System

Abstract

An object of a system according to an embodiment is to provide an appropriate and human-like answer to a user's trouble or question.SOLUTION: A system includes a reception unit, an analysis unit, a generation unit, and an interaction unit. The reception unit receives a question from a user. The analysis unit analyzes the question received by the reception unit. The generation unit generates an answer based on the question analyzed by the analysis unit. The interaction unit proceeds with an interaction based on the answer generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to provide appropriate and human-like answers to users' concerns and questions.

[0005] The system according to the embodiment aims to provide appropriate and human-like answers to the worries and questions of users. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a dialogue unit. The reception unit receives a question from a user. The analysis unit analyzes the question received by the reception unit. The generation unit generates an answer based on the question analyzed by the analysis unit. The dialogue unit advances the dialogue based on the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate and human-like answers to the worries and questions of users. [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) An online consultation system according to an embodiment of the present invention allows users to easily seek online advice about career, family, and other concerns. This online consultation system begins when a user speaks to a mobile phone or voice assistant device. For example, the user may ask, "I've been having trouble at work lately. What should I do?" This question is then input into an AI. The AI ​​then analyzes the input question and generates an answer based on the words of great figures and famous books. For example, the AI ​​may generate an answer such as, "Great figure A said, 'Difficulties are opportunities for growth.' You too can grow by overcoming these difficulties." Furthermore, the AI ​​adds nods and proactive questions to the answer to further the dialogue. For example, it may ask, "What specific difficulties are you facing?" This allows the user to delve deeper into their concerns and receive more specific advice. This system not only allows users to easily seek advice, but also allows them to receive highly specialized advice. For example, for career concerns, users can receive specific advice based on the words of great figures and famous books. Similarly, for family concerns, users can receive highly specialized advice. This service allows users to ask questions not only by asking questions but also by having the AI ​​proactively ask questions, resulting in a smooth dialogue. For example, by asking questions such as "What problems have you been having at home recently?", users can more easily organize their concerns. This makes the online consultation system a useful tool for resolving career and family concerns faced by office workers in their 30s to 50s. The online consultation system is extremely convenient, as users can receive highly specialized advice simply by speaking to their mobile phone or voice assistant device.

[0029] The online consultation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a dialogue unit. The reception unit receives a question from a user. The question may be in a text format, a voice format, an image format, or the like, but is not limited to these examples. The reception unit may receive a voice question using a voice assistant device, for example. The reception unit may also receive a text question using a mobile device. The reception unit may also receive an image question using image analysis technology. For example, the reception unit may receive a voice question from a user in real time using a voice assistant device. The reception unit may also instantly receive a text question entered by a user using a mobile device. The reception unit may analyze an image question sent by a user using image analysis technology and extract the content of the question. The analysis unit analyzes the question received by the reception unit. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may convert a voice question into text using natural language processing technology and analyze the content of the question. The analysis unit may also analyze the intent of the text question using a machine learning algorithm. The analysis unit can also analyze the content of the image question using image analysis technology. For example, the analysis unit converts the voice question into text and analyzes the text. The analysis unit uses a machine learning algorithm to analyze the intent of the text question and generate an appropriate answer. The image analysis technology is used to analyze the content of the image question and identify the intent of the question. The generation unit generates an answer based on the question analyzed by the analysis unit. The answer may be generated in, for example, a text format, a voice format, or an image format, but is not limited to these examples. The generation unit generates an answer based on, for example, the words of great people of the past or famous books. The generation unit can also generate an answer based on data of great people of the past or famous books contained in a database. The generation unit can also be equipped with an algorithm that actively asks questions in response to the user's question. For example, the generation unit generates an answer based on the words of great people of the past. The generation unit generates an answer based on data of famous books contained in a database. The generation unit actively asks questions in response to the user's question and progresses the dialogue. The dialogue unit progresses the dialogue based on the answer generated by the generation unit.The dialogue progresses, for example, by analyzing the user's reaction and nodding or asking questions at appropriate times, but is not limited to such examples. For example, the dialogue unit analyzes the user's facial expression and tone of voice and nods at appropriate times. The dialogue unit can also ask appropriate questions based on the user's reaction. The dialogue unit can also analyze the user's reaction in real time and adjust the progress of the dialogue. For example, the dialogue unit analyzes the user's facial expression and nods at appropriate times. The dialogue unit analyzes the user's tone of voice and asks appropriate questions. The dialogue unit analyzes the user's reaction in real time and adjusts the progress of the dialogue. This enables the online consultation system according to the embodiment to efficiently receive and analyze user questions, generate answers, and progress the dialogue.

[0030] The generation unit can generate an answer based on a specific data source. Specific data sources include, for example, sayings of great people from the past and famous books, but are not limited to these examples. For example, the generation unit obtains sayings of great people from the past from a database and generates an answer based on them. The generation unit can also generate an answer based on data from famous books. The generation unit can also update the data source in real time and generate an answer based on the latest information. For example, the generation unit obtains sayings of great people from the past from a database and generates an answer based on them. An answer is generated based on data from famous books. The data source is updated in real time and generates an answer based on the latest information. In this way, highly specialized answers are generated by being based on sayings of great people from the past and famous books.

[0031] The dialogue unit can analyze the user's reaction and nod or ask a question under specific conditions. Specific conditions include, but are not limited to, the user's facial expression and voice tone, for example. The dialogue unit can analyze the user's facial expression and nod at an appropriate timing. The dialogue unit can also analyze the user's voice tone and ask an appropriate question. The dialogue unit can also analyze the user's reaction in real time and adjust the progress of the dialogue. For example, the dialogue unit can analyze the user's facial expression and nod at an appropriate timing. The dialogue unit can analyze the user's voice tone and ask an appropriate question. The dialogue unit can analyze the user's reaction in real time and adjust the progress of the dialogue. This allows the dialogue to proceed based on the user's reaction, making it possible to have a more natural dialogue.

[0032] The analysis unit may be provided with an algorithm for analyzing a user's question and generating an appropriate answer. Examples of algorithms include, but are not limited to, natural language processing algorithms and machine learning algorithms. For example, the analysis unit may use a natural language processing algorithm to analyze a user's question and generate an appropriate answer. The analysis unit may also use a machine learning algorithm to analyze the intent of the user's question and generate an appropriate answer. The analysis unit may also combine multiple algorithms to perform more accurate analysis. For example, the analysis unit may use a natural language processing algorithm to analyze a user's question and generate an appropriate answer. The analysis unit may use a machine learning algorithm to analyze the intent of the user's question and generate an appropriate answer. The analysis unit may combine multiple algorithms to perform more accurate analysis. As a result, by providing an algorithm for analyzing a user's question, an appropriate answer can be generated.

[0033] The generation unit can generate answers based on specific data sources contained in the database. Specific data sources include, but are not limited to, for example, quotes from great people of the past and famous books contained in the database. For example, the generation unit obtains quotes from great people of the past from the database and generates answers based on the quotes. The generation unit can also generate answers based on data from famous books contained in the database. The generation unit can also update the database in real time and generate answers based on the latest information. For example, the generation unit obtains quotes from great people of the past from the database and generates answers based on the quotes. An answer is generated based on data from famous books. The database is updated in real time and answers are generated based on the latest information. In this way, by using the database as the basis, more diverse answers can be generated.

[0034] The dialogue unit may include an algorithm that actively asks questions in response to a user's question. Examples of the algorithm include, but are not limited to, a dialogue progression algorithm and a question generation algorithm. For example, the dialogue unit may actively ask questions in response to a user's question using a dialogue progression algorithm. The dialogue unit may also generate appropriate questions in response to a user's question using a question generation algorithm. The dialogue unit may also include an algorithm that analyzes a user's response and adjusts the progress of the dialogue. For example, the dialogue unit may actively ask questions in response to a user's question using a dialogue progression algorithm, or may generate appropriate questions in response to a user's question using a question generation algorithm, or may include an algorithm that analyzes a user's response and adjusts the progress of the dialogue. As a result, by actively asking questions, the dialogue progresses smoothly.

[0035] The reception unit can analyze the user's past question history and select the optimal reception method. The optimal reception method includes, for example, a method based on the user's past question history, but is not limited to such an example. For example, the reception unit preferentially receives topics that the user has frequently asked about in the past. The reception unit can also preferentially suggest question formats (audio, text, etc.) that the user has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past question history. For example, the reception unit preferentially receives topics that the user has frequently asked about in the past. The reception unit preferentially suggests question formats that the user has used in the past. The reception unit suggests the optimal reception method for a specific time period based on the user's past question history. In this way, the optimal reception method can be provided to the user by analyzing the past question history.

[0036] When receiving a question, the reception unit can filter the questions based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, the user's location information and time period. Examples of areas of interest include, but are not limited to, the content of past questions and the user's profile information. For example, when the user inputs their current work situation, the reception unit prioritizes receiving questions related to that situation. The reception unit can also filter questions based on the user's areas of interest (career, family, etc.). The reception unit can also suggest optimal questions based on the user's current situation (time period, location, etc.). For example, when the user inputs their current work situation, the reception unit prioritizes receiving questions related to that situation. The reception unit filters questions based on the user's areas of interest. The reception unit suggests optimal questions based on the user's current situation. By filtering questions based on the user's current situation and areas of interest, more relevant questions can be received.

[0037] When accepting a question, the acceptance unit can select an optimal acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of optimal acceptance means include, but are not limited to, voice recognition technology and text analysis technology. For example, when a user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when a user inputs a question by text, the acceptance unit can accept the question using text analysis technology. Furthermore, when a user inputs a question using an image, the acceptance unit can accept the question using image analysis technology. For example, when a user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. When a user inputs a question by text, the acceptance unit accepts the question using text analysis technology. When a user inputs a question using an image, the acceptance unit accepts the question using image analysis technology. This allows for smoother question acceptance by selecting an optimal acceptance means depending on the user's input method.

[0038] When accepting a question, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific area, the acceptance unit can prioritize accepting questions related to the area. Furthermore, when the user is traveling, the acceptance unit can also prioritize accepting questions related to the travel. Furthermore, when the user is at home, the acceptance unit can also prioritize accepting questions related to the home. For example, when the user is in a specific area, the acceptance unit prioritizes accepting questions related to the area. When the user is traveling, the acceptance unit prioritizes accepting questions related to the travel. When the user is at home, the acceptance unit prioritizes accepting questions related to the home. In this way, by taking into account the user's geographical location information, it is possible to prioritize accepting more relevant questions.

[0039] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to these examples. The reception unit, for example, receives related questions based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. For example, the reception unit receives related questions based on the content posted by the user on social media. The reception unit analyzes the user's social media activity history and suggests related questions. The reception unit receives related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to receive more relevant questions.

[0040] When receiving a question, the reception unit can customize the reception method by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments, survey results, etc., but is not limited to these examples. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. The reception unit can also analyze the user's past feedback and select an optimal reception method. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit preferentially suggests a specific question format based on the user's past feedback. The reception unit analyzes the user's past feedback and selects the optimal reception method. In this way, a more optimal reception method can be provided by reflecting the user's past feedback.

[0041] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. The importance includes, for example, but is not limited to, the content of the question and the urgency of the user. The adjustment of the level of detail includes, for example, but is not limited to, the depth and scope of the analysis. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit can also perform a concise analysis on a question with a low level of importance. The analysis unit can also optimally allocate analysis resources according to the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit performs a concise analysis on a question with a low level of importance. The analysis resources are optimally allocated according to the importance of the question. By adjusting the level of detail of the analysis based on the importance of the question, more appropriate analysis is possible.

[0042] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. Examples of categories include, but are not limited to, technology categories and business categories. Examples of analysis algorithms include, but are not limited to, natural language processing algorithms and machine learning algorithms. For example, the analysis unit applies a career-specialized analysis algorithm to questions about careers. The analysis unit can also apply a family-issue-specialized analysis algorithm to questions about family. The analysis unit can also apply a health-specialized analysis algorithm to questions about health. For example, the analysis unit applies a career-specialized analysis algorithm to questions about careers. The analysis unit applies a family-issue-specialized analysis algorithm to questions about family. The analysis unit applies a health-specialized analysis algorithm to questions about health. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the question.

[0043] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question results. Past question results include, for example, past answer contents, user feedback, etc., but are not limited to these examples. The analysis unit improves the accuracy of the analysis, for example, based on the user's past question results. The analysis unit can also extract specific patterns from the user's past question results and reflect them in the analysis. The analysis unit can also analyze the user's past question results and select the optimal analysis method. For example, the analysis unit improves the accuracy of the analysis based on the user's past question results. The analysis unit extracts specific patterns from the user's past question results and reflects them in the analysis. The analysis unit analyzes the user's past question results and selects the optimal analysis method. In this way, the analysis accuracy is improved by referring to the user's past question results.

[0044] When analyzing a question, the analysis unit can determine the priority of analysis based on the time of submission of the question. The time of submission includes, for example, but is not limited to, the date and time of submission, the time period of submission, etc. The determination of the priority includes, for example, but is not limited to, the recency of the submission, the importance of the time period of submission, etc. The analysis unit can determine the priority of analysis based on, for example, the time period when the question was submitted. The analysis unit can also determine the priority of analysis based on the date when the question was submitted. The analysis unit can also determine the priority of analysis based on the timing when the question was submitted. For example, the analysis unit can determine the priority of analysis based on the time period when the question was submitted. The analysis unit can determine the priority of analysis based on the date when the question was submitted. The analysis unit can determine the priority of analysis based on the timing when the question was submitted. In this way, by determining the priority of analysis based on the time when the question was submitted, more appropriate analysis can be performed.

[0045] When analyzing questions, the analysis unit can adjust the order of analysis based on the relevance of the questions. Relevance includes, for example, similarity of question content, presence or absence of related topics, etc., but is not limited to these examples. Adjusting the order includes, for example, prioritizing highly relevant questions, etc., but is not limited to these examples. For example, the analysis unit prioritizes analysis of highly relevant questions. The analysis unit can also postpone analysis of less relevant questions. The analysis unit can also optimize the order of analysis based on the relevance of the questions. For example, the analysis unit prioritizes analysis of highly relevant questions. Questions with less relevance are postponed. The analysis order is optimized based on the relevance of the questions. In this way, adjusting the order of analysis based on the relevance of the questions enables more appropriate analysis.

[0046] When analyzing a question, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and the content of previous questions. Examples of the use of technical terms include, but are not limited to, the definitions and frequency of use of technical terms. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid technical terms when the user's level of expertise is low. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit avoids technical terms when the user's level of expertise is low. The use of technical terms in the analysis can be adjusted according to the user's level of expertise. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0047] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. Examples of the importance include, but are not limited to, the content of the question and the urgency of the user. Examples of the adjustment of the level of detail include, but are not limited to, the depth of the answer and the range of the answer. For example, the generation unit provides a detailed answer to a question of high importance. The generation unit can also provide a concise answer to a question of low importance. The generation unit can also optimally allocate answer resources according to the importance of the question. For example, the generation unit provides a detailed answer to a question of high importance. The generation unit provides a concise answer to a question of low importance. Answer resources are optimally allocated according to the importance of the question. In this way, by adjusting the level of detail of the answer based on the importance of the question, a more appropriate answer can be provided.

[0048] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. Examples of categories include, but are not limited to, technical categories and business categories. Examples of generation algorithms include, but are not limited to, natural language generation algorithms and machine learning algorithms. For example, the generation unit applies a career-specialized generation algorithm to a question about career. The generation unit can also apply a family-issue-specialized generation algorithm to a question about family. The generation unit can also apply a health-specialized generation algorithm to a question about health. For example, the generation unit applies a career-specialized generation algorithm to a question about career. The generation unit applies a family-issue-specialized generation algorithm to a question about family. The generation unit applies a health-specialized generation algorithm to a question about health. By applying different generation algorithms depending on the category of the question, more appropriate answers can be provided.

[0049] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. Past answer results include, for example, past answer content, user feedback, etc., but are not limited to these examples. The generation unit improves the accuracy of the answer, for example, based on the user's past answer results. The generation unit can also extract a specific pattern from the user's past answer results and reflect it in the answer. The generation unit can also analyze the user's past answer results and select an optimal answer method. For example, the generation unit improves the accuracy of the answer based on the user's past answer results. A specific pattern is extracted from the user's past answer results and reflected in the answer. The user's past answer results are analyzed and an optimal answer method is selected. In this way, the accuracy of the answer is improved by referring to the user's past answer results.

[0050] When generating answers, the generation unit can determine the priority of answers based on the time of submission of the question. The submission time includes, for example, but is not limited to, the date and time of submission, the time period of submission, etc. The priority determination includes, for example, but is not limited to, the recency of the submission time, the importance of the time period of submission, etc. The generation unit can determine the priority of answers based on, for example, the time period when the question was submitted. The generation unit can also determine the priority of answers based on the date when the question was submitted. The generation unit can also determine the priority of answers based on the timing when the question was submitted. For example, the generation unit can determine the priority of answers based on the time period when the question was submitted. The generation unit can determine the priority of answers based on the date when the question was submitted. The generation unit can determine the priority of answers based on the timing when the question was submitted. In this way, by determining the priority of answers based on the time when the question was submitted, more appropriate answers can be provided.

[0051] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. Relevance includes, for example, similarity of question content, presence or absence of related topics, etc., but is not limited to these examples. Adjusting the order includes, for example, prioritizing highly relevant questions, etc., but is not limited to these examples. For example, the generation unit prioritizes answers to questions with high relevance. The generation unit can also postpone questions with low relevance. The generation unit can also optimize the order of answers based on the relevance of the questions. For example, the generation unit prioritizes answers to questions with high relevance. Questions with low relevance are postponed. The order of answers is optimized based on the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, more appropriate answers can be provided.

[0052] When generating an answer, the generation unit can adjust the use of technical terms in the answer according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, the content of previous questions, etc. The use of technical terms includes, but are not limited to, the definition and frequency of use of technical terms, for example. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the generation unit can avoid using technical terms. Furthermore, the generation unit can adjust the use of technical terms in the answer according to the user's level of expertise. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terms. If the user's level of expertise is low, the generation unit avoids using technical terms. The use of technical terms in the answer is adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terms in the answer according to the user's level of expertise, a more appropriate answer can be provided.

[0053] The dialogue unit can analyze the user's reaction as the dialogue progresses and nod or ask a question at the optimal timing. The optimal timing includes, but is not limited to, the user's reaction speed, the flow of the dialogue, and the like. For example, the dialogue unit inserts a nod at an appropriate timing while the user is speaking. The dialogue unit can also ask an appropriate question after the user has finished speaking. The dialogue unit can also analyze the user's reaction and adjust the nod or question according to the flow of the dialogue. For example, the dialogue unit inserts a nod at an appropriate timing while the user is speaking. The dialogue unit asks an appropriate question after the user has finished speaking. The dialogue unit analyzes the user's reaction and adjusts the nod or question according to the flow of the dialogue. In this way, by analyzing the user's reaction, a more natural dialogue is possible.

[0054] The dialogue unit can improve the accuracy of the dialogue by referring to the user's past dialogue history as the dialogue progresses. The past dialogue history includes, for example, past dialogue content, user feedback, etc., but is not limited to these examples. The dialogue unit improves the accuracy of the dialogue, for example, based on the user's past dialogue history. The dialogue unit can also extract specific patterns from the user's past dialogue history and reflect them in the dialogue. The dialogue unit can also analyze the user's past dialogue history and select an optimal dialogue method. For example, the dialogue unit improves the accuracy of the dialogue based on the user's past dialogue history. The dialogue unit extracts specific patterns from the user's past dialogue history and reflects them in the dialogue. The dialogue unit analyzes the user's past dialogue history and selects an optimal dialogue method. In this way, the dialogue accuracy is improved by referring to the user's past dialogue history.

[0055] The dialogue unit can customize the content of the dialogue based on the user's current situation during the dialogue. The current situation includes, but is not limited to, for example, the user's location information, time of day, etc. For example, when the user inputs their current work situation, the dialogue unit provides dialogue content related to that situation. Furthermore, when the user inputs their current home situation, the dialogue unit can also provide dialogue content related to that situation. Furthermore, the dialogue unit can suggest optimal dialogue content based on the user's current situation (time of day, location, etc.). For example, when the user inputs their current work situation, the dialogue unit provides dialogue content related to that situation. When the user inputs their current home situation, the dialogue unit provides dialogue content related to that situation. The optimal dialogue content is suggested based on the user's current situation. This allows for more appropriate dialogue by customizing the content of the dialogue based on the user's current situation.

[0056] The dialogue unit may select an optimal dialogue method while the dialogue is in progress, taking into consideration the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, etc. Examples of the optimal dialogue method include, but are not limited to, customizing dialogue content based on the geographical location. For example, if the user is in a specific area, the dialogue unit may provide dialogue content related to the area. Furthermore, if the user is traveling, the dialogue unit may provide dialogue content related to travel. Furthermore, if the user is at home, the dialogue unit may provide dialogue content related to home. For example, if the user is in a specific area, the dialogue unit may provide dialogue content related to the area. If the user is traveling, the dialogue unit may provide dialogue content related to travel. If the user is at home, the dialogue unit may provide dialogue content related to home. This enables more appropriate dialogue by taking into consideration the user's geographical location information.

[0057] The dialogue unit may analyze the user's social media activity to suggest dialogue content as the dialogue progresses. Examples of social media activity include, but are not limited to, posted content and the number of likes. Examples of dialogue content include, but are not limited to, customization of dialogue content based on social media activity. For example, the dialogue unit may suggest related dialogue content based on content posted by the user on social media. The dialogue unit may also analyze the user's social media activity history to suggest related dialogue content. The dialogue unit may also refer to the activities of the user's friends on social media to suggest related dialogue content. For example, the dialogue unit may suggest related dialogue content based on content posted by the user on social media. The dialogue unit may analyze the user's social media activity history to suggest related dialogue content. The dialogue unit may also refer to the activities of the user's friends on social media to suggest related dialogue content. This enables more appropriate dialogue by analyzing the user's social media activity.

[0058] The dialogue unit can customize the dialogue method by reflecting the user's past feedback as the dialogue progresses. Examples of past feedback include, but are not limited to, the user's evaluation comments and survey results. Examples of dialogue methods include, but are not limited to, adjusting the dialogue content based on the past feedback. For example, the dialogue unit can suggest an optimal dialogue method based on feedback provided by the user in the past. The dialogue unit can also preferentially suggest a specific dialogue format based on the user's past feedback. The dialogue unit can also analyze the user's past feedback and select an optimal dialogue means. For example, the dialogue unit can suggest an optimal dialogue method based on feedback provided by the user in the past. The dialogue unit can preferentially suggest a specific dialogue format based on the user's past feedback. The dialogue unit can analyze the user's past feedback and select an optimal dialogue means. This enables a more appropriate dialogue by reflecting the user's past feedback.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The reception unit can analyze the content of a user's past consultations and provide answers that refer to the results of past consultations to users with similar consultations. For example, if a user has previously consulted about their career, the reception unit can analyze the content of that consultation and provide similar answers to other users with similar consultations. The reception unit can also extract specific patterns from the content of a user's past consultations and apply them to future consultations. For example, if a user has previously consulted about family problems, the reception unit can analyze the content of that consultation and provide appropriate answers to other users with similar problems. In this way, the reception unit can provide more appropriate answers by utilizing the content of a user's past consultations.

[0061] The generation unit can generate multiple answer options based on the content of the user's consultation and provide the user with choices. For example, in response to a career-related question, multiple career paths can be proposed, allowing the user to select the most appropriate one. In addition, in response to a question about a family problem, multiple solutions can be presented, allowing the user to select the most appropriate solution. Furthermore, the generation unit can also suggest the next step based on the user's selection. This allows the generation unit to provide the user with multiple options, allowing for more flexible responses.

[0062] The reception unit can prioritize receiving consultations specific to a region by taking into account the user's current geographical location information. For example, if the user is in a specific region, consultations about career or family issues related to that region can be prioritized. Also, if the user is traveling, consultations related to travel can be prioritized. Furthermore, if the user is at home, consultations related to family can be prioritized. In this way, the reception unit can prioritize receiving more relevant consultations by taking into account the user's geographical location information.

[0063] When analyzing the content of a user's consultation, the analysis unit can improve the accuracy of the analysis by referring to the user's past consultation history. For example, if the user has previously consulted about their career, the analysis unit can perform analysis based on that consultation history to provide a more appropriate answer. Also, if the user has previously consulted about family issues, the analysis unit can perform analysis based on that consultation history to provide a more appropriate answer. Furthermore, specific patterns can be extracted from the user's past consultation history and applied to future consultations. In this way, the analysis unit can improve the accuracy of the analysis by utilizing the user's past consultation history.

[0064] The dialogue unit can customize the way the dialogue proceeds by referring to the user's past dialogue history. For example, if the user has frequently consulted about a particular topic in the past, dialogue content related to that topic can be preferentially provided. Also, if the user has preferred a particular dialogue style in the past, that style can be preferentially used. Furthermore, specific patterns can be extracted from the user's past dialogue history and applied to future dialogues. In this way, the dialogue unit can customize the way the dialogue proceeds by utilizing the user's past dialogue history.

[0065] The reception unit can analyze the user's social media activity and prioritize receiving related consultation content. For example, if the user frequently posts about their career on social media, the reception unit can prioritize receiving consultations related to that content. Also, if the user posts about family problems on social media, the reception unit can prioritize receiving consultations related to that content. Furthermore, the reception unit can suggest related consultation content by taking into account the activities of the user's friends on social media. In this way, the reception unit can prioritize receiving more relevant consultation content by analyzing the user's social media activity.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives a question from a user. The question may be in text, voice, or image format. For example, a voice question may be received using a voice assistant device, a text question may be received using a mobile device, or an image question may be received using image analysis technology. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis is performed using natural language processing technology and machine learning algorithms. For example, it can convert voice questions into text and analyze the content of the question, analyze the intent of text questions, or analyze the content of image questions. Step 3: The generator generates an answer based on the question analyzed by the analyzer. The answer may be generated in text, audio, image, or other formats. For example, the generator may generate answers based on the words of great people or famous books, or based on data contained in a database, or it may be equipped with an algorithm that actively asks questions in response to the user's questions. Step 4: The dialogue unit advances the dialogue based on the answers generated by the generation unit. The dialogue progresses by analyzing the user's reactions and nodding or asking questions at the appropriate times. For example, the dialogue unit can analyze the user's facial expressions and tone of voice to nod at the appropriate times, ask appropriate questions based on the user's reactions, and analyze the user's reactions in real time to adjust the progress of the dialogue.

[0068] (Example 2) An online consultation system according to an embodiment of the present invention allows users to easily seek online advice about career, family, and other concerns. This online consultation system begins when a user speaks to a mobile phone or voice assistant device. For example, the user may ask, "I've been having trouble at work lately. What should I do?" This question is then input into an AI. The AI ​​then analyzes the input question and generates an answer based on the words of great figures and famous books. For example, the AI ​​may generate an answer such as, "Great figure A said, 'Difficulties are opportunities for growth.' You too can grow by overcoming these difficulties." Furthermore, the AI ​​adds nods and proactive questions to the answer to further the dialogue. For example, it may ask, "What specific difficulties are you facing?" This allows the user to delve deeper into their concerns and receive more specific advice. This system not only allows users to easily seek advice, but also allows them to receive highly specialized advice. For example, for career concerns, users can receive specific advice based on the words of great figures and famous books. Similarly, for family concerns, users can receive highly specialized advice. This service allows users to ask questions not only by asking questions but also by having the AI ​​proactively ask questions, resulting in a smooth dialogue. For example, by asking questions such as "What problems have you been having at home recently?", users can more easily organize their concerns. This makes the online consultation system a useful tool for resolving career and family concerns faced by office workers in their 30s to 50s. The online consultation system is extremely convenient, as users can receive highly specialized advice simply by speaking to their mobile phone or voice assistant device.

[0069] The online consultation system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a dialogue unit. The reception unit receives a question from a user. The question may be in a text format, a voice format, an image format, or the like, but is not limited to these examples. The reception unit may receive a voice question using a voice assistant device, for example. The reception unit may also receive a text question using a mobile device. The reception unit may also receive an image question using image analysis technology. For example, the reception unit may receive a voice question from a user in real time using a voice assistant device. The reception unit may also instantly receive a text question entered by a user using a mobile device. The reception unit may analyze an image question sent by a user using image analysis technology and extract the content of the question. The analysis unit analyzes the question received by the reception unit. The analysis may be performed using, for example, natural language processing technology or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may convert a voice question into text using natural language processing technology and analyze the content of the question. The analysis unit may also analyze the intent of the text question using a machine learning algorithm. The analysis unit can also analyze the content of the image question using image analysis technology. For example, the analysis unit converts the voice question into text and analyzes the text. The analysis unit uses a machine learning algorithm to analyze the intent of the text question and generate an appropriate answer. The image analysis technology is used to analyze the content of the image question and identify the intent of the question. The generation unit generates an answer based on the question analyzed by the analysis unit. The answer may be generated in, for example, a text format, a voice format, or an image format, but is not limited to these examples. The generation unit generates an answer based on, for example, the words of great people of the past or famous books. The generation unit can also generate an answer based on data of great people of the past or famous books contained in a database. The generation unit can also be equipped with an algorithm that actively asks questions in response to the user's question. For example, the generation unit generates an answer based on the words of great people of the past. The generation unit generates an answer based on data of famous books contained in a database. The generation unit actively asks questions in response to the user's question and progresses the dialogue. The dialogue unit progresses the dialogue based on the answer generated by the generation unit.The dialogue progresses, for example, by analyzing the user's reaction and nodding or asking questions at appropriate times, but is not limited to such examples. For example, the dialogue unit analyzes the user's facial expression and tone of voice and nods at appropriate times. The dialogue unit can also ask appropriate questions based on the user's reaction. The dialogue unit can also analyze the user's reaction in real time and adjust the progress of the dialogue. For example, the dialogue unit analyzes the user's facial expression and nods at appropriate times. The dialogue unit analyzes the user's tone of voice and asks appropriate questions. The dialogue unit analyzes the user's reaction in real time and adjusts the progress of the dialogue. This enables the online consultation system according to the embodiment to efficiently receive and analyze user questions, generate answers, and progress the dialogue.

[0070] The generation unit can generate an answer based on a specific data source. Specific data sources include, for example, sayings of great people from the past and famous books, but are not limited to these examples. For example, the generation unit obtains sayings of great people from the past from a database and generates an answer based on them. The generation unit can also generate an answer based on data from famous books. The generation unit can also update the data source in real time and generate an answer based on the latest information. For example, the generation unit obtains sayings of great people from the past from a database and generates an answer based on them. An answer is generated based on data from famous books. The data source is updated in real time and generates an answer based on the latest information. In this way, highly specialized answers are generated by being based on sayings of great people from the past and famous books.

[0071] The dialogue unit can analyze the user's reaction and nod or ask a question under specific conditions. Specific conditions include, but are not limited to, the user's facial expression and voice tone, for example. The dialogue unit can analyze the user's facial expression and nod at an appropriate timing. The dialogue unit can also analyze the user's voice tone and ask an appropriate question. The dialogue unit can also analyze the user's reaction in real time and adjust the progress of the dialogue. For example, the dialogue unit can analyze the user's facial expression and nod at an appropriate timing. The dialogue unit can analyze the user's voice tone and ask an appropriate question. The dialogue unit can analyze the user's reaction in real time and adjust the progress of the dialogue. This allows the dialogue to proceed based on the user's reaction, making it possible to have a more natural dialogue.

[0072] The analysis unit may be provided with an algorithm for analyzing a user's question and generating an appropriate answer. Examples of algorithms include, but are not limited to, natural language processing algorithms and machine learning algorithms. For example, the analysis unit may use a natural language processing algorithm to analyze a user's question and generate an appropriate answer. The analysis unit may also use a machine learning algorithm to analyze the intent of the user's question and generate an appropriate answer. The analysis unit may also combine multiple algorithms to perform more accurate analysis. For example, the analysis unit may use a natural language processing algorithm to analyze a user's question and generate an appropriate answer. The analysis unit may use a machine learning algorithm to analyze the intent of the user's question and generate an appropriate answer. The analysis unit may combine multiple algorithms to perform more accurate analysis. As a result, by providing an algorithm for analyzing a user's question, an appropriate answer can be generated.

[0073] The generation unit can generate answers based on specific data sources contained in the database. Specific data sources include, but are not limited to, for example, quotes from great people of the past and famous books contained in the database. For example, the generation unit obtains quotes from great people of the past from the database and generates answers based on the quotes. The generation unit can also generate answers based on data from famous books contained in the database. The generation unit can also update the database in real time and generate answers based on the latest information. For example, the generation unit obtains quotes from great people of the past from the database and generates answers based on the quotes. An answer is generated based on data from famous books. The database is updated in real time and answers are generated based on the latest information. In this way, by using the database as the basis, more diverse answers can be generated.

[0074] The dialogue unit may include an algorithm that actively asks questions in response to a user's question. Examples of the algorithm include, but are not limited to, a dialogue progression algorithm and a question generation algorithm. For example, the dialogue unit may actively ask questions in response to a user's question using a dialogue progression algorithm. The dialogue unit may also generate appropriate questions in response to a user's question using a question generation algorithm. The dialogue unit may also include an algorithm that analyzes a user's response and adjusts the progress of the dialogue. For example, the dialogue unit may actively ask questions in response to a user's question using a dialogue progression algorithm, or may generate appropriate questions in response to a user's question using a question generation algorithm, or may include an algorithm that analyzes a user's response and adjusts the progress of the dialogue. As a result, by actively asking questions, the dialogue progresses smoothly.

[0075] The reception unit can estimate the user's emotions and adjust the timing of receiving questions based on the estimated user emotions. Estimation of emotions includes, but is not limited to, facial expression analysis and voice analysis. For example, the reception unit analyzes the user's facial expression to estimate the emotion. The reception unit can also analyze the user's voice to estimate the emotion. The reception unit can also adjust the timing of receiving questions based on the estimated emotion. For example, if the user is feeling stressed, the reception unit can immediately receive the question and respond quickly. If the user is relaxed, the reception unit can take a little more time to receive the question and elicit more detailed information. If the user is in a hurry, the reception unit can receive a concise question and provide a quick answer. By adjusting the timing of receiving questions based on the user's emotions, the questions can be received at a more appropriate time.

[0076] The reception unit can analyze the user's past question history and select the optimal reception method. The optimal reception method includes, for example, a method based on the user's past question history, but is not limited to such an example. For example, the reception unit preferentially receives topics that the user has frequently asked about in the past. The reception unit can also preferentially suggest question formats (audio, text, etc.) that the user has used in the past. The reception unit can also suggest the optimal reception method for a specific time period based on the user's past question history. For example, the reception unit preferentially receives topics that the user has frequently asked about in the past. The reception unit preferentially suggests question formats that the user has used in the past. The reception unit suggests the optimal reception method for a specific time period based on the user's past question history. In this way, the optimal reception method can be provided to the user by analyzing the past question history.

[0077] When receiving a question, the reception unit can filter the questions based on the user's current situation and areas of interest. Examples of the current situation include, but are not limited to, the user's location information and time period. Examples of areas of interest include, but are not limited to, the content of past questions and the user's profile information. For example, when the user inputs their current work situation, the reception unit prioritizes receiving questions related to that situation. The reception unit can also filter questions based on the user's areas of interest (career, family, etc.). The reception unit can also suggest optimal questions based on the user's current situation (time period, location, etc.). For example, when the user inputs their current work situation, the reception unit prioritizes receiving questions related to that situation. The reception unit filters questions based on the user's areas of interest. The reception unit suggests optimal questions based on the user's current situation. By filtering questions based on the user's current situation and areas of interest, more relevant questions can be received.

[0078] When accepting a question, the acceptance unit can select an optimal acceptance means depending on the user's input method. Examples of input methods include, but are not limited to, voice input, text input, and image input. Examples of optimal acceptance means include, but are not limited to, voice recognition technology and text analysis technology. For example, when a user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. Furthermore, when a user inputs a question by text, the acceptance unit can accept the question using text analysis technology. Furthermore, when a user inputs a question using an image, the acceptance unit can accept the question using image analysis technology. For example, when a user inputs a question by voice, the acceptance unit accepts the question using voice recognition technology. When a user inputs a question by text, the acceptance unit accepts the question using text analysis technology. When a user inputs a question using an image, the acceptance unit accepts the question using image analysis technology. This allows for smoother question acceptance by selecting an optimal acceptance means depending on the user's input method.

[0079] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis and voice analysis. Examples of determining the priority include, but are not limited to, the intensity of emotions and the urgency of questions. For example, when the user is stressed, the reception unit can prioritize receiving urgent questions. Furthermore, when the user is relaxed, the reception unit can prioritize receiving detailed questions. Furthermore, when the user is in a hurry, the reception unit can prioritize receiving brief questions. For example, when the user is stressed, the reception unit prioritizes receiving urgent questions. When the user is relaxed, the reception unit prioritizes receiving detailed questions. When the user is in a hurry, the reception unit prioritizes receiving brief questions. In this way, by determining the priority of questions according to the user's emotions, more appropriate questions can be received preferentially.

[0080] When accepting a question, the acceptance unit can prioritize accepting highly relevant questions by taking into account the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, IP address, etc. For example, when the user is in a specific area, the acceptance unit can prioritize accepting questions related to the area. Furthermore, when the user is traveling, the acceptance unit can also prioritize accepting questions related to the travel. Furthermore, when the user is at home, the acceptance unit can also prioritize accepting questions related to the home. For example, when the user is in a specific area, the acceptance unit prioritizes accepting questions related to the area. When the user is traveling, the acceptance unit prioritizes accepting questions related to the travel. When the user is at home, the acceptance unit prioritizes accepting questions related to the home. In this way, by taking into account the user's geographical location information, it is possible to prioritize accepting more relevant questions.

[0081] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. Social media activity includes, for example, the content of posts, the number of likes, and the like, but is not limited to these examples. The reception unit, for example, receives related questions based on the content posted by the user on social media. The reception unit can also analyze the user's social media activity history and suggest related questions. The reception unit can also receive related questions by referring to the activities of the user's friends on social media. For example, the reception unit receives related questions based on the content posted by the user on social media. The reception unit analyzes the user's social media activity history and suggests related questions. The reception unit receives related questions by referring to the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to receive more relevant questions.

[0082] When receiving a question, the reception unit can customize the reception method by reflecting the user's past feedback. Past feedback includes, for example, the user's evaluation comments, survey results, etc., but is not limited to these examples. The reception unit, for example, suggests an optimal reception method based on feedback provided by the user in the past. The reception unit can also preferentially suggest a specific question format based on the user's past feedback. The reception unit can also analyze the user's past feedback and select an optimal reception method. For example, the reception unit suggests an optimal reception method based on feedback provided by the user in the past. The reception unit preferentially suggests a specific question format based on the user's past feedback. The reception unit analyzes the user's past feedback and selects the optimal reception method. In this way, a more optimal reception method can be provided by reflecting the user's past feedback.

[0083] The analysis unit can estimate the user's emotions and adjust the question analysis method based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis and voice analysis. Examples of adjusting the analysis method include, but are not limited to, selecting an analysis algorithm according to the emotions. For example, when the user is stressed, the analysis unit can quickly perform an analysis and provide a concise answer. Furthermore, when the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insight. Furthermore, when the user is in a hurry, the analysis unit can perform a concise and to-the-point analysis. For example, when the user is stressed, the analysis unit can quickly perform an analysis and provide a concise answer. When the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insight. When the user is in a hurry, the analysis unit can perform a concise and to-the-point analysis. This allows for more appropriate analysis by adjusting the question analysis method according to the user's emotions.

[0084] When analyzing a question, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. The importance includes, for example, but is not limited to, the content of the question and the urgency of the user. The adjustment of the level of detail includes, for example, but is not limited to, the depth and scope of the analysis. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit can also perform a concise analysis on a question with a low level of importance. The analysis unit can also optimally allocate analysis resources according to the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit performs a concise analysis on a question with a low level of importance. The analysis resources are optimally allocated according to the importance of the question. By adjusting the level of detail of the analysis based on the importance of the question, more appropriate analysis is possible.

[0085] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. Examples of categories include, but are not limited to, technology categories and business categories. Examples of analysis algorithms include, but are not limited to, natural language processing algorithms and machine learning algorithms. For example, the analysis unit applies a career-specialized analysis algorithm to questions about careers. The analysis unit can also apply a family-issue-specialized analysis algorithm to questions about family. The analysis unit can also apply a health-specialized analysis algorithm to questions about health. For example, the analysis unit applies a career-specialized analysis algorithm to questions about careers. The analysis unit applies a family-issue-specialized analysis algorithm to questions about family. The analysis unit applies a health-specialized analysis algorithm to questions about health. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the question.

[0086] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question results. Past question results include, for example, past answer contents, user feedback, etc., but are not limited to these examples. The analysis unit improves the accuracy of the analysis, for example, based on the user's past question results. The analysis unit can also extract specific patterns from the user's past question results and reflect them in the analysis. The analysis unit can also analyze the user's past question results and select the optimal analysis method. For example, the analysis unit improves the accuracy of the analysis based on the user's past question results. The analysis unit extracts specific patterns from the user's past question results and reflects them in the analysis. The analysis unit analyzes the user's past question results and selects the optimal analysis method. In this way, the analysis accuracy is improved by referring to the user's past question results.

[0087] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis and voice analysis. Examples of determining priorities include, but are not limited to, the intensity of emotions and the urgency of questions. For example, if the user is stressed, the analysis unit prioritizes analyzing urgent questions. Furthermore, if the user is relaxed, the analysis unit can prioritize analyzing detailed questions. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing concise questions. For example, if the user is stressed, the analysis unit prioritizes analyzing urgent questions. If the user is relaxed, the analysis unit prioritizes analyzing detailed questions. If the user is in a hurry, the analysis unit prioritizes analyzing concise questions. This enables more appropriate analysis by determining analysis priorities according to the user's emotions.

[0088] When analyzing a question, the analysis unit can determine the priority of analysis based on the time of submission of the question. The time of submission includes, for example, but is not limited to, the date and time of submission, the time period of submission, etc. The determination of the priority includes, for example, but is not limited to, the recency of the submission, the importance of the time period of submission, etc. The analysis unit can determine the priority of analysis based on, for example, the time period when the question was submitted. The analysis unit can also determine the priority of analysis based on the date when the question was submitted. The analysis unit can also determine the priority of analysis based on the timing when the question was submitted. For example, the analysis unit can determine the priority of analysis based on the time period when the question was submitted. The analysis unit can determine the priority of analysis based on the date when the question was submitted. The analysis unit can determine the priority of analysis based on the timing when the question was submitted. In this way, by determining the priority of analysis based on the time when the question was submitted, more appropriate analysis can be performed.

[0089] When analyzing questions, the analysis unit can adjust the order of analysis based on the relevance of the questions. Relevance includes, for example, similarity of question content, presence or absence of related topics, etc., but is not limited to these examples. Adjusting the order includes, for example, prioritizing highly relevant questions, etc., but is not limited to these examples. For example, the analysis unit prioritizes analysis of highly relevant questions. The analysis unit can also postpone analysis of less relevant questions. The analysis unit can also optimize the order of analysis based on the relevance of the questions. For example, the analysis unit prioritizes analysis of highly relevant questions. Questions with less relevance are postponed. The analysis order is optimized based on the relevance of the questions. In this way, adjusting the order of analysis based on the relevance of the questions enables more appropriate analysis.

[0090] When analyzing a question, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation and the content of previous questions. Examples of the use of technical terms include, but are not limited to, the definitions and frequency of use of technical terms. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit can also avoid technical terms when the user's level of expertise is low. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit uses a lot of technical terms when the user's level of expertise is high. The analysis unit avoids technical terms when the user's level of expertise is low. The use of technical terms in the analysis can be adjusted according to the user's level of expertise. This allows for more appropriate analysis by adjusting the use of technical terms in the analysis according to the user's level of expertise.

[0091] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated user's emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis and voice analysis. Examples of expression adjustment include, but are not limited to, wording and tone of expression according to the emotion. For example, if the user is stressed, the generation unit uses a concise and reassuring expression. Also, if the user is relaxed, the generation unit can use a detailed and friendly expression. Also, if the user is in a hurry, the generation unit can use a concise and to-the-point expression. For example, if the user is stressed, the generation unit uses a concise and reassuring expression. If the user is relaxed, the generation unit uses a detailed and friendly expression. If the user is in a hurry, the generation unit uses a concise and to-the-point expression. In this way, by adjusting the way the answer is expressed according to the user's emotions, a more appropriate answer can be provided.

[0092] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. Examples of the importance include, but are not limited to, the content of the question and the urgency of the user. Examples of the adjustment of the level of detail include, but are not limited to, the depth of the answer and the range of the answer. For example, the generation unit provides a detailed answer to a question of high importance. The generation unit can also provide a concise answer to a question of low importance. The generation unit can also optimally allocate answer resources according to the importance of the question. For example, the generation unit provides a detailed answer to a question of high importance. The generation unit provides a concise answer to a question of low importance. Answer resources are optimally allocated according to the importance of the question. In this way, by adjusting the level of detail of the answer based on the importance of the question, a more appropriate answer can be provided.

[0093] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. Examples of categories include, but are not limited to, technical categories and business categories. Examples of generation algorithms include, but are not limited to, natural language generation algorithms and machine learning algorithms. For example, the generation unit applies a career-specialized generation algorithm to a question about career. The generation unit can also apply a family-issue-specialized generation algorithm to a question about family. The generation unit can also apply a health-specialized generation algorithm to a question about health. For example, the generation unit applies a career-specialized generation algorithm to a question about career. The generation unit applies a family-issue-specialized generation algorithm to a question about family. The generation unit applies a health-specialized generation algorithm to a question about health. By applying different generation algorithms depending on the category of the question, more appropriate answers can be provided.

[0094] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. Past answer results include, for example, past answer content, user feedback, etc., but are not limited to these examples. The generation unit improves the accuracy of the answer, for example, based on the user's past answer results. The generation unit can also extract a specific pattern from the user's past answer results and reflect it in the answer. The generation unit can also analyze the user's past answer results and select an optimal answer method. For example, the generation unit improves the accuracy of the answer based on the user's past answer results. A specific pattern is extracted from the user's past answer results and reflected in the answer. The user's past answer results are analyzed and an optimal answer method is selected. In this way, the accuracy of the answer is improved by referring to the user's past answer results.

[0095] The generation unit can estimate the user's emotion and adjust the length of the answer based on the estimated user's emotion. Examples of emotion estimation include, but are not limited to, facial expression analysis and voice analysis. Examples of answer length adjustment include, but are not limited to, shortening or detailing the answer depending on the emotion. For example, if the user is stressed, the generation unit provides a short and to-the-point answer. Also, if the user is relaxed, the generation unit can provide a longer answer with detailed explanation. Also, if the user is in a hurry, the generation unit can provide a concise and quick answer. For example, if the user is stressed, the generation unit provides a short and to-the-point answer. If the user is relaxed, the generation unit provides a longer answer with detailed explanation. If the user is in a hurry, the generation unit provides a concise and quick answer. In this way, by adjusting the length of the answer according to the user's emotion, a more appropriate answer can be provided.

[0096] When generating answers, the generation unit can determine the priority of answers based on the time of submission of the question. The submission time includes, for example, but is not limited to, the date and time of submission, the time period of submission, etc. The priority determination includes, for example, but is not limited to, the recency of the submission time, the importance of the time period of submission, etc. The generation unit can determine the priority of answers based on, for example, the time period when the question was submitted. The generation unit can also determine the priority of answers based on the date when the question was submitted. The generation unit can also determine the priority of answers based on the timing when the question was submitted. For example, the generation unit can determine the priority of answers based on the time period when the question was submitted. The generation unit can determine the priority of answers based on the date when the question was submitted. The generation unit can determine the priority of answers based on the timing when the question was submitted. In this way, by determining the priority of answers based on the time when the question was submitted, more appropriate answers can be provided.

[0097] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. Relevance includes, for example, similarity of question content, presence or absence of related topics, etc., but is not limited to these examples. Adjusting the order includes, for example, prioritizing highly relevant questions, etc., but is not limited to these examples. For example, the generation unit prioritizes answers to questions with high relevance. The generation unit can also postpone questions with low relevance. The generation unit can also optimize the order of answers based on the relevance of the questions. For example, the generation unit prioritizes answers to questions with high relevance. Questions with low relevance are postponed. The order of answers is optimized based on the relevance of the questions. In this way, by adjusting the order of answers based on the relevance of the questions, more appropriate answers can be provided.

[0098] When generating an answer, the generation unit can adjust the use of technical terms in the answer according to the user's level of expertise. Examples of the level of expertise include, but are not limited to, the user's occupation, the content of previous questions, etc. The use of technical terms includes, but are not limited to, the definition and frequency of use of technical terms, for example. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terms. Furthermore, if the user's level of expertise is low, the generation unit can avoid using technical terms. Furthermore, the generation unit can adjust the use of technical terms in the answer according to the user's level of expertise. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terms. If the user's level of expertise is low, the generation unit avoids using technical terms. The use of technical terms in the answer is adjusted according to the user's level of expertise. In this way, by adjusting the use of technical terms in the answer according to the user's level of expertise, a more appropriate answer can be provided.

[0099] The dialogue unit can estimate the user's emotions and adjust the dialogue progression method based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis and voice analysis. Examples of dialogue progression adjustment include, but are not limited to, adjusting the dialogue progression speed and content according to the user's emotions. For example, if the user is stressed, the dialogue unit can proceed quickly to provide a sense of security. Furthermore, if the user is relaxed, the dialogue unit can proceed slowly to elicit detailed information. Furthermore, if the user is in a hurry, the dialogue unit can proceed briefly to provide a quick response. For example, if the user is stressed, the dialogue unit can proceed quickly to provide a sense of security. If the user is relaxed, the dialogue unit can proceed slowly to elicit detailed information. If the user is in a hurry, the dialogue unit can proceed briefly to provide a quick response. This allows for more appropriate dialogue by adjusting the dialogue progression method according to the user's emotions.

[0100] The dialogue unit can analyze the user's reaction as the dialogue progresses and nod or ask a question at the optimal timing. The optimal timing includes, but is not limited to, the user's reaction speed, the flow of the dialogue, and the like. For example, the dialogue unit inserts a nod at an appropriate timing while the user is speaking. The dialogue unit can also ask an appropriate question after the user has finished speaking. The dialogue unit can also analyze the user's reaction and adjust the nod or question according to the flow of the dialogue. For example, the dialogue unit inserts a nod at an appropriate timing while the user is speaking. The dialogue unit asks an appropriate question after the user has finished speaking. The dialogue unit analyzes the user's reaction and adjusts the nod or question according to the flow of the dialogue. In this way, by analyzing the user's reaction, a more natural dialogue is possible.

[0101] The dialogue unit can improve the accuracy of the dialogue by referring to the user's past dialogue history as the dialogue progresses. The past dialogue history includes, for example, past dialogue content, user feedback, etc., but is not limited to these examples. The dialogue unit improves the accuracy of the dialogue, for example, based on the user's past dialogue history. The dialogue unit can also extract specific patterns from the user's past dialogue history and reflect them in the dialogue. The dialogue unit can also analyze the user's past dialogue history and select an optimal dialogue method. For example, the dialogue unit improves the accuracy of the dialogue based on the user's past dialogue history. The dialogue unit extracts specific patterns from the user's past dialogue history and reflects them in the dialogue. The dialogue unit analyzes the user's past dialogue history and selects an optimal dialogue method. In this way, the dialogue accuracy is improved by referring to the user's past dialogue history.

[0102] The dialogue unit can customize the content of the dialogue based on the user's current situation during the dialogue. The current situation includes, but is not limited to, for example, the user's location information, time of day, etc. For example, when the user inputs their current work situation, the dialogue unit provides dialogue content related to that situation. Furthermore, when the user inputs their current home situation, the dialogue unit can also provide dialogue content related to that situation. Furthermore, the dialogue unit can suggest optimal dialogue content based on the user's current situation (time of day, location, etc.). For example, when the user inputs their current work situation, the dialogue unit provides dialogue content related to that situation. When the user inputs their current home situation, the dialogue unit provides dialogue content related to that situation. The optimal dialogue content is suggested based on the user's current situation. This allows for more appropriate dialogue by customizing the content of the dialogue based on the user's current situation.

[0103] The dialogue unit can estimate the user's emotions and determine the priority of dialogues based on the estimated user emotions. Examples of emotion estimation include, but are not limited to, facial expression analysis and voice analysis. Examples of priority determination include, but are not limited to, the intensity of emotions and the urgency of dialogue. For example, if the user is stressed, the dialogue unit can prioritize dialogues with a high level of urgency. Furthermore, if the user is relaxed, the dialogue unit can prioritize dialogues with a high level of detail. Furthermore, if the user is in a hurry, the dialogue unit can prioritize dialogues with a high level of urgency. For example, if the user is stressed, the dialogue unit can prioritize dialogues with a high level of urgency. If the user is relaxed, the dialogue unit can prioritize dialogues with a high level of detail. If the user is in a hurry, the dialogue unit can prioritize dialogues with a high level of detail. In this way, by determining the priority of dialogues according to the user's emotions, more appropriate dialogues can be achieved.

[0104] The dialogue unit may select an optimal dialogue method while the dialogue is in progress, taking into consideration the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data, IP address, etc. Examples of the optimal dialogue method include, but are not limited to, customizing dialogue content based on the geographical location. For example, if the user is in a specific area, the dialogue unit may provide dialogue content related to the area. Furthermore, if the user is traveling, the dialogue unit may provide dialogue content related to travel. Furthermore, if the user is at home, the dialogue unit may provide dialogue content related to home. For example, if the user is in a specific area, the dialogue unit may provide dialogue content related to the area. If the user is traveling, the dialogue unit may provide dialogue content related to travel. If the user is at home, the dialogue unit may provide dialogue content related to home. This enables more appropriate dialogue by taking into consideration the user's geographical location information.

[0105] The dialogue unit may analyze the user's social media activity to suggest dialogue content as the dialogue progresses. Examples of social media activity include, but are not limited to, posted content and the number of likes. Examples of dialogue content include, but are not limited to, customization of dialogue content based on social media activity. For example, the dialogue unit may suggest related dialogue content based on content posted by the user on social media. The dialogue unit may also analyze the user's social media activity history to suggest related dialogue content. The dialogue unit may also refer to the activities of the user's friends on social media to suggest related dialogue content. For example, the dialogue unit may suggest related dialogue content based on content posted by the user on social media. The dialogue unit may analyze the user's social media activity history to suggest related dialogue content. The dialogue unit may also refer to the activities of the user's friends on social media to suggest related dialogue content. This enables more appropriate dialogue by analyzing the user's social media activity.

[0106] The dialogue unit can customize the dialogue method by reflecting the user's past feedback as the dialogue progresses. Examples of past feedback include, but are not limited to, the user's evaluation comments and survey results. Examples of dialogue methods include, but are not limited to, adjusting the dialogue content based on the past feedback. For example, the dialogue unit can suggest an optimal dialogue method based on feedback provided by the user in the past. The dialogue unit can also preferentially suggest a specific dialogue format based on the user's past feedback. The dialogue unit can also analyze the user's past feedback and select an optimal dialogue means. For example, the dialogue unit can suggest an optimal dialogue method based on feedback provided by the user in the past. The dialogue unit can preferentially suggest a specific dialogue format based on the user's past feedback. The dialogue unit can analyze the user's past feedback and select an optimal dialogue means. This enables a more appropriate dialogue by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, and dialogue unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive a user's voice question in real time using the reception device 38 or microphone 38B of the smart device 14. The reception unit can also receive a text-based question via the communication I / F 26 of the data processing device 12. The analysis unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, analyzes the question content using natural language processing technology or a machine learning algorithm. The generation unit, implemented, for example, by the specific processing unit 290 of the data processing device 12, generates an answer based on the words of great people or famous books. The dialogue unit, implemented, for example, by the control unit 46A of the smart device 14, analyzes the user's reaction and nods or asks questions at the appropriate time. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, and dialogue 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 can receive a user's voice question in real time using the microphone 238 of the smart glasses 214. The reception unit can also receive a text-format question via the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question content using natural language processing technology or a machine learning algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the words of great people of the past or famous books. The dialogue unit is realized, for example, by the control unit 46A of the smart glasses 214 and analyzes the user's reaction and nods or asks questions at the appropriate time. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, and dialogue unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive a user's voice question in real time using the microphone 238 of the headset-type terminal 314. The reception unit can also receive a text-format question via the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question content using natural language processing technology or a machine learning algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the words of great people of the past or famous books. The dialogue unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and analyzes the user's reaction and nods or asks questions at the appropriate time. === Hard Collateral 1-4 === Each of the multiple elements, including the reception unit, analysis unit, generation unit, and dialogue 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 can receive a user's voice question in real time using the microphone 238 of the robot 414. The reception unit can also receive a text-based question via the communication I / F 26 of the data processing device 12. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the question content using natural language processing technology or a machine learning algorithm. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an answer based on the words of great people of the past or famous books. The dialogue unit is realized, for example, by the control unit 46A of the robot 414 and analyzes the user's reaction and nods or asks questions at the appropriate time.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the content of a user's past consultations and provide answers that refer to the results of past consultations to users with similar consultations. For example, if a user has previously consulted about their career, the reception unit can analyze the content of that consultation and provide similar answers to other users with similar consultations. The reception unit can also extract specific patterns from the content of a user's past consultations and apply them to future consultations. For example, if a user has previously consulted about family problems, the reception unit can analyze the content of that consultation and provide appropriate answers to other users with similar problems. In this way, the reception unit can provide more appropriate answers by utilizing the content of a user's past consultations.

[0109] When analyzing the content of a user's consultation, the analysis unit can improve the accuracy of the analysis by taking into account the user's current emotional state. For example, if the user is feeling stressed, the analysis unit can perform a quick analysis and provide a concise answer. If the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insight. Furthermore, if the user is in a hurry, the analysis unit can perform a concise, to-the-point analysis. This allows the analysis unit to adjust its analysis method according to the user's emotional state, enabling more appropriate analysis.

[0110] The generation unit can generate multiple answer options based on the content of the user's consultation and provide the user with choices. For example, in response to a career-related question, multiple career paths can be proposed, allowing the user to select the most appropriate one. In addition, in response to a question about a family problem, multiple solutions can be presented, allowing the user to select the most appropriate solution. Furthermore, the generation unit can also suggest the next step based on the user's selection. This allows the generation unit to provide the user with multiple options, allowing for more flexible responses.

[0111] The dialogue unit can estimate the user's emotions and adjust the tone and content of the dialogue based on the estimated emotions. For example, if the user is sad, the dialogue unit can speak to the user in a gentle tone and provide comforting content. If the user is excited, the dialogue unit can speak to the user in a calm tone and provide calming content. Furthermore, if the user is feeling anxious, the dialogue unit can provide reassuring content. This allows the dialogue unit to adjust the tone and content of the dialogue according to the user's emotions, enabling more appropriate dialogue.

[0112] The reception unit can prioritize receiving consultations specific to a region by taking into account the user's current geographical location information. For example, if the user is in a specific region, consultations about career or family issues related to that region can be prioritized. Also, if the user is traveling, consultations related to travel can be prioritized. Furthermore, if the user is at home, consultations related to family can be prioritized. In this way, the reception unit can prioritize receiving more relevant consultations by taking into account the user's geographical location information.

[0113] When analyzing the content of a user's consultation, the analysis unit can improve the accuracy of the analysis by referring to the user's past consultation history. For example, if the user has previously consulted about their career, the analysis unit can perform analysis based on that consultation history to provide a more appropriate answer. Also, if the user has previously consulted about family issues, the analysis unit can perform analysis based on that consultation history to provide a more appropriate answer. Furthermore, specific patterns can be extracted from the user's past consultation history and applied to future consultations. In this way, the analysis unit can improve the accuracy of the analysis by utilizing the user's past consultation history.

[0114] The generation unit can estimate the user's emotions and adjust the way the answer is expressed based on the estimated emotions. For example, if the user is feeling stressed, a concise and reassuring way of expression can be used. If the user is relaxed, a detailed and friendly way of expression can be used. Furthermore, if the user is in a hurry, a concise way of expression that gets straight to the point can be used. In this way, the generation unit can provide a more appropriate answer by adjusting the way the answer is expressed based on the user's emotions.

[0115] The dialogue unit can customize the way the dialogue proceeds by referring to the user's past dialogue history. For example, if the user has frequently consulted about a particular topic in the past, dialogue content related to that topic can be preferentially provided. Also, if the user has preferred a particular dialogue style in the past, that style can be preferentially used. Furthermore, specific patterns can be extracted from the user's past dialogue history and applied to future dialogues. In this way, the dialogue unit can customize the way the dialogue proceeds by utilizing the user's past dialogue history.

[0116] The reception unit can analyze the user's social media activity and prioritize receiving related consultation content. For example, if the user frequently posts about their career on social media, the reception unit can prioritize receiving consultations related to that content. Also, if the user posts about family problems on social media, the reception unit can prioritize receiving consultations related to that content. Furthermore, the reception unit can suggest related consultation content by taking into account the activities of the user's friends on social media. In this way, the reception unit can prioritize receiving more relevant consultation content by analyzing the user's social media activity.

[0117] The dialogue unit can estimate the user's emotions and determine the priority of dialogues based on the estimated emotions. For example, if the user is feeling stressed, a dialogue with a high urgency can be prioritized. Also, if the user is relaxed, a detailed dialogue can be prioritized. Furthermore, if the user is in a hurry, a brief dialogue can be prioritized. In this way, the dialogue unit can determine the priority of dialogues according to the user's emotions, thereby enabling more appropriate dialogues.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives a question from a user. The question may be in text, voice, or image format. For example, a voice question may be received using a voice assistant device, a text question may be received using a mobile device, or an image question may be received using image analysis technology. Step 2: The analysis unit analyzes the question received by the reception unit. The analysis is performed using natural language processing technology and machine learning algorithms. For example, it can convert voice questions into text and analyze the content of the question, analyze the intent of text questions, or analyze the content of image questions. Step 3: The generator generates an answer based on the question analyzed by the analyzer. The answer may be generated in text, audio, image, or other formats. For example, the generator may generate answers based on the words of great people or famous books, or based on data contained in a database, or it may be equipped with an algorithm that actively asks questions in response to the user's questions. Step 4: The dialogue unit advances the dialogue based on the answers generated by the generation unit. The dialogue progresses by analyzing the user's reactions and nodding or asking questions at the appropriate times. For example, the dialogue unit can analyze the user's facial expressions and tone of voice to nod at the appropriate times, ask appropriate questions based on the user's reactions, and analyze the user's reactions in real time to adjust the progress of the dialogue.

[0120] 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.

[0121] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 unit that receives questions from users; an analysis unit that analyzes the question received by the reception unit; a generation unit that generates an answer based on the question analyzed by the analysis unit; a dialogue unit that advances dialogue based on the answer generated by the generation unit; A system characterized by:

2. The generation unit Generate answers based on specific data sources 2. The system of claim 1.

3. The dialogue unit Analyze user reactions and nod or ask questions under specific conditions 2. The system of claim 1.

4. The analysis unit Equipped with algorithms to analyze user questions and generate appropriate answers 2. The system of claim 1.

5. The generation unit Generate answers based on specific data sources contained in the database 2. The system of claim 1.

6. The dialogue unit Equipped with an algorithm that proactively asks questions in response to user questions 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past question history and select the optimal reception method 2. The system of claim 1.

Images