System

Abstract

An object of a system according to an embodiment is to generate a personalized avatar based on life data, a voice, and an image of a user.SOLUTION: A system according to an embodiment includes a data acquisition unit, a data analysis unit, and an avatar generation unit. The data acquisition unit acquires life data, a voice, an image, and the like of the user. The data-analyzing unit analyzes the life information, voices, images, etc. of the user acquired by the data-acquiring unit in combination with the generated AI. The avatar generation unit generates an avatar of the user based on the data analyzed by the data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have difficulty generating personalized avatars based on a user's lifestyle data, voice, and image, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a personalized avatar based on the user's lifestyle data, voice, and image. [Means for solving the problem]

[0006] The system according to the embodiment includes a data acquisition unit, a data analysis unit, and an avatar generation unit. The data acquisition unit acquires the user's lifestyle data, voice, images, etc. The data analysis unit combines the user's lifestyle data, voice, images, etc. acquired by the data acquisition unit with a generation AI for analysis. The avatar generation unit creates an avatar for the user based on the data analyzed by the data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate a personalized avatar based on the user's lifestyle data, voice, and image. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The My Generation AI system according to an embodiment of the present invention is a system that acquires a user's daily life data, voice, images, etc., combines this data with a generation AI, analyzes it, and creates an avatar for the user. This allows the My Generation AI system to reproduce the user's thoughts, speaking style, and habits, enabling communication and discussion.

[0029] The My generation AI system according to the embodiment includes a data acquisition unit, a data analysis unit, and an avatar generation unit. The data acquisition unit acquires the user's lifestyle data, voice, images, etc. For example, the data acquisition unit collects daily activity data using a wearable device. The data acquisition unit can also acquire conversation data through a communication tool. The data acquisition unit can also accept data registration by the user themselves. The data analysis unit combines the user's lifestyle data, voice, images, etc. acquired by the data acquisition unit with the generation AI to analyze them. For example, the data analysis unit uses the generation AI to analyze the user's speech patterns, specific habits, and thinking tendencies. The data analysis unit also uses the generation AI to generate prompts based on the user data and create a replica of the user. The avatar generation unit creates a user avatar based on the data analyzed by the data analysis unit. For example, the avatar generation unit generates an avatar that reproduces the user's thoughts, speaking style, and habits. The avatar generation unit can communicate and discuss using the user's avatar. The avatar generation unit can also use the user's avatar to engage in conversations with the user. As a result, the My generation AI system according to the embodiment can reproduce the user's thoughts, speaking style, and habits, enabling communication and discussion.

[0030] The data acquisition unit can acquire biometric information such as the user's heart rate, body temperature, and stress level using a wearable device. For example, the data acquisition unit acquires biometric information such as the user's heart rate, body temperature, and stress level in real time using a wearable device. This allows for a detailed understanding of the user's health condition and lifestyle rhythm. The data acquisition unit also analyzes the biometric information acquired from the wearable device in combination with daily activity data. For example, it records fluctuations in heart rate and stress level during exercise to understand the user's lifestyle patterns in detail. The data acquisition unit also builds a system that monitors the user's stress level and changes in physical condition in real time based on the biometric information and issues an alert if an abnormality is detected. In this way, by acquiring the user's biometric information, a more detailed understanding of the user's lifestyle patterns can be obtained.

[0031] The data acquisition unit can analyze a user's SNS activity or online shopping history to reflect the user's interests and preferences. For example, the data acquisition unit analyzes the user's SNS activity and extracts interests and preferences from the content of posts and trends in likes. For example, if there are many posts about a particular topic, data related to that topic is preferentially acquired. The data acquisition unit also analyzes online shopping history to understand the user's preferences from the category and brand of purchased products. For example, if a user frequently purchases products from a particular brand, data related to that brand is acquired. The data acquisition unit also analyzes SNS activity and online shopping history in an integrated manner to comprehensively understand the user's interests and preferences. For example, the content of posts on SNS can be compared with purchase history to more accurately reflect the user's interests. This allows for the creation of a more personalized avatar by reflecting the user's interests and preferences.

[0032] The data acquisition unit also acquires data on the user's pets and family, and can recreate the user's entire living environment. The data acquisition unit, for example, acquires activity data of the user's pets and recreates the user's entire living environment. For example, the data acquisition unit records the pet's exercise amount and meal times and reflects them in the user's lifestyle rhythm. The data acquisition unit also acquires family data and recreates the user's living environment in more detail. For example, the data acquisition unit analyzes the family's schedule and communication history and reflects them in the user's lifestyle pattern. The data acquisition unit also comprehensively analyzes the data on the pets and family, and builds a system that recreates the user's entire living environment. For example, the user's living environment is recreated in detail based on the content of conversations with family members and the behavior of the pets. This allows the user's entire living environment to be recreated, making it possible to create a more detailed avatar.

[0033] The data acquisition unit can analyze the user's past photos and videos and reflect changes over time. For example, the data acquisition unit analyzes the user's past photos and videos and records changes in appearance and environment over time. For example, the data acquisition unit extracts the user's growth and changes from past photos and reflects them in the data. The data acquisition unit also analyzes past videos and records changes in the user's speaking style and behavior. For example, the data acquisition unit extracts changes in speaking patterns and gestures from past videos and reflects them in the data. The data acquisition unit also builds a system that reproduces changes in the user's living environment and appearance based on the results of the photo and video analysis. For example, the data acquisition unit changes the user's avatar over time based on past data. This allows for the creation of a more realistic avatar by reflecting changes over time.

[0034] The data analysis unit analyzes the user's past emails and memos and can learn specific phrases and expressions to reproduce their thought patterns. For example, the data analysis unit analyzes the user's past emails and memos and extracts specific phrases and expressions. For example, it learns frequently used phrases and word patterns. The data analysis unit also analyzes the content of emails and memos and generates data to reproduce the user's thought patterns. For example, it extracts opinions and thoughts on specific topics and trains the generation AI. The data analysis unit also builds a system that reproduces the user's thought patterns based on the analysis results of past emails and memos. For example, it generates data to reproduce reactions and opinions in specific situations. This allows the user's thought patterns to be reproduced, enabling more natural conversations.

[0035] The data analysis unit can analyze the tone and rhythm of the user's voice and reproduce the speaking style. The data analysis unit, for example, analyzes the tone and rhythm of the user's voice and extracts speaking style characteristics. For example, it learns patterns of voice pitch, speed, and intonation. The data analysis unit also generates data for reproducing the user's speaking style based on the analysis results of the voice tone and rhythm. For example, it reproduces changes in speaking style according to a specific emotional state. The data analysis unit also builds a system that reproduces a more natural speaking style based on the characteristics of the user's voice. For example, it adjusts the tone and rhythm of the voice to realistically reproduce the user's speaking style. In this way, by reproducing the characteristics of the user's voice, a more natural speaking style is possible.

[0036] The data analysis unit can add data related to the user's hobbies and special skills and reproduce reactions in specific situations. The data analysis unit, for example, acquires data related to the user's hobbies and special skills and reproduces reactions in specific situations. For example, it reproduces conversations related to hobbies and reactions when showing off special skills. The data analysis unit also generates data for reproducing reactions in specific situations based on the data related to hobbies and special skills. For example, it reproduces answers to questions about hobbies and the way of speaking when showing off special skills. The data analysis unit also analyzes the data related to the user's hobbies and special skills and builds a system that reproduces reactions in specific situations. For example, it generates data for natural conversations related to hobbies and special skills. In this way, reactions in specific situations can be reproduced by adding data related to the user's hobbies and special skills.

[0037] The data analysis unit analyzes data on the user's friends and colleagues and can reflect their influence in a conversation. The data analysis unit, for example, acquires data on the user's friends and colleagues and reflects their influence in a conversation. For example, it analyzes the content of conversations and communication patterns with friends and colleagues. The data analysis unit also generates data based on the data on friends and colleagues to reflect their influence in a conversation. For example, it reproduces the conversation style with a specific friend or colleague. The data analysis unit also analyzes the data on the user's friends and colleagues and builds a system that reflects their influence in a conversation. For example, it adjusts the user's speaking style and reactions based on the content of conversations with friends and colleagues. In this way, by analyzing the data on the user's friends and colleagues, it is possible to reflect their influence in a conversation.

[0038] The avatar generation unit can reflect the user's clothing and accessory preferences. For example, the avatar generation unit acquires the user's clothing and accessory preferences as data and reflects them in the avatar. For example, the avatar generation unit recreates the user's favorite clothes and favorite accessories in the avatar. The avatar generation unit also builds a system that customizes the user's avatar based on the clothing and accessory data. For example, it automatically selects clothing and accessories according to the user's preferences. The avatar generation unit also analyzes the user's past photos and videos, extracts clothing and accessory preferences, and reflects them in the avatar. For example, it recreates clothing from a specific event or scene. This allows a more personalized avatar to be created by reflecting the user's clothing and accessory preferences.

[0039] The avatar generation unit can reproduce the user's gestures and body language. For example, the avatar generation unit acquires the user's gestures and body language as data and reflects them in the avatar. For example, the avatar reproduces specific movements and poses. The avatar generation unit also builds a system that customizes the user's avatar based on the gesture and body language data. For example, the avatar generation unit reproduces the user's characteristic movements. The avatar generation unit also analyzes the user's past videos, extracts features of gestures and body language, and reflects them in the avatar. For example, it reproduces movements from a specific scene. In this way, a more realistic avatar can be created by reproducing the user's gestures and body language.

[0040] The avatar generation unit can also add avatars of the user's pets and family members, thereby recreating more realistic communication. The avatar generation unit, for example, acquires data on the user's pets and family members and creates avatars of the pets and family members based on the data. For example, it reproduces the pet's movements and the family members' ways of speaking. The avatar generation unit also combines avatars of the pets and family members with the user's avatar to build a system that reproduces more realistic communication. For example, it reproduces conversations with family members and playing with pets. The avatar generation unit also reproduces interactions between avatars based on the data on the user's pets and family members. For example, it reproduces the movements of the pets in response to the user's avatar. In this way, by adding avatars of the user's pets and family members, it is possible to reproduce more realistic communication.

[0041] The avatar generation unit can conduct a dialogue based on a scenario based on the user's past events and memories. The avatar generation unit, for example, acquires the user's past events and memories as data and generates a scenario in which the avatar will conduct a dialogue based on the data. For example, a conversation related to a specific event or memory is recreated. The avatar generation unit also builds a system that customizes a scenario in which the avatar will conduct a dialogue based on data on past events and memories. For example, a conversation scenario based on the user's memories is created. The avatar generation unit also analyzes the user's past events and memories and generates a scenario in which the avatar will conduct a dialogue based on the data. For example, a conversation related to a specific memory is recreated. This enables a more personalized dialogue by conducting a dialogue based on a scenario based on the user's past events and memories.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The data acquisition unit can acquire data related to the user's hobbies and special skills and reflect it in the avatar. For example, if the user plays a particular instrument, the performance data can be acquired and the avatar can reproduce the movements of playing the same instrument. Also, if the user plays a particular sport, the movement data can be acquired and the avatar can reproduce the movements of playing the same sport. Furthermore, if the user cooks a particular dish, the steps and movements can be acquired and the avatar can reproduce the movements of cooking the same dish. This allows for a more personalized experience by creating an avatar that reflects the user's hobbies and special skills.

[0044] The data analysis unit analyzes the user's past travel data, allowing the avatar to recreate experiences at travel destinations. For example, data on tourist spots and accommodations visited by the user is acquired, allowing the avatar to recreate experiences at those locations. The data analysis unit can also analyze photos and videos taken by the user, allowing the avatar to recreate events that occurred at those locations. Furthermore, data on activities and meals taken by the user during the trip can be acquired, allowing the avatar to recreate the same experiences. This allows the creation of an avatar that reflects the user's travel experiences, providing a more realistic experience.

[0045] The data acquisition unit can acquire the user's health data and reflect it in the avatar. For example, data on the user's diet and exercise habits can be acquired, and the avatar can replicate the same diet and exercise routines. It can also acquire data on the user's sleep patterns and stress levels, and have the avatar reflect those conditions. Furthermore, it can acquire data on the user's health goals and progress, and have the avatar replicate efforts toward those goals. This allows for the creation of an avatar that reflects the user's health data, helping to support a healthier lifestyle.

[0046] The data acquisition unit can acquire the user's reading history and reflect it in the avatar. For example, data on the titles and genres of books read by the user can be acquired so that the avatar has knowledge about those books. It can also analyze the content and impressions of books read by the user so that the avatar can talk about those books. Furthermore, based on the data on the books read by the user, the avatar can recreate characters and scenes from those books. This allows for the creation of an avatar that reflects the user's reading experience, providing a more knowledgeable experience.

[0047] The data acquisition unit can acquire data about the user's work or studies and reflect it in the avatar. For example, data about the projects and learning content the user has completed can be acquired so that the avatar has that knowledge and skills. It can also acquire data about the tools and methods the user used during work or learning so that the avatar can perform tasks in the same way. It can also acquire data about the results and goals the user achieved during work or learning so that the avatar can reproduce those results. This allows for a more practical experience by creating an avatar that reflects the user's work or learning data.

[0048] The data acquisition unit can acquire the user's cooking data and reflect it in the avatar. For example, it can acquire data on the recipe and steps for a dish made by the user, and have the avatar reproduce the actions of making the same dish. It can also acquire data on the ingredients and cooking utensils used by the user while cooking, allowing the avatar to prepare the dish using the same method. It can also acquire data on the ingenuity and arrangements made by the user while cooking, and have the avatar reproduce those ingenuity. This allows for the creation of an avatar that reflects the user's cooking data, providing a more practical experience.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The data acquisition unit acquires the user's lifestyle data, voice, images, etc. For example, the data acquisition unit may collect daily activity data using a wearable device. The data acquisition unit may also acquire conversation data through a communication tool. The data acquisition unit may also accept data registration by the user themselves. Step 2: The data analysis unit combines the user's lifestyle data, voice, images, etc. acquired by the data acquisition unit with the generation AI to analyze them. For example, the data analysis unit uses the generation AI to analyze the user's speech patterns, specific habits, and thinking tendencies. The data analysis unit also uses the generation AI to generate prompts based on the user's data and create a clone of the user. Step 3: The avatar generation unit creates an avatar for the user based on the data analyzed by the data analysis unit. For example, the avatar generation unit generates an avatar that reproduces the user's thoughts, speaking style, and habits. The avatar generation unit can also use the user's avatar to communicate and discuss. The avatar generation unit can also use the user's avatar to have a conversation with the user.

[0051] (Example 2) The My Generation AI system according to an embodiment of the present invention is a system that acquires a user's daily life data, voice, images, etc., combines this data with a generation AI, analyzes it, and creates an avatar for the user. This allows the My Generation AI system to reproduce the user's thoughts, speaking style, and habits, enabling communication and discussion.

[0052] The My generation AI system according to the embodiment includes a data acquisition unit, a data analysis unit, and an avatar generation unit. The data acquisition unit acquires the user's lifestyle data, voice, images, etc. For example, the data acquisition unit collects daily activity data using a wearable device. The data acquisition unit can also acquire conversation data through a communication tool. The data acquisition unit can also accept data registration by the user themselves. The data analysis unit combines the user's lifestyle data, voice, images, etc. acquired by the data acquisition unit with the generation AI to analyze them. For example, the data analysis unit uses the generation AI to analyze the user's speech patterns, specific habits, and thinking tendencies. The data analysis unit also uses the generation AI to generate prompts based on the user data and create a replica of the user. The avatar generation unit creates a user avatar based on the data analyzed by the data analysis unit. For example, the avatar generation unit generates an avatar that reproduces the user's thoughts, speaking style, and habits. The avatar generation unit can communicate and discuss using the user's avatar. The avatar generation unit can also use the user's avatar to engage in conversations with the user. As a result, the My generation AI system according to the embodiment can reproduce the user's thoughts, speaking style, and habits, enabling communication and discussion.

[0053] The data acquisition unit can acquire biometric information such as the user's heart rate, body temperature, and stress level using a wearable device. For example, the data acquisition unit acquires biometric information such as the user's heart rate, body temperature, and stress level in real time using a wearable device. This allows for a detailed understanding of the user's health condition and lifestyle rhythm. The data acquisition unit also analyzes the biometric information acquired from the wearable device in combination with daily activity data. For example, it records fluctuations in heart rate and stress level during exercise to understand the user's lifestyle patterns in detail. The data acquisition unit also builds a system that monitors the user's stress level and changes in physical condition in real time based on the biometric information and issues an alert if an abnormality is detected. In this way, by acquiring the user's biometric information, a more detailed understanding of the user's lifestyle patterns can be obtained.

[0054] The data acquisition unit can analyze a user's SNS activity or online shopping history to reflect the user's interests and preferences. For example, the data acquisition unit analyzes the user's SNS activity and extracts interests and preferences from the content of posts and trends in likes. For example, if there are many posts about a particular topic, data related to that topic is preferentially acquired. The data acquisition unit also analyzes online shopping history to understand the user's preferences from the category and brand of purchased products. For example, if a user frequently purchases products from a particular brand, data related to that brand is acquired. The data acquisition unit also analyzes SNS activity and online shopping history in an integrated manner to comprehensively understand the user's interests and preferences. For example, the content of posts on SNS can be compared with purchase history to more accurately reflect the user's interests. This allows for the creation of a more personalized avatar by reflecting the user's interests and preferences.

[0055] The data acquisition unit uses the emotion estimation function to record the emotional state of the user when entering data, and can reflect emotional fluctuations in the data. For example, the data acquisition unit uses the emotion estimation function to analyze the facial expressions and vocal tone of the user when entering data and record the emotional state in real time. For example, the emotion of joy or sadness is estimated from the facial expression at the time of entry. The data acquisition unit also records the user's emotional state and analyzes emotional fluctuations based on the data. For example, emotional fluctuations in specific time periods or situations are graphed and reflected in the data. The data acquisition unit also builds a system that monitors the user's emotional state in real time based on the emotion estimation data and issues an alert if emotional fluctuations are significant. This allows for the creation of a more realistic avatar by reflecting the user's emotional state.

[0056] The data acquisition unit also acquires data on the user's pets and family, and can recreate the user's entire living environment. The data acquisition unit, for example, acquires activity data of the user's pets and recreates the user's entire living environment. For example, the data acquisition unit records the pet's exercise amount and meal times and reflects them in the user's lifestyle rhythm. The data acquisition unit also acquires family data and recreates the user's living environment in more detail. For example, the data acquisition unit analyzes the family's schedule and communication history and reflects them in the user's lifestyle pattern. The data acquisition unit also comprehensively analyzes the data on the pets and family, and builds a system that recreates the user's entire living environment. For example, the user's living environment is recreated in detail based on the content of conversations with family members and the behavior of the pets. This allows the user's entire living environment to be recreated, making it possible to create a more detailed avatar.

[0057] The data acquisition unit can analyze the user's past photos and videos and reflect changes over time. For example, the data acquisition unit analyzes the user's past photos and videos and records changes in appearance and environment over time. For example, the data acquisition unit extracts the user's growth and changes from past photos and reflects them in the data. The data acquisition unit also analyzes past videos and records changes in the user's speaking style and behavior. For example, the data acquisition unit extracts changes in speaking patterns and gestures from past videos and reflects them in the data. The data acquisition unit also builds a system that reproduces changes in the user's living environment and appearance based on the results of the photo and video analysis. For example, the data acquisition unit changes the user's avatar over time based on past data. This allows for the creation of a more realistic avatar by reflecting changes over time.

[0058] The data acquisition unit can use the emotion estimation function to record the emotional state felt by the user in a specific location or situation and reflect the emotional fluctuations in the data. For example, the data acquisition unit can use the emotion estimation function to record the emotions felt by the user in a specific location or situation in real time. For example, the emotional state at a specific location can be recorded in combination with GPS data. The data acquisition unit can also record the user's emotional state in a specific situation and analyze the emotional fluctuations based on the data. For example, the emotional fluctuations in response to a specific event or occurrence can be graphed and reflected in the data. The data acquisition unit can also build a system that reproduces the emotions felt by the user in a specific location or situation based on the emotion estimation data. For example, the facial expressions and behavior of the user's avatar can be changed based on the emotional state at a specific location. This allows for a more realistic avatar to be created by reflecting the user's emotional state.

[0059] The data analysis unit analyzes the user's past emails and memos and can learn specific phrases and expressions to reproduce their thought patterns. For example, the data analysis unit analyzes the user's past emails and memos and extracts specific phrases and expressions. For example, it learns frequently used phrases and word patterns. The data analysis unit also analyzes the content of emails and memos and generates data to reproduce the user's thought patterns. For example, it extracts opinions and thoughts on specific topics and trains the generation AI. The data analysis unit also builds a system that reproduces the user's thought patterns based on the analysis results of past emails and memos. For example, it generates data to reproduce reactions and opinions in specific situations. This allows the user's thought patterns to be reproduced, enabling more natural conversations.

[0060] The data analysis unit can analyze the tone and rhythm of the user's voice and reproduce the speaking style. The data analysis unit, for example, analyzes the tone and rhythm of the user's voice and extracts speaking style characteristics. For example, it learns patterns of voice pitch, speed, and intonation. The data analysis unit also generates data for reproducing the user's speaking style based on the analysis results of the voice tone and rhythm. For example, it reproduces changes in speaking style according to a specific emotional state. The data analysis unit also builds a system that reproduces a more natural speaking style based on the characteristics of the user's voice. For example, it adjusts the tone and rhythm of the voice to realistically reproduce the user's speaking style. In this way, by reproducing the characteristics of the user's voice, a more natural speaking style is possible.

[0061] The data analysis unit can use the emotion estimation function to reproduce speaking styles and expressions according to the user's emotional state. For example, the data analysis unit uses the emotion estimation function to analyze the user's emotional state in real time and adjust speaking styles and expressions based on the results. For example, the tone and rhythm of voice are changed according to the emotional state. The data analysis unit also generates data that reproduces speaking styles and expressions according to specific emotions based on the user's emotional state. For example, it reproduces changes in speaking styles according to emotions such as joy and sadness. The data analysis unit also builds a system that reproduces speaking styles and expressions according to the user's emotional state based on the emotion estimation data. For example, it dynamically adjusts speaking styles and expressions according to the emotional state. This allows for more natural conversations by reproducing speaking styles and expressions according to the user's emotional state.

[0062] The data analysis unit can add data related to the user's hobbies and special skills and reproduce reactions in specific situations. The data analysis unit, for example, acquires data related to the user's hobbies and special skills and reproduces reactions in specific situations. For example, it reproduces conversations related to hobbies and reactions when showing off special skills. The data analysis unit also generates data for reproducing reactions in specific situations based on the data related to hobbies and special skills. For example, it reproduces answers to questions about hobbies and the way of speaking when showing off special skills. The data analysis unit also analyzes the data related to the user's hobbies and special skills and builds a system that reproduces reactions in specific situations. For example, it generates data for natural conversations related to hobbies and special skills. In this way, reactions in specific situations can be reproduced by adding data related to the user's hobbies and special skills.

[0063] The data analysis unit analyzes data on the user's friends and colleagues and can reflect their influence in a conversation. The data analysis unit, for example, acquires data on the user's friends and colleagues and reflects their influence in a conversation. For example, it analyzes the content of conversations and communication patterns with friends and colleagues. The data analysis unit also generates data based on the data on friends and colleagues to reflect their influence in a conversation. For example, it reproduces the conversation style with a specific friend or colleague. The data analysis unit also analyzes the data on the user's friends and colleagues and builds a system that reflects their influence in a conversation. For example, it adjusts the user's speaking style and reactions based on the content of conversations with friends and colleagues. In this way, by analyzing the data on the user's friends and colleagues, it is possible to reflect their influence in a conversation.

[0064] The data analysis unit can use the emotion estimation function to reproduce a user's reaction in a situation where the user has a specific emotion. For example, the data analysis unit uses the emotion estimation function to analyze a user's reaction in a situation where the user has a specific emotion in real time and reproduces the reaction based on the result. For example, it reproduces a reaction corresponding to an emotion such as joy or sadness. The data analysis unit also generates data for reproducing a reaction in a situation where the user has a specific emotion based on the user's emotional state. For example, it reproduces a speech style or expression corresponding to the specific emotional state. The data analysis unit also builds a system that reproduces a user's reaction in a situation where the user has a specific emotion based on the emotion estimation data. For example, it dynamically adjusts the speech style or expression according to the emotional state. This allows for more natural dialogue by reproducing a user's reaction in a situation where the user has a specific emotion.

[0065] The avatar generation unit can reflect the user's clothing and accessory preferences. For example, the avatar generation unit acquires the user's clothing and accessory preferences as data and reflects them in the avatar. For example, the avatar generation unit recreates the user's favorite clothes and favorite accessories in the avatar. The avatar generation unit also builds a system that customizes the user's avatar based on the clothing and accessory data. For example, it automatically selects clothing and accessories according to the user's preferences. The avatar generation unit also analyzes the user's past photos and videos, extracts clothing and accessory preferences, and reflects them in the avatar. For example, it recreates clothing from a specific event or scene. This allows a more personalized avatar to be created by reflecting the user's clothing and accessory preferences.

[0066] The avatar generation unit can reproduce the user's gestures and body language. For example, the avatar generation unit acquires the user's gestures and body language as data and reflects them in the avatar. For example, the avatar reproduces specific movements and poses. The avatar generation unit also builds a system that customizes the user's avatar based on the gesture and body language data. For example, the avatar generation unit reproduces the user's characteristic movements. The avatar generation unit also analyzes the user's past videos, extracts features of gestures and body language, and reflects them in the avatar. For example, it reproduces movements from a specific scene. In this way, a more realistic avatar can be created by reproducing the user's gestures and body language.

[0067] The avatar generation unit can use the emotion estimation function to make the user's avatar display facial expressions and movements according to the emotion. The avatar generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and adjust the avatar's facial expressions and movements based on the results. For example, it reproduces facial expressions according to emotions such as joy and sadness. The avatar generation unit also generates data for the avatar to display facial expressions and movements according to the emotion based on the user's emotional state. For example, it reproduces movements and facial expressions according to a specific emotional state. The avatar generation unit also builds a system for making the user's avatar display facial expressions and movements according to the emotion based on the emotion estimation data. For example, it dynamically adjusts the avatar's movements and facial expressions according to the emotional state. This allows for the creation of a more realistic avatar by reproducing facial expressions and movements according to the user's emotions.

[0068] The avatar generation unit can also add avatars of the user's pets and family members, thereby recreating more realistic communication. The avatar generation unit, for example, acquires data on the user's pets and family members and creates avatars of the pets and family members based on the data. For example, it reproduces the pet's movements and the family members' ways of speaking. The avatar generation unit also combines avatars of the pets and family members with the user's avatar to build a system that reproduces more realistic communication. For example, it reproduces conversations with family members and playing with pets. The avatar generation unit also reproduces interactions between avatars based on the data on the user's pets and family members. For example, it reproduces the movements of the pets in response to the user's avatar. In this way, by adding avatars of the user's pets and family members, it is possible to reproduce more realistic communication.

[0069] The avatar generation unit can conduct a dialogue based on a scenario based on the user's past events and memories. The avatar generation unit, for example, acquires the user's past events and memories as data and generates a scenario in which the avatar will conduct a dialogue based on the data. For example, a conversation related to a specific event or memory is recreated. The avatar generation unit also builds a system that customizes a scenario in which the avatar will conduct a dialogue based on data on past events and memories. For example, a conversation scenario based on the user's memories is created. The avatar generation unit also analyzes the user's past events and memories and generates a scenario in which the avatar will conduct a dialogue based on the data. For example, a conversation related to a specific memory is recreated. This enables a more personalized dialogue by conducting a dialogue based on a scenario based on the user's past events and memories.

[0070] The avatar generation unit can use the emotion estimation function to cause the user's avatar to reflect emotions during a conversation with another avatar. For example, the avatar generation unit uses the emotion estimation function to cause the user's avatar to reflect emotions in real time during a conversation with another avatar. For example, the avatar generation unit changes facial expressions and movements according to the emotional state during the conversation. The avatar generation unit also generates data for the avatar to reflect emotions during a conversation with another avatar based on the user's emotional state. For example, the avatar generation unit reproduces a reaction according to a specific emotional state. The avatar generation unit also builds a system for the user's avatar to reflect emotions during a conversation with another avatar based on the emotion estimation data. For example, the avatar generation unit dynamically adjusts facial expressions and movements during the conversation according to the emotional state. This allows the user's avatar to reflect emotions during a conversation with another avatar, enabling a more natural conversation.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The data acquisition unit can acquire data related to the user's hobbies and special skills and reflect it in the avatar. For example, if the user plays a particular instrument, the performance data can be acquired and the avatar can reproduce the movements of playing the same instrument. Also, if the user plays a particular sport, the movement data can be acquired and the avatar can reproduce the movements of playing the same sport. Furthermore, if the user cooks a particular dish, the steps and movements can be acquired and the avatar can reproduce the movements of cooking the same dish. This allows for a more personalized experience by creating an avatar that reflects the user's hobbies and special skills.

[0073] The data analysis unit analyzes the user's past travel data, allowing the avatar to recreate experiences at travel destinations. For example, data on tourist spots and accommodations visited by the user is acquired, allowing the avatar to recreate experiences at those locations. The data analysis unit can also analyze photos and videos taken by the user, allowing the avatar to recreate events that occurred at those locations. Furthermore, data on activities and meals taken by the user during the trip can be acquired, allowing the avatar to recreate the same experiences. This allows the creation of an avatar that reflects the user's travel experiences, providing a more realistic experience.

[0074] The data acquisition unit can acquire the user's health data and reflect it in the avatar. For example, data on the user's diet and exercise habits can be acquired, and the avatar can replicate the same diet and exercise routines. It can also acquire data on the user's sleep patterns and stress levels, and have the avatar reflect those conditions. Furthermore, it can acquire data on the user's health goals and progress, and have the avatar replicate efforts toward those goals. This allows for the creation of an avatar that reflects the user's health data, helping to support a healthier lifestyle.

[0075] The data acquisition unit can use the emotion estimation function to record the emotional state of the user when listening to specific music and reflect it in the avatar. For example, the data acquisition unit can analyze the user's facial expressions and heart rate when listening to specific music and reflect that emotional state in the avatar. It can also record the emotional fluctuations of the user when listening to specific music and have the avatar express the same emotions when listening to that music. Furthermore, based on the user's emotional state when listening to specific music, the avatar can change its movements and facial expressions to match the music. This allows for the creation of an avatar that reflects the user's musical experience, providing a more emotionally rich experience.

[0076] The data acquisition unit can acquire the user's reading history and reflect it in the avatar. For example, data on the titles and genres of books read by the user can be acquired so that the avatar has knowledge about those books. It can also analyze the content and impressions of books read by the user so that the avatar can talk about those books. Furthermore, based on the data on the books read by the user, the avatar can recreate characters and scenes from those books. This allows for the creation of an avatar that reflects the user's reading experience, providing a more knowledgeable experience.

[0077] The data acquisition unit can use the emotion estimation function to record the emotional state of the user when watching a specific movie or drama and reflect it in the avatar. For example, the data acquisition unit can analyze the user's facial expressions and heart rate when watching a specific movie or drama and reflect that emotional state in the avatar. It can also record the emotional fluctuations of the user when watching a specific movie or drama, so that the avatar can express the same emotions when watching that work. Furthermore, based on the user's emotional state when watching a specific movie or drama, the avatar can change its movements and facial expressions to suit the work. This allows for the creation of an avatar that reflects the user's viewing experience, providing a more emotionally rich experience.

[0078] The data acquisition unit can acquire data about the user's work or studies and reflect it in the avatar. For example, data about the projects and learning content the user has completed can be acquired so that the avatar has that knowledge and skills. It can also acquire data about the tools and methods the user used during work or learning so that the avatar can perform tasks in the same way. It can also acquire data about the results and goals the user achieved during work or learning so that the avatar can reproduce those results. This allows for a more practical experience by creating an avatar that reflects the user's work or learning data.

[0079] The data acquisition unit can use the emotion estimation function to record the emotional state of the user when playing a specific sport and reflect it in the avatar. For example, the data acquisition unit can analyze the user's facial expressions and heart rate when playing a specific sport and reflect that emotional state in the avatar. It can also record the emotional fluctuations of the user when playing a specific sport and have the avatar express the same emotions when playing that sport. Furthermore, based on the user's emotional state when playing a specific sport, the avatar can change its movements and facial expressions to suit the sport. This allows for the creation of an avatar that reflects the user's sports experience, providing a more emotionally rich experience.

[0080] The data acquisition unit can acquire the user's cooking data and reflect it in the avatar. For example, it can acquire data on the recipe and steps for a dish made by the user, and have the avatar reproduce the actions of making the same dish. It can also acquire data on the ingredients and cooking utensils used by the user while cooking, allowing the avatar to prepare the dish using the same method. It can also acquire data on the ingenuity and arrangements made by the user while cooking, and have the avatar reproduce those ingenuity. This allows for the creation of an avatar that reflects the user's cooking data, providing a more practical experience.

[0081] The data acquisition unit can use the emotion estimation function to record the user's emotional state regarding a specific event or occurrence and reflect that in the avatar. For example, the system can analyze the user's emotional state regarding a specific event or occurrence in real time and reflect the results in the avatar. The user can also record their emotional fluctuations regarding a specific event or occurrence, and the avatar can express the same emotions regarding that event or occurrence. Furthermore, based on the user's emotional state regarding a specific event or occurrence, the avatar can change its movements and facial expressions to suit the situation. This allows the system to create an avatar that reflects the user's emotional state, providing a more emotionally rich experience.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The data acquisition unit acquires the user's lifestyle data, voice, images, etc. For example, the data acquisition unit may collect daily activity data using a wearable device. The data acquisition unit may also acquire conversation data through a communication tool. The data acquisition unit may also accept data registration by the user themselves. Step 2: The data analysis unit combines the user's lifestyle data, voice, images, etc. acquired by the data acquisition unit with the generation AI to analyze them. For example, the data analysis unit uses the generation AI to analyze the user's speech patterns, specific habits, and thinking tendencies. The data analysis unit also uses the generation AI to generate prompts based on the user's data and create a clone of the user. Step 3: The avatar generation unit creates an avatar for the user based on the data analyzed by the data analysis unit. For example, the avatar generation unit generates an avatar that reproduces the user's thoughts, speaking style, and habits. The avatar generation unit can also use the user's avatar to communicate and discuss. The avatar generation unit can also use the user's avatar to have a conversation with the user.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data acquisition unit that acquires user's life data, voice, image, etc.; a data analysis unit that analyzes the user's life data, voice, image, etc. acquired by the data acquisition unit in combination with a generation AI; an avatar generation unit that creates an avatar of a user based on the data analyzed by the data analysis unit. A system characterized by:

2. The data acquisition unit A wearable device is used to acquire biometric information of the user, such as heart rate, body temperature, and stress level.

2. The system of claim 1.

3. The data acquisition unit Analyzing the user's social media activity or online shopping history to reflect the user's interests and preferences 2. The system of claim 1.

4. The data acquisition unit Recording the emotional state of the user when entering data, and reflecting emotional fluctuations in the data 2. The system of claim 1.

5. The data acquisition unit Data on the user's pets and family members is also acquired, and the user's entire living environment is reproduced.

2. The system of claim 1.

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