system

Abstract

The system according to the embodiment aims to provide a personalized virtual partner to effectively relieve the user's stress. [Solution] A system according to an embodiment includes a creation unit, a communication unit, a monitoring unit, and an advice unit. The creation unit creates a virtual partner desired by the user. The communication unit communicates in real time with the virtual partner created by the creation unit. The monitoring unit monitors the user's stress level based on information obtained by the communication unit. The advice unit provides relaxation methods and stress relief advice based on the stress level obtained by the monitoring 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 techniques have not adequately provided a personalized virtual partner to effectively relieve users' stress, and there is room for improvement.

[0005] The system according to the embodiment aims to provide a personalized virtual partner to effectively relieve the user's stress. [Means for solving the problem]

[0006] The system according to the embodiment includes a creation unit, a communication unit, a monitoring unit, and an advice unit. The creation unit creates a virtual partner desired by the user. The communication unit communicates in real time with the virtual partner created by the creation unit. The monitoring unit monitors the user's stress level based on information obtained by the communication unit. The advice unit provides advice on relaxation methods and stress relief based on the stress level obtained by the monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide a personalized virtual partner to effectively relieve the user's stress. [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 virtual partner providing system according to an embodiment of the present invention allows a user to create their ideal virtual partner and communicate with that virtual partner in real time. The virtual partner providing system allows a user to create a virtual partner with their ideal speaking style, personality, face, body shape, and knowledge, and then talk to that virtual partner face-to-face and confide their worries via LINE (registered trademark) or the web. For example, the virtual partner providing system allows a user to create their ideal virtual partner through a dedicated application. For example, the virtual partner providing system allows the user to configure detailed settings such as speaking style, personality, face, body shape, and knowledge. The virtual partner providing system then communicates with the user in real time using a generation AI. For example, the virtual partner providing system allows a user to talk to a virtual partner face-to-face via LINE or the web, confide their worries, and share daily events. The generation AI analyzes the user's comments and generates an appropriate response. For example, if a user confides in the virtual partner about work-related worries, the virtual partner can empathize and offer words of encouragement. Furthermore, the virtual partner providing system monitors the user's stress level and provides relaxation and stress relief advice as needed. For example, the system can reduce the user's stress by suggesting deep breathing or light exercise. In this way, the virtual partner provision system allows users to communicate with their ideal partner, thereby relieving mental stress and achieving peace of mind. In addition, virtual partners are available 24 hours a day and can provide advice to users at any time, allowing users to spend their time with peace of mind without feeling lonely.

[0029] A virtual partner providing system according to an embodiment includes a creation unit, a communication unit, a monitoring unit, and an advice unit. The creation unit creates a virtual partner desired by the user. For example, the creation unit accepts user input and creates a virtual partner with an ideal speaking style, personality, face, body shape, and knowledge. The creation unit can also estimate the user's emotions and adjust the virtual partner's speaking style and personality based on the estimated user emotions. For example, if the user is feeling stressed, the creation unit can adjust the virtual partner's speaking style to be gentler and its personality to be more empathetic. The creation unit can also analyze the user's past input history and suggest optimal virtual partner settings. For example, the creation unit can suggest optimal virtual partner settings based on the speaking style and personality patterns previously selected by the user. The communication unit uses a generation AI to communicate in real time with the virtual partner created by the creation unit. The generation AI analyzes the user's comments and generates appropriate responses. For example, if the user confides in the creation unit about work-related worries, the generation AI can empathize and offer words of encouragement. The communication unit can also estimate the user's emotions and adjust the way the response is expressed based on the estimated user emotions. For example, if the user is feeling stressed, the virtual partner's responses will be gentler and they will use empathetic expressions. The monitoring unit monitors the user's stress level based on information obtained by the communication unit. The monitoring unit determines the stress level, for example, by analyzing the user's statements and facial expressions. The monitoring unit can also estimate the user's emotions and adjust the criteria for determining the stress level based on the estimated user emotions. For example, if the user is feeling stressed, the criteria for determining the stress level will be made stricter. The advice unit provides relaxation methods and stress relief advice based on the stress level obtained by the monitoring unit. The advice unit suggests relaxation methods such as deep breathing and light exercise. The advice unit can also estimate the user's emotions and adjust the relaxation methods and stress relief advice based on the estimated user emotions.For example, if the user is feeling stressed, the virtual partner providing system according to the embodiment suggests deep breathing or meditation, allowing the user to communicate with their ideal virtual partner, thereby relieving mental stress and achieving peace of mind.

[0030] The creation unit can accept user input and create a virtual partner with the user's desired speaking style, personality, face, body shape, and knowledge. The creation unit creates a virtual partner based on, for example, detailed settings of the user's speaking style, personality, face, body shape, and knowledge. For example, if the user desires a partner who speaks gently, the creation unit creates a virtual partner reflecting those settings. Also, if the user desires a knowledgeable partner who is willing to give advice, the creation unit can create a virtual partner reflecting those settings. Furthermore, the creation unit can estimate the user's emotions and adjust the virtual partner's speaking style and personality based on the estimated emotions. For example, if the user is feeling stressed, the creation unit adjusts the virtual partner's speaking style to be gentle and its personality to be empathetic. This allows the user to create a virtual partner that matches their ideal.

[0031] The communication unit can use a generation AI to analyze the content of a user's statements and generate an appropriate response. For example, if a user confides in the communication unit about work-related worries, the generation AI can empathize and offer words of encouragement. The communication unit can also estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is feeling stressed, the communication unit can make the virtual partner's responses gentler and use empathetic expressions. Furthermore, the communication unit can improve the accuracy of responses by referring to the user's past statement history. For example, it can provide relevant responses based on what the user has said in the past. This enables real-time communication by generating appropriate responses based on the user's statements.

[0032] The monitoring unit can determine the stress level by analyzing the content of the user's statements and facial expressions. The monitoring unit, for example, determines the stress level by analyzing the content of the user's statements. For example, if the user frequently makes statements about stress, the monitoring unit determines the stress level to be high. The monitoring unit can also determine the stress level by analyzing the user's facial expressions. For example, if the user's facial expression is tense, the monitoring unit determines the stress level to be high. Furthermore, the monitoring unit can estimate the user's emotions and adjust the criteria for determining the stress level based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit tightens the criteria for determining the stress level. In this way, the stress level can be accurately determined by analyzing the content of the user's statements and facial expressions.

[0033] The advice unit can provide relaxation methods such as deep breathing and light exercise and stress relief advice. The advice unit suggests relaxation methods such as deep breathing and light exercise. For example, if the user is feeling stressed, the advice unit suggests deep breathing. The advice unit can also estimate the user's emotions and adjust the relaxation methods and stress relief advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit suggests deep breathing and meditation. Furthermore, the advice unit can analyze the user's past stress relief methods and provide optimal advice. For example, optimal advice is provided based on stress relief methods that have been effective for the user in the past. In this way, the user's stress is reduced by providing relaxation methods and stress relief advice according to the user's stress level.

[0034] The creation unit can analyze the user's past input history and suggest virtual partner settings. The creation unit, for example, analyzes the user's past input history and suggests optimal virtual partner settings. For example, the creation unit suggests optimal virtual partner settings based on the speech patterns and personality patterns selected by the user in the past. The creation unit can also suggest optimal virtual partner appearances based on facial and body features that the user has preferred in the past. Furthermore, the creation unit can suggest optimal virtual partner knowledge levels based on areas of knowledge that the user has previously sought. In this way, the creation unit can suggest optimal virtual partner settings by analyzing the user's past input history.

[0035] When creating a virtual partner, the creation unit can select a creation means according to the user's input method. The creation unit selects the optimal creation means according to the user's input method (voice, text, image, etc.), for example. For example, if the user uses voice input, the creation unit can create a virtual partner using voice recognition technology. Also, if the user uses text input, the creation unit can also create a virtual partner using text analysis technology. Furthermore, if the user uses image input, the creation unit can also create a virtual partner using image analysis technology. This allows the creation of a virtual partner to be made more efficient by selecting the optimal creation means according to the user's input method.

[0036] When creating a virtual partner, the creation unit can propose highly relevant settings by taking into account the user's geographical location information. The creation unit proposes virtual partner settings by taking into account, for example, the user's geographical location information. For example, if the user lives in a specific area, the creation unit can propose virtual partners who have topics and knowledge related to that area. Also, if the user is traveling, the creation unit can propose virtual partners who have topics and knowledge related to the travel destination. Furthermore, if the user belongs to a specific cultural sphere, the creation unit can propose virtual partners who have topics and knowledge related to that culture. In this way, highly relevant virtual partners can be proposed by taking into account the user's geographical location information.

[0037] The creation unit can analyze the user's social media activity and suggest related settings when creating a virtual partner. The creation unit, for example, analyzes the user's social media activity and suggests related settings. For example, the creation unit can suggest virtual partners with related topics and knowledge based on the content the user frequently posts on social media. The creation unit can also analyze the user's social media friendships and suggest virtual partners with common interests. Furthermore, the creation unit can analyze the user's social media activity time and suggest virtual partners with optimal communication time. In this way, related settings can be suggested by analyzing the user's social media activity.

[0038] When creating a virtual partner, the creation unit can adjust the creation method by reflecting the user's past feedback. The creation unit adjusts the virtual partner creation method by reflecting the user's past feedback, for example. For example, the creation unit adjusts the virtual partner's speaking style and personality based on the user's past feedback. The creation unit can also adjust the virtual partner's appearance and body shape based on the user's past feedback. Furthermore, the virtual partner's knowledge level can be adjusted based on the user's past feedback. In this way, a more appropriate virtual partner can be created by reflecting the user's past feedback.

[0039] The communication unit can adjust the details of a response based on the importance of the user's statement during communication. The communication unit adjusts the details of a response based on the importance of the user's statement, for example. For example, if a user confides an important concern, the communication unit can make the virtual partner's response detailed and provide specific advice. Also, if the user is talking about a light topic, the communication unit can make the virtual partner's response brief and provide a light reply. Furthermore, if the user asks a question, the details of the response can be adjusted based on the importance of the question. As a result, a more appropriate response can be provided by adjusting the details of the response based on the importance of the user's statement.

[0040] The communication unit can apply different response algorithms depending on the user's comment category during communication. The communication unit applies different response algorithms depending on the user's comment category, for example. For example, if the user is talking about work-related worries, the communication unit applies a work-related response algorithm. Also, if the user is talking about a private topic, the communication unit can apply a private-related response algorithm. Furthermore, if the user is talking about a hobby, the communication unit can apply a hobby-related response algorithm. In this way, applying different response algorithms depending on the user's comment category enables more appropriate responses.

[0041] The communication unit can improve the accuracy of responses during communication by referring to the user's past utterance history. The communication unit improves the accuracy of responses, for example, by referring to the user's past utterance history. For example, the communication unit provides a relevant response based on what the user has said in the past. It can also provide a detailed response based on what the user has asked in the past. It can also improve the accuracy of responses based on feedback the user has provided in the past. In this way, the accuracy of responses is improved by referring to the user's past utterance history.

[0042] During communication, the communication unit can determine the order of responses based on the time when the user submitted the utterance. The communication unit determines the order of responses based on, for example, the time when the user submitted the utterance. For example, responses are given priority to utterances recently submitted by the user. It is also possible to respond appropriately to utterances submitted by the user in the past. Furthermore, it is also possible to give priority to utterances submitted by the user during a specific time period. In this way, by determining the order of responses based on the time when the user submitted the utterance, more appropriate responses are possible.

[0043] The communication unit can adjust the order of responses based on the relevance of the user's utterances during communication. The communication unit adjusts the order of responses based on, for example, the relevance of the user's utterances. For example, if the user's utterances are highly relevant, the response is given priority. Also, if the user's utterances are less relevant, the response can be postponed. Furthermore, if the user's utterances are related to a specific theme, the order of responses can be adjusted based on that theme. In this way, adjusting the order of responses based on the relevance of the user's utterances enables more appropriate responses.

[0044] The communication unit can change the use of technical terms in responses during communication depending on the user's level of expertise. The communication unit, for example, adjusts the use of technical terms in responses depending on the user's level of expertise. For example, if the user has technical expertise, the communication unit responds using technical terms. Alternatively, if the user does not have technical expertise, the communication unit can respond in simple language. Furthermore, the use of appropriate technical terms can be adjusted depending on the user's level of expertise. This enables more appropriate communication by adjusting the use of technical terms in responses depending on the user's level of expertise.

[0045] During monitoring, the monitoring unit can improve the accuracy of the stress level by taking into account the interrelationships between the user's statements. The monitoring unit, for example, improves the accuracy of the stress level by taking into account the interrelationships between the user's statements. For example, if the user's statements are consistent, the monitoring unit can improve the accuracy of the stress level. Also, if the user's statements are contradictory, the monitoring unit can lower the accuracy of the stress level. Furthermore, if the user's statements are related to a specific theme, the accuracy of the stress level can be improved based on that theme. In this way, the accuracy of the stress level is improved by taking into account the interrelationships between the user's statements.

[0046] During monitoring, the monitoring unit can determine the stress level by taking into account the user's attribute information. The monitoring unit determines the stress level by taking into account the user's attribute information, for example. For example, the stress level is determined by taking into account the user's age and gender. The stress level can also be determined by taking into account the user's occupation and living environment. Furthermore, the stress level can also be determined by taking into account the user's health condition and past stress history. In this way, by taking into account the user's attribute information, a more accurate determination of the stress level is possible.

[0047] During monitoring, the monitoring unit can weight the stress level based on the frequency of the user's speech. The monitoring unit weights the stress level based on, for example, the frequency of the user's speech. For example, if the user speaks frequently, the monitoring unit may weight the stress level higher. Also, if the user does not speak much, the monitoring unit may weight the stress level lower. Furthermore, if the frequency of the user's speech suddenly changes, the weighting of the stress level can be adjusted based on that change. Thus, by weighting the stress level based on the frequency of the user's speech, a more accurate determination of the stress level is possible.

[0048] During monitoring, the monitoring unit can determine the stress level taking into account the geographical distribution of the user. The monitoring unit determines the stress level, for example, taking into account the geographical distribution of the user. For example, if the user lives in an urban area, the stress level can be determined taking into account stress factors specific to the city. Also, if the user lives in a rural area, the stress level can be determined taking into account stress factors specific to the rural area. Furthermore, if the user is traveling, the stress level can be determined taking into account the environment of the travel destination. In this way, by taking into account the geographical distribution of the user, a more accurate determination of the stress level is possible.

[0049] The monitoring unit can improve the accuracy of the stress level by referring to literature related to the user during monitoring. The monitoring unit, for example, improves the accuracy of the stress level by referring to literature related to the user. For example, the accuracy of the stress level can be improved based on literature that the user has read in the past. The accuracy of the stress level can also be improved based on literature that the user is currently reading. Furthermore, the accuracy of the stress level can also be improved based on literature in which the user is interested. In this way, the accuracy of the stress level can be improved by referring to literature related to the user.

[0050] During monitoring, the monitoring unit can evaluate the stress level taking into account the market value of the user. The monitoring unit determines the stress level, for example, taking into account the market value of the user. For example, the stress level is determined based on the user's occupation and income. The stress level can also be determined based on the user's social status. Furthermore, if the user's market value fluctuates, the stress level can also be determined based on that fluctuation. In this way, by taking the user's market value into account, a more accurate determination of the stress level is possible.

[0051] When giving advice, the advice unit can analyze the user's past stress relief methods and provide the advice. For example, the advice unit analyzes the user's past stress relief methods and provides optimal advice. For example, the advice unit provides optimal advice based on stress relief methods that have been effective for the user in the past. It can also suggest new methods based on stress relief methods that the user has tried in the past. Furthermore, it can also provide optimal advice based on feedback that the user has provided in the past. In this way, it is possible to provide optimal advice by analyzing the user's past stress relief methods.

[0052] When providing advice, the advice unit can customize a relaxation method based on the user's current living situation. The advice unit customizes the relaxation method based on the user's current living situation, for example. For example, if the user is busy, the advice unit can suggest a relaxation method that can be done in a short amount of time. Also, if the user has plenty of time, the advice unit can suggest a relaxation method that takes longer. Furthermore, if the user is in a specific environment, the advice unit can suggest a relaxation method that is suitable for that environment. In this way, by customizing the relaxation method based on the user's current living situation, more appropriate advice can be provided.

[0053] The advice unit can improve the method of giving advice by reflecting user feedback when giving advice. The advice unit, for example, improves the method of giving advice by reflecting user feedback. For example, the content of the advice is improved based on the feedback provided by the user. The method of giving advice can also be improved based on the feedback provided by the user. Furthermore, the frequency of advice can be adjusted based on the feedback provided by the user. In this way, the method of giving advice is improved by reflecting user feedback.

[0054] When giving advice, the advice unit can suggest a relaxation method taking into account the user's geographical location information. The advice unit, for example, suggests a relaxation method taking into account the user's geographical location information. For example, if the user lives in an urban area, the advice unit can suggest a relaxation method specific to the city. Also, if the user lives in the countryside, the advice unit can also suggest a relaxation method specific to the countryside. Furthermore, if the user is traveling, the advice unit can also suggest a relaxation method that is suitable for the environment of the travel destination. In this way, the optimal relaxation method can be suggested by taking into account the user's geographical location information.

[0055] When giving advice, the advice unit can analyze the user's social media activity and suggest relaxation methods. The advice unit, for example, analyzes the user's social media activity to suggest relaxation methods. For example, the advice unit can suggest related relaxation methods based on the content the user frequently posts on social media. It can also analyze the user's friendships on social media to suggest relaxation methods that share common interests. It can also analyze the user's social media activity time to suggest optimal relaxation methods. In this way, it is possible to suggest related relaxation methods by analyzing the user's social media activity.

[0056] When giving advice, the advice unit can adjust the relaxation method by reflecting the user's past feedback. The advice unit, for example, customizes the relaxation method by reflecting the user's past feedback. For example, the advice unit customizes the relaxation method based on feedback provided by the user in the past. It can also suggest a new method based on relaxation methods that the user has tried in the past. It can also suggest an optimal method based on relaxation methods that have been effective for the user in the past. In this way, a more appropriate relaxation method can be suggested by reflecting the user's past feedback.

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

[0058] The virtual partner providing system may also include a health management unit that acquires the user's health data and adjusts the virtual partner's responses. For example, the virtual partner may provide advice based on the user's health condition based on the user's heart rate and sleep data. The health management unit may also analyze the user's diet and exercise history and make suggestions to support the virtual partner in maintaining a healthy lifestyle. Furthermore, the health management unit may monitor the user's health data over the long term and, if an abnormality is detected, prompt the user to consult a medical institution. This allows the user to receive appropriate support based on their health condition.

[0059] The creation unit can also suggest virtual partner settings taking into account the user's hobbies and interests. For example, if the user is interested in music, the creation unit can suggest a virtual partner who is knowledgeable about music. If the user likes sports, the creation unit can suggest a virtual partner who has knowledge about sports. Furthermore, if the user likes traveling, the creation unit can suggest a virtual partner who can provide information about travel. In this way, a virtual partner that suits the user's hobbies and interests is provided.

[0060] The communication unit can learn based on what the user says and evolve the virtual partner's responses. For example, if a user frequently talks about a particular topic, the communication unit will deepen its knowledge of that topic. Also, if the user prefers a certain expression, the communication unit can incorporate that expression into its responses. Furthermore, the quality of responses can be improved based on user feedback. This makes communication with the user more natural and effective.

[0061] The monitoring unit can also analyze the user's environmental sounds to determine the stress level. For example, if the noise level around the user is high, the monitoring unit can determine the stress level as high. Alternatively, if the user's surroundings are quiet, the monitoring unit can determine the stress level as low. Furthermore, if the user's environmental sounds have a specific pattern, the stress level can be determined based on that pattern. This makes it possible to determine the user's stress level taking the user's environmental sounds into consideration.

[0062] The creation unit can also suggest virtual partner settings taking into account the user's geographical location information. For example, if the user lives in a specific area, it can suggest virtual partners who have topics and knowledge related to that area. Also, if the user is traveling, it can suggest virtual partners who have topics and knowledge related to the travel destination. Furthermore, if the user belongs to a specific cultural sphere, it can suggest virtual partners who have topics and knowledge related to that culture. In this way, by taking the user's geographical location information into account, it is possible to suggest highly relevant virtual partners.

[0063] The creation unit can also analyze the user's social media activity and suggest related settings. For example, it can suggest virtual partners with related topics and knowledge based on the content the user frequently posts on social media. It can also analyze the user's social media friendships and suggest virtual partners with common interests. It can also analyze the user's social media activity time and suggest virtual partners with optimal communication time. In this way, it is possible to suggest related settings by analyzing the user's social media activity.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The creation unit creates the virtual partner desired by the user. The creation unit accepts the user's input and creates a virtual partner with the user's ideal speaking style, personality, face, body shape, and knowledge. It can also estimate the user's emotions and adjust the virtual partner's speaking style and personality based on the estimated emotions. It also analyzes the user's past input history and suggests optimal virtual partner settings. Step 2: The communication unit uses the generation AI to communicate in real time with the virtual partner created by the creation unit. The generation AI analyzes the user's comments and generates appropriate responses. It can also estimate the user's emotions and adjust the way the responses are expressed based on the estimated emotions. Step 3: The monitoring unit monitors the user's stress level based on the information obtained by the communication unit. The monitoring unit determines the stress level by analyzing the user's speech and facial expressions. It can also estimate the user's emotions and adjust the criteria for determining the stress level based on the estimated emotions. Step 4: The advice unit provides relaxation methods and stress relief advice based on the stress level obtained by the monitoring unit. The advice unit suggests relaxation methods such as deep breathing and light exercise. It can also estimate the user's emotions and adjust the relaxation methods and stress relief advice based on the estimated emotions.

[0066] (Example 2) A virtual partner provision system according to an embodiment of the present invention allows users to create their ideal virtual partner and communicate with them in real time. The virtual partner provision system allows users to create a virtual partner with their ideal speaking style, personality, face, body shape, and knowledge, and then talk to that virtual partner face-to-face and confide their worries via LINE or the web. For example, the virtual partner provision system allows users to create their ideal virtual partner through a dedicated application. For example, the virtual partner provision system allows users to configure detailed settings such as speaking style, personality, face, body shape, and knowledge. The virtual partner provision system then communicates with the user in real time using a generation AI. For example, the virtual partner provision system allows users to talk to a virtual partner face-to-face via LINE or the web, confide their worries, and share daily events. The generation AI analyzes the user's comments and generates an appropriate response. For example, if a user confides in the virtual partner about work-related worries, the virtual partner can empathize and offer words of encouragement. Furthermore, the virtual partner provision system monitors the user's stress level and provides relaxation and stress relief advice as needed. For example, the system can reduce the user's stress by suggesting deep breathing or light exercise. In this way, the virtual partner provision system allows users to communicate with their ideal partner, thereby relieving mental stress and achieving peace of mind. In addition, virtual partners are available 24 hours a day and can provide advice to users at any time, allowing users to spend their time with peace of mind without feeling lonely.

[0067] A virtual partner providing system according to an embodiment includes a creation unit, a communication unit, a monitoring unit, and an advice unit. The creation unit creates a virtual partner desired by the user. For example, the creation unit accepts user input and creates a virtual partner with an ideal speaking style, personality, face, body shape, and knowledge. The creation unit can also estimate the user's emotions and adjust the virtual partner's speaking style and personality based on the estimated user emotions. For example, if the user is feeling stressed, the creation unit can adjust the virtual partner's speaking style to be gentler and its personality to be more empathetic. The creation unit can also analyze the user's past input history and suggest optimal virtual partner settings. For example, the creation unit can suggest optimal virtual partner settings based on the speaking style and personality patterns previously selected by the user. The communication unit uses a generation AI to communicate in real time with the virtual partner created by the creation unit. The generation AI analyzes the user's comments and generates appropriate responses. For example, if the user confides in the creation unit about work-related worries, the generation AI can empathize and offer words of encouragement. The communication unit can also estimate the user's emotions and adjust the way the response is expressed based on the estimated user emotions. For example, if the user is feeling stressed, the virtual partner's responses will be gentler and they will use empathetic expressions. The monitoring unit monitors the user's stress level based on information obtained by the communication unit. The monitoring unit determines the stress level, for example, by analyzing the user's statements and facial expressions. The monitoring unit can also estimate the user's emotions and adjust the criteria for determining the stress level based on the estimated user emotions. For example, if the user is feeling stressed, the criteria for determining the stress level will be made stricter. The advice unit provides relaxation methods and stress relief advice based on the stress level obtained by the monitoring unit. The advice unit suggests relaxation methods such as deep breathing and light exercise. The advice unit can also estimate the user's emotions and adjust the relaxation methods and stress relief advice based on the estimated user emotions.For example, if the user is feeling stressed, the virtual partner providing system according to the embodiment suggests deep breathing or meditation, allowing the user to communicate with their ideal virtual partner, thereby relieving mental stress and achieving peace of mind.

[0068] The creation unit can accept user input and create a virtual partner with the user's desired speaking style, personality, face, body shape, and knowledge. The creation unit creates a virtual partner based on, for example, detailed settings of the user's speaking style, personality, face, body shape, and knowledge. For example, if the user desires a partner who speaks gently, the creation unit creates a virtual partner reflecting those settings. Also, if the user desires a knowledgeable partner who is willing to give advice, the creation unit can create a virtual partner reflecting those settings. Furthermore, the creation unit can estimate the user's emotions and adjust the virtual partner's speaking style and personality based on the estimated emotions. For example, if the user is feeling stressed, the creation unit adjusts the virtual partner's speaking style to be gentle and its personality to be empathetic. This allows the user to create a virtual partner that matches their ideal.

[0069] The communication unit can use a generation AI to analyze the content of a user's statements and generate an appropriate response. For example, if a user confides in the communication unit about work-related worries, the generation AI can empathize and offer words of encouragement. The communication unit can also estimate the user's emotions and adjust the way the response is expressed based on the estimated emotions. For example, if the user is feeling stressed, the communication unit can make the virtual partner's responses gentler and use empathetic expressions. Furthermore, the communication unit can improve the accuracy of responses by referring to the user's past statement history. For example, it can provide relevant responses based on what the user has said in the past. This enables real-time communication by generating appropriate responses based on the user's statements.

[0070] The monitoring unit can determine the stress level by analyzing the content of the user's statements and facial expressions. The monitoring unit, for example, determines the stress level by analyzing the content of the user's statements. For example, if the user frequently makes statements about stress, the monitoring unit determines the stress level to be high. The monitoring unit can also determine the stress level by analyzing the user's facial expressions. For example, if the user's facial expression is tense, the monitoring unit determines the stress level to be high. Furthermore, the monitoring unit can estimate the user's emotions and adjust the criteria for determining the stress level based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit tightens the criteria for determining the stress level. In this way, the stress level can be accurately determined by analyzing the content of the user's statements and facial expressions.

[0071] The advice unit can provide relaxation methods such as deep breathing and light exercise and stress relief advice. The advice unit suggests relaxation methods such as deep breathing and light exercise. For example, if the user is feeling stressed, the advice unit suggests deep breathing. The advice unit can also estimate the user's emotions and adjust the relaxation methods and stress relief advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit suggests deep breathing and meditation. Furthermore, the advice unit can analyze the user's past stress relief methods and provide optimal advice. For example, optimal advice is provided based on stress relief methods that have been effective for the user in the past. In this way, the user's stress is reduced by providing relaxation methods and stress relief advice according to the user's stress level.

[0072] The creation unit can estimate the user's emotions and adjust the virtual partner's speaking style and personality based on the estimated user emotions. The creation unit, for example, estimates the user's emotions and adjusts the virtual partner's speaking style and personality based on the estimated emotions. For example, if the user is feeling stressed, the creation unit can adjust the virtual partner's speaking style to be gentle and the personality to be empathetic. Also, if the user is relaxed, the creation unit can adjust the virtual partner's speaking style to be friendly and the personality to be fun. Furthermore, if the user is angry, the creation unit can adjust the virtual partner's speaking style to be calm and the personality to be cool. In this way, more appropriate communication is possible by adjusting the virtual partner's speaking style and personality according to the user's emotions.

[0073] The creation unit can analyze the user's past input history and suggest virtual partner settings. The creation unit, for example, analyzes the user's past input history and suggests optimal virtual partner settings. For example, the creation unit suggests optimal virtual partner settings based on the speech patterns and personality patterns selected by the user in the past. The creation unit can also suggest optimal virtual partner appearances based on facial and body features that the user has preferred in the past. Furthermore, the creation unit can suggest optimal virtual partner knowledge levels based on areas of knowledge that the user has previously sought. In this way, the creation unit can suggest optimal virtual partner settings by analyzing the user's past input history.

[0074] The creation unit can perform filtering based on the user's current psychological state when creating a virtual partner. The creation unit filters virtual partners based on the user's current psychological state, for example. For example, if the user is tired, the creation unit can preferentially suggest virtual partners with a relaxing speaking style and personality. Also, if the user is excited, the creation unit can suggest virtual partners with a calm and collected speaking style and personality. Furthermore, if the user is sad, the creation unit can suggest virtual partners with a comforting speaking style and personality. In this way, by filtering virtual partners based on the user's current psychological state, more appropriate partners can be suggested.

[0075] When creating a virtual partner, the creation unit can select a creation means according to the user's input method. The creation unit selects the optimal creation means according to the user's input method (voice, text, image, etc.), for example. For example, if the user uses voice input, the creation unit can create a virtual partner using voice recognition technology. Also, if the user uses text input, the creation unit can also create a virtual partner using text analysis technology. Furthermore, if the user uses image input, the creation unit can also create a virtual partner using image analysis technology. This allows the creation of a virtual partner to be made more efficient by selecting the optimal creation means according to the user's input method.

[0076] The creation unit can estimate the user's emotions and adjust the virtual partner's appearance and knowledge based on the estimated user's emotions. The creation unit, for example, estimates the user's emotions and adjusts the virtual partner's appearance and knowledge based on the estimated emotions. For example, if the user is feeling stressed, the creation unit can adjust the virtual partner's appearance to be relaxing and its knowledge to be empathetic. Also, if the user is relaxed, the creation unit can adjust the virtual partner's appearance to be friendly and its knowledge to be about fun topics. Furthermore, if the user is angry, the creation unit can adjust the virtual partner's appearance to be calm and its knowledge to be cool. In this way, by adjusting the virtual partner's appearance and knowledge according to the user's emotions, a more appropriate partner can be provided.

[0077] When creating a virtual partner, the creation unit can propose highly relevant settings by taking into account the user's geographical location information. The creation unit proposes virtual partner settings by taking into account, for example, the user's geographical location information. For example, if the user lives in a specific area, the creation unit can propose virtual partners who have topics and knowledge related to that area. Also, if the user is traveling, the creation unit can propose virtual partners who have topics and knowledge related to the travel destination. Furthermore, if the user belongs to a specific cultural sphere, the creation unit can propose virtual partners who have topics and knowledge related to that culture. In this way, highly relevant virtual partners can be proposed by taking into account the user's geographical location information.

[0078] The creation unit can analyze the user's social media activity and suggest related settings when creating a virtual partner. The creation unit, for example, analyzes the user's social media activity and suggests related settings. For example, the creation unit can suggest virtual partners with related topics and knowledge based on the content the user frequently posts on social media. The creation unit can also analyze the user's social media friendships and suggest virtual partners with common interests. Furthermore, the creation unit can analyze the user's social media activity time and suggest virtual partners with optimal communication time. In this way, related settings can be suggested by analyzing the user's social media activity.

[0079] When creating a virtual partner, the creation unit can adjust the creation method by reflecting the user's past feedback. The creation unit adjusts the virtual partner creation method by reflecting the user's past feedback, for example. For example, the creation unit adjusts the virtual partner's speaking style and personality based on the user's past feedback. The creation unit can also adjust the virtual partner's appearance and body shape based on the user's past feedback. Furthermore, the virtual partner's knowledge level can be adjusted based on the user's past feedback. In this way, a more appropriate virtual partner can be created by reflecting the user's past feedback.

[0080] The communication unit can estimate the user's emotions and adjust the way responses are expressed based on the estimated user emotions. The communication unit, for example, estimates the user's emotions and adjusts the way responses are expressed based on the estimated emotions. For example, if the user is feeling stressed, the communication unit can make the virtual partner's responses gentler and use empathetic expressions. Also, if the user is relaxed, the communication unit can make the virtual partner's responses friendly and use fun expressions. Furthermore, if the user is angry, the communication unit can make the virtual partner's responses calmer and use calm expressions. This allows for more appropriate communication by adjusting the way responses are expressed according to the user's emotions.

[0081] The communication unit can adjust the details of a response based on the importance of the user's statement during communication. The communication unit adjusts the details of a response based on the importance of the user's statement, for example. For example, if a user confides an important concern, the communication unit can make the virtual partner's response detailed and provide specific advice. Also, if the user is talking about a light topic, the communication unit can make the virtual partner's response brief and provide a light reply. Furthermore, if the user asks a question, the details of the response can be adjusted based on the importance of the question. As a result, a more appropriate response can be provided by adjusting the details of the response based on the importance of the user's statement.

[0082] The communication unit can apply different response algorithms depending on the user's comment category during communication. The communication unit applies different response algorithms depending on the user's comment category, for example. For example, if the user is talking about work-related worries, the communication unit applies a work-related response algorithm. Also, if the user is talking about a private topic, the communication unit can apply a private-related response algorithm. Furthermore, if the user is talking about a hobby, the communication unit can apply a hobby-related response algorithm. In this way, applying different response algorithms depending on the user's comment category enables more appropriate responses.

[0083] The communication unit can improve the accuracy of responses during communication by referring to the user's past utterance history. The communication unit improves the accuracy of responses, for example, by referring to the user's past utterance history. For example, the communication unit provides a relevant response based on what the user has said in the past. It can also provide a detailed response based on what the user has asked in the past. It can also improve the accuracy of responses based on feedback the user has provided in the past. In this way, the accuracy of responses is improved by referring to the user's past utterance history.

[0084] The communication unit can estimate the user's emotions and adjust the length of the response based on the estimated user emotions. The communication unit, for example, estimates the user's emotions and adjusts the length of the response based on the estimated emotions. For example, if the user is feeling stressed, the communication unit can shorten the virtual partner's response and make it concise. Also, if the user is relaxed, the communication unit can lengthen the virtual partner's response and make it more detailed. Furthermore, if the user is in a hurry, the communication unit can shorten the virtual partner's response and get to the point. In this way, more appropriate communication is possible by adjusting the length of the response according to the user's emotions.

[0085] During communication, the communication unit can determine the order of responses based on the time when the user submitted the utterance. The communication unit determines the order of responses based on, for example, the time when the user submitted the utterance. For example, responses are given priority to utterances recently submitted by the user. It is also possible to respond appropriately to utterances submitted by the user in the past. Furthermore, it is also possible to give priority to utterances submitted by the user during a specific time period. In this way, by determining the order of responses based on the time when the user submitted the utterance, more appropriate responses are possible.

[0086] The communication unit can adjust the order of responses based on the relevance of the user's utterances during communication. The communication unit adjusts the order of responses based on, for example, the relevance of the user's utterances. For example, if the user's utterances are highly relevant, the response is given priority. Also, if the user's utterances are less relevant, the response can be postponed. Furthermore, if the user's utterances are related to a specific theme, the order of responses can be adjusted based on that theme. In this way, adjusting the order of responses based on the relevance of the user's utterances enables more appropriate responses.

[0087] The communication unit can change the use of technical terms in responses during communication depending on the user's level of expertise. The communication unit, for example, adjusts the use of technical terms in responses depending on the user's level of expertise. For example, if the user has technical expertise, the communication unit responds using technical terms. Alternatively, if the user does not have technical expertise, the communication unit can respond in simple language. Furthermore, the use of appropriate technical terms can be adjusted depending on the user's level of expertise. This enables more appropriate communication by adjusting the use of technical terms in responses depending on the user's level of expertise.

[0088] The monitoring unit can estimate the user's emotions and adjust the criteria for determining the stress level based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the criteria for determining the stress level based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit can tighten the criteria for determining the stress level. Also, if the user is relaxed, the monitoring unit can loosen the criteria for determining the stress level. Furthermore, if the user is angry, the monitoring unit can neutralize the criteria for determining the stress level. In this way, by adjusting the criteria for determining the stress level according to the user's emotions, more accurate determination of the stress level is possible.

[0089] During monitoring, the monitoring unit can improve the accuracy of the stress level by taking into account the interrelationships between the user's statements. The monitoring unit, for example, improves the accuracy of the stress level by taking into account the interrelationships between the user's statements. For example, if the user's statements are consistent, the monitoring unit can improve the accuracy of the stress level. Also, if the user's statements are contradictory, the monitoring unit can lower the accuracy of the stress level. Furthermore, if the user's statements are related to a specific theme, the accuracy of the stress level can be improved based on that theme. In this way, the accuracy of the stress level is improved by taking into account the interrelationships between the user's statements.

[0090] During monitoring, the monitoring unit can determine the stress level by taking into account the user's attribute information. The monitoring unit determines the stress level by taking into account the user's attribute information, for example. For example, the stress level is determined by taking into account the user's age and gender. The stress level can also be determined by taking into account the user's occupation and living environment. Furthermore, the stress level can also be determined by taking into account the user's health condition and past stress history. In this way, by taking into account the user's attribute information, a more accurate determination of the stress level is possible.

[0091] During monitoring, the monitoring unit can weight the stress level based on the frequency of the user's speech. The monitoring unit weights the stress level based on, for example, the frequency of the user's speech. For example, if the user speaks frequently, the monitoring unit may weight the stress level higher. Also, if the user does not speak much, the monitoring unit may weight the stress level lower. Furthermore, if the frequency of the user's speech suddenly changes, the weighting of the stress level can be adjusted based on that change. Thus, by weighting the stress level based on the frequency of the user's speech, a more accurate determination of the stress level is possible.

[0092] The monitoring unit can estimate the user's emotions and adjust the display method of the stress level based on the estimated user emotions. The monitoring unit, for example, estimates the user's emotions and adjusts the display method of the stress level based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit can display the stress level in a visually easy-to-understand manner. Also, if the user is relaxed, the monitoring unit can display the stress level in a concise manner. Furthermore, if the user is angry, the monitoring unit can display the stress level calmly. In this way, by adjusting the display method of the stress level according to the user's emotions, a more appropriate display is possible.

[0093] During monitoring, the monitoring unit can determine the stress level taking into account the geographical distribution of the user. The monitoring unit determines the stress level, for example, taking into account the geographical distribution of the user. For example, if the user lives in an urban area, the stress level can be determined taking into account stress factors specific to the city. Also, if the user lives in a rural area, the stress level can be determined taking into account stress factors specific to the rural area. Furthermore, if the user is traveling, the stress level can be determined taking into account the environment of the travel destination. In this way, by taking into account the geographical distribution of the user, a more accurate determination of the stress level is possible.

[0094] The monitoring unit can improve the accuracy of the stress level by referring to literature related to the user during monitoring. The monitoring unit, for example, improves the accuracy of the stress level by referring to literature related to the user. For example, the accuracy of the stress level can be improved based on literature that the user has read in the past. The accuracy of the stress level can also be improved based on literature that the user is currently reading. Furthermore, the accuracy of the stress level can also be improved based on literature in which the user is interested. In this way, the accuracy of the stress level can be improved by referring to literature related to the user.

[0095] During monitoring, the monitoring unit can evaluate the stress level taking into account the market value of the user. The monitoring unit determines the stress level, for example, taking into account the market value of the user. For example, the stress level is determined based on the user's occupation and income. The stress level can also be determined based on the user's social status. Furthermore, if the user's market value fluctuates, the stress level can also be determined based on that fluctuation. In this way, by taking the user's market value into account, a more accurate determination of the stress level is possible.

[0096] The advice unit can estimate the user's emotions and adjust the relaxation method or stress relief advice based on the estimated user's emotions. The advice unit, for example, estimates the user's emotions and adjusts the relaxation method or stress relief advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit can suggest deep breathing or meditation. Also, if the user is relaxed, the advice unit can suggest light exercise or a hobby. Furthermore, if the user is angry, the advice unit can suggest ways to calm down. In this way, more appropriate advice can be provided by adjusting the relaxation method or stress relief advice according to the user's emotions.

[0097] When giving advice, the advice unit can analyze the user's past stress relief methods and provide the advice. For example, the advice unit analyzes the user's past stress relief methods and provides optimal advice. For example, the advice unit provides optimal advice based on stress relief methods that have been effective for the user in the past. It can also suggest new methods based on stress relief methods that the user has tried in the past. Furthermore, it can also provide optimal advice based on feedback that the user has provided in the past. In this way, it is possible to provide optimal advice by analyzing the user's past stress relief methods.

[0098] When providing advice, the advice unit can customize a relaxation method based on the user's current living situation. The advice unit customizes the relaxation method based on the user's current living situation, for example. For example, if the user is busy, the advice unit can suggest a relaxation method that can be done in a short amount of time. Also, if the user has plenty of time, the advice unit can suggest a relaxation method that takes longer. Furthermore, if the user is in a specific environment, the advice unit can suggest a relaxation method that is suitable for that environment. In this way, by customizing the relaxation method based on the user's current living situation, more appropriate advice can be provided.

[0099] The advice unit can improve the method of giving advice by reflecting user feedback when giving advice. The advice unit, for example, improves the method of giving advice by reflecting user feedback. For example, the content of the advice is improved based on the feedback provided by the user. The method of giving advice can also be improved based on the feedback provided by the user. Furthermore, the frequency of advice can be adjusted based on the feedback provided by the user. In this way, the method of giving advice is improved by reflecting user feedback.

[0100] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. The advice unit, for example, estimates the user's emotions and determines the priority of advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit can prioritize providing advice on stress relief. Also, if the user is relaxed, the advice unit can also prioritize providing advice on relaxation methods. Furthermore, if the user is angry, the advice unit can also prioritize providing advice on how to stay calm. In this way, by determining the priority of advice according to the user's emotions, more appropriate advice can be provided.

[0101] When giving advice, the advice unit can suggest a relaxation method taking into account the user's geographical location information. The advice unit, for example, suggests a relaxation method taking into account the user's geographical location information. For example, if the user lives in an urban area, the advice unit can suggest a relaxation method specific to the city. Also, if the user lives in the countryside, the advice unit can also suggest a relaxation method specific to the countryside. Furthermore, if the user is traveling, the advice unit can also suggest a relaxation method that is suitable for the environment of the travel destination. In this way, the optimal relaxation method can be suggested by taking into account the user's geographical location information.

[0102] When giving advice, the advice unit can analyze the user's social media activity and suggest relaxation methods. The advice unit, for example, analyzes the user's social media activity to suggest relaxation methods. For example, the advice unit can suggest related relaxation methods based on the content the user frequently posts on social media. It can also analyze the user's friendships on social media to suggest relaxation methods that share common interests. It can also analyze the user's social media activity time to suggest optimal relaxation methods. In this way, it is possible to suggest related relaxation methods by analyzing the user's social media activity.

[0103] When giving advice, the advice unit can adjust the relaxation method by reflecting the user's past feedback. The advice unit, for example, customizes the relaxation method by reflecting the user's past feedback. For example, the advice unit customizes the relaxation method based on feedback provided by the user in the past. It can also suggest a new method based on relaxation methods that the user has tried in the past. It can also suggest an optimal method based on relaxation methods that have been effective for the user in the past. In this way, a more appropriate relaxation method can be suggested by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the creation unit, communication unit, monitoring unit, and advice unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the creation unit receives user input via the control unit 46A of the smart device 14 and creates an ideal virtual partner. The communication unit communicates with the user in real time using a generation AI via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's stress level via the specific processing unit 290 of the data processing device 12. The advice unit provides advice on relaxation methods and stress relief via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned creation unit, communication unit, monitoring unit, and advice unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the creation unit accepts user input via the control unit 46A of the smart glasses 214 and creates an ideal virtual partner. The communication unit communicates with the user in real time using a generation AI via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's stress level via the specific processing unit 290 of the data processing device 12. The advice unit provides advice on relaxation methods and stress relief via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the creation unit, communication unit, monitoring unit, and advice unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the creation unit receives user input via the control unit 46A of the headset-type terminal 314 and creates an ideal virtual partner. The communication unit communicates with the user in real time using a generation AI via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's stress level via the specific processing unit 290 of the data processing device 12. The advice unit provides advice on relaxation methods and stress relief via the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the creation unit, communication unit, monitoring unit, and advice unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the creation unit receives user input via the control unit 46A of the robot 414 and creates an ideal virtual partner. The communication unit communicates with the user in real time using a generation AI via the specific processing unit 290 of the data processing device 12. The monitoring unit monitors the user's stress level via the specific processing unit 290 of the data processing device 12. The advice unit provides advice on relaxation methods and stress relief via the specific processing unit 290 of the data processing device 12.

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

[0105] The virtual partner providing system may also include a health management unit that acquires the user's health data and adjusts the virtual partner's responses. For example, the virtual partner may provide advice based on the user's health condition based on the user's heart rate and sleep data. The health management unit may also analyze the user's diet and exercise history and make suggestions to support the virtual partner in maintaining a healthy lifestyle. Furthermore, the health management unit may monitor the user's health data over the long term and, if an abnormality is detected, prompt the user to consult a medical institution. This allows the user to receive appropriate support based on their health condition.

[0106] The creation unit can also suggest virtual partner settings taking into account the user's hobbies and interests. For example, if the user is interested in music, the creation unit can suggest a virtual partner who is knowledgeable about music. If the user likes sports, the creation unit can suggest a virtual partner who has knowledge about sports. Furthermore, if the user likes traveling, the creation unit can suggest a virtual partner who can provide information about travel. In this way, a virtual partner that suits the user's hobbies and interests is provided.

[0107] The communication unit can learn based on what the user says and evolve the virtual partner's responses. For example, if a user frequently talks about a particular topic, the communication unit will deepen its knowledge of that topic. Also, if the user prefers a certain expression, the communication unit can incorporate that expression into its responses. Furthermore, the quality of responses can be improved based on user feedback. This makes communication with the user more natural and effective.

[0108] The monitoring unit can also analyze the user's environmental sounds to determine the stress level. For example, if the noise level around the user is high, the monitoring unit can determine the stress level as high. Alternatively, if the user's surroundings are quiet, the monitoring unit can determine the stress level as low. Furthermore, if the user's environmental sounds have a specific pattern, the stress level can be determined based on that pattern. This makes it possible to determine the user's stress level taking the user's environmental sounds into consideration.

[0109] The advice unit can also estimate the user's emotions and adjust the timing of advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit can immediately suggest ways to relax. Also, if the user is relaxed, the advice unit can also suggest ways to relax later. Furthermore, if the user is angry, the advice unit can immediately suggest ways to calm down. In this way, advice is provided at an appropriate timing according to the user's emotions.

[0110] The creation unit can also estimate the user's emotions and adjust the virtual partner's appearance and knowledge based on the estimated emotions. For example, if the user is feeling stressed, the creation unit can adjust the virtual partner's appearance to be relaxing and its knowledge to be empathetic. Also, if the user is relaxed, the creation unit can adjust the virtual partner's appearance to be friendly and its knowledge to be about pleasant topics. Furthermore, if the user is angry, the creation unit can adjust the virtual partner's appearance to be calm and its knowledge to be cool. In this way, by adjusting the virtual partner's appearance and knowledge according to the user's emotions, a more suitable partner can be provided.

[0111] The creation unit can also suggest virtual partner settings taking into account the user's geographical location information. For example, if the user lives in a specific area, it can suggest virtual partners who have topics and knowledge related to that area. Also, if the user is traveling, it can suggest virtual partners who have topics and knowledge related to the travel destination. Furthermore, if the user belongs to a specific cultural sphere, it can suggest virtual partners who have topics and knowledge related to that culture. In this way, by taking the user's geographical location information into account, it is possible to suggest highly relevant virtual partners.

[0112] The creation unit can also analyze the user's social media activity and suggest related settings. For example, it can suggest virtual partners with related topics and knowledge based on the content the user frequently posts on social media. It can also analyze the user's social media friendships and suggest virtual partners with common interests. It can also analyze the user's social media activity time and suggest virtual partners with optimal communication time. In this way, it is possible to suggest related settings by analyzing the user's social media activity.

[0113] The communication unit can also estimate the user's emotions and adjust the length of the response based on the estimated emotions. For example, if the user is feeling stressed, the communication unit can shorten the virtual partner's response and make it more concise. If the user is relaxed, the communication unit can lengthen the virtual partner's response and make it more detailed. Furthermore, if the user is in a hurry, the communication unit can shorten the virtual partner's response and get to the point. This allows for more appropriate communication by adjusting the length of the response according to the user's emotions.

[0114] The monitoring unit can also estimate the user's emotions and adjust the display method of the stress level based on the estimated emotions. For example, if the user is feeling stressed, the monitoring unit can display the stress level in a visually easy-to-understand manner. Also, if the user is relaxed, the monitoring unit can display the stress level in a concise manner. Furthermore, if the user is angry, the monitoring unit can display the stress level calmly. This allows for a more appropriate display by adjusting the display method of the stress level according to the user's emotions.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The creation unit creates the virtual partner desired by the user. The creation unit accepts the user's input and creates a virtual partner with the user's ideal speaking style, personality, face, body shape, and knowledge. It can also estimate the user's emotions and adjust the virtual partner's speaking style and personality based on the estimated emotions. It also analyzes the user's past input history and suggests optimal virtual partner settings. Step 2: The communication unit uses the generation AI to communicate in real time with the virtual partner created by the creation unit. The generation AI analyzes the user's comments and generates appropriate responses. It can also estimate the user's emotions and adjust the way the responses are expressed based on the estimated emotions. Step 3: The monitoring unit monitors the user's stress level based on the information obtained by the communication unit. The monitoring unit determines the stress level by analyzing the user's speech and facial expressions. It can also estimate the user's emotions and adjust the criteria for determining the stress level based on the estimated emotions. Step 4: The advice unit provides relaxation methods and stress relief advice based on the stress level obtained by the monitoring unit. The advice unit suggests relaxation methods such as deep breathing and light exercise. It can also estimate the user's emotions and adjust the relaxation methods and stress relief advice based on the estimated emotions.

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

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

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

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0155] The data processing device 12 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 creation unit that creates a virtual partner desired by a user; a communication unit that communicates in real time with the virtual partner created by the creation unit; a monitoring unit that monitors a stress level of a user based on information obtained by the communication unit; an advice unit that provides advice on relaxation methods and stress relief based on the stress level obtained by the monitoring unit; A system characterized by:

2. The creation unit Accepts user input and creates a virtual partner with the user's desired speech style, personality, face, body type, and knowledge 2. The system of claim 1.

3. The communication unit Generative AI is used to analyze what the user says and generate an appropriate response.

2. The system of claim 1.

4. The monitoring unit Analyzing user speech and facial expressions to determine stress levels 2. The system of claim 1.

5. The advice unit Providing relaxation and stress relief advice, such as deep breathing and gentle exercise 2. The system of claim 1.

6. The creation unit Estimate the user's emotions and adjust the virtual partner's speech style and personality based on the estimated user emotions.

2. The system of claim 1.

7. The creation unit Analyzes the user's past input history and suggests virtual partner settings 2. The system of claim 1.

8. The creation unit Filtering virtual partners based on the user's current state of mind when creating them 2. The system of claim 1.

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