System

Abstract

An object of the system according to the embodiment is to provide a device that efficiently summarizes the main points of a meeting at home and is easy to carry.SOLUTION: A system includes a generation AI, a conversation response unit, a wrap-up unit, and a carrying unit. The generation AI generates a response based on the user's input. The conversation response unit generates a response based on the input of the user. The wrap-up section summarizes the meeting based on the responses provided by the conversation response section. The carrying part has a mascot shape to facilitate carrying.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently summarize the key points of meetings when working from home, and there was a lack of portable devices.

[0005] The system according to the embodiment aims to provide a portable device that efficiently summarizes the key points of meetings when working from home. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a conversation response unit, a wrap-up unit, and a carrying unit. The generation AI generates a response based on a user's input. The conversation response unit generates a response based on the user's input. The wrap-up unit summarizes the main points of the meeting based on the response provided by the conversation response unit. The carrying unit is shaped like a mascot, making it easy to carry. [Effects of the Invention]

[0007] The system according to the embodiment can provide a portable device that efficiently summarizes the main points of meetings when working from home. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 device according to an embodiment of the present invention utilizes generative AI to provide a constant conversation partner. This device has functions for wrapping up meetings and providing self-awareness when working from home, and is shaped like a mascot, making it easy to carry and endearing in appearance. This allows the user to always have a conversation partner through generative AI, improving the efficiency of working from home. Furthermore, its portable design and endearing appearance allow generative AI to be utilized in everyday life. Furthermore, it allows for balanced communication amid diversity, building an environment in which people can coexist and receive kindness from those around them.

[0029] A device according to an embodiment includes a generation AI, a conversation response unit, a wrap-up unit, and a portable unit. The generation AI generates a response based on a user's input. For example, when a user asks, "What's the weather like today?", the generation AI responds, "It's sunny today. The temperature is 25 degrees." When a user asks, "What did you notice at work today?", the generation AI provides feedback, such as, "You were actively working on a new project." The conversation response unit provides a response generated by the generation AI. For example, the conversation response unit conveys the response generated by the generation AI to the user via voice. The conversation response unit can also display the text response generated by the generation AI on a display. The wrap-up unit summarizes the key points of a meeting. For example, after a meeting ends, the wrap-up unit provides a summary, such as, "The key points of the meeting are as follows." The wrap-up unit can also automatically record important remarks and decisions made during the meeting so that they can be searched later. The portable unit makes the device easy to carry. For example, the portable unit is palm-sized and lightweight, making it easy to store in a bag or pocket. The portable portion may also allow the exterior of the device to be customizable, allowing the user to change the design to suit their preferences, allowing the device to generate responses based on user input, summarize meeting highlights, and provide an easily portable device.

[0030] The conversation response unit can refer to the user's past conversation history and provide more personalized responses based on the context. For example, in the conversation response unit, the generation AI refers to the user's past conversation history and generates a response based on what the user previously said. For example, if the user previously said, "I want to go on a trip," the generation AI responds, "How are your travel plans progressing these days?" The conversation response unit also refers to the user's past conversation history and generates a response based on what the user previously said. For example, if the user previously said, "I want to read a new book," the generation AI responds, "Have you found any interesting books recently?" The conversation response unit also refers to the user's past conversation history and generates a response based on what the user previously said. For example, if the user previously said, "I want to start exercising," the generation AI responds, "Have you started exercising recently?" This enables more personalized conversations by providing responses that are appropriate for the context based on the past conversation history.

[0031] The conversation response unit can support conversations in different languages ​​and accommodate international users. For example, if the generation AI supports conversations in different languages, it will respond in English when the user speaks to it in English. For example, if the user asks, "What is the weather like today?", the generation AI will respond, "Today is sunny. The temperature is 25 degrees Celsius." The conversation response unit can also support conversations in different languages, and if the user speaks to it in Spanish, it will respond in Spanish. For example, if the user asks, "What is the weather like today?", the generation AI will respond, "Today is sunny. The temperature is 25 degrees Celsius." The conversation response unit can also support conversations in different languages, and if the user speaks to it in Chinese, it will respond in Chinese. For example, if the user asks, "What is the weather like today???," the generation AI will respond, "Today is sunny. The temperature is 25 degrees Celsius." This allows for international users to be accommodated by supporting conversations in different languages.

[0032] The conversation response unit can suggest topics based on the user's hobbies and interests, broadening the scope of the conversation. For example, the generation AI in the conversation response unit learns the user's hobbies and interests, and if the user is interested in music, it will suggest a topic related to music. For example, the generation AI will suggest, "What kind of music have you been listening to recently?" The conversation response unit also learns the user's hobbies and interests, and if the user is interested in sports, it will suggest a topic related to sports. For example, the generation AI will suggest, "What sports have you been playing recently?" The conversation response unit also learns the user's hobbies and interests, and if the user is interested in reading, it will suggest a topic related to reading. For example, the generation AI will suggest, "What books have you been reading recently?" This allows the scope of the conversation to be broadened by suggesting topics based on the user's hobbies and interests.

[0033] Generative AI can automatically record the key points of a meeting and save them in a format that can be searched later. For example, generative AI can automatically record the key points of a meeting and save them in text format. For example, it can automatically record important remarks and decisions made during a meeting so that they can be searched later. Generative AI can also automatically record the key points of a meeting and save them in audio format. For example, it can record important remarks and decisions made during a meeting as audio so that they can be searched later. Generative AI can also automatically record the key points of a meeting and tag and save them. For example, it can tag important remarks and decisions made during a meeting and save them so that they can be searched later. This enables efficient information management by automatically recording the key points of a meeting and saving them in a format that can be searched later.

[0034] The generation AI can learn the user's work patterns and suggest optimal break times. For example, the generation AI can learn the user's work patterns and suggest breaks at regular intervals. For example, if the user has been working for an hour, the generation AI might suggest, "Let's take a short break." The generation AI can also learn the user's work patterns and suggest breaks based on the user's progress. For example, if the user completes a specific task, the generation AI might suggest, "Let's take a short break before moving on to the next task." The generation AI can also learn the user's work patterns and suggest breaks based on the user's level of fatigue. For example, if the generation AI estimates that the user is tired, it might suggest, "Let's take a short break. You'll feel refreshed." This allows the AI ​​to learn the user's work patterns and suggest optimal break times, enabling efficient work.

[0035] The generation AI can work with project management tools to report task progress in real time. For example, if a user asks, "What's the progress on this task?", the generation AI reports, "The current progress is 50%." The generation AI also works with project management tools to report task progress in real time. For example, if a user asks, "What's the next task?", the generation AI reports, "The next task is to write a report." The generation AI also works with project management tools to report task progress in real time. For example, if a user asks, "When is the deadline for this task?", the generation AI reports, "The deadline for this task is tomorrow." This enables efficient project management by working with project management tools to report task progress in real time.

[0036] Generative AI can manage users' schedules and remind them of important meetings and deadlines. For example, generative AI can manage users' schedules and remind them of important meetings. For example, it can notify them 30 minutes before a meeting by saying, "An important meeting starts in 30 minutes." Generative AI can also manage users' schedules and remind them of important deadlines. For example, it can notify them one hour before the deadline by saying, "An important deadline is approaching in one hour." Generative AI can also manage users' schedules and remind them of important task deadlines. For example, it can notify them the day before a task deadline by saying, "The deadline for an important task is tomorrow." This allows users to manage their schedules and remind them of important meetings and deadlines, enabling efficient time management.

[0037] The device may have a customizable exterior, allowing the user to change the design to suit their preferences. For example, the device may have a customizable exterior, allowing the user to change the design to suit their preferences. For example, the device may have a removable cover, allowing the user to choose a cover with their favorite design. The device may also have a customizable exterior, allowing the user to change the design to suit their preferences. For example, the device may have a paintable exterior, allowing the user to paint it in their favorite color or design. The device may also have a customizable exterior, allowing the user to change the design to suit their preferences. For example, the device may have stickers that can be attached to the exterior, allowing the user to customize the design by attaching their favorite stickers. In this way, by making the exterior of the device customizable, the user can change the design to suit their preferences.

[0038] A device can be added with a haptic feedback function to react to a user's touch. For example, a device can be added with a haptic feedback function to react to a user's touch with vibration. For example, when a user touches the device, the device reacts by vibrating lightly. Furthermore, a device can be added with a haptic feedback function to react to a user's touch with sound. For example, when a user touches the device, the device reacts by emitting a sound. Furthermore, a device can be added with a haptic feedback function to react to a user's touch with light. For example, when a user touches the device, the device reacts by emitting a light. In this way, adding a haptic feedback function to a device allows it to react to a user's touch.

[0039] The device may be equipped with a GPS function, allowing the location of the device to be determined if the user loses it. For example, the device may be equipped with a GPS function, allowing the location of the device to be determined using a smartphone app if the user loses the device. For example, if the user loses the device, the app displays the current location of the device. The device may also be equipped with a GPS function, allowing the location of the device to be determined if the user loses the device. For example, if the user loses the device, the device may automatically transmit location information, allowing the user to determine the location. The device may also be equipped with a GPS function, allowing the location of the device to be determined if the user loses the device. For example, if the user loses the device, the device may emit a sound to determine the location. Thus, by equipping the device with a GPS function, the location of the device can be determined if the user loses the device.

[0040] A device may add a function to display the remaining battery level, allowing the user to know when it is time to charge. For example, the device may add a function to display the remaining battery level, allowing the user to know when it is time to charge. For example, the device may display the remaining battery level as a percentage on the device screen. A device may also add a function to display the remaining battery level, allowing the user to know when it is time to charge. For example, the device may display a notification when the battery level is low. Thus, adding a function to display the remaining battery level to the device allows the user to know when it is time to charge.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The device can monitor the user's health status and provide appropriate advice. For example, the device can measure the user's heart rate and blood pressure and, if an abnormality is detected, advise the user, "Your heart rate seems high. Please take a short rest." The device can also monitor the user's sleep patterns and provide advice on how to get better quality sleep. For example, the device can notify the user, "It seems you haven't been sleeping much lately. I recommend you go to bed earlier tonight." The device can also manage the user's food log and provide advice on how to eat a balanced diet. For example, the device can suggest, "Today's meal lacked vegetables. Why not add a salad to your next meal?" In this way, the device can monitor the user's health status and provide appropriate advice to support the user's health.

[0043] The device can analyze the user's study patterns and suggest effective study methods. For example, if the user tends to study in short bursts, the device can suggest, "Try the Pomodoro technique, which involves 25 minutes of concentrated study followed by a 5-minute break." If the user prefers visual learning, the device can suggest, "Try organizing your learning using diagrams and graphs." If the user prefers repetitive learning, the device can suggest, "Use flashcards to repeatedly review important points." In this way, the device can analyze the user's study patterns and suggest effective study methods, thereby improving learning efficiency.

[0044] The device can provide event information based on the user's hobbies and interests. For example, if the user is interested in music, the device can suggest, "Why not check out information about concerts happening nearby?" If the user is interested in sports, the device can suggest, "We will provide information about sporting events happening this weekend." If the user is interested in art, the device can suggest, "We will provide information about exhibitions at nearby art museums." In this way, the device can enrich the user's life by providing event information based on the user's hobbies and interests.

[0045] The device can monitor the user's exercise habits and suggest appropriate exercise plans. For example, the device can record the number of steps the user has walked in a day and suggest, "Try walking a little more today" if the user has not reached their target number of steps. The device can also analyze the user's exercise history and suggest a balanced exercise plan. For example, it can suggest, "Try combining three days of aerobic exercise with two days of strength training per week." Furthermore, the device can monitor the user's exercise performance and suggest areas for improvement. For example, it can suggest, "Try paying attention to your foot landing to improve your running form." In this way, the device can monitor the user's exercise habits and suggest appropriate exercise plans to support the user's health.

[0046] The device can monitor the user's sleep patterns and provide advice on how to get better quality sleep. For example, the device can record the user's sleep time and advise the user, "It's recommended that you go to bed earlier tonight," if the user continues to have insufficient sleep. The device can also analyze the user's sleep environment and suggest improvements. For example, the device can suggest, "Try lowering the temperature in your bedroom a little." The device can also suggest relaxation techniques to improve the user's sleep quality. For example, the device can suggest, "Try listening to relaxing music before going to bed." In this way, the device can support the user's health by monitoring the user's sleep patterns and providing advice on how to get better quality sleep.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The conversation response unit provides a response generated by the generation AI based on the user's input. For example, if the user asks, "What's the weather like today?", the conversation response unit responds, "It's sunny today. The temperature is 25 degrees." The conversation response unit can also display the text response generated by the generation AI on the display. Step 2: The wrap-up section summarizes the key points of the meeting. For example, after the meeting is over, it provides a summary such as, "The key points of the meeting are as follows." The wrap-up section can also automatically record important statements and decisions made during the meeting so that they can be searched for later. Step 3: The carrying part makes the device portable. For example, it can be designed to be palm-sized and lightweight, so that it can be easily stored in a bag or pocket. The carrying part can also make the exterior of the device customizable, allowing users to change the design to suit their preferences.

[0049] (Example 2) A device according to an embodiment of the present invention utilizes generative AI to provide a constant conversation partner. This device has functions for wrapping up meetings and providing self-awareness when working from home, and is shaped like a mascot, making it easy to carry and endearing in appearance. This allows the user to always have a conversation partner through generative AI, improving the efficiency of working from home. Furthermore, its portable design and endearing appearance allow generative AI to be utilized in everyday life. Furthermore, it allows for balanced communication amid diversity, building an environment in which people can coexist and receive kindness from those around them.

[0050] A device according to an embodiment includes a generation AI, a conversation response unit, a wrap-up unit, and a portable unit. The generation AI generates a response based on a user's input. For example, when a user asks, "What's the weather like today?", the generation AI responds, "It's sunny today. The temperature is 25 degrees." When a user asks, "What did you notice at work today?", the generation AI provides feedback, such as, "You were actively working on a new project." The conversation response unit provides a response generated by the generation AI. For example, the conversation response unit conveys the response generated by the generation AI to the user via voice. The conversation response unit can also display the text response generated by the generation AI on a display. The wrap-up unit summarizes the key points of a meeting. For example, after a meeting ends, the wrap-up unit provides a summary, such as, "The key points of the meeting are as follows." The wrap-up unit can also automatically record important remarks and decisions made during the meeting so that they can be searched later. The portable unit makes the device easy to carry. For example, the portable unit is palm-sized and lightweight, making it easy to store in a bag or pocket. The portable portion may also allow the exterior of the device to be customizable, allowing the user to change the design to suit their preferences, allowing the device to generate responses based on user input, summarize meeting highlights, and provide an easily portable device.

[0051] The conversation response unit can learn the user's tone of voice and speaking style and generate responses that correspond to the user's emotional state. For example, the generation AI analyzes the user's tone of voice and speaking style and generates a calming response if the user is angry. For example, if the user speaks in an angry tone, the generation AI responds, "Please calm down. Is there anything I can help you with?" The conversation response unit also analyzes the user's tone of voice and speaking style and generates an encouraging response if the user is sad. For example, if the user speaks in a sad tone, the generation AI responds, "Are you okay? I'm here to listen if you want to talk." The conversation response unit also analyzes the user's tone of voice and speaking style and generates an empathetic response if the user is happy. For example, if the user speaks in a happy tone, the generation AI responds, "That's wonderful! Congratulations!" This enables more personalized conversations by generating responses that correspond to the user's emotional state.

[0052] The conversation response unit can refer to the user's past conversation history and provide more personalized responses based on the context. For example, in the conversation response unit, the generation AI refers to the user's past conversation history and generates a response based on what the user previously said. For example, if the user previously said, "I want to go on a trip," the generation AI responds, "How are your travel plans progressing these days?" The conversation response unit also refers to the user's past conversation history and generates a response based on what the user previously said. For example, if the user previously said, "I want to read a new book," the generation AI responds, "Have you found any interesting books recently?" The conversation response unit also refers to the user's past conversation history and generates a response based on what the user previously said. For example, if the user previously said, "I want to start exercising," the generation AI responds, "Have you started exercising recently?" This enables more personalized conversations by providing responses that are appropriate for the context based on the past conversation history.

[0053] The conversation response unit can use the emotion estimation function to analyze the user's emotions in real time and provide a conversation to relax the user if the user is feeling stressed. The conversation response unit, for example, uses the emotion estimation function to provide a conversation to relax the user if the user is feeling stressed. For example, if it is estimated that the user is feeling stressed, the generation AI responds, "Try taking a deep breath. You'll feel a little more relaxed." The conversation response unit also uses the emotion estimation function to provide a conversation to relax the user if the user is feeling stressed. For example, if it is estimated that the user is feeling stressed, the generation AI responds, "Let's take a short break. Why don't you listen to some relaxing music?" The conversation response unit also uses the emotion estimation function to provide a conversation to relax the user if the user is feeling stressed. For example, if it is estimated that the user is feeling stressed, the generation AI responds, "Try doing some stretches to relax." In this way, by analyzing the user's emotions in real time and providing a conversation to relax the user if the user is feeling stressed, it is possible to reduce the user's psychological burden.

[0054] The conversation response unit can support conversations in different languages ​​and accommodate international users. For example, if the generation AI supports conversations in different languages, it will respond in English when the user speaks to it in English. For example, if the user asks, "What is the weather like today?", the generation AI will respond, "Today is sunny. The temperature is 25 degrees Celsius." The conversation response unit can also support conversations in different languages, and if the user speaks to it in Spanish, it will respond in Spanish. For example, if the user asks, "What is the weather like today?", the generation AI will respond, "Today is sunny. The temperature is 25 degrees Celsius." The conversation response unit can also support conversations in different languages, and if the user speaks to it in Chinese, it will respond in Chinese. For example, if the user asks, "What is the weather like today???," the generation AI will respond, "Today is sunny. The temperature is 25 degrees Celsius." This allows for international users to be accommodated by supporting conversations in different languages.

[0055] The conversation response unit can suggest topics based on the user's hobbies and interests, broadening the scope of the conversation. For example, the generation AI in the conversation response unit learns the user's hobbies and interests, and if the user is interested in music, it will suggest a topic related to music. For example, the generation AI will suggest, "What kind of music have you been listening to recently?" The conversation response unit also learns the user's hobbies and interests, and if the user is interested in sports, it will suggest a topic related to sports. For example, the generation AI will suggest, "What sports have you been playing recently?" The conversation response unit also learns the user's hobbies and interests, and if the user is interested in reading, it will suggest a topic related to reading. For example, the generation AI will suggest, "What books have you been reading recently?" This allows the scope of the conversation to be broadened by suggesting topics based on the user's hobbies and interests.

[0056] The conversation response unit can use the emotion estimation function to suggest appropriate music or videos when the user is feeling a specific emotion. For example, the conversation response unit can use the emotion estimation function to suggest relaxing music when the user is sad. For example, if it is estimated that the user is sad, the generation AI can suggest, "Would you like to listen to some relaxing music?" The conversation response unit can also use the emotion estimation function to suggest refreshing videos when the user is tired. For example, if it is estimated that the user is tired, the generation AI can suggest, "Would you like to watch some refreshing videos?" The conversation response unit can also use the emotion estimation function to suggest relaxing music when the user is feeling stressed. For example, if it is estimated that the user is feeling stressed, the generation AI can suggest, "Would you like to listen to some relaxing music?" This makes it possible to support the user's emotions by suggesting appropriate music or videos when the user is feeling a specific emotion.

[0057] Generative AI can automatically record the key points of a meeting and save them in a format that can be searched later. For example, generative AI can automatically record the key points of a meeting and save them in text format. For example, it can automatically record important remarks and decisions made during a meeting so that they can be searched later. Generative AI can also automatically record the key points of a meeting and save them in audio format. For example, it can record important remarks and decisions made during a meeting as audio so that they can be searched later. Generative AI can also automatically record the key points of a meeting and tag and save them. For example, it can tag important remarks and decisions made during a meeting and save them so that they can be searched later. This enables efficient information management by automatically recording the key points of a meeting and saving them in a format that can be searched later.

[0058] The generation AI can learn the user's work patterns and suggest optimal break times. For example, the generation AI can learn the user's work patterns and suggest breaks at regular intervals. For example, if the user has been working for an hour, the generation AI might suggest, "Let's take a short break." The generation AI can also learn the user's work patterns and suggest breaks based on the user's progress. For example, if the user completes a specific task, the generation AI might suggest, "Let's take a short break before moving on to the next task." The generation AI can also learn the user's work patterns and suggest breaks based on the user's level of fatigue. For example, if the generation AI estimates that the user is tired, it might suggest, "Let's take a short break. You'll feel refreshed." This allows the AI ​​to learn the user's work patterns and suggest optimal break times, enabling efficient work.

[0059] The generation AI can use the emotion estimation function to suggest short exercises or stretches to refresh the user when the user feels tired. For example, if the generation AI estimates that the user is tired, it suggests, "Let's do a little exercise to refresh yourself." The generation AI also uses the emotion estimation function to suggest stretches to refresh the user when the user feels tired. For example, if the generation AI estimates that the user is tired, it suggests, "Let's do a little stretch to refresh yourself." The generation AI also uses the emotion estimation function to suggest short exercises or stretches to refresh the user when the user feels tired. For example, if the generation AI estimates that the user is tired, it suggests, "Let's do a little exercise or stretch to refresh yourself." This allows the user's health to be supported by suggesting exercises or stretches to refresh the user when they feel tired.

[0060] The generation AI can work with project management tools to report task progress in real time. For example, if a user asks, "What's the progress on this task?", the generation AI reports, "The current progress is 50%." The generation AI also works with project management tools to report task progress in real time. For example, if a user asks, "What's the next task?", the generation AI reports, "The next task is to write a report." The generation AI also works with project management tools to report task progress in real time. For example, if a user asks, "When is the deadline for this task?", the generation AI reports, "The deadline for this task is tomorrow." This enables efficient project management by working with project management tools to report task progress in real time.

[0061] Generative AI can manage users' schedules and remind them of important meetings and deadlines. For example, generative AI can manage users' schedules and remind them of important meetings. For example, it can notify them 30 minutes before a meeting by saying, "An important meeting starts in 30 minutes." Generative AI can also manage users' schedules and remind them of important deadlines. For example, it can notify them one hour before the deadline by saying, "An important deadline is approaching in one hour." Generative AI can also manage users' schedules and remind them of important task deadlines. For example, it can notify them the day before a task deadline by saying, "The deadline for an important task is tomorrow." This allows users to manage their schedules and remind them of important meetings and deadlines, enabling efficient time management.

[0062] The generation AI can use the emotion estimation function to suggest environmental settings to improve concentration when the user is lacking in concentration. For example, when the generation AI estimates that the user is lacking in concentration using the emotion estimation function, it can suggest lighting settings to improve concentration. For example, the generation AI can suggest, "Turn the lights a little brighter to improve concentration." The generation AI can also use the emotion estimation function to suggest music to improve concentration when the generation AI estimates that the user is lacking in concentration. For example, the generation AI can suggest, "Listen to relaxing music to improve concentration." The generation AI can also use the emotion estimation function to suggest environmental settings to improve concentration when the generation AI estimates that the user is lacking in concentration. For example, the generation AI can suggest, "Take a short break to improve concentration." This allows the user to work more efficiently by suggesting environmental settings to improve concentration when they are lacking in concentration.

[0063] The device may have a customizable exterior, allowing the user to change the design to suit their preferences. For example, the device may have a customizable exterior, allowing the user to change the design to suit their preferences. For example, the device may have a removable cover, allowing the user to choose a cover with their favorite design. The device may also have a customizable exterior, allowing the user to change the design to suit their preferences. For example, the device may have a paintable exterior, allowing the user to paint it in their favorite color or design. The device may also have a customizable exterior, allowing the user to change the design to suit their preferences. For example, the device may have stickers that can be attached to the exterior, allowing the user to customize the design by attaching their favorite stickers. In this way, by making the exterior of the device customizable, the user can change the design to suit their preferences.

[0064] A device can be added with a haptic feedback function to react to a user's touch. For example, a device can be added with a haptic feedback function to react to a user's touch with vibration. For example, when a user touches the device, the device reacts by vibrating lightly. Furthermore, a device can be added with a haptic feedback function to react to a user's touch with sound. For example, when a user touches the device, the device reacts by emitting a sound. Furthermore, a device can be added with a haptic feedback function to react to a user's touch with light. For example, when a user touches the device, the device reacts by emitting a light. In this way, adding a haptic feedback function to a device allows it to react to a user's touch.

[0065] The device can provide visual feedback by using the emotion estimation function to change color or shape depending on the emotional state of the user. For example, the device uses the emotion estimation function to change the color of the device to a brighter color when the user is happy. For example, if it is estimated that the user is happy, the color of the device changes to bright yellow. The device also uses the emotion estimation function to change the color of the device to a darker color when the user is sad. For example, if it is estimated that the user is sad, the color of the device changes to dark blue. The device also uses the emotion estimation function to change the shape of the device when the user is feeling stressed. For example, if it is estimated that the user is feeling stressed, the shape of the device changes to a relaxing shape. In this way, the device provides visual feedback by changing color or shape depending on the emotional state of the user.

[0066] The device may be equipped with a GPS function, allowing the location of the device to be determined if the user loses it. For example, the device may be equipped with a GPS function, allowing the location of the device to be determined using a smartphone app if the user loses the device. For example, if the user loses the device, the app displays the current location of the device. The device may also be equipped with a GPS function, allowing the location of the device to be determined if the user loses the device. For example, if the user loses the device, the device may automatically transmit location information, allowing the user to determine the location. The device may also be equipped with a GPS function, allowing the location of the device to be determined if the user loses the device. For example, if the user loses the device, the device may emit a sound to determine the location. Thus, by equipping the device with a GPS function, the location of the device can be determined if the user loses the device.

[0067] A device may add a function to display the remaining battery level, allowing the user to know when it is time to charge. For example, the device may add a function to display the remaining battery level, allowing the user to know when it is time to charge. For example, the device may display the remaining battery level as a percentage on the device screen. A device may also add a function to display the remaining battery level, allowing the user to know when it is time to charge. For example, the device may display a notification when the battery level is low. Thus, adding a function to display the remaining battery level to the device allows the user to know when it is time to charge.

[0068] The device can use the emotion estimation function to display an appropriate message or animation according to the emotional state of the user. For example, using the emotion estimation function, the device displays a congratulatory message when the user is happy. For example, if it is estimated that the user is happy, the device displays "Congratulations!". Also, using the emotion estimation function, the device displays an encouraging message when the user is sad. For example, if it is estimated that the user is sad, the device displays "Are you okay? Is there anything I can help you with?" Also, using the emotion estimation function, the device displays a message to relax when the user is feeling stressed. For example, if it is estimated that the user is feeling stressed, the device displays "Take a short break." In this way, the device can support the user's emotions by displaying appropriate messages or animations according to the user's emotional state.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The device can monitor the user's health status and provide appropriate advice. For example, the device can measure the user's heart rate and blood pressure and, if an abnormality is detected, advise the user, "Your heart rate seems high. Please take a short rest." The device can also monitor the user's sleep patterns and provide advice on how to get better quality sleep. For example, the device can notify the user, "It seems you haven't been sleeping much lately. I recommend you go to bed earlier tonight." The device can also manage the user's food log and provide advice on how to eat a balanced diet. For example, the device can suggest, "Today's meal lacked vegetables. Why not add a salad to your next meal?" In this way, the device can monitor the user's health status and provide appropriate advice to support the user's health.

[0071] The device can estimate the user's emotions and suggest an environment that helps the user relax based on the estimated emotions. For example, if the device estimates that the user is feeling stressed, it can suggest, "Why not try a relaxing scent?". If the device estimates that the user is feeling tired, it can suggest, "Let's change the lighting to something more relaxing." Furthermore, if the device estimates that the user is feeling anxious, it can suggest, "Why not try listening to relaxing music?" In this way, the device can estimate the user's emotions and suggest a relaxing environment, thereby reducing the user's psychological burden.

[0072] The device can analyze the user's study patterns and suggest effective study methods. For example, if the user tends to study in short bursts, the device can suggest, "Try the Pomodoro technique, which involves 25 minutes of concentrated study followed by a 5-minute break." If the user prefers visual learning, the device can suggest, "Try organizing your learning using diagrams and graphs." If the user prefers repetitive learning, the device can suggest, "Use flashcards to repeatedly review important points." In this way, the device can analyze the user's study patterns and suggest effective study methods, thereby improving learning efficiency.

[0073] Using the emotion estimation function, the device can suggest appropriate exercises when the user is feeling a specific emotion. For example, if the device estimates that the user is feeling stressed, it can suggest, "Why not try a relaxing yoga pose?" If the device estimates that the user is feeling tired, it can suggest, "Let's do some light stretching to refresh ourselves." Furthermore, if the device estimates that the user is feeling anxious, it can suggest, "Take a deep breath and relax." In this way, the device can estimate the user's emotions and suggest appropriate exercises to support the user's health.

[0074] The device can provide event information based on the user's hobbies and interests. For example, if the user is interested in music, the device can suggest, "Why not check out information about concerts happening nearby?" If the user is interested in sports, the device can suggest, "We will provide information about sporting events happening this weekend." If the user is interested in art, the device can suggest, "We will provide information about exhibitions at nearby art museums." In this way, the device can enrich the user's life by providing event information based on the user's hobbies and interests.

[0075] Using its emotion estimation function, the device can suggest appropriate relaxation methods when the user is experiencing a specific emotion. For example, if the device estimates that the user is feeling stressed, it can suggest, "Why not try relaxing aromatherapy?" If the device estimates that the user is tired, it can suggest, "Try taking a bath using relaxing bath salts." If the device estimates that the user is feeling anxious, it can suggest, "Why not try relaxing meditation?" In this way, the device can estimate the user's emotions and suggest appropriate relaxation methods, thereby reducing the user's psychological burden.

[0076] The device can monitor the user's exercise habits and suggest appropriate exercise plans. For example, the device can record the number of steps the user has walked in a day and suggest, "Try walking a little more today" if the user has not reached their target number of steps. The device can also analyze the user's exercise history and suggest a balanced exercise plan. For example, it can suggest, "Try combining three days of aerobic exercise with two days of strength training per week." Furthermore, the device can monitor the user's exercise performance and suggest areas for improvement. For example, it can suggest, "Try paying attention to your foot landing to improve your running form." In this way, the device can monitor the user's exercise habits and suggest appropriate exercise plans to support the user's health.

[0077] Using the emotion estimation function, the device can suggest an appropriate meal plan when the user is feeling a specific emotion. For example, if the device estimates that the user is feeling stressed, it can suggest, "Why not try some relaxing herbal tea?" If the device estimates that the user is feeling tired, it can suggest, "Eat some bananas and nuts to replenish your energy." If the device estimates that the user is feeling anxious, it can suggest, "Why not try some relaxing chamomile tea?" In this way, the device can estimate the user's emotions and suggest an appropriate meal plan to support the user's health.

[0078] The device can monitor the user's sleep patterns and provide advice on how to get better quality sleep. For example, the device can record the user's sleep time and advise the user, "It's recommended that you go to bed earlier tonight," if the user continues to have insufficient sleep. The device can also analyze the user's sleep environment and suggest improvements. For example, the device can suggest, "Try lowering the temperature in your bedroom a little." The device can also suggest relaxation techniques to improve the user's sleep quality. For example, the device can suggest, "Try listening to relaxing music before going to bed." In this way, the device can support the user's health by monitoring the user's sleep patterns and providing advice on how to get better quality sleep.

[0079] Using the emotion estimation function, the device can suggest appropriate mental health care when the user is experiencing a specific emotion. For example, if the device estimates that the user is feeling stressed, it can suggest, "Why not try a relaxing meditation?" If the device estimates that the user is tired, it can suggest, "Try taking a short, refreshing walk." If the device estimates that the user is feeling anxious, it can suggest, "Try taking some deep, relaxing breaths." In this way, the device can estimate the user's emotions and suggest appropriate mental health care, thereby reducing the user's psychological burden.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The conversation response unit provides a response generated by the generation AI based on the user's input. For example, if the user asks, "What's the weather like today?", the conversation response unit responds, "It's sunny today. The temperature is 25 degrees." The conversation response unit can also display the text response generated by the generation AI on the display. Step 2: The wrap-up section summarizes the key points of the meeting. For example, after the meeting is over, it provides a summary such as, "The key points of the meeting are as follows." The wrap-up section can also automatically record important statements and decisions made during the meeting so that they can be searched for later. Step 3: The carrying part makes the device portable. For example, it can be designed to be palm-sized and lightweight, so that it can be easily stored in a bag or pocket. The carrying part can also make the exterior of the device customizable, allowing users to change the design to suit their preferences.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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. [Explanation of symbols]

[0149] 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. Equipped with generative AI, The generated AI is a conversational response unit that generates a response based on a user's input; a wrap-up unit that summarizes the main points of the meeting based on the responses provided by the conversation response unit; A carrying part in a mascot shape that makes it easy to carry A system characterized by:

2. The conversation response unit Learn the user's tone of voice and speaking style and generate responses that correspond to the user's emotional state 2. The system of claim 1.

3. The conversation response unit Supports conversations in different languages ​​and caters to international users 2. The system of claim 1.

4. The generated AI is Automatically record the key points of said meetings and store them in a searchable format for later use 2. The system of claim 1.

5. The generated AI is Integrate with project management tools to report task progress in real time 2. The system of claim 1.

6. The device is The exterior can be customized, allowing the user to change the design to suit their preferences.

2. The system of claim 1.

7. The device is Provide visual feedback by changing color and shape depending on the user's emotional state 2. The system of claim 1.

8. The device is Displaying appropriate messages and animations depending on the user's emotional state 2. The system of claim 1.

Images