System

Abstract

An object of the system according to the embodiment is to change a moment in daily life to a special experience.SOLUTION: A system according to an embodiment includes smart glasses and a generation AI. The smart glasses include a camera and a microphone that collect video and audio around the user. The generation AI includes a video analysis unit, an audio analysis unit, and a live commentary generation unit. The video analysis unit and the audio analysis unit analyze the video and audio collected by the smart glasses. The live commentary generation unit generates live commentary based on the data analyzed by the video analysis unit and the audio analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the challenge of making it difficult to turn every moment of everyday life into a special experience.

[0005] The system according to the embodiment aims to turn every moment of daily life into a special experience. [Means for solving the problem]

[0006] The system according to the embodiment includes smart glasses and a generation AI. The smart glasses include a camera and a microphone that collect video and audio around the user. The generation AI includes a video analysis unit, an audio analysis unit, and a commentary generation unit. The video analysis unit and the audio analysis unit analyze the video and audio collected by the smart glasses. The commentary generation unit generates commentary based on the data analyzed by the video analysis unit and the audio analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can turn every moment of daily life into a special experience. [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 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) The "Live! Your Life" system according to an embodiment of the present invention is a system that makes every moment of daily life more exciting. This system allows users to hear a live commentary dedicated to them through smart glasses. In this way, the "Live! Your Life" system can make the user's daily life more exciting.

[0029] A "Live! Your Life" system according to an embodiment includes smart glasses and a generation AI. The smart glasses include a camera and a microphone that collect video and audio of the user's surroundings. For example, the smart glasses use a built-in camera to collect video of the user's surroundings in real time. The smart glasses also use a built-in microphone to collect audio of the user's surroundings in real time. The generation AI includes a video analysis unit and an audio analysis unit that analyze the video and audio collected by the smart glasses, and a commentary generation unit that generates commentary based on the data analyzed by the video analysis unit and the audio analysis unit. For example, the video analysis unit analyzes the collected video data to recognize the user's behavior and surrounding circumstances. The audio analysis unit analyzes the collected audio data to recognize environmental sounds and conversations. The commentary generation unit generates enthusiastic commentary, such as a sports commentary, based on the data analyzed by the video analysis unit and the audio analysis unit. For example, if a user is preparing breakfast in the kitchen, the commentary generation unit generates commentary such as, "He's running through the kitchen at incredible speed! Breakfast is ready, and he's crossed the finish line in an amazing time!" This allows the "Live! Your Life" system to make users' daily lives more exciting.

[0030] The commentary generation unit can generate customized commentary according to the user's actions. For example, the commentary generation unit monitors the user's heart rate in real time using a heart rate sensor built into the smart glasses. For example, when the user's heart rate rises while exercising, the generation AI generates commentary such as, "Your heart rate is skyrocketing! That's an athlete's performance!" This allows for customized commentary according to the user's actions.

[0031] Smart glasses can collect the user's biometric information and generate commentary based on that information. For example, smart glasses can use a built-in camera to analyze the user's facial expressions and infer their emotions. For example, when the user smiles, the AI ​​can generate a commentary such as, "Your smile is shining! It's like a scene from a movie!" This allows the glasses to provide commentary based on the user's biometric information.

[0032] Smart glasses can analyze surrounding environmental sounds and generate commentary based on the sounds. For example, smart glasses can analyze surrounding environmental sounds and have a generation AI generate commentary based on that information. For example, while a user is running, an AR display can be used to create a scene of the user crossing the finish line, providing commentary such as, "Finished! Finished in amazing time!" This allows for commentary to be provided based on the surrounding environmental sounds.

[0033] Smart glasses can link with other smart devices and integrate data from multiple devices to generate live commentary. For example, smart glasses can link with a smartwatch to acquire the user's heart rate and exercise data. For example, while the user is running, the glasses can generate live commentary such as, "Your heart rate is rising! You're running at the pace of a marathon runner!" based on the data from the smartwatch. This allows the glasses to link with other smart devices and provide live commentary.

[0034] Smart glasses can add AR functionality and provide commentary with added visual effects. For example, smart glasses can use emotion estimation functionality to provide commentary that emphasizes a particular emotion the moment the user feels it. For example, when a user reunites with a friend, the smart glasses can generate a commentary such as, "What a joyous reunion! It's like a scene from a movie!" This allows for commentary with added visual effects.

[0035] The generation AI can learn the user's past behavioral data and generate commentary based on past experiences of success and failure. For example, the generation AI can learn the user's past successful experiences and generate commentary based on those experiences. For example, based on the user's past experience of completing a marathon, it can provide commentary such as, "Remember your past marathon completions! You made it to the finish line beautifully this time too!" This makes it possible to provide commentary based on past behavioral data.

[0036] The generative AI can analyze the actions of people around the user and simultaneously provide commentary on their actions. For example, when a user is playing sports with a friend, the generative AI can generate a commentary such as, "Your friend is making a great play! He's like a part of the team!" This allows the AI ​​to provide commentary on the actions of people around the user.

[0037] The generation AI can provide a customized commentary according to the user's hobbies and interests. For example, if the user likes music, the generation AI can generate a commentary specialized for music, such as, "Amazing performance! It's like being in a concert hall!" This allows the generation AI to provide a customized commentary according to the user's hobbies and interests.

[0038] The generation AI can predict the user's behavior and generate commentary for the predicted behavior in advance. For example, the generation AI learns the user's behavioral patterns and generates commentary for the predicted behavior in advance. For example, if the user has the habit of jogging every morning, the generation AI can generate commentary in advance such as, "Great start again today! Picking up the jogging pace!" This allows commentary for predicted behavior to be generated in advance.

[0039] The generation AI can stage the user's actions like a scene from a movie and add background music and sound effects that match the specific scene. For example, when the user is running, the generation AI can provide commentary such as, "BGM like the climax of a movie is playing!" This allows the user to perform the action like a scene from a movie and add background music and sound effects that match the specific scene.

[0040] Generative AI can interpret the user's actions as part of a story and provide commentary that matches the progress of the story. For example, when a user starts a new project, it can provide commentary such as, "A new chapter in the story begins!" This allows it to provide commentary that matches the progress of the story.

[0041] The generation AI can animate the user's actions and provide commentary linked to the animation. For example, when the user is running, the generation AI can provide commentary such as, "An animated character is running with you!" This allows the generation AI to animate the user's actions and provide commentary linked to the animation.

[0042] The generation AI can interpret the user's actions as if they were a game, and present them as if they were a commentary within the game. For example, the generation AI can interpret the user's actions as if they were a game, and provide commentary on those actions. For example, when the user is running, it can provide commentary such as, "A commentary like a game level-up will be played!" This allows the user's actions to be interpreted as if they were a game, and present them as if they were a commentary within the game.

[0043] The generation AI can learn the user's behavioral patterns and provide the optimal commentary based on the behavioral patterns. For example, if the user has the habit of jogging every morning, the generation AI can provide a commentary such as, "Another great start today! My jogging pace is increasing!" This allows the generation AI to provide the optimal commentary based on the user's behavioral patterns.

[0044] The generation AI can learn the user's preferences and interests and provide a customized commentary that matches their preferences. For example, if the user likes music, the generation AI can provide a commentary that is specialized in music, such as, "Amazing performance! It's like being in a concert hall!" This makes it possible to provide a customized commentary that matches the user's preferences and interests.

[0045] The generation AI can analyze the user's behavior in real time and provide a customized commentary according to the behavior. For example, when the user is running, the generation AI can provide a commentary such as, "What an amazing pace! Just like a professional runner!" This makes it possible to provide a customized commentary according to the user's behavior in real time.

[0046] The generation AI can predict the user's behavior and generate a customized commentary in advance according to the predicted behavior. For example, the generation AI learns the user's behavioral patterns and generates a customized commentary in advance according to the predicted behavior. For example, if the user has the habit of jogging every morning, the generation AI can generate a commentary in advance such as, "Great start again today! Picking up the jogging pace!" This allows a customized commentary to be generated in advance according to the predicted behavior.

[0047] Generative AI can capture the user's actions from different perspectives and provide commentary based on those different perspectives. For example, when a user is running, it can provide commentary such as, "The running course seen from a bird's eye view!" This allows it to provide commentary based on different perspectives.

[0048] Generative AI can analyze a user's behavior and provide a running commentary that explains the background and meaning of the behavior. For example, when a user is running, a running commentary such as "Explaining the effects of running and its impact on health!" can be provided. This allows a running commentary that explains the background and meaning of the behavior to be provided.

[0049] The generation AI can compare the user's actions with other users and generate commentary that provides a new perspective based on the comparison. For example, the generation AI can compare the user's actions with other users and generate commentary that provides a new perspective based on the comparison. For example, when the user is running, the generation AI can provide commentary such as, "What an amazing pace compared to other runners!" This makes it possible to generate commentary that provides a new perspective compared to other users.

[0050] Generative AI can associate a user's actions with historical events and cultural background, and generate commentary that provides a new perspective. For example, generative AI can associate a user's actions with historical events and generate commentary that provides a new perspective based on that association. For example, when a user is running, a commentary such as "Performance like an ancient Olympic runner!" can be provided. This makes it possible to generate commentary that provides a new perspective that is associated with historical events and cultural background.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The "Live! Your Life" system can also be equipped with a health management unit that monitors the user's health condition. For example, the health management unit can measure the user's blood pressure and blood sugar levels in real time, and if an abnormality is detected, provide health advice such as "Your blood pressure is rising! We recommend you take a break!". Also, if the user is not hydrating properly, the system can send a notification such as "You need to hydrate! Drink some water now!". This can support the user's health management.

[0053] The "Live! Your Life" system can also be equipped with a schedule management section that manages the user's schedule. For example, the schedule management section can link with the user's calendar app and provide a reminder such as "10 minutes until your next appointment!" when an appointment is approaching. It can also send a notification such as "An important meeting is about to begin! Get ready!" before an important meeting or event. This can support the user's schedule management.

[0054] The "Live! Your Life" system can also be equipped with a diet management unit that manages the user's diet. For example, the diet management unit can record the calories and nutrients of the meals the user eats and provide advice such as, "Your calorie intake today is above your goal!" It can also make suggestions such as, "Try incorporating more vegetables into your next meal!" to help the user eat a balanced diet. This can support the user's healthy eating habits.

[0055] The "Live! Your Life" system can also be equipped with a sleep management unit that manages the user's sleep. For example, the sleep management unit can monitor the user's sleep patterns and provide advice such as, "You didn't get enough sleep last night. Go to bed early tonight!". It can also notify the user when they fall into a deep sleep by saying, "You've fallen into a deep sleep. Sweet dreams!" This can support the user's quality sleep.

[0056] The "Live! Your Life" system can also be equipped with an exercise management unit that manages the user's exercise. For example, the exercise management unit can record the user's exercise data and provide feedback such as, "You achieved your exercise goal today! Great!". If the user is slacking off on exercise, the system can also make suggestions such as, "You're not getting enough exercise. Try walking a little!" This can support the user's exercise habits.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The smart glasses are equipped with a camera and a microphone that collect video and audio around the user. For example, the smart glasses use a built-in camera to collect video around the user in real time, and a built-in microphone to collect audio around the user in real time. Step 2: The generation AI is equipped with a video analysis unit and an audio analysis unit that analyze the video and audio collected by the smart glasses. The video analysis unit analyzes the collected video data and recognizes the user's behavior and surrounding circumstances. The audio analysis unit analyzes the collected audio data and recognizes environmental sounds and conversations. Step 3: The commentary generation unit generates commentary based on the data analyzed by the video analysis unit and the audio analysis unit. For example, if the user is preparing breakfast in the kitchen, the commentary generation unit generates commentary such as, "He runs through the kitchen at incredible speed! Breakfast is ready, and he crosses the finish line in an amazing time!"

[0059] (Example 2) The "Live! Your Life" system according to an embodiment of the present invention is a system that makes every moment of daily life more exciting. This system allows users to hear a live commentary dedicated to them through smart glasses. In this way, the "Live! Your Life" system can make the user's daily life more exciting.

[0060] A "Live! Your Life" system according to an embodiment includes smart glasses and a generation AI. The smart glasses include a camera and a microphone that collect video and audio of the user's surroundings. For example, the smart glasses use a built-in camera to collect video of the user's surroundings in real time. The smart glasses also use a built-in microphone to collect audio of the user's surroundings in real time. The generation AI includes a video analysis unit and an audio analysis unit that analyze the video and audio collected by the smart glasses, and a commentary generation unit that generates commentary based on the data analyzed by the video analysis unit and the audio analysis unit. For example, the video analysis unit analyzes the collected video data to recognize the user's behavior and surrounding circumstances. The audio analysis unit analyzes the collected audio data to recognize environmental sounds and conversations. The commentary generation unit generates enthusiastic commentary, such as a sports commentary, based on the data analyzed by the video analysis unit and the audio analysis unit. For example, if a user is preparing breakfast in the kitchen, the commentary generation unit generates commentary such as, "He's running through the kitchen at incredible speed! Breakfast is ready, and he's crossed the finish line in an amazing time!" This allows the "Live! Your Life" system to make users' daily lives more exciting.

[0061] The commentary generation unit can generate customized commentary according to the user's actions. For example, the commentary generation unit monitors the user's heart rate in real time using a heart rate sensor built into the smart glasses. For example, when the user's heart rate rises while exercising, the generation AI generates commentary such as, "Your heart rate is skyrocketing! That's an athlete's performance!" This allows for customized commentary according to the user's actions.

[0062] The commentary generation unit can generate commentary that corresponds to the user's emotions. For example, the smart glasses analyze the sound of the surrounding wind, and the generation AI generates commentary based on that information. For example, when a user is walking outside on a windy day, the AI ​​generates a commentary such as, "You're making great progress in the strong wind! You're like an adventurer!" This allows the AI ​​to provide commentary that corresponds to the user's emotions.

[0063] Smart glasses can collect the user's biometric information and generate commentary based on that information. For example, smart glasses can use a built-in camera to analyze the user's facial expressions and infer their emotions. For example, when the user smiles, the AI ​​can generate a commentary such as, "Your smile is shining! It's like a scene from a movie!" This allows the glasses to provide commentary based on the user's biometric information.

[0064] Smart glasses can analyze surrounding environmental sounds and generate commentary based on the sounds. For example, smart glasses can analyze surrounding environmental sounds and have a generation AI generate commentary based on that information. For example, while a user is running, an AR display can be used to create a scene of the user crossing the finish line, providing commentary such as, "Finished! Finished in amazing time!" This allows for commentary to be provided based on the surrounding environmental sounds.

[0065] Smart glasses can link with other smart devices and integrate data from multiple devices to generate live commentary. For example, smart glasses can link with a smartwatch to acquire the user's heart rate and exercise data. For example, while the user is running, the glasses can generate live commentary such as, "Your heart rate is rising! You're running at the pace of a marathon runner!" based on the data from the smartwatch. This allows the glasses to link with other smart devices and provide live commentary.

[0066] Smart glasses can add AR functionality and provide commentary with added visual effects. For example, smart glasses can use emotion estimation functionality to provide commentary that emphasizes a particular emotion the moment the user feels it. For example, when a user reunites with a friend, the smart glasses can generate a commentary such as, "What a joyous reunion! It's like a scene from a movie!" This allows for commentary with added visual effects.

[0067] The generation AI can learn the user's past behavioral data and generate commentary based on past experiences of success and failure. For example, the generation AI can learn the user's past successful experiences and generate commentary based on those experiences. For example, based on the user's past experience of completing a marathon, it can provide commentary such as, "Remember your past marathon completions! You made it to the finish line beautifully this time too!" This makes it possible to provide commentary based on past behavioral data.

[0068] The generative AI can analyze the actions of people around the user and simultaneously provide commentary on their actions. For example, when a user is playing sports with a friend, the generative AI can generate a commentary such as, "Your friend is making a great play! He's like a part of the team!" This allows the AI ​​to provide commentary on the actions of people around the user.

[0069] The generation AI can use the emotion estimation function to adjust the tone and content of the commentary in real time according to changes in the user's emotions. For example, the generation AI uses the emotion estimation function to increase the tone of the commentary when the user is excited. For example, when a user is excited while watching a sports game, the generation AI can provide commentary such as, "The excitement is reaching its peak! It's like being at the stadium!" This allows the tone and content of the commentary to be adjusted according to changes in the user's emotions.

[0070] The generation AI can provide a customized commentary according to the user's hobbies and interests. For example, if the user likes music, the generation AI can generate a commentary specialized for music, such as, "Amazing performance! It's like being in a concert hall!" This allows the generation AI to provide a customized commentary according to the user's hobbies and interests.

[0071] The generation AI can predict the user's behavior and generate commentary for the predicted behavior in advance. For example, the generation AI learns the user's behavioral patterns and generates commentary for the predicted behavior in advance. For example, if the user has the habit of jogging every morning, the generation AI can generate commentary in advance such as, "Great start again today! Picking up the jogging pace!" This allows commentary for predicted behavior to be generated in advance.

[0072] The generation AI can use the emotion estimation function to add music and sound effects to emphasize a particular emotion when the user feels that emotion. For example, the generation AI can use the emotion estimation function to add music to emphasize a particular emotion when the user feels joy. For example, when the user achieves success, the generation AI can provide a commentary such as, "Music will play to celebrate the joyful moment!" This allows the generation AI to add music and sound effects to emphasize a particular emotion.

[0073] The generation AI can stage the user's actions like a scene from a movie and add background music and sound effects that match the specific scene. For example, when the user is running, the generation AI can provide commentary such as, "BGM like the climax of a movie is playing!" This allows the user to perform the action like a scene from a movie and add background music and sound effects that match the specific scene.

[0074] Generative AI can interpret the user's actions as part of a story and provide commentary that matches the progress of the story. For example, when a user starts a new project, it can provide commentary such as, "A new chapter in the story begins!" This allows it to provide commentary that matches the progress of the story.

[0075] The generation AI can use its emotion estimation function to add special effects when the user's emotions are at their peak, emphasizing the heightened emotions. For example, the generation AI can use its emotion estimation function to add special background music when the user's emotions are at their peak, emphasizing the heightened emotions. For example, when the user reaches a moving moment, it can provide commentary such as, "BGM will play to bring the emotional climax to life!" This allows special effects to be added when emotions are at their peak, emphasizing the heightened emotions.

[0076] The generation AI can animate the user's actions and provide commentary linked to the animation. For example, when the user is running, the generation AI can provide commentary such as, "An animated character is running with you!" This allows the generation AI to animate the user's actions and provide commentary linked to the animation.

[0077] The generation AI can interpret the user's actions as if they were a game, and present them as if they were a commentary within the game. For example, the generation AI can interpret the user's actions as if they were a game, and provide commentary on those actions. For example, when the user is running, it can provide commentary such as, "A commentary like a game level-up will be played!" This allows the user's actions to be interpreted as if they were a game, and present them as if they were a commentary within the game.

[0078] The generation AI can use the emotion estimation function to add visual effects to emphasize a particular emotion when the user feels that emotion. For example, when the user feels joy, the generation AI can use the emotion estimation function to add visual effects to emphasize that emotion. For example, when the user achieves success, the generation AI can provide a commentary such as, "A visual effect that highlights the moment of joy will be displayed!" This allows the generation AI to add visual effects to emphasize a particular emotion.

[0079] The generation AI can learn the user's behavioral patterns and provide the optimal commentary based on the behavioral patterns. For example, if the user has the habit of jogging every morning, the generation AI can provide a commentary such as, "Another great start today! My jogging pace is increasing!" This allows the generation AI to provide the optimal commentary based on the user's behavioral patterns.

[0080] The generation AI can learn the user's preferences and interests and provide a customized commentary that matches their preferences. For example, if the user likes music, the generation AI can provide a commentary that is specialized in music, such as, "Amazing performance! It's like being in a concert hall!" This makes it possible to provide a customized commentary that matches the user's preferences and interests.

[0081] The generation AI can use the emotion estimation function to provide a customized commentary according to the user's emotions. For example, when the user feels joy, the generation AI can use the emotion estimation function to provide a customized commentary according to that emotion. For example, when the user achieves success, the generation AI can provide a commentary such as, "A commentary celebrating your joyous moment will be broadcast!" This makes it possible to provide a customized commentary according to emotions.

[0082] The generation AI can analyze the user's behavior in real time and provide a customized commentary according to the behavior. For example, when the user is running, the generation AI can provide a commentary such as, "What an amazing pace! Just like a professional runner!" This makes it possible to provide a customized commentary according to the user's behavior in real time.

[0083] The generation AI can predict the user's behavior and generate a customized commentary in advance according to the predicted behavior. For example, the generation AI learns the user's behavioral patterns and generates a customized commentary in advance according to the predicted behavior. For example, if the user has the habit of jogging every morning, the generation AI can generate a commentary in advance such as, "Great start again today! Picking up the jogging pace!" This allows a customized commentary to be generated in advance according to the predicted behavior.

[0084] The generation AI can use the emotion estimation function to provide a customized commentary that corresponds to a specific emotion when the user feels that emotion. For example, when the user feels joy, the generation AI can use the emotion estimation function to provide a customized commentary that corresponds to that emotion. For example, when the user achieves success, the generation AI can provide a commentary such as, "A commentary celebrating your joyful moment will be broadcast!" This makes it possible to provide a customized commentary that corresponds to a specific emotion.

[0085] Generative AI can capture the user's actions from different perspectives and provide commentary based on those different perspectives. For example, when a user is running, it can provide commentary such as, "The running course seen from a bird's eye view!" This allows it to provide commentary based on different perspectives.

[0086] Generative AI can analyze a user's behavior and provide a running commentary that explains the background and meaning of the behavior. For example, when a user is running, a running commentary such as "Explaining the effects of running and its impact on health!" can be provided. This allows a running commentary that explains the background and meaning of the behavior to be provided.

[0087] The generation AI can use the emotion estimation function to generate commentary that provides a new perspective based on the user's emotions. For example, when a user feels joy, the generation AI uses the emotion estimation function to generate commentary that provides a new perspective based on that emotion. For example, when a user achieves success, the generation AI provides commentary such as, "A commentary that captures that moment of joy from a new perspective will be broadcast!" This makes it possible to generate commentary that provides a new perspective based on emotions.

[0088] The generation AI can compare the user's actions with other users and generate commentary that provides a new perspective based on the comparison. For example, the generation AI can compare the user's actions with other users and generate commentary that provides a new perspective based on the comparison. For example, when the user is running, the generation AI can provide commentary such as, "What an amazing pace compared to other runners!" This makes it possible to generate commentary that provides a new perspective compared to other users.

[0089] Generative AI can associate a user's actions with historical events and cultural background, and generate commentary that provides a new perspective. For example, generative AI can associate a user's actions with historical events and generate commentary that provides a new perspective based on that association. For example, when a user is running, a commentary such as "Performance like an ancient Olympic runner!" can be provided. This makes it possible to generate commentary that provides a new perspective that is associated with historical events and cultural background.

[0090] The generation AI can use the emotion estimation function to generate commentary that provides a new perspective based on a specific emotion when the user feels that emotion. For example, when the user feels joy, the generation AI can use the emotion estimation function to generate commentary that provides a new perspective based on that emotion. For example, when the user achieves success, the generation AI can provide commentary such as, "A commentary that captures that moment of joy from a new perspective will be broadcast!" This makes it possible to generate commentary that provides a new perspective based on a specific emotion.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The "Live! Your Life" system can also be equipped with a health management unit that monitors the user's health condition. For example, the health management unit can measure the user's blood pressure and blood sugar levels in real time, and if an abnormality is detected, provide health advice such as "Your blood pressure is rising! We recommend you take a break!". Also, if the user is not hydrating properly, the system can send a notification such as "You need to hydrate! Drink some water now!". This can support the user's health management.

[0093] The "Live! Your Life" system can also be equipped with a schedule management section that manages the user's schedule. For example, the schedule management section can link with the user's calendar app and provide a reminder such as "10 minutes until your next appointment!" when an appointment is approaching. It can also send a notification such as "An important meeting is about to begin! Get ready!" before an important meeting or event. This can support the user's schedule management.

[0094] The "Live! Your Life" system can also be equipped with a diet management unit that manages the user's diet. For example, the diet management unit can record the calories and nutrients of the meals the user eats and provide advice such as, "Your calorie intake today is above your goal!" It can also make suggestions such as, "Try incorporating more vegetables into your next meal!" to help the user eat a balanced diet. This can support the user's healthy eating habits.

[0095] The "Live! Your Life" system can also be equipped with a sleep management unit that manages the user's sleep. For example, the sleep management unit can monitor the user's sleep patterns and provide advice such as, "You didn't get enough sleep last night. Go to bed early tonight!". It can also notify the user when they fall into a deep sleep by saying, "You've fallen into a deep sleep. Sweet dreams!" This can support the user's quality sleep.

[0096] The "Live! Your Life" system can also be equipped with an exercise management unit that manages the user's exercise. For example, the exercise management unit can record the user's exercise data and provide feedback such as, "You achieved your exercise goal today! Great!". If the user is slacking off on exercise, the system can also make suggestions such as, "You're not getting enough exercise. Try walking a little!" This can support the user's exercise habits.

[0097] The "Live! Your Life" system can also estimate the user's emotions and suggest relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest, "Take a deep breath and relax!". If the user is feeling sad, the system can suggest, "Listen to your favorite music to cheer yourself up!". This makes it possible to provide relaxation methods that suit the user's emotions.

[0098] The "Live! Your Life" system can further estimate the user's emotions and provide encouraging messages based on the estimated emotions. For example, if the user is feeling down, the system can provide a message such as "You're great! Have confidence!". If the user is feeling nervous, the system can provide a message such as "Relax, you can do it!". This makes it possible to provide encouraging messages that correspond to the user's emotions.

[0099] The "Live! Your Life" system can also estimate the user's emotions and suggest appropriate activities based on the estimated emotions. For example, if the user is tired, it can suggest "Try some relaxing yoga!". If the user is energetic, it can suggest "Go for a run!". This allows the system to provide activities that match the user's emotions.

[0100] The "Live! Your Life" system can also estimate the user's emotions and suggest appropriate reading lists based on the estimated emotions. For example, if the user wants to relax, the system can suggest, "Let's read a relaxing novel!". Or, if the user wants to increase their motivation, the system can suggest, "Let's read a self-help book!". This makes it possible to provide reading lists that correspond to the user's emotions.

[0101] The "Live! Your Life" system can also estimate the user's emotions and suggest appropriate movies and dramas based on the estimated emotions. For example, if the user wants to laugh, it can suggest "Let's watch a comedy movie!". If the user wants to be moved, it can suggest "Let's watch an emotional drama!". This makes it possible to provide movies and dramas that match the user's emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The smart glasses are equipped with a camera and a microphone that collect video and audio around the user. For example, the smart glasses use a built-in camera to collect video around the user in real time, and a built-in microphone to collect audio around the user in real time. Step 2: The generation AI is equipped with a video analysis unit and an audio analysis unit that analyze the video and audio collected by the smart glasses. The video analysis unit analyzes the collected video data and recognizes the user's behavior and surrounding circumstances. The audio analysis unit analyzes the collected audio data and recognizes environmental sounds and conversations. Step 3: The commentary generation unit generates commentary based on the data analyzed by the video analysis unit and the audio analysis unit. For example, if the user is preparing breakfast in the kitchen, the commentary generation unit generates commentary such as, "He runs through the kitchen at incredible speed! Breakfast is ready, and he crosses the finish line in an amazing time!"

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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. Smart glasses and Generative AI and Equipped with The smart glasses include: equipped with a camera and a microphone for collecting images and sounds around the user; The generated AI is A video analysis unit and an audio analysis unit that analyze the video and audio collected by the smart glasses; a commentary generating unit that generates commentary based on the data analyzed by the video analyzing unit and the audio analyzing unit. A system characterized by:

2. The smart glasses include: Collecting the user's biometric information; Generate commentary based on the biometric information 2. The system of claim 1.

3. The smart glasses include: Link with other smart devices Integrating data from multiple devices to generate commentary 2. The system of claim 1.

4. The generated AI is learning past behavioral data of the user; Generate commentary based on past successes and failures 2. The system of claim 1.

5. The generated AI is The user's actions are staged like a movie scene, Add background music and sound effects to specific scenes 2. The system of claim 1.

6. The generated AI is learning the user's behavioral patterns; Providing optimal commentary based on behavioral patterns 2. The system of claim 1.

7. The generated AI is Capture the user's behavior from different perspectives, Providing commentary from a different perspective 2. The system of claim 1.

8. The commentary generation unit Generate commentary according to the user's emotions 2. The system of claim 1.

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