system

Abstract

The system according to the embodiment aims to provide an environment in which users can easily seek advice about their mental worries. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a navigation unit. The reception unit receives input from a user. The analysis unit analyzes the input received by the reception unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The navigation unit performs navigation in video format based on the advice provided by the provision unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the problem of a lack of people with whom users can easily talk about their mental worries, making it difficult to improve their mental health.

[0005] The system according to the embodiment aims to provide an environment in which users can easily seek advice about their mental worries. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, and a navigation unit. The reception unit receives input from a user. The analysis unit analyzes the input received by the reception unit. The provision unit provides advice based on the analysis results obtained by the analysis unit. The navigation unit performs navigation in video format based on the advice provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which users can easily seek advice about their mental worries. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A healthcare consultant system according to an embodiment of the present invention is a system that allows users to play the role of a talking healthcare consultant with the appearance of a hamster via chat or video. This system allows users to easily confide their concerns to a hamster-like AI via chat or video. For example, if a user inputs, "I've been feeling stressed lately," the hamster-like AI responds, "That's tough. What's causing you stress?" In this way, users can easily confide their concerns. The AI ​​then analyzes the user's input and provides appropriate advice and training methods. For example, if a user inputs, "I'm under a lot of pressure at work," the AI ​​provides specific advice such as, "Try some breathing exercises to relax." The AI ​​also responds affirmatively and encouragingly to the user. For example, it might respond, "You're doing a great job. Don't forget that you're making progress, even if it's just a little at a time." Furthermore, as a future service development, it is possible to consider generating personal AIs (PAIs) specialized for specific concerns, other than hamsters. For example, it is possible to generate AIs tailored to the user's concerns, such as an AI with the appearance of a businessman for work concerns and an AI with the appearance of a counselor for love concerns. This service allows users to easily confide their concerns and improve their mental health. AI-provided advice and training methods allow users to take specific measures. For example, specific advice tailored to the user's concerns, such as breathing techniques for relaxation or stress management methods, is provided. This allows the healthcare consultant system to accept and analyze user input, provide advice, and navigate in video format.

[0029] A healthcare consultant system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a navigation unit. The reception unit receives input from a user. The input from the user may include, but is not limited to, text input, voice input, image input, and the like. The reception unit provides, for example, an interface for receiving the text input. The reception unit may also use a microphone or voice recognition technology to receive voice input. The reception unit may also use a camera or image analysis technology to receive image input. The analysis unit analyzes the input received by the reception unit. The analysis may be performed using, for example, natural language processing, image analysis, or voice analysis, but is not limited to, these examples. For example, the analysis unit may analyze the text input using natural language processing technology. The analysis unit may also analyze the image input using image analysis technology. The analysis unit may also analyze the voice input using voice analysis technology. The provision unit provides advice based on the analysis results obtained by the analysis unit. The advice may be provided in the form of, for example, a text message, a voice message, a video message, or the like, but is not limited to these examples. For example, the providing unit provides the advice as a text message. The providing unit may also provide the advice as a voice message. Furthermore, the providing unit may also provide the advice as a video message. The navigation unit performs navigation in a video format based on the advice provided by the providing unit. Navigation may be performed, for example, as a video guide, an interactive map, or the like, but is not limited to these examples. For example, the navigation unit performs navigation as a video guide. Furthermore, the navigation unit may also perform navigation as an interactive map. This allows the healthcare consultant system according to the embodiment to accept and analyze user input, provide advice, and perform navigation in a video format.

[0030] The providing unit can provide specific advice or a training method based on the user's input. The providing unit provides specific advice based on the user's input, for example. For example, if the user inputs "I'm under a lot of pressure at work," the providing unit provides specific advice such as "Try some breathing exercises to relax." The providing unit can also provide specific training methods based on the user's input. For example, if the user inputs "I'm feeling stressed," the providing unit provides specific training methods such as "Try some meditation for stress management." This allows the providing unit to provide specific advice or a training method based on the user's input.

[0031] The providing unit can provide a response that affirms and encourages the user. The providing unit, for example, provides a response that affirms and encourages the user. For example, if the user inputs, "Work hasn't been going well lately," the providing unit will provide a response such as, "You're working hard. Don't forget that you're making progress, even if it's just a little at a time." The providing unit can also provide a response that affirms and encourages the user. For example, if the user inputs, "I'm having trouble with a lot of stress," the providing unit will provide a response such as, "That's tough. But you're working hard." In this way, the providing unit can provide a response that affirms and encourages the user.

[0032] Furthermore, the healthcare consultant system includes a generation unit that generates a personal AI according to the user's concerns. The generation unit generates a personal AI according to the user's concerns, for example. For example, if the user inputs "I have work-related concerns," the generation unit generates an AI that looks like a businessman. Also, if the user inputs "I have love concerns," the generation unit can generate an AI that looks like a counselor. In this way, the generation unit can generate a personal AI according to the user's concerns.

[0033] The generation unit can generate a character that corresponds to the user's worries. For example, if the user inputs "I have worries about work," the generation unit generates a character that looks like a businessman. Also, if the user inputs "I have worries about love," the generation unit can generate a character that looks like a counselor. In this way, the generation unit can generate a character that corresponds to the user's worries.

[0034] The providing unit can provide specific measures, such as breathing techniques for relaxation or stress management methods. The providing unit provides, for example, breathing techniques for relaxation. For example, if a user inputs "I'm feeling stressed," the providing unit provides a specific breathing technique, such as "Try deep breathing." The providing unit can also provide stress management methods. For example, if a user inputs "I'm under a lot of pressure at work," the providing unit provides a specific stress management method, such as "Try meditation." This allows the providing unit to provide specific measures, such as breathing techniques for relaxation or stress management methods.

[0035] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal reception method. For example, it preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze the content that the user has input in the past and suggest the optimal reception method to users who have similar concerns. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. In this way, the reception unit can analyze the user's past input history and select the optimal reception method.

[0036] The reception unit can perform filtering based on the user's current living situation and areas of interest when receiving input. For example, the reception unit performs filtering based on the user's current living situation and areas of interest when receiving input. For example, when the user inputs their current living situation, the reception unit performs optimal filtering based on that information. The reception unit can also register the user's areas of interest in advance and filter the input content based on that information. Furthermore, the reception unit can preferentially receive information that is highly relevant based on the user's living situation and areas of interest. This allows the reception unit to perform filtering based on the user's current living situation and areas of interest.

[0037] The reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information when receiving input. For example, the reception unit prioritizes receiving highly relevant inputs in consideration of the user's geographical location information when receiving input. For example, if the user is in a specific area, the reception unit can prioritize receiving information related to that area. Also, if the user is traveling, the reception unit can prioritize receiving highly relevant information based on the user's current location. Furthermore, if the user is participating in a specific event, the reception unit can prioritize receiving information related to that event. This allows the reception unit to prioritize receiving highly relevant inputs in consideration of the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and receive related input when receiving input. For example, the reception unit analyzes the user's social media activity and receives related input when receiving input. For example, the reception unit analyzes the user's social media activity and receives related worries and concerns with priority. The reception unit can also receive related input with priority based on information shared by the user on social media. Furthermore, the reception unit can also receive input related to a specific topic with priority from the user's social media activity. This allows the reception unit to analyze the user's social media activity and receive related input.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit performs a detailed analysis for an input with high importance. Also, the analysis unit can perform a simplified analysis for an input with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages depending on the importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the input.

[0040] The analysis unit can apply different analysis algorithms depending on the category of the input during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the input during analysis. For example, for input related to stress, the analysis unit applies an analysis algorithm specialized for stress management. Furthermore, for input related to health, the analysis unit can also apply an analysis algorithm specialized for health management. Furthermore, for input related to work, the analysis unit can apply an analysis algorithm specialized for improving work efficiency. This allows the analysis unit to apply different analysis algorithms depending on the category of the input.

[0041] The analysis unit can determine the analysis priority based on the time of input submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of input submission during analysis. For example, the analysis unit prioritizes analysis of input with high urgency. The analysis unit can also prioritize analysis of input that was submitted earlier. Furthermore, the analysis unit can gradually adjust the analysis priority based on the time of submission. This allows the analysis unit to determine the analysis priority based on the time of input submission.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the input during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the input during analysis. For example, the analysis unit prioritizes analysis of highly relevant input. Also, the analysis unit can postpone analysis of less relevant input. Furthermore, the analysis unit can adjust the order of analysis in stages based on the relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the input.

[0043] The providing unit can provide optimal advice by referring to the user's past behavioral history when providing advice. For example, the providing unit can provide optimal advice by referring to the user's past behavioral history when providing advice. For example, the providing unit can provide optimal advice based on advice that the user has tried in the past. The providing unit can also select effective advice from the user's past behavioral history. Furthermore, the providing unit can analyze the user's past behavioral history and provide the most appropriate advice. This allows the providing unit to provide optimal advice by referring to the user's past behavioral history.

[0044] The providing unit can customize the content of the advice based on the user's current living situation when providing advice. For example, the providing unit customizes the content of the advice based on the user's current living situation when providing advice. For example, when the user inputs their current living situation, the providing unit provides optimal advice based on that information. The providing unit can also provide customized advice based on the user's living situation. Furthermore, the providing unit can provide specific advice taking into account the user's current living situation. This allows the providing unit to customize the content of the advice based on the user's current living situation.

[0045] The providing unit can provide optimal advice by taking into account the user's geographical location information when providing advice. For example, the providing unit can provide optimal advice by taking into account the user's geographical location information when providing advice. For example, if the user is in a specific area, the providing unit can provide advice related to that area. Also, if the user is traveling, the providing unit can provide optimal advice based on the user's current location. Furthermore, if the user is participating in a specific event, the providing unit can provide advice related to the event. This allows the providing unit to provide optimal advice by taking into account the user's geographical location information.

[0046] The providing unit can adjust the content of the advice by analyzing the user's social media activity when providing advice. For example, the providing unit can analyze the user's social media activity when providing advice and adjust the content of the advice. For example, the providing unit can analyze the user's social media activity and provide related advice. The providing unit can also provide optimal advice based on information shared by the user on social media. Furthermore, the providing unit can provide advice related to a specific topic from the user's social media activity. This allows the providing unit to adjust the content of the advice by analyzing the user's social media activity.

[0047] The navigation unit can provide optimal navigation by referring to the user's past behavior history during navigation. For example, the navigation unit can provide optimal navigation by referring to the user's past behavior history during navigation. For example, the navigation unit can provide optimal navigation based on routes the user has used in the past. The navigation unit can also provide navigation that avoids congestion based on the user's past behavior history. Furthermore, the navigation unit can analyze the user's past behavior history and provide the most efficient navigation. This allows the navigation unit to provide optimal navigation by referring to the user's past behavior history.

[0048] The navigation unit can customize the navigation means based on the user's current living situation during navigation. For example, the navigation unit customizes the navigation means based on the user's current living situation during navigation. For example, when the user inputs their current living situation, the navigation unit provides the optimal navigation means based on that information. The navigation unit can also provide a customized navigation means based on the user's living situation. Furthermore, the navigation unit can provide a specific navigation means taking into account the user's current living situation. This allows the navigation unit to customize the navigation means based on the user's current living situation.

[0049] The navigation unit can select an optimal navigation method during navigation by taking into account the user's geographical location information. For example, the navigation unit selects an optimal navigation method during navigation by taking into account the user's geographical location information. For example, if the user is in a specific area, the navigation unit can provide a navigation method related to that area. Also, if the user is traveling, the navigation unit can provide an optimal navigation method based on the user's current location. Furthermore, if the user is participating in a specific event, the navigation unit can provide a navigation method related to the event. In this way, the navigation unit can select an optimal navigation method by taking into account the user's geographical location information.

[0050] The navigation unit can analyze the user's social media activity and suggest navigation methods during navigation. For example, the navigation unit can analyze the user's social media activity and suggest navigation methods during navigation. For example, the navigation unit can analyze the user's social media activity and provide related navigation methods. The navigation unit can also provide optimal navigation methods based on information shared by the user on social media. Furthermore, the navigation unit can provide navigation methods related to specific topics from the user's social media activity. This allows the navigation unit to analyze the user's social media activity and suggest navigation methods.

[0051] The generation unit can generate an optimal character by referring to the user's past behavior history when generating a character. For example, the generation unit generates an optimal character by referring to the user's past behavior history when generating a character. For example, the generation unit generates an optimal character based on the characteristics of characters that the user has previously preferred. The generation unit can also select an effective character from the user's past behavior history. Furthermore, the generation unit can analyze the user's past behavior history and generate the most suitable character. This allows the generation unit to generate an optimal character by referring to the user's past behavior history.

[0052] The generation unit can customize the characteristics of the character based on the user's current living situation when generating the character. For example, the generation unit customizes the characteristics of the character based on the user's current living situation when generating the character. For example, when the user inputs their current living situation, the generation unit generates an optimal character based on that information. The generation unit can also generate a customized character based on the user's living situation. Furthermore, the generation unit can generate a specific character taking into account the user's current living situation. This allows the generation unit to customize the characteristics of the character based on the user's current living situation.

[0053] The generation unit can generate an optimal character by taking into account the user's geographical location information when generating a character. For example, the generation unit generates an optimal character by taking into account the user's geographical location information when generating a character. For example, if the user is in a specific area, the generation unit generates a character related to that area. Also, if the user is traveling, the generation unit can generate an optimal character based on the user's current location. Furthermore, if the user is participating in a specific event, the generation unit can generate a character related to that event. In this way, the generation unit can generate an optimal character by taking into account the user's geographical location information.

[0054] The generation unit may analyze the user's social media activities and adjust the character's characteristics when generating a character. For example, the generation unit may analyze the user's social media activities and adjust the character's characteristics when generating a character. For example, the generation unit may analyze the user's social media activities and generate a related character. The generation unit may also generate an optimal character based on information shared by the user on social media. Furthermore, the generation unit may generate a character related to a specific topic from the user's social media activities. This allows the generation unit to analyze the user's social media activities and adjust the character's characteristics.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] When accepting input from a user, the acceptance unit can refer to the user's past input history and suggest the optimal input method. For example, if the user has frequently used text input in the past, the acceptance unit can preferentially suggest text input. Also, if the user prefers voice input, the acceptance unit can also suggest voice input. Furthermore, if the user tends to input during a specific time period, the acceptance unit can also suggest the optimal input method for that time period. In this way, the acceptance unit can utilize the user's past input history to provide the optimal input method for the user.

[0057] When providing specific advice based on the user's input, the providing unit can customize the content of the advice by taking into account the user's current living situation. For example, if the user inputs "I'm under a lot of pressure at work," the providing unit can provide specific advice such as "Try some breathing exercises to relax" by taking into account the user's living situation. Also, if the user inputs "I'm feeling stressed," the providing unit can provide a specific training method such as "Try some meditation to manage stress." This allows the providing unit to provide specific advice and training methods according to the user's living situation.

[0058] When providing a response that affirms and encourages the user, the providing unit can provide an optimal response by referring to the user's past behavioral history. For example, if the user inputs, "Work hasn't been going well lately," the providing unit can refer to the user's past behavioral history and provide a response such as, "You have overcome difficulties in the past. I'm sure you can overcome this one too." Also, if the user inputs, "I'm having trouble with a lot of stress," the providing unit can provide a response such as, "You have overcome similar situations before. You'll be fine this time too." In this way, the providing unit can utilize the user's past behavioral history to provide a response that affirms and encourages the user.

[0059] When generating a character that corresponds to the user's concerns, the generation unit can analyze the user's social media activity and generate a related character. For example, the generation unit generates an optimal character based on information shared by the user on social media. It can also generate a character related to a specific topic from the user's social media activity. It can also generate a character that incorporates the characteristics of characters that the user likes on social media. In this way, the generation unit can analyze the user's social media activity and generate a character that is optimal for the user.

[0060] When receiving input, the reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information. For example, if the user is in a specific area, information related to that area is prioritized for reception. Also, if the user is traveling, the reception unit can prioritize receiving highly relevant information based on the user's current location. Furthermore, if the user is participating in a specific event, the reception unit can prioritize receiving information related to that event. This allows the reception unit to prioritize receiving highly relevant inputs in consideration of the user's geographical location information.

[0061] When providing advice, the providing unit can provide optimal advice by referring to the user's past behavioral history. For example, the providing unit provides optimal advice based on advice that the user has tried in the past. The providing unit can also select effective advice from the user's past behavioral history. Furthermore, the providing unit can analyze the user's past behavioral history and provide the most appropriate advice. This allows the providing unit to provide optimal advice by referring to the user's past behavioral history.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit receives input from the user. The input from the user includes text input, voice input, image input, etc. The reception unit can use an interface for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image analysis technology for receiving image input. Step 2: The analysis unit analyzes the input received by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. For example, the analysis unit can analyze text input using natural language processing technology, image input using image analysis technology, and voice input using voice analysis technology. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice is provided in the form of a text message, a voice message, a video message, etc. For example, the providing unit can provide the advice as a text message, a voice message, or a video message. Step 4: The navigation unit performs navigation in a video format based on the advice provided by the providing unit. The navigation is performed by a method such as a video guide or an interactive map. For example, the navigation unit can perform navigation as a video guide or an interactive map.

[0064] (Example 2) A healthcare consultant system according to an embodiment of the present invention is a system that allows users to play the role of a talking healthcare consultant with the appearance of a hamster via chat or video. This system allows users to easily confide their concerns to a hamster-like AI via chat or video. For example, if a user inputs, "I've been feeling stressed lately," the hamster-like AI responds, "That's tough. What's causing you stress?" In this way, users can easily confide their concerns. The AI ​​then analyzes the user's input and provides appropriate advice and training methods. For example, if a user inputs, "I'm under a lot of pressure at work," the AI ​​provides specific advice such as, "Try some breathing exercises to relax." The AI ​​also responds affirmatively and encouragingly to the user. For example, it might respond, "You're doing a great job. Don't forget that you're making progress, even if it's just a little at a time." Furthermore, as a future service development, it is possible to consider generating personal AIs (PAIs) specialized for specific concerns, other than hamsters. For example, it is possible to generate AIs tailored to the user's concerns, such as an AI with the appearance of a businessman for work concerns and an AI with the appearance of a counselor for love concerns. This service allows users to easily confide their concerns and improve their mental health. AI-provided advice and training methods allow users to take specific measures. For example, specific advice tailored to the user's concerns, such as breathing techniques for relaxation or stress management methods, is provided. This allows the healthcare consultant system to accept and analyze user input, provide advice, and navigate in video format.

[0065] A healthcare consultant system according to an embodiment includes a reception unit, an analysis unit, a provision unit, and a navigation unit. The reception unit receives input from a user. The input from the user may include, but is not limited to, text input, voice input, image input, and the like. The reception unit provides, for example, an interface for receiving the text input. The reception unit may also use a microphone or voice recognition technology to receive voice input. The reception unit may also use a camera or image analysis technology to receive image input. The analysis unit analyzes the input received by the reception unit. The analysis may be performed using, for example, natural language processing, image analysis, or voice analysis, but is not limited to, these examples. For example, the analysis unit may analyze the text input using natural language processing technology. The analysis unit may also analyze the image input using image analysis technology. The analysis unit may also analyze the voice input using voice analysis technology. The provision unit provides advice based on the analysis results obtained by the analysis unit. The advice may be provided in the form of, for example, a text message, a voice message, a video message, or the like, but is not limited to these examples. For example, the providing unit provides the advice as a text message. The providing unit may also provide the advice as a voice message. Furthermore, the providing unit may also provide the advice as a video message. The navigation unit performs navigation in a video format based on the advice provided by the providing unit. Navigation may be performed, for example, as a video guide, an interactive map, or the like, but is not limited to these examples. For example, the navigation unit performs navigation as a video guide. Furthermore, the navigation unit may also perform navigation as an interactive map. This allows the healthcare consultant system according to the embodiment to accept and analyze user input, provide advice, and perform navigation in a video format.

[0066] The providing unit can provide specific advice or a training method based on the user's input. The providing unit provides specific advice based on the user's input, for example. For example, if the user inputs "I'm under a lot of pressure at work," the providing unit provides specific advice such as "Try some breathing exercises to relax." The providing unit can also provide specific training methods based on the user's input. For example, if the user inputs "I'm feeling stressed," the providing unit provides specific training methods such as "Try some meditation for stress management." This allows the providing unit to provide specific advice or a training method based on the user's input.

[0067] The providing unit can provide a response that affirms and encourages the user. The providing unit, for example, provides a response that affirms and encourages the user. For example, if the user inputs, "Work hasn't been going well lately," the providing unit will provide a response such as, "You're working hard. Don't forget that you're making progress, even if it's just a little at a time." The providing unit can also provide a response that affirms and encourages the user. For example, if the user inputs, "I'm having trouble with a lot of stress," the providing unit will provide a response such as, "That's tough. But you're working hard." In this way, the providing unit can provide a response that affirms and encourages the user.

[0068] Furthermore, the healthcare consultant system includes a generation unit that generates a personal AI according to the user's concerns. The generation unit generates a personal AI according to the user's concerns, for example. For example, if the user inputs "I have work-related concerns," the generation unit generates an AI that looks like a businessman. Also, if the user inputs "I have love concerns," the generation unit can generate an AI that looks like a counselor. In this way, the generation unit can generate a personal AI according to the user's concerns.

[0069] The generation unit can generate a character that corresponds to the user's worries. For example, if the user inputs "I have worries about work," the generation unit generates a character that looks like a businessman. Also, if the user inputs "I have worries about love," the generation unit can generate a character that looks like a counselor. In this way, the generation unit can generate a character that corresponds to the user's worries.

[0070] The providing unit can provide specific measures, such as breathing techniques for relaxation or stress management methods. The providing unit provides, for example, breathing techniques for relaxation. For example, if a user inputs "I'm feeling stressed," the providing unit provides a specific breathing technique, such as "Try deep breathing." The providing unit can also provide stress management methods. For example, if a user inputs "I'm under a lot of pressure at work," the providing unit provides a specific stress management method, such as "Try meditation." This allows the providing unit to provide specific measures, such as breathing techniques for relaxation or stress management methods.

[0071] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the timing of input reception based on the estimated user's emotion. For example, if the user is feeling stressed, the reception unit can estimate the emotion and prompt the user to input at a timing when the user can relax. Also, if the user is feeling depressed, the reception unit can estimate the emotion and prompt the user to input after waiting a while so that the user can sort out their feelings. Furthermore, if the user is excited, the reception unit can estimate the emotion and prompt the user to input at a timing when the user has calmed down. In this way, the reception unit can adjust the timing of input reception based on the user's emotion.

[0072] The reception unit can analyze the user's past input history and select the optimal reception method. The reception unit, for example, analyzes the user's past input history and selects the optimal reception method. For example, it preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze the content that the user has input in the past and suggest the optimal reception method to users who have similar concerns. Furthermore, the reception unit can also suggest the optimal reception method for a specific time period based on the user's past input history. In this way, the reception unit can analyze the user's past input history and select the optimal reception method.

[0073] The reception unit can perform filtering based on the user's current living situation and areas of interest when receiving input. For example, the reception unit performs filtering based on the user's current living situation and areas of interest when receiving input. For example, when the user inputs their current living situation, the reception unit performs optimal filtering based on that information. The reception unit can also register the user's areas of interest in advance and filter the input content based on that information. Furthermore, the reception unit can preferentially receive information that is highly relevant based on the user's living situation and areas of interest. This allows the reception unit to perform filtering based on the user's current living situation and areas of interest.

[0074] The reception unit can estimate the user's emotion and determine the priority of inputs to be received based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of inputs to be received based on the estimated user's emotion. For example, if the user is feeling strong stress, the reception unit can estimate the emotion and preferentially receive the input. Also, if the user is depressed, the reception unit can estimate the emotion and preferentially receive the input. Furthermore, if the user is excited, the reception unit can estimate the emotion and preferentially receive the input. In this way, the reception unit can determine the priority of inputs to be received based on the user's emotion.

[0075] The reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information when receiving input. For example, the reception unit prioritizes receiving highly relevant inputs in consideration of the user's geographical location information when receiving input. For example, if the user is in a specific area, the reception unit can prioritize receiving information related to that area. Also, if the user is traveling, the reception unit can prioritize receiving highly relevant information based on the user's current location. Furthermore, if the user is participating in a specific event, the reception unit can prioritize receiving information related to that event. This allows the reception unit to prioritize receiving highly relevant inputs in consideration of the user's geographical location information.

[0076] The reception unit can analyze the user's social media activity and receive related input when receiving input. For example, the reception unit analyzes the user's social media activity and receives related input when receiving input. For example, the reception unit analyzes the user's social media activity and receives related worries and concerns with priority. The reception unit can also receive related input with priority based on information shared by the user on social media. Furthermore, the reception unit can also receive input related to a specific topic with priority from the user's social media activity. This allows the reception unit to analyze the user's social media activity and receive related input.

[0077] The analysis unit can estimate the user's emotion and adjust the method of presentation of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the method of presentation of the analysis based on the estimated user's emotion. For example, if the user is feeling stressed, the analysis unit can estimate the emotion and select a simple and easy-to-understand method of presentation. Also, if the user is relaxed, the analysis unit can estimate the emotion and provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can estimate the emotion and select a visually stimulating method of presentation. This allows the analysis unit to adjust the method of presentation of the analysis based on the user's emotion.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the input during analysis. For example, the analysis unit performs a detailed analysis for an input with high importance. Also, the analysis unit can perform a simplified analysis for an input with low importance. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages depending on the importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the input.

[0079] The analysis unit can apply different analysis algorithms depending on the category of the input during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the input during analysis. For example, for input related to stress, the analysis unit applies an analysis algorithm specialized for stress management. Furthermore, for input related to health, the analysis unit can also apply an analysis algorithm specialized for health management. Furthermore, for input related to work, the analysis unit can apply an analysis algorithm specialized for improving work efficiency. This allows the analysis unit to apply different analysis algorithms depending on the category of the input.

[0080] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, the analysis unit can estimate the emotions and perform a short, to-the-point analysis. Also, if the user is relaxed, the analysis unit can estimate the emotions and perform a detailed analysis. Furthermore, if the user is excited, the analysis unit can estimate the emotions and perform a visually stimulating analysis. This allows the analysis unit to adjust the length of the analysis based on the user's emotions.

[0081] The analysis unit can determine the analysis priority based on the time of input submission during analysis. The analysis unit, for example, determines the analysis priority based on the time of input submission during analysis. For example, the analysis unit prioritizes analysis of input with high urgency. The analysis unit can also prioritize analysis of input that was submitted earlier. Furthermore, the analysis unit can gradually adjust the analysis priority based on the time of submission. This allows the analysis unit to determine the analysis priority based on the time of input submission.

[0082] The analysis unit can adjust the order of analysis based on the relevance of the input during analysis. For example, the analysis unit adjusts the order of analysis based on the relevance of the input during analysis. For example, the analysis unit prioritizes analysis of highly relevant input. Also, the analysis unit can postpone analysis of less relevant input. Furthermore, the analysis unit can adjust the order of analysis in stages based on the relevance. This allows the analysis unit to adjust the order of analysis based on the relevance of the input.

[0083] The providing unit can estimate the user's emotion and adjust the way in which advice is expressed based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and adjusts the way in which advice is expressed based on the estimated user's emotion. For example, if the user is feeling stressed, the providing unit can estimate the emotion and provide advice in kind words. Also, if the user is relaxed, the providing unit can estimate the emotion and provide detailed advice. Furthermore, if the user is excited, the providing unit can estimate the emotion and provide visually stimulating advice. This allows the providing unit to adjust the way in which advice is expressed based on the user's emotion.

[0084] The providing unit can provide optimal advice by referring to the user's past behavioral history when providing advice. For example, the providing unit can provide optimal advice by referring to the user's past behavioral history when providing advice. For example, the providing unit can provide optimal advice based on advice that the user has tried in the past. The providing unit can also select effective advice from the user's past behavioral history. Furthermore, the providing unit can analyze the user's past behavioral history and provide the most appropriate advice. This allows the providing unit to provide optimal advice by referring to the user's past behavioral history.

[0085] The providing unit can customize the content of the advice based on the user's current living situation when providing advice. For example, the providing unit customizes the content of the advice based on the user's current living situation when providing advice. For example, when the user inputs their current living situation, the providing unit provides optimal advice based on that information. The providing unit can also provide customized advice based on the user's living situation. Furthermore, the providing unit can provide specific advice taking into account the user's current living situation. This allows the providing unit to customize the content of the advice based on the user's current living situation.

[0086] The providing unit can estimate the user's emotion and determine the priority of advice based on the estimated user's emotion. The providing unit, for example, estimates the user's emotion and determines the priority of advice based on the estimated user's emotion. For example, if the user is feeling strong stress, the providing unit can estimate the emotion and provide the advice with priority. Also, if the user is depressed, the providing unit can estimate the emotion and provide the advice with priority. Furthermore, if the user is excited, the providing unit can estimate the emotion and provide the advice with priority. In this way, the providing unit can determine the priority of advice based on the user's emotion.

[0087] The providing unit can provide optimal advice by taking into account the user's geographical location information when providing advice. For example, the providing unit can provide optimal advice by taking into account the user's geographical location information when providing advice. For example, if the user is in a specific area, the providing unit can provide advice related to that area. Also, if the user is traveling, the providing unit can provide optimal advice based on the user's current location. Furthermore, if the user is participating in a specific event, the providing unit can provide advice related to the event. This allows the providing unit to provide optimal advice by taking into account the user's geographical location information.

[0088] The providing unit can adjust the content of the advice by analyzing the user's social media activity when providing advice. For example, the providing unit can analyze the user's social media activity when providing advice and adjust the content of the advice. For example, the providing unit can analyze the user's social media activity and provide related advice. The providing unit can also provide optimal advice based on information shared by the user on social media. Furthermore, the providing unit can provide advice related to a specific topic from the user's social media activity. This allows the providing unit to adjust the content of the advice by analyzing the user's social media activity.

[0089] The navigation unit can estimate the user's emotion and adjust the navigation method based on the estimated user's emotion. The navigation unit, for example, estimates the user's emotion and adjusts the navigation method based on the estimated user's emotion. For example, if the user is feeling stressed, the navigation unit can estimate the emotion and select a simple and easy-to-understand navigation method. Also, if the user is relaxed, the navigation unit can estimate the emotion and provide a detailed navigation method. Furthermore, if the user is excited, the navigation unit can estimate the emotion and select a visually stimulating navigation method. In this way, the navigation unit can adjust the navigation method based on the user's emotion.

[0090] The navigation unit can provide optimal navigation by referring to the user's past behavior history during navigation. For example, the navigation unit can provide optimal navigation by referring to the user's past behavior history during navigation. For example, the navigation unit can provide optimal navigation based on routes the user has used in the past. The navigation unit can also provide navigation that avoids congestion based on the user's past behavior history. Furthermore, the navigation unit can analyze the user's past behavior history and provide the most efficient navigation. This allows the navigation unit to provide optimal navigation by referring to the user's past behavior history.

[0091] The navigation unit can customize the navigation means based on the user's current living situation during navigation. For example, the navigation unit customizes the navigation means based on the user's current living situation during navigation. For example, when the user inputs their current living situation, the navigation unit provides the optimal navigation means based on that information. The navigation unit can also provide a customized navigation means based on the user's living situation. Furthermore, the navigation unit can provide a specific navigation means taking into account the user's current living situation. This allows the navigation unit to customize the navigation means based on the user's current living situation.

[0092] The navigation unit can estimate the user's emotion and determine the priority of navigation based on the estimated user's emotion. The navigation unit, for example, estimates the user's emotion and determines the priority of navigation based on the estimated user's emotion. For example, if the user is feeling strong stress, the navigation unit can estimate the emotion and provide the navigation with priority. Also, if the user is depressed, the navigation unit can estimate the emotion and provide the navigation with priority. Furthermore, if the user is excited, the navigation unit can estimate the emotion and provide the navigation with priority. In this way, the navigation unit can determine the priority of navigation based on the user's emotion.

[0093] The navigation unit can select an optimal navigation method during navigation by taking into account the user's geographical location information. For example, the navigation unit selects an optimal navigation method during navigation by taking into account the user's geographical location information. For example, if the user is in a specific area, the navigation unit can provide a navigation method related to that area. Also, if the user is traveling, the navigation unit can provide an optimal navigation method based on the user's current location. Furthermore, if the user is participating in a specific event, the navigation unit can provide a navigation method related to the event. In this way, the navigation unit can select an optimal navigation method by taking into account the user's geographical location information.

[0094] The navigation unit can analyze the user's social media activity and suggest navigation methods during navigation. For example, the navigation unit can analyze the user's social media activity and suggest navigation methods during navigation. For example, the navigation unit can analyze the user's social media activity and provide related navigation methods. The navigation unit can also provide optimal navigation methods based on information shared by the user on social media. Furthermore, the navigation unit can provide navigation methods related to specific topics from the user's social media activity. This allows the navigation unit to analyze the user's social media activity and suggest navigation methods.

[0095] The generation unit can estimate the user's emotion and adjust the expression method of the generated character based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the generated character based on the estimated user's emotion. For example, if the user is feeling stressed, the generation unit can estimate the emotion and generate a character with a gentle expression. Also, if the user is relaxed, the generation unit can estimate the emotion and generate a character with detailed expressions. Furthermore, if the user is excited, the generation unit can estimate the emotion and generate a visually stimulating character. This allows the generation unit to adjust the expression method of the generated character based on the user's emotion.

[0096] The generation unit can generate an optimal character by referring to the user's past behavior history when generating a character. For example, the generation unit generates an optimal character by referring to the user's past behavior history when generating a character. For example, the generation unit generates an optimal character based on the characteristics of characters that the user has previously preferred. The generation unit can also select an effective character from the user's past behavior history. Furthermore, the generation unit can analyze the user's past behavior history and generate the most suitable character. This allows the generation unit to generate an optimal character by referring to the user's past behavior history.

[0097] The generation unit can customize the characteristics of the character based on the user's current living situation when generating the character. For example, the generation unit customizes the characteristics of the character based on the user's current living situation when generating the character. For example, when the user inputs their current living situation, the generation unit generates an optimal character based on that information. The generation unit can also generate a customized character based on the user's living situation. Furthermore, the generation unit can generate a specific character taking into account the user's current living situation. This allows the generation unit to customize the characteristics of the character based on the user's current living situation.

[0098] The generation unit can estimate the user's emotion and determine the priority of characters to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and determines the priority of characters to be generated based on the estimated user's emotion. For example, if the user is feeling strong stress, the generation unit can estimate the emotion and generate a character that matches the emotion with priority. Also, if the user is depressed, the generation unit can estimate the emotion and generate a character that matches the emotion with priority. Furthermore, if the user is excited, the generation unit can estimate the emotion and generate a character that matches the emotion with priority. This allows the generation unit to determine the priority of characters to be generated based on the user's emotion.

[0099] The generation unit can generate an optimal character by taking into account the user's geographical location information when generating a character. For example, the generation unit generates an optimal character by taking into account the user's geographical location information when generating a character. For example, if the user is in a specific area, the generation unit generates a character related to that area. Also, if the user is traveling, the generation unit can generate an optimal character based on the user's current location. Furthermore, if the user is participating in a specific event, the generation unit can generate a character related to that event. In this way, the generation unit can generate an optimal character by taking into account the user's geographical location information.

[0100] The generation unit may analyze the user's social media activities and adjust the character's characteristics when generating a character. For example, the generation unit may analyze the user's social media activities and adjust the character's characteristics when generating a character. For example, the generation unit may analyze the user's social media activities and generate a related character. The generation unit may also generate an optimal character based on information shared by the user on social media. Furthermore, the generation unit may generate a character related to a specific topic from the user's social media activities. This allows the generation unit to analyze the user's social media activities and adjust the character's characteristics. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, navigation unit, and generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives text input or voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs natural language processing and image analysis. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides text messages or voice messages to the user. The navigation unit is realized, for example, by the output device 40 of the smart device 14 and provides video guides. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates personal AI that responds to the user's concerns. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, navigation unit, and generation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs natural language processing and voice analysis. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides voice messages to the user. The navigation unit is realized, for example, by the display of the smart glasses 214 and provides video-format guidance. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates a personal AI that responds to the user's concerns. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, navigation unit, and generation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives voice input from the user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and performs natural language processing and voice analysis. The provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides voice messages to the user. The navigation unit is realized by the display 343 of the headset-type terminal 314 and provides video-format guidance. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates personal AI that responds to the user's concerns. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, navigation unit, and generation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs natural language processing and voice analysis. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides voice messages to the user. The navigation unit is realized, for example, by the display of the robot 414 and provides video-format guidance. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates personal AI that responds to the user's concerns.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] When accepting input from a user, the acceptance unit can refer to the user's past input history and suggest the optimal input method. For example, if the user has frequently used text input in the past, the acceptance unit can preferentially suggest text input. Also, if the user prefers voice input, the acceptance unit can also suggest voice input. Furthermore, if the user tends to input during a specific time period, the acceptance unit can also suggest the optimal input method for that time period. In this way, the acceptance unit can utilize the user's past input history to provide the optimal input method for the user.

[0103] When providing specific advice based on the user's input, the providing unit can customize the content of the advice by taking into account the user's current living situation. For example, if the user inputs "I'm under a lot of pressure at work," the providing unit can provide specific advice such as "Try some breathing exercises to relax" by taking into account the user's living situation. Also, if the user inputs "I'm feeling stressed," the providing unit can provide a specific training method such as "Try some meditation to manage stress." This allows the providing unit to provide specific advice and training methods according to the user's living situation.

[0104] When providing a response that affirms and encourages the user, the providing unit can provide an optimal response by referring to the user's past behavioral history. For example, if the user inputs, "Work hasn't been going well lately," the providing unit can refer to the user's past behavioral history and provide a response such as, "You have overcome difficulties in the past. I'm sure you can overcome this one too." Also, if the user inputs, "I'm having trouble with a lot of stress," the providing unit can provide a response such as, "You have overcome similar situations before. You'll be fine this time too." In this way, the providing unit can utilize the user's past behavioral history to provide a response that affirms and encourages the user.

[0105] When generating a personal AI that responds to the user's concerns, the generation unit can estimate the user's emotions and adjust the expression method of the generated AI based on the estimated emotions. For example, if the user is feeling stressed, the generation unit can estimate the emotions and generate an AI with a gentle expression. Also, if the user is relaxed, the generation unit can estimate the emotions and generate an AI with detailed expressions. Furthermore, if the user is excited, the generation unit can estimate the emotions and generate a visually stimulating AI. This allows the generation unit to adjust the expression method of the generated AI based on the user's emotions.

[0106] When generating a character that corresponds to the user's concerns, the generation unit can analyze the user's social media activity and generate a related character. For example, the generation unit generates an optimal character based on information shared by the user on social media. It can also generate a character related to a specific topic from the user's social media activity. It can also generate a character that incorporates the characteristics of characters that the user likes on social media. In this way, the generation unit can analyze the user's social media activity and generate a character that is optimal for the user.

[0107] When providing breathing techniques for relaxation or stress management methods, the providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated emotions. For example, if the user is feeling stressed, the providing unit can estimate the emotions and provide advice in gentle words. Also, if the user is relaxed, the providing unit can estimate the emotions and provide detailed advice. Furthermore, if the user is excited, the providing unit can estimate the emotions and provide visually stimulating advice. This allows the providing unit to adjust the way the advice is presented based on the user's emotions.

[0108] The reception unit can estimate the user's emotions and adjust the timing of input reception based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can estimate the emotions and prompt the user to input at a time when the user can relax. Also, if the user is feeling depressed, the reception unit can estimate the emotions and prompt the user to input after waiting a while so that the user can organize their feelings. Furthermore, if the user is excited, the reception unit can estimate the emotions and prompt the user to input at a time when the user has calmed down. This allows the reception unit to adjust the timing of input reception based on the user's emotions.

[0109] When receiving input, the reception unit can prioritize receiving highly relevant inputs in consideration of the user's geographical location information. For example, if the user is in a specific area, information related to that area is prioritized for reception. Also, if the user is traveling, the reception unit can prioritize receiving highly relevant information based on the user's current location. Furthermore, if the user is participating in a specific event, the reception unit can prioritize receiving information related to that event. This allows the reception unit to prioritize receiving highly relevant inputs in consideration of the user's geographical location information.

[0110] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can estimate the emotions and select a simple, easy-to-understand way of presentation. If the user is relaxed, the analysis unit can also estimate the emotions and provide detailed analysis results. Furthermore, if the user is excited, the analysis unit can also estimate the emotions and select a visually stimulating way of presentation. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions.

[0111] When providing advice, the providing unit can provide optimal advice by referring to the user's past behavioral history. For example, the providing unit provides optimal advice based on advice that the user has tried in the past. The providing unit can also select effective advice from the user's past behavioral history. Furthermore, the providing unit can analyze the user's past behavioral history and provide the most appropriate advice. This allows the providing unit to provide optimal advice by referring to the user's past behavioral history.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit receives input from the user. The input from the user includes text input, voice input, image input, etc. The reception unit can use an interface for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image analysis technology for receiving image input. Step 2: The analysis unit analyzes the input received by the reception unit. The analysis is performed using methods such as natural language processing, image analysis, and voice analysis. For example, the analysis unit can analyze text input using natural language processing technology, image input using image analysis technology, and voice input using voice analysis technology. Step 3: The providing unit provides advice based on the analysis results obtained by the analyzing unit. The advice is provided in the form of a text message, a voice message, a video message, etc. For example, the providing unit can provide the advice as a text message, a voice message, or a video message. Step 4: The navigation unit performs navigation in a video format based on the advice provided by the providing unit. The navigation is performed by a method such as a video guide or an interactive map. For example, the navigation unit can perform navigation as a video guide or an interactive map.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; an analysis unit that analyzes the input received by the reception unit; a providing unit that provides advice based on the analysis result obtained by the analyzing unit; a navigation unit that performs navigation in a video format based on the advice provided by the providing unit. A system characterized by:

2. The providing unit Providing specific advice or training methods based on user input 2. The system of claim 1.

3. The providing unit Respond with affirmation and encouragement 2. The system of claim 1.

4. The navigation unit Equipped with a generation unit that generates personal AI according to the user's concerns 2. The system of claim 1.

5. The generation unit Generate a character that responds to the user's concerns 5. The system of claim 4.

6. The providing unit Providing breathing exercises or stress management techniques and specific strategies for relaxation 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1.

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

9. The reception unit When accepting input, filter based on the user's current life situation and areas of interest.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

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