system

Abstract

The system according to the embodiment aims to provide quick and visual support for digital-related questions. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, and a visual support unit. The receiving unit receives digital-related questions. The generating unit generates answers to the questions received by the receiving unit. The visual support unit provides visual support for the answers generated by the generating unit.

Description

[Technical Field]

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

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

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

[0004] Current technologies do not adequately provide fast, visual support for digital-related questions, and there is room for improvement.

[0005] The system according to the embodiment aims to provide quick and visual support for digital-related questions. [Means for solving the problem]

[0006] A system according to an embodiment includes a reception unit, a generation unit, and a visual support unit. The reception unit receives a digital-related question. The generation unit generates an answer to the question received by the reception unit. The visual support unit provides visual support for the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide quick and visual support for digital-related questions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) The digital support system according to an embodiment of the present invention accepts digital-related questions, generates answers using a generation AI, and provides visual support. This digital support system is designed to address the concerns and anxieties of local government employees working on digitalization and business improvement. Specifically, employees input digital-related questions, and the generation AI generates answers in a question-and-answer format. The generated answers are provided not only in text format but also as visual support. Furthermore, the generation AI's training data includes a mutual conversion between digital terminology and administrative terminology, improving employees' understanding of digitalization. This system improves the efficiency of administrative work overall, from identifying issues to proposing solutions. For example, an employee inputs a question such as, "What are the benefits of digitalization?" This information is input into the generation AI. The generation AI then analyzes the input question and generates an answer in a question-and-answer format. The generation AI converts between digital terminology and administrative terminology to generate answers that are easy for employees to understand. For example, a generated answer might be, "The benefits of digitalization are improved business efficiency and cost reduction." The generated answers are provided not only in text format but also with visual support. For example, illustrating the benefits of digitalization makes it easier for employees to understand. Furthermore, the generative AI's learning data includes mutual conversion between digital terms and administrative terminology, improving employees' understanding of digitalization. This allows employees to easily understand technical terms and content related to digitalization. This system improves the efficiency of administrative work in general, from identifying issues to proposing solutions. For example, an employee can input a question about digitalization, and the generative AI can generate an answer to that question and provide visual support, improving the employee's understanding and streamlining their work. In this way, the digital support system can improve employees' understanding of digitalization and realize efficiency improvements in administrative work in general.

[0029] A digital assistance system according to an embodiment includes a reception unit, a generation unit, and a visual support unit. The reception unit receives digital-related questions. When an employee inputs a digital-related question, the reception unit provides a simple, conversational input interface. For example, an employee inputs a question such as, "What are the benefits of digitalization?" This information is input to a generation AI. The generation unit generates an answer to the question received by the reception unit. The generation unit converts digital terms into administrative terms and generates an answer that is easy for employees to understand. For example, an answer such as, "The benefits of digitalization are improved work efficiency and cost reduction" may be generated. The visual support unit provides visual support for the answer generated by the generation unit. The generated answer is provided not only in text format but also in visual format. For example, illustrating the benefits of digitalization makes it easier for employees to understand. This allows the digital assistance system according to an embodiment to improve employees' understanding of digitalization and achieve efficiency improvements in administrative work overall.

[0030] The generation unit can perform mutual conversion between digital terms and administrative terms. The generation unit, for example, performs mutual conversion between digital terms and administrative terms. For example, the generation unit converts digital terms into administrative terms and generates answers in a format that is easy for employees to understand. The generation unit can also convert administrative terms into digital terms and provide appropriate answers to digital-related questions. This allows the generation unit to improve employees' understanding of digitalization. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate answers using a generation AI model that converts between digital terms and administrative terms.

[0031] The reception unit can provide a simple conversational input interface. The reception unit, for example, provides a simple conversational input interface. For example, the reception unit can provide a chatbot-style interface, allowing staff members to input questions in a natural conversational format. The reception unit can also accept questions in the form of questions and answers. For example, when a staff member inputs, "What are the benefits of digitalization?", the reception unit sends the question to the generation unit. This makes it easier for staff members to input questions. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to analyze the input content of the staff member and send it to the generation unit in an appropriate format.

[0032] The visual support unit can provide visual support using visuals. For example, the visual support unit provides the answer generated by the generation unit in a visual format such as an illustration, graph, or animation. The visual support unit can also use videos or infographics to make the answer easier for staff to understand. In this way, the visual support unit can make the answer easier for staff to understand. Some or all of the above-described processing in the visual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visual support unit can use a generation AI to convert the generated answer into a visual format and provide it to the staff.

[0033] The reception unit can cooperate with smart home devices. The reception unit cooperates with, for example, smart home devices. For example, the reception unit cooperates with smart home devices such as smart speakers, smart lights, and smart thermostats to allow staff to input questions by voice. The reception unit can also provide answers to staff through the smart home devices. This allows the reception unit to more conveniently allow staff to input questions. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input voice data acquired from a smart home device into a generation AI and have the generation AI convert the voice data into text data.

[0034] The generation unit can provide easy-to-understand explanations of technical terms and content related to digitalization. For example, the generation unit provides easy-to-understand explanations of technical terms and content related to digitalization. For example, the generation unit explains technical terms such as cloud computing, blockchain, and AI algorithms in a format that is easy for employees to understand. The generation unit can also provide explanations of digitalization content in visual formats such as diagrams, graphs, and animations. This allows the generation unit to improve employees' understanding of digitalization. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can use a generation AI to generate text and visuals that provide easy-to-understand explanations of technical terms and content.

[0035] The reception unit can analyze past question reception history and select the optimal reception method. The reception unit, for example, analyzes past question reception history and selects the optimal reception method. For example, the reception unit suggests the optimal reception method based on the content of questions frequently asked by the user in the past. The reception unit can also select the optimal reception method for a specific time period from the user's past question reception history. Furthermore, the reception unit can analyze the user's past question reception history and select the most efficient reception method. In this way, the reception unit can select the optimal reception method by analyzing the past question reception history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input past question reception history into the generation AI and have the generation AI perform analysis to select the optimal reception method.

[0036] The reception unit may filter questions based on the user's current project or area of ​​interest when receiving the question. For example, the reception unit may preferentially receive questions related to the user's current project or area of ​​interest when receiving the question. For example, the reception unit may preferentially receive questions related to the project the user is currently working on. The reception unit may also filter and receive related questions based on the user's area of ​​interest. Furthermore, the reception unit may also receive optimal questions taking into account the user's current project or area of ​​interest. By filtering questions based on the user's current project or area of ​​interest, the reception unit can preferentially receive highly relevant questions. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's project data and area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0037] When accepting a question, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, when accepting a question, the reception unit prioritizes accepting highly relevant questions by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes accepting questions related to that area. The reception unit can also filter and accept highly relevant questions based on the user's geographical location information. Furthermore, when the user is moving, the reception unit can prioritize accepting questions related to the user's current location. In this way, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to perform analysis to filter highly relevant questions.

[0038] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the user's social media activity and prioritize receiving related questions. The reception unit can also receive questions related to topics in which the user has shown interest on social media. Furthermore, the reception unit can receive optimal questions based on the user's social media activity. In this way, the reception unit can analyze the user's social media activity and prioritize receiving related questions. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to perform analysis to filter related questions.

[0039] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit adjusts the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit generates a detailed answer for an important question. The generation unit can also generate a concise answer for a general question. Furthermore, the generation unit can also quickly generate an answer for a highly urgent question. This allows the generation unit to adjust the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail of the answer using a generation AI model that evaluates the importance of the question.

[0040] The generation unit can apply different answer algorithms depending on the category of the question when generating an answer. For example, the generation unit can apply different answer algorithms depending on the category of the question when generating an answer. For example, the generation unit can apply a specialized algorithm to generate an answer for a technical question. The generation unit can also apply a simple algorithm to generate an answer for a general question. Furthermore, the generation unit can also apply a legal algorithm to generate an answer for a legal question. This allows the generation unit to apply different answer algorithms depending on the category of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can apply an appropriate answer algorithm using a generation AI model that evaluates the category of the question.

[0041] The generation unit can determine the priority of answers based on the time when the question was submitted when generating an answer. For example, the generation unit determines the priority of answers based on the time when the question was submitted when generating an answer. For example, the generation unit prioritizes answers to recently submitted questions. The generation unit can also prioritize answers to questions with high urgency. Furthermore, the generation unit can postpone answering questions that were submitted earlier. In this way, the generation unit can determine the priority of answers based on the time when the question was submitted. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the priority of answers using a generation AI model that evaluates the time when the question was submitted.

[0042] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit adjusts the order of answers based on the relevance of the questions when generating answers. For example, the generation unit prioritizes answers to highly relevant questions. The generation unit can also postpone answers to less relevant questions. Furthermore, the generation unit can group highly relevant questions and answer them. This allows the generation unit to adjust the order of answers based on the relevance of the questions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order of answers using a generation AI model that evaluates the relevance of questions.

[0043] The visual support unit can select the optimal display method by referring to the user's past operation history when displaying visual support. For example, the visual support unit selects the optimal display method by referring to the user's past operation history when displaying visual support. For example, the visual support unit preferentially provides a display method that the user has previously preferred. The visual support unit can also select the optimal display method from the user's past operation history. Furthermore, the visual support unit can analyze the user's past operation history and provide a visually easy-to-understand display method. In this way, the visual support unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the visual support unit may be performed using, or without, a generation AI. For example, the visual support unit can input the user's operation history data into the generation AI and cause the generation AI to perform analysis to select the optimal display method.

[0044] The visual support unit can apply different visual support techniques depending on the content of the question when displaying the visual support. For example, the visual support unit can apply different visual support techniques depending on the content of the question when displaying the visual support. For example, the visual support unit can provide visual support using diagrams and flowcharts for technical questions. The visual support unit can also provide visual support using simple diagrams and icons for general questions. Furthermore, the visual support unit can provide visual support using legal documents and diagrams for legal questions. This allows the visual support unit to apply different visual support techniques depending on the content of the question. Some or all of the above-mentioned processing in the visual support unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the visual support unit can apply an appropriate visual support technique using a generative AI model that evaluates the content of the question.

[0045] The visual support unit can select the optimal display method by taking into account the user's device information when displaying visual support. For example, when displaying visual support, the visual support unit selects the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the visual support unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the visual support unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the visual support unit can provide a simple, highly visible display method. In this way, the visual support unit can provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the visual support unit may be performed using, or without, a generation AI. For example, the visual support unit can input the user's device information into the generation AI and cause the generation AI to perform an analysis to select the optimal display method.

[0046] The visual support unit can improve the accuracy of the visual support by referring to literature related to the question when displaying the visual support. For example, the visual support unit can improve the accuracy of the visual support by referring to literature related to the question when displaying the visual support. For example, the visual support unit provides detailed visual support based on the related literature. The visual support unit can also improve the accuracy of the visual support by referring to the related literature. Furthermore, the visual support unit can provide visually easy-to-understand support based on the related literature. In this way, the visual support unit can improve the accuracy of the visual support by referring to literature related to the question. Some or all of the above-mentioned processing in the visual support unit can be performed using, or without, a generation AI. For example, the visual support unit can input related literature data into the generation AI and cause the generation AI to perform analysis to improve the accuracy of the visual support.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reception department can analyze the user's past question history and automatically suggest similar questions. For example, if a staff member has previously asked, "What are the benefits of digitalization?", the reception department can use that history to suggest related questions such as, "What are the disadvantages of digitalization?" or "What are some specific examples of digitalization?" The reception department can also identify topics that staff members frequently ask about based on the past question history and provide the latest information related to those topics. Furthermore, the reception department can analyze the past question history to identify topics that staff members found difficult to understand and provide detailed explanations about those topics. This allows the reception department to provide more effective support by utilizing the staff member's past question history.

[0049] The generation unit can incorporate the opinions of external experts when generating answers to user questions. For example, the generation unit can generate an answer to a question about digitalization that incorporates the opinions of external digital experts. The generation unit can also generate an answer to a question about government that incorporates the opinions of external government experts. Furthermore, the generation unit can provide detailed explanations that incorporate the opinions of external experts for questions about specific fields. This allows the generation unit to utilize the opinions of external experts to provide more reliable answers.

[0050] The visual support unit can monitor the user's device usage status in real time and provide optimal visual support. For example, if the user is using a smartphone, the visual support unit can provide visual support optimized for the screen size. Also, if the user is using a tablet, the visual support unit can provide detailed visual support suitable for a large screen. Furthermore, if the user is using a desktop, the visual support unit can provide visual support using multiple windows. As a result, the visual support unit can provide optimal visual support according to the user's device usage status.

[0051] When generating an answer to a user's question, the generation unit can refer to a related database to improve the accuracy of the answer. For example, the generation unit can generate a detailed answer to a question about digitalization by referring to a related database. The generation unit can also generate an accurate answer to a question about government by referring to a related database. Furthermore, the generation unit can generate a reliable answer to a question about a specific field by referring to a related database. In this way, the generation unit can provide a more accurate answer by utilizing the related database.

[0052] When generating an answer to a user's question, the generation unit can provide a consistent answer by referring to the past answer history. For example, if the generation unit previously answered the question, "What are the benefits of digitalization?" with "Business efficiency and cost reduction," the generation unit can provide a consistent answer to similar questions. The generation unit can also generate answers in a format that is easy for the user to understand based on the past answer history. Furthermore, the generation unit can also provide detailed answers on topics that the user is particularly interested in by referring to the past answer history. In this way, the generation unit can utilize the past answer history to provide consistent and reliable answers.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reception desk accepts digital-related questions. When employees input digital-related questions, the reception desk provides a simple conversational input interface. For example, an employee might input a question such as, "What are the benefits of digitalization?" This information is then input into the generative AI. Step 2: The generation unit generates answers to the questions received by the reception unit. The generation unit converts between digital and administrative terms to generate answers that are easy for employees to understand. For example, a generated answer might be, "The benefits of digitalization are increased work efficiency and cost reduction." Step 3: The visual support section provides visual support for the answers generated by the generation section. The generated answers are provided not only in text format but also in visual support. For example, illustrating the benefits of digitalization can help employees understand more easily.

[0055] (Example 2) The digital support system according to an embodiment of the present invention accepts digital-related questions, generates answers using a generation AI, and provides visual support. This digital support system is designed to address the concerns and anxieties of local government employees working on digitalization and business improvement. Specifically, employees input digital-related questions, and the generation AI generates answers in a question-and-answer format. The generated answers are provided not only in text format but also as visual support. Furthermore, the generation AI's training data includes a mutual conversion between digital terminology and administrative terminology, improving employees' understanding of digitalization. This system improves the efficiency of administrative work overall, from identifying issues to proposing solutions. For example, an employee inputs a question such as, "What are the benefits of digitalization?" This information is input into the generation AI. The generation AI then analyzes the input question and generates an answer in a question-and-answer format. The generation AI converts between digital terminology and administrative terminology to generate answers that are easy for employees to understand. For example, a generated answer might be, "The benefits of digitalization are improved business efficiency and cost reduction." The generated answers are provided not only in text format but also with visual support. For example, illustrating the benefits of digitalization makes it easier for employees to understand. Furthermore, the generative AI's learning data includes mutual conversion between digital terms and administrative terminology, improving employees' understanding of digitalization. This allows employees to easily understand technical terms and content related to digitalization. This system improves the efficiency of administrative work in general, from identifying issues to proposing solutions. For example, an employee can input a question about digitalization, and the generative AI can generate an answer to that question and provide visual support, improving the employee's understanding and streamlining their work. In this way, the digital support system can improve employees' understanding of digitalization and realize efficiency improvements in administrative work in general.

[0056] A digital assistance system according to an embodiment includes a reception unit, a generation unit, and a visual support unit. The reception unit receives digital-related questions. When an employee inputs a digital-related question, the reception unit provides a simple, conversational input interface. For example, an employee inputs a question such as, "What are the benefits of digitalization?" This information is input to a generation AI. The generation unit generates an answer to the question received by the reception unit. The generation unit converts digital terms into administrative terms and generates an answer that is easy for employees to understand. For example, an answer such as, "The benefits of digitalization are improved work efficiency and cost reduction" may be generated. The visual support unit provides visual support for the answer generated by the generation unit. The generated answer is provided not only in text format but also in visual format. For example, illustrating the benefits of digitalization makes it easier for employees to understand. This allows the digital assistance system according to an embodiment to improve employees' understanding of digitalization and achieve efficiency improvements in administrative work overall.

[0057] The generation unit can perform mutual conversion between digital terms and administrative terms. The generation unit, for example, performs mutual conversion between digital terms and administrative terms. For example, the generation unit converts digital terms into administrative terms and generates answers in a format that is easy for employees to understand. The generation unit can also convert administrative terms into digital terms and provide appropriate answers to digital-related questions. This allows the generation unit to improve employees' understanding of digitalization. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can generate answers using a generation AI model that converts between digital terms and administrative terms.

[0058] The reception unit can provide a simple conversational input interface. The reception unit, for example, provides a simple conversational input interface. For example, the reception unit can provide a chatbot-style interface, allowing staff members to input questions in a natural conversational format. The reception unit can also accept questions in the form of questions and answers. For example, when a staff member inputs, "What are the benefits of digitalization?", the reception unit sends the question to the generation unit. This makes it easier for staff members to input questions. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can use a generation AI to analyze the input content of the staff member and send it to the generation unit in an appropriate format.

[0059] The visual support unit can provide visual support using visuals. For example, the visual support unit provides the answer generated by the generation unit in a visual format such as an illustration, graph, or animation. The visual support unit can also use videos or infographics to make the answer easier for staff to understand. In this way, the visual support unit can make the answer easier for staff to understand. Some or all of the above-described processing in the visual support unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the visual support unit can use a generation AI to convert the generated answer into a visual format and provide it to the staff.

[0060] The reception unit can cooperate with smart home devices. The reception unit cooperates with, for example, smart home devices. For example, the reception unit cooperates with smart home devices such as smart speakers, smart lights, and smart thermostats to allow staff to input questions by voice. The reception unit can also provide answers to staff through the smart home devices. This allows the reception unit to more conveniently allow staff to input questions. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input voice data acquired from a smart home device into a generation AI and have the generation AI convert the voice data into text data.

[0061] The generation unit can provide easy-to-understand explanations of technical terms and content related to digitalization. For example, the generation unit provides easy-to-understand explanations of technical terms and content related to digitalization. For example, the generation unit explains technical terms such as cloud computing, blockchain, and AI algorithms in a format that is easy for employees to understand. The generation unit can also provide explanations of digitalization content in visual formats such as diagrams, graphs, and animations. This allows the generation unit to improve employees' understanding of digitalization. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can use a generation AI to generate text and visuals that provide easy-to-understand explanations of technical terms and content.

[0062] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of question reception based on the estimated user emotions. For example, if the user is stressed, the reception unit can quickly receive the question and provide an immediate answer. Alternatively, if the user is relaxed, the reception unit can slowly receive the question and provide a detailed answer. Furthermore, if the user is in a hurry, the reception unit can quickly receive the question and provide a concise answer. This allows the reception unit to adjust the timing of question reception according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the reception unit may be performed using the generation AI, for example, or without the generation AI. For example, the reception unit can input the user's facial expression data and voice data into the generation AI and have the generation AI perform emotion estimation.

[0063] The reception unit can analyze past question reception history and select the optimal reception method. The reception unit, for example, analyzes past question reception history and selects the optimal reception method. For example, the reception unit suggests the optimal reception method based on the content of questions frequently asked by the user in the past. The reception unit can also select the optimal reception method for a specific time period from the user's past question reception history. Furthermore, the reception unit can analyze the user's past question reception history and select the most efficient reception method. In this way, the reception unit can select the optimal reception method by analyzing the past question reception history. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input past question reception history into the generation AI and have the generation AI perform analysis to select the optimal reception method.

[0064] The reception unit may filter questions based on the user's current project or area of ​​interest when receiving the question. For example, the reception unit may preferentially receive questions related to the user's current project or area of ​​interest when receiving the question. For example, the reception unit may preferentially receive questions related to the project the user is currently working on. The reception unit may also filter and receive related questions based on the user's area of ​​interest. Furthermore, the reception unit may also receive optimal questions taking into account the user's current project or area of ​​interest. By filtering questions based on the user's current project or area of ​​interest, the reception unit can preferentially receive highly relevant questions. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's project data and area of ​​interest data into the generation AI and have the generation AI perform the filtering.

[0065] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of questions to be received based on the estimated user emotions. For example, when the user is stressed, the reception unit can prioritize important questions. Furthermore, when the user is relaxed, the reception unit can prioritize detailed questions. Furthermore, when the user is in a hurry, the reception unit can prioritize concise questions. This allows the reception unit to prioritize questions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the reception unit can input the user's facial expression data and voice data into the generation AI and have the generation AI perform emotion estimation.

[0066] When accepting a question, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. For example, when accepting a question, the reception unit prioritizes accepting highly relevant questions by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit prioritizes accepting questions related to that area. The reception unit can also filter and accept highly relevant questions based on the user's geographical location information. Furthermore, when the user is moving, the reception unit can prioritize accepting questions related to the user's current location. In this way, the reception unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI and cause the generation AI to perform analysis to filter highly relevant questions.

[0067] The reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the user's social media activity when receiving a question and receive related questions. For example, the reception unit can analyze the user's social media activity and prioritize receiving related questions. The reception unit can also receive questions related to topics in which the user has shown interest on social media. Furthermore, the reception unit can receive optimal questions based on the user's social media activity. In this way, the reception unit can analyze the user's social media activity and prioritize receiving related questions. Some or all of the above-described processing in the reception unit can be performed, for example, using a generation AI or without using a generation AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to perform analysis to filter related questions.

[0068] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, the generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate a detailed and polite answer. Furthermore, if the user is in a hurry, the generation unit can generate a concise and to-the-point answer. Furthermore, if the user is stressed, the generation unit can generate a gentle and reassuring answer. This allows the generation unit to adjust the way the answer is expressed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's facial expression data and voice data into the generation AI and cause the generation AI to estimate the emotion.

[0069] The generation unit can adjust the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit adjusts the level of detail of the answer based on the importance of the question when generating an answer. For example, the generation unit generates a detailed answer for an important question. The generation unit can also generate a concise answer for a general question. Furthermore, the generation unit can also quickly generate an answer for a highly urgent question. This allows the generation unit to adjust the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the level of detail of the answer using a generation AI model that evaluates the importance of the question.

[0070] The generation unit can apply different answer algorithms depending on the category of the question when generating an answer. For example, the generation unit can apply different answer algorithms depending on the category of the question when generating an answer. For example, the generation unit can apply a specialized algorithm to generate an answer for a technical question. The generation unit can also apply a simple algorithm to generate an answer for a general question. Furthermore, the generation unit can also apply a legal algorithm to generate an answer for a legal question. This allows the generation unit to apply different answer algorithms depending on the category of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can apply an appropriate answer algorithm using a generation AI model that evaluates the category of the question.

[0071] The generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the length of the answer based on the estimated user emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point answer. Furthermore, if the user is relaxed, the generation unit can generate a longer answer with detailed explanations. Furthermore, if the user is stressed, the generation unit can generate an answer of appropriate length to provide a sense of security. This allows the generation unit to adjust the length of the answer according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can input the user's facial expression data and voice data into the generation AI and cause the generation AI to estimate the emotion.

[0072] The generation unit can determine the priority of answers based on the time when the question was submitted when generating an answer. For example, the generation unit determines the priority of answers based on the time when the question was submitted when generating an answer. For example, the generation unit prioritizes answers to recently submitted questions. The generation unit can also prioritize answers to questions with high urgency. Furthermore, the generation unit can postpone answering questions that were submitted earlier. In this way, the generation unit can determine the priority of answers based on the time when the question was submitted. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can determine the priority of answers using a generation AI model that evaluates the time when the question was submitted.

[0073] The generation unit can adjust the order of answers based on the relevance of the questions when generating answers. For example, the generation unit adjusts the order of answers based on the relevance of the questions when generating answers. For example, the generation unit prioritizes answers to highly relevant questions. The generation unit can also postpone answers to less relevant questions. Furthermore, the generation unit can group highly relevant questions and answer them. This allows the generation unit to adjust the order of answers based on the relevance of the questions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can adjust the order of answers using a generation AI model that evaluates the relevance of questions.

[0074] The visual support unit can estimate the user's emotions and adjust the display method of the visual support based on the estimated user emotions. For example, the visual support unit can estimate the user's emotions and adjust the display method of the visual support based on the estimated user emotions. For example, if the user is nervous, the visual support unit can provide a simple, highly visible display method. If the user is relaxed, the visual support unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the visual support unit can also provide a display method that focuses on the main points. This allows the visual support unit to adjust the display method of the visual support according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the visual support unit can be performed using, for example, the generation AI. For example, the visual support unit can input the user's facial expression data and voice data into the generation AI and have the generation AI perform emotion estimation.

[0075] The visual support unit can select the optimal display method by referring to the user's past operation history when displaying visual support. For example, the visual support unit selects the optimal display method by referring to the user's past operation history when displaying visual support. For example, the visual support unit preferentially provides a display method that the user has previously preferred. The visual support unit can also select the optimal display method from the user's past operation history. Furthermore, the visual support unit can analyze the user's past operation history and provide a visually easy-to-understand display method. In this way, the visual support unit can provide the optimal display method by referring to the user's past operation history. Some or all of the above-described processing in the visual support unit may be performed using, or without, a generation AI. For example, the visual support unit can input the user's operation history data into the generation AI and cause the generation AI to perform analysis to select the optimal display method.

[0076] The visual support unit can apply different visual support techniques depending on the content of the question when displaying the visual support. For example, the visual support unit can apply different visual support techniques depending on the content of the question when displaying the visual support. For example, the visual support unit can provide visual support using diagrams and flowcharts for technical questions. The visual support unit can also provide visual support using simple diagrams and icons for general questions. Furthermore, the visual support unit can provide visual support using legal documents and diagrams for legal questions. This allows the visual support unit to apply different visual support techniques depending on the content of the question. Some or all of the above-mentioned processing in the visual support unit can be performed using, for example, a generative AI, or can be performed without using a generative AI. For example, the visual support unit can apply an appropriate visual support technique using a generative AI model that evaluates the content of the question.

[0077] The visual support unit can estimate the user's emotions and determine the priority of visual support based on the estimated user emotions. For example, the visual support unit can estimate the user's emotions and determine the priority of visual support based on the estimated user emotions. For example, if the user is feeling stressed, the visual support unit can prioritize providing important visual support. Furthermore, if the user is relaxed, the visual support unit can prioritize providing detailed visual support. Furthermore, if the user is in a hurry, the visual support unit can prioritize providing concise visual support. This allows the visual support unit to determine the priority of visual support according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the visual support unit can be performed using, for example, the generation AI. For example, the visual support unit can input the user's facial expression data and voice data into the generation AI and cause the generation AI to estimate emotions.

[0078] The visual support unit can select the optimal display method by taking into account the user's device information when displaying visual support. For example, when displaying visual support, the visual support unit selects the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the visual support unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the visual support unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the visual support unit can provide a simple, highly visible display method. In this way, the visual support unit can provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the visual support unit may be performed using, or without, a generation AI. For example, the visual support unit can input the user's device information into the generation AI and cause the generation AI to perform an analysis to select the optimal display method.

[0079] The visual support unit can improve the accuracy of the visual support by referring to literature related to the question when displaying the visual support. For example, the visual support unit can improve the accuracy of the visual support by referring to literature related to the question when displaying the visual support. For example, the visual support unit provides detailed visual support based on the related literature. The visual support unit can also improve the accuracy of the visual support by referring to the related literature. Furthermore, the visual support unit can provide visually easy-to-understand support based on the related literature. In this way, the visual support unit can improve the accuracy of the visual support by referring to literature related to the question. Some or all of the above-mentioned processing in the visual support unit can be performed using, or without, a generation AI. For example, the visual support unit can input related literature data into the generation AI and cause the generation AI to perform analysis to improve the accuracy of the visual support. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, generation unit, and visual support unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and provides a simple conversational input interface when employees input digital-related questions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates answers that are easy for employees to understand while converting between digital terms and administrative terms. The visual support unit is realized, for example, by the output device 40 of the smart device 14 and provides visual support for the generated answers. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, generation unit, and visual support unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and provides a simple conversational input interface when employees input digital-related questions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates answers that are easy for employees to understand while converting between digital terms and administrative terms. The visual support unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides visual support for the generated answers. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, and visual support unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and provides a simple conversational input interface when employees input digital-related questions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates answers that are easy for employees to understand while converting between digital terms and administrative terms. The visual support unit is realized, for example, by the display 343 of the headset terminal 314 and provides visual support for the generated answers. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, generation unit, and visual support unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and provides a simple conversational input interface when employees input digital-related questions. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates answers that are easy for employees to understand while converting between digital terms and administrative terms. The visual support unit is realized, for example, by the speaker 240 of the robot 414, and provides visual support for the generated answers.

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

[0081] The reception department can analyze the user's past question history and automatically suggest similar questions. For example, if a staff member has previously asked, "What are the benefits of digitalization?", the reception department can use that history to suggest related questions such as, "What are the disadvantages of digitalization?" or "What are some specific examples of digitalization?" The reception department can also identify topics that staff members frequently ask about based on the past question history and provide the latest information related to those topics. Furthermore, the reception department can analyze the past question history to identify topics that staff members found difficult to understand and provide detailed explanations about those topics. This allows the reception department to provide more effective support by utilizing the staff member's past question history.

[0082] The generation unit can incorporate the opinions of external experts when generating answers to user questions. For example, the generation unit can generate an answer to a question about digitalization that incorporates the opinions of external digital experts. The generation unit can also generate an answer to a question about government that incorporates the opinions of external government experts. Furthermore, the generation unit can provide detailed explanations that incorporate the opinions of external experts for questions about specific fields. This allows the generation unit to utilize the opinions of external experts to provide more reliable answers.

[0083] The reception unit can estimate the user's emotions and adjust the method of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface to allow the user to quickly input a question. If the user is relaxed, the reception unit can provide a detailed interface to allow the user to carefully input a question. Furthermore, if the user is in a hurry, the reception unit can quickly receive a question using voice input. This allows the reception unit to adjust the method of receiving questions according to the user's emotions.

[0084] The visual support unit can monitor the user's device usage status in real time and provide optimal visual support. For example, if the user is using a smartphone, the visual support unit can provide visual support optimized for the screen size. Also, if the user is using a tablet, the visual support unit can provide detailed visual support suitable for a large screen. Furthermore, if the user is using a desktop, the visual support unit can provide visual support using multiple windows. As a result, the visual support unit can provide optimal visual support according to the user's device usage status.

[0085] The reception unit can estimate the user's emotions and determine the priority of questions based on the estimated user's emotions. For example, when the user is feeling stressed, the reception unit can prioritize receiving important questions. When the user is relaxed, the reception unit can also prioritize receiving detailed questions. Furthermore, when the user is in a hurry, the reception unit can also prioritize receiving concise questions. In this way, the reception unit can prioritize questions according to the user's emotions.

[0086] When generating an answer to a user's question, the generation unit can refer to a related database to improve the accuracy of the answer. For example, the generation unit can generate a detailed answer to a question about digitalization by referring to a related database. The generation unit can also generate an accurate answer to a question about government by referring to a related database. Furthermore, the generation unit can generate a reliable answer to a question about a specific field by referring to a related database. In this way, the generation unit can provide a more accurate answer by utilizing the related database.

[0087] The reception unit can estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can quickly receive the question and provide an immediate answer. Alternatively, if the user is relaxed, the reception unit can slowly receive the question and provide a detailed answer. Furthermore, if the user is in a hurry, the reception unit can quickly receive the question and provide a concise answer. This allows the reception unit to adjust the timing of receiving questions according to the user's emotions.

[0088] The visual support unit can estimate the user's emotions and adjust the display method of the visual support based on the estimated user's emotions. For example, if the user is nervous, the visual support unit can provide a simple, highly visible display method. If the user is relaxed, the visual support unit can also provide a display method including detailed information. Furthermore, if the user is in a hurry, the visual support unit can also provide a display method that focuses on the main points. In this way, the visual support unit can adjust the display method of the visual support according to the user's emotions.

[0089] When generating an answer to a user's question, the generation unit can provide a consistent answer by referring to the past answer history. For example, if the generation unit previously answered the question, "What are the benefits of digitalization?" with "Business efficiency and cost reduction," the generation unit can provide a consistent answer to similar questions. The generation unit can also generate answers in a format that is easy for the user to understand based on the past answer history. Furthermore, the generation unit can also provide detailed answers on topics that the user is particularly interested in by referring to the past answer history. In this way, the generation unit can utilize the past answer history to provide consistent and reliable answers.

[0090] The visual support unit can estimate the user's emotion and determine the priority of visual support based on the estimated user's emotion. For example, if the user is feeling stressed, the visual support unit can prioritize providing important visual support. Also, if the user is relaxed, the visual support unit can prioritize providing detailed visual support. Furthermore, if the user is in a hurry, the visual support unit can prioritize providing brief visual support. In this way, the visual support unit can prioritize visual support according to the user's emotion.

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

[0092] Step 1: The reception desk accepts digital-related questions. When employees input digital-related questions, the reception desk provides a simple conversational input interface. For example, an employee might input a question such as, "What are the benefits of digitalization?" This information is then input into the generative AI. Step 2: The generation unit generates answers to the questions received by the reception unit. The generation unit converts between digital and administrative terms to generate answers that are easy for employees to understand. For example, a generated answer might be, "The benefits of digitalization are increased work efficiency and cost reduction." Step 3: The visual support section provides visual support for the answers generated by the generation section. The generated answers are provided not only in text format but also in visual support. For example, illustrating the benefits of digitalization can help employees understand more easily.

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

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

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

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a 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.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0137] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0138] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0139] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0142] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0143] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0164] [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk for digital-related questions, a generator that generates an answer to the question received by the receiver; A system comprising: a visual support unit that visually supports the answer generated by the generation unit.

2. The generation unit Convert between digital and administrative terms 2. The system of claim 1.

3. The reception unit Provides a simple conversational input interface 2. The system of claim 1.

4. The visual support portion includes: Providing visual support 2. The system of claim 1.

5. The reception unit Integrate with smart home devices 2. The system of claim 1.

6. The generation unit Easy-to-understand explanations of technical terms and content related to digitalization 2. The system of claim 1.

7. The reception unit Estimates user emotions and adjusts timing for accepting questions based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze past inquiries and choose the best method of inquiry 2. The system of claim 1.

Images