the invention is incorporated into actual business operations, reducing the risk of sending emails to the wrong recipient and improving operational efficiency.

[0458] The following describes the processing flow.

[0459] Step 1:

[0460] Users (project planners) upload project-related data to the system using their terminals. This data includes product information and market analysis results.

[0461] Step 2:

[0462] The server receives the uploaded data and stores it in its internal database. Here, it automatically checks for data integrity and proper formatting.

[0463] Step 3:

[0464] The server passes the data to the AI ​​agent, which analyzes similar past cases. Based on the data, the AI ​​agent selects the optimal processing strategy and determines the processing procedure.

[0465] Step 4:

[0466] The server processes data based on instructions from the AI ​​agent. This includes tasks such as filtering, aggregation, and formatting.

[0467] Step 5:

[0468] The server automatically generates an email draft using the processed data. This draft clearly indicates the message to be conveyed to the customer, and the proposal is created based on the template.

[0469] Step 6:

[0470] The server compares the generated email draft with the customer database. Here, it automatically verifies that the correct recipients are specified.

[0471] Step 7:

[0472] The terminal (sales representative) receives a request to review an email draft sent from the server. The representative reviews the draft and makes revisions as needed.

[0473] Step 8:

[0474] The terminal (sales representative) approves the revised draft and instructs the server to send the email. At this time, a confirmation prompt will be displayed in the message content.

[0475] Step 9:

[0476] The server sends the approved email to the designated customer. After sending, it updates the record as a basis for timely follow-up with the customer.

[0477] Step 10:

[0478] The server automatically receives customer responses, which are then analyzed by an AI agent. The analysis results are presented to sales representatives to help them create future emails and improve customer service.

[0479] (Example 1)

[0480] 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."

[0481] In today's business environment, efficient and highly accurate data processing and communication are essential. However, traditional systems require significant human effort and time for data management, analysis of similar cases, and the creation and transmission of electronic messages, and also carry the risk of sending messages to the wrong recipient. This hinders operational efficiency and prevents effective customer engagement.

[0482] 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.

[0483] In this invention, the server includes means for receiving and storing data, means for identifying similar cases and selecting the optimal data processing method, and means for automatically generating drafts of electronic messages. This enables efficient data management, fast and accurate email transmission, and prevention of erroneous transmissions.

[0484] A "computer" is a device that has the function of processing and recording data via a communication network.

[0485] A "communication network" is a network system that enables the exchange of information.

[0486] "Data" refers to information that is collected and analyzed for a specific purpose.

[0487] "Storage" refers to the safe and long-term retention of data.

[0488] "Similar cases" refer to similar incidents or activities that have been handled in the past.

[0489] "Identification" refers to finding objects that are related.

[0490] A "data processing method" is a technique for analyzing, transforming, or manipulating data.

[0491] An "electronic message" is information that is sent and received using electronic means.

[0492] A "draft" refers to a preliminary version or draft before it becomes the final version.

[0493] "Misdirected transmission" refers to information being sent to an unintended recipient.

[0494] A "customer information database" is a structured collection of data in which information about customers is gathered and recorded.

[0495] "Contrast" refers to comparing different data or pieces of information.

[0496] A "user device" is a computer or electronic device used by an end user to input or output information.

[0497] "Approval" refers to acknowledging or acknowledging the content or actions of something.

[0498] "Target recipient" refers to the person to whom specific information should be sent.

[0499] "Analysis" refers to the process of breaking down complex information and then understanding or interpreting it.

[0500] To implement this invention, first, a server, acting as a computing device, is installed within the company and connected to terminals and users via a communication network. Users access the company's portal using their terminals and upload business-related data such as product information, customer lists, and transaction history to the server.

[0501] The server stores the received data in a database. This database efficiently stores structured data and allows for quick access when needed. Based on the data stored in the database, the server activates a generative AI model to identify similar cases. Analysis by this generative AI model extracts past successful cases and relevant examples.

[0502] Next, the server uses an AI agent to select the optimal data processing method based on the identified similar cases and processes the data. This process often includes data cleaning and summarization.

[0503] The server then automatically generates a draft of an electronic message to send to the customer through an AI agent. For example, it might use a prompt such as, "Explain the features of product A to the customer and state its benefits," to create the email content. This draft would include product details, promotional information, and suggestions for the customer.

[0504] To prevent accidental sending, the generated email draft is cross-referenced by the server with a customer information database to verify the recipient information. Users review the draft content via their terminal, make any necessary corrections, and approve it only after confirming its accuracy.

[0505] After the email is approved, the server sends an electronic message to the designated recipient. The server then automatically receives the customer's response, and an AI agent analyzes its content. The analysis results are provided to the user as information useful for future sales activities.

[0506] In this way, the invention is integrated into business operations, enabling efficient and effective communication, reducing the risk of sending emails to the wrong recipient, and improving overall work efficiency.

[0507] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0508] Step 1:

[0509] Users access the company's portal using their devices and upload business-related data such as product information, customer lists, and transaction history. Input is in the form of a CSV file or similar, and output is data sent to the server via the network. Users submit the data by clicking the "Upload" button on the portal.

[0510] Step 2:

[0511] The server receives data sent from users via the communication network and stores it in a database. The input is raw data sent by the user, and the output is structured database entries. The server parses the data format and uses SQL to save the data to the database.

[0512] Step 3:

[0513] The server accesses the database, activates a generative AI model, and identifies similar cases based on past data. The input is the stored data and the generative AI model, and the output is a list of similar cases. The server utilizes natural language processing and machine learning techniques to extract highly relevant cases from past data.

[0514] Step 4:

[0515] The AI ​​agent selects the optimal data processing method based on identified similar cases and processes the data. The input is the identified similar cases, and the output is the processed data. The AI ​​agent organizes the necessary information by performing tasks such as data cleaning and summarization.

[0516] Step 5:

[0517] The server automatically generates drafts of electronic messages to be sent to customers via an AI agent. The input is processed data and prompt text, and the output is a draft of the electronic message. For example, the server might use the prompt text "Explain the features of product A to the customer and state its benefits" to generate an email.

[0518] Step 6:

[0519] The server verifies the recipient information in the draft against the customer information database to prevent misdeliveries. The input is the recipient information in the draft, and the output is the verified recipient information after verification. The server automatically checks whether the recipient is correct.

[0520] Step 7:

[0521] The user reviews the draft on their device, makes any necessary corrections, and then approves it. The input is the draft content, and the output is the corrected, approved draft. The user edits on the device interface and clicks the "Approve" button.

[0522] Step 8:

[0523] The server sends the approved electronic message to the designated recipient. The input is the approved draft, and the output is the sent electronic message. The server sends the email using the SMTP protocol.

[0524] Step 9:

[0525] The server automatically receives responses from customers, and an AI agent analyzes their content. The input is the customer response message, and the output is the analyzed customer feedback information. The server receives responses using the POP3 or IMAP protocol, and the AI ​​agent performs semantic analysis.

[0526] (Application Example 1)

[0527] 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."

[0528] Traditional business processes require significant time and effort to optimize advertising campaigns, and there are challenges in effectively utilizing historical data. Furthermore, misdirected emails to customers and insufficient targeting accuracy for advertising messages are also problems. These issues lead to decreased operational efficiency and reduced customer satisfaction, thus necessitating a new system to address these challenges.

[0529] 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.

[0530] In this invention, the server includes means for receiving and storing data, means for analyzing similar cases and selecting the optimal data processing method, means for automatically generating email drafts, and means for analyzing past advertising data and customer response data to optimize advertising campaigns and propose the optimal advertising strategy to the user. This enables effective optimization of advertising campaigns and improves the accuracy of delivering targeted messages to customers.

[0531] A "computing device" is a device designed to collect, store, process, and analyze data.

[0532] A "communication network" is an infrastructure for sending and receiving data between different devices.

[0533] "Means for receiving and storing data" refers to a function for taking in data from external sources and saving it.

[0534] "A means of analyzing similar cases and selecting the optimal data processing method" refers to a function that analyzes past data and determines an appropriate data processing method based on that analysis.

[0535] "Methods for automatically generating email drafts" refers to a function that uses AI technology to automatically create a draft of an email that is scheduled to be sent.

[0536] "A means of analyzing past advertising data and customer response data to optimize advertising campaigns and propose the optimal advertising strategy to users" refers to a process of evaluating past advertising performance and customer responses to derive effective advertising methods.

[0537] This invention is primarily implemented through a system using a server and user terminals. The server is located within the company and connected to the user terminals via a communication network. Since this system performs a series of processes from data collection and analysis to proposals and notifications, it is implemented in the following specific forms.

[0538] The server utilizes a database management system to receive and store data. Specific software options for this include MySQL and PostgreSQL. For analyzing similar cases and selecting the optimal data processing method, Python-based data processing libraries such as Pandas and the machine learning library Scikit-learn are used.

[0539] Generative AI models using natural language processing technology are used to generate email drafts. Specifically, generative models such as OpenAI's GPT series can be used. The generated email draft is notified to the user's terminal, where the user can review and revise it.

[0540] Furthermore, the server analyzes historical advertising data and customer response data to optimize advertising campaigns. This process includes data cluster analysis and predictive modeling. In this case, using Python's Pandas or Scikit-learn is also suitable.

[0541] For example, when planning a promotional campaign for a new product, data from similar past products can be used to suggest the optimal targeting method and advertising content. Users can input prompts such as, "Generate an effective advertising message that appeals to the target audience. The target audience is women in their 20s, and the product is a new cosmetic product," into the AI ​​model to generate the advertising message. This entire process leads to the execution of a more effective advertising strategy.

[0542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0543] Step 1:

[0544] The server receives advertising data and customer response data from the user's terminal. Input data includes past advertising campaign information and customer behavior history. The server uses a database management system such as MySQL to store this data in a database. The output is structured data stored in the database.

[0545] Step 2:

[0546] The server retrieves advertising data stored in the database and analyzes similar campaigns. In this process, it preprocesses the data using Python's Pandas library, and then uses Scikit-learn to create a predictive model based on past successful campaigns. The input is advertising data retrieved from the database, and the output is advertising strategy suggestions generated by the predictive model.

[0547] Step 3:

[0548] The server uses a generative AI model (e.g., GPT) to generate email drafts suitable for the target audience. By providing the generative AI model with prompts based on user-specified conditions, the system generates the optimal advertising message. The input consists of advertising strategy suggestions from the predictive model and user-specified prompts, while the output is the generated advertising message.

[0549] Step 4:

[0550] The generated advertising message is sent from the server to the user's device. The user reviews it on their device and makes any necessary modifications. They then return the modified message to the server as approval. The input to this process is the generated advertising message, and the output is the final, modified advertising message.

[0551] Step 5:

[0552] The server verifies the revised advertising message against the customer database to prevent accidental sending. It compares the customer information in the database with the message recipients to check for any discrepancies. The input for this step is the revised advertising message, and the output is the final recipient list.

[0553] Step 6:

[0554] The server sends the advertising message, whose final destination has been confirmed, to the designated recipient. After delivery is complete, it automatically receives customer responses and analyzes them to help optimize future campaigns. The input for this step is customer response data after delivery is complete, and the output is optimization suggestions for future campaigns based on that data.

[0555] 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.

[0556] This invention is a communication system using a computing device that incorporates an emotion recognition engine to analyze the user's emotional state and optimize the email sending process. In an embodiment of the invention, a server is first installed and connected to the user's terminal via a communication network. The user uploads business data and related information using the terminal, and processing by the computing device begins.

[0557] The server stores the data provided by the user in a database and then uses an emotion engine to infer the user's emotional state from their conversations and text input. This emotional information is reflected in the creation of email drafts, and the content is adjusted accordingly; for example, if the user is feeling stressed, a softer tone is suggested to alleviate the situation.

[0558] Furthermore, the server uses an AI agent to analyze similar cases in the data and select the most effective data processing method. Based on the processed information, an email draft is automatically generated, and a system is in place to prevent accidental sending by cross-referencing it with the customer database.

[0559] Users can review the draft via their device and make revisions in line with the communication strategy suggested by the sentiment engine. Once approved, the server sends an email and analyzes the customer's response immediately upon receipt. The sentiment engine also analyzes the customer's reaction and provides the user with suggestions for the next communication activity.

[0560] As a concrete example, consider a case where a user sends an email introducing a new product to a customer. The server analyzes the raw data entered by the user and, if it determines that the user is excited, creates an email that appropriately conveys that enthusiasm. If the user is highly anxious, the server suggests a message structure that provides reassurance to alleviate that anxiety. Through this process, communication with customers becomes more effective and personalized, improving operational efficiency.

[0561] This system offers a new form of automated email management that takes user emotions into account, significantly reducing misdeliveries and communication breakdowns.

[0562] The following describes the processing flow.

[0563] Step 1:

[0564] Users log in to the system using their terminals and input or upload necessary business data. This data includes product information, related documents, customer lists, and more.

[0565] Step 2:

[0566] The server receives the uploaded data and stores it in the database. Simultaneously, it performs integrity checks to ensure the data is stored correctly.

[0567] Step 3:

[0568] The server activates the emotion engine and analyzes the user's current emotional state based on the context and keywords obtained from the user's input. This analysis identifies states such as tension, anxiety, and joy.

[0569] Step 4:

[0570] The server uses an AI agent to research similar cases and determine the optimal processing method. The processed information is then enhanced by referencing past success stories.

[0571] Step 5:

[0572] The server automatically generates an email draft. This draft is adjusted based on the user's emotional state, incorporating a softer tone and highlighting key points.

[0573] Step 6:

[0574] The server compares the generated email draft with the customer database to verify that the correct recipients have been selected.

[0575] Step 7:

[0576] The terminal displays a request for review of an email draft sent from the server. The user reviews the draft and makes necessary revisions based on suggestions from the sentiment engine.

[0577] Step 8:

[0578] The user approves the final revised draft and sends a command to the server from their terminal.

[0579] Step 9:

[0580] The server, upon receiving user approval, sends the email to the designated customer. The sending result is recorded and saved as part of the correspondence history.

[0581] Step 10:

[0582] The server automatically aggregates customer responses received after emails are sent and analyzes them through an emotion engine. The analysis results are then provided to the user as strategic proposals for future sales activities.

[0583] (Example 2)

[0584] 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."

[0585] In recent years, with the advancement of information and communication technology, communication using email has increased, but problems such as misunderstandings of emotions, communication failures, and accidental sending of emails have become more pronounced. There is a need for methods to solve these problems and achieve efficient and effective communication.

[0586] 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.

[0587] In this invention, the server includes means for receiving and storing data via a communication network, means for inferring the user's emotional state using an emotion analysis mechanism, and means for automatically generating the content of the communication message based on the emotional state. This enables communication that is more appropriate to the user's emotions, prevents accidental transmissions, and facilitates the building of good relationships with customers.

[0588] A "computing device" is a hardware or software device used to process, store, analyze, and output data.

[0589] A "communication network" refers to a transmission path for information that can send and receive data, and constitutes a broad network infrastructure that includes the internet and corporate networks.

[0590] "Data storage means" refers to a device or process for temporarily or permanently recording received data and keeping it in a state where it can be retrieved later.

[0591] An "emotion analysis mechanism" is a technology that analyzes user input information, such as text data, and uses that information to infer the user's emotional state.

[0592] "Automatic communication message generation means" refers to a method or device for automatically generating appropriate communication messages based on user input or analyzed sentiment information.

[0593] "Similar case analysis methods" refer to analytical processes that extract similar cases based on past data and select the optimal processing method.

[0594] A "recipient verification means" is a means for verifying the correct recipient by comparing the recipient information contained in the message with a data storage device.

[0595] A "display device" is a device that can visually present information to a user and provides an interface for the user to check and edit that information.

[0596] A "received response processing means" is a means for automatically receiving responses to transmitted messages, analyzing their content, and using that information to determine the next action.

[0597] This invention is an advanced communication system using a computing device that analyzes the user's emotional state to optimize email transmission. The server functions as a processing unit for receiving and storing business data and related information from the user's terminal via a communication network. The emotion analysis mechanism implemented in the server is used to infer emotions from text provided by the user, applying natural language processing technology.

[0598] As a concrete example, a user creates an email introducing a new product using their device. Based on the data uploaded to the server, the emotion analysis mechanism analyzes the user's input, and if, for example, the user is excited, the AI ​​model for generating communication text automatically generates content that reflects that passion. A concrete example of a prompt would be an instruction such as, "Please generate an email to excitedly inform customers about the new product."

[0599] The server further analyzes similar cases using an AI agent and selects the optimal data processing method. The automatically generated email draft includes recipient information and is cross-referenced with the data storage device to reduce the risk of sending emails to the wrong recipient. Finally, the user can review the generated email draft on their device and make revisions based on sentiment analysis as needed.

[0600] This system enables users to engage in emotionally-driven communication, improving work efficiency and the quality of customer service.

[0601] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0602] Step 1:

[0603] Users input business data and related information using a terminal. This includes email drafts and past communication history. The entered data is sent to the server via the communication network. The server receives the data and stores it in a database. This stored data becomes the foundational information used in subsequent processing.

[0604] Step 2:

[0605] The server analyzes the received data using an emotion analysis mechanism. It analyzes the received text data using natural language processing techniques to infer the user's emotional state. For example, if the word "happy" is used frequently, the user's emotion is classified as "excited." The output is the result of the emotion analysis.

[0606] Step 3:

[0607] The server uses a generative AI model to automatically generate email drafts based on sentiment analysis results and referencing prompt text. Inputs include sentiment states and prompt text (e.g., "Generate an email to excitedly inform customers about a new product"). The output is an email draft tailored to the sentiment.

[0608] Step 4:

[0609] The server uses an AI agent to extract similar past cases from the database and select the optimal data processing method. The input is data from similar past cases, and the output is the optimized data processing method. This process further refines the generated email.

[0610] Step 5:

[0611] The user reviews the generated email draft using their device. This draft includes content based on sentiment analysis and can be manually adjusted. Input includes user feedback and correction instructions, and output is the finalized email draft.

[0612] Step 6:

[0613] The server checks for misdeliveries by comparing the confirmed email recipient information with customer information in the database. The input is the recipient information, and the output is a verified recipient list. After obtaining user approval, the email is sent.

[0614] Step 7:

[0615] After an email is sent, the server automatically receives the customer's response and analyzes it using an emotion analysis mechanism. The input is the customer's response, and the output is the analyzed emotional response of the customer. This is used to suggest future communication strategies.

[0616] (Application Example 2)

[0617] 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."

[0618] Traditional email systems create messages uniformly without considering the user's emotional state, making personalized communication difficult. Furthermore, the lack of campaign and incentive offers tailored to user emotions makes maximizing customer satisfaction a challenge.

[0619] 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.

[0620] In this invention, the server includes a data processing device that receives information via a communication network and stores the information, analyzes similar cases based on the information and selects the optimal information processing method, and automatically generates a draft message. This makes it possible to analyze the user's emotional state, create personalized messages that correspond to that emotion, and propose optimal campaigns and special offers.

[0621] A "data processing device" is a device that has the function of receiving information via a communication network, and storing and processing that information.

[0622] "Analysis of similar cases" is the process of finding the optimal processing method by comparing past cases with current data and extracting patterns.

[0623] An "information processing method" is a technique for converting received information into a format suitable for a specific purpose, enabling its efficient use.

[0624] A "message draft" is a preliminary version of a message created before sending, and is provided in a state where it can be reviewed and edited.

[0625] "Preventing accidental sending" means implementing a checking mechanism to avoid sending messages to unintended recipients.

[0626] "Analyzing emotional state" is the process of inferring the user's emotions and psychological state at a given time based on data obtained from the user.

[0627] "Suggesting campaigns and special offers" refers to the act of selecting and presenting appropriate campaigns and special offers based on the user's current status and past behavior.

[0628] To realize this application, the server operates as a system integrating multiple functions. The server receives information from users via a communication network using a data processing unit and stores that information in a database. It is equipped with a function to analyze the user's emotional state using an emotion recognition engine. Based on the analyzed data, the server proposes optimal campaigns and reward information. A natural language processing engine (such as Google Cloud Natural Language) is used for the analysis.

[0629] The user's device receives and displays this campaign and promotional information to the user. It also continuously updates the database by sending user feedback back to the server.

[0630] A concrete example is a case where, when a user makes an electronic payment, heart rate sensors and voice input devices are used to collect real-time emotional data, and appropriate promotions are suggested based on that emotional state. This enables personalized and effective communication.

[0631] Examples of prompt statements to input into a generative AI model are as follows:

[0632] "How can we recognize a user's emotions based on their current heart rate and voice tone, and suggest an appropriate relaxation campaign if we determine they are experiencing stress?"

[0633] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0634] Step 1:

[0635] The server receives data from the user's terminal device via a communication network. Inputs include the user's past payment history and real-time sensor information (e.g., heart rate, voice input). This information is stored in a database, forming the foundation for subsequent processing.

[0636] Step 2:

[0637] The server uses an emotion recognition engine to analyze the user's emotional state based on the stored data. Sensor data is used as input, and a natural language processing engine performs emotion analysis, obtaining the results as output. Specifically, the analysis determines stress levels and agitation levels.

[0638] Step 3:

[0639] The server selects the most suitable campaigns and reward information based on the analysis results. The input involves matching the sentiment analysis results with the user's past preference data and retrieving appropriate promotional information from the commercial database. As a result, personalized campaign information is output.

[0640] Step 4:

[0641] The server sends the selected campaign information to the user's device. The device displays the received information on its screen for the user to review. The campaign information details the specific benefits offered.

[0642] Step 5:

[0643] Users make selections based on the suggested campaigns and offers. These selections are then sent back to the server from the device as feedback data. This allows feedback information to be accumulated in a database, contributing to improvements in system accuracy.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] [Fourth Embodiment]

[0648] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0649] 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.

[0650] 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).

[0651] 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.

[0652] 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.

[0653] 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).

[0654] 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.

[0655] 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.

[0656] 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.

[0657] 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.

[0658] 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.

[0659] 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.

[0660] 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".

[0661] To implement this invention, first, a server acting as a computing device is installed within the company and connected via a communication network. Users access a portal through a terminal and upload various business-related data to the server. This data includes product information, customer lists, transaction history, etc.

[0662] The server stores the uploaded data in a database, which the AI ​​agent accesses to identify similar past cases. Based on these similar cases, the AI ​​agent selects the optimal data processing method and processes the data in a way that conforms to the business objectives set by the user.

[0663] Next, the server automatically generates a draft email to send to the customer. The email draft includes product details, promotional information, and customer suggestions, designed to capture the customer's interest. The server, through an AI agent, verifies that the draft is accurate and appropriate, and that the correct recipients have been selected. At this stage, a check against the customer database is performed to prevent accidental sending.

[0664] Sales representatives, who are users with terminals, receive notifications from the server and review the email draft. After making any necessary revisions, they approve sending the email. Upon receiving this approval, the server sends the email to the customer. After sending, the server automatically receives the customer's response, which is then analyzed by an AI agent. This process organizes the customer's reaction and helps improve future sales activities.

[0665] As a concrete example, consider sending a campaign email targeting a particular product. A product planner inputs product promotion information from their terminal into a server, and the server uses AI to analyze the relevant data and create a draft. A sales representative reviews this draft, verifies the appropriate customer list, and after approval, the email is sent quickly and accurately.

[0666] In this way, the invention is incorporated into actual business operations, reducing the risk of sending emails to the wrong recipient and improving operational efficiency.

[0667] The following describes the processing flow.

[0668] Step 1:

[0669] Users (project planners) upload project-related data to the system using their terminals. This data includes product information and market analysis results.

[0670] Step 2:

[0671] The server receives the uploaded data and stores it in its internal database. Here, it automatically checks for data integrity and proper formatting.

[0672] Step 3:

[0673] The server passes the data to the AI ​​agent, which analyzes similar past cases. Based on the data, the AI ​​agent selects the optimal processing strategy and determines the processing procedure.

[0674] Step 4:

[0675] The server processes data based on instructions from the AI ​​agent. This includes tasks such as filtering, aggregation, and formatting.

[0676] Step 5:

[0677] The server automatically generates an email draft using the processed data. This draft clearly indicates the message to be conveyed to the customer, and the proposal is created based on the template.

[0678] Step 6:

[0679] The server compares the generated email draft with the customer database. Here, it automatically verifies that the correct recipients are specified.

[0680] Step 7:

[0681] The terminal (sales representative) receives a request to review an email draft sent from the server. The representative reviews the draft and makes revisions as needed.

[0682] Step 8:

[0683] The terminal (sales representative) approves the revised draft and instructs the server to send the email. At this time, a confirmation prompt will be displayed in the message content.

[0684] Step 9:

[0685] The server sends the approved email to the designated customer. After sending, it updates the record as a basis for timely follow-up with the customer.

[0686] Step 10:

[0687] The server automatically receives customer responses, which are then analyzed by an AI agent. The analysis results are presented to sales representatives to help them create future emails and improve customer service.

[0688] (Example 1)

[0689] 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".

[0690] In today's business environment, efficient and highly accurate data processing and communication are essential. However, traditional systems require significant human effort and time for data management, analysis of similar cases, and the creation and transmission of electronic messages, and also carry the risk of sending messages to the wrong recipient. This hinders operational efficiency and prevents effective customer engagement.

[0691] 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.

[0692] In this invention, the server includes means for receiving and storing data, means for identifying similar cases and selecting the optimal data processing method, and means for automatically generating drafts of electronic messages. This enables efficient data management, fast and accurate email transmission, and prevention of erroneous transmissions.

[0693] A "computer" is a device that has the function of processing and recording data via a communication network.

[0694] A "communication network" is a network system that enables the exchange of information.

[0695] "Data" refers to information that is collected and analyzed for a specific purpose.

[0696] "Storage" refers to the safe and long-term retention of data.

[0697] "Similar cases" refer to similar incidents or activities that have been handled in the past.

[0698] "Identification" refers to finding objects that are related.

[0699] A "data processing method" is a technique for analyzing, transforming, or manipulating data.

[0700] An "electronic message" is information that is sent and received using electronic means.

[0701] A "draft" refers to a preliminary version or draft before it becomes the final version.

[0702] "Misdirected transmission" refers to information being sent to an unintended recipient.

[0703] A "customer information database" is a structured collection of data in which information about customers is gathered and recorded.

[0704] "Contrast" refers to comparing different data or pieces of information.

[0705] A "user device" is a computer or electronic device used by an end user to input or output information.

[0706] "Approval" refers to acknowledging or acknowledging the content or actions of something.

[0707] "Target recipient" refers to the person to whom specific information should be sent.

[0708] "Analysis" refers to the process of breaking down complex information and then understanding or interpreting it.

[0709] To implement this invention, first, a server, acting as a computing device, is installed within the company and connected to terminals and users via a communication network. Users access the company's portal using their terminals and upload business-related data such as product information, customer lists, and transaction history to the server.

[0710] The server stores the received data in a database. This database efficiently stores structured data and allows for quick access when needed. Based on the data stored in the database, the server activates a generative AI model to identify similar cases. Analysis by this generative AI model extracts past successful cases and relevant examples.

[0711] Next, the server uses an AI agent to select the optimal data processing method based on the identified similar cases and processes the data. This process often includes data cleaning and summarization.

[0712] The server then automatically generates a draft of an electronic message to send to the customer through an AI agent. For example, it might use a prompt such as, "Explain the features of product A to the customer and state its benefits," to create the email content. This draft would include product details, promotional information, and suggestions for the customer.

[0713] To prevent accidental sending, the generated email draft is cross-referenced by the server with a customer information database to verify the recipient information. Users review the draft content via their terminal, make any necessary corrections, and approve it only after confirming its accuracy.

[0714] After the email is approved, the server sends an electronic message to the designated recipient. The server then automatically receives the customer's response, and an AI agent analyzes its content. The analysis results are provided to the user as information useful for future sales activities.

[0715] In this way, the invention is integrated into business operations, enabling efficient and effective communication, reducing the risk of sending emails to the wrong recipient, and improving overall work efficiency.

[0716] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0717] Step 1:

[0718] Users access the company's portal using their devices and upload business-related data such as product information, customer lists, and transaction history. Input is in the form of a CSV file or similar, and output is data sent to the server via the network. Users submit the data by clicking the "Upload" button on the portal.

[0719] Step 2:

[0720] The server receives data sent from users via the communication network and stores it in a database. The input is raw data sent by the user, and the output is structured database entries. The server parses the data format and uses SQL to save the data to the database.

[0721] Step 3:

[0722] The server accesses the database, activates a generative AI model, and identifies similar cases based on past data. The input is the stored data and the generative AI model, and the output is a list of similar cases. The server utilizes natural language processing and machine learning techniques to extract highly relevant cases from past data.

[0723] Step 4:

[0724] The AI ​​agent selects the optimal data processing method based on identified similar cases and processes the data. The input is the identified similar cases, and the output is the processed data. The AI ​​agent organizes the necessary information by performing tasks such as data cleaning and summarization.

[0725] Step 5:

[0726] The server automatically generates drafts of electronic messages to be sent to customers via an AI agent. The input is processed data and prompt text, and the output is a draft of the electronic message. For example, the server might use the prompt text "Explain the features of product A to the customer and state its benefits" to generate an email.

[0727] Step 6:

[0728] The server verifies the recipient information in the draft against the customer information database to prevent misdeliveries. The input is the recipient information in the draft, and the output is the verified recipient information after verification. The server automatically checks whether the recipient is correct.

[0729] Step 7:

[0730] The user reviews the draft on their device, makes any necessary corrections, and then approves it. The input is the draft content, and the output is the corrected, approved draft. The user edits on the device interface and clicks the "Approve" button.

[0731] Step 8:

[0732] The server sends the approved electronic message to the designated recipient. The input is the approved draft, and the output is the sent electronic message. The server sends the email using the SMTP protocol.

[0733] Step 9:

[0734] The server automatically receives responses from customers, and an AI agent analyzes their content. The input is the customer response message, and the output is the analyzed customer feedback information. The server receives responses using the POP3 or IMAP protocol, and the AI ​​agent performs semantic analysis.

[0735] (Application Example 1)

[0736] 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".

[0737] Traditional business processes require significant time and effort to optimize advertising campaigns, and there are challenges in effectively utilizing historical data. Furthermore, misdirected emails to customers and insufficient targeting accuracy for advertising messages are also problems. These issues lead to decreased operational efficiency and reduced customer satisfaction, thus necessitating a new system to address these challenges.

[0738] 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.

[0739] In this invention, the server includes means for receiving and storing data, means for analyzing similar cases and selecting the optimal data processing method, means for automatically generating email drafts, and means for analyzing past advertising data and customer response data to optimize advertising campaigns and propose the optimal advertising strategy to the user. This enables effective optimization of advertising campaigns and improves the accuracy of delivering targeted messages to customers.

[0740] A "computing device" is a device designed to collect, store, process, and analyze data.

[0741] A "communication network" is an infrastructure for sending and receiving data between different devices.

[0742] "Means for receiving and storing data" refers to a function for taking in data from external sources and saving it.

[0743] "A means of analyzing similar cases and selecting the optimal data processing method" refers to a function that analyzes past data and determines an appropriate data processing method based on that analysis.

[0744] "Methods for automatically generating email drafts" refers to a function that uses AI technology to automatically create a draft of an email that is scheduled to be sent.

[0745] "A means of analyzing past advertising data and customer response data to optimize advertising campaigns and propose the optimal advertising strategy to users" refers to a process of evaluating past advertising performance and customer responses to derive effective advertising methods.

[0746] This invention is primarily implemented through a system using a server and user terminals. The server is located within the company and connected to the user terminals via a communication network. Since this system performs a series of processes from data collection and analysis to proposals and notifications, it is implemented in the following specific forms.

[0747] The server utilizes a database management system to receive and store data. Specific software options for this include MySQL and PostgreSQL. For analyzing similar cases and selecting the optimal data processing method, Python-based data processing libraries such as Pandas and the machine learning library Scikit-learn are used.

[0748] Generative AI models using natural language processing technology are used to generate email drafts. Specifically, generative models such as OpenAI's GPT series can be used. The generated email draft is notified to the user's terminal, where the user can review and revise it.

[0749] Furthermore, the server analyzes historical advertising data and customer response data to optimize advertising campaigns. This process includes data cluster analysis and predictive modeling. In this case, using Python's Pandas or Scikit-learn is also suitable.

[0750] For example, when planning a promotional campaign for a new product, data from similar past products can be used to suggest the optimal targeting method and advertising content. Users can input prompts such as, "Generate an effective advertising message that appeals to the target audience. The target audience is women in their 20s, and the product is a new cosmetic product," into the AI ​​model to generate the advertising message. This entire process leads to the execution of a more effective advertising strategy.

[0751] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0752] Step 1:

[0753] The server receives advertising data and customer response data from the user's terminal. Input data includes past advertising campaign information and customer behavior history. The server uses a database management system such as MySQL to store this data in a database. The output is structured data stored in the database.

[0754] Step 2:

[0755] The server retrieves advertising data stored in the database and analyzes similar campaigns. In this process, it preprocesses the data using Python's Pandas library, and then uses Scikit-learn to create a predictive model based on past successful campaigns. The input is advertising data retrieved from the database, and the output is advertising strategy suggestions generated by the predictive model.

[0756] Step 3:

[0757] The server uses a generative AI model (e.g., GPT) to generate email drafts suitable for the target audience. By providing the generative AI model with prompts based on user-specified conditions, the system generates the optimal advertising message. The input consists of advertising strategy suggestions from the predictive model and user-specified prompts, while the output is the generated advertising message.

[0758] Step 4:

[0759] The generated advertising message is sent from the server to the user's device. The user reviews it on their device and makes any necessary modifications. They then return the modified message to the server as approval. The input to this process is the generated advertising message, and the output is the final, modified advertising message.

[0760] Step 5:

[0761] The server verifies the revised advertising message against the customer database to prevent accidental sending. It compares the customer information in the database with the message recipients to check for any discrepancies. The input for this step is the revised advertising message, and the output is the final recipient list.

[0762] Step 6:

[0763] The server sends the advertising message, whose final destination has been confirmed, to the designated recipient. After delivery is complete, it automatically receives customer responses and analyzes them to help optimize future campaigns. The input for this step is customer response data after delivery is complete, and the output is optimization suggestions for future campaigns based on that data.

[0764] 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.

[0765] This invention is a communication system using a computing device that incorporates an emotion recognition engine to analyze the user's emotional state and optimize the email sending process. In an embodiment of the invention, a server is first installed and connected to the user's terminal via a communication network. The user uploads business data and related information using the terminal, and processing by the computing device begins.

[0766] The server stores the data provided by the user in a database and then uses an emotion engine to infer the user's emotional state from their conversations and text input. This emotional information is reflected in the creation of email drafts, and the content is adjusted accordingly; for example, if the user is feeling stressed, a softer tone is suggested to alleviate the situation.

[0767] Furthermore, the server uses an AI agent to analyze similar cases in the data and select the most effective data processing method. Based on the processed information, an email draft is automatically generated, and a system is in place to prevent accidental sending by cross-referencing it with the customer database.

[0768] Users can review the draft via their device and make revisions in line with the communication strategy suggested by the sentiment engine. Once approved, the server sends an email and analyzes the customer's response immediately upon receipt. The sentiment engine also analyzes the customer's reaction and provides the user with suggestions for the next communication activity.

[0769] As a concrete example, consider a case where a user sends an email introducing a new product to a customer. The server analyzes the raw data entered by the user and, if it determines that the user is excited, creates an email that appropriately conveys that enthusiasm. If the user is highly anxious, the server suggests a message structure that provides reassurance to alleviate that anxiety. Through this process, communication with customers becomes more effective and personalized, improving operational efficiency.

[0770] This system offers a new form of automated email management that takes user emotions into account, significantly reducing misdeliveries and communication breakdowns.

[0771] The following describes the processing flow.

[0772] Step 1:

[0773] Users log in to the system using their terminals and input or upload necessary business data. This data includes product information, related documents, customer lists, and more.

[0774] Step 2:

[0775] The server receives the uploaded data and stores it in the database. Simultaneously, it performs integrity checks to ensure the data is stored correctly.

[0776] Step 3:

[0777] The server activates the emotion engine and analyzes the user's current emotional state based on the context and keywords obtained from the user's input. This analysis identifies states such as tension, anxiety, and joy.

[0778] Step 4:

[0779] The server uses an AI agent to research similar cases and determine the optimal processing method. The processed information is then enhanced by referencing past success stories.

[0780] Step 5:

[0781] The server automatically generates an email draft. This draft is adjusted based on the user's emotional state, incorporating a softer tone and highlighting key points.

[0782] Step 6:

[0783] The server compares the generated email draft with the customer database to verify that the correct recipients have been selected.

[0784] Step 7:

[0785] The terminal displays a request for review of an email draft sent from the server. The user reviews the draft and makes necessary revisions based on suggestions from the sentiment engine.

[0786] Step 8:

[0787] The user approves the final revised draft and sends a command to the server from their terminal.

[0788] Step 9:

[0789] The server, upon receiving user approval, sends the email to the designated customer. The sending result is recorded and saved as part of the correspondence history.

[0790] Step 10:

[0791] The server automatically aggregates customer responses received after emails are sent and analyzes them through an emotion engine. The analysis results are then provided to the user as strategic proposals for future sales activities.

[0792] (Example 2)

[0793] 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".

[0794] In recent years, with the advancement of information and communication technology, communication using email has increased, but problems such as misunderstandings of emotions, communication failures, and accidental sending of emails have become more pronounced. There is a need for methods to solve these problems and achieve efficient and effective communication.

[0795] 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.

[0796] In this invention, the server includes means for receiving and storing data via a communication network, means for inferring the user's emotional state using an emotion analysis mechanism, and means for automatically generating the content of the communication message based on the emotional state. This enables communication that is more appropriate to the user's emotions, prevents accidental transmissions, and facilitates the building of good relationships with customers.

[0797] A "computing device" is a hardware or software device used to process, store, analyze, and output data.

[0798] A "communication network" refers to a transmission path for information that can send and receive data, and constitutes a broad network infrastructure that includes the internet and corporate networks.

[0799] "Data storage means" refers to a device or process for temporarily or permanently recording received data and keeping it in a state where it can be retrieved later.

[0800] An "emotion analysis mechanism" is a technology that analyzes user input information, such as text data, and uses that information to infer the user's emotional state.

[0801] "Automatic communication message generation means" refers to a method or device for automatically generating appropriate communication messages based on user input or analyzed sentiment information.

[0802] "Similar case analysis methods" refer to analytical processes that extract similar cases based on past data and select the optimal processing method.

[0803] A "recipient verification means" is a means for verifying the correct recipient by comparing the recipient information contained in the message with a data storage device.

[0804] A "display device" is a device that can visually present information to a user and provides an interface for the user to check and edit that information.

[0805] A "received response processing means" is a means for automatically receiving responses to transmitted messages, analyzing their content, and using that information to determine the next action.

[0806] This invention is an advanced communication system using a computing device that analyzes the user's emotional state to optimize email transmission. The server functions as a processing unit for receiving and storing business data and related information from the user's terminal via a communication network. The emotion analysis mechanism implemented in the server is used to infer emotions from text provided by the user, applying natural language processing technology.

[0807] As a concrete example, a user creates an email introducing a new product using their device. Based on the data uploaded to the server, the emotion analysis mechanism analyzes the user's input, and if, for example, the user is excited, the AI ​​model for generating communication text automatically generates content that reflects that passion. A concrete example of a prompt would be an instruction such as, "Please generate an email to excitedly inform customers about the new product."

[0808] The server further analyzes similar cases using an AI agent and selects the optimal data processing method. The automatically generated email draft includes recipient information and is cross-referenced with the data storage device to reduce the risk of sending emails to the wrong recipient. Finally, the user can review the generated email draft on their device and make revisions based on sentiment analysis as needed.

[0809] This system enables users to engage in emotionally-driven communication, improving work efficiency and the quality of customer service.

[0810] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0811] Step 1:

[0812] Users input business data and related information using a terminal. This includes email drafts and past communication history. The entered data is sent to the server via the communication network. The server receives the data and stores it in a database. This stored data becomes the foundational information used in subsequent processing.

[0813] Step 2:

[0814] The server analyzes the received data using an emotion analysis mechanism. It analyzes the received text data using natural language processing techniques to infer the user's emotional state. For example, if the word "happy" is used frequently, the user's emotion is classified as "excited." The output is the result of the emotion analysis.

[0815] Step 3:

[0816] The server uses a generative AI model to automatically generate email drafts based on sentiment analysis results and referencing prompt text. Inputs include sentiment states and prompt text (e.g., "Generate an email to excitedly inform customers about a new product"). The output is an email draft tailored to the sentiment.

[0817] Step 4:

[0818] The server uses an AI agent to extract similar past cases from the database and select the optimal data processing method. The input is data from similar past cases, and the output is the optimized data processing method. This process further refines the generated email.

[0819] Step 5:

[0820] The user reviews the generated email draft using their device. This draft includes content based on sentiment analysis and can be manually adjusted. Input includes user feedback and correction instructions, and output is the finalized email draft.

[0821] Step 6:

[0822] The server checks for misdeliveries by comparing the confirmed email recipient information with customer information in the database. The input is the recipient information, and the output is a verified recipient list. After obtaining user approval, the email is sent.

[0823] Step 7:

[0824] After an email is sent, the server automatically receives the customer's response and analyzes it using an emotion analysis mechanism. The input is the customer's response, and the output is the analyzed emotional response of the customer. This is used to suggest future communication strategies.

[0825] (Application Example 2)

[0826] 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".

[0827] Traditional email systems create messages uniformly without considering the user's emotional state, making personalized communication difficult. Furthermore, the lack of campaign and incentive offers tailored to user emotions makes maximizing customer satisfaction a challenge.

[0828] 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.

[0829] In this invention, the server includes a data processing device that receives information via a communication network and stores the information, analyzes similar cases based on the information and selects the optimal information processing method, and automatically generates a draft message. This makes it possible to analyze the user's emotional state, create personalized messages that correspond to that emotion, and propose optimal campaigns and special offers.

[0830] A "data processing device" is a device that has the function of receiving information via a communication network, and storing and processing that information.

[0831] "Analysis of similar cases" is the process of finding the optimal processing method by comparing past cases with current data and extracting patterns.

[0832] An "information processing method" is a technique for converting received information into a format suitable for a specific purpose, enabling its efficient use.

[0833] A "message draft" is a preliminary version of a message created before sending, and is provided in a state where it can be reviewed and edited.

[0834] "Preventing accidental sending" means implementing a checking mechanism to avoid sending messages to unintended recipients.

[0835] "Analyzing emotional state" is the process of inferring the user's emotions and psychological state at a given time based on data obtained from the user.

[0836] "Suggesting campaigns and special offers" refers to the act of selecting and presenting appropriate campaigns and special offers based on the user's current status and past behavior.

[0837] To realize this application, the server operates as a system integrating multiple functions. The server receives information from users via a communication network using a data processing unit and stores that information in a database. It is equipped with a function to analyze the user's emotional state using an emotion recognition engine. Based on the analyzed data, the server proposes optimal campaigns and reward information. A natural language processing engine (such as Google Cloud Natural Language) is used for the analysis.

[0838] The user's device receives and displays this campaign and promotional information to the user. It also continuously updates the database by sending user feedback back to the server.

[0839] A concrete example is a case where, when a user makes an electronic payment, heart rate sensors and voice input devices are used to collect real-time emotional data, and appropriate promotions are suggested based on that emotional state. This enables personalized and effective communication.

[0840] Examples of prompt statements to input into a generative AI model are as follows:

[0841] "How can we recognize a user's emotions based on their current heart rate and voice tone, and suggest an appropriate relaxation campaign if we determine they are experiencing stress?"

[0842] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0843] Step 1:

[0844] The server receives data from the user's terminal device via a communication network. Inputs include the user's past payment history and real-time sensor information (e.g., heart rate, voice input). This information is stored in a database, forming the foundation for subsequent processing.

[0845] Step 2:

[0846] The server uses an emotion recognition engine to analyze the user's emotional state based on the stored data. Sensor data is used as input, and a natural language processing engine performs emotion analysis, obtaining the results as output. Specifically, the analysis determines stress levels and agitation levels.

[0847] Step 3:

[0848] The server selects the most suitable campaigns and reward information based on the analysis results. The input involves matching the sentiment analysis results with the user's past preference data and retrieving appropriate promotional information from the commercial database. As a result, personalized campaign information is output.

[0849] Step 4:

[0850] The server sends the selected campaign information to the user's device. The device displays the received information on its screen for the user to review. The campaign information details the specific benefits offered.

[0851] Step 5:

[0852] Users make selections based on the suggested campaigns and offers. These selections are then sent back to the server from the device as feedback data. This allows feedback information to be accumulated in a database, contributing to improvements in system accuracy.

[0853] 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.

[0854] 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.

[0855] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0856] 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.

[0857] 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. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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."

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0874] The following is further disclosed regarding the embodiments described above.

[0875] (Claim 1)

[0876] A computing device includes means for receiving data via a communication network and storing said data,

[0877] A means for analyzing similar cases based on the data and selecting the optimal data processing method,

[0878] A method for automatically generating email drafts,

[0879] A means to prevent misdelivery by comparing the recipient information included in the draft with the customer database,

[0880] A means of providing the user device with a draft to review and obtain approval,

[0881] A means of sending approved emails to designated recipients.

[0882] A system that includes this.

[0883] (Claim 2)

[0884] The system according to claim 1, further comprising means for analyzing past customer responses and providing the user with suggestions for future business activities.

[0885] (Claim 3)

[0886] The system according to claim 1, further comprising means for automatically receiving customer responses to emails after they have been sent and processing the content of those responses.

[0887] "Example 1"

[0888] (Claim 1)

[0889] A computing device has means for receiving data via a communication network and storing said data,

[0890] A means for identifying similar cases based on the data and selecting the optimal data processing method,

[0891] A means for automatically generating drafts of electronic messages,

[0892] A means to prevent misdelivery by comparing the recipient information included in the draft with the customer information database,

[0893] A means of providing the user's device with a draft to review and approve,

[0894] A means of sending an approved electronic message to the intended recipient,

[0895] A means for automatically receiving customer responses and analyzing the content of those responses.

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising means for analyzing past response results and providing suggestions for future business activities.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising means for generating prompt text using an AI model generated from similar cases and generating an email draft.

[0901] "Application Example 1"

[0902] (Claim 1)

[0903] A computing device includes means for receiving data via a communication network and storing said data,

[0904] A means for analyzing similar cases based on the data and selecting the optimal data processing method,

[0905] A method for automatically generating email drafts,

[0906] A means to prevent misdelivery by comparing the recipient information included in the draft with the customer database,

[0907] A means of providing the user device with a draft to review and obtain approval,

[0908] A means of sending approved emails to designated recipients,

[0909] To optimize advertising campaigns, we analyze past advertising data and customer response data to propose the optimal advertising strategy to users.

[0910] A system that includes this.

[0911] (Claim 2)

[0912] The system according to claim 1, further comprising means for analyzing past customer responses and providing the user with suggestions for future business activities.

[0913] (Claim 3)

[0914] The system according to claim 1, further comprising means for automatically receiving customer responses to emails after they have been sent and processing the content of those responses.

[0915] "Example 2 of combining an emotion engine"

[0916] (Claim 1)

[0917] A computing device includes means for receiving data via a communication network and storing said data,

[0918] A means of inferring a user's emotional state using an emotion analysis mechanism,

[0919] An automatic generation means for adjusting the content of a message based on the emotional state,

[0920] A means of analyzing similar cases and selecting the optimal data processing method,

[0921] A means for preventing erroneous transmission by comparing recipient information contained in the content of a communication message with a data storage device,

[0922] A means of providing content confirmation to a display device and obtaining approval,

[0923] Means for sending approved messages to a designated recipient.

[0924] A system that includes this.

[0925] (Claim 2)

[0926] The system according to claim 1, further comprising means for analyzing past responses and providing further suggestions for future business activities.

[0927] (Claim 3)

[0928] The system according to claim 1, further comprising means for automatically processing received responses to a transmission message.

[0929] "Application example 2 when combining with an emotional engine"

[0930] (Claim 1)

[0931] A data processing device includes means for receiving information via a communication network and storing said information,

[0932] A means for analyzing similar cases based on the information and selecting the optimal information processing method,

[0933] A means of automatically generating a draft of a message,

[0934] A means to prevent misdelivery by comparing the recipient information included in the draft with the user database,

[0935] A means of providing a terminal device with a draft to review and obtain approval,

[0936] A means for sending an approved message to a designated recipient,

[0937] A means of analyzing the emotional state of users and proposing the most suitable campaigns and special offers to users based on that emotional state.

[0938] A system that includes this.

[0939] (Claim 2)

[0940] The system according to claim 1, further comprising means for analyzing past user responses and providing suggestions for subsequent work activities to the terminal.

[0941] (Claim 3)

[0942] The system according to claim 1, further comprising means for automatically receiving a user's response to a message after it has been sent and processing the content of the response. [Explanation of Symbols]

[0943] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A computing device includes means for receiving data via a communication network and storing said data, A means for analyzing similar cases based on the data and selecting the optimal data processing method, A method for automatically generating email drafts, A means to prevent misdelivery by comparing the recipient information included in the draft with the customer database, A means of providing the user device with a draft to review and obtain approval, A means of sending approved emails to designated recipients, To optimize advertising campaigns, we analyze past advertising data and customer response data to propose the optimal advertising strategy to users. A system that includes this.

2. The system according to claim 1, further comprising means for analyzing past customer responses and providing the user with suggestions for future business activities.

3. The system according to claim 1, further comprising means for automatically receiving customer responses to emails after they have been sent and processing the content of those responses.

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