system

Abstract

The system according to this embodiment aims to analyze the user's emotions and mental state and provide appropriate advice. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit receives the user's statements. The analysis unit analyzes the content of the statements received by the reception unit and grasps the user's emotions and mental state. The provision unit provides advice based on the emotions and mental state grasped by the analysis unit. The customization unit customizes the chatbot's personality and dialogue style.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, accurately grasping the user's emotions and mental state and providing appropriate advice have not been fully achieved, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the user's emotions and mental state and provide appropriate advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit receives user statements. The analysis unit analyzes the content of the statements received by the reception unit and grasps the user's emotions and mental state. The provision unit provides advice based on the emotions and mental state grasped by the analysis unit. The customization unit customizes the chatbot's personality and dialogue style. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's emotions and mental state and provide appropriate advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The chatbot system according to an embodiment of the present invention is a system targeted at modern people exposed to stressful environments. This system aims to correctly understand the user's emotions and mental state and provide appropriate care methods. Specifically, it consists of the following steps. First, the user starts a conversation with the chatbot. The chatbot analyzes the user's statements and grasps their emotions and mental state. This analysis is performed using AI to extract emotions from the user's statements. For example, if the user says, "I'm very tired today," the AI ​​extracts "fatigue" as the emotion. Next, based on the analysis results, the chatbot provides insights into the current situation and advice for improvement. For example, it provides specific advice such as, "Take a good rest today" or "Try taking some deep breaths to relax." This advice is customized according to the user's emotions and mental state. Furthermore, the chatbot's personality and dialogue style can be freely customized according to the user's preferences. For example, it can be set to a chatbot that speaks in a gentle tone or a chatbot with an encouraging dialogue style, according to the user's needs. Through this system, users can deepen their self-understanding and improve their quality of life while effectively coping with stress. For example, by expressing daily emotions to a chatbot, users can receive insights into their current situation and advice for improvement. This allows users to correctly understand their own emotions and mental state and find appropriate ways to care for themselves. In this way, chatbot systems are useful tools for modern people exposed to stressful environments to deepen their self-understanding and improve their quality of life. This enables chatbot systems to correctly understand users' emotions and mental state and provide appropriate care methods.

[0029] The chatbot system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit receives user statements. User statements include, but are not limited to, text messages and voice messages. The reception unit analyzes text messages using natural language processing technology to understand the content of the user's statements. The reception unit can also convert voice messages into text using speech recognition technology and analyze its content. For example, if a user says, "I'm very tired today," the reception unit analyzes the statement as text data and extracts the emotion. The analysis unit analyzes the content of the statements received by the reception unit to understand the user's emotions and mental state. The analysis unit analyzes the content of the user's statements using natural language processing technology and extracts the emotion. The analysis unit can also evaluate the user's mental state using an emotion analysis algorithm. For example, the analysis unit extracts "fatigue" and "stress" as emotions from the user's statements and evaluates their intensity. The provision unit provides advice based on the emotions and mental state understood by the analysis unit. The service provider unit provides specific advice based on the user's emotions. For example, if the user is feeling "tired," the service provider unit might advise, "Take it easy today." Similarly, if the user is feeling "stressed," the service provider unit might advise, "Try taking some deep breaths to relax." The customization unit customizes the chatbot's personality and dialogue style. For example, the customization unit sets the chatbot's personality and dialogue style according to the user's preferences. For instance, the customization unit can set the chatbot to speak in a gentle tone or to have an encouraging dialogue style, according to the user's needs. This allows the chatbot system according to the embodiment to analyze user statements and provide appropriate advice, thereby deepening the user's self-understanding and improving their quality of life.

[0030] The reception desk receives user messages. User messages include, but are not limited to, text messages and voice messages. The reception desk analyzes text messages using natural language processing techniques to understand the content of the user's message. Specifically, natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis can be used. Morphological analysis divides the user's message into words and identifies the part of speech of each word. Grammatical analysis analyzes the order of words and grammatical structure to understand the meaning of the message. Semantic analysis considers the context and the meaning of words to grasp the intent of the message. The reception desk can also convert voice messages into text using speech recognition technology and analyze its content. Common speech recognition techniques include deep learning techniques using acoustic models and language models. Acoustic models convert speech signals into features, and language models convert those features into text. For example, if a user says, "I'm very tired today," the reception desk analyzes that message as text data and extracts the emotion. Furthermore, the reception desk can refer to the user's statement history and past conversation content, and perform analysis considering the background and context of the statement. This allows the reception desk to accurately understand the user's statement and provide a foundation for taking appropriate action.

[0031] The analysis unit analyzes the content of statements received by the reception unit to understand the user's emotions and mental state. The analysis uses natural language processing technology to analyze the user's statements and extract emotions. Specifically, it uses an emotion analysis algorithm to extract emotions from the user's statements and evaluate their intensity. Machine learning and deep learning methods are common for emotion analysis algorithms. For example, the analysis unit extracts "fatigue" and "stress" as emotions from the user's statements and evaluates their intensity. The intensity of an emotion is evaluated based on the content and context of the statement, as well as the user's past statement history. Furthermore, the analysis unit can combine multiple emotions to perform a comprehensive evaluation in order to assess the user's mental state. For example, if both "fatigue" and "stress" are high simultaneously, it can be determined that the user's mental state is deteriorating. The analysis unit can also consider the tone and expression of the user's statements, as well as the choice of words, to perform more accurate emotion analysis. This allows the analysis unit to accurately understand the user's emotions and mental state and provide information for appropriate responses.

[0032] The service provider provides advice based on the emotions and mental state identified by the analysis unit. Specifically, it provides specific advice according to the user's emotions. For example, if the user is feeling "tired," the service provider might advise, "Take it easy and rest today." Similarly, if the user is feeling "stressed," the service provider might advise, "Try taking some deep breaths to relax." The service provider refers to a pre-configured advice database to select appropriate advice based on the user's emotions and mental state. This database contains advice corresponding to various emotions and mental states, and the service provider selects the most suitable advice based on the information provided by the analysis unit. Furthermore, the service provider can customize the content of the advice based on the user's past responses and feedback. For example, if previously provided advice was effective, similar advice can be provided again. The service provider can also adjust the expression and tone of the advice according to the user's preferences and needs. This allows the service provider to provide appropriate and effective advice to the user, thereby improving the user's mental state.

[0033] The customization department customizes the chatbot's personality and dialogue style. Specifically, it sets the chatbot's personality and dialogue style according to the user's preferences. For example, the customization department can set the chatbot to speak in a gentle tone or to have an encouraging dialogue style, according to the user's needs. The customization department makes optimal settings based on the user's profile information and past dialogue history. For example, if a user has previously preferred a chatbot with a gentle tone, the customization department will set the chatbot's personality based on that information. Furthermore, the customization department can collect user feedback and continuously improve the chatbot's personality and dialogue style. For example, if a user provides feedback such as "I want to be more encouraging," the customization department will adjust the chatbot's dialogue style based on that feedback. In addition, the customization department can combine the personalities and dialogue styles of multiple chatbots to provide the user with the optimal dialogue experience. In this way, the customization department can provide a chatbot that meets the user's preferences and needs, improving user satisfaction.

[0034] The service provider can offer specific advice based on the user's emotions and mental state. For example, if the user is feeling "tired," the service provider might advise, "Take it easy and rest today." Similarly, if the user is feeling "stressed," the service provider might advise, "Try taking some deep breaths to relax." Furthermore, if the user is feeling "anxious," the service provider might advise, "Try meditating to relax." This allows for the optimization of user care methods by providing specific advice tailored to the user's emotions and mental state. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider could input user emotion data into a generating AI, which could then generate advice.

[0035] The customization section allows users to configure the chatbot's personality and dialogue style according to their preferences. For example, if a user desires a chatbot that speaks in a gentle tone, the customization section can configure that setting. It can also configure a chatbot with an encouraging dialogue style if the user desires one. Furthermore, it can configure a chatbot with a more formal dialogue style if the user desires one. This allows for improved user satisfaction by tailoring the chatbot's personality and dialogue style to the user's preferences. Some or all of the above-described processes in the customization section may be performed using AI, or without AI. For example, the customization section can input user preference data into a generating AI, which can then generate an optimal dialogue style.

[0036] The analysis unit can extract emotions from user statements. For example, the analysis unit analyzes the content of user statements using natural language processing technology and extracts emotions. The analysis unit can also evaluate user emotions using emotion analysis algorithms. For example, the analysis unit can extract "fatigue" or "stress" as emotions from user statements and evaluate their intensity. Furthermore, the analysis unit can also extract "joy" or "sadness" as emotions from user statements. This allows for more accurate emotion analysis by extracting emotions from user statements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user statement data into a generating AI, which can then extract emotions.

[0037] The service provider can offer advice on deep breathing to help users relax. For example, if a user is feeling "stressed," the service provider might advise, "Try taking some deep breaths to relax." Similarly, if a user is feeling "anxious," the service provider might advise, "Try taking some deep breaths to relax." Furthermore, if a user is feeling "tense," the service provider might advise, "Try taking some deep breaths to relax." By providing specific advice on relaxation, the service provider can help reduce user stress. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider could input user emotion data into a generating AI, which could then generate advice on deep breathing.

[0038] The service unit may include a recording unit that records the user's emotional history. The recording unit, for example, records the user's emotional data along with the date and time. The recording unit may also record the type of emotion the user is feeling. Furthermore, the recording unit may also record the intensity of the user's emotion. This allows for advice based on past emotional data by recording the user's emotional history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's emotional data into a generating AI, which can then record the emotional history.

[0039] The reception desk can analyze the user's past communication history and select the most suitable reception method. For example, the reception desk may prioritize receiving communication formats that the user has frequently used in the past. Furthermore, the reception desk can suggest the most suitable reception method for a specific time period based on the user's past communication history. In addition, the reception desk can analyze the user's past communication history and select the most effective reception method. This allows for more effective communication by selecting the optimal reception method based on the user's past communication history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's communication history data into a generating AI, which can then select the most suitable reception method.

[0040] The reception unit can filter messages based on the user's current living situation and areas of interest when receiving them. For example, if the user enters their current living situation, the reception unit will filter messages based on that information. The reception unit can also prioritize receiving relevant messages based on the user's areas of interest. Furthermore, the reception unit can filter messages to be most relevant, taking into account the user's living situation and areas of interest. This allows for more relevant conversations by filtering messages based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's living situation data into a generating AI, which can then filter the messages.

[0041] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit will prioritize receiving messages related to that location. The reception unit can also filter messages based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can prioritize receiving relevant messages based on their current location. This allows for the priority of receiving more relevant messages by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI, which can then filter the messages.

[0042] The reception unit can analyze the user's social media activity when receiving a message and accept relevant messages. For example, the reception unit can analyze the user's social media activity and prioritize accepting relevant messages. The reception unit can also filter for the most relevant messages based on the information the user has shared on social media. Furthermore, the reception unit can accept relevant messages while considering the user's social media activity. This allows for the priority acceptance of relevant messages by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI, which can then filter the messages.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the statements during the analysis. For example, the analysis unit performs a detailed analysis for important statements. The analysis unit can also perform a simplified analysis for ordinary statements. Furthermore, the analysis unit can perform a rapid analysis for urgent statements. This allows for detailed analysis of important statements by adjusting the level of detail of the analysis based on the importance of the statements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, which can then adjust the level of detail of the analysis based on the importance of the statements.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the statement during analysis. For example, the analysis unit can apply an emotion analysis algorithm to statements related to emotions. It can also apply a mental analysis algorithm to statements related to mental state. Furthermore, it can apply a living situation analysis algorithm to statements related to living situation. By applying different analysis algorithms depending on the category of the statement, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, and the generating AI can apply different analysis algorithms depending on the category of the statement.

[0045] The analysis unit can determine the priority of analysis based on when the statements were submitted. For example, the analysis unit may prioritize the analysis of the most recent statements. The analysis unit can also analyze current statements while referring to past statements. Furthermore, the analysis unit may prioritize the analysis of statements submitted within a specific time period. This allows for the prioritization of the analysis of the most recent statements by determining the priority of analysis based on when the statements were submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, which can then determine the priority of analysis based on when the statements were submitted.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the statements during analysis. For example, the analysis unit may prioritize the analysis of highly relevant statements. It can also postpone the analysis of less relevant statements. Furthermore, the analysis unit can determine the optimal analysis order by considering the relevance of the statements. This allows for the prioritization of highly relevant statements by adjusting the analysis order based on the relevance of the statements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, which can then adjust the analysis order based on the relevance of the statements.

[0047] The service provider can adjust the level of detail in the advice based on the importance of the emotion when providing advice. For example, the service provider can provide detailed advice for important emotions. It can also provide simplified advice for ordinary emotions. Furthermore, it can provide advice quickly for urgent emotions. This allows for detailed advice to be provided for important emotions by adjusting the level of detail in the advice based on the importance of the emotion. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input emotion data into a generating AI, which can then adjust the level of detail in the advice based on the importance of the emotion.

[0048] The service provider can apply different advice algorithms depending on the category of emotion when providing advice. For example, for emotions related to stress, the service provider can apply a stress reduction advice algorithm. It can also apply a relaxation advice algorithm for emotions related to relaxation. Furthermore, it can apply a calming advice algorithm for emotions related to excitement. By applying different advice algorithms depending on the category of emotion, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input emotion data into a generating AI, which can then apply different advice algorithms depending on the category of emotion.

[0049] The service provider can prioritize advice based on when the emotions were submitted. For example, it may prioritize advice for the most recent emotions. It can also provide advice for the current emotions while referring to past emotions. Furthermore, it can prioritize advice for emotions submitted within a specific time period. This allows for prioritizing advice based on when the emotions were submitted, thereby ensuring that the most recent emotions are given priority. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input emotion data into a generating AI, which can then prioritize advice based on when the emotions were submitted.

[0050] The advice delivery unit can adjust the order of advice based on the relevance of emotions when providing advice. For example, the delivery unit can prioritize advice for highly relevant emotions. It can also postpone advice for less relevant emotions. Furthermore, the delivery unit can determine the optimal order of advice, taking into account the relevance of emotions. This allows for prioritizing advice for highly relevant emotions by adjusting the order of advice based on emotional relevance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input emotional data into a generating AI, which can then adjust the order of advice based on the relevance of emotions.

[0051] The customization unit can select the optimal customization method by referring to the user's past settings history during customization. For example, the customization unit can suggest the optimal customization method based on the chatbot's personality and dialogue style previously set by the user. The customization unit can also select the optimal customization method for a specific time period based on the user's past settings history. Furthermore, the customization unit can analyze the user's past settings history and select the most effective customization method. This allows for more effective customization by selecting the optimal customization method based on the user's past settings history. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's settings history data into a generating AI, which can then select the optimal customization method.

[0052] The customization unit can customize the means of customization based on the user's current living situation during the customization process. For example, if the user inputs their current living situation, the customization unit will propose customization methods based on that information. The customization unit can also select the optimal customization method based on the user's living situation. Furthermore, the customization unit can adjust the means of customization considering the user's living situation. This allows for more appropriate customization by adjusting the means of customization based on the user's living situation. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's living situation data into a generating AI, which can then propose customization methods.

[0053] The customization unit can select the optimal customization method by considering the user's geographical location information during customization. For example, if the user is in a specific location, the customization unit will suggest a customization method related to that location. The customization unit can also select the optimal customization method based on the user's geographical location information. Furthermore, if the user is on the move, the customization unit can adjust the customization method based on the user's current location information. This allows for the selection of a more appropriate customization method by considering the user's geographical location information. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location information data into a generating AI, which can then select the optimal customization method.

[0054] The customization unit can analyze the user's social media activity during customization and propose customization methods. For example, the customization unit can analyze the user's social media activity and propose the optimal customization method. The customization unit can also adjust the customization methods based on information shared by the user on social media. Furthermore, the customization unit can select the optimal customization method considering the user's social media activity. This allows for the proposal of more appropriate customization methods by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media data into a generating AI, which can then propose customization methods.

[0055] The recording unit can optimize the recording algorithm by referring to past recording data during recording. For example, the recording unit can select the optimal recording algorithm based on past recording data. The recording unit can also analyze past recording data and adjust the recording algorithm. Furthermore, the recording unit can optimize the current recording data while referring to past recording data. This makes it possible to record data more effectively by optimizing the recording algorithm based on past recording data. Some or all of the above processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input past recording data into a generating AI, and the generating AI can optimize the recording algorithm.

[0056] The recording unit can weight the recorded data based on when the statements were submitted. For example, the recording unit can prioritize recording the most recent statements. The recording unit can also record current statements while referring to past statements. Furthermore, the recording unit can prioritize recording statements submitted within a specific time period. By weighting the recorded data based on when the statements were submitted, more important data can be recorded preferentially. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input statement data into a generating AI, which can then weight the recorded data based on when the statements were submitted.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception desk can adjust how it receives user messages, taking into account the user's current activity status. For example, if the user is exercising, the reception desk will prioritize short messages. If the user is working, the reception desk may temporarily delay receiving messages. Furthermore, if the user is relaxed, the reception desk can accept normal messages. By adjusting how messages are received according to the user's activity status, it becomes possible to have conversations at more appropriate times. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input user activity data into a generating AI, which can then adjust how messages are received.

[0059] The customization unit can analyze the user's past utterance history and select the optimal customization method. For example, it can prioritize customizing the utterance format that the user has frequently used in the past. The customization unit can also suggest the optimal customization method for a specific time period based on the user's past utterance history. Furthermore, the customization unit can analyze the user's past utterance history and select the most effective customization method. This enables more effective dialogue by selecting the optimal customization method based on the user's past utterance history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's utterance history data into a generating AI, which can then select the optimal customization method.

[0060] The service provider can adjust the level of detail in the advice based on the importance of the emotion when providing advice. For example, it can provide detailed advice for important emotions, and simplified advice for ordinary emotions. Furthermore, it can provide rapid advice for urgent emotions. By adjusting the level of detail in the advice based on the importance of the emotion, it is possible to provide detailed advice for important emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input emotion data into a generating AI, and the generating AI can adjust the level of detail in the advice based on the importance of the emotion.

[0061] The service provider can apply different advice algorithms depending on the category of emotion when providing advice. For example, for emotions related to stress, an advice algorithm for stress reduction can be applied. Similarly, for emotions related to relaxation, an advice algorithm for relaxation methods can be applied. Furthermore, for emotions related to excitement, an advice algorithm for calming excitement can be applied. By applying different advice algorithms depending on the category of emotion, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input emotion data into a generating AI, and the generating AI can apply different advice algorithms depending on the category of emotion.

[0062] The analysis unit can apply different analysis algorithms depending on the category of the statement during analysis. For example, an emotion analysis algorithm can be applied to statements related to emotions. A mental state analysis algorithm can also be applied to statements related to mental state. Furthermore, a living situation analysis algorithm can be applied to statements related to living situation. By applying different analysis algorithms depending on the category of the statement, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, and the generating AI can apply different analysis algorithms depending on the category of the statement.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives user messages. User messages can include text messages and voice messages. The reception desk analyzes text messages using natural language processing technology to understand the content of the user's message. It can also convert voice messages into text using speech recognition technology and analyze the content. For example, if a user says, "I'm very tired today," the system analyzes that message as text data and extracts the emotion. Step 2: The analysis unit analyzes the content of the statements received by the reception unit to understand the user's emotions and mental state. The analysis unit uses natural language processing technology to analyze the user's statements and extract emotions. It can also evaluate the user's mental state using an emotion analysis algorithm. For example, it can extract emotions such as "fatigue" and "stress" from the user's statements and evaluate their intensity. Step 3: The service provider provides advice based on the emotions and mental state identified by the analysis unit. The service provider provides specific advice according to the user's emotions. For example, if the user is feeling "tired," it might advise, "Take it easy and rest today." If the user is feeling "stressed," it might advise, "Try taking some deep breaths to relax." Step 4: The customization section allows you to customize the chatbot's personality and conversation style. The customization section allows you to set the chatbot's personality and conversation style according to user preferences. For example, you can set it to speak in a gentle tone or have an encouraging conversation style, depending on the user's needs.

[0065] (Example of form 2) The chatbot system according to an embodiment of the present invention is a system targeted at modern people exposed to stressful environments. This system aims to correctly understand the user's emotions and mental state and provide appropriate care methods. Specifically, it consists of the following steps. First, the user starts a conversation with the chatbot. The chatbot analyzes the user's statements and grasps their emotions and mental state. This analysis is performed using AI to extract emotions from the user's statements. For example, if the user says, "I'm very tired today," the AI ​​extracts "fatigue" as the emotion. Next, based on the analysis results, the chatbot provides insights into the current situation and advice for improvement. For example, it provides specific advice such as, "Take a good rest today" or "Try taking some deep breaths to relax." This advice is customized according to the user's emotions and mental state. Furthermore, the chatbot's personality and dialogue style can be freely customized according to the user's preferences. For example, it can be set to a chatbot that speaks in a gentle tone or a chatbot with an encouraging dialogue style, according to the user's needs. Through this system, users can deepen their self-understanding and improve their quality of life while effectively coping with stress. For example, by expressing daily emotions to a chatbot, users can receive insights into their current situation and advice for improvement. This allows users to correctly understand their own emotions and mental state and find appropriate ways to care for themselves. In this way, chatbot systems are useful tools for modern people exposed to stressful environments to deepen their self-understanding and improve their quality of life. This enables chatbot systems to correctly understand users' emotions and mental state and provide appropriate care methods.

[0066] The chatbot system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a customization unit. The reception unit receives user statements. User statements include, but are not limited to, text messages and voice messages. The reception unit analyzes text messages using natural language processing technology to understand the content of the user's statements. The reception unit can also convert voice messages into text using speech recognition technology and analyze its content. For example, if a user says, "I'm very tired today," the reception unit analyzes the statement as text data and extracts the emotion. The analysis unit analyzes the content of the statements received by the reception unit to understand the user's emotions and mental state. The analysis unit analyzes the content of the user's statements using natural language processing technology and extracts the emotion. The analysis unit can also evaluate the user's mental state using an emotion analysis algorithm. For example, the analysis unit extracts "fatigue" and "stress" as emotions from the user's statements and evaluates their intensity. The provision unit provides advice based on the emotions and mental state understood by the analysis unit. The service provider unit provides specific advice based on the user's emotions. For example, if the user is feeling "tired," the service provider unit might advise, "Take it easy today." Similarly, if the user is feeling "stressed," the service provider unit might advise, "Try taking some deep breaths to relax." The customization unit customizes the chatbot's personality and dialogue style. For example, the customization unit sets the chatbot's personality and dialogue style according to the user's preferences. For instance, the customization unit can set the chatbot to speak in a gentle tone or to have an encouraging dialogue style, according to the user's needs. This allows the chatbot system according to the embodiment to analyze user statements and provide appropriate advice, thereby deepening the user's self-understanding and improving their quality of life.

[0067] The reception desk receives user messages. User messages include, but are not limited to, text messages and voice messages. The reception desk analyzes text messages using natural language processing techniques to understand the content of the user's message. Specifically, natural language processing techniques such as morphological analysis, grammatical analysis, and semantic analysis can be used. Morphological analysis divides the user's message into words and identifies the part of speech of each word. Grammatical analysis analyzes the order of words and grammatical structure to understand the meaning of the message. Semantic analysis considers the context and the meaning of words to grasp the intent of the message. The reception desk can also convert voice messages into text using speech recognition technology and analyze its content. Common speech recognition techniques include deep learning techniques using acoustic models and language models. Acoustic models convert speech signals into features, and language models convert those features into text. For example, if a user says, "I'm very tired today," the reception desk analyzes that message as text data and extracts the emotion. Furthermore, the reception desk can refer to the user's statement history and past conversation content, and perform analysis considering the background and context of the statement. This allows the reception desk to accurately understand the user's statement and provide a foundation for taking appropriate action.

[0068] The analysis unit analyzes the content of statements received by the reception unit to understand the user's emotions and mental state. The analysis uses natural language processing technology to analyze the user's statements and extract emotions. Specifically, it uses an emotion analysis algorithm to extract emotions from the user's statements and evaluate their intensity. Machine learning and deep learning methods are common for emotion analysis algorithms. For example, the analysis unit extracts "fatigue" and "stress" as emotions from the user's statements and evaluates their intensity. The intensity of an emotion is evaluated based on the content and context of the statement, as well as the user's past statement history. Furthermore, the analysis unit can combine multiple emotions to perform a comprehensive evaluation in order to assess the user's mental state. For example, if both "fatigue" and "stress" are high simultaneously, it can be determined that the user's mental state is deteriorating. The analysis unit can also consider the tone and expression of the user's statements, as well as the choice of words, to perform more accurate emotion analysis. This allows the analysis unit to accurately understand the user's emotions and mental state and provide information for appropriate responses.

[0069] The service provider provides advice based on the emotions and mental state identified by the analysis unit. Specifically, it provides specific advice according to the user's emotions. For example, if the user is feeling "tired," the service provider might advise, "Take it easy and rest today." Similarly, if the user is feeling "stressed," the service provider might advise, "Try taking some deep breaths to relax." The service provider refers to a pre-configured advice database to select appropriate advice based on the user's emotions and mental state. This database contains advice corresponding to various emotions and mental states, and the service provider selects the most suitable advice based on the information provided by the analysis unit. Furthermore, the service provider can customize the content of the advice based on the user's past responses and feedback. For example, if previously provided advice was effective, similar advice can be provided again. The service provider can also adjust the expression and tone of the advice according to the user's preferences and needs. This allows the service provider to provide appropriate and effective advice to the user, thereby improving the user's mental state.

[0070] The customization department customizes the chatbot's personality and dialogue style. Specifically, it sets the chatbot's personality and dialogue style according to the user's preferences. For example, the customization department can set the chatbot to speak in a gentle tone or to have an encouraging dialogue style, according to the user's needs. The customization department makes optimal settings based on the user's profile information and past dialogue history. For example, if a user has previously preferred a chatbot with a gentle tone, the customization department will set the chatbot's personality based on that information. Furthermore, the customization department can collect user feedback and continuously improve the chatbot's personality and dialogue style. For example, if a user provides feedback such as "I want to be more encouraging," the customization department will adjust the chatbot's dialogue style based on that feedback. In addition, the customization department can combine the personalities and dialogue styles of multiple chatbots to provide the user with the optimal dialogue experience. In this way, the customization department can provide a chatbot that meets the user's preferences and needs, improving user satisfaction.

[0071] The service provider can offer specific advice based on the user's emotions and mental state. For example, if the user is feeling "tired," the service provider might advise, "Take it easy and rest today." Similarly, if the user is feeling "stressed," the service provider might advise, "Try taking some deep breaths to relax." Furthermore, if the user is feeling "anxious," the service provider might advise, "Try meditating to relax." This allows for the optimization of user care methods by providing specific advice tailored to the user's emotions and mental state. Some or all of the above processing in the service provider may be performed using AI, or without AI. For example, the service provider could input user emotion data into a generating AI, which could then generate advice.

[0072] The customization section allows users to configure the chatbot's personality and dialogue style according to their preferences. For example, if a user desires a chatbot that speaks in a gentle tone, the customization section can configure that setting. It can also configure a chatbot with an encouraging dialogue style if the user desires one. Furthermore, it can configure a chatbot with a more formal dialogue style if the user desires one. This allows for improved user satisfaction by tailoring the chatbot's personality and dialogue style to the user's preferences. Some or all of the above-described processes in the customization section may be performed using AI, or without AI. For example, the customization section can input user preference data into a generating AI, which can then generate an optimal dialogue style.

[0073] The analysis unit can extract emotions from user statements. For example, the analysis unit analyzes the content of user statements using natural language processing technology and extracts emotions. The analysis unit can also evaluate user emotions using emotion analysis algorithms. For example, the analysis unit can extract "fatigue" or "stress" as emotions from user statements and evaluate their intensity. Furthermore, the analysis unit can also extract "joy" or "sadness" as emotions from user statements. This allows for more accurate emotion analysis by extracting emotions from user statements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user statement data into a generating AI, which can then extract emotions.

[0074] The service provider can offer advice on deep breathing to help users relax. For example, if a user is feeling "stressed," the service provider might advise, "Try taking some deep breaths to relax." Similarly, if a user is feeling "anxious," the service provider might advise, "Try taking some deep breaths to relax." Furthermore, if a user is feeling "tense," the service provider might advise, "Try taking some deep breaths to relax." By providing specific advice on relaxation, the service provider can help reduce user stress. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider could input user emotion data into a generating AI, which could then generate advice on deep breathing.

[0075] The service unit may include a recording unit that records the user's emotional history. The recording unit, for example, records the user's emotional data along with the date and time. The recording unit may also record the type of emotion the user is feeling. Furthermore, the recording unit may also record the intensity of the user's emotion. This allows for advice based on past emotional data by recording the user's emotional history. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the user's emotional data into a generating AI, which can then record the emotional history.

[0076] The reception unit can estimate the user's emotions and adjust the timing of receiving their statements based on the estimated emotions. For example, if the user is stressed, the reception unit can delay receiving their statements and wait until the user is relaxed. Conversely, if the user is agitated, the reception unit can immediately receive their statements and respond quickly. Furthermore, if the user is tired, the reception unit can adjust the timing of receiving their statements to allow the user time to rest. By adjusting the timing of receiving statements according to the user's emotions, more appropriate conversations can be conducted. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI, which can then adjust the timing of receiving statements.

[0077] The reception desk can analyze the user's past communication history and select the most suitable reception method. For example, the reception desk may prioritize receiving communication formats that the user has frequently used in the past. Furthermore, the reception desk can suggest the most suitable reception method for a specific time period based on the user's past communication history. In addition, the reception desk can analyze the user's past communication history and select the most effective reception method. This allows for more effective communication by selecting the optimal reception method based on the user's past communication history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's communication history data into a generating AI, which can then select the most suitable reception method.

[0078] The reception unit can filter messages based on the user's current living situation and areas of interest when receiving them. For example, if the user enters their current living situation, the reception unit will filter messages based on that information. The reception unit can also prioritize receiving relevant messages based on the user's areas of interest. Furthermore, the reception unit can filter messages to be most relevant, taking into account the user's living situation and areas of interest. This allows for more relevant conversations by filtering messages based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the user's living situation data into a generating AI, which can then filter the messages.

[0079] The reception unit can estimate the user's emotions and determine the priority of messages to receive based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize important messages. If the user is relaxed, the reception unit can also prioritize normal messages. Furthermore, if the user is agitated, the reception unit can prioritize urgent messages. This ensures that important messages are received preferentially by prioritizing messages according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI, which can then determine the priority of messages.

[0080] The reception unit can prioritize receiving messages that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific location, the reception unit will prioritize receiving messages related to that location. The reception unit can also filter messages based on the user's geographical location. Furthermore, if the user is on the move, the reception unit can prioritize receiving relevant messages based on their current location. This allows for the priority of receiving more relevant messages by considering the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI, which can then filter the messages.

[0081] The reception unit can analyze the user's social media activity when receiving a message and accept relevant messages. For example, the reception unit can analyze the user's social media activity and prioritize accepting relevant messages. The reception unit can also filter for the most relevant messages based on the information the user has shared on social media. Furthermore, the reception unit can accept relevant messages while considering the user's social media activity. This allows for the priority acceptance of relevant messages by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media data into a generating AI, which can then filter the messages.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide the analysis results in a simple presentation. If the user is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can provide the analysis results in a visually stimulating presentation. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's emotion data into the generative AI, which can then adjust the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the statements during the analysis. For example, the analysis unit performs a detailed analysis for important statements. The analysis unit can also perform a simplified analysis for ordinary statements. Furthermore, the analysis unit can perform a rapid analysis for urgent statements. This allows for detailed analysis of important statements by adjusting the level of detail of the analysis based on the importance of the statements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, which can then adjust the level of detail of the analysis based on the importance of the statements.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the statement during analysis. For example, the analysis unit can apply an emotion analysis algorithm to statements related to emotions. It can also apply a mental analysis algorithm to statements related to mental state. Furthermore, it can apply a living situation analysis algorithm to statements related to living situation. By applying different analysis algorithms depending on the category of the statement, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, and the generating AI can apply different analysis algorithms depending on the category of the statement.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI, which can then adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on when the statements were submitted. For example, the analysis unit may prioritize the analysis of the most recent statements. The analysis unit can also analyze current statements while referring to past statements. Furthermore, the analysis unit may prioritize the analysis of statements submitted within a specific time period. This allows for the prioritization of the analysis of the most recent statements by determining the priority of analysis based on when the statements were submitted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, which can then determine the priority of analysis based on when the statements were submitted.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the statements during analysis. For example, the analysis unit may prioritize the analysis of highly relevant statements. It can also postpone the analysis of less relevant statements. Furthermore, the analysis unit can determine the optimal analysis order by considering the relevance of the statements. This allows for the prioritization of highly relevant statements by adjusting the analysis order based on the relevance of the statements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, which can then adjust the analysis order based on the relevance of the statements.

[0088] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is stressed, the service provider will provide advice in a gentle tone. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user is excited, the service provider can provide encouraging advice. This allows for more appropriate advice to be provided by adjusting the way advice is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI, which can then adjust the way advice is expressed.

[0089] The service provider can adjust the level of detail in the advice based on the importance of the emotion when providing advice. For example, the service provider can provide detailed advice for important emotions. It can also provide simplified advice for ordinary emotions. Furthermore, it can provide advice quickly for urgent emotions. This allows for detailed advice to be provided for important emotions by adjusting the level of detail in the advice based on the importance of the emotion. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input emotion data into a generating AI, which can then adjust the level of detail in the advice based on the importance of the emotion.

[0090] The service provider can apply different advice algorithms depending on the category of emotion when providing advice. For example, for emotions related to stress, the service provider can apply a stress reduction advice algorithm. It can also apply a relaxation advice algorithm for emotions related to relaxation. Furthermore, it can apply a calming advice algorithm for emotions related to excitement. By applying different advice algorithms depending on the category of emotion, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input emotion data into a generating AI, which can then apply different advice algorithms depending on the category of emotion.

[0091] The service provider can estimate the user's emotions and adjust the length of the advice based on the estimated emotions. For example, if the user is in a hurry, the service provider can provide short, concise advice. If the user is relaxed, the service provider can also provide detailed advice. Furthermore, if the user is excited, the service provider can provide visually stimulating advice. By adjusting the length of the advice according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not using AI. For example, the service provider can input emotion data into the generative AI, which can then adjust the length of the advice.

[0092] The service provider can prioritize advice based on when the emotions were submitted. For example, it may prioritize advice for the most recent emotions. It can also provide advice for the current emotions while referring to past emotions. Furthermore, it can prioritize advice for emotions submitted within a specific time period. This allows for prioritizing advice based on when the emotions were submitted, thereby ensuring that the most recent emotions are given priority. Some or all of the above processing in the service provider may be performed using AI, or not. For example, the service provider can input emotion data into a generating AI, which can then prioritize advice based on when the emotions were submitted.

[0093] The advice delivery unit can adjust the order of advice based on the relevance of emotions when providing advice. For example, the delivery unit can prioritize advice for highly relevant emotions. It can also postpone advice for less relevant emotions. Furthermore, the delivery unit can determine the optimal order of advice, taking into account the relevance of emotions. This allows for prioritizing advice for highly relevant emotions by adjusting the order of advice based on emotional relevance. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input emotional data into a generating AI, which can then adjust the order of advice based on the relevance of emotions.

[0094] The customization unit can estimate the user's emotions and adjust the chatbot's personality and dialogue style based on the estimated emotions. For example, if the user is stressed, the customization unit can set the chatbot to have a gentle personality. It can also set the chatbot to have an encouraging dialogue style if the user is relaxed. Furthermore, it can set the chatbot to have a calm dialogue style if the user is excited. This allows for more appropriate dialogue by adjusting the chatbot's personality and dialogue style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not. For example, the customization unit can input user emotion data into the generative AI, which can then adjust the chatbot's personality and dialogue style.

[0095] The customization unit can select the optimal customization method by referring to the user's past settings history during customization. For example, the customization unit can suggest the optimal customization method based on the chatbot's personality and dialogue style previously set by the user. The customization unit can also select the optimal customization method for a specific time period based on the user's past settings history. Furthermore, the customization unit can analyze the user's past settings history and select the most effective customization method. This allows for more effective customization by selecting the optimal customization method based on the user's past settings history. Some or all of the above processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's settings history data into a generating AI, which can then select the optimal customization method.

[0096] The customization unit can customize the means of customization based on the user's current living situation during the customization process. For example, if the user inputs their current living situation, the customization unit will propose customization methods based on that information. The customization unit can also select the optimal customization method based on the user's living situation. Furthermore, the customization unit can adjust the means of customization considering the user's living situation. This allows for more appropriate customization by adjusting the means of customization based on the user's living situation. Some or all of the above-described processes in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's living situation data into a generating AI, which can then propose customization methods.

[0097] The customization unit can estimate the user's emotions and determine the priority of customization based on the estimated emotions. For example, if the user is stressed, the customization unit will prioritize customization. It can also perform normal customization if the user is relaxed. Furthermore, if the user is excited, the customization unit can perform urgent customization. This allows for more appropriate customization by determining the priority of customization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI, or not. For example, the customization unit can input user emotion data into a generative AI, which can then determine the priority of customization.

[0098] The customization unit can select the optimal customization method by considering the user's geographical location information during customization. For example, if the user is in a specific location, the customization unit will suggest a customization method related to that location. The customization unit can also select the optimal customization method based on the user's geographical location information. Furthermore, if the user is on the move, the customization unit can adjust the customization method based on the user's current location information. This allows for the selection of a more appropriate customization method by considering the user's geographical location information. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's geographical location information data into a generating AI, which can then select the optimal customization method.

[0099] The customization unit can analyze the user's social media activity during customization and propose customization methods. For example, the customization unit can analyze the user's social media activity and propose the optimal customization method. The customization unit can also adjust the customization methods based on information shared by the user on social media. Furthermore, the customization unit can select the optimal customization method considering the user's social media activity. This allows for the proposal of more appropriate customization methods by analyzing the user's social media activity. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's social media data into a generating AI, which can then propose customization methods.

[0100] The recording unit can estimate the user's emotions and select recording data based on the estimated emotions. For example, if the user is stressed, the recording unit will prioritize recording data related to stress. It can also record data related to relaxation if the user is relaxed. Furthermore, if the user is excited, it can record data related to excitement. This allows for more appropriate data recording by selecting recording data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, or not. For example, the recording unit can input user emotion data into a generative AI, which can then select the recording data.

[0101] The recording unit can optimize the recording algorithm by referring to past recording data during recording. For example, the recording unit can select the optimal recording algorithm based on past recording data. The recording unit can also analyze past recording data and adjust the recording algorithm. Furthermore, the recording unit can optimize the current recording data while referring to past recording data. This makes it possible to record data more effectively by optimizing the recording algorithm based on past recording data. Some or all of the above processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input past recording data into a generating AI, and the generating AI can optimize the recording algorithm.

[0102] The recording unit can estimate the user's emotions and adjust the recording frequency based on the estimated emotions. For example, if the user is stressed, the recording unit will record more frequently. It can also record at a normal frequency if the user is relaxed. Furthermore, if the user is excited, the recording unit can record urgently. This allows for more appropriate data recording by adjusting the recording frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, or not. For example, the recording unit can input user emotion data into the generative AI, which can then adjust the recording frequency.

[0103] The recording unit can weight the recorded data based on when the statements were submitted. For example, the recording unit can prioritize recording the most recent statements. The recording unit can also record current statements while referring to past statements. Furthermore, the recording unit can prioritize recording statements submitted within a specific time period. By weighting the recorded data based on when the statements were submitted, more important data can be recorded preferentially. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input statement data into a generating AI, which can then weight the recorded data based on when the statements were submitted.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception desk can adjust how it receives user messages, taking into account the user's current activity status. For example, if the user is exercising, the reception desk will prioritize short messages. If the user is working, the reception desk may temporarily delay receiving messages. Furthermore, if the user is relaxed, the reception desk can accept normal messages. By adjusting how messages are received according to the user's activity status, it becomes possible to have conversations at more appropriate times. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input user activity data into a generating AI, which can then adjust how messages are received.

[0106] The service provider can estimate the user's emotions and adjust the way advice is delivered based on the estimated emotions. For example, if the user is stressed, the service provider will provide advice in a gentle tone. If the user is relaxed, it can also provide detailed advice. Furthermore, if the user is excited, it can provide encouraging advice. In this way, by adjusting the way advice is delivered according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can adjust the way advice is delivered.

[0107] The customization unit can analyze the user's past utterance history and select the optimal customization method. For example, it can prioritize customizing the utterance format that the user has frequently used in the past. The customization unit can also suggest the optimal customization method for a specific time period based on the user's past utterance history. Furthermore, the customization unit can analyze the user's past utterance history and select the most effective customization method. This enables more effective dialogue by selecting the optimal customization method based on the user's past utterance history. Some or all of the above processing in the customization unit may be performed using AI, for example, or without AI. For example, the customization unit can input the user's utterance history data into a generating AI, which can then select the optimal customization method.

[0108] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis results can be provided in a simple presentation. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is excited, the analysis results can be provided in a visually stimulating presentation. In this way, by adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis.

[0109] The service provider can adjust the level of detail in the advice based on the importance of the emotion when providing advice. For example, it can provide detailed advice for important emotions, and simplified advice for ordinary emotions. Furthermore, it can provide rapid advice for urgent emotions. By adjusting the level of detail in the advice based on the importance of the emotion, it is possible to provide detailed advice for important emotions. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input emotion data into a generating AI, and the generating AI can adjust the level of detail in the advice based on the importance of the emotion.

[0110] The reception unit can estimate the user's emotions and adjust the timing of receiving their statements based on the estimated emotions. For example, if the user is stressed, the reception timing can be delayed, waiting until the user is relaxed. Conversely, if the user is excited, the reception can be accepted immediately, allowing for a quick response. Furthermore, if the user is tired, the reception timing can be adjusted to allow the user time to rest. By adjusting the timing of receiving statements according to the user's emotions, more appropriate conversations can be conducted. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can input user emotion data into a generative AI, which can then adjust the timing of receiving statements.

[0111] The service provider can estimate the user's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the user is stressed, it can provide advice in a gentle tone. If the user is relaxed, it can provide detailed advice. Furthermore, if the user is excited, it can provide encouraging advice. By adjusting the way advice is expressed according to the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then adjust the way advice is expressed.

[0112] The customization unit can estimate the user's emotions and adjust the chatbot's personality and dialogue style based on the estimated emotions. For example, if the user is stressed, the chatbot can be set to have a gentle personality. If the user is relaxed, the chatbot can be set to have an encouraging dialogue style. Furthermore, if the user is excited, the chatbot can be set to have a calm dialogue style. This allows for more appropriate conversations by adjusting the chatbot's personality and dialogue style according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit may be performed using AI or not using AI. For example, the customization unit can input user emotion data into the generative AI, which can then adjust the chatbot's personality and dialogue style.

[0113] The service provider can apply different advice algorithms depending on the category of emotion when providing advice. For example, for emotions related to stress, an advice algorithm for stress reduction can be applied. Similarly, for emotions related to relaxation, an advice algorithm for relaxation methods can be applied. Furthermore, for emotions related to excitement, an advice algorithm for calming excitement can be applied. By applying different advice algorithms depending on the category of emotion, more appropriate advice can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input emotion data into a generating AI, and the generating AI can apply different advice algorithms depending on the category of emotion.

[0114] The analysis unit can apply different analysis algorithms depending on the category of the statement during analysis. For example, an emotion analysis algorithm can be applied to statements related to emotions. A mental state analysis algorithm can also be applied to statements related to mental state. Furthermore, a living situation analysis algorithm can be applied to statements related to living situation. By applying different analysis algorithms depending on the category of the statement, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input statement data into a generating AI, and the generating AI can apply different analysis algorithms depending on the category of the statement.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives user messages. User messages can include text messages and voice messages. The reception desk analyzes text messages using natural language processing technology to understand the content of the user's message. It can also convert voice messages into text using speech recognition technology and analyze the content. For example, if a user says, "I'm very tired today," the system analyzes that message as text data and extracts the emotion. Step 2: The analysis unit analyzes the content of the statements received by the reception unit to understand the user's emotions and mental state. The analysis unit uses natural language processing technology to analyze the user's statements and extract emotions. It can also evaluate the user's mental state using an emotion analysis algorithm. For example, it can extract emotions such as "fatigue" and "stress" from the user's statements and evaluate their intensity. Step 3: The service provider provides advice based on the emotions and mental state identified by the analysis unit. The service provider provides specific advice according to the user's emotions. For example, if the user is feeling "tired," it might advise, "Take it easy and rest today." If the user is feeling "stressed," it might advise, "Try taking some deep breaths to relax." Step 4: The customization section allows you to customize the chatbot's personality and conversation style. The customization section allows you to set the chatbot's personality and conversation style according to user preferences. For example, you can set it to speak in a gentle tone or have an encouraging conversation style, depending on the user's needs.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and customization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's statements. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the user's statements to understand their emotions and mental state. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice based on the analysis results. The customization unit is implemented by the control unit 46A of the smart device 14 and customizes the chatbot's personality and dialogue style. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and customization unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's statements. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the user's statements to understand their emotions and mental state. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice based on the analysis results. The customization unit is implemented by the control unit 46A of the smart glasses 214 and customizes the chatbot's personality and dialogue style. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and customization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's statements. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the content of the user's statements to understand their emotions and mental state. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides advice based on the analysis results. The customization unit is implemented by the control unit 46A of the headset terminal 314 and customizes the chatbot's personality and dialogue style. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and customization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's statements. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the content of the user's statements to understand their emotions and mental state. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides advice based on the analysis results. The customization unit is implemented by, for example, the control unit 46A of the robot 414 and customizes the chatbot's personality and dialogue style. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) A reception desk that receives user comments, The analysis unit analyzes the content of statements received by the reception unit and grasps the emotions and mental state, A provision unit that provides advice based on the emotions and mental state grasped by the aforementioned analysis unit, It includes a customization section for customizing the chatbot's personality and conversation style. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Provide specific advice tailored to the user's emotions and mental state. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned customization unit is Customize the chatbot's personality and conversation style according to user preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Extracting emotions from user statements. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We offer advice on deep breathing techniques for relaxation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, It includes a recording unit that records the user's emotional history. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving comments based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past communication history and select the most suitable method of receiving their message. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving a message, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of messages to accept based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving messages, the system prioritizes receiving messages that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving a comment, the system analyzes the user's social media activity and accepts relevant comments. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of each statement. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the statement. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analysis is determined based on when the statements were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the statements. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing advice, adjust the level of detail based on the importance of the emotions involved. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing advice, different advice algorithms are applied depending on the emotional category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, It estimates the user's emotions and adjusts the length of the advice based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing advice, prioritize the advice based on when the emotions were expressed. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing advice, adjust the order of advice based on its emotional relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned customization unit is It estimates the user's emotions and adjusts the chatbot's personality and conversation style based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned customization unit is During customization, the system selects the optimal customization method by referring to the user's past settings history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned customization unit is During customization, the customization methods are tailored based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned customization unit is It estimates the user's emotions and determines the priority of customization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned customization unit is During customization, we analyze the user's social media activity and suggest customization options. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned recording unit is The system estimates the user's emotions and selects the recording data based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned recording unit is During recording, the recording algorithm is optimized by referring to past recording data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned recording unit is It estimates the user's emotions and adjusts the recording frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned recording unit is During recording, the recorded data is weighted based on when the statements were submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives user comments, The analysis unit analyzes the content of statements received by the reception unit and grasps the emotions and mental state, A provision unit that provides advice based on the emotions and mental state grasped by the aforementioned analysis unit, It includes a customization section for customizing the chatbot's personality and conversation style. A system characterized by the following features.

2. The aforementioned supply unit is, Provide specific advice tailored to the user's emotions and mental state. The system according to feature 1.

3. The aforementioned customization unit is Customize the chatbot's personality and conversation style according to user preferences. The system according to feature 1.

4. The aforementioned analysis unit, Extracting emotions from user statements. The system according to feature 1.

5. The aforementioned supply unit is, We offer advice on deep breathing techniques for relaxation. The system according to feature 1.

6. The aforementioned supply unit is, It includes a recording unit that records the user's emotional history. The system according to feature 1.

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

8. The aforementioned reception unit is Analyze the user's past communication history and select the most suitable method of receiving their message. The system according to feature 1.

9. The aforementioned reception unit is When receiving a message, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of messages to accept based on the estimated emotions. The system according to feature 1.

Images