System

Abstract

An object of a system according to an embodiment is to effectively eliminate daily stress and dissatisfaction of a user.SOLUTION: A system includes an avatar selection unit, a reception unit, an analysis unit, and a provision unit. The avatar selection unit allows the user to select an avatar. The reception unit receives the user's dissatisfaction. The analysis unit analyzes the data received by the reception unit. The providing unit provides advice on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide users with means to effectively relieve everyday stress and frustration, and there is room for improvement.

[0005] The system according to the embodiment aims to enable users to effectively relieve everyday stress and frustration. [Means for solving the problem]

[0006] The system according to the embodiment includes an avatar selection unit, a reception unit, an analysis unit, and a provision unit. The avatar selection unit allows a user to select an avatar. The reception unit receives complaints from the user. The analysis unit analyzes the data received by the reception unit. The provision unit provides advice based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to effectively relieve everyday stress and frustration. [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) In a virtual friend system according to an embodiment of the present invention, when a user selects an avatar and vents their grievances, an AI listens to, understands, and provides appropriate advice. The virtual friend system includes an avatar selection unit through which the user selects an avatar, a reception unit that receives the user's grievances, an analysis unit that analyzes the grievances received by the reception unit, and a provision unit that provides advice based on the analysis results. For example, the virtual friend system allows a user to select an avatar of their choice. For example, avatars with different genders, ages, and clothing are available, allowing users to select an avatar that suits their preferences. Next, when the user vents their grievances, an AI listens to and understands them. The AI ​​analyzes the user's utterances using natural language processing technology and understands the user's emotions using emotion analysis technology. For example, if a user says, "Work was tough today," the AI ​​analyzes the utterance and understands that the user is tired. Based on the analysis results, the AI ​​provides appropriate advice. For example, if the user is tired, the AI ​​provides advice such as, "Take it easy and rest today." It is also possible to provide advice customized to the user's needs. For example, if a user wants to relax, the AI ​​will offer advice such as, "Try listening to some relaxing music." This allows the virtual friend system to empathize with everyday stress and complaints and provide support through dialogue. This allows the virtual friend system to support the mental health of people who have limited time to relieve stress in today's busy society. For example, by providing a place where users can vent and sort out their feelings while maintaining anonymity, the system can alleviate the complexities of interpersonal relationships and privacy concerns. Advances in AI are enabling more natural conversations and the provision of customized advice tailored to individual needs.

[0029] The virtual friend system according to the embodiment includes an avatar selection unit, a reception unit, an analysis unit, and a provision unit. The avatar selection unit allows a user to select an avatar. The user can select from avatars with different genders, ages, clothing, etc. For example, the avatar selection unit displays multiple avatars, allowing the user to select one based on their preferences. The reception unit receives a user's complaints. The user can express their complaints using text input or voice input. For example, the reception unit receives text or voice data entered by the user. The analysis unit analyzes the content received by the reception unit. The analysis unit analyzes the user's utterances using, for example, natural language processing technology. For example, the analysis unit performs morphological analysis or grammatical analysis on the user's utterances to understand their meaning. The analysis unit can also understand the user's emotions using emotion analysis technology. For example, the analysis unit extracts emotions from the user's utterances and understands the user's emotions. The provision unit provides advice based on the results of the analysis by the analysis unit. For example, the provision unit generates appropriate advice based on the analysis results. For example, if the user is tired, the providing unit provides advice such as "Take a good rest today." The providing unit can also provide advice customized to the user's needs. For example, if the user wants to relax, the providing unit provides advice such as "Try listening to relaxing music." In this way, the virtual friend system according to the embodiment can support the user's mental health by receiving and analyzing the user's complaints and providing appropriate advice.

[0030] The avatar selection unit may allow the user to select from several avatars. For example, the avatar selection unit may display a plurality of avatars, allowing the user to select one that suits their preferences. For example, the avatar selection unit may provide different types of avatars, such as animal characters and robot characters. The avatar selection unit may also allow the user to customize the appearance and clothing of the avatar. For example, the avatar selection unit may allow the user to select clothing and accessories for the avatar. This allows the user to select an avatar that suits their preferences.

[0031] The reception unit can analyze the user's utterances using natural language processing technology. The reception unit receives, for example, text or voice data input by the user. For example, if the user uses text input to complain, the reception unit receives the text data. Also, if the user uses voice input to complain, the reception unit receives the voice data. The reception unit analyzes the received data using natural language processing technology. For example, the reception unit performs morphological analysis to break down the user's utterances into words. The reception unit can also perform grammatical analysis to analyze the grammatical structure of the user's utterances. Furthermore, the reception unit can perform semantic analysis to understand the meaning of the user's utterances. This allows the reception unit to accurately analyze the user's utterances.

[0032] The analysis unit can understand the user's emotions using emotion analysis technology. The analysis unit, for example, extracts emotions from the user's speech. For example, the analysis unit detects emotional expressions contained in the user's speech and understands those emotions. The analysis unit can also analyze emotions from audio data. For example, the analysis unit analyzes the tone and speed of the user's voice to understand the user's emotions. Furthermore, the analysis unit can also understand the user's emotions using facial expression analysis technology. For example, the analysis unit captures the user's facial expressions with a camera and analyzes emotions from the facial expressions. This allows the analysis unit to accurately understand the user's emotions.

[0033] The providing unit can provide advice customized based on the analysis results. The providing unit generates appropriate advice based on the analysis results, for example. For example, if the user is tired, the providing unit provides advice such as "Try to get plenty of rest today." The providing unit can also provide advice customized to the user's needs. For example, if the user wants to relax, the providing unit provides advice such as "Try listening to some relaxing music." Furthermore, the providing unit can customize advice based on the user's past data and current situation. For example, the providing unit analyzes the user's past utterance history and provides advice tailored to the user's preferences. This allows the providing unit to provide advice that meets the user's needs.

[0034] The providing unit can suggest music that promotes relaxation according to the user's needs. For example, if the user wants to relax, the providing unit suggests music that promotes relaxation. For example, the providing unit suggests music that has a relaxing effect, such as classical music, nature sounds, or healing music. The providing unit can also customize music according to the user's preferences. For example, the providing unit analyzes the history of music that the user has listened to in the past and suggests music that matches the user's preferences. In this way, the providing unit can support the user's relaxation.

[0035] The avatar selection unit can analyze the user's past avatar selection history and suggest the most suitable avatar. The avatar selection unit, for example, analyzes the user's past avatar selection history. For example, the avatar selection unit analyzes the tendency of avatars selected by the user in the past and suggests a similar type of avatar. The avatar selection unit can also refer to an avatar selected by the user in a specific time period and suggest the most suitable avatar for the same time period. Furthermore, the avatar selection unit can suggest the most suitable avatar for a similar situation based on an avatar selected by the user in a specific event or situation. In this way, the avatar selection unit can suggest the most suitable avatar based on the user's past selection history.

[0036] When selecting an avatar, the avatar selection unit can customize the appearance of the avatar based on the user's current mood and situation. The avatar selection unit, for example, evaluates the user's current mood and situation. For example, the avatar selection unit evaluates the user's mood and situation based on survey results, sensor data, and user comments. Next, the avatar selection unit customizes the avatar's appearance based on the user's current mood and situation. For example, if the user wants to relax, the avatar can be customized with colors and a design that has a relaxing effect. Also, if the user wants to cheer up, the avatar can be customized with bright colors and a lively design. Furthermore, if the user wants to concentrate, the avatar can be customized with a simple and calm design. In this way, the avatar selection unit can customize the avatar according to the user's mood and situation.

[0037] When selecting an avatar, the avatar selection unit can suggest avatar clothing and accessories based on the user's preferences and interests. The avatar selection unit, for example, evaluates the user's preferences and interests. For example, the avatar selection unit evaluates the user's preferences and interests based on the user's past selection history, survey results, and social media activity. Next, the avatar selection unit suggests avatar clothing and accessories based on the user's preferences and interests. For example, the avatar selection unit can suggest avatar clothing and accessories based on the user's favorite colors and designs. The avatar selection unit can also suggest avatar clothing and accessories based on themes and characters in which the user is interested. Furthermore, the avatar selection unit can analyze trends in avatar clothing and accessories selected by the user in the past and make optimal suggestions. This allows the avatar selection unit to suggest avatars that match the user's preferences and interests.

[0038] When selecting an avatar, the avatar selection unit can suggest a highly relevant avatar by taking into consideration the user's geographical location information. The avatar selection unit, for example, acquires the user's geographical location information. For example, the avatar selection unit acquires the user's geographical location information using GPS data, an IP address, or a location information service. Next, the avatar selection unit suggests a highly relevant avatar by taking into consideration the user's geographical location information. For example, if the user is in a specific area, an avatar related to that area can be suggested. Also, if the user is traveling, an avatar related to the travel destination can be suggested. Furthermore, if the user is participating in a specific event, an avatar related to the event can be suggested. In this way, the avatar selection unit can suggest a highly relevant avatar based on the user's geographical location information.

[0039] The avatar selection unit can analyze the user's social media activity and suggest related avatars when selecting an avatar. The avatar selection unit, for example, analyzes the user's social media activity. For example, the avatar selection unit analyzes the themes and characters the user follows on social media. Then, the avatar selection unit suggests related avatars based on the user's social media activity. For example, the avatar selection unit suggests avatars based on the characters and themes the user follows on social media. The avatar selection unit can also analyze content posted by the user on social media to suggest related avatars. Furthermore, the avatar selection unit can suggest related avatars based on the activities of the user's friends on social media. This allows the avatar selection unit to suggest related avatars based on the user's social media activity.

[0040] The avatar selection unit can customize the avatar suggestion method by reflecting the user's past feedback when selecting an avatar. The avatar selection unit, for example, collects the user's past feedback. For example, the avatar selection unit collects survey results, user comments, and evaluation data. Next, the avatar selection unit customizes the avatar suggestion method by reflecting the user's past feedback. For example, the avatar suggestion method can be adjusted based on feedback provided by the user in the past. Also, if a user gives a high rating to a specific avatar, a similar avatar can be preferentially suggested. Furthermore, if a user gives a low rating to a specific avatar, it can be configured not to suggest that type of avatar. In this way, the avatar selection unit can customize the avatar suggestion method based on the user's past feedback.

[0041] The reception unit can analyze the user's past speech history and select the optimal reception method. The reception unit, for example, analyzes the user's past speech history. For example, the reception unit analyzes speech methods that the user has frequently used in the past. Next, the reception unit selects the optimal reception method based on the user's past speech history. For example, the reception unit preferentially accepts speech methods that the user has frequently used in the past. The reception unit can also predict the speech methods that the user will use in a specific time period and select the optimal reception method. Furthermore, the reception unit can analyze the user's past speech history and select the most efficient reception method. This allows the reception unit to select the optimal reception method based on the user's past speech history.

[0042] The reception unit can filter comments based on the user's current living situation and areas of interest when receiving the comments. The reception unit, for example, evaluates the user's current living situation and areas of interest. For example, the reception unit evaluates the user's living situation and areas of interest based on survey results, sensor data, and the user's comments. Next, the reception unit filters the comments based on the user's current living situation and areas of interest. For example, if the user is at work, it can preferentially accept comments related to work. Also, if the user is on vacation, it can preferentially accept comments related to relaxation. Furthermore, if the user is interested in a specific area of ​​interest, it can preferentially accept comments related to that area. In this way, the reception unit can filter comments according to the user's living situation and areas of interest.

[0043] When receiving a utterance, the reception unit can select the optimal reception means according to the user's input method. The reception unit, for example, evaluates the user's input method. For example, the reception unit evaluates which input method the user is using, such as voice input, text input, or image input. Next, the reception unit selects the optimal reception means according to the user's input method. For example, if the user uses voice input, the reception unit may preferentially accept voice input. Also, if the user uses text input, the reception unit may preferentially accept text input. Furthermore, if the user uses image input, the reception unit may preferentially accept image input. This allows the reception unit to select the optimal reception means according to the user's input method.

[0044] When receiving a utterance, the reception unit can prioritize receiving highly relevant utterances by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. For example, the reception unit acquires the user's geographical location information by using GPS data, an IP address, or a location information service. Next, the reception unit prioritizes receiving highly relevant utterances by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving utterances related to that area. Also, if the user is traveling, the reception unit can prioritize receiving utterances related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize receiving utterances related to that event. This allows the reception unit to prioritize receiving highly relevant utterances based on the user's geographical location information.

[0045] The reception unit can analyze the user's social media activity when receiving a comment and receive related comments. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit analyzes the themes and characters the user follows on social media. Next, the reception unit receives related comments based on the user's social media activity. For example, the reception unit receives comments based on the themes the user follows on social media. The reception unit can also analyze content posted by the user on social media and receive related comments. Furthermore, the reception unit can receive related comments by referring to the activities of the user's friends on social media. In this way, the reception unit can receive related comments based on the user's social media activity.

[0046] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a utterance. The reception unit, for example, collects the user's past feedback. For example, the reception unit collects survey results, user comments, and evaluation data. Next, the reception unit customizes the reception method by reflecting the user's past feedback. For example, the reception unit adjusts the reception method for utterances based on feedback provided by the user in the past. Also, if the user gives a high rating to a specific utterance method, that method can be preferentially accepted. Furthermore, if the user gives a low rating to a specific utterance method, that method can be avoided. In this way, the reception unit can customize the reception method based on the user's past feedback.

[0047] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the statement. The analysis unit, for example, evaluates the importance of the statement. For example, the analysis unit evaluates the importance of the statement based on the content of the statement, the user's situation, and the system load situation. Next, the analysis unit adjusts the level of detail of the analysis based on the importance of the statement. For example, a detailed analysis is performed for important statements. Also, a standard analysis can be performed for general statements. Furthermore, a simplified analysis can be performed for statements with low importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the statement.

[0048] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the statement. The analysis unit, for example, classifies the category of the statement. For example, the analysis unit classifies the statement into categories such as work, life, and human relationships based on the content of the statement. Next, the analysis unit applies different analysis algorithms depending on the category of the statement. For example, an emotion analysis algorithm can be applied to statements related to emotions. A fact analysis algorithm can also be applied to statements related to facts. Furthermore, an opinion analysis algorithm can be applied to statements related to opinions. This allows the analysis unit to apply an analysis algorithm depending on the category of the statement.

[0049] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, collects the user's past analysis results. For example, the analysis unit collects a database of the user's comment history and analysis results. Next, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis algorithm can also be adjusted by referring to feedback provided by the user in the past. Furthermore, the user's past comment history can be analyzed to improve the accuracy of the analysis. This allows the analysis unit to improve the accuracy of the analysis based on the user's past analysis results.

[0050] During analysis, the analysis unit can determine the priority of analysis based on the time when the comment was submitted. The analysis unit, for example, evaluates the time when the comment was submitted. For example, the analysis unit evaluates the time when the comment was submitted based on the timestamp of the comment, the user's status, and the system load status. Next, the analysis unit determines the priority of analysis based on the time when the comment was submitted. For example, the analysis unit can prioritize analysis of recently submitted comments. It can also prioritize analysis of comments submitted during a specific time period. Furthermore, if the user is in a hurry, it can also prioritize analysis of those comments. This allows the analysis unit to determine the priority of analysis based on the time when the comment was submitted.

[0051] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the comments. The analysis unit, for example, evaluates the relevance of the comments. For example, the analysis unit evaluates the relevance of the comments based on the content of the comments, the user's situation, and the system load situation. Next, the analysis unit adjusts the order of analysis based on the relevance of the comments. For example, it prioritizes analysis of highly relevant comments. It is also possible to postpone less relevant comments. Furthermore, it is also possible to prioritize analysis of highly relevant comments based on the user's past comment history. This allows the analysis unit to adjust the order of analysis based on the relevance of the comments.

[0052] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation, educational background, and past utterances. Next, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, technical terms are used. Also, if the user does not have technical expertise, simple words can be used. Furthermore, the use of technical terms can be adjusted based on the user's past utterance history. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0053] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the analysis result. The providing unit, for example, evaluates the importance of the analysis result. For example, the providing unit evaluates the importance of the analysis result based on the content of the analysis result, the user's status, and the system load status. Next, the providing unit adjusts the level of detail of the advice based on the importance of the analysis result. For example, detailed advice can be provided for important analysis results. Standard advice can also be provided for general analysis results. Furthermore, simplified advice can be provided for analysis results with low importance. In this way, the providing unit can adjust the level of detail of the advice according to the importance of the analysis result.

[0054] When providing advice, the providing unit can apply different advice algorithms according to the user's needs. The providing unit, for example, evaluates the user's needs. For example, the providing unit evaluates the user's needs based on the user's past data and current situation. Next, the providing unit applies different advice algorithms according to the user's needs. For example, if the user wants to relax, advice that has a relaxing effect can be provided. Also, if the user wants to cheer up, encouraging advice can be provided. Furthermore, if the user wants to concentrate, advice to improve concentration can be provided. This allows the providing unit to apply advice algorithms according to the user's needs.

[0055] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit, for example, collects the user's past advice results. For example, the providing unit collects the user's feedback and the effects of advice. Next, the providing unit improves the accuracy of the advice by referring to the user's past advice results. For example, the providing unit improves the accuracy of the current advice based on the user's past advice results. The advice algorithm can also be adjusted by referring to feedback provided by the user in the past. Furthermore, the accuracy of the advice can be improved by analyzing the user's past statement history. This allows the providing unit to improve the accuracy of the advice based on the user's past advice results.

[0056] When providing advice, the providing unit can determine the priority of advice based on the time when the analysis results were submitted. The providing unit, for example, evaluates the time when the analysis results were submitted. For example, the providing unit evaluates the time when the analysis results were submitted based on the timestamp of the analysis results, the user's status, and the system load status. Next, the providing unit determines the priority of advice based on the time when the analysis results were submitted. For example, advice can be provided based on the most recently submitted analysis results. Advice can also be provided based on analysis results submitted within a specific time period. Furthermore, if the user is in a hurry, advice can be preferentially provided based on those analysis results. This allows the providing unit to determine the priority of advice based on the time when the analysis results were submitted.

[0057] When providing advice, the providing unit can adjust the order of advice based on the relevance of the analysis results. The providing unit, for example, evaluates the relevance of the analysis results. For example, the providing unit evaluates the relevance of the analysis results based on the content of the analysis results, the user's situation, and the system load situation. Next, the providing unit adjusts the order of advice based on the relevance of the analysis results. For example, advice can be provided based on highly relevant analysis results. Advice can also be postponed based on less relevant analysis results. Furthermore, advice can be provided based on highly relevant analysis results based on the user's past comment history. This allows the providing unit to adjust the order of advice based on the relevance of the analysis results.

[0058] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise. The providing unit, for example, evaluates the user's level of expertise. For example, the providing unit evaluates the user's level of expertise based on the user's occupation, educational background, and past comments. Next, the providing unit adjusts the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical expertise, technical terms are used. Also, if the user does not have technical expertise, simple words can be used. Furthermore, the use of technical terms can be adjusted based on the user's past comment history. In this way, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise.

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

[0060] The virtual friend system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit measures the user's heart rate and stress level and transmits this data to the analysis unit. The analysis unit evaluates the user's health condition based on this data, and the provision unit can provide advice according to the user's health condition. For example, if the user's heart rate is high, the provision unit can provide advice such as "Take a deep breath and relax." Also, if the stress level is high, the provision unit can provide advice such as "Take a short break." This allows the virtual friend system to comprehensively support the user's physical and mental health.

[0061] The virtual friend system can further include a hobby learning unit that learns the user's hobbies and interests. The hobby learning unit analyzes the user's past comments and behavioral history to identify the user's hobbies and interests. The analysis unit transmits information related to the user's hobbies and interests based on this data to the providing unit. The providing unit can provide advice and suggestions based on the user's hobbies and interests. For example, if the user is interested in music, the providing unit can make a suggestion such as "Try listening to the new album." Or, if the user is interested in sports, the providing unit can make a suggestion such as "Try jogging on the weekend." In this way, the virtual friend system can support users in enriching their lives.

[0062] The virtual friend system can further include a sleep monitoring unit that monitors the user's sleep patterns. The sleep monitoring unit measures the user's sleep duration and quality and transmits this data to the analysis unit. The analysis unit evaluates the user's sleep state based on this data, and the provision unit can provide advice according to the user's sleep state. For example, if the user's sleep duration is short, the provision unit can provide advice such as "Try to go to bed earlier." Furthermore, if the sleep quality is low, the provision unit can also provide advice such as "Try listening to relaxing music before going to bed." In this way, the virtual friend system can support the user in improving their sleep quality.

[0063] The virtual friend system can further include a dietary monitoring unit that monitors the user's eating habits. The dietary monitoring unit records the user's dietary content and calorie intake and transmits this data to the analysis unit. The analysis unit evaluates the user's eating habits based on this data, and the provision unit can provide advice based on the user's eating habits. For example, if the user's calorie intake is high, the provision unit can provide advice such as "Try to eat more vegetables." If the user's diet is unbalanced, the provision unit can also provide advice such as "Try to eat a balanced diet." In this way, the virtual friend system can support the user's healthy eating habits.

[0064] The virtual friend system can further include an exercise monitoring unit that monitors the user's exercise habits. The exercise monitoring unit records the amount and type of exercise the user does and sends this data to the analysis unit. The analysis unit evaluates the user's exercise habits based on this data, and the providing unit can provide advice based on the user's exercise habits. For example, if the user does not exercise much, the providing unit can provide advice such as "Try to exercise a little every day." Furthermore, if a particular exercise is not effective, the providing unit can also provide advice such as "Try a different exercise." In this way, the virtual friend system can support the user's healthy exercise habits.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The avatar selection unit allows the user to select an avatar. The user can choose from avatars with different genders, ages, clothing, etc. For example, the avatar selection unit displays multiple avatars, allowing the user to select one that suits their preferences. Step 2: The reception unit receives a complaint from the user. The user can speak the complaint using text input or voice input. For example, the reception unit receives text or voice data input by the user. Step 3: The analysis unit analyzes the content received by the reception unit. The analysis unit uses natural language processing technology to analyze the user's utterance. For example, the analysis unit performs morphological analysis and grammatical analysis on the user's utterance to understand its meaning. The analysis unit can also use emotion analysis technology to understand the user's emotions. For example, the analysis unit extracts emotions from the user's utterance to understand what emotions the user is feeling. Step 4: The providing unit provides advice based on the results of the analysis by the analyzing unit. The providing unit generates appropriate advice based on the analysis results. For example, if the user is tired, the providing unit provides advice such as "Try to get plenty of rest today." The providing unit can also provide advice customized to the user's needs. For example, if the user wants to relax, the providing unit provides advice such as "Try listening to some relaxing music."

[0067] (Example 2) In a virtual friend system according to an embodiment of the present invention, when a user selects an avatar and vents their grievances, an AI listens to, understands, and provides appropriate advice. The virtual friend system includes an avatar selection unit through which the user selects an avatar, a reception unit that receives the user's grievances, an analysis unit that analyzes the grievances received by the reception unit, and a provision unit that provides advice based on the analysis results. For example, the virtual friend system allows a user to select an avatar of their choice. For example, avatars with different genders, ages, and clothing are available, allowing users to select an avatar that suits their preferences. Next, when the user vents their grievances, an AI listens to and understands them. The AI ​​analyzes the user's utterances using natural language processing technology and understands the user's emotions using emotion analysis technology. For example, if a user says, "Work was tough today," the AI ​​analyzes the utterance and understands that the user is tired. Based on the analysis results, the AI ​​provides appropriate advice. For example, if the user is tired, the AI ​​provides advice such as, "Take it easy and rest today." It is also possible to provide advice customized to the user's needs. For example, if a user wants to relax, the AI ​​will offer advice such as, "Try listening to some relaxing music." This allows the virtual friend system to empathize with everyday stress and complaints and provide support through dialogue. This allows the virtual friend system to support the mental health of people who have limited time to relieve stress in today's busy society. For example, by providing a place where users can vent and sort out their feelings while maintaining anonymity, the system can alleviate the complexities of interpersonal relationships and privacy concerns. Advances in AI are enabling more natural conversations and the provision of customized advice tailored to individual needs.

[0068] The virtual friend system according to the embodiment includes an avatar selection unit, a reception unit, an analysis unit, and a provision unit. The avatar selection unit allows a user to select an avatar. The user can select from avatars with different genders, ages, clothing, etc. For example, the avatar selection unit displays multiple avatars, allowing the user to select one based on their preferences. The reception unit receives a user's complaints. The user can express their complaints using text input or voice input. For example, the reception unit receives text or voice data entered by the user. The analysis unit analyzes the content received by the reception unit. The analysis unit analyzes the user's utterances using, for example, natural language processing technology. For example, the analysis unit performs morphological analysis or grammatical analysis on the user's utterances to understand their meaning. The analysis unit can also understand the user's emotions using emotion analysis technology. For example, the analysis unit extracts emotions from the user's utterances and understands the user's emotions. The provision unit provides advice based on the results of the analysis by the analysis unit. For example, the provision unit generates appropriate advice based on the analysis results. For example, if the user is tired, the providing unit provides advice such as "Take a good rest today." The providing unit can also provide advice customized to the user's needs. For example, if the user wants to relax, the providing unit provides advice such as "Try listening to relaxing music." In this way, the virtual friend system according to the embodiment can support the user's mental health by receiving and analyzing the user's complaints and providing appropriate advice.

[0069] The avatar selection unit may allow the user to select from several avatars. For example, the avatar selection unit may display a plurality of avatars, allowing the user to select one that suits their preferences. For example, the avatar selection unit may provide different types of avatars, such as animal characters and robot characters. The avatar selection unit may also allow the user to customize the appearance and clothing of the avatar. For example, the avatar selection unit may allow the user to select clothing and accessories for the avatar. This allows the user to select an avatar that suits their preferences.

[0070] The reception unit can analyze the user's utterances using natural language processing technology. The reception unit receives, for example, text or voice data input by the user. For example, if the user uses text input to complain, the reception unit receives the text data. Also, if the user uses voice input to complain, the reception unit receives the voice data. The reception unit analyzes the received data using natural language processing technology. For example, the reception unit performs morphological analysis to break down the user's utterances into words. The reception unit can also perform grammatical analysis to analyze the grammatical structure of the user's utterances. Furthermore, the reception unit can perform semantic analysis to understand the meaning of the user's utterances. This allows the reception unit to accurately analyze the user's utterances.

[0071] The analysis unit can understand the user's emotions using emotion analysis technology. The analysis unit, for example, extracts emotions from the user's speech. For example, the analysis unit detects emotional expressions contained in the user's speech and understands those emotions. The analysis unit can also analyze emotions from audio data. For example, the analysis unit analyzes the tone and speed of the user's voice to understand the user's emotions. Furthermore, the analysis unit can also understand the user's emotions using facial expression analysis technology. For example, the analysis unit captures the user's facial expressions with a camera and analyzes emotions from the facial expressions. This allows the analysis unit to accurately understand the user's emotions.

[0072] The providing unit can provide advice customized based on the analysis results. The providing unit generates appropriate advice based on the analysis results, for example. For example, if the user is tired, the providing unit provides advice such as "Try to get plenty of rest today." The providing unit can also provide advice customized to the user's needs. For example, if the user wants to relax, the providing unit provides advice such as "Try listening to some relaxing music." Furthermore, the providing unit can customize advice based on the user's past data and current situation. For example, the providing unit analyzes the user's past utterance history and provides advice tailored to the user's preferences. This allows the providing unit to provide advice that meets the user's needs.

[0073] The providing unit can suggest music that promotes relaxation according to the user's needs. For example, if the user wants to relax, the providing unit suggests music that promotes relaxation. For example, the providing unit suggests music that has a relaxing effect, such as classical music, nature sounds, or healing music. The providing unit can also customize music according to the user's preferences. For example, the providing unit analyzes the history of music that the user has listened to in the past and suggests music that matches the user's preferences. In this way, the providing unit can support the user's relaxation.

[0074] The avatar selection unit can estimate the user's emotion and suggest an avatar based on the estimated user's emotion. The avatar selection unit, for example, estimates the user's emotion. For example, the avatar selection unit analyzes the user's facial expression, voice, and text data to estimate the user's emotion. Next, the avatar selection unit suggests an avatar based on the estimated user's emotion. For example, if the user is feeling stressed, an avatar with a relaxing effect can be suggested. Also, if the user is in a happy mood, a bright and cheerful avatar can be suggested. Furthermore, if the user is in a sad mood, an avatar with a comforting expression can be suggested. In this way, the avatar selection unit can suggest an avatar according to the user's emotion.

[0075] The avatar selection unit can analyze the user's past avatar selection history and suggest the most suitable avatar. The avatar selection unit, for example, analyzes the user's past avatar selection history. For example, the avatar selection unit analyzes the tendency of avatars selected by the user in the past and suggests a similar type of avatar. The avatar selection unit can also refer to an avatar selected by the user in a specific time period and suggest the most suitable avatar for the same time period. Furthermore, the avatar selection unit can suggest the most suitable avatar for a similar situation based on an avatar selected by the user in a specific event or situation. In this way, the avatar selection unit can suggest the most suitable avatar based on the user's past selection history.

[0076] When selecting an avatar, the avatar selection unit can customize the appearance of the avatar based on the user's current mood and situation. The avatar selection unit, for example, evaluates the user's current mood and situation. For example, the avatar selection unit evaluates the user's mood and situation based on survey results, sensor data, and user comments. Next, the avatar selection unit customizes the avatar's appearance based on the user's current mood and situation. For example, if the user wants to relax, the avatar can be customized with colors and a design that has a relaxing effect. Also, if the user wants to cheer up, the avatar can be customized with bright colors and a lively design. Furthermore, if the user wants to concentrate, the avatar can be customized with a simple and calm design. In this way, the avatar selection unit can customize the avatar according to the user's mood and situation.

[0077] When selecting an avatar, the avatar selection unit can suggest avatar clothing and accessories based on the user's preferences and interests. The avatar selection unit, for example, evaluates the user's preferences and interests. For example, the avatar selection unit evaluates the user's preferences and interests based on the user's past selection history, survey results, and social media activity. Next, the avatar selection unit suggests avatar clothing and accessories based on the user's preferences and interests. For example, the avatar selection unit can suggest avatar clothing and accessories based on the user's favorite colors and designs. The avatar selection unit can also suggest avatar clothing and accessories based on themes and characters in which the user is interested. Furthermore, the avatar selection unit can analyze trends in avatar clothing and accessories selected by the user in the past and make optimal suggestions. This allows the avatar selection unit to suggest avatars that match the user's preferences and interests.

[0078] The avatar selection unit can estimate the user's emotions and adjust the avatar's facial expressions and movements based on the estimated user's emotions. The avatar selection unit, for example, estimates the user's emotions. For example, the avatar selection unit analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. Next, the avatar selection unit adjusts the avatar's facial expressions and movements based on the estimated user's emotions. For example, if the user is feeling sad, the avatar's facial expressions can be adjusted to be gentle and comforting. Also, if the user is feeling happy, the avatar's facial expressions can be adjusted to be bright and cheerful. Furthermore, if the user is feeling nervous, the avatar's movements can be adjusted to be calming. In this way, the avatar selection unit can adjust the avatar's facial expressions and movements according to the user's emotions.

[0079] When selecting an avatar, the avatar selection unit can suggest a highly relevant avatar by taking into consideration the user's geographical location information. The avatar selection unit, for example, acquires the user's geographical location information. For example, the avatar selection unit acquires the user's geographical location information using GPS data, an IP address, or a location information service. Next, the avatar selection unit suggests a highly relevant avatar by taking into consideration the user's geographical location information. For example, if the user is in a specific area, an avatar related to that area can be suggested. Also, if the user is traveling, an avatar related to the travel destination can be suggested. Furthermore, if the user is participating in a specific event, an avatar related to the event can be suggested. In this way, the avatar selection unit can suggest a highly relevant avatar based on the user's geographical location information.

[0080] The avatar selection unit can analyze the user's social media activity and suggest related avatars when selecting an avatar. The avatar selection unit, for example, analyzes the user's social media activity. For example, the avatar selection unit analyzes the themes and characters the user follows on social media. Then, the avatar selection unit suggests related avatars based on the user's social media activity. For example, the avatar selection unit suggests avatars based on the characters and themes the user follows on social media. The avatar selection unit can also analyze content posted by the user on social media to suggest related avatars. Furthermore, the avatar selection unit can suggest related avatars based on the activities of the user's friends on social media. This allows the avatar selection unit to suggest related avatars based on the user's social media activity.

[0081] The avatar selection unit can customize the avatar suggestion method by reflecting the user's past feedback when selecting an avatar. The avatar selection unit, for example, collects the user's past feedback. For example, the avatar selection unit collects survey results, user comments, and evaluation data. Next, the avatar selection unit customizes the avatar suggestion method by reflecting the user's past feedback. For example, the avatar suggestion method can be adjusted based on feedback provided by the user in the past. Also, if a user gives a high rating to a specific avatar, a similar avatar can be preferentially suggested. Furthermore, if a user gives a low rating to a specific avatar, it can be configured not to suggest that type of avatar. In this way, the avatar selection unit can customize the avatar suggestion method based on the user's past feedback.

[0082] The reception unit can estimate the user's emotions and adjust the timing of receiving utterances based on the estimated user emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. Next, the reception unit adjusts the timing of receiving utterances based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can immediately receive utterances. Also, if the user is relaxed, the reception unit can receive utterances at a slower pace. Furthermore, if the user is in a hurry, the reception unit can quickly receive utterances. This allows the reception unit to adjust the timing of receiving utterances according to the user's emotions.

[0083] The reception unit can analyze the user's past speech history and select the optimal reception method. The reception unit, for example, analyzes the user's past speech history. For example, the reception unit analyzes speech methods that the user has frequently used in the past. Next, the reception unit selects the optimal reception method based on the user's past speech history. For example, the reception unit preferentially accepts speech methods that the user has frequently used in the past. The reception unit can also predict the speech methods that the user will use in a specific time period and select the optimal reception method. Furthermore, the reception unit can analyze the user's past speech history and select the most efficient reception method. This allows the reception unit to select the optimal reception method based on the user's past speech history.

[0084] The reception unit can filter comments based on the user's current living situation and areas of interest when receiving the comments. The reception unit, for example, evaluates the user's current living situation and areas of interest. For example, the reception unit evaluates the user's living situation and areas of interest based on survey results, sensor data, and the user's comments. Next, the reception unit filters the comments based on the user's current living situation and areas of interest. For example, if the user is at work, it can preferentially accept comments related to work. Also, if the user is on vacation, it can preferentially accept comments related to relaxation. Furthermore, if the user is interested in a specific area of ​​interest, it can preferentially accept comments related to that area. In this way, the reception unit can filter comments according to the user's living situation and areas of interest.

[0085] When receiving a utterance, the reception unit can select the optimal reception means according to the user's input method. The reception unit, for example, evaluates the user's input method. For example, the reception unit evaluates which input method the user is using, such as voice input, text input, or image input. Next, the reception unit selects the optimal reception means according to the user's input method. For example, if the user uses voice input, the reception unit may preferentially accept voice input. Also, if the user uses text input, the reception unit may preferentially accept text input. Furthermore, if the user uses image input, the reception unit may preferentially accept image input. This allows the reception unit to select the optimal reception means according to the user's input method.

[0086] The reception unit can estimate the user's emotions and determine the priority of utterances to be received based on the estimated user's emotions. The reception unit, for example, estimates the user's emotions. For example, the reception unit analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. Next, the reception unit determines the priority of utterances based on the estimated user's emotions. For example, if the user is feeling stressed, the utterance can be received preferentially. Also, if the user is relaxed, the utterance can be received equally with other utterances. Furthermore, if the user is in a hurry, the utterance can be received quickly. In this way, the reception unit can determine the priority of utterances according to the user's emotions.

[0087] When receiving a utterance, the reception unit can prioritize receiving highly relevant utterances by taking into account the user's geographical location information. The reception unit, for example, acquires the user's geographical location information. For example, the reception unit acquires the user's geographical location information by using GPS data, an IP address, or a location information service. Next, the reception unit prioritizes receiving highly relevant utterances by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize receiving utterances related to that area. Also, if the user is traveling, the reception unit can prioritize receiving utterances related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize receiving utterances related to that event. This allows the reception unit to prioritize receiving highly relevant utterances based on the user's geographical location information.

[0088] The reception unit can analyze the user's social media activity when receiving a comment and receive related comments. The reception unit, for example, analyzes the user's social media activity. For example, the reception unit analyzes the themes and characters the user follows on social media. Next, the reception unit receives related comments based on the user's social media activity. For example, the reception unit receives comments based on the themes the user follows on social media. The reception unit can also analyze content posted by the user on social media and receive related comments. Furthermore, the reception unit can receive related comments by referring to the activities of the user's friends on social media. In this way, the reception unit can receive related comments based on the user's social media activity.

[0089] The reception unit can customize the reception method by reflecting the user's past feedback when receiving a utterance. The reception unit, for example, collects the user's past feedback. For example, the reception unit collects survey results, user comments, and evaluation data. Next, the reception unit customizes the reception method by reflecting the user's past feedback. For example, the reception unit adjusts the reception method for utterances based on feedback provided by the user in the past. Also, if the user gives a high rating to a specific utterance method, that method can be preferentially accepted. Furthermore, if the user gives a low rating to a specific utterance method, that method can be avoided. In this way, the reception unit can customize the reception method based on the user's past feedback.

[0090] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. Next, the analysis unit adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand presentation method can be used. Also, if the user is relaxed, a presentation method including detailed information can be used. Furthermore, if the user is in a hurry, a presentation method that gets to the point can be used. This allows the analysis unit to adjust the way the analysis is presented according to the user's emotions.

[0091] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the statement. The analysis unit, for example, evaluates the importance of the statement. For example, the analysis unit evaluates the importance of the statement based on the content of the statement, the user's situation, and the system load situation. Next, the analysis unit adjusts the level of detail of the analysis based on the importance of the statement. For example, a detailed analysis is performed for important statements. Also, a standard analysis can be performed for general statements. Furthermore, a simplified analysis can be performed for statements with low importance. This allows the analysis unit to adjust the level of detail of the analysis according to the importance of the statement.

[0092] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the statement. The analysis unit, for example, classifies the category of the statement. For example, the analysis unit classifies the statement into categories such as work, life, and human relationships based on the content of the statement. Next, the analysis unit applies different analysis algorithms depending on the category of the statement. For example, an emotion analysis algorithm can be applied to statements related to emotions. A fact analysis algorithm can also be applied to statements related to facts. Furthermore, an opinion analysis algorithm can be applied to statements related to opinions. This allows the analysis unit to apply an analysis algorithm depending on the category of the statement.

[0093] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, collects the user's past analysis results. For example, the analysis unit collects a database of the user's comment history and analysis results. Next, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the accuracy of the current analysis based on the user's past analysis results. The analysis algorithm can also be adjusted by referring to feedback provided by the user in the past. Furthermore, the user's past comment history can be analyzed to improve the accuracy of the analysis. This allows the analysis unit to improve the accuracy of the analysis based on the user's past analysis results.

[0094] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. Next, the analysis unit adjusts the length of the analysis based on the estimated user's emotions. For example, if the user is in a hurry, a short and to-the-point analysis can be performed. Alternatively, if the user is relaxed, a detailed analysis can be performed. Furthermore, if the user is feeling stressed, a simple and easy-to-understand analysis can be performed. This allows the analysis unit to adjust the length of the analysis according to the user's emotions.

[0095] During analysis, the analysis unit can determine the priority of analysis based on the time when the comment was submitted. The analysis unit, for example, evaluates the time when the comment was submitted. For example, the analysis unit evaluates the time when the comment was submitted based on the timestamp of the comment, the user's status, and the system load status. Next, the analysis unit determines the priority of analysis based on the time when the comment was submitted. For example, the analysis unit can prioritize analysis of recently submitted comments. It can also prioritize analysis of comments submitted during a specific time period. Furthermore, if the user is in a hurry, it can also prioritize analysis of those comments. This allows the analysis unit to determine the priority of analysis based on the time when the comment was submitted.

[0096] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the comments. The analysis unit, for example, evaluates the relevance of the comments. For example, the analysis unit evaluates the relevance of the comments based on the content of the comments, the user's situation, and the system load situation. Next, the analysis unit adjusts the order of analysis based on the relevance of the comments. For example, it prioritizes analysis of highly relevant comments. It is also possible to postpone less relevant comments. Furthermore, it is also possible to prioritize analysis of highly relevant comments based on the user's past comment history. This allows the analysis unit to adjust the order of analysis based on the relevance of the comments.

[0097] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise. For example, the analysis unit evaluates the user's level of expertise based on the user's occupation, educational background, and past utterances. Next, the analysis unit adjusts the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, technical terms are used. Also, if the user does not have technical expertise, simple words can be used. Furthermore, the use of technical terms can be adjusted based on the user's past utterance history. This allows the analysis unit to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0098] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. Next, the providing unit adjusts the way in which advice is expressed based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide advice in gentle words. Also, if the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise advice can be provided. This allows the providing unit to adjust the way in which advice is expressed according to the user's emotions.

[0099] When providing advice, the providing unit can adjust the level of detail of the advice based on the importance of the analysis result. The providing unit, for example, evaluates the importance of the analysis result. For example, the providing unit evaluates the importance of the analysis result based on the content of the analysis result, the user's status, and the system load status. Next, the providing unit adjusts the level of detail of the advice based on the importance of the analysis result. For example, detailed advice can be provided for important analysis results. Standard advice can also be provided for general analysis results. Furthermore, simplified advice can be provided for analysis results with low importance. In this way, the providing unit can adjust the level of detail of the advice according to the importance of the analysis result.

[0100] When providing advice, the providing unit can apply different advice algorithms according to the user's needs. The providing unit, for example, evaluates the user's needs. For example, the providing unit evaluates the user's needs based on the user's past data and current situation. Next, the providing unit applies different advice algorithms according to the user's needs. For example, if the user wants to relax, advice that has a relaxing effect can be provided. Also, if the user wants to cheer up, encouraging advice can be provided. Furthermore, if the user wants to concentrate, advice to improve concentration can be provided. This allows the providing unit to apply advice algorithms according to the user's needs.

[0101] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. The providing unit, for example, collects the user's past advice results. For example, the providing unit collects the user's feedback and the effects of advice. Next, the providing unit improves the accuracy of the advice by referring to the user's past advice results. For example, the providing unit improves the accuracy of the current advice based on the user's past advice results. The advice algorithm can also be adjusted by referring to feedback provided by the user in the past. Furthermore, the accuracy of the advice can be improved by analyzing the user's past statement history. This allows the providing unit to improve the accuracy of the advice based on the user's past advice results.

[0102] The providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit analyzes the user's facial expressions, voice, and text data to estimate the user's emotions. Next, the providing unit adjusts the length of the advice based on the estimated user's emotions. For example, if the user is in a hurry, short and to the point advice can be provided. Also, if the user is relaxed, detailed advice can be provided. Furthermore, if the user is feeling stressed, simple and easy-to-understand advice can be provided. In this way, the providing unit can adjust the length of the advice according to the user's emotions.

[0103] When providing advice, the providing unit can determine the priority of advice based on the time when the analysis results were submitted. The providing unit, for example, evaluates the time when the analysis results were submitted. For example, the providing unit evaluates the time when the analysis results were submitted based on the timestamp of the analysis results, the user's status, and the system load status. Next, the providing unit determines the priority of advice based on the time when the analysis results were submitted. For example, advice can be provided based on the most recently submitted analysis results. Advice can also be provided based on analysis results submitted within a specific time period. Furthermore, if the user is in a hurry, advice can be preferentially provided based on those analysis results. This allows the providing unit to determine the priority of advice based on the time when the analysis results were submitted.

[0104] When providing advice, the providing unit can adjust the order of advice based on the relevance of the analysis results. The providing unit, for example, evaluates the relevance of the analysis results. For example, the providing unit evaluates the relevance of the analysis results based on the content of the analysis results, the user's situation, and the system load situation. Next, the providing unit adjusts the order of advice based on the relevance of the analysis results. For example, advice can be provided based on highly relevant analysis results. Advice can also be postponed based on less relevant analysis results. Furthermore, advice can be provided based on highly relevant analysis results based on the user's past comment history. This allows the providing unit to adjust the order of advice based on the relevance of the analysis results.

[0105] When providing advice, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise. The providing unit, for example, evaluates the user's level of expertise. For example, the providing unit evaluates the user's level of expertise based on the user's occupation, educational background, and past comments. Next, the providing unit adjusts the use of technical terms in the advice according to the user's level of expertise. For example, if the user has technical expertise, technical terms are used. Also, if the user does not have technical expertise, simple words can be used. Furthermore, the use of technical terms can be adjusted based on the user's past comment history. In this way, the providing unit can adjust the use of technical terms in the advice according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements including the avatar selection unit, the reception unit, the analysis unit, and the provision 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 avatar selection unit is realized by the control unit 46A of the smart device 14. For example, the reception unit receives a user's complaints using the microphone 38B or the touch panel 38A of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's comments using natural language processing technology or emotion analysis technology. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate advice based on the analysis results and provides the advice to the user via the display 40A or the speaker 40B of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described avatar selection unit, reception unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the avatar selection unit is realized by the control unit 46A of the smart glasses 214. For example, the reception unit receives a user's complaints using the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's comments using natural language processing technology or emotion analysis technology. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12 and generates appropriate advice based on the analysis results and provides the advice to the user through the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-described avatar selection unit, reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the avatar selection unit is realized by the control unit 46A of the headset-type terminal 314. For example, the reception unit receives a user's complaints using the microphone 238 of the headset-type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's comments using natural language processing technology or emotion analysis technology. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12, and generates appropriate advice based on the analysis results and provides the advice to the user through the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-described avatar selection unit, reception unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the avatar selection unit is realized by the control unit 46A of the robot 414. For example, the reception unit receives a user's complaints using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the user's comments using natural language processing technology or emotion analysis technology. For example, the provision unit is realized by the specific processing unit 290 of the data processing device 12, and generates appropriate advice based on the analysis results and provides the advice to the user through the speaker 240 of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The virtual friend system can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit measures the user's heart rate and stress level and transmits this data to the analysis unit. The analysis unit evaluates the user's health condition based on this data, and the provision unit can provide advice according to the user's health condition. For example, if the user's heart rate is high, the provision unit can provide advice such as "Take a deep breath and relax." Also, if the stress level is high, the provision unit can provide advice such as "Take a short break." This allows the virtual friend system to comprehensively support the user's physical and mental health.

[0108] The virtual friend system can further include a hobby learning unit that learns the user's hobbies and interests. The hobby learning unit analyzes the user's past comments and behavioral history to identify the user's hobbies and interests. The analysis unit transmits information related to the user's hobbies and interests based on this data to the providing unit. The providing unit can provide advice and suggestions based on the user's hobbies and interests. For example, if the user is interested in music, the providing unit can make a suggestion such as "Try listening to the new album." Or, if the user is interested in sports, the providing unit can make a suggestion such as "Try jogging on the weekend." In this way, the virtual friend system can support users in enriching their lives.

[0109] The virtual friend system can further include a sleep monitoring unit that monitors the user's sleep patterns. The sleep monitoring unit measures the user's sleep duration and quality and transmits this data to the analysis unit. The analysis unit evaluates the user's sleep state based on this data, and the provision unit can provide advice according to the user's sleep state. For example, if the user's sleep duration is short, the provision unit can provide advice such as "Try to go to bed earlier." Furthermore, if the sleep quality is low, the provision unit can also provide advice such as "Try listening to relaxing music before going to bed." In this way, the virtual friend system can support the user in improving their sleep quality.

[0110] The virtual friend system can further include a dietary monitoring unit that monitors the user's eating habits. The dietary monitoring unit records the user's dietary content and calorie intake and transmits this data to the analysis unit. The analysis unit evaluates the user's eating habits based on this data, and the provision unit can provide advice based on the user's eating habits. For example, if the user's calorie intake is high, the provision unit can provide advice such as "Try to eat more vegetables." If the user's diet is unbalanced, the provision unit can also provide advice such as "Try to eat a balanced diet." In this way, the virtual friend system can support the user's healthy eating habits.

[0111] The virtual friend system can further include an exercise monitoring unit that monitors the user's exercise habits. The exercise monitoring unit records the amount and type of exercise the user does and sends this data to the analysis unit. The analysis unit evaluates the user's exercise habits based on this data, and the providing unit can provide advice based on the user's exercise habits. For example, if the user does not exercise much, the providing unit can provide advice such as "Try to exercise a little every day." Furthermore, if a particular exercise is not effective, the providing unit can also provide advice such as "Try a different exercise." In this way, the virtual friend system can support the user's healthy exercise habits.

[0112] The virtual friend system can also estimate the user's emotions and adjust the avatar's facial expressions and movements in real time based on the estimated user emotions. For example, if the user is sad, the avatar's facial expressions can be adjusted to be gentle and comforting. If the user is happy, the avatar's facial expressions can be adjusted to be bright and cheerful. Furthermore, if the user is nervous, the avatar's movements can be adjusted to be calming. This allows the virtual friend system to adjust the avatar's facial expressions and movements in real time according to the user's emotions, enabling more natural and empathetic interactions.

[0113] The virtual friend system can further estimate the user's emotions and adjust the content of advice based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide advice such as "Try something to relax." If the user is feeling happy, the providing unit can also provide advice such as "Continue doing what you like to maintain that feeling." If the user is feeling sad, the providing unit can also provide advice such as "Try going outside to change your mood." This allows the virtual friend system to provide advice that is appropriate for the user's emotions, allowing for more effective support.

[0114] The virtual friend system can further estimate the user's emotions and adjust the tone of the conversation based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide the conversation in a gentle tone. If the user is relaxed, the providing unit can provide the conversation in a friendly tone. Furthermore, if the user is in a hurry, the providing unit can provide the conversation in a quick and concise tone. This allows the virtual friend system to adjust the tone of the conversation according to the user's emotions, thereby achieving more natural and effective communication.

[0115] The virtual friend system can further estimate the user's emotions and customize the content of the dialogue based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide information that helps relieve stress. If the user is feeling happy, the providing unit can also provide information that will further enhance that feeling. Furthermore, if the user is feeling sad, the providing unit can also provide information that will help change the user's mood. This allows the virtual friend system to customize the content of the dialogue according to the user's emotions and provide more effective support.

[0116] The virtual friend system can further estimate the user's emotions and adjust the frequency of conversations based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can frequently engage in conversations to support the user. If the user is relaxed, the providing unit can also engage in conversations at a moderate frequency. Furthermore, if the user is in a hurry, the providing unit can reduce the frequency of conversations to reduce the user's burden. This allows the virtual friend system to adjust the frequency of conversations according to the user's emotions and provide more effective support.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The avatar selection unit allows the user to select an avatar. The user can choose from avatars with different genders, ages, clothing, etc. For example, the avatar selection unit displays multiple avatars, allowing the user to select one that suits their preferences. Step 2: The reception unit receives a complaint from the user. The user can speak the complaint using text input or voice input. For example, the reception unit receives text or voice data input by the user. Step 3: The analysis unit analyzes the content received by the reception unit. The analysis unit uses natural language processing technology to analyze the user's utterance. For example, the analysis unit performs morphological analysis and grammatical analysis on the user's utterance to understand its meaning. The analysis unit can also use emotion analysis technology to understand the user's emotions. For example, the analysis unit extracts emotions from the user's utterance to understand what emotions the user is feeling. Step 4: The providing unit provides advice based on the results of the analysis by the analyzing unit. The providing unit generates appropriate advice based on the analysis results. For example, if the user is tired, the providing unit provides advice such as "Try to get plenty of rest today." The providing unit can also provide advice customized to the user's needs. For example, if the user wants to relax, the providing unit provides advice such as "Try listening to some relaxing music."

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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. an avatar selection unit where a user selects an avatar; a reception unit for receiving user complaints; an analysis unit that analyzes the data accepted by the acceptance unit; a providing unit that provides advice based on the results of the analysis by the analyzing unit. A system characterized by:

2. The avatar selection unit Allow users to choose from several avatars 2. The system of claim 1.

3. The reception unit Analyze user comments using natural language processing technology 2. The system of claim 1.

4. The analysis unit Understanding user emotions using emotion analysis technology 2. The system of claim 1.

5. The providing unit Providing customized advice based on analysis results 2. The system of claim 1.

6. The providing unit Suggesting music to promote relaxation according to user needs 2. The system of claim 1.

7. The avatar selection unit Estimate the user's emotions and suggest avatars based on the estimated user emotions.

2. The system of claim 1.

8. The avatar selection unit Analyzes the user's past avatar selection history and suggests the most suitable avatar 2. The system of claim 1.

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