system

Abstract

The system according to the embodiment aims to realize high-level interaction with the user and link with home appliances. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a linking unit, an operation unit, and a conversation unit. The reception unit receives input from a user. The generation unit materializes an image based on the information received by the reception unit. The connection unit links with home appliances based on the image generated by the generation unit. The operation unit operates the home appliances linked by the linking unit. The conversation unit converses with the user based on information about the home appliances operated by the operation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide systems that enable high-level interaction with users and link with home appliances, and there is room for improvement.

[0005] The system according to the embodiment aims to realize high-level interaction with the user and link with home appliances. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a linking unit, an operation unit, and a conversation unit. The reception unit receives input from a user. The generation unit materializes an image based on the information received by the reception unit. The connection unit links with home appliances based on the image generated by the generation unit. The operation unit operates the home appliances linked by the connection unit. The conversation unit converses with the user based on information about the home appliances operated by the operation unit. [Effects of the Invention]

[0007] The system according to the embodiment can realize high-level interaction with the user and can be linked with home appliances. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention creates AI friends with personalities (or so they are said to be) and visualizes them through visual materialization. This system allows users to select an AI friend through a smartphone application, which then visually materializes the friend. Furthermore, the AI ​​friends interact with home appliances to support daily life. For example, the AI ​​friends can adjust the temperature of the air conditioner and turn the lights on and off. The AI ​​friends also respond to daily conversations with the user on a high level, providing appropriate advice in response to concerns or questions. This allows the system to enrich the user's life and provide daily support by operating home appliances. For example, a character selected by the user can be visually materialized and support the user's daily life through operating home appliances and daily conversations.

[0029] The AI ​​Friends system according to the embodiment includes a reception unit, a generation unit, a linkage unit, an operation unit, and a conversation unit. The reception unit receives input from a user. For example, it can receive information from the user in the form of voice input, text input, gesture input, or the like. The generation unit uses a generation AI to create a visual representation based on the information received by the reception unit. For example, it can create a visual representation of a character using 3D modeling, AR technology, VR technology, or the like. The connection unit links with home appliances based on the image generated by the generation unit. For example, it links with home appliances using protocols such as Wi-Fi, Bluetooth (registered trademark), and Zigbee (registered trademark). The operation unit operates the home appliances linked by the connection unit. For example, it adjusts the temperature of an air conditioner, turns lights on and off, adjusts the volume, etc. The conversation unit converses with the user based on information about the home appliance operated by the operation unit. For example, it converses with the user using voice recognition, natural language processing, a dialogue system, or the like. As a result, the AI ​​Friends system according to the embodiment can create a visual representation of an AI Friends based on user input, operate the AI ​​Friends in conjunction with home appliances, and converse with the user.

[0030] The reception unit can receive information about a character selected by a user. The character information includes, but is not limited to, the character's name, attributes, and appearance. For example, the reception unit receives information about a character selected by a user through a smartphone application. The reception unit can also provide character information using voice input or text input by the user. In this way, by receiving information about a character selected by a user, an individual character can be visualized.

[0031] The generation unit can materialize the selected character in a visual form. The selected character is, for example, a character selected by a user through a smartphone application. The generation unit uses a generation AI to materialize the selected character in a visual form. For example, the character is represented three-dimensionally using 3D modeling technology. The generation unit can also use AR technology to display the character superimposed on the real world. Furthermore, the generation unit can also use VR technology to display the character in a virtual space. By materializing the selected character in a visual form, the user can visually recognize it.

[0032] The interlocking unit can interlock with the home appliances using a smart home protocol. Examples of smart home protocols include, but are not limited to, Z-Wave, Zigbee, and Matter. The interlocking unit can interlock with the home appliances using, for example, Wi-Fi. The interlocking unit can also interlock with the home appliances using Bluetooth. The interlocking unit can also interlock with the home appliances using Zigbee. This makes it possible to interlock with the home appliances using the smart home protocol.

[0033] The operation unit can adjust the temperature of home appliances and turn lights on and off. The operation unit, for example, sets the temperature of an air conditioner. For example, the operation unit adjusts the temperature of the air conditioner based on a user's instruction. The operation unit can also turn lights on and off. For example, the operation unit turns lights on and off by accepting a voice command. The operation unit can also turn lights on and off using a timer setting. This supports daily life by adjusting the temperature of home appliances and turning lights on and off.

[0034] The conversation unit can provide appropriate advice for the user's worries and concerns. The conversation unit, for example, uses speech recognition technology to understand the user's worries and concerns. For example, the conversation unit analyzes the user's voice to understand the content of the worry or concern. The conversation unit can also generate appropriate advice using natural language processing technology. For example, the conversation unit can provide psychological advice based on the content of the user's concern. The conversation unit can also provide technical advice. In this way, the system can support the user's daily life by providing appropriate advice for the user's worries and concerns.

[0035] The reception unit can analyze the user's past selection history and recommend the most suitable character. The reception unit, for example, analyzes the history of characters selected by the user in the past. For example, the reception unit analyzes the tendency of characters selected by the user in the past and recommends characters with similar characteristics. The reception unit can also preferentially display characters that the user has frequently selected in the past. For example, the reception unit recommends characters that are likely to be selected during specific time periods based on the user's past selection history. In this way, the most suitable character can be recommended by analyzing the user's past selection history.

[0036] When selecting a character, the reception unit can perform filtering based on the user's current living situation and areas of interest. The reception unit, for example, filters characters taking into account the user's current living situation. For example, when the user is at work, the reception unit may preferentially display characters that support the user's work. Furthermore, when the user is spending time on a hobby, the reception unit may preferentially display characters related to the hobby. For example, when the user is relaxing, the reception unit may preferentially display characters that have a relaxing effect. In this way, by filtering based on the user's current living situation and areas of interest, a more appropriate character can be selected.

[0037] The reception unit can provide an optimal selection means depending on the user's input method when selecting a character. For example, if the user is using voice input, the reception unit provides an interface that allows the user to select a character by voice. For example, the reception unit uses voice recognition technology to analyze the user's voice input and select a character. Furthermore, if the user is using text input, the reception unit can also provide an interface that allows the user to search for a character by text. For example, the reception unit uses text analysis technology to analyze the user's text input and select a character. Furthermore, if the user is using image input, the reception unit can also provide an interface that allows the user to select a character using image recognition. For example, the reception unit uses image analysis technology to analyze the user's image input and select a character. This facilitates character selection by providing an optimal selection means depending on the user's input method.

[0038] The generation unit can adjust the level of detail based on the importance of a character when materializing the video. For example, the generation unit assigns a high level of detail to a main character and a low level of detail to a background character. For example, the generation unit assigns a high level of detail to a character that the user frequently uses. The generation unit can also assign a high level of detail to a character that the user feels is important in a particular scene. In this way, the quality of the video can be optimized by adjusting the level of detail based on the importance of the character.

[0039] The generation unit can apply different generation algorithms depending on the character category when materializing the image. For example, the generation unit applies a generation algorithm that emphasizes realistic expression to a human-type character. For example, the generation unit applies a generation algorithm that emphasizes cute expression to an animal-type character. The generation unit can also apply a generation algorithm that emphasizes mechanical expression to a robot-type character. In this way, by applying different generation algorithms depending on the character category, more realistic images can be generated.

[0040] When materializing a video, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit adjusts the generation algorithm based on, for example, the user's evaluation of videos generated in the past. For example, the generation unit analyzes the characteristics of videos generated in the past by the user and reflects them in the next generation. The generation unit can also learn the user's preference trends from the user's past generation results and improve the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0041] The interlocking unit can improve the accuracy of interlocking by taking into account the interrelationships between home appliances when interlocking home appliances. For example, the interlocking unit can interlock lighting and air conditioners to improve the overall comfort of a room. For example, the interlocking unit can interlock televisions and speakers to improve the entertainment experience. The interlocking unit can also interlock lighting and curtains to let in natural light. In this way, the accuracy of interlocking can be improved by taking into account the interrelationships between home appliances.

[0042] When interlocking home appliances, the interlocking unit can perform interlocking by taking into account attribute information of the user of the home appliance. For example, in a household with children, the interlocking unit performs home appliance interlocking that emphasizes safety. For example, in a household with elderly people, the interlocking unit can perform home appliance interlocking that emphasizes ease of use. Furthermore, in a household with pets, the interlocking unit can also perform home appliance interlocking that emphasizes the comfort of the pet. This makes it possible to perform more appropriate home appliance interlocking by taking into account attribute information of the user of the home appliance.

[0043] When interlocking home appliances, the interlocking unit can weight the interlocking based on the frequency of use of the home appliances. For example, the interlocking unit prioritizes interlocking with home appliances that are used more frequently. For example, the interlocking unit can lower the priority of interlocking with home appliances that are used less frequently. The interlocking unit can also adjust the timing of interlocking depending on the frequency of use of the home appliances. Thus, by weighting interlocking based on the frequency of use of the home appliances, more efficient home appliance operation is possible.

[0044] When operating a home appliance, the operation unit can select the optimal operation method by referring to the user's past operation history. For example, the operation unit can preferentially provide operation methods that the user has frequently used in the past. For example, the operation unit can suggest the optimal operation method based on the user's past operation history. The operation unit can also preferentially provide operation methods that the user has given high ratings to in the past. In this way, the optimal operation method can be provided by referring to the user's past operation history.

[0045] The operation unit can customize the operation means based on the user's current lifestyle when operating a home appliance. For example, when the user is at work, the operation unit provides an operation method that does not interfere with the user's work. For example, when the user is relaxing, the operation unit provides an operation method that has a relaxing effect. Furthermore, when the user is busy, the operation unit can also provide a quick and easy operation method. This allows the user to customize the operation means based on the user's current lifestyle, enabling more appropriate home appliance operation.

[0046] The operation unit can improve the operation method by reflecting user feedback when operating a home appliance. The operation unit improves the operation method, for example, based on feedback provided by the user in the past. For example, the operation unit reflects user feedback in real time and adjusts the operation method. The operation unit can also analyze user feedback and provide the optimal operation method. In this way, by reflecting user feedback, the operation method can be improved, enabling more appropriate home appliance operation.

[0047] During a conversation, the conversation unit can provide optimal advice by referring to the user's past conversation history. The conversation unit provides relevant advice based on, for example, the content of a consultation the user has had in the past. For example, the conversation unit suggests optimal advice from the user's past conversation history. The conversation unit can also prioritize providing advice that the user has given a high rating to in the past. This allows optimal advice to be provided by referring to the user's past conversation history.

[0048] The conversation unit can customize the conversation content based on the user's current living situation and areas of interest during the conversation. For example, if the user is at work, the conversation unit provides work-related conversation content. For example, if the user is spending time on a hobby, the conversation unit provides conversation content related to that hobby. The conversation unit can also provide conversation content that has a relaxing effect when the user is relaxing. This allows for more appropriate conversation by customizing the conversation content based on the user's current living situation and areas of interest.

[0049] The conversation unit can improve the conversation method by reflecting the user's feedback during the conversation. The conversation unit improves the conversation method, for example, based on feedback provided by the user in the past. For example, the conversation unit reflects the user's feedback in real time and adjusts the conversation method. The conversation unit can also analyze the user's feedback and provide the optimal conversation method. In this way, by reflecting the user's feedback, the conversation method can be improved, enabling more appropriate conversation.

[0050] The conversation unit can provide optimal conversation content during a conversation by taking into account the user's geographical location information. For example, if the user is in a specific area, the conversation unit provides conversation content related to that area. For example, if the user is traveling, the conversation unit provides conversation content related to the travel destination. Furthermore, if the user is at home, the conversation unit can also provide conversation content to support the user at home. In this way, more appropriate conversation content can be provided by taking into account the user's geographical location information.

[0051] The conversation unit can analyze the user's social media activity during a conversation and suggest conversation content. The conversation unit, for example, provides conversation related to content shared by the user on social media. For example, the conversation unit analyzes the content posted by the user on social media and provides related conversation content. The conversation unit can also provide related conversation content by referring to the activity of the user's friends on social media. In this way, more appropriate conversation content can be suggested by analyzing the user's social media activity.

[0052] The conversation unit can customize the conversation content by reflecting the user's past feedback during the conversation. For example, the conversation unit preferentially provides conversation content that the user has previously rated highly. For example, the conversation unit prevents the display of conversation content that the user has previously rated poorly. The conversation unit can also customize the conversation content based on the user's past feedback. This allows the conversation content to be customized by reflecting the user's past feedback, enabling more appropriate conversations.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The generation unit can also customize the character's appearance based on the user's preferences. For example, the generation unit can change the character's clothing based on the color and style selected by the user. The generation unit can also reflect the user's preferred hairstyle and accessories. Furthermore, the generation unit can customize the character's voice and speaking style according to the user's preferences. In this way, by customizing the character's appearance based on the user's preferences, it is possible to provide a more familiar character.

[0055] The interlocking unit can also operate home appliances taking into account the user's schedule. For example, the interlocking unit can obtain the user's calendar information and set the air conditioner to an appropriate temperature before a meeting. The interlocking unit can also gradually brighten the lights based on the user's alarm settings. Furthermore, the interlocking unit can automatically turn off home appliances when the user plans to go out. This allows home appliances to be operated based on the user's schedule, supporting a more efficient lifestyle.

[0056] The reception unit can also suggest characters based on the user's hobbies and interests. For example, if the user likes music, the reception unit can suggest characters related to music. If the user likes sports, the reception unit can also suggest characters related to sports. Furthermore, if the user likes cooking, the reception unit can also suggest characters related to cooking. In this way, by suggesting characters based on the user's hobbies and interests, a more appropriate character can be selected.

[0057] The interlocking unit can also operate home appliances taking into account the user's geographical location information. For example, the interlocking unit can automatically turn on lights and air conditioners when the user is at home. The interlocking unit can also automatically turn off home appliances when the user is out. Furthermore, when the user is in a specific area, the interlocking unit can adjust the settings of the home appliances to suit the local climate. This allows for the operation of home appliances to take into account the user's geographical location information, thereby supporting a more efficient lifestyle.

[0058] The operation unit can also analyze the user's past operation history and suggest the optimal operation method. For example, the operation unit can prioritize providing operation methods that the user has used frequently in the past. The operation unit can also prioritize providing operation methods that the user has given high ratings in the past. Furthermore, the operation unit can learn and suggest the optimal operation method from the user's past operation history. In this way, the optimal operation method can be offered by analyzing the user's past operation history.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives input from the user. For example, information from the user can be received in the form of voice input, text input, gesture input, or the like. Step 2: The generation unit uses the generation AI to materialize the image based on the information received by the reception unit. For example, the character is materialized using 3D modeling, AR technology, VR technology, etc. Step 3: The linking unit links with the home appliances based on the video generated by the generating unit, for example, using a protocol such as Wi-Fi, Bluetooth, or Zigbee. Step 4: The operation unit controls the home appliances linked by the linking unit, for example, adjusting the temperature of the air conditioner, turning the lights on and off, adjusting the volume, etc. Step 5: The conversation unit converses with the user based on information about the home appliance operated by the operation unit, for example, by using voice recognition, natural language processing, a dialogue system, or the like.

[0061] (Example 2) A system according to an embodiment of the present invention creates AI friends with personalities (or so they are said to be) and visualizes them through visual materialization. This system allows users to select an AI friend through a smartphone application, which then visually materializes the friend. Furthermore, the AI ​​friends interact with home appliances to support daily life. For example, the AI ​​friends can adjust the temperature of the air conditioner and turn the lights on and off. The AI ​​friends also respond to daily conversations with the user on a high level, providing appropriate advice in response to concerns or questions. This allows the system to enrich the user's life and provide daily support by operating home appliances. For example, a character selected by the user can be visually materialized and support the user's daily life through operating home appliances and daily conversations.

[0062] The AI ​​Friends system according to the embodiment includes a reception unit, a generation unit, a linking unit, an operation unit, and a conversation unit. The reception unit receives input from a user. For example, it can receive information from the user in the form of voice input, text input, gesture input, or the like. The generation unit uses a generation AI to create a visual representation based on the information received by the reception unit. For example, it can create a visual representation of a character using 3D modeling, AR technology, VR technology, or the like. The connection unit links with home appliances based on the visual representation generated by the generation unit. For example, it links with home appliances using protocols such as Wi-Fi, Bluetooth, and Zigbee. The operation unit operates the linked home appliances via the connection unit. For example, it adjusts the temperature of an air conditioner, turns lights on and off, adjusts the volume, and so on. The conversation unit converses with the user based on information about the home appliance operated by the operation unit. For example, it converses with the user using voice recognition, natural language processing, a dialogue system, or the like. As a result, the AI ​​Friends system according to the embodiment can create a visual representation of an AI Friends based on user input, link with the home appliances, and converse with the user.

[0063] The reception unit can receive information about a character selected by a user. The character information includes, but is not limited to, the character's name, attributes, and appearance. For example, the reception unit receives information about a character selected by a user through a smartphone application. The reception unit can also provide character information using voice input or text input by the user. In this way, by receiving information about a character selected by a user, an individual character can be visualized.

[0064] The generation unit can materialize the selected character in a visual form. The selected character is, for example, a character selected by a user through a smartphone application. The generation unit uses a generation AI to materialize the selected character in a visual form. For example, the character is represented three-dimensionally using 3D modeling technology. The generation unit can also use AR technology to display the character superimposed on the real world. Furthermore, the generation unit can also use VR technology to display the character in a virtual space. By materializing the selected character in a visual form, the user can visually recognize it.

[0065] The interlocking unit can interlock with the home appliances using a smart home protocol. Examples of smart home protocols include, but are not limited to, Z-Wave, Zigbee, and Matter. The interlocking unit can interlock with the home appliances using, for example, Wi-Fi. The interlocking unit can also interlock with the home appliances using Bluetooth. The interlocking unit can also interlock with the home appliances using Zigbee. This makes it possible to interlock with the home appliances using the smart home protocol.

[0066] The operation unit can adjust the temperature of home appliances and turn lights on and off. The operation unit, for example, sets the temperature of an air conditioner. For example, the operation unit adjusts the temperature of the air conditioner based on a user's instruction. The operation unit can also turn lights on and off. For example, the operation unit turns lights on and off by accepting a voice command. The operation unit can also turn lights on and off using a timer setting. This supports daily life by adjusting the temperature of home appliances and turning lights on and off.

[0067] The conversation unit can provide appropriate advice for the user's worries and concerns. The conversation unit, for example, uses speech recognition technology to understand the user's worries and concerns. For example, the conversation unit analyzes the user's voice to understand the content of the worry or concern. The conversation unit can also generate appropriate advice using natural language processing technology. For example, the conversation unit can provide psychological advice based on the content of the user's concern. The conversation unit can also provide technical advice. In this way, the system can support the user's daily life by providing appropriate advice for the user's worries and concerns.

[0068] The reception unit can estimate the user's emotions and present character options based on the estimated user's emotions. The reception unit estimates the user's emotions using, for example, facial expression recognition technology. For example, the reception unit captures the user's facial expressions with a camera and estimates the emotions. The reception unit can also estimate the user's emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the user's voice and estimates the emotions. The reception unit can also estimate the user's emotions using text analysis technology. For example, the reception unit analyzes the user's text input and estimates the emotions. This allows the user to select a more appropriate character by presenting character options based on the user's emotions.

[0069] The reception unit can analyze the user's past selection history and recommend the most suitable character. The reception unit, for example, analyzes the history of characters selected by the user in the past. For example, the reception unit analyzes the tendency of characters selected by the user in the past and recommends characters with similar characteristics. The reception unit can also preferentially display characters that the user has frequently selected in the past. For example, the reception unit recommends characters that are likely to be selected during specific time periods based on the user's past selection history. In this way, the most suitable character can be recommended by analyzing the user's past selection history.

[0070] When selecting a character, the reception unit can perform filtering based on the user's current living situation and areas of interest. The reception unit, for example, filters characters taking into account the user's current living situation. For example, when the user is at work, the reception unit may preferentially display characters that support the user's work. Furthermore, when the user is spending time on a hobby, the reception unit may preferentially display characters related to the hobby. For example, when the user is relaxing, the reception unit may preferentially display characters that have a relaxing effect. In this way, by filtering based on the user's current living situation and areas of interest, a more appropriate character can be selected.

[0071] The reception unit can provide an optimal selection means depending on the user's input method when selecting a character. For example, if the user is using voice input, the reception unit provides an interface that allows the user to select a character by voice. For example, the reception unit uses voice recognition technology to analyze the user's voice input and select a character. Furthermore, if the user is using text input, the reception unit can also provide an interface that allows the user to search for a character by text. For example, the reception unit uses text analysis technology to analyze the user's text input and select a character. Furthermore, if the user is using image input, the reception unit can also provide an interface that allows the user to select a character using image recognition. For example, the reception unit uses image analysis technology to analyze the user's image input and select a character. This facilitates character selection by providing an optimal selection means depending on the user's input method.

[0072] The generation unit can estimate the user's emotion and adjust the image materialization expression method based on the estimated user's emotion. The generation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the generation unit can capture the user's facial expression with a camera and estimate the emotion. The generation unit can also estimate the user's emotion using voice analysis technology. For example, the generation unit can analyze the tone and speed of the user's voice to estimate the emotion. The generation unit can also estimate the user's emotion using text analysis technology. For example, the generation unit can analyze the user's text input to estimate the emotion. The generation unit can also adjust the image materialization expression method based on the estimated user's emotion. For example, the generation unit can generate an image with calm colors and movements when the user is relaxed. The generation unit can also generate an image with vivid colors and movements when the user is excited. The generation unit can also generate an image with a soothing effect when the user is tired. This makes it possible to generate more appropriate images by adjusting the image materialization expression method based on the user's emotion.

[0073] The generation unit can adjust the level of detail based on the importance of a character when materializing the video. For example, the generation unit assigns a high level of detail to a main character and a low level of detail to a background character. For example, the generation unit assigns a high level of detail to a character that the user frequently uses. The generation unit can also assign a high level of detail to a character that the user feels is important in a particular scene. In this way, the quality of the video can be optimized by adjusting the level of detail based on the importance of the character.

[0074] The generation unit can apply different generation algorithms depending on the character category when materializing the image. For example, the generation unit applies a generation algorithm that emphasizes realistic expression to a human-type character. For example, the generation unit applies a generation algorithm that emphasizes cute expression to an animal-type character. The generation unit can also apply a generation algorithm that emphasizes mechanical expression to a robot-type character. In this way, by applying different generation algorithms depending on the character category, more realistic images can be generated.

[0075] When materializing a video, the generation unit can improve the accuracy of generation by referring to the user's past generation results. The generation unit adjusts the generation algorithm based on, for example, the user's evaluation of videos generated in the past. For example, the generation unit analyzes the characteristics of videos generated in the past by the user and reflects them in the next generation. The generation unit can also learn the user's preference trends from the user's past generation results and improve the accuracy of generation. In this way, the accuracy of generation can be improved by referring to the user's past generation results.

[0076] The interlocking unit can estimate a user's emotion and adjust the criteria for interlocking home appliances based on the estimated user's emotion. The interlocking unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the interlocking unit can capture the user's facial expression with a camera and estimate the emotion. The interlocking unit can also estimate the user's emotion using voice analysis technology. For example, the interlocking unit can analyze the tone and speed of the user's voice to estimate the emotion. The interlocking unit can also estimate the user's emotion using text analysis technology. For example, the interlocking unit can analyze the user's text input to estimate the emotion. The interlocking unit can also adjust the criteria for interlocking home appliances based on the estimated user's emotion. For example, the interlocking unit can adjust the lighting to a warm color when the user is relaxed. The interlocking unit can also adjust the air conditioner temperature to a comfortable temperature when the user is stressed. The interlocking unit can also play music to relax the user when the user is tired. This allows the interlocking unit to adjust the criteria for interlocking home appliances based on the user's emotion, enabling more appropriate home appliance operation.

[0077] The interlocking unit can improve the accuracy of interlocking by taking into account the interrelationships between home appliances when interlocking home appliances. For example, the interlocking unit can interlock lighting and air conditioners to improve the overall comfort of a room. For example, the interlocking unit can interlock televisions and speakers to improve the entertainment experience. The interlocking unit can also interlock lighting and curtains to let in natural light. In this way, the accuracy of interlocking can be improved by taking into account the interrelationships between home appliances.

[0078] When interlocking home appliances, the interlocking unit can perform interlocking by taking into account attribute information of the user of the home appliance. For example, in a household with children, the interlocking unit performs home appliance interlocking that emphasizes safety. For example, in a household with elderly people, the interlocking unit can perform home appliance interlocking that emphasizes ease of use. Furthermore, in a household with pets, the interlocking unit can also perform home appliance interlocking that emphasizes the comfort of the pet. This makes it possible to perform more appropriate home appliance interlocking by taking into account attribute information of the user of the home appliance.

[0079] When interlocking home appliances, the interlocking unit can weight the interlocking based on the frequency of use of the home appliances. For example, the interlocking unit prioritizes interlocking with home appliances that are used more frequently. For example, the interlocking unit can lower the priority of interlocking with home appliances that are used less frequently. The interlocking unit can also adjust the timing of interlocking depending on the frequency of use of the home appliances. Thus, by weighting interlocking based on the frequency of use of the home appliances, more efficient home appliance operation is possible.

[0080] The operation unit can estimate a user's emotion and adjust a home appliance operation method based on the estimated user's emotion. The operation unit estimates the user's emotion using, for example, facial expression recognition technology. For example, the operation unit captures the user's facial expression with a camera and estimates the emotion. The operation unit can also estimate the user's emotion using voice analysis technology. For example, the operation unit analyzes the tone and speed of the user's voice to estimate the emotion. The operation unit can also estimate the user's emotion using text analysis technology. For example, the operation unit analyzes the user's text input to estimate the emotion. Furthermore, the operation unit adjusts the home appliance operation method based on the estimated user's emotion. For example, the operation unit provides a gentle operation method when the user is relaxed. The operation unit can also provide a simple and intuitive operation method when the user is stressed. Furthermore, the operation unit can provide a method that requires minimal operation when the user is tired. This allows the home appliance operation method to be adjusted based on the user's emotion, enabling more appropriate home appliance operation.

[0081] When operating a home appliance, the operation unit can select the optimal operation method by referring to the user's past operation history. For example, the operation unit can preferentially provide operation methods that the user has frequently used in the past. For example, the operation unit can suggest the optimal operation method based on the user's past operation history. The operation unit can also preferentially provide operation methods that the user has given high ratings to in the past. In this way, the optimal operation method can be provided by referring to the user's past operation history.

[0082] The operation unit can customize the operation means based on the user's current lifestyle when operating a home appliance. For example, when the user is at work, the operation unit provides an operation method that does not interfere with the user's work. For example, when the user is relaxing, the operation unit provides an operation method that has a relaxing effect. Furthermore, when the user is busy, the operation unit can also provide a quick and easy operation method. This allows the user to customize the operation means based on the user's current lifestyle, enabling more appropriate home appliance operation.

[0083] The operation unit can improve the operation method by reflecting user feedback when operating a home appliance. The operation unit improves the operation method, for example, based on feedback provided by the user in the past. For example, the operation unit reflects user feedback in real time and adjusts the operation method. The operation unit can also analyze user feedback and provide the optimal operation method. In this way, by reflecting user feedback, the operation method can be improved, enabling more appropriate home appliance operation.

[0084] The conversation unit can estimate the user's emotions and adjust the content of the conversation based on the estimated user's emotions. The conversation unit, for example, estimates the user's emotions using facial expression recognition technology. For example, the conversation unit captures the user's facial expressions with a camera and estimates the user's emotions. The conversation unit can also estimate the user's emotions using voice analysis technology. For example, the conversation unit analyzes the tone and speed of the user's voice and estimates the user's emotions. The conversation unit can also estimate the user's emotions using text analysis technology. For example, the conversation unit analyzes the user's text input and estimates the user's emotions. The conversation unit also adjusts the content of the conversation based on the estimated user's emotions. For example, the conversation unit provides calming content when the user is relaxed. Furthermore, the conversation unit can provide stress-reducing content when the user is stressed. Furthermore, the conversation unit can provide soothing content when the user is tired. This allows for more appropriate conversation by adjusting the content of the conversation based on the user's emotions.

[0085] During a conversation, the conversation unit can provide optimal advice by referring to the user's past conversation history. The conversation unit provides relevant advice based on, for example, the content of a consultation the user has had in the past. For example, the conversation unit suggests optimal advice from the user's past conversation history. The conversation unit can also prioritize providing advice that the user has given a high rating to in the past. This allows optimal advice to be provided by referring to the user's past conversation history.

[0086] The conversation unit can customize the conversation content based on the user's current living situation and areas of interest during the conversation. For example, if the user is at work, the conversation unit provides work-related conversation content. For example, if the user is spending time on a hobby, the conversation unit provides conversation content related to that hobby. The conversation unit can also provide conversation content that has a relaxing effect when the user is relaxing. This allows for more appropriate conversation by customizing the conversation content based on the user's current living situation and areas of interest.

[0087] The conversation unit can improve the conversation method by reflecting the user's feedback during the conversation. The conversation unit improves the conversation method, for example, based on feedback provided by the user in the past. For example, the conversation unit reflects the user's feedback in real time and adjusts the conversation method. The conversation unit can also analyze the user's feedback and provide the optimal conversation method. In this way, by reflecting the user's feedback, the conversation method can be improved, enabling more appropriate conversation.

[0088] The conversation unit can estimate the user's emotions and determine the priority of conversations based on the estimated user's emotions. The conversation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the conversation unit can capture the user's facial expressions with a camera and estimate the emotions. The conversation unit can also estimate the user's emotions using voice analysis technology. For example, the conversation unit can analyze the tone and speed of the user's voice to estimate the emotions. The conversation unit can also estimate the user's emotions using text analysis technology. For example, the conversation unit can analyze the user's text input to estimate the emotions. The conversation unit can also determine the priority of conversations based on the estimated user's emotions. For example, the conversation unit can prioritize conversations that have a relaxing effect when the user is relaxed. Furthermore, the conversation unit can prioritize conversations that have a stress-reducing effect when the user is stressed. Furthermore, the conversation unit can prioritize conversations that have a soothing effect when the user is tired. As a result, by determining the priority of conversations based on the user's emotions, more appropriate conversations can be held.

[0089] The conversation unit can provide optimal conversation content during a conversation by taking into account the user's geographical location information. For example, if the user is in a specific area, the conversation unit provides conversation content related to that area. For example, if the user is traveling, the conversation unit provides conversation content related to the travel destination. Furthermore, if the user is at home, the conversation unit can also provide conversation content to support the user at home. In this way, more appropriate conversation content can be provided by taking into account the user's geographical location information.

[0090] The conversation unit can analyze the user's social media activity during a conversation and suggest conversation content. The conversation unit, for example, provides conversation related to content shared by the user on social media. For example, the conversation unit analyzes the content posted by the user on social media and provides related conversation content. The conversation unit can also provide related conversation content by referring to the activity of the user's friends on social media. In this way, more appropriate conversation content can be suggested by analyzing the user's social media activity.

[0091] The conversation unit can customize the conversation content by reflecting the user's past feedback during the conversation. For example, the conversation unit preferentially provides conversation content that the user has previously rated highly. For example, the conversation unit prevents the display of conversation content that the user has previously rated poorly. The conversation unit can also customize the conversation content based on the user's past feedback. This allows the conversation content to be customized by reflecting the user's past feedback, enabling more appropriate conversations. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, linking unit, operation unit, and conversation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives voice input and text input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and visually materializes a character using a generation AI. The connection unit is connected to home appliances via the communication I / F 44 of the smart device 14. The operation unit is realized by the control unit 46A of the smart device 14 and operates the home appliances. The conversation unit is realized by the specific processing unit 290 of the data processing device 12 and converses with the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, linkage unit, operation unit, and conversation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives voice input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and visually materializes a character using a generation AI. The connection unit links with home appliances via the communication I / F 44 of the smart glasses 214. The operation unit is realized by the control unit 46A of the smart glasses 214 and operates the home appliances. The conversation unit is realized by the specific processing unit 290 of the data processing device 12 and converses with the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, linking unit, operation unit, and conversation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives voice input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and visually materializes a character using a generation AI. The connection unit is connected to a home appliance via the communication I / F 44 of the headset type terminal 314. The operation unit is realized by the control unit 46A of the headset type terminal 314 and operates the home appliance. The conversation unit is realized by the specific processing unit 290 of the data processing device 12 and converses with the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, linking unit, operation unit, and conversation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives voice input from the user. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and visually materializes the character using a generation AI. The connection unit is connected to the home appliance via the communication I / F 44 of the robot 414. The operation unit is realized by the control unit 46A of the robot 414 and operates the home appliance. The conversation unit is realized by the specific processing unit 290 of the data processing device 12 and converses with the user.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can also monitor the user's health condition and present a selection of characters based on the health condition. For example, the reception unit can measure the user's heart rate and blood pressure and recommend a character that has a relaxing effect. The reception unit can also analyze the user's sleep patterns and present a character that improves the quality of sleep. Furthermore, the reception unit can track the user's exercise volume and recommend a character that supports exercise. In this way, by presenting a selection of characters based on the user's health condition, it is possible to support a healthier lifestyle.

[0094] The generation unit can also customize the character's appearance based on the user's preferences. For example, the generation unit can change the character's clothing based on the color and style selected by the user. The generation unit can also reflect the user's preferred hairstyle and accessories. Furthermore, the generation unit can customize the character's voice and speaking style according to the user's preferences. In this way, by customizing the character's appearance based on the user's preferences, it is possible to provide a more familiar character.

[0095] The interlocking unit can also operate home appliances taking into account the user's schedule. For example, the interlocking unit can obtain the user's calendar information and set the air conditioner to an appropriate temperature before a meeting. The interlocking unit can also gradually brighten the lights based on the user's alarm settings. Furthermore, the interlocking unit can automatically turn off home appliances when the user plans to go out. This allows home appliances to be operated based on the user's schedule, supporting a more efficient lifestyle.

[0096] The operation unit can also adjust the operation method of the home appliance based on the tone and speed of the user's voice. For example, the operation unit can provide a quick operation method when the user is in a hurry. The operation unit can also provide a gentle operation method when the user is relaxed. Furthermore, the operation unit can also provide a simple and intuitive operation method when the user is feeling stressed. This allows for more appropriate home appliance operation by adjusting the operation method of the home appliance based on the tone and speed of the user's voice.

[0097] The conversation unit can also estimate the user's emotions and select a conversation topic based on the estimated user's emotions. For example, if the user is sad, the conversation unit can provide words of encouragement. If the user is happy, the conversation unit can also provide words of sympathy. Furthermore, if the user is feeling anxious, the conversation unit can also provide words of reassurance. This allows for more appropriate conversation by selecting a conversation topic based on the user's emotions.

[0098] The reception unit can also suggest characters based on the user's hobbies and interests. For example, if the user likes music, the reception unit can suggest characters related to music. If the user likes sports, the reception unit can also suggest characters related to sports. Furthermore, if the user likes cooking, the reception unit can also suggest characters related to cooking. In this way, by suggesting characters based on the user's hobbies and interests, a more appropriate character can be selected.

[0099] The generation unit can also estimate the user's emotions and adjust the character's facial expression based on the estimated user's emotions. For example, if the user is sad, the generation unit can make the character's facial expression gentle. If the user is happy, the generation unit can also make the character's facial expression cheerful. Furthermore, if the user is angry, the generation unit can also make the character's facial expression calm. In this way, by adjusting the character's facial expression based on the user's emotions, it is possible to provide a more friendly character.

[0100] The interlocking unit can also operate home appliances taking into account the user's geographical location information. For example, the interlocking unit can automatically turn on lights and air conditioners when the user is at home. The interlocking unit can also automatically turn off home appliances when the user is out. Furthermore, when the user is in a specific area, the interlocking unit can adjust the settings of the home appliances to suit the local climate. This allows for the operation of home appliances to take into account the user's geographical location information, thereby supporting a more efficient lifestyle.

[0101] The operation unit can also analyze the user's past operation history and suggest the optimal operation method. For example, the operation unit can prioritize providing operation methods that the user has used frequently in the past. The operation unit can also prioritize providing operation methods that the user has given high ratings in the past. Furthermore, the operation unit can learn and suggest the optimal operation method from the user's past operation history. In this way, the optimal operation method can be offered by analyzing the user's past operation history.

[0102] The conversation unit can also estimate the user's emotions and adjust the tone of the conversation based on the estimated user's emotions. For example, the conversation unit can use a calm tone when the user is relaxed. Also, the conversation unit can use a lively tone when the user is excited. Furthermore, the conversation unit can use a calm tone when the user is tired. In this way, adjusting the tone of the conversation based on the user's emotions enables more appropriate conversation.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives input from the user. For example, information from the user can be received in the form of voice input, text input, gesture input, or the like. Step 2: The generation unit uses the generation AI to materialize the image based on the information received by the reception unit. For example, the character is materialized using 3D modeling, AR technology, VR technology, etc. Step 3: The linking unit links with the home appliances based on the video generated by the generating unit, for example, using a protocol such as Wi-Fi, Bluetooth, or Zigbee. Step 4: The operation unit controls the home appliances linked by the linking unit, for example, adjusting the temperature of the air conditioner, turning the lights on and off, adjusting the volume, etc. Step 5: The conversation unit converses with the user based on information about the home appliance operated by the operation unit, for example, by using voice recognition, natural language processing, a dialogue system, or the like.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input from a user; a generation unit that materializes the video based on the information received by the reception unit; a linking unit that links with a home appliance based on the video generated by the generation unit; an operation unit that operates the home appliances linked by the linking unit; a conversation unit that converses with a user based on information about the home appliance operated by the operation unit; Equipped with A system characterized by:

2. The reception unit Accepts information about the character selected by the user 2. The system of claim 1.

3. The generation unit Visualize the selected character 2. The system of claim 1.

4. The interlocking portion is Linking with home appliances using smart home protocols 2. The system of claim 1.

5. The operation unit includes: Adjust the temperature of home appliances and turn lights on and off 2. The system of claim 1.

6. The conversation unit is Providing appropriate advice for users' concerns and questions 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and present character options based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past selection history and recommend the most suitable character 2. The system of claim 1.

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