system

Abstract

The system according to this embodiment aims to provide a relaxation method that is tailored to the user's emotions and circumstances. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a learning unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the user's emotions or situation based on the information received by the reception unit. The learning unit learns the user's preferences and past conversation history based on the information obtained by the analysis unit. The provision unit provides a method for playing music or stretching to help the user relax, based on the information obtained by the learning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide an appropriate relaxation method according to the user's emotions and situation.

[0005] The system according to the embodiment aims to provide a relaxation method according to the user's emotions and situation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a learning unit, and a provision unit. The reception unit receives user input. The analysis unit analyzes the user's emotions or situation based on the information received by the reception unit. The learning unit learns the user's preferences and past conversation history based on the information obtained by the analysis unit. The provision unit provides a method for playing music or stretching to help the user relax, based on the information obtained by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide a relaxation method tailored to the user's emotions and circumstances. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI bot system according to an embodiment of the present invention is a tool in which a favorite character from a favorite manga becomes an AI bot that interacts with the user and deepens the relationship with the character. Unlike a real person, this AI bot system will listen to you for hours, even at night. This is expected to relieve stress and provide a change of pace for people of all ages, from children to adults. For example, the user selects a favorite manga character, the AI ​​bot learns the character's personality and way of speaking, and then begins a conversation with the user. If the user says, "I'm tired today," the AI ​​bot will respond with something like, "You must be tired, what happened today?" The AI ​​bot provides appropriate responses according to the user's emotions and situation, deepening its relationship with the user. The AI ​​bot also learns the user's preferences and past conversation history to provide more personalized conversations. For example, if the user previously said, "My favorite food is curry," in the next conversation it can bring up the topic by asking, "Did you eat curry today?" This allows the user to build a closer relationship with the AI ​​bot through conversation. Furthermore, the AI ​​bot also has functions to alleviate the user's stress. For example, it can play music to help users relax or teach them simple stretching exercises. This allows users to refresh their minds and bodies through interaction with the AI ​​bot. In this way, the AI ​​bot acts as a companion in the user's life, supporting stress relief and mood changes. The manga market is a steadily growing sector, with a projected growth rate of 16.9% from 2022 and reaching US$42.2 billion by 2030, and this invention holds great potential in that market. Thus, the AI ​​bot system can help users relieve stress and support mood changes.

[0029] The AI ​​bot system according to this embodiment comprises a reception unit, an analysis unit, a learning unit, and a provision unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone or voice recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera or image recognition technology for receiving image input. The analysis unit analyzes the user's emotions and situation based on the information received by the reception unit. Analysis of emotions and situations includes, but is not limited to, emotions and situations such as joy, sadness, stress, and fatigue. The analysis unit may, for example, use facial recognition technology to analyze the user's emotions. The analysis unit may also use voice analysis technology to analyze the user's emotions. Furthermore, the analysis unit may also use text analysis technology to analyze the user's emotions. The learning unit learns the user's preferences and past dialogue history based on the information obtained by the analysis unit. Learning preferences and dialogue history includes, but is not limited to, music genres and past conversation content. The learning unit learns the user's preferences using, for example, machine learning algorithms. The learning unit can also learn past dialogue history using natural language processing techniques. The providing unit provides relaxation music playback and stretching methods based on the information obtained by the learning unit. Relaxation music playback and stretching methods include, but is not limited to, classical music and yoga stretching methods. The providing unit includes, for example, speakers or headphones with music playback capabilities. The providing unit can also include a display or projector for displaying stretching methods. Thus, the AI ​​bot system according to the embodiment can support the user's stress relief and mood change by receiving user input, analyzing emotions and situations, learning preferences and past dialogue history, and providing relaxation music playback and stretching methods.

[0030] The learning unit can learn the user's preferences and past conversation history. For example, if the user says, "My favorite food is curry," the learning unit can record that information and bring up the topic in the next conversation by asking, "Did you eat curry today?" Similarly, if the user says, "My favorite music is classical music," the learning unit can record that information and bring up the topic in the next conversation by asking, "Have you been listening to classical music lately?" Furthermore, if the user says, "My hobby is reading," the learning unit can record that information and bring up the topic in the next conversation by asking, "What book have you read recently?" In this way, the learning unit can provide more personalized conversations by learning the user's preferences and past conversation history. Some or all of the above processing in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input the user's conversation history data into a generating AI and have the generating AI learn the user's preferences and conversation history.

[0031] The service provider can provide relaxation music or stretching methods. For example, if the user is feeling stressed, the service provider can play relaxing classical music. The service provider can also teach the user simple yoga stretches if the user is tired. Furthermore, if the user wants to relax, the service provider can play nature sounds. In this way, the service provider can support the user's stress relief by providing relaxation music or stretching methods. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's emotional data into a generating AI and have the generating AI provide relaxation music or stretching methods.

[0032] The reception desk can analyze the user's past input history and select an appropriate input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can suggest relevant input methods based on the content the user has previously entered. In this way, the reception desk can select the optimal input method by analyzing the user's past input history, thereby improving user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0033] The reception unit can filter input based on the user's current activity status or areas of interest. For example, if the user is working, the reception unit will prioritize receiving work-related topics. Similarly, if the user is enjoying a hobby, the reception unit can prioritize receiving topics related to that hobby. Furthermore, if the user is relaxing, the reception unit can prioritize receiving relaxation-related topics. This allows the reception unit to receive more relevant input by filtering based on the user's current activity status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current activity status data into a generating AI and have the generating AI perform the filtering.

[0034] The reception unit can prioritize receiving inputs that are highly relevant based on the user's geographical location information. For example, if the user is at home, the reception unit can prioritize receiving inputs related to relaxation methods that can be done at home. Furthermore, if the user is out, the reception unit can prioritize receiving inputs related to stress relief methods while away from home. Additionally, if the user is traveling, the reception unit can prioritize receiving inputs related to relaxation methods at their travel destination. This allows the reception unit to prioritize receiving highly relevant inputs by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI determine the priority of highly relevant inputs.

[0035] The reception unit can analyze the user's social media activity when receiving input and accept appropriate input. For example, if the user is experiencing stress on social media, the reception unit can prioritize accepting input related to stress relief. Similarly, if the user is experiencing excitement on social media, the reception unit can prioritize accepting input to calm the excitement. Furthermore, if the user is experiencing fatigue on social media, the reception unit can prioritize accepting input encouraging rest. In this way, the reception unit can prioritize accepting relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of accepting appropriate input.

[0036] The analysis unit can improve the accuracy of its analysis based on user interactions. For example, if a user is spending time with family, the analysis unit will consider their relationship with family. Similarly, if a user is spending time with friends, the analysis unit can consider their relationship with friends. Furthermore, if a user is spending time with colleagues at work, the analysis unit can consider their relationship with colleagues. This allows the analysis unit to improve the accuracy of its analysis by considering user interactions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user interaction data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0037] The analysis unit can perform analysis based on user attribute information during the analysis process. For example, the analysis unit can analyze appropriate information by considering the user's age. It can also analyze appropriate information by considering the user's gender. Furthermore, it can analyze appropriate information by considering the user's occupation. This allows the analysis unit to perform more appropriate analysis by considering the user's attribute information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information data into a generating AI and have the generating AI perform the analysis.

[0038] The analysis unit can perform analysis based on the geographical distribution of users. For example, if a user lives in an urban area, the analysis unit will prioritize analyzing information related to urban areas. Similarly, if a user lives in a suburban area, the analysis unit can prioritize analyzing information related to suburban areas. Furthermore, if a user lives overseas, the analysis unit can prioritize analyzing information related to overseas locations. This allows the analysis unit to perform more appropriate analysis by considering the geographical distribution of users. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0039] The analysis unit can improve the accuracy of its analysis based on relevant literature during the analysis process. For example, the analysis unit may perform its analysis by referring to the latest research related to user emotions. It may also perform its analysis by referring to past research related to the user's situation. Furthermore, the analysis unit may perform its analysis by referring to literature related to user attributes. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0040] The learning unit can optimize its learning algorithm based on past learning data during the learning process. For example, the learning unit can optimize algorithms related to stress reduction based on past learning data. It can also optimize algorithms for calming excitement based on past learning data. Furthermore, it can optimize algorithms for promoting rest based on past learning data. In this way, the learning unit can optimize its learning algorithm and improve the accuracy of learning by referring to past learning data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0041] The learning unit can improve the accuracy of its learning based on the user's past conversation history. For example, the learning unit can perform learning related to stress reduction based on the user's past conversation history. It can also perform learning to calm excitement based on the user's past conversation history. Furthermore, it can perform learning to encourage rest based on the user's past conversation history. In this way, the learning unit can improve the accuracy of its learning by analyzing the user's past conversation history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past conversation history data into a generating AI and have the generating AI perform the learning accuracy improvement.

[0042] The learning unit can weight the training data based on the timing of user submissions during training. For example, the learning unit can prioritize data submitted when the user was feeling stressed. It can also prioritize data submitted when the user was excited. Furthermore, it can prioritize data submitted when the user was tired. This allows the learning unit to learn more appropriate data by weighting the training data based on the timing of user submissions. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user submission timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0043] The learning unit can supplement its learning data based on the user's social media activity during the learning process. For example, if the user is experiencing stress on social media, the learning unit can supplement it with data related to stress relief. It can also supplement it with data to calm the user if they are feeling excited on social media. Furthermore, if the user is feeling fatigued on social media, it can supplement it with data to encourage rest. This allows the learning unit to improve the accuracy of its learning by supplementing its learning data through analysis of the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity data into a generating AI and have the generating AI perform the supplementation of the learning data.

[0044] The service provider can select an appropriate service delivery method based on the user's past behavioral history at the time of delivery. For example, the service provider can provide optimal music based on music the user has listened to in the past to relax. It can also provide optimal music based on music the user has listened to in the past to calm excitement. Furthermore, it can provide optimal music based on music the user has listened to in the past to induce rest. In this way, the service provider can select the optimal service delivery method by analyzing the user's past behavioral history and improve user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal service delivery method.

[0045] The service provider can customize the content offered based on the user's current lifestyle at the time of delivery. For example, if the user is working, the service provider can offer relaxing music during work breaks. If the user is enjoying a hobby, the service provider can offer music related to that hobby. Furthermore, if the user is resting, the service provider can offer music that promotes rest. This allows the service provider to offer more appropriate content by customizing it based on the user's current lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current lifestyle data into a generating AI and have the generating AI customize the content offered.

[0046] The service provider can select an appropriate service delivery method based on the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can provide a way to relax at home. If the user is out, the service provider can also provide a way to relax while out. Furthermore, if the user is traveling, the service provider can provide a way to relax at their travel destination. In this way, the service provider can select the optimal service delivery method by considering the user's geographical location information, thereby improving user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI select an appropriate service delivery method.

[0047] The service provider can supplement the content provided based on the user's social media activity at the time of delivery. For example, if the user is feeling stressed on social media, the service provider can provide content related to stress relief. It can also provide content to calm the user if they are feeling excited on social media. Furthermore, if the user is feeling tired on social media, it can provide content to encourage rest. This allows the service provider to supplement its content and provide more appropriate content by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the supplementation of the content.

[0048] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0049] The learning unit can learn not only the user's preferences and past conversation history, but also their behavioral patterns. For example, if a user jogs every morning, it can record this information and bring up the topic in the next conversation, such as, "Did you jog this morning?" Similarly, if a user reads every night, it can record this information and bring up the topic in the next conversation, such as, "What book did you read last night?" Furthermore, if a user often watches movies on weekends, it can record this information and bring up the topic in the next conversation, such as, "What movie did you watch this weekend?" In this way, the learning unit can provide more personalized conversations by learning the user's behavioral patterns.

[0050] The service provider can offer not only relaxing music and stretching exercises, but also content related to the user's interests. For example, if the user likes music, the service provider can offer recommendations for new music albums and concert information. If the user likes movies, the service provider can offer trailers and reviews of new films. Furthermore, if the user likes cooking, the service provider can offer new recipes and cooking tips. In this way, the service provider can attract the user's interest and enhance the relaxation effect by providing content related to the user's interests.

[0051] The reception desk can analyze the user's past input history and select the appropriate input method, as well as provide an auto-completion function based on the user's input. For example, if the user types "Today," the reception desk can suggest an auto-completion such as "How was your day today?" If the user types "Tired," the reception desk can suggest an auto-completion such as "What caused you to be tired?" Furthermore, if the user types "Happy," the reception desk can suggest an auto-completion such as "Did something happy happen?" In this way, the reception desk can reduce the effort required for input and improve the smoothness of the conversation by providing an auto-completion function based on the user's input.

[0052] The reception system can not only filter input based on the user's current activities or areas of interest, but also prioritize input based on the user's schedule. For example, if a user wants to relax before an important meeting, the reception system will prioritize input related to relaxation. Similarly, if a user is enjoying a hobby, it can prioritize input related to that hobby. Furthermore, if a user is resting, it can prioritize input related to rest. This allows the reception system to prioritize input based on the user's schedule, enabling it to receive input at a more appropriate time.

[0053] The reception desk not only prioritizes receiving input that is highly relevant based on the user's geographical location, but can also suggest appropriate conversation topics based on the user's geographical location. For example, if the user is at home, it can suggest relaxation methods or topics related to their hobbies. If the user is out, it can suggest ways to relieve stress while out and information on sightseeing. Furthermore, if the user is traveling, it can suggest ways to relax or sightseeing spots at their destination. In this way, the reception desk can suggest more relevant conversation topics by taking the user's geographical location into consideration.

[0054] The reception desk, upon receiving input, analyzes the user's social media activity and not only accepts appropriate input but can also suggest conversation topics based on the user's social media activity. For example, if a user posts about a specific topic on social media, it can suggest conversations related to that topic. Similarly, if a user participates in a specific event on social media, it can suggest conversations related to that event. Furthermore, if a user shows a specific interest on social media, it can suggest conversations related to that interest. This allows the reception desk to suggest more relevant conversation topics by analyzing the user's social media activity.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception area receives user input. User input includes text input, voice input, and image input. The reception area is equipped with a keyboard or touchscreen for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The analysis unit analyzes the user's emotions and situation based on the information received by the reception unit. The analysis of emotions and situation includes joy, sadness, stress, fatigue, etc. The analysis unit uses facial recognition technology, voice analysis technology, and text analysis technology to analyze the user's emotions. Step 3: The learning unit learns the user's preferences and past conversation history based on the information obtained by the analysis unit. This learning of preferences and conversation history includes music genres and past conversation content. The learning unit uses machine learning algorithms and natural language processing techniques to learn the user's preferences and conversation history. Step 4: The provider unit provides relaxation music and stretching methods based on the information obtained by the learning unit. Relaxation music and stretching methods include classical music and yoga stretching methods. The provider unit is equipped with speakers or headphones with music playback capabilities, and a display or projector for displaying stretching methods.

[0057] (Example of form 2) An AI bot system according to an embodiment of the present invention is a tool in which a favorite character from a favorite manga becomes an AI bot that interacts with the user and deepens the relationship with the character. Unlike a real person, this AI bot system will listen to you for hours, even at night. This is expected to relieve stress and provide a change of pace for people of all ages, from children to adults. For example, the user selects a favorite manga character, the AI ​​bot learns the character's personality and way of speaking, and then begins a conversation with the user. If the user says, "I'm tired today," the AI ​​bot will respond with something like, "You must be tired, what happened today?" The AI ​​bot provides appropriate responses according to the user's emotions and situation, deepening its relationship with the user. The AI ​​bot also learns the user's preferences and past conversation history to provide more personalized conversations. For example, if the user previously said, "My favorite food is curry," in the next conversation it can bring up the topic by asking, "Did you eat curry today?" This allows the user to build a closer relationship with the AI ​​bot through conversation. Furthermore, the AI ​​bot also has functions to alleviate the user's stress. For example, it can play music to help users relax or teach them simple stretching exercises. This allows users to refresh their minds and bodies through interaction with the AI ​​bot. In this way, the AI ​​bot acts as a companion in the user's life, supporting stress relief and mood changes. The manga market is a steadily growing sector, with a projected growth rate of 16.9% from 2022 and reaching US$42.2 billion by 2030, and this invention holds great potential in that market. Thus, the AI ​​bot system can help users relieve stress and support mood changes.

[0058] The AI ​​bot system according to this embodiment comprises a reception unit, an analysis unit, a learning unit, and a provision unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit may include, for example, a keyboard or touchscreen for receiving text input. The reception unit may also include a microphone or voice recognition technology for receiving voice input. Furthermore, the reception unit may also include a camera or image recognition technology for receiving image input. The analysis unit analyzes the user's emotions and situation based on the information received by the reception unit. Analysis of emotions and situations includes, but is not limited to, emotions and situations such as joy, sadness, stress, and fatigue. The analysis unit may, for example, use facial recognition technology to analyze the user's emotions. The analysis unit may also use voice analysis technology to analyze the user's emotions. Furthermore, the analysis unit may also use text analysis technology to analyze the user's emotions. The learning unit learns the user's preferences and past dialogue history based on the information obtained by the analysis unit. Learning preferences and dialogue history includes, but is not limited to, music genres and past conversation content. The learning unit learns the user's preferences using, for example, machine learning algorithms. The learning unit can also learn past dialogue history using natural language processing techniques. The providing unit provides relaxation music playback and stretching methods based on the information obtained by the learning unit. Relaxation music playback and stretching methods include, but is not limited to, classical music and yoga stretching methods. The providing unit includes, for example, speakers or headphones with music playback capabilities. The providing unit can also include a display or projector for displaying stretching methods. Thus, the AI ​​bot system according to the embodiment can support the user's stress relief and mood change by receiving user input, analyzing emotions and situations, learning preferences and past dialogue history, and providing relaxation music playback and stretching methods.

[0059] The analysis unit can provide appropriate responses depending on the user's emotions or situation. For example, if the user says, "I'm tired today," the analysis unit might respond, "You must be tired. What happened today?" If the user says, "Something good happened," the analysis unit might respond, "That's great! What happened?" Furthermore, if the user says, "I'm sad," the analysis unit might respond, "What happened? Tell me." In this way, the analysis unit can improve the quality of the interaction with the user by providing appropriate responses according to the user's emotions and situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user input data into a generative AI and have the generative AI perform emotion estimation and generate appropriate responses.

[0060] The learning unit can learn the user's preferences and past conversation history. For example, if the user says, "My favorite food is curry," the learning unit can record that information and bring up the topic in the next conversation by asking, "Did you eat curry today?" Similarly, if the user says, "My favorite music is classical music," the learning unit can record that information and bring up the topic in the next conversation by asking, "Have you been listening to classical music lately?" Furthermore, if the user says, "My hobby is reading," the learning unit can record that information and bring up the topic in the next conversation by asking, "What book have you read recently?" In this way, the learning unit can provide more personalized conversations by learning the user's preferences and past conversation history. Some or all of the above processing in the learning unit may be performed using AI, for example, or not. For example, the learning unit can input the user's conversation history data into a generating AI and have the generating AI learn the user's preferences and conversation history.

[0061] The service provider can provide relaxation music or stretching methods. For example, if the user is feeling stressed, the service provider can play relaxing classical music. The service provider can also teach the user simple yoga stretches if the user is tired. Furthermore, if the user wants to relax, the service provider can play nature sounds. In this way, the service provider can support the user's stress relief by providing relaxation music or stretching methods. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's emotional data into a generating AI and have the generating AI provide relaxation music or stretching methods.

[0062] The reception unit can estimate the user's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the timing of input acceptance to provide time to relax. Alternatively, if the user is excited, the reception unit can immediately accept input and initiate a quick conversation. Furthermore, if the user is tired, the reception unit can adjust the timing of input acceptance and display a message encouraging rest. This allows the reception unit to accept input at a more appropriate time by adjusting the timing of input acceptance based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI adjust the timing of input acceptance.

[0063] The reception desk can analyze the user's past input history and select an appropriate input method. For example, the reception desk can prioritize suggesting input methods (such as voice or text) that the user has frequently used in the past. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. Furthermore, the reception desk can suggest relevant input methods based on the content the user has previously entered. In this way, the reception desk can select the optimal input method by analyzing the user's past input history, thereby improving user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI select the optimal input method.

[0064] The reception unit can filter input based on the user's current activity status or areas of interest. For example, if the user is working, the reception unit will prioritize receiving work-related topics. Similarly, if the user is enjoying a hobby, the reception unit can prioritize receiving topics related to that hobby. Furthermore, if the user is relaxing, the reception unit can prioritize receiving relaxation-related topics. This allows the reception unit to receive more relevant input by filtering based on the user's current activity status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's current activity status data into a generating AI and have the generating AI perform the filtering.

[0065] The reception unit can estimate the user's emotions and determine the priority of inputs to receive based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize inputs related to stress relief. It can also prioritize inputs that calm the user if they are excited. Furthermore, if the user is tired, it can prioritize inputs that encourage rest. This allows the reception unit to prioritize more appropriate inputs by determining the priority of inputs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input user emotion data into a generative AI and have the generative AI determine the priority of inputs.

[0066] The reception unit can prioritize receiving inputs that are highly relevant based on the user's geographical location information. For example, if the user is at home, the reception unit can prioritize receiving inputs related to relaxation methods that can be done at home. Furthermore, if the user is out, the reception unit can prioritize receiving inputs related to stress relief methods while away from home. Additionally, if the user is traveling, the reception unit can prioritize receiving inputs related to relaxation methods at their travel destination. This allows the reception unit to prioritize receiving highly relevant inputs by considering the user's geographical location information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's geographical location data into a generating AI and have the generating AI determine the priority of highly relevant inputs.

[0067] The reception unit can analyze the user's social media activity when receiving input and accept appropriate input. For example, if the user is experiencing stress on social media, the reception unit can prioritize accepting input related to stress relief. Similarly, if the user is experiencing excitement on social media, the reception unit can prioritize accepting input to calm the excitement. Furthermore, if the user is experiencing fatigue on social media, the reception unit can prioritize accepting input encouraging rest. In this way, the reception unit can prioritize accepting relevant input by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI perform the task of accepting appropriate input.

[0068] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing information related to stress relief. It can also prioritize analyzing information to calm the user if they are excited. Furthermore, if the user is tired, it can prioritize analyzing information to encourage rest. This allows the analysis unit to perform more appropriate analysis by adjusting the analysis criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis criteria.

[0069] The analysis unit can improve the accuracy of its analysis based on user interactions. For example, if a user is spending time with family, the analysis unit will consider their relationship with family. Similarly, if a user is spending time with friends, the analysis unit can consider their relationship with friends. Furthermore, if a user is spending time with colleagues at work, the analysis unit can consider their relationship with colleagues. This allows the analysis unit to improve the accuracy of its analysis by considering user interactions. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user interaction data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0070] The analysis unit can perform analysis based on user attribute information during the analysis process. For example, the analysis unit can analyze appropriate information by considering the user's age. It can also analyze appropriate information by considering the user's gender. Furthermore, it can analyze appropriate information by considering the user's occupation. This allows the analysis unit to perform more appropriate analysis by considering the user's attribute information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user attribute information data into a generating AI and have the generating AI perform the analysis.

[0071] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying information related to stress relief. It can also prioritize displaying information to calm the user if they are excited. Furthermore, if the user is tired, it can prioritize displaying information to encourage rest. In this way, the analysis unit can prioritize displaying more appropriate information by adjusting the display order of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display order of the analysis results.

[0072] The analysis unit can perform analysis based on the geographical distribution of users. For example, if a user lives in an urban area, the analysis unit will prioritize analyzing information related to urban areas. Similarly, if a user lives in a suburban area, the analysis unit can prioritize analyzing information related to suburban areas. Furthermore, if a user lives overseas, the analysis unit can prioritize analyzing information related to overseas locations. This allows the analysis unit to perform more appropriate analysis by considering the geographical distribution of users. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0073] The analysis unit can improve the accuracy of its analysis based on relevant literature during the analysis process. For example, the analysis unit may perform its analysis by referring to the latest research related to user emotions. It may also perform its analysis by referring to past research related to the user's situation. Furthermore, the analysis unit may perform its analysis by referring to literature related to user attributes. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may input relevant literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0074] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is stressed, the learning unit will prioritize learning data related to stress reduction. It can also prioritize learning data to calm the user if they are excited. Furthermore, if the user is tired, it can prioritize learning data to encourage rest. This allows the learning unit to learn more appropriate data by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI select the training data.

[0075] The learning unit can optimize its learning algorithm based on past learning data during the learning process. For example, the learning unit can optimize algorithms related to stress reduction based on past learning data. It can also optimize algorithms for calming excitement based on past learning data. Furthermore, it can optimize algorithms for promoting rest based on past learning data. In this way, the learning unit can optimize its learning algorithm and improve the accuracy of learning by referring to past learning data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0076] The learning unit can improve the accuracy of its learning based on the user's past conversation history. For example, the learning unit can perform learning related to stress reduction based on the user's past conversation history. It can also perform learning to calm excitement based on the user's past conversation history. Furthermore, it can perform learning to encourage rest based on the user's past conversation history. In this way, the learning unit can improve the accuracy of its learning by analyzing the user's past conversation history. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past conversation history data into a generating AI and have the generating AI perform the learning accuracy improvement.

[0077] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated emotions. For example, if the user is stressed, the learning unit can learn more frequently and update data related to stress reduction. It can also learn more frequently and update data to calm the user if they are excited. Furthermore, if the user is tired, the learning unit can learn more frequently and update data to encourage rest. This allows the learning unit to learn at more appropriate times by adjusting the frequency of learning based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the learning frequency.

[0078] The learning unit can weight the training data based on the timing of user submissions during training. For example, the learning unit can prioritize data submitted when the user was feeling stressed. It can also prioritize data submitted when the user was excited. Furthermore, it can prioritize data submitted when the user was tired. This allows the learning unit to learn more appropriate data by weighting the training data based on the timing of user submissions. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user submission timing data into a generating AI and have the generating AI perform the weighting of the training data.

[0079] The learning unit can supplement its learning data based on the user's social media activity during the learning process. For example, if the user is experiencing stress on social media, the learning unit can supplement it with data related to stress relief. It can also supplement it with data to calm the user if they are feeling excited on social media. Furthermore, if the user is feeling fatigued on social media, it can supplement it with data to encourage rest. This allows the learning unit to improve the accuracy of its learning by supplementing its learning data through analysis of the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity data into a generating AI and have the generating AI perform the supplementation of the learning data.

[0080] The service provider can estimate the user's emotions and adjust the content it provides based on those emotions. For example, if the user is stressed, the service provider can provide music to help them relax. It can also provide music to calm the user if they are excited. Furthermore, if the user is tired, it can provide music to encourage rest. This allows the service provider to provide more appropriate content by adjusting it based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the content it provides.

[0081] The service provider can select an appropriate service delivery method based on the user's past behavioral history at the time of delivery. For example, the service provider can provide optimal music based on music the user has listened to in the past to relax. It can also provide optimal music based on music the user has listened to in the past to calm excitement. Furthermore, it can provide optimal music based on music the user has listened to in the past to induce rest. In this way, the service provider can select the optimal service delivery method by analyzing the user's past behavioral history and improve user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past behavioral history data into a generating AI and have the generating AI select the optimal service delivery method.

[0082] The service provider can customize the content offered based on the user's current lifestyle at the time of delivery. For example, if the user is working, the service provider can offer relaxing music during work breaks. If the user is enjoying a hobby, the service provider can offer music related to that hobby. Furthermore, if the user is resting, the service provider can offer music that promotes rest. This allows the service provider to offer more appropriate content by customizing it based on the user's current lifestyle. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current lifestyle data into a generating AI and have the generating AI customize the content offered.

[0083] The service provider can estimate the user's emotions and prioritize the content offered based on those emotions. For example, if the user is stressed, the service provider will prioritize content related to stress relief. Similarly, if the user is agitated, the service provider can prioritize content to calm them down. Furthermore, if the user is tired, the service provider can prioritize content to encourage rest. This allows the service provider to prioritize more appropriate content by prioritizing it based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of the content offered.

[0084] The service provider can select an appropriate service delivery method based on the user's geographical location information at the time of delivery. For example, if the user is at home, the service provider can provide a way to relax at home. If the user is out, the service provider can also provide a way to relax while out. Furthermore, if the user is traveling, the service provider can provide a way to relax at their travel destination. In this way, the service provider can select the optimal service delivery method by considering the user's geographical location information, thereby improving user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location information data into a generating AI and have the generating AI select an appropriate service delivery method.

[0085] The service provider can supplement the content provided based on the user's social media activity at the time of delivery. For example, if the user is feeling stressed on social media, the service provider can provide content related to stress relief. It can also provide content to calm the user if they are feeling excited on social media. Furthermore, if the user is feeling tired on social media, it can provide content to encourage rest. This allows the service provider to supplement its content and provide more appropriate content by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the supplementation of the content.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The reception desk can not only receive user input but also provide appropriate feedback based on that input. For example, if a user inputs "I'm tired today," the reception desk can provide feedback such as, "You must be tired. Why don't you take a short break to relax?" If a user inputs "Something good happened," the reception desk can provide feedback such as, "That's wonderful! Please tell us what happened." Furthermore, if a user inputs "I'm sad," the reception desk can provide feedback such as, "What happened? Please tell us." In this way, the reception desk can improve the quality of interaction with users by providing appropriate feedback based on their input.

[0088] The analytics unit can analyze not only the user's emotions and situation, but also their health condition. For example, if a user says, "I have a headache," the analytics unit can offer advice such as, "What do you think might be causing your headache? Try drinking some water or taking a break." If a user says, "I can't sleep," the analytics unit can offer advice such as, "Try taking some deep breaths to relax. It's also effective to avoid using your smartphone before bed." Furthermore, if a user says, "I have no appetite," the analytics unit can offer advice such as, "How has your diet been lately? It's important to try to eat a balanced diet." In this way, the analytics unit can provide more appropriate advice by conducting analyses that take the user's health condition into consideration.

[0089] The learning unit can learn not only the user's preferences and past conversation history, but also their behavioral patterns. For example, if a user jogs every morning, it can record this information and bring up the topic in the next conversation, such as, "Did you jog this morning?" Similarly, if a user reads every night, it can record this information and bring up the topic in the next conversation, such as, "What book did you read last night?" Furthermore, if a user often watches movies on weekends, it can record this information and bring up the topic in the next conversation, such as, "What movie did you watch this weekend?" In this way, the learning unit can provide more personalized conversations by learning the user's behavioral patterns.

[0090] The service provider can offer not only relaxing music and stretching exercises, but also content related to the user's interests. For example, if the user likes music, the service provider can offer recommendations for new music albums and concert information. If the user likes movies, the service provider can offer trailers and reviews of new films. Furthermore, if the user likes cooking, the service provider can offer new recipes and cooking tips. In this way, the service provider can attract the user's interest and enhance the relaxation effect by providing content related to the user's interests.

[0091] The reception desk can estimate the user's emotions and adjust the timing of input acceptance based on those emotions, as well as modify the interface according to the user's feelings. For example, if the user is stressed, the reception desk can change the screen's color tone to a calming color to provide a relaxing environment. If the user is excited, the reception desk can brighten the screen's color tone to provide a lively environment. Furthermore, if the user is tired, the reception desk can adjust the screen brightness to provide an eye-friendly environment. In this way, the reception desk can provide a more comfortable user experience by adjusting the interface based on the user's emotions.

[0092] The reception desk can analyze the user's past input history and select the appropriate input method, as well as provide an auto-completion function based on the user's input. For example, if the user types "Today," the reception desk can suggest an auto-completion such as "How was your day today?" If the user types "Tired," the reception desk can suggest an auto-completion such as "What caused you to be tired?" Furthermore, if the user types "Happy," the reception desk can suggest an auto-completion such as "Did something happy happen?" In this way, the reception desk can reduce the effort required for input and improve the smoothness of the conversation by providing an auto-completion function based on the user's input.

[0093] The reception system can not only filter input based on the user's current activities or areas of interest, but also prioritize input based on the user's schedule. For example, if a user wants to relax before an important meeting, the reception system will prioritize input related to relaxation. Similarly, if a user is enjoying a hobby, it can prioritize input related to that hobby. Furthermore, if a user is resting, it can prioritize input related to rest. This allows the reception system to prioritize input based on the user's schedule, enabling it to receive input at a more appropriate time.

[0094] The reception desk can estimate the user's emotions and, based on that estimation, determine the priority of inputs to be received. It can also select notification methods appropriate to the user's emotions. For example, if the user is stressed, the reception desk can notify them with a calming sound to reduce stress. If the user is excited, the reception desk can notify them with a bright sound to calm their excitement. Furthermore, if the user is tired, the reception desk can notify them with a quiet sound to encourage rest. This allows the reception desk to deliver notifications in a more appropriate way by selecting notification methods based on the user's emotions.

[0095] The reception desk not only prioritizes receiving input that is highly relevant based on the user's geographical location, but can also suggest appropriate conversation topics based on the user's geographical location. For example, if the user is at home, it can suggest relaxation methods or topics related to their hobbies. If the user is out, it can suggest ways to relieve stress while out and information on sightseeing. Furthermore, if the user is traveling, it can suggest ways to relax or sightseeing spots at their destination. In this way, the reception desk can suggest more relevant conversation topics by taking the user's geographical location into consideration.

[0096] The reception desk, upon receiving input, analyzes the user's social media activity and not only accepts appropriate input but can also suggest conversation topics based on the user's social media activity. For example, if a user posts about a specific topic on social media, it can suggest conversations related to that topic. Similarly, if a user participates in a specific event on social media, it can suggest conversations related to that event. Furthermore, if a user shows a specific interest on social media, it can suggest conversations related to that interest. This allows the reception desk to suggest more relevant conversation topics by analyzing the user's social media activity.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reception area receives user input. User input includes text input, voice input, and image input. The reception area is equipped with a keyboard or touchscreen for receiving text input, a microphone and voice recognition technology for receiving voice input, and a camera and image recognition technology for receiving image input. Step 2: The analysis unit analyzes the user's emotions and situation based on the information received by the reception unit. The analysis of emotions and situation includes joy, sadness, stress, fatigue, etc. The analysis unit uses facial recognition technology, voice analysis technology, and text analysis technology to analyze the user's emotions. Step 3: The learning unit learns the user's preferences and past conversation history based on the information obtained by the analysis unit. This learning of preferences and conversation history includes music genres and past conversation content. The learning unit uses machine learning algorithms and natural language processing techniques to learn the user's preferences and conversation history. Step 4: The provider unit provides relaxation music and stretching methods based on the information obtained by the learning unit. Relaxation music and stretching methods include classical music and yoga stretching methods. The provider unit is equipped with speakers or headphones with music playback capabilities, and a display or projector for displaying stretching methods.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements described above, including the reception unit, analysis unit, learning unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user input using the touchscreen or microphone 38B of the smart device 14. The analysis unit analyzes the user's emotions and situation using the specific processing unit 290 of the data processing unit 12. The learning unit learns the user's preferences and past conversation history using the specific processing unit 290 of the data processing unit 12. The provision unit provides methods for playing music to relax or stretching using the speaker 40B or display 40A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0106] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0107] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0109] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0110] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0111] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0112] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0113] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0114] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, analysis unit, learning unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 of the smart glasses 214. The analysis unit analyzes the user's emotions and situation using the specific processing unit 290 of the data processing unit 12. The learning unit learns the user's preferences and past conversation history using the specific processing unit 290 of the data processing unit 12. The provision unit provides relaxation music playback or stretching methods using the speaker 240 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0122] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0126] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, analysis unit, learning unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 of the headset terminal 314. The analysis unit analyzes the user's emotions and situation using the specific processing unit 290 of the data processing unit 12. The learning unit learns the user's preferences and past conversation history using the specific processing unit 290 of the data processing unit 12. The provision unit provides relaxation music playback or stretching methods using the speaker 240 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0142] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0143] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the reception unit, analysis unit, learning unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 of the robot 414. The analysis unit analyzes the user's emotions and situation using the specific processing unit 290 of the data processing unit 12. The learning unit learns the user's preferences and past dialogue history using the specific processing unit 290 of the data processing unit 12. The provision unit provides methods for relaxation, such as playing music or stretching, using the speaker 240 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0154] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0155] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0156] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0160] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0161] 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.

[0162] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0163] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0164] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0165] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0167] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0168] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0169] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0170] (Note 1) A reception area that receives user input, An analysis unit analyzes the user's emotions or situation based on the information received by the reception unit, A learning unit learns the user's preferences and past dialogue history based on the information obtained by the aforementioned analysis unit, The system includes a providing unit that provides a method for playing music or stretching to relax based on the information obtained by the learning unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Provide appropriate responses based on the user's emotions or situation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned learning unit, Learns user preferences and past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Provides methods for relaxing, such as playing music or stretching. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past input history and select the appropriate input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving input, the system filters it based on the user's current activities or areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving input, the system analyzes the user's social media activity and accepts appropriate input. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved based on the interactions between users. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the analysis is performed based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis is performed based on the geographical distribution of users. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, improve the accuracy of the analysis based on relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During training, the learning algorithm is optimized based on past training data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, During training, the accuracy of the learning process is improved based on the user's past conversation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, During training, the training data is weighted based on when the users submitted their work. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, During training, the training data is supplemented based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, We estimate the user's emotions and adjust the content we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, the appropriate delivery method will be selected based on the user's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing the service, the content will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content offered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, the appropriate delivery method will be selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the content will be supplemented based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, An analysis unit analyzes the user's emotions or situation based on the information received by the reception unit, A learning unit learns the user's preferences and past dialogue history based on the information obtained by the aforementioned analysis unit, The system includes a providing unit that provides a method for playing music or stretching to relax based on the information obtained by the learning unit. A system characterized by the following features.

2. The aforementioned analysis unit, Provide appropriate responses based on the user's emotions or situation. The system according to feature 1.

3. The aforementioned learning unit, Learns user preferences and past conversation history. The system according to feature 1.

4. The aforementioned supply unit is, Provides methods for relaxing, such as playing music or stretching. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past input history and select the appropriate input method. The system according to feature 1.

7. The aforementioned reception unit is When receiving input, the system filters it based on the user's current activities or areas of interest. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and determines the priority of incoming inputs based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving input, the system prioritizes accepting inputs that are highly relevant based on the user's geographical location. The system according to feature 1.

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