System

Abstract

An object of the system according to the embodiment is to organize information regarding a customer, a business partner, and a person in charge of media, and to respond according to an attribute or a personality of the other party.SOLUTION: A system according to an embodiment includes an information collection unit, an analysis unit, a bot creation unit, and an interaction unit. The information collector collects all information about the customer, the merchant, the media representative, etc. The analysis unit analyzes the information collected by the information collection unit, and specifies the attribute and personality of the other party. The bot creation unit creates a bot reflecting the attributes and personality of the other party specified by the analysis unit. The interaction unit allows the user to interact with the bot created by the bot creation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently organize information about customers, business partners, and media representatives, and to respond in accordance with the attributes and personalities of the other party.

[0005] The system according to the embodiment aims to organize information about customers, business partners, and media representatives, and to respond in accordance with the attributes and personalities of the other party. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a bot creation unit, and a dialogue unit. The information collection unit collects all information related to customers, business partners, media representatives, etc. The analysis unit analyzes the information collected by the information collection unit and identifies the attributes and personality of the other party. The bot creation unit creates a bot that reflects the attributes and personality of the other party identified by the analysis unit. The dialogue unit allows the user to interact with the bot created by the bot creation unit. [Effects of the Invention]

[0007] The system according to the embodiment organizes information about customers, business partners, and media representatives, and can respond according to the attributes and personalities of the other party. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The information organization system according to an embodiment of the present invention organizes all information related to customers, business partners, media representatives, etc., and creates a bot based on that information, reflecting the attributes and personalities of the other party. This bot functions as a tool to assist users in generating proposal ideas and writing emails through chat-style interactions. This allows the information organization system to communicate efficiently and effectively with customers, business partners, media representatives, etc.

[0029] The information organization system according to the embodiment includes an information collection unit, an analysis unit, a bot creation unit, and a dialogue unit. The information collection unit collects all information related to customers, business partners, media representatives, etc. For example, the information collection unit collects email content. The information collection unit can also collect materials. The information collection unit can also collect past communication histories. For example, the information collection unit analyzes email content and extracts important information. When collecting materials, the information collection unit converts them into digital data using OCR technology. When collecting past communication histories, the information collection unit analyzes chat logs and call records. The analysis unit analyzes the information collected by the information collection unit and identifies the attributes and personality of the other party. For example, the analysis unit uses text mining technology to identify the other party's occupation and position from the email content. The analysis unit can also use sentiment analysis technology to identify the other party's personality traits. The analysis unit can also use clustering technology to identify the other party's interests. For example, the analysis unit uses text mining technology to identify the other party's occupation and position from the email content. The system uses sentiment analysis technology to identify the recipient's personality traits from the text of the email. It uses clustering technology to group the recipient's interests. The bot creation unit creates a bot that reflects the recipient's attributes and personality identified by the analysis unit. For example, the bot creation unit uses a generation AI to generate responses that match the recipient's attributes and personality. The bot creation unit can also use the generation AI to generate short, clear responses if the recipient prefers a quick response. The bot creation unit can also use the generation AI to generate flexible responses if the recipient prefers a flexible response. For example, the generation AI uses a text generation AI (e.g., GPT-3) to generate responses that match the recipient's attributes and personality. The generation AI generates short, clear responses if the recipient prefers a quick response. The generation AI generates flexible responses if the recipient prefers a flexible response. The dialogue unit allows the user to interact with the bot created by the bot creation unit. For example, the dialogue unit allows the user to interact with the bot in a chat format. The dialogue unit also allows the user to interact with the bot using voice input.The dialogue unit also allows the user to interact with the bot via video call. For example, the dialogue unit supports dialogue in chat format. Using voice input, the user inputs a question by voice, and the bot responds in text. Using video call, the user interacts with the bot via video call. This allows the information organization system according to the embodiment to efficiently and effectively communicate with customers, business partners, media representatives, etc. For example, it allows for smoother idea generation for proposals and reduces the time required to create emails. It also enables responses that are tailored to the attributes and personality of the other party, improving the quality of communication.

[0030] The information collection unit can analyze the social media activities of customers and business partners to identify their online behavioral patterns and interests. For example, the information collection unit can analyze the social media accounts of customers and business partners to identify their interests from the content of their posts and trends in likes. For example, if there are many posts about a particular brand or product, it can be determined that the customer has a strong interest in that brand or product. The information collection unit can also use text mining technology to analyze social media activities. For example, it can analyze the content of posts to identify their interests. The information collection unit can also use image analysis technology to analyze social media activities. For example, it can identify their interests from posted images. This can identify the online behavioral patterns and interests of customers and business partners.

[0031] The information collection unit can estimate the other party's health condition and lifestyle habits based on the collected information, and identify their attributes and personality based on that. The information collection unit, for example, analyzes the email and chat history of customers or business partners to estimate their health condition and lifestyle habits. For example, if health-related topics are frequently discussed, it can be determined that the person is highly health-conscious. The information collection unit also uses text mining technology to estimate their health condition and lifestyle habits. For example, it can estimate their health condition from the content of emails. The information collection unit can also use clustering technology to estimate their health condition and lifestyle habits. For example, it can group lifestyle patterns. This makes it possible to estimate the other party's health condition and lifestyle habits and identify their attributes and personality based on that.

[0032] The information collection unit can integrate different data sources to identify more detailed attributes and characteristics. For example, the information collection unit analyzes a customer's purchasing history to identify attributes and characteristics based on the type and frequency of purchased items. For example, if the customer frequently purchases expensive items, it can identify the customer as being wealthy. The information collection unit also uses data mining technology to integrate different data sources. For example, it can integrate purchasing history with website browsing history. The information collection unit can also use database technology to integrate different data sources. For example, it can integrate social media data with purchasing history. This allows the integration of different data sources to identify more detailed attributes and characteristics.

[0033] The bot creation unit can create bots specialized for different industries or occupations, reflecting industry-specific knowledge and terminology. The bot creation unit, for example, uses generation AI to create bots specialized for different industries or occupations. For example, a bot specialized for the medical industry understands medical terminology and generates appropriate responses. The bot creation unit also uses generation AI to use a technical dictionary to reflect industry-specific knowledge and terminology. For example, a medical terminology dictionary is used to generate responses specialized for the medical industry. The bot creation unit can also use generation AI to refer to industry best practices to reflect industry-specific knowledge and terminology. For example, best practices in the legal industry are referenced to generate responses using legal terminology. This makes it possible to create bots specialized for different industries or occupations, reflecting industry-specific knowledge and terminology.

[0034] The bot creation unit can create a bot that learns the other party's past behavioral patterns and predicts future behavior. The bot creation unit, for example, uses a generation AI to create a bot that learns the other party's past behavioral patterns and predicts future behavior. For example, predicting the next purchase based on past purchase history. The bot creation unit can also use machine learning technology to learn the other party's past behavioral patterns using the generation AI. For example, predicting the next purchase using purchase history data. The bot creation unit can also use trend analysis technology to predict the other party's future behavior using the generation AI. For example, predicting future behavior based on past behavioral patterns and current trends. This makes it possible to create a bot that learns the other party's past behavioral patterns and predicts future behavior.

[0035] The dialogue unit can understand the context of the dialogue and generate a consistent response by referring to past dialogue history. The dialogue unit, for example, uses a generation AI to understand the context of the dialogue and generate a consistent response by referring to past dialogue history. For example, it may refer again to topics that came up in past dialogues. The dialogue unit also uses natural language processing technology to understand the context of the dialogue using the generation AI. For example, it may understand the context from the text of the chat and generate a response. The dialogue unit can also use database technology to refer to past dialogue history using the generation AI. For example, it may search past chat logs and reflect related content in the response. This makes it possible to understand the context of the dialogue and generate a consistent response by referring to past dialogue history.

[0036] The dialogue unit can combine voice input and video calls in chat-style dialogue to provide a wider variety of communication means. The dialogue unit, for example, uses a generation AI to combine voice input with chat-style dialogue. For example, a user inputs a question by voice, and the generation AI responds in text. The dialogue unit can also use a generation AI to combine video calls with chat-style dialogue. For example, a user inputs a question via video call, and the generation AI responds in text. The dialogue unit can also use speech recognition technology to combine voice input and video calls in chat-style dialogue. For example, the voice input is converted into text and a response is generated. This makes it possible to combine voice input and video calls in chat-style dialogue to provide a wider variety of communication means.

[0037] The dialogue unit can visually display ideas proposed by the generation AI during a dialogue, allowing the user to select one. The dialogue unit, for example, uses the generation AI to visually display ideas proposed during a dialogue. For example, the ideas can be displayed in list form, allowing the user to select one. The dialogue unit also uses a graphical user interface (GUI) to visually display ideas proposed during a dialogue using the generation AI. For example, the ideas can be displayed as icons or images, allowing the user to select one. The dialogue unit can also use data visualization technology to visually display ideas proposed during a dialogue using the generation AI. For example, the ideas can be displayed as graphs or charts, allowing the user to select one. In this way, the ideas proposed by the generation AI during a dialogue can be visually displayed, allowing the user to select one.

[0038] The dialogue unit can refer to success stories from different industries and fields and generate proposals based on them. For example, the dialogue unit uses a generation AI to refer to success stories from different industries and fields and generate proposals based on them. For example, a proposal is generated based on success stories from the medical industry. The dialogue unit also uses database technology to refer to success stories from different industries and fields using the generation AI. For example, it searches past project data and extracts success stories. The dialogue unit can also use text mining technology to refer to success stories from different industries and fields using the generation AI. For example, it extracts industry best practices and reflects them in proposals. This makes it possible to refer to success stories from different industries and fields and generate proposals based on them.

[0039] The dialogue unit can visually display the proposed ideas, allowing the user to select one. The dialogue unit, for example, uses a generation AI to visually display the proposed ideas. For example, the ideas can be displayed in list form, allowing the user to select one. The dialogue unit can also use a graphical user interface (GUI) to visually display the proposed ideas, using the generation AI. For example, the ideas can be displayed as icons or images, allowing the user to select one. The dialogue unit can also use data visualization technology to visually display the proposed ideas, using the generation AI. For example, the ideas can be displayed as graphs or charts, allowing the user to select one. In this way, the proposed ideas can be visually displayed, allowing the user to select one.

[0040] The dialogue unit can refer to past email history and generate consistent email content. The dialogue unit, for example, uses generation AI to refer to past email history and generate consistent email content. For example, it creates a new email based on the expressions and tone used in past emails. The dialogue unit also uses database technology to refer to past email history using generation AI. For example, it searches past email logs and reflects related content in the email. The dialogue unit can also use text mining technology to refer to past email history using generation AI. For example, it extracts important information from the text of past emails and reflects it in new emails. In this way, it is possible to refer to past email history and generate consistent email content.

[0041] The dialogue unit can combine images and videos when creating an email to generate a visually appealing email. The dialogue unit, for example, uses a generation AI to combine images when creating an email to generate a visually appealing email. For example, a product photo is inserted into the email. The dialogue unit can also use a generation AI to combine videos when creating an email. For example, a product introduction video is inserted into the email. The dialogue unit can also use a generation AI to use data visualization technology to combine images and videos when creating an email. For example, images and videos are effectively arranged to generate a visually appealing email. This makes it possible to combine images and videos when creating an email to generate a visually appealing email.

[0042] The dialogue unit can automatically translate into different languages ​​to support international communication. For example, the dialogue unit uses generation AI to automatically translate the contents of emails into different languages ​​to support international communication. For example, it translates an English email into Japanese. The dialogue unit also uses a translation algorithm to automatically translate into different languages ​​using generation AI. For example, it uses neural machine translation (NMT) technology to translate the contents of emails. The dialogue unit can also increase the number of supported languages ​​to automatically translate into different languages ​​using generation AI. For example, it supports multiple languages ​​such as English, Japanese, Chinese, and French. This makes it possible to automatically translate into different languages ​​and support international communication.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The information collection unit can analyze the purchasing history of customers and business partners to identify their purchasing patterns and preferences. For example, if a customer frequently purchases a particular product, it can be determined that the customer has a strong interest in that product. The information collection unit also uses data mining technology to analyze the purchasing history. For example, it can analyze purchase frequency and purchase amount to identify the customer's purchasing pattern. The information collection unit can also use clustering technology to analyze the purchasing history. For example, it can group customers with similar purchasing patterns to identify common preferences. This can identify the purchasing patterns and preferences of customers and business partners, enabling more personalized proposals.

[0045] The information collection unit can analyze the website browsing history of customers and business partners to identify their interests. For example, if a user frequently views pages in a particular category, it can identify that the user has a strong interest in that category. The information collection unit can also use text mining technology to analyze the website browsing history. For example, it can analyze the content of the pages viewed to identify the user's interests. The information collection unit can also use clustering technology to analyze the website browsing history. For example, it can group users with similar browsing patterns to identify their common interests. This makes it possible to identify the interests of customers and business partners from their website browsing history and make more personalized suggestions.

[0046] The information gathering unit can analyze feedback from customers and business partners to identify satisfaction levels and dissatisfaction points. For example, if there is a lot of positive feedback about a product or service, it can identify high satisfaction levels for that product or service. The information gathering unit can also use text mining technology to analyze the feedback. For example, it can analyze the content of the feedback to identify satisfaction levels and dissatisfaction points. The information gathering unit can also use sentiment analysis technology to analyze the feedback. For example, it can identify emotions from the text of the feedback to identify satisfaction levels and dissatisfaction points. This makes it possible to identify satisfaction levels and dissatisfaction points from customer and business partner feedback and find areas for improvement.

[0047] The information collection unit can analyze the location information of customers and business partners to identify behavioral patterns and visit frequency. For example, if a customer frequently visits a particular store, it can be determined that the customer has a strong interest in that store. The information collection unit also uses data mining technology to analyze the location information. For example, it can analyze the frequency of visits and length of stay to identify behavioral patterns. The information collection unit can also use clustering technology to analyze the location information. For example, it can group customers with similar behavioral patterns to identify common interests. This makes it possible to identify behavioral patterns and visit frequency from the location information of customers and business partners, enabling more personalized proposals.

[0048] The information gathering unit can integrate the purchase history and social media activity of customers or business partners to identify more detailed attributes and characteristics. For example, purchase history can be integrated with social media posts to identify common interests. The information gathering unit also uses data mining technology to integrate purchase history and social media activity. For example, purchase history can be integrated with social media data to identify detailed attributes. The information gathering unit can also use clustering technology to integrate purchase history and social media activity. For example, customers with similar purchasing patterns and social media activity can be grouped together to identify common characteristics. This makes it possible to integrate the purchase history and social media activity of customers or business partners to identify more detailed attributes and characteristics.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The information collection department collects all information related to customers, business partners, media representatives, etc. For example, they collect email content, documents, and past communication history, analyze email content to extract important information, convert documents into digital data using OCR technology, and analyze chat logs and call records. Step 2: The analysis unit analyzes the information collected by the information collection unit and identifies the other person's attributes and personality. For example, text mining technology is used to identify occupations and job titles, sentiment analysis technology is used to identify personality traits, and clustering technology is used to identify interests. Step 3: The bot creation unit creates a bot that reflects the attributes and personality of the other party identified by the analysis unit. For example, it uses generation AI to generate responses that match the other party's attributes and personality, generating short, clear answers if the other party prefers a quick response, and flexible answers if the other party prefers a flexible response. Step 4: In the dialogue unit, the user interacts with the bot created by the bot creation unit. For example, the interaction can be performed using chat, voice input, or video calling.

[0051] (Example 2) The information organization system according to an embodiment of the present invention organizes all information related to customers, business partners, media representatives, etc., and creates a bot based on that information, reflecting the attributes and personalities of the other party. This bot functions as a tool to assist users in generating proposal ideas and writing emails through chat-style interactions. This allows the information organization system to communicate efficiently and effectively with customers, business partners, media representatives, etc.

[0052] The information organization system according to the embodiment includes an information collection unit, an analysis unit, a bot creation unit, and a dialogue unit. The information collection unit collects all information related to customers, business partners, media representatives, etc. For example, the information collection unit collects email content. The information collection unit can also collect materials. The information collection unit can also collect past communication histories. For example, the information collection unit analyzes email content and extracts important information. When collecting materials, the information collection unit converts them into digital data using OCR technology. When collecting past communication histories, the information collection unit analyzes chat logs and call records. The analysis unit analyzes the information collected by the information collection unit and identifies the attributes and personality of the other party. For example, the analysis unit uses text mining technology to identify the other party's occupation and position from the email content. The analysis unit can also use sentiment analysis technology to identify the other party's personality traits. The analysis unit can also use clustering technology to identify the other party's interests. For example, the analysis unit uses text mining technology to identify the other party's occupation and position from the email content. The system uses sentiment analysis technology to identify the recipient's personality traits from the text of the email. It uses clustering technology to group the recipient's interests. The bot creation unit creates a bot that reflects the recipient's attributes and personality identified by the analysis unit. For example, the bot creation unit uses a generation AI to generate responses that match the recipient's attributes and personality. The bot creation unit can also use the generation AI to generate short, clear responses if the recipient prefers a quick response. The bot creation unit can also use the generation AI to generate flexible responses if the recipient prefers a flexible response. For example, the generation AI uses a text generation AI (e.g., GPT-3) to generate responses that match the recipient's attributes and personality. The generation AI generates short, clear responses if the recipient prefers a quick response. The generation AI generates flexible responses if the recipient prefers a flexible response. The dialogue unit allows the user to interact with the bot created by the bot creation unit. For example, the dialogue unit allows the user to interact with the bot in a chat format. The dialogue unit also allows the user to interact with the bot using voice input.The dialogue unit also allows the user to interact with the bot via video call. For example, the dialogue unit supports dialogue in chat format. Using voice input, the user inputs a question by voice, and the bot responds in text. Using video call, the user interacts with the bot via video call. This allows the information organization system according to the embodiment to efficiently and effectively communicate with customers, business partners, media representatives, etc. For example, it allows for smoother idea generation for proposals and reduces the time required to create emails. It also enables responses that are tailored to the attributes and personality of the other party, improving the quality of communication.

[0053] The analysis unit can analyze the changes in the other person's emotions from the information collected by the information collection unit and identify attributes and personality traits corresponding to the changes in emotions. The analysis unit, for example, analyzes collected emails and communication history to identify the changes in the other person's emotions. For example, it reads changes in emotions from the text of emails and the speed of replies, and identifies attributes and personality traits corresponding to the changes in emotions. The analysis unit also uses natural language processing technology to analyze the changes in emotions. For example, it uses text mining technology to identify changes in emotions from the content of emails. The analysis unit can also analyze changes in emotions over time to analyze the changes in emotions. For example, it identifies changes in emotions based on the date and time an email was sent. This makes it possible to identify attributes and personality traits corresponding to changes in the other person's emotions.

[0054] The information collection unit can analyze the social media activities of customers and business partners to identify their online behavioral patterns and interests. For example, the information collection unit can analyze the social media accounts of customers and business partners to identify their interests from the content of their posts and trends in likes. For example, if there are many posts about a particular brand or product, it can be determined that the customer has a strong interest in that brand or product. The information collection unit can also use text mining technology to analyze social media activities. For example, it can analyze the content of posts to identify their interests. The information collection unit can also use image analysis technology to analyze social media activities. For example, it can identify their interests from posted images. This can identify the online behavioral patterns and interests of customers and business partners.

[0055] The analysis unit can use the emotion estimation function to estimate the other party's emotions from past communication history and identify attributes and personality based on those emotions. The analysis unit, for example, analyzes past email and chat history and uses the emotion estimation function to estimate the other party's emotions. For example, if there are many positive expressions, it is determined that the other party has an optimistic personality, and if there are many negative expressions, it is determined that the other party has a cautious personality. The analysis unit also uses the emotion estimation function to estimate emotions using natural language processing technology. For example, it estimates emotions from the text of emails. The analysis unit can also use the emotion estimation function to estimate emotions using image analysis technology. For example, it estimates emotions from facial expression images in chats. In this way, it is possible to estimate the other party's emotions from past communication history and identify attributes and personality based on those emotions.

[0056] The information collection unit can estimate the other party's health condition and lifestyle habits based on the collected information, and identify their attributes and personality based on that. The information collection unit, for example, analyzes the email and chat history of customers or business partners to estimate their health condition and lifestyle habits. For example, if health-related topics are frequently discussed, it can be determined that the person is highly health-conscious. The information collection unit also uses text mining technology to estimate their health condition and lifestyle habits. For example, it can estimate their health condition from the content of emails. The information collection unit can also use clustering technology to estimate their health condition and lifestyle habits. For example, it can group lifestyle patterns. This makes it possible to estimate the other party's health condition and lifestyle habits and identify their attributes and personality based on that.

[0057] The information collection unit can integrate different data sources to identify more detailed attributes and characteristics. For example, the information collection unit analyzes a customer's purchasing history to identify attributes and characteristics based on the type and frequency of purchased items. For example, if the customer frequently purchases expensive items, it can identify the customer as being wealthy. The information collection unit also uses data mining technology to integrate different data sources. For example, it can integrate purchasing history with website browsing history. The information collection unit can also use database technology to integrate different data sources. For example, it can integrate social media data with purchasing history. This allows the integration of different data sources to identify more detailed attributes and characteristics.

[0058] The analysis unit uses the emotion estimation function to analyze the emotions of the other party from the collected information in real time and can identify attributes and personality based on those emotions. The analysis unit, for example, analyzes email and chat history of customers or business partners in real time and uses the emotion estimation function to identify the other party's emotions. For example, if there are a lot of positive expressions, it can identify the other party as having an optimistic personality. The analysis unit also uses the emotion estimation function to analyze emotions in real time using natural language processing technology. For example, it can identify emotions from the text of emails in real time. The analysis unit can also use the emotion estimation function to analyze emotions in real time using image analysis technology. For example, it can identify emotions from facial expression images in chats in real time. This makes it possible to analyze the emotions of the other party from the collected information in real time and identify attributes and personality based on those emotions.

[0059] The bot creation unit uses a generation AI to create a bot that generates a response according to the other party's emotions, thereby responding to changes in emotions. The bot creation unit, for example, uses a generation AI to create a bot that generates a response according to the other party's emotions. For example, if the other party is angry, it generates a calm and polite response. The bot creation unit also uses natural language processing technology to generate a response according to the other party's emotions using the generation AI. For example, it identifies emotions from the text of an email and generates a response. The bot creation unit can also use emotion analysis technology to generate a response according to the other party's emotions using the generation AI. For example, it identifies emotions from facial expressions in a chat and generates a response. This makes it possible to generate a response according to the other party's emotions and respond to changes in emotions.

[0060] The bot creation unit uses the emotion estimation function to create a bot that generates a response based on the other party's emotions, thereby eliciting emotional empathy. The bot creation unit, for example, uses the emotion estimation function to create a bot that generates a response based on the other party's emotions. For example, if the other party is sad, the bot creation unit generates words of encouragement. The bot creation unit also uses natural language processing technology to generate a response based on the other party's emotions using the emotion estimation function. For example, the bot creation unit identifies emotions from the text of an email and generates a response. The bot creation unit can also use emotion analysis technology to generate a response based on the other party's emotions using the emotion estimation function. For example, the bot creation unit identifies emotions from facial expressions in a chat and generates a response. This makes it possible to generate a response based on the other party's emotions and elicit emotional empathy.

[0061] The bot creation unit can create bots specialized for different industries or occupations, reflecting industry-specific knowledge and terminology. The bot creation unit, for example, uses generation AI to create bots specialized for different industries or occupations. For example, a bot specialized for the medical industry understands medical terminology and generates appropriate responses. The bot creation unit also uses generation AI to use a technical dictionary to reflect industry-specific knowledge and terminology. For example, a medical terminology dictionary is used to generate responses specialized for the medical industry. The bot creation unit can also use generation AI to refer to industry best practices to reflect industry-specific knowledge and terminology. For example, best practices in the legal industry are referenced to generate responses using legal terminology. This makes it possible to create bots specialized for different industries or occupations, reflecting industry-specific knowledge and terminology.

[0062] The bot creation unit can create a bot that learns the other party's past behavioral patterns and predicts future behavior. The bot creation unit, for example, uses a generation AI to create a bot that learns the other party's past behavioral patterns and predicts future behavior. For example, predicting the next purchase based on past purchase history. The bot creation unit can also use machine learning technology to learn the other party's past behavioral patterns using the generation AI. For example, predicting the next purchase using purchase history data. The bot creation unit can also use trend analysis technology to predict the other party's future behavior using the generation AI. For example, predicting future behavior based on past behavioral patterns and current trends. This makes it possible to create a bot that learns the other party's past behavioral patterns and predicts future behavior.

[0063] The bot creation unit uses the emotion estimation function to create a bot that generates a response in real time according to the emotion of the other party, thereby improving the quality of the dialogue. For example, the bot creation unit uses the emotion estimation function to create a bot that generates a response in real time according to the emotion of the other party. For example, if the other party is angry, a calm and polite response is generated. The bot creation unit also uses natural language processing technology to use the emotion estimation function to generate a response in real time according to the emotion of the other party. For example, the bot creation unit identifies the emotion from the text of an email in real time and generates a response. The bot creation unit can also use emotion analysis technology to use the emotion estimation function to generate a response in real time according to the emotion of the other party. For example, the bot creation unit identifies the emotion from facial expression images in chat in real time and generates a response. This makes it possible to generate a response in real time according to the emotion of the other party, thereby improving the quality of the dialogue.

[0064] The dialogue unit can use a generation AI to analyze the emotions of the other party during a dialogue in real time and generate a response that corresponds to that emotion. For example, the dialogue unit can use a generation AI to analyze the emotions of the other party during a dialogue in real time and generate a response that corresponds to that emotion. For example, if the other party is angry, it can generate a calm and polite response. The dialogue unit can also use a generation AI to use natural language processing technology to analyze the emotions of the other party during a dialogue in real time. For example, it can identify emotions from chat text in real time and generate a response. The dialogue unit can also use emotion analysis technology to analyze the emotions of the other party during a dialogue in real time. For example, it can identify emotions from facial expressions in a video call in real time and generate a response. This makes it possible to analyze the emotions of the other party during a dialogue in real time and generate a response that corresponds to those emotions.

[0065] The dialogue unit can understand the context of the dialogue and generate a consistent response by referring to past dialogue history. The dialogue unit, for example, uses a generation AI to understand the context of the dialogue and generate a consistent response by referring to past dialogue history. For example, it may refer again to topics that came up in past dialogues. The dialogue unit also uses natural language processing technology to understand the context of the dialogue using the generation AI. For example, it may understand the context from the text of the chat and generate a response. The dialogue unit can also use database technology to refer to past dialogue history using the generation AI. For example, it may search past chat logs and reflect related content in the response. This makes it possible to understand the context of the dialogue and generate a consistent response by referring to past dialogue history.

[0066] The dialogue unit can use the emotion estimation function to estimate the emotion of the other party in a dialogue and generate a response based on that emotion. For example, the dialogue unit can use the emotion estimation function to estimate the emotion of the other party in a dialogue and generate a response based on that emotion. For example, if the other party is sad, the dialogue unit can generate words of encouragement. The dialogue unit can also use the emotion estimation function to use natural language processing technology to estimate the emotion of the other party in a dialogue. For example, the dialogue unit can estimate the emotion from chat text and generate a response. The dialogue unit can also use emotion analysis technology to estimate the emotion of the other party in a dialogue. For example, the dialogue unit can estimate the emotion from facial expression images in a video call and generate a response. This makes it possible to estimate the emotion of the other party in a dialogue and generate a response based on that emotion.

[0067] The dialogue unit can combine voice input and video calls in chat-style dialogue to provide a wider variety of communication means. The dialogue unit, for example, uses a generation AI to combine voice input with chat-style dialogue. For example, a user inputs a question by voice, and the generation AI responds in text. The dialogue unit can also use a generation AI to combine video calls with chat-style dialogue. For example, a user inputs a question via video call, and the generation AI responds in text. The dialogue unit can also use speech recognition technology to combine voice input and video calls in chat-style dialogue. For example, the voice input is converted into text and a response is generated. This makes it possible to combine voice input and video calls in chat-style dialogue to provide a wider variety of communication means.

[0068] The dialogue unit can visually display ideas proposed by the generation AI during a dialogue, allowing the user to select one. The dialogue unit, for example, uses the generation AI to visually display ideas proposed during a dialogue. For example, the ideas can be displayed in list form, allowing the user to select one. The dialogue unit also uses a graphical user interface (GUI) to visually display ideas proposed during a dialogue using the generation AI. For example, the ideas can be displayed as icons or images, allowing the user to select one. The dialogue unit can also use data visualization technology to visually display ideas proposed during a dialogue using the generation AI. For example, the ideas can be displayed as graphs or charts, allowing the user to select one. In this way, the ideas proposed by the generation AI during a dialogue can be visually displayed, allowing the user to select one.

[0069] The dialogue unit can use the emotion estimation function to analyze the emotions of the other party during a dialogue in real time and generate a response according to that emotion. For example, the dialogue unit can use the emotion estimation function to analyze the emotions of the other party during a dialogue in real time and generate a response according to that emotion. For example, if the other party is angry, the dialogue unit can generate a calm and polite response. The dialogue unit can also use natural language processing technology to analyze the emotions of the other party during a dialogue in real time using the emotion estimation function. For example, the dialogue unit can identify emotions from chat text in real time and generate a response. The dialogue unit can also use emotion analysis technology to analyze the emotions of the other party during a dialogue in real time using the emotion estimation function. For example, the dialogue unit can identify emotions from facial expression images in a video call in real time and generate a response. This makes it possible to analyze the emotions of the other party during a dialogue in real time and generate a response according to those emotions.

[0070] The dialogue unit can use the emotion estimation function to estimate the emotion of the other party and generate a proposal based on that emotion. For example, the dialogue unit can use the emotion estimation function to estimate the emotion of the other party and generate a proposal based on that emotion. For example, if the other party is sad, the dialogue unit can generate an encouraging proposal. The dialogue unit can also use the emotion estimation function to use natural language processing technology to estimate the emotion of the other party. For example, the dialogue unit can estimate the emotion from chat text and generate a proposal. The dialogue unit can also use emotion analysis technology to estimate the emotion of the other party using the emotion estimation function. For example, the dialogue unit can estimate the emotion from facial expression images in a video call and generate a proposal. This makes it possible to estimate the emotion of the other party and generate a proposal based on that emotion.

[0071] The dialogue unit can refer to success stories from different industries and fields and generate proposals based on them. For example, the dialogue unit uses a generation AI to refer to success stories from different industries and fields and generate proposals based on them. For example, a proposal is generated based on success stories from the medical industry. The dialogue unit also uses database technology to refer to success stories from different industries and fields using the generation AI. For example, it searches past project data and extracts success stories. The dialogue unit can also use text mining technology to refer to success stories from different industries and fields using the generation AI. For example, it extracts industry best practices and reflects them in proposals. This makes it possible to refer to success stories from different industries and fields and generate proposals based on them.

[0072] The dialogue unit can visually display the proposed ideas, allowing the user to select one. The dialogue unit, for example, uses a generation AI to visually display the proposed ideas. For example, the ideas can be displayed in list form, allowing the user to select one. The dialogue unit can also use a graphical user interface (GUI) to visually display the proposed ideas, using the generation AI. For example, the ideas can be displayed as icons or images, allowing the user to select one. The dialogue unit can also use data visualization technology to visually display the proposed ideas, using the generation AI. For example, the ideas can be displayed as graphs or charts, allowing the user to select one. In this way, the proposed ideas can be visually displayed, allowing the user to select one.

[0073] The dialogue unit can use the emotion estimation function to analyze the emotion of the other party in real time and generate a proposal based on that emotion. For example, the dialogue unit can use the emotion estimation function to analyze the emotion of the other party in real time and generate a proposal based on that emotion. For example, if the other party is sad, the dialogue unit can generate an encouraging proposal. The dialogue unit can also use natural language processing technology to analyze the emotion of the other party in real time using the emotion estimation function. For example, the dialogue unit can identify emotion from chat text in real time and generate a proposal. The dialogue unit can also use emotion analysis technology to analyze the emotion of the other party in real time using the emotion estimation function. For example, the dialogue unit can identify emotion from facial expression images in a video call in real time and generate a proposal. This makes it possible to analyze the emotion of the other party in real time and generate a proposal based on that emotion.

[0074] The dialogue unit uses a generation AI to generate email content based on the other party's emotions, thereby eliciting emotional empathy. The dialogue unit, for example, uses a generation AI to generate email content based on the other party's emotions. For example, if the other party is happy, it generates an email with positive content. The dialogue unit also uses natural language processing technology to generate email content based on the other party's emotions using the generation AI. For example, it identifies emotions from chat text and generates email content. The dialogue unit can also use emotion analysis technology to generate email content based on the other party's emotions using the generation AI. For example, it identifies emotions from facial expressions in a video call and generates email content. This makes it possible to generate email content based on the other party's emotions and elicit emotional empathy.

[0075] The dialogue unit can refer to past email history and generate consistent email content. The dialogue unit, for example, uses generation AI to refer to past email history and generate consistent email content. For example, it creates a new email based on the expressions and tone used in past emails. The dialogue unit also uses database technology to refer to past email history using generation AI. For example, it searches past email logs and reflects related content in the email. The dialogue unit can also use text mining technology to refer to past email history using generation AI. For example, it extracts important information from the text of past emails and reflects it in new emails. In this way, it is possible to refer to past email history and generate consistent email content.

[0076] The dialogue unit can use the emotion estimation function to estimate the emotion of the other party and generate email content based on that emotion. For example, the dialogue unit can use the emotion estimation function to estimate the emotion of the other party and generate email content based on that emotion. For example, if the other party is sad, the dialogue unit can generate an email with encouraging content. The dialogue unit can also use natural language processing technology to estimate the emotion of the other party using the emotion estimation function. For example, the dialogue unit can estimate the emotion from chat text and generate email content. The dialogue unit can also use emotion analysis technology to estimate the emotion of the other party using the emotion estimation function. For example, the dialogue unit can estimate the emotion from facial expression images during a video call and generate email content. This makes it possible to estimate the emotion of the other party and generate email content based on that emotion.

[0077] The dialogue unit can combine images and videos when creating an email to generate a visually appealing email. The dialogue unit, for example, uses a generation AI to combine images when creating an email to generate a visually appealing email. For example, a product photo is inserted into the email. The dialogue unit can also use a generation AI to combine videos when creating an email. For example, a product introduction video is inserted into the email. The dialogue unit can also use a generation AI to use data visualization technology to combine images and videos when creating an email. For example, images and videos are effectively arranged to generate a visually appealing email. This makes it possible to combine images and videos when creating an email to generate a visually appealing email.

[0078] The dialogue unit can automatically translate into different languages ​​to support international communication. For example, the dialogue unit uses generation AI to automatically translate the contents of emails into different languages ​​to support international communication. For example, it translates an English email into Japanese. The dialogue unit also uses a translation algorithm to automatically translate into different languages ​​using generation AI. For example, it uses neural machine translation (NMT) technology to translate the contents of emails. The dialogue unit can also increase the number of supported languages ​​to automatically translate into different languages ​​using generation AI. For example, it supports multiple languages ​​such as English, Japanese, Chinese, and French. This makes it possible to automatically translate into different languages ​​and support international communication.

[0079] The dialogue unit can use the emotion estimation function to analyze the emotion of the other party in real time and generate email content based on that emotion. The dialogue unit, for example, uses the emotion estimation function to analyze the emotion of the other party in real time and generate email content based on that emotion. For example, if the other party is sad, the dialogue unit generates an email with encouraging content. The dialogue unit also uses natural language processing technology to analyze the emotion of the other party in real time using the emotion estimation function. For example, the dialogue unit identifies emotion from chat text in real time and generates email content. The dialogue unit can also use emotion analysis technology to analyze the emotion of the other party in real time using the emotion estimation function. For example, the dialogue unit identifies emotion from facial expression images in a video call in real time and generates email content. This makes it possible to analyze the emotion of the other party in real time and generate email content based on that emotion.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The information collection unit can analyze the purchasing history of customers and business partners to identify their purchasing patterns and preferences. For example, if a customer frequently purchases a particular product, it can be determined that the customer has a strong interest in that product. The information collection unit also uses data mining technology to analyze the purchasing history. For example, it can analyze purchase frequency and purchase amount to identify the customer's purchasing pattern. The information collection unit can also use clustering technology to analyze the purchasing history. For example, it can group customers with similar purchasing patterns to identify common preferences. This can identify the purchasing patterns and preferences of customers and business partners, enabling more personalized proposals.

[0082] The analysis unit can use the emotion estimation function to estimate emotions from a customer's purchase history and identify purchasing trends based on those emotions. For example, if there are many positive emotions when a particular product is purchased, it can identify favorable emotions toward that product. The analysis unit can also use the emotion estimation function to estimate emotions from text data in the purchase history. For example, it can identify emotions from reviews and feedback after a purchase. The analysis unit can also use the emotion estimation function to estimate emotions from image data in the purchase history. For example, it can identify emotions from photos and videos taken after a purchase. This makes it possible to estimate emotions from a customer's purchase history and identify purchasing trends based on those emotions.

[0083] The information collection unit can analyze the website browsing history of customers and business partners to identify their interests. For example, if a user frequently views pages in a particular category, it can identify that the user has a strong interest in that category. The information collection unit can also use text mining technology to analyze the website browsing history. For example, it can analyze the content of the pages viewed to identify the user's interests. The information collection unit can also use clustering technology to analyze the website browsing history. For example, it can group users with similar browsing patterns to identify their common interests. This makes it possible to identify the interests of customers and business partners from their website browsing history and make more personalized suggestions.

[0084] The analysis unit can use the emotion estimation function to estimate emotions from customers' social media posts and identify their interests based on those emotions. For example, if there are many positive posts about a particular brand, it can identify positive emotions toward that brand. The analysis unit can also use the emotion estimation function to estimate emotions from text data on social media. For example, it can identify emotions from the content of posts and comments. The analysis unit can also use the emotion estimation function to estimate emotions from image data on social media. For example, it can identify emotions from posted photos and videos. This makes it possible to estimate emotions from customers' social media posts and identify their interests based on those emotions.

[0085] The information gathering unit can analyze feedback from customers and business partners to identify satisfaction levels and dissatisfaction points. For example, if there is a lot of positive feedback about a product or service, it can identify high satisfaction levels for that product or service. The information gathering unit can also use text mining technology to analyze the feedback. For example, it can analyze the content of the feedback to identify satisfaction levels and dissatisfaction points. The information gathering unit can also use sentiment analysis technology to analyze the feedback. For example, it can identify emotions from the text of the feedback to identify satisfaction levels and dissatisfaction points. This makes it possible to identify satisfaction levels and dissatisfaction points from customer and business partner feedback and find areas for improvement.

[0086] The analysis unit uses the emotion estimation function to estimate emotions from customer feedback and can identify satisfaction levels and dissatisfaction points based on those emotions. For example, if there is a lot of positive feedback, it will indicate high satisfaction, and if there is a lot of negative feedback, it will identify dissatisfaction points. The analysis unit also uses the emotion estimation function to estimate emotions from text data of feedback. For example, it will identify emotions from the written content of the feedback. The analysis unit can also use the emotion estimation function to estimate emotions from audio data of feedback. For example, it will identify emotions from feedback over the phone. In this way, it is possible to estimate emotions from customer feedback and identify satisfaction levels and dissatisfaction points based on those emotions.

[0087] The information collection unit can analyze the location information of customers and business partners to identify behavioral patterns and visit frequency. For example, if a customer frequently visits a particular store, it can be determined that the customer has a strong interest in that store. The information collection unit also uses data mining technology to analyze the location information. For example, it can analyze the frequency of visits and length of stay to identify behavioral patterns. The information collection unit can also use clustering technology to analyze the location information. For example, it can group customers with similar behavioral patterns to identify common interests. This makes it possible to identify behavioral patterns and visit frequency from the location information of customers and business partners, enabling more personalized proposals.

[0088] The analysis unit can use the emotion estimation function to estimate emotions from customer location information and identify behavioral patterns based on those emotions. For example, if there are many positive emotions when visiting a specific place, it can identify favorable emotions toward that place. The analysis unit can also use the emotion estimation function to estimate emotions from text data of location information. For example, it can identify emotions from reviews and comments about the place visited. The analysis unit can also use the emotion estimation function to estimate emotions from image data of location information. For example, it can identify emotions from photos and videos of the place visited. This makes it possible to estimate emotions from customer location information and identify behavioral patterns based on those emotions.

[0089] The information gathering unit can integrate the purchase history and social media activity of customers or business partners to identify more detailed attributes and characteristics. For example, purchase history can be integrated with social media posts to identify common interests. The information gathering unit also uses data mining technology to integrate purchase history and social media activity. For example, purchase history can be integrated with social media data to identify detailed attributes. The information gathering unit can also use clustering technology to integrate purchase history and social media activity. For example, customers with similar purchasing patterns and social media activity can be grouped together to identify common characteristics. This makes it possible to integrate the purchase history and social media activity of customers or business partners to identify more detailed attributes and characteristics.

[0090] The analysis unit uses the emotion estimation function to infer emotions from a customer's purchase history and social media activity, and can identify detailed attributes and personality based on those emotions. For example, if there are many positive emotions based on the purchase history and social media posts, the customer's optimistic personality can be identified. The analysis unit also uses the emotion estimation function to infer emotions from purchase history and social media text data. For example, emotions can be identified from post-purchase reviews and social media comments. The analysis unit can also use the emotion estimation function to infer emotions from purchase history and social media image data. For example, emotions can be identified from photos taken after a purchase or images posted on social media. In this way, emotions can be inferred from a customer's purchase history and social media activity, and detailed attributes and personality can be identified based on those emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The information collection department collects all information related to customers, business partners, media representatives, etc. For example, they collect email content, documents, and past communication history, analyze email content to extract important information, convert documents into digital data using OCR technology, and analyze chat logs and call records. Step 2: The analysis unit analyzes the information collected by the information collection unit and identifies the other person's attributes and personality. For example, text mining technology is used to identify occupations and job titles, sentiment analysis technology is used to identify personality traits, and clustering technology is used to identify interests. Step 3: The bot creation unit creates a bot that reflects the attributes and personality of the other party identified by the analysis unit. For example, it uses generation AI to generate responses that match the other party's attributes and personality, generating short, clear answers if the other party prefers a quick response, and flexible answers if the other party prefers a flexible response. Step 4: In the dialogue unit, the user interacts with the bot created by the bot creation unit. For example, the interaction can be performed using chat, voice input, or video calling.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The information gathering department collects all information related to customers, business partners, media representatives, etc. an analysis unit that analyzes the information collected by the information collection unit and identifies the attributes and characteristics of the other party; a bot creation unit that creates a bot that reflects the attributes and personality of the other party identified by the analysis unit; a dialogue unit through which a user dialogues with the bot created by the bot creation unit; A system characterized by:

2. The information collecting unit Analyzing the social media activity of such customers and business partners to identify their online behavioral patterns and interests 2. The system of claim 1.

3. The information collecting unit Integrate different data sources to identify more detailed attributes and characteristics 2. The system of claim 1.

4. The bot creation unit Using generative AI, we create a bot that generates a response according to the emotions of the other person, and respond to changes in emotions.

2. The system of claim 1.

5. The bot creation unit Using emotion estimation functionality, a bot is created that generates responses in real time according to the emotions of the other party, thereby improving the quality of the conversation.

2. The system of claim 1.

6. The dialogue unit Using generative AI, the emotions of the other person during a conversation are analyzed in real time, and a response is generated based on those emotions.

2. The system of claim 1.

7. The dialogue unit Visually display ideas proposed by the generative AI during the dialogue and allow the user to select one.

2. The system of claim 1.

8. The dialogue unit Using generative AI, the content of the email is generated based on the recipient's emotions, eliciting emotional empathy.

2. The system of claim 1.

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