system

Abstract

The system according to this embodiment aims to improve the efficiency and quality of consultation services at consumer affairs centers. [Solution] The system according to the embodiment comprises a reception unit, a conversion unit, an automation unit, a storage unit, and a visualization unit. The reception unit receives the content of the consultation. The conversion unit converts the content of the consultation received by the reception unit into text. The automation unit automates the work based on the text converted by the conversion unit. The storage unit stores information on the work automated by the automation unit. The visualization unit visualizes the information stored by the storage unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that consultations received at the consumer life center are not effectively utilized and the problems of the poor cannot be solved.

[0005] The system according to the embodiment aims to improve the efficiency and quality of the consultation services at the consumer life center.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a conversion unit, an automation unit, a storage unit, and a visualization unit. The reception unit receives inquiries. The conversion unit converts the inquiries received by the reception unit into text. The automation unit automates tasks based on the text converted by the conversion unit. The storage unit stores information about the tasks automated by the automation unit. The visualization unit visualizes the information stored by the storage unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline and improve the quality of consultation services at consumer affairs centers. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The DX system for consumer affairs centers according to an embodiment of the present invention is a system that aims to improve the efficiency and quality of consultation services, and further contribute to reducing social anxiety and the well-being of people by applying the data to the field of information security. The DX system for consumer affairs centers incorporates AI chatbots, speech recognition technology, RPA, knowledge base construction, and data visualization. This is expected to reduce the burden on counselors, shorten response times, improve the analysis of consultation content, and increase consumer satisfaction. Next, the data accumulated at consumer affairs centers will be utilized and applied to the field of information security. Specifically, this will involve analysis of fraud patterns, anomaly detection using AI, application to information security products, awareness-raising activities to improve security awareness, and collaboration with local communities. This is expected to enable early detection of new fraudulent activities, improve the accuracy of information security products, and strengthen consumer protection. Furthermore, by utilizing data from consumer affairs centers, the AI's training data will be enriched, enabling more accurate analysis. In addition, it is expected to create new business models and improve the safety and security of society as a whole. Through this mechanism, consultation services at consumer affairs centers will not only be made more efficient and of higher quality, but the application to the field of information security will reduce social anxiety and contribute to the well-being of people. As a result, the DX system at consumer affairs centers can improve the efficiency and quality of consultation services, and by applying the data to the field of information security, it can alleviate social anxiety and contribute to people's well-being.

[0029] The DX system for a consumer affairs center according to this embodiment comprises a reception unit, a conversion unit, an automation unit, a storage unit, and a visualization unit. The reception unit receives inquiries. The reception unit can receive inquiries using, for example, an AI chatbot. The reception unit can receive, for example, text or voice input from a user. The reception unit can process user input in real time and receive inquiries. The conversion unit converts the inquiries received by the reception unit into text. The conversion unit can convert inquiries into text using, for example, speech recognition technology. The conversion unit can convert inquiries into text using, for example, handwriting recognition technology. The conversion unit can convert inquiries into text using, for example, image recognition technology. The automation unit automates tasks based on the text converted by the conversion unit. The automation unit can automate tasks using, for example, RPA. The automation unit can automate tasks using, for example, AI technology. The automation unit can automate tasks using, for example, scripts. The storage unit stores information on tasks automated by the automation unit. The storage unit can store information using, for example, a database. The storage unit can store information using, for example, cloud storage. The storage unit can store information using, for example, distributed storage. The visualization unit visualizes the information stored by the storage unit. The visualization unit can visualize the information using, for example, graphs. The visualization unit can visualize the information using, for example, charts. The visualization unit can visualize the information using, for example, a dashboard. As a result, the DX system for consumer affairs centers according to this embodiment can streamline and improve the quality of a series of operations from receiving consultations to visualization.

[0030] The reception desk receives inquiries. The reception desk can, for example, use an AI chatbot to receive inquiries. The AI ​​chatbot uses natural language processing technology to interact with the user and processes the text and voice input by the user in real time. For example, if the user enters the inquiry in text, the chatbot analyzes the content and generates an appropriate response. In the case of voice input, it uses speech recognition technology to convert the voice into text and then processes it. Furthermore, in order to understand the user's intent, the chatbot manages the conversation while considering the context and asks additional questions as needed to grasp the content of the inquiry in detail. As a result, the reception desk can handle various input formats from users and receive inquiries quickly and accurately.

[0031] The conversion unit converts the consultation content received by the reception unit into text. The conversion unit can, for example, convert the consultation content into text using speech recognition technology. Speech recognition technology utilizes a deep learning-based speech model to achieve highly accurate speech recognition. For example, if a user inputs the consultation content by voice, the voice data is first preprocessed to remove noise and normalize the speech. Then, the speech recognition model analyzes the voice data and converts it into corresponding text. When using handwriting recognition technology, the content entered by the user by hand is acquired as an image, and the handwritten characters are analyzed using image recognition technology and converted into text. Image recognition technology uses a convolutional neural network (CNN) to extract features of handwritten characters and perform highly accurate character recognition. As a result, the conversion unit can accurately convert various input formats such as voice and handwritten characters into text, making them available for subsequent processing.

[0032] The automation unit automates tasks based on the text converted by the conversion unit. The automation unit can, for example, automate tasks using RPA (Robotic Process Automation). RPA is a technology for automating routine business processes, such as data entry, information retrieval, and report creation. When using AI technology, more advanced task automation can be achieved by leveraging natural language processing and machine learning. For example, it can automate tasks such as classifying inquiries, determining priorities, and proposing appropriate solutions. When using scripts, customized scripts are created for specific business processes to achieve automation. This allows the automation unit to efficiently and accurately automate tasks based on the converted text, improving both efficiency and quality.

[0033] The storage unit stores information about tasks automated by the automation unit. The storage unit can store information using, for example, a database. A database is a system for efficiently managing structured data; for example, an SQL database can be used to store information. When using cloud storage, data can be stored via the internet and accessed as needed. Cloud storage offers excellent scalability and availability, allowing for efficient management of large amounts of data. When using distributed storage, data can be distributed and stored across multiple nodes, ensuring fault tolerance and data redundancy. This allows the storage unit to securely and efficiently store information about automated tasks, making it available for subsequent analysis and visualization.

[0034] The visualization unit visualizes the information accumulated by the storage unit. For example, the visualization unit can visualize information using graphs. Graphs are a means of intuitively understanding data trends and patterns, and information can be visualized using line graphs, bar graphs, pie charts, etc. Charts are a means of clearly showing data comparisons and relationships, and information can be visualized using scatter plots, heatmaps, etc. When using a dashboard, multiple visualization elements are integrated and displayed on a single screen, allowing the overall situation to be grasped at a glance. Dashboards can display data that is updated in real time, enabling users to quickly obtain the information they need. In this way, the visualization unit can effectively visualize accumulated information, contributing to decision-making support and business improvement.

[0035] The reception desk can receive inquiries using an AI chatbot. The reception desk can, for example, use natural language processing technology to understand user input and generate appropriate responses. The reception desk can, for example, use a dialogue management system to manage interactions with users and ensure smooth inquiry reception. The reception desk can, for example, have the AI ​​chatbot process user input in real time and quickly receive inquiries. This makes the reception of inquiries more efficient by using an AI chatbot. The AI ​​chatbot uses, for example, natural language processing technology to analyze user input and generate appropriate responses. The AI ​​chatbot uses, for example, a dialogue management system to manage interactions with users. The AI ​​chatbot processes user input in real time and quickly receives inquiries. Some or all of the above processing in the AI ​​chatbot may be performed using, for example, a generative AI, or not using a generative AI. For example, the AI ​​chatbot inputs user input to a generative AI, and the generative AI generates an appropriate response.

[0036] The conversion unit can convert the content of the consultation into text using speech recognition technology. The conversion unit can convert the user's voice into text with high accuracy using, for example, deep learning-based speech recognition technology. The conversion unit can convert the user's voice into text using, for example, speech recognition technology using HMM (Hidden Markov Model). The conversion unit can convert the user's voice into text in real time using, for example, speech recognition technology. This makes the conversion of consultation content into text more efficient by using speech recognition technology. The speech recognition technology uses, for example, deep learning-based speech recognition technology. The speech recognition technology uses, for example, speech recognition technology using HMM (Hidden Markov Model). The speech recognition technology converts the user's voice into text in real time. Some or all of the above processing in the speech recognition technology may be performed using, for example, a generative AI, or without a generative AI. For example, the conversion unit inputs the user's voice data into a generative AI, and the generative AI converts the voice data into text.

[0037] The automation unit can automate tasks using RPA. The automation unit can automate business processes using, for example, a specific RPA tool. The automation unit can automate business processes by, for example, creating scripts. The automation unit can automate business processes using, for example, AI technology. This makes the automation of tasks more efficient by using RPA. RPA uses, for example, a specific RPA tool. RPA automates business processes by, for example, creating scripts. RPA automates business processes using, for example, AI technology. Some or all of the above-mentioned processes in RPA may be performed using, for example, generative AI, or not using generative AI. For example, the automation unit can use generative AI to automate business processes.

[0038] The storage unit can build a knowledge base and store information. The storage unit can systematically organize and store information, for example, using a knowledge graph. The storage unit can store specialized knowledge, for example, using an expert system. The storage unit can store information, for example, using a database. This makes information storage more efficient by building a knowledge base. The knowledge base uses, for example, a knowledge graph. The knowledge base uses, for example, an expert system. The knowledge base uses, for example, a database. Some or all of the above-described processes in the knowledge base may be performed, for example, using generative AI, or not using generative AI. For example, the storage unit can use generative AI to store information.

[0039] The visualization unit can visualize data. The visualization unit can visualize data using, for example, graphs. The visualization unit can visualize data using, for example, charts. The visualization unit can visualize data using, for example, dashboards. This makes it easier to understand the information by visualizing the data. Data visualization can be done using, for example, graphs. Data visualization can be done using, for example, charts. Data visualization can be done using, for example, dashboards. Some or all of the above-described processes in data visualization may be performed using, for example, generative AI, or not using generative AI. For example, the visualization unit can use generative AI for data visualization.

[0040] The reception desk can select the most suitable reception method by referring to the user's past consultation history at the time of reception. For example, the reception desk can automatically display as candidates the topics the user has frequently consulted about in the past. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest a reception method to be used during a specific time period based on the user's past consultation history. This allows the reception desk to select the most suitable reception method by referring to the past consultation history. The past consultation history can be referred to, for example, using a database search method. The past consultation history can be referred to, for example, based on the history storage format. Some or all of the above processing of the past consultation history may be performed using, for example, a generation AI, or not using a generation AI. For example, the reception desk inputs the user's past consultation history into a generation AI, and the generation AI selects the most suitable reception method.

[0041] The reception unit can filter the user's current situation and areas of interest upon receiving a request. For example, the reception unit can prioritize displaying consultations related to problems the user is currently facing. For example, the reception unit can filter relevant consultations based on the user's areas of interest. For example, the reception unit can suggest appropriate consultations according to the user's current situation (e.g., urgency). In this way, appropriate consultations can be received by filtering according to the user's situation and areas of interest. The current situation and areas of interest can be identified, for example, using survey results. The current situation and areas of interest can be identified, for example, using behavioral history. Some or all of the above processing regarding the current situation and areas of interest may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit inputs the user's current situation and areas of interest into a generative AI, and the generative AI performs the filtering.

[0042] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location information. For example, the reception desk can prioritize receiving inquiries related to the user's region based on the user's current location. For example, the reception desk can prioritize receiving inquiries related to nearby consumer affairs centers, taking into account the user's geographical location information. For example, the reception desk can prioritize receiving inquiries related to region-specific issues, based on the user's location information. In this way, by considering geographical location information, highly relevant inquiries can be prioritized. Geographical location information can be considered using, for example, GPS data. Geographical location information can be considered using, for example, IP addresses. Some or all of the above processing of geographical location information may be performed using, for example, a generating AI, or without using a generating AI. For example, the reception desk inputs the user's geographical location information into a generating AI, and the generating AI selects highly relevant inquiries.

[0043] The reception department can analyze a user's social media activity and receive relevant inquiries at the time of reception. For example, the reception department can analyze a user's social media posts and automatically receive relevant inquiries. For example, the reception department can identify a user's current interests from their social media activity and prioritize receiving relevant inquiries. For example, the reception department can suggest appropriate inquiries based on the user's feedback on social media. In this way, relevant inquiries can be appropriately received by analyzing social media activity. Social media activity can be analyzed, for example, by analyzing the content of posts. Social media activity can be analyzed, for example, by analyzing followers. Some or all of the above processing of social media activity may be performed using, for example, generative AI, or without generative AI. For example, the reception department inputs the user's social media activity into generative AI, and the generative AI selects relevant inquiries.

[0044] The conversion unit can adjust the level of detail in the conversion based on the importance of the consultation content during the conversion process. For example, the conversion unit converts highly important consultation content into detailed text. For example, the conversion unit converts less important consultation content into concise text. For example, the conversion unit converts the text to include necessary information according to its importance. This allows for appropriate text conversion by adjusting the level of detail in the conversion according to the importance of the consultation content. The importance of the consultation content can be evaluated using, for example, impact. The importance of the consultation content can be evaluated using, for example, urgency. Some or all of the above processing regarding the importance of the consultation content may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit inputs the importance of the consultation content into a generating AI, and the generating AI adjusts the level of detail in the conversion.

[0045] The conversion unit can apply different conversion algorithms depending on the category of the consultation content during conversion. For example, the conversion unit converts financial consultation content into text containing technical terms. For example, health consultation content into text containing easy-to-understand explanations. For example, legal consultation content into text containing legal expressions. This allows for appropriate text conversion by applying a conversion algorithm according to the category of the consultation content. The categories of consultation content may include, for example, technical consultations. The categories of consultation content may include, for example, business consultations. Some or all of the above processing in the categories of consultation content may be performed using, for example, a generative AI, or without using a generative AI. For example, the conversion unit inputs the category of consultation content into a generative AI, and the generative AI applies an appropriate conversion algorithm.

[0046] The conversion unit can determine the conversion priority based on the submission date of the consultation content during the conversion process. For example, the conversion unit prioritizes converting urgent consultation content to text. For example, the conversion unit postpones converting older consultation content to text. For example, the conversion unit converts to text in an appropriate order according to the submission date. This makes it possible to convert to text in an appropriate order by determining the conversion priority according to the submission date of the consultation content. The submission date of the consultation content can be evaluated using, for example, the submission date and time. For example, the submission date of the consultation content can be evaluated using, for example, the submission order. Some or all of the above processing regarding the submission date of the consultation content may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit inputs the submission date of the consultation content to the generating AI, and the generating AI determines the conversion priority.

[0047] The conversion unit can adjust the order of conversion based on the relevance of the consultation content during the conversion process. For example, the conversion unit prioritizes converting highly relevant consultation content into text. For example, the conversion unit postpones converting less relevant consultation content into text. For example, the conversion unit converts to text in an appropriate order according to relevance. This allows for text conversion in an appropriate order by adjusting the order of conversion according to the relevance of the consultation content. The relevance of the consultation content can be evaluated, for example, using content similarity. The relevance of the consultation content can be evaluated, for example, using related topics. Some or all of the above processing regarding the relevance of the consultation content may be performed, for example, using a generative AI, or without using a generative AI. For example, the conversion unit inputs the relevance of the consultation content into a generative AI, and the generative AI adjusts the order of conversion.

[0048] The automation unit can select the optimal automation method by referring to past business data during automation. For example, the automation unit analyzes past business data and selects the optimal automation method. For example, the automation unit selects an efficient automation method from past business data. For example, the automation unit applies the optimal automation algorithm based on past business data. This allows the optimal automation method to be selected by referring to past business data. Past business data is referred to, for example, using a database search method. Past business data is referred to, for example, based on the history storage format. Some or all of the above processing on past business data may be performed using, for example, a generating AI, or without using a generating AI. For example, the automation unit inputs past business data into a generating AI, and the generating AI selects the optimal automation method.

[0049] The automation unit can apply different automation algorithms depending on the category of the task during automation. For example, the automation unit applies a specialized automation algorithm to financial tasks. For example, the automation unit applies an easy-to-understand automation algorithm to health-related tasks. For example, the automation unit applies an automation algorithm that includes legal terminology to legal tasks. This enables appropriate task automation by applying automation algorithms according to the category of the task. Examples of task categories include classifying technical tasks. Examples of task categories include classifying administrative tasks. Some or all of the above processing in the task categories may be performed using, for example, a generative AI, or without a generative AI. For example, the automation unit inputs the task category into the generative AI, and the generative AI applies an appropriate automation algorithm.

[0050] The automation unit can select the optimal automation method when automating, taking into account the geographical distribution of the work. For example, the automation unit selects the optimal automation method for each region, taking into account the geographical distribution of the work. For example, the automation unit selects an efficient automation method based on the geographical distribution. For example, the automation unit applies the optimal automation algorithm based on the geographical distribution. This allows the optimal automation method to be selected by considering the geographical distribution of the work. The geographical distribution of the work can be considered, for example, using the workload for each region. The geographical distribution of the work can be considered, for example, using geographical characteristics. Some or all of the above processing regarding the geographical distribution of the work may be performed, for example, using a generative AI, or without using a generative AI. For example, the automation unit inputs the geographical distribution of the work into a generative AI, and the generative AI selects the optimal automation method.

[0051] The automation unit can improve the accuracy of automation by referring to relevant business literature during automation. For example, the automation unit selects the optimal automation method by referring to relevant business literature. For example, the automation unit selects an efficient automation method based on the relevant literature. For example, the automation unit applies the optimal automation algorithm by referring to the relevant literature. In this way, the accuracy of automation can be improved by referring to relevant business literature. Relevant business literature is referred to, for example, using a literature search method. Relevant business literature is referred to, for example, using the type of literature to be referred to. Some or all of the above processing of relevant business literature may be performed, for example, using a generating AI, or without using a generating AI. For example, the automation unit inputs relevant business literature into a generating AI, and the generating AI selects the optimal automation method.

[0052] The storage unit can optimize the storage algorithm by referring to past stored data during storage. For example, the storage unit can analyze past stored data and select the optimal storage algorithm. For example, the storage unit can select an efficient storage method from past stored data. For example, the storage unit can apply the optimal storage algorithm based on past stored data. This allows the optimal storage algorithm to be applied by referring to past stored data. Past stored data can be referred to, for example, using a database search method. Past stored data can be referred to, for example, based on the history storage format. Some or all of the above processing on past stored data may be performed using, for example, a generation AI, or without using a generation AI. For example, the storage unit inputs past stored data into a generation AI, and the generation AI selects the optimal storage algorithm.

[0053] The storage unit can apply different storage methods depending on the data category during storage. For example, the storage unit applies a specialized storage method to financial data. For example, the storage unit applies an easy-to-understand storage method to health-related data. For example, the storage unit applies a storage method that includes legal terminology to legal data. This enables appropriate data storage by applying storage methods according to the data category. Data categories include, for example, classifying technical data. Data categories include, for example, classifying business data. Some or all of the above processing in data categories may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit inputs the data category into a generative AI, and the generative AI applies an appropriate storage method.

[0054] The storage unit can weight the stored data based on the data submission date during storage. For example, the storage unit prioritizes storing urgent data. For example, the storage unit postpones storing older data. For example, the storage unit stores data in an appropriate order according to the submission date. This makes it possible to store data in an appropriate order by weighting it based on the data submission date. The data submission date can be evaluated using, for example, the submission date and time. The data submission date can be evaluated using, for example, the submission order. Some or all of the above processing regarding the data submission date may be performed using, for example, a generation AI, or not using a generation AI. For example, the storage unit inputs the data submission date to the generation AI, and the generation AI performs the weighting.

[0055] The storage unit can improve the accuracy of data storage by referring to relevant market data during storage. For example, the storage unit selects the optimal storage method by referring to relevant market data. For example, the storage unit selects an efficient storage method based on market data. For example, the storage unit applies the optimal storage algorithm by referring to market data. This improves the accuracy of data storage by referring to relevant market data. Relevant market data is referred to, for example, using the type of market data. Relevant market data is referred to, for example, using the referencing method. Some or all of the above processing on the relevant market data may be performed, for example, using a generating AI, or without using a generating AI. For example, the storage unit inputs the relevant market data into a generating AI, and the generating AI selects the optimal storage method.

[0056] The visualization unit can select the optimal visualization method by referring to past visualization data during visualization. For example, the visualization unit analyzes past visualization data and selects the optimal visualization method. For example, the visualization unit selects an efficient visualization method from past visualization data. For example, the visualization unit applies the optimal visualization algorithm based on past visualization data. This allows the optimal visualization method to be selected by referring to past visualization data. Past visualization data can be referred to, for example, using a database search method. Past visualization data can be referred to, for example, based on the history storage format. Some or all of the above processing on past visualization data may be performed, for example, using a generation AI, or without using a generation AI. For example, the visualization unit inputs past visualization data into a generation AI, and the generation AI selects the optimal visualization method.

[0057] The visualization unit can apply different visualization methods depending on the data category during visualization. For example, the visualization unit can apply specialized visualization methods to financial data. For example, the visualization unit can apply easy-to-understand visualization methods to health data. For example, the visualization unit can apply visualization methods that include legal terminology to legal data. This allows for appropriate data visualization by applying visualization methods according to the data category. Data categories include, for example, classifying technical data. Data categories include, for example, classifying business data. Some or all of the above processing in data categories may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit inputs the data category into a generative AI, and the generative AI applies an appropriate visualization method.

[0058] The visualization unit can weight the visualized data based on the data submission date during visualization. For example, the visualization unit prioritizes the visualization of urgent data. For example, the visualization unit postpones the visualization of older data. For example, the visualization unit visualizes the data in an appropriate order according to the submission date. This makes it possible to visualize the data in an appropriate order by weighting it based on the data submission date. The data submission date can be evaluated using, for example, the submission date and time. The data submission date can be evaluated using, for example, the submission order. Some or all of the above processing regarding the data submission date may be performed using, for example, a generating AI, or without using a generating AI. For example, the visualization unit inputs the data submission date to the generating AI, and the generating AI performs the weighting.

[0059] The visualization unit can improve the accuracy of visualization by referring to relevant market data during visualization. For example, the visualization unit selects the optimal visualization method by referring to relevant market data. For example, the visualization unit selects an efficient visualization method based on market data. For example, the visualization unit applies the optimal visualization algorithm by referring to market data. This allows for improved visualization accuracy by referring to relevant market data. Relevant market data is referred to, for example, using the type of market data. Relevant market data is referred to, for example, using the referencing method. Some or all of the above processing on the relevant market data may be performed, for example, using a generating AI, or without using a generating AI. For example, the visualization unit inputs the relevant market data into a generating AI, and the generating AI selects the optimal visualization method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can select the most suitable reception method by referring to the user's past consultation history at the time of reception. For example, it can automatically display as suggestions the topics the user has frequently consulted about in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest reception methods to be used during specific time periods based on the user's past consultation history. This allows for the selection of the most suitable reception method by referring to past consultation history. Past consultation history can be referred to, for example, using a database search method. Past consultation history can also be referred to, for example, based on the history storage format. Some or all of the above processing of past consultation history may be performed using, for example, a generation AI, or without a generation AI. For example, the reception desk inputs the user's past consultation history into a generation AI, and the generation AI selects the most suitable reception method.

[0062] The reception unit can filter the user's current situation and areas of interest upon receiving a request. For example, it can prioritize displaying consultations related to problems the user is currently facing. It can also filter relevant consultations based on the user's areas of interest. Furthermore, it can suggest appropriate consultations according to the user's current situation (e.g., urgency). This allows for the reception of appropriate consultations by filtering according to the user's situation and areas of interest. The current situation and areas of interest can be identified, for example, using survey results. The current situation and areas of interest can also be identified, for example, using behavioral history. Some or all of the above processing regarding the current situation and areas of interest may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception unit inputs the user's current situation and areas of interest into a generative AI, and the generative AI performs the filtering.

[0063] The conversion unit can adjust the level of detail in the conversion based on the importance of the consultation content. For example, highly important consultation content can be converted into detailed text. Conversely, less important consultation content can be converted into concise text. Furthermore, depending on the importance, it can be converted into text that includes necessary information. This allows for appropriate text conversion by adjusting the level of detail in the conversion according to the importance of the consultation content. The importance of the consultation content can be evaluated, for example, using impact. The importance of the consultation content can be evaluated, for example, using urgency. Some or all of the above processing regarding the importance of the consultation content may be performed, for example, using a generation AI, or without using a generation AI. For example, the conversion unit inputs the importance of the consultation content to the generation AI, and the generation AI adjusts the level of detail in the conversion.

[0064] The automation unit can select the optimal automation method by referring to past business data during automation. For example, it can analyze past business data and select the optimal automation method. It can also select an efficient automation method from past business data. Furthermore, it can apply the optimal automation algorithm based on past business data. In this way, the optimal automation method can be selected by referring to past business data. Past business data can be referred to, for example, using a database search method. Past business data can be referred to, for example, based on the history storage format. Some or all of the above processing on past business data may be performed using, for example, a generation AI, or without using a generation AI. For example, the automation unit inputs past business data into a generation AI, and the generation AI selects the optimal automation method.

[0065] The storage unit can apply different storage methods depending on the data category during storage. For example, a specialized storage method can be applied to financial data. A user-friendly storage method can be applied to health-related data. Furthermore, a storage method that includes legal terminology can be applied to legal data. This allows for appropriate data storage by applying storage methods according to the data category. Data categories can include, for example, classification of technical data. Data categories can also include, for example, classification of business data. Some or all of the above processing in data categories may be performed using, for example, a generative AI, or without using a generative AI. For example, the storage unit inputs the data category into the generative AI, and the generative AI applies an appropriate storage method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives the inquiry. For example, an AI chatbot can be used to receive the inquiry, accepting text or voice input from the user. It can also process the user's input in real time and accept the inquiry. Step 2: The conversion unit converts the consultation content received by the reception unit into text. For example, the consultation content can be converted into text using speech recognition technology, handwriting recognition technology, or image recognition technology. Step 3: The automation unit automates tasks based on the text converted by the conversion unit. For example, tasks can be automated using RPA, AI technology, or scripts. Step 4: The storage unit stores information about the tasks automated by the automation unit. For example, information can be stored using a database, cloud storage, or distributed storage. Step 5: The visualization unit visualizes the information accumulated by the storage unit. For example, the information can be visualized using graphs, charts, and dashboards.

[0068] (Example of form 2) The DX system for consumer affairs centers according to an embodiment of the present invention is a system that aims to improve the efficiency and quality of consultation services, and further contribute to reducing social anxiety and the well-being of people by applying the data to the field of information security. The DX system for consumer affairs centers incorporates AI chatbots, speech recognition technology, RPA, knowledge base construction, and data visualization. This is expected to reduce the burden on counselors, shorten response times, improve the analysis of consultation content, and increase consumer satisfaction. Next, the data accumulated at consumer affairs centers will be utilized and applied to the field of information security. Specifically, this will involve analysis of fraud patterns, anomaly detection using AI, application to information security products, awareness-raising activities to improve security awareness, and collaboration with local communities. This is expected to enable early detection of new fraudulent activities, improve the accuracy of information security products, and strengthen consumer protection. Furthermore, by utilizing data from consumer affairs centers, the AI's training data will be enriched, enabling more accurate analysis. In addition, it is expected to create new business models and improve the safety and security of society as a whole. Through this mechanism, consultation services at consumer affairs centers will not only be made more efficient and of higher quality, but the application to the field of information security will reduce social anxiety and contribute to the well-being of people. As a result, the DX system at consumer affairs centers can improve the efficiency and quality of consultation services, and by applying the data to the field of information security, it can alleviate social anxiety and contribute to people's well-being.

[0069] The DX system for a consumer affairs center according to this embodiment comprises a reception unit, a conversion unit, an automation unit, a storage unit, and a visualization unit. The reception unit receives inquiries. The reception unit can receive inquiries using, for example, an AI chatbot. The reception unit can receive, for example, text or voice input from a user. The reception unit can process user input in real time and receive inquiries. The conversion unit converts the inquiries received by the reception unit into text. The conversion unit can convert inquiries into text using, for example, speech recognition technology. The conversion unit can convert inquiries into text using, for example, handwriting recognition technology. The conversion unit can convert inquiries into text using, for example, image recognition technology. The automation unit automates tasks based on the text converted by the conversion unit. The automation unit can automate tasks using, for example, RPA. The automation unit can automate tasks using, for example, AI technology. The automation unit can automate tasks using, for example, scripts. The storage unit stores information on tasks automated by the automation unit. The storage unit can store information using, for example, a database. The storage unit can store information using, for example, cloud storage. The storage unit can store information using, for example, distributed storage. The visualization unit visualizes the information stored by the storage unit. The visualization unit can visualize the information using, for example, graphs. The visualization unit can visualize the information using, for example, charts. The visualization unit can visualize the information using, for example, a dashboard. As a result, the DX system for consumer affairs centers according to this embodiment can streamline and improve the quality of a series of operations from receiving consultations to visualization.

[0070] The reception desk receives inquiries. The reception desk can, for example, use an AI chatbot to receive inquiries. The AI ​​chatbot uses natural language processing technology to interact with the user and processes the text and voice input by the user in real time. For example, if the user enters the inquiry in text, the chatbot analyzes the content and generates an appropriate response. In the case of voice input, it uses speech recognition technology to convert the voice into text and then processes it. Furthermore, in order to understand the user's intent, the chatbot manages the conversation while considering the context and asks additional questions as needed to grasp the content of the inquiry in detail. As a result, the reception desk can handle various input formats from users and receive inquiries quickly and accurately.

[0071] The conversion unit converts the consultation content received by the reception unit into text. The conversion unit can, for example, convert the consultation content into text using speech recognition technology. Speech recognition technology utilizes a deep learning-based speech model to achieve highly accurate speech recognition. For example, if a user inputs the consultation content by voice, the voice data is first preprocessed to remove noise and normalize the speech. Then, the speech recognition model analyzes the voice data and converts it into corresponding text. When using handwriting recognition technology, the content entered by the user by hand is acquired as an image, and the handwritten characters are analyzed using image recognition technology and converted into text. Image recognition technology uses a convolutional neural network (CNN) to extract features of handwritten characters and perform highly accurate character recognition. As a result, the conversion unit can accurately convert various input formats such as voice and handwritten characters into text, making them available for subsequent processing.

[0072] The automation unit automates tasks based on the text converted by the conversion unit. The automation unit can, for example, automate tasks using RPA (Robotic Process Automation). RPA is a technology for automating routine business processes, such as data entry, information retrieval, and report creation. When using AI technology, more advanced task automation can be achieved by leveraging natural language processing and machine learning. For example, it can automate tasks such as classifying inquiries, determining priorities, and proposing appropriate solutions. When using scripts, customized scripts are created for specific business processes to achieve automation. This allows the automation unit to efficiently and accurately automate tasks based on the converted text, improving both efficiency and quality.

[0073] The storage unit stores information about tasks automated by the automation unit. The storage unit can store information using, for example, a database. A database is a system for efficiently managing structured data; for example, an SQL database can be used to store information. When using cloud storage, data can be stored via the internet and accessed as needed. Cloud storage offers excellent scalability and availability, allowing for efficient management of large amounts of data. When using distributed storage, data can be distributed and stored across multiple nodes, ensuring fault tolerance and data redundancy. This allows the storage unit to securely and efficiently store information about automated tasks, making it available for subsequent analysis and visualization.

[0074] The visualization unit visualizes the information accumulated by the storage unit. For example, the visualization unit can visualize information using graphs. Graphs are a means of intuitively understanding data trends and patterns, and information can be visualized using line graphs, bar graphs, pie charts, etc. Charts are a means of clearly showing data comparisons and relationships, and information can be visualized using scatter plots, heatmaps, etc. When using a dashboard, multiple visualization elements are integrated and displayed on a single screen, allowing the overall situation to be grasped at a glance. Dashboards can display data that is updated in real time, enabling users to quickly obtain the information they need. In this way, the visualization unit can effectively visualize accumulated information, contributing to decision-making support and business improvement.

[0075] The reception desk can receive inquiries using an AI chatbot. The reception desk can, for example, use natural language processing technology to understand user input and generate appropriate responses. The reception desk can, for example, use a dialogue management system to manage interactions with users and ensure smooth inquiry reception. The reception desk can, for example, have the AI ​​chatbot process user input in real time and quickly receive inquiries. This makes the reception of inquiries more efficient by using an AI chatbot. The AI ​​chatbot uses, for example, natural language processing technology to analyze user input and generate appropriate responses. The AI ​​chatbot uses, for example, a dialogue management system to manage interactions with users. The AI ​​chatbot processes user input in real time and quickly receives inquiries. Some or all of the above processing in the AI ​​chatbot may be performed using, for example, a generative AI, or not using a generative AI. For example, the AI ​​chatbot inputs user input to a generative AI, and the generative AI generates an appropriate response.

[0076] The conversion unit can convert the content of the consultation into text using speech recognition technology. The conversion unit can convert the user's voice into text with high accuracy using, for example, deep learning-based speech recognition technology. The conversion unit can convert the user's voice into text using, for example, speech recognition technology using HMM (Hidden Markov Model). The conversion unit can convert the user's voice into text in real time using, for example, speech recognition technology. This makes the conversion of consultation content into text more efficient by using speech recognition technology. The speech recognition technology uses, for example, deep learning-based speech recognition technology. The speech recognition technology uses, for example, speech recognition technology using HMM (Hidden Markov Model). The speech recognition technology converts the user's voice into text in real time. Some or all of the above processing in the speech recognition technology may be performed using, for example, a generative AI, or without a generative AI. For example, the conversion unit inputs the user's voice data into a generative AI, and the generative AI converts the voice data into text.

[0077] The automation unit can automate tasks using RPA. The automation unit can automate business processes using, for example, a specific RPA tool. The automation unit can automate business processes by, for example, creating scripts. The automation unit can automate business processes using, for example, AI technology. This makes the automation of tasks more efficient by using RPA. RPA uses, for example, a specific RPA tool. RPA automates business processes by, for example, creating scripts. RPA automates business processes using, for example, AI technology. Some or all of the above-mentioned processes in RPA may be performed using, for example, generative AI, or not using generative AI. For example, the automation unit can use generative AI to automate business processes.

[0078] The storage unit can build a knowledge base and store information. The storage unit can systematically organize and store information, for example, using a knowledge graph. The storage unit can store specialized knowledge, for example, using an expert system. The storage unit can store information, for example, using a database. This makes information storage more efficient by building a knowledge base. The knowledge base uses, for example, a knowledge graph. The knowledge base uses, for example, an expert system. The knowledge base uses, for example, a database. Some or all of the above-described processes in the knowledge base may be performed, for example, using generative AI, or not using generative AI. For example, the storage unit can use generative AI to store information.

[0079] The visualization unit can visualize data. The visualization unit can visualize data using, for example, graphs. The visualization unit can visualize data using, for example, charts. The visualization unit can visualize data using, for example, dashboards. This makes it easier to understand the information by visualizing the data. Data visualization can be done using, for example, graphs. Data visualization can be done using, for example, charts. Data visualization can be done using, for example, dashboards. Some or all of the above-described processes in data visualization may be performed using, for example, generative AI, or not using generative AI. For example, the visualization unit can use generative AI for data visualization.

[0080] The reception desk can estimate the user's emotions and adjust the method of receiving the consultation based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. For example, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of the consultation details. This allows for more appropriate consultation reception by adjusting the reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk inputs the user's emotion data into the generative AI, and the generative AI estimates the emotion.

[0081] The reception desk can select the most suitable reception method by referring to the user's past consultation history at the time of reception. For example, the reception desk can automatically display as candidates the topics the user has frequently consulted about in the past. For example, the reception desk can prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. For example, the reception desk can predict and suggest a reception method to be used during a specific time period based on the user's past consultation history. This allows the reception desk to select the most suitable reception method by referring to the past consultation history. The past consultation history can be referred to, for example, using a database search method. The past consultation history can be referred to, for example, based on the history storage format. Some or all of the above processing of the past consultation history may be performed using, for example, a generation AI, or not using a generation AI. For example, the reception desk inputs the user's past consultation history into a generation AI, and the generation AI selects the most suitable reception method.

[0082] The reception unit can filter the user's current situation and areas of interest upon receiving a request. For example, the reception unit can prioritize displaying consultations related to problems the user is currently facing. For example, the reception unit can filter relevant consultations based on the user's areas of interest. For example, the reception unit can suggest appropriate consultations according to the user's current situation (e.g., urgency). In this way, appropriate consultations can be received by filtering according to the user's situation and areas of interest. The current situation and areas of interest can be identified, for example, using survey results. The current situation and areas of interest can be identified, for example, using behavioral history. Some or all of the above processing regarding the current situation and areas of interest may be performed using, for example, a generative AI, or without a generative AI. For example, the reception unit inputs the user's current situation and areas of interest into a generative AI, and the generative AI performs the filtering.

[0083] The reception desk can estimate the user's emotions and determine the priority of the consultation content to be received based on the estimated user emotions. For example, if the user has an urgent problem, the reception desk will prioritize that consultation. For example, if the user is feeling stressed, the reception desk will raise the priority so that a quick response can be made. For example, if the user is relaxed, the reception desk will receive the consultation with the normal priority. In this way, by determining the priority according to the user's emotions, consultation content can be received in the appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk inputs the user's emotion data into the generative AI, and the generative AI estimates the emotion.

[0084] The reception desk can prioritize receiving inquiries that are highly relevant to the user, taking into account the user's geographical location information. For example, the reception desk can prioritize receiving inquiries related to the user's region based on the user's current location. For example, the reception desk can prioritize receiving inquiries related to nearby consumer affairs centers, taking into account the user's geographical location information. For example, the reception desk can prioritize receiving inquiries related to region-specific issues, based on the user's location information. In this way, by considering geographical location information, highly relevant inquiries can be prioritized. Geographical location information can be considered using, for example, GPS data. Geographical location information can be considered using, for example, IP addresses. Some or all of the above processing of geographical location information may be performed using, for example, a generating AI, or without using a generating AI. For example, the reception desk inputs the user's geographical location information into a generating AI, and the generating AI selects highly relevant inquiries.

[0085] The reception department can analyze a user's social media activity and receive relevant inquiries at the time of reception. For example, the reception department can analyze a user's social media posts and automatically receive relevant inquiries. For example, the reception department can identify a user's current interests from their social media activity and prioritize receiving relevant inquiries. For example, the reception department can suggest appropriate inquiries based on the user's feedback on social media. In this way, relevant inquiries can be appropriately received by analyzing social media activity. Social media activity can be analyzed, for example, by analyzing the content of posts. Social media activity can be analyzed, for example, by analyzing followers. Some or all of the above processing of social media activity may be performed using, for example, generative AI, or without generative AI. For example, the reception department inputs the user's social media activity into generative AI, and the generative AI selects relevant inquiries.

[0086] The conversion unit can estimate the user's emotions and adjust the expression of the text conversion based on the estimated emotions. For example, if the user is stressed, the conversion unit converts the text into concise and clear text. For example, if the user is relaxed, the conversion unit converts the text into detailed and explanatory text. For example, if the user is in a hurry, the conversion unit converts the text into short, to-the-point text. This allows for appropriate text conversion by adjusting the expression of the text conversion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the conversion unit may be performed using AI, for example, or not using AI. For example, the conversion unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotions.

[0087] The conversion unit can adjust the level of detail in the conversion based on the importance of the consultation content during the conversion process. For example, the conversion unit converts highly important consultation content into detailed text. For example, the conversion unit converts less important consultation content into concise text. For example, the conversion unit converts the text to include necessary information according to its importance. This allows for appropriate text conversion by adjusting the level of detail in the conversion according to the importance of the consultation content. The importance of the consultation content can be evaluated using, for example, impact. The importance of the consultation content can be evaluated using, for example, urgency. Some or all of the above processing regarding the importance of the consultation content may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit inputs the importance of the consultation content into a generating AI, and the generating AI adjusts the level of detail in the conversion.

[0088] The conversion unit can apply different conversion algorithms depending on the category of the consultation content during conversion. For example, the conversion unit converts financial consultation content into text containing technical terms. For example, health consultation content into text containing easy-to-understand explanations. For example, legal consultation content into text containing legal expressions. This allows for appropriate text conversion by applying a conversion algorithm according to the category of the consultation content. The categories of consultation content may include, for example, technical consultations. The categories of consultation content may include, for example, business consultations. Some or all of the above processing in the categories of consultation content may be performed using, for example, a generative AI, or without using a generative AI. For example, the conversion unit inputs the category of consultation content into a generative AI, and the generative AI applies an appropriate conversion algorithm.

[0089] The conversion unit can estimate the user's emotions and adjust the length of the text conversion based on the estimated emotions. For example, if the user is in a hurry, the conversion unit converts the text to a short, concise format. If the user is relaxed, the conversion unit converts the text to a longer format that includes detailed explanations. If the user is excited, the conversion unit converts the text to a format with visually stimulating effects. This allows for appropriate text conversion by adjusting the length of the text conversion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the conversion unit may be performed using AI or not. For example, the conversion unit inputs the user's emotion data into the generative AI, which then estimates the emotions.

[0090] The conversion unit can determine the conversion priority based on the submission date of the consultation content during the conversion process. For example, the conversion unit prioritizes converting urgent consultation content to text. For example, the conversion unit postpones converting older consultation content to text. For example, the conversion unit converts to text in an appropriate order according to the submission date. This makes it possible to convert to text in an appropriate order by determining the conversion priority according to the submission date of the consultation content. The submission date of the consultation content can be evaluated using, for example, the submission date and time. For example, the submission date of the consultation content can be evaluated using, for example, the submission order. Some or all of the above processing regarding the submission date of the consultation content may be performed using, for example, a generating AI, or without using a generating AI. For example, the conversion unit inputs the submission date of the consultation content to the generating AI, and the generating AI determines the conversion priority.

[0091] The conversion unit can adjust the order of conversion based on the relevance of the consultation content during the conversion process. For example, the conversion unit prioritizes converting highly relevant consultation content into text. For example, the conversion unit postpones converting less relevant consultation content into text. For example, the conversion unit converts to text in an appropriate order according to relevance. This allows for text conversion in an appropriate order by adjusting the order of conversion according to the relevance of the consultation content. The relevance of the consultation content can be evaluated, for example, using content similarity. The relevance of the consultation content can be evaluated, for example, using related topics. Some or all of the above processing regarding the relevance of the consultation content may be performed, for example, using a generative AI, or without using a generative AI. For example, the conversion unit inputs the relevance of the consultation content into a generative AI, and the generative AI adjusts the order of conversion.

[0092] The automation unit can estimate the user's emotions and adjust the method of automating tasks based on the estimated user emotions. For example, if the user is stressed, the automation unit will automate tasks to respond quickly. For example, if the user is relaxed, the automation unit will perform task automation that includes detailed explanations. For example, if the user is in a hurry, the automation unit will automate tasks to respond in the shortest possible time. This allows for appropriate task automation by adjusting the method of task automation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI, for example, or not using AI. For example, the automation unit inputs user emotion data into the generative AI, and the generative AI estimates the emotions.

[0093] The automation unit can select the optimal automation method by referring to past business data during automation. For example, the automation unit analyzes past business data and selects the optimal automation method. For example, the automation unit selects an efficient automation method from past business data. For example, the automation unit applies the optimal automation algorithm based on past business data. This allows the optimal automation method to be selected by referring to past business data. Past business data is referred to, for example, using a database search method. Past business data is referred to, for example, based on the history storage format. Some or all of the above processing on past business data may be performed using, for example, a generating AI, or without using a generating AI. For example, the automation unit inputs past business data into a generating AI, and the generating AI selects the optimal automation method.

[0094] The automation unit can apply different automation algorithms depending on the category of the task during automation. For example, the automation unit applies a specialized automation algorithm to financial tasks. For example, the automation unit applies an easy-to-understand automation algorithm to health-related tasks. For example, the automation unit applies an automation algorithm that includes legal terminology to legal tasks. This enables appropriate task automation by applying automation algorithms according to the category of the task. Examples of task categories include classifying technical tasks. Examples of task categories include classifying administrative tasks. Some or all of the above processing in the task categories may be performed using, for example, a generative AI, or without a generative AI. For example, the automation unit inputs the task category into the generative AI, and the generative AI applies an appropriate automation algorithm.

[0095] The automation unit can estimate the user's emotions and determine the priority of task automation based on the estimated user emotions. For example, if the user has an urgent problem, the automation unit will prioritize automating that task. For example, if the user is stressed, the automation unit will raise the priority of task automation to allow for a quicker response. For example, if the user is relaxed, the automation unit will automate tasks with the normal priority. This allows for task automation in the appropriate order by determining the priority of task automation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI or not using AI. For example, the automation unit inputs user emotion data into the generative AI, and the generative AI estimates the emotions.

[0096] The automation unit can select the optimal automation method when automating, taking into account the geographical distribution of the work. For example, the automation unit selects the optimal automation method for each region, taking into account the geographical distribution of the work. For example, the automation unit selects an efficient automation method based on the geographical distribution. For example, the automation unit applies the optimal automation algorithm based on the geographical distribution. This allows the optimal automation method to be selected by considering the geographical distribution of the work. The geographical distribution of the work can be considered, for example, using the workload for each region. The geographical distribution of the work can be considered, for example, using geographical characteristics. Some or all of the above processing regarding the geographical distribution of the work may be performed, for example, using a generative AI, or without using a generative AI. For example, the automation unit inputs the geographical distribution of the work into a generative AI, and the generative AI selects the optimal automation method.

[0097] The automation unit can improve the accuracy of automation by referring to relevant business literature during automation. For example, the automation unit selects the optimal automation method by referring to relevant business literature. For example, the automation unit selects an efficient automation method based on the relevant literature. For example, the automation unit applies the optimal automation algorithm by referring to the relevant literature. In this way, the accuracy of automation can be improved by referring to relevant business literature. Relevant business literature is referred to, for example, using a literature search method. Relevant business literature is referred to, for example, using the type of literature to be referred to. Some or all of the above processing of relevant business literature may be performed, for example, using a generating AI, or without using a generating AI. For example, the automation unit inputs relevant business literature into a generating AI, and the generating AI selects the optimal automation method.

[0098] The data storage unit can estimate the user's emotions and select data to store based on the estimated emotions. For example, if the user is stressed, the storage unit will prioritize storing important data. For example, if the user is relaxed, the storage unit will store detailed data. For example, if the user is in a hurry, the storage unit will store concise data. This allows for appropriate data storage by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, or not using AI. For example, the storage unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotions.

[0099] The storage unit can optimize the storage algorithm by referring to past stored data during storage. For example, the storage unit can analyze past stored data and select the optimal storage algorithm. For example, the storage unit can select an efficient storage method from past stored data. For example, the storage unit can apply the optimal storage algorithm based on past stored data. This allows the optimal storage algorithm to be applied by referring to past stored data. Past stored data can be referred to, for example, using a database search method. Past stored data can be referred to, for example, based on the history storage format. Some or all of the above processing on past stored data may be performed using, for example, a generation AI, or without using a generation AI. For example, the storage unit inputs past stored data into a generation AI, and the generation AI selects the optimal storage algorithm.

[0100] The storage unit can apply different storage methods depending on the data category during storage. For example, the storage unit applies a specialized storage method to financial data. For example, the storage unit applies an easy-to-understand storage method to health-related data. For example, the storage unit applies a storage method that includes legal terminology to legal data. This enables appropriate data storage by applying storage methods according to the data category. Data categories include, for example, classifying technical data. Data categories include, for example, classifying business data. Some or all of the above processing in data categories may be performed using, for example, a generative AI, or without a generative AI. For example, the storage unit inputs the data category into a generative AI, and the generative AI applies an appropriate storage method.

[0101] The storage unit can estimate the user's emotions and determine the priority of stored data based on the estimated user emotions. For example, if the user has an urgent problem, the storage unit will prioritize storing that data. For example, if the user is stressed, the storage unit will raise the priority of the data so that a quick response can be made. For example, if the user is relaxed, the storage unit will store the data with the normal priority. This allows for data to be stored in the appropriate order by determining the priority of stored data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI or not using AI. For example, the storage unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotions.

[0102] The storage unit can weight the stored data based on the data submission date during storage. For example, the storage unit prioritizes storing urgent data. For example, the storage unit postpones storing older data. For example, the storage unit stores data in an appropriate order according to the submission date. This makes it possible to store data in an appropriate order by weighting it based on the data submission date. The data submission date can be evaluated using, for example, the submission date and time. The data submission date can be evaluated using, for example, the submission order. Some or all of the above processing regarding the data submission date may be performed using, for example, a generation AI, or not using a generation AI. For example, the storage unit inputs the data submission date to the generation AI, and the generation AI performs the weighting.

[0103] The storage unit can improve the accuracy of data storage by referring to relevant market data during storage. For example, the storage unit selects the optimal storage method by referring to relevant market data. For example, the storage unit selects an efficient storage method based on market data. For example, the storage unit applies the optimal storage algorithm by referring to market data. This improves the accuracy of data storage by referring to relevant market data. Relevant market data is referred to, for example, using the type of market data. Relevant market data is referred to, for example, using the referencing method. Some or all of the above processing on the relevant market data may be performed, for example, using a generating AI, or without using a generating AI. For example, the storage unit inputs the relevant market data into a generating AI, and the generating AI selects the optimal storage method.

[0104] The visualization unit can estimate the user's emotions and adjust the data visualization method based on the estimated user emotions. For example, if the user is stressed, the visualization unit provides a simple and highly visible visualization method. For example, if the user is relaxed, the visualization unit provides a visualization method that includes detailed information. For example, if the user is in a hurry, the visualization unit provides a visualization method that gets straight to the point. This allows for appropriate data visualization by adjusting the data visualization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotion.

[0105] The visualization unit can select the optimal visualization method by referring to past visualization data during visualization. For example, the visualization unit analyzes past visualization data and selects the optimal visualization method. For example, the visualization unit selects an efficient visualization method from past visualization data. For example, the visualization unit applies the optimal visualization algorithm based on past visualization data. This allows the optimal visualization method to be selected by referring to past visualization data. Past visualization data can be referred to, for example, using a database search method. Past visualization data can be referred to, for example, based on the history storage format. Some or all of the above processing on past visualization data may be performed, for example, using a generation AI, or without using a generation AI. For example, the visualization unit inputs past visualization data into a generation AI, and the generation AI selects the optimal visualization method.

[0106] The visualization unit can apply different visualization methods depending on the data category during visualization. For example, the visualization unit can apply specialized visualization methods to financial data. For example, the visualization unit can apply easy-to-understand visualization methods to health data. For example, the visualization unit can apply visualization methods that include legal terminology to legal data. This allows for appropriate data visualization by applying visualization methods according to the data category. Data categories include, for example, classifying technical data. Data categories include, for example, classifying business data. Some or all of the above processing in data categories may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit inputs the data category into a generative AI, and the generative AI applies an appropriate visualization method.

[0107] The visualization unit can estimate the user's emotions and determine the priority of the visualization data based on the estimated user emotions. For example, if the user has an urgent problem, the visualization unit will prioritize the visualization of that data. For example, if the user is stressed, the visualization unit will raise the priority of the data so that a quick response can be made. For example, if the user is relaxed, the visualization unit will visualize the data with normal priority. This allows for data visualization in the appropriate order by determining the priority of the visualization data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotions.

[0108] The visualization unit can weight the visualized data based on the data submission date during visualization. For example, the visualization unit prioritizes the visualization of urgent data. For example, the visualization unit postpones the visualization of older data. For example, the visualization unit visualizes the data in an appropriate order according to the submission date. This makes it possible to visualize the data in an appropriate order by weighting it based on the data submission date. The data submission date can be evaluated using, for example, the submission date and time. The data submission date can be evaluated using, for example, the submission order. Some or all of the above processing regarding the data submission date may be performed using, for example, a generating AI, or without using a generating AI. For example, the visualization unit inputs the data submission date to the generating AI, and the generating AI performs the weighting.

[0109] The visualization unit can improve the accuracy of visualization by referring to relevant market data during visualization. For example, the visualization unit selects the optimal visualization method by referring to relevant market data. For example, the visualization unit selects an efficient visualization method based on market data. For example, the visualization unit applies the optimal visualization algorithm by referring to market data. This allows for improved visualization accuracy by referring to relevant market data. Relevant market data is referred to, for example, using the type of market data. Relevant market data is referred to, for example, using the referencing method. Some or all of the above processing on the relevant market data may be performed, for example, using a generating AI, or without using a generating AI. For example, the visualization unit inputs the relevant market data into a generating AI, and the generating AI selects the optimal visualization method.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception desk can estimate the user's emotions and adjust the method of receiving the consultation based on the estimated emotions. For example, if the user is stressed, a simple interface can be provided and the input steps can be minimized. If the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow for quick input of the consultation details. This allows for more appropriate consultation reception by adjusting the reception method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk inputs the user's emotion data into the generative AI, and the generative AI estimates the emotions.

[0112] The conversion unit can estimate the user's emotions and adjust the expression of the text conversion based on the estimated emotions. For example, if the user is stressed, the text can be converted to concise and clear text. If the user is relaxed, the text can be converted to text that includes detailed explanations. Furthermore, if the user is in a hurry, the text can be converted to short, to-the-point text. In this way, appropriate text conversion is possible by adjusting the expression of the text conversion according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotions.

[0113] The automation unit can estimate the user's emotions and adjust the method of automating tasks based on the estimated user emotions. For example, if the user is stressed, tasks can be automated to respond quickly. If the user is relaxed, tasks can be automated to include detailed explanations. Furthermore, if the user is in a hurry, tasks can be automated to respond in the shortest possible time. In this way, appropriate task automation is possible by adjusting the method of automating tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI, for example, or not using AI. For example, the automation unit inputs user emotion data into the generative AI, and the generative AI estimates the emotions.

[0114] The data storage unit can estimate the user's emotions and select data to store based on the estimated emotions. For example, if the user is stressed, important data can be prioritized for storage. If the user is relaxed, detailed data can be stored. Furthermore, if the user is in a hurry, concise data can be stored. This allows for appropriate data storage by selecting data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data storage unit may be performed using AI or not. For example, the data storage unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotions.

[0115] The visualization unit can estimate the user's emotions and adjust the data visualization method based on the estimated user emotions. For example, if the user is stressed, it can provide a simple and highly visible visualization method. If the user is relaxed, it can provide a visualization method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a visualization method that gets straight to the point. By adjusting the data visualization method according to the user's emotions, appropriate data visualization becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the visualization unit may be performed using AI, or not using AI. For example, the visualization unit inputs the user's emotion data into the generative AI, and the generative AI estimates the emotions.

[0116] The reception desk can select the most suitable reception method by referring to the user's past consultation history at the time of reception. For example, it can automatically display as suggestions the topics the user has frequently consulted about in the past. It can also prioritize suggesting reception methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest reception methods to be used during specific time periods based on the user's past consultation history. This allows for the selection of the most suitable reception method by referring to past consultation history. Past consultation history can be referred to, for example, using a database search method. Past consultation history can also be referred to, for example, based on the history storage format. Some or all of the above processing of past consultation history may be performed using, for example, a generation AI, or without a generation AI. For example, the reception desk inputs the user's past consultation history into a generation AI, and the generation AI selects the most suitable reception method.

[0117] The reception unit can filter the user's current situation and areas of interest upon receiving a request. For example, it can prioritize displaying consultations related to problems the user is currently facing. It can also filter relevant consultations based on the user's areas of interest. Furthermore, it can suggest appropriate consultations according to the user's current situation (e.g., urgency). This allows for the reception of appropriate consultations by filtering according to the user's situation and areas of interest. The current situation and areas of interest can be identified, for example, using survey results. The current situation and areas of interest can also be identified, for example, using behavioral history. Some or all of the above processing regarding the current situation and areas of interest may be performed using, for example, a generative AI, or without using a generative AI. For example, the reception unit inputs the user's current situation and areas of interest into a generative AI, and the generative AI performs the filtering.

[0118] The conversion unit can adjust the level of detail in the conversion based on the importance of the consultation content. For example, highly important consultation content can be converted into detailed text. Conversely, less important consultation content can be converted into concise text. Furthermore, depending on the importance, it can be converted into text that includes necessary information. This allows for appropriate text conversion by adjusting the level of detail in the conversion according to the importance of the consultation content. The importance of the consultation content can be evaluated, for example, using impact. The importance of the consultation content can be evaluated, for example, using urgency. Some or all of the above processing regarding the importance of the consultation content may be performed, for example, using a generation AI, or without using a generation AI. For example, the conversion unit inputs the importance of the consultation content to the generation AI, and the generation AI adjusts the level of detail in the conversion.

[0119] The automation unit can select the optimal automation method by referring to past business data during automation. For example, it can analyze past business data and select the optimal automation method. It can also select an efficient automation method from past business data. Furthermore, it can apply the optimal automation algorithm based on past business data. In this way, the optimal automation method can be selected by referring to past business data. Past business data can be referred to, for example, using a database search method. Past business data can be referred to, for example, based on the history storage format. Some or all of the above processing on past business data may be performed using, for example, a generation AI, or without using a generation AI. For example, the automation unit inputs past business data into a generation AI, and the generation AI selects the optimal automation method.

[0120] The storage unit can apply different storage methods depending on the data category during storage. For example, a specialized storage method can be applied to financial data. A user-friendly storage method can be applied to health-related data. Furthermore, a storage method that includes legal terminology can be applied to legal data. This allows for appropriate data storage by applying storage methods according to the data category. Data categories can include, for example, classification of technical data. Data categories can also include, for example, classification of business data. Some or all of the above processing in data categories may be performed using, for example, a generative AI, or without using a generative AI. For example, the storage unit inputs the data category into the generative AI, and the generative AI applies an appropriate storage method.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk receives the inquiry. For example, an AI chatbot can be used to receive the inquiry, accepting text or voice input from the user. It can also process the user's input in real time and accept the inquiry. Step 2: The conversion unit converts the consultation content received by the reception unit into text. For example, the consultation content can be converted into text using speech recognition technology, handwriting recognition technology, or image recognition technology. Step 3: The automation unit automates tasks based on the text converted by the conversion unit. For example, tasks can be automated using RPA, AI technology, or scripts. Step 4: The storage unit stores information about the tasks automated by the automation unit. For example, information can be stored using a database, cloud storage, or distributed storage. Step 5: The visualization unit visualizes the information accumulated by the storage unit. For example, the information can be visualized using graphs, charts, and dashboards.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the reception unit, conversion unit, automation unit, storage unit, and visualization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives inquiries using an AI chatbot. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts inquiries into text using speech recognition technology. The automation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automates tasks using RPA. The storage unit is implemented by the database 24 of the data processing unit 12 and stores information. The visualization unit is implemented by the control unit 46A of the smart device 14 and visualizes information using graphs and charts. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the reception unit, conversion unit, automation unit, storage unit, and visualization unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives inquiries using an AI chatbot. The conversion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and converts inquiries into text using speech recognition technology. The automation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automates tasks using RPA. The storage unit is implemented, for example, by the database 24 of the data processing unit 12 and stores information. The visualization unit is implemented, for example, by the control unit 46A of the smart glasses 214 and visualizes information using graphs and charts. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, conversion unit, automation unit, storage unit, and visualization unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives inquiries using an AI chatbot. The conversion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and converts inquiries into text using speech recognition technology. The automation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates tasks using RPA. The storage unit is implemented by, for example, the database 24 of the data processing unit 12 and stores information. The visualization unit is implemented by, for example, the control unit 46A of the headset terminal 314 and visualizes information using graphs and charts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the reception unit, conversion unit, automation unit, storage unit, and visualization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives inquiries using an AI chatbot. The conversion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and converts inquiries into text using speech recognition technology. The automation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates tasks using RPA. The storage unit is implemented by, for example, the database 24 of the data processing unit 12 and stores information. The visualization unit is implemented by, for example, the control unit 46A of the robot 414 and visualizes information using graphs and charts. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The reception desk that receives inquiries, A conversion unit that converts the consultation content received by the reception unit into text, An automation unit that automates tasks based on the text converted by the aforementioned conversion unit, A storage unit that stores information on the tasks automated by the aforementioned automation unit, The system includes a visualization unit that visualizes the information stored by the storage unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept inquiries using an AI chatbot. The system described in Appendix 1, characterized by the features described herein. (Note 3) The conversion unit is Using speech recognition technology, the content of the consultation is converted into text. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned automation unit, Automate business processes using RPA. The system described in Appendix 1, characterized by the features described herein. (Note 5) The storage unit is Build a knowledge base and accumulate information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The visualization unit, Visualize data The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of receiving inquiries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is During the registration process, the system will refer to the user's past consultation history to select the most appropriate registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During registration, filtering is performed based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the types of inquiries it will accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving a request, the system prioritizes accepting inquiries that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Upon receiving a request, the system analyzes the user's social media activity and receives related inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 13) The conversion unit is It estimates the user's emotions and adjusts the way text is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The conversion unit is During the conversion process, the level of detail in the conversion will be adjusted based on the importance of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The conversion unit is During conversion, different conversion algorithms are applied depending on the category of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The conversion unit is It estimates the user's emotions and adjusts the length of the text conversion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is During the conversion process, the priority of conversions will be determined based on when the consultation details were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is During the conversion process, the order of conversions will be adjusted based on the relevance of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned automation unit, It estimates user sentiment and adjusts the method of automating tasks based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned automation unit, When automating processes, the optimal automation method is selected by referring to past business data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned automation unit, When automating tasks, different automation algorithms are applied depending on the category of the task. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned automation unit, It estimates user sentiment and determines the priority of business process automation based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned automation unit, When automating tasks, the optimal automation method should be selected considering the geographical distribution of the work processes. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned automation unit, When automating processes, refer to relevant documentation to improve the accuracy of the automation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The storage unit is The system estimates the user's emotions and selects stored data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The storage unit is During data storage, the storage algorithm is optimized by referring to past stored data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The storage unit is When accumulating data, different accumulation methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The storage unit is It estimates user emotions and prioritizes accumulated data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The storage unit is During data storage, the stored data is weighted based on when it was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The storage unit is During data accumulation, the accuracy of the accumulation is improved by referencing relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The visualization unit, We estimate user sentiment and adjust the data visualization method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The visualization unit, When creating visualizations, the optimal visualization method is selected by referring to past visualization data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The visualization unit, When visualizing data, apply different visualization techniques depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The visualization unit, It estimates user emotions and prioritizes visualization data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The visualization unit, When creating visualizations, weight the visualized data based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The visualization unit, When creating visualizations, referencing relevant market data improves the accuracy of the visualizations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk that receives inquiries, A conversion unit that converts the consultation content received by the reception unit into text, An automation unit that automates tasks based on the text converted by the aforementioned conversion unit, A storage unit that stores information on the tasks automated by the aforementioned automation unit, The system includes a visualization unit that visualizes the information stored by the storage unit. A system characterized by the following features.

2. The aforementioned reception unit is We use an AI chatbot to receive inquiries. The system according to feature 1.

3. The conversion unit is Using speech recognition technology, the content of the consultation is converted into text. The system according to feature 1.

4. The aforementioned automation unit, Automate business processes using RPA. The system according to feature 1.

5. The storage unit is Build a knowledge base and accumulate information. The system according to feature 1.

6. The visualization unit, Visualize data The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the method of receiving inquiries based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is During the registration process, the system will refer to the user's past consultation history to select the most appropriate registration method. The system according to feature 1.

9. The aforementioned reception unit is During registration, filtering is performed based on the user's current situation and areas of interest. The system according to feature 1.

Images