system

Abstract

An object of the system according to the embodiment is to reproduce communication with the deceased and perform mental support for people who have lost loved ones.SOLUTION: A system includes a data collection unit, an analysis unit, a generation unit, and a response unit. The data collection unit collects past conversation data, diaries, SNS posts, and the like of individuals. The analysis unit analyzes the data collected by the data collection unit. The generation unit learns a feature of an individual based on the data analyzed by the analysis unit. The response unit generates a response based on the input from the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to recreate communication with the deceased, and there was a problem that mental support for those who had lost loved ones was not provided sufficiently.

[0005] The system according to the embodiment aims to recreate communication with the deceased and provide mental support to people who have lost loved ones. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a response unit. The data collection unit collects an individual's past conversation data, diary, social media posts, etc. The analysis unit analyzes the data collected by the data collection unit. The generation unit learns the individual's characteristics based on the data analyzed by the analysis unit. The response unit generates a response based on input from a user. [Effects of the Invention]

[0007] The system according to the embodiment can recreate communication with the deceased and provide mental support to people who have lost loved ones. [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 digital clone system according to an embodiment of the present invention uses AI technology to digitize an individual's will, thoughts, and experiences, and recreate a being that behaves like that individual. This allows the digital clone system to communicate with the deceased via a smartphone or dedicated device, and provide mental support.

[0029] The digital clone system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a response unit. The data collection unit collects an individual's past conversation data, diary entries, social media posts, and the like. For example, it collects data from text messages, voice calls, and video calls. It can also collect data from handwritten diaries, digital diaries, blogs, and other sources. It can also collect data from social media posts such as Facebook, Twitter, and Instagram. The analysis unit analyzes the collected data. For example, it performs text analysis to extract an individual's emotions and topics. It can also perform sentiment analysis to identify changes in an individual's emotions. It can also use topic modeling to identify an individual's interests. The generation unit learns individual characteristics based on the analyzed data. For example, it learns personality traits, behavioral patterns, and emotional states. It also uses generation AI (e.g., text generation AI or multimodal generation AI) to reproduce the individual's characteristics. The response unit generates responses based on user input. For example, it uses natural language generation technology to generate responses to user questions. It can also provide standard responses using template-based response generation. This allows the digital clone system according to the embodiment to learn the characteristics of an individual and generate responses based on input from the user.

[0030] The analysis unit can learn an individual's non-verbal communication and reflect it in the digital clone. For example, the analysis unit analyzes an individual's video messages and learns facial expressions and gestures. For example, non-verbal communication such as smiles and hand movements is reflected in the digital clone. The analysis unit can also analyze past photos and videos to identify an individual's non-verbal communication patterns. For example, specific facial expressions and postures are reproduced in the digital clone. In addition, the analysis unit can collect an individual's movement data to learn non-verbal communication and reflect it in the digital clone. For example, the way they walk and hand movements are reproduced. This allows an individual's non-verbal communication to be reflected in the digital clone.

[0031] The data collection unit collects an individual's voice data and video messages, and the generation unit can use these data to generate a digital clone. The data collection unit, for example, collects an individual's voice data and reflects it in the digital clone's voice responses. For example, it reproduces specific phrases and intonation. The data collection unit also analyzes the video messages and reflects it in the digital clone's movements and facial expressions. For example, it reproduces specific gestures and facial expressions. The data collection unit also integrates the voice data and video messages to more realistically reproduce the digital clone's responses. For example, it generates responses in which voice and movement are synchronized. This makes it possible to generate a digital clone using an individual's voice data and video messages.

[0032] The data collection unit collects personal data from different cultures and languages, and the generation unit can generate a multilingual digital clone. The data collection unit, for example, collects conversation data in different languages ​​to generate a multilingual digital clone. For example, it supports languages ​​such as English, Japanese, and French. The data collection unit also learns non-verbal communication patterns from different cultures and reflects them in the digital clone. For example, it reproduces gestures and facial expressions in a particular culture. The data collection unit also analyzes audio data and video messages in different languages ​​and reflects them in responses to generate a multilingual digital clone. For example, it enables conversations in multiple languages. This makes it possible to generate a multilingual digital clone using personal data from different cultures and languages.

[0033] The response unit can analyze the user's past conversation history and generate responses to achieve more natural dialogue. For example, the response unit collects the user's past conversation history, and the generation AI analyzes it to generate responses to achieve natural dialogue. For example, it learns past conversation patterns and generates similar responses. The response unit also identifies the user's preferences and interests based on the conversation history and generates responses based on them. For example, it prioritizes topics that the user likes. The response unit also analyzes the past conversation history and learns the user's language and expression. For example, it reproduces specific phrases and expressions. This makes it possible to achieve natural dialogue based on the user's past conversation history.

[0034] The response unit can recreate specific memories and episodes of the deceased and use them in dialogue with the user. For example, the response unit collects the deceased's past memories and episodes, which the generation AI analyzes and recreates. For example, it recreates detailed memories of specific events. The response unit also generates dialogue with the user based on the deceased's episodes. For example, it can share memories of specific trips or events. The response unit also analyzes photos and videos to recreate the deceased's memories and episodes and use them in the dialogue. For example, it can recreate an episode related to a specific photo. This allows the deceased's specific memories and episodes to be recreated and used in dialogue with the user.

[0035] The response unit can learn the user's lifestyle rhythm and start communication at the appropriate time. The response unit, for example, learns the user's lifestyle rhythm and builds a system in which the digital clone starts communication at the appropriate time. For example, it talks to the user when the user is relaxing. The response unit also analyzes the user's schedule data and the digital clone starts communication at the optimal time. For example, it selects a time when the user is not busy. The response unit also monitors the user's lifestyle rhythm in real time and the digital clone dynamically adjusts the timing of communication. For example, it talks to the user when the user is active. This allows communication to start at the appropriate time based on the user's lifestyle rhythm.

[0036] The response unit can analyze the user's past mental health data and provide individually customized support. For example, the response unit collects the user's past mental health data, and the generation AI analyzes it to provide individually customized support. For example, it may suggest relaxation methods that have been effective in the past. The response unit also provides support for the user's specific problems and challenges based on the mental health data. For example, it may suggest measures to address stress factors that the user has experienced in the past. The response unit also analyzes the user's mental health data and creates an individually customized mental support plan. For example, it may set regular check-ins and reminders. This makes it possible to provide individually customized support based on the user's past mental health data.

[0037] The response unit can provide advice regarding the user's daily life and enhance mental support. The response unit, for example, builds a system in which a digital clone provides advice regarding the user's daily life. For example, it makes suggestions about healthy eating and exercise. The response unit also analyzes the user's daily life data and the digital clone provides appropriate advice. For example, it makes suggestions to improve sleep quality. The response unit also enables the digital clone to provide advice regarding the user's daily life in real time and enhance mental support. For example, it suggests methods of stress management. This makes it possible to provide advice regarding the user's daily life and enhance mental support.

[0038] The response unit can suggest relaxation methods based on the user's hobbies and interests. The response unit, for example, analyzes the user's hobbies and interests to build a system in which the digital clone suggests relaxation methods. For example, the response unit suggests music or movies that the user likes. The response unit also allows the digital clone to suggest relaxation methods based on the user's past activity data. For example, the response unit re-suggests activities that the user enjoyed in the past. The response unit also allows the digital clone to suggest relaxation methods based on the user's hobbies and interests in real time. For example, the response unit makes suggestions for finding new hobbies or interests. This makes it possible to suggest relaxation methods based on the user's hobbies and interests.

[0039] The generation unit can customize the digital clone generation process based on the user's life events. The generation unit, for example, collects the user's life event data and builds a system that customizes the digital clone generation process. For example, it reflects important events such as marriage and childbirth. The generation unit also customizes the digital clone's responses based on the user's life events. For example, it recreates memories related to specific events. The generation unit also dynamically adjusts the digital clone generation process based on the life event data. For example, it generates responses according to the user's life stage. This makes it possible to customize the digital clone generation process based on the user's life events.

[0040] The generation unit can learn the user's relationships with family and friends and provide more personalized services. For example, the generation unit collects relationship data with the user's family and friends, and the digital clone learns from this to provide personalized services. For example, memories related to specific family members and friends are recreated. The generation unit also generates appropriate responses based on the relationships with family and friends. For example, memories related to specific family events are shared. The generation unit also analyzes the relationship data with the user's family and friends, and the digital clone provides personalized services. For example, specific episodes related to family and friends are recreated. This allows personalized services to be provided based on the user's relationships with family and friends.

[0041] The generation unit can learn the user's lifestyle habits and make suggestions for lifestyle improvements. The generation unit, for example, collects the user's lifestyle data, and the digital clone learns this data to build a system that makes suggestions for lifestyle improvements. For example, it makes suggestions for healthy eating and exercise. The generation unit also monitors the user's lifestyle habits in real time based on the lifestyle data, and makes appropriate suggestions for improvements. For example, it makes suggestions for improving sleep quality. The generation unit also learns the user's lifestyle habits, and the digital clone dynamically makes suggestions for lifestyle improvements. For example, it suggests methods of stress management. In this way, the user's lifestyle habits can be learned and suggestions for lifestyle improvements can be made.

[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 collection unit collects personal health and fitness data, and the generation unit can use this data to generate health advice for the digital clone. For example, the data collection unit can collect an individual's step count, heart rate, and sleep data to monitor their health status. The data collection unit can also collect food records and exercise history to suggest healthy lifestyle habits. Furthermore, the generation unit can provide individually customized fitness and meal plans based on the collected health data. This allows the digital clone to provide health advice using the individual's health data.

[0044] The analysis unit analyzes an individual's hobbies and interests, allowing the digital clone to make hobby-related suggestions to the user. For example, the analysis unit can collect data on an individual's past hobbies and suggest events and activities related to the hobbies. The analysis unit can also analyze an individual's interests and make suggestions for discovering new hobbies and interests. Furthermore, the analysis unit can provide information and news related to hobbies based on the data on an individual's hobbies. This allows the digital clone to make suggestions based on an individual's hobbies and interests.

[0045] The data collection unit collects travel data and sightseeing data of an individual, and the generation unit uses this data to enable the digital clone to propose a travel plan. For example, data on an individual's past travel history and tourist destinations is collected to propose the next travel destination. The data collection unit can also provide a customized travel plan based on the individual's preferences and interests. Furthermore, the generation unit can suggest activities and tourist spots during the trip based on the collected travel data. This allows the digital clone to propose a travel plan using the individual's travel data.

[0046] The data collection unit collects individual learning data and educational data, and the generation unit uses this data to enable the digital clone to provide learning advice. For example, the data collection unit can collect an individual's past learning history and grade data and propose a learning plan. The data collection unit can also provide customized learning advice based on the individual's interests and goals. Furthermore, the generation unit can suggest learning activities and materials based on the collected learning data. This allows the digital clone to provide learning advice using the individual's learning data.

[0047] The data collection unit collects individual purchasing data and consumption data, and the generation unit uses this data to enable the digital clone to provide purchasing advice. For example, the data collection unit can collect an individual's past purchasing history and consumption patterns and suggest the next purchase. The data collection unit can also provide customized purchasing advice based on the individual's preferences and budget. Furthermore, the generation unit can suggest activities and products to purchase based on the collected purchasing data. This allows the digital clone to provide purchasing advice using the individual's purchasing data.

[0048] The data collection unit collects personal environmental data and weather data, and the generation unit uses this data to enable the digital clone to provide environmental advice. For example, data on the individual's living environment and weather can be collected to suggest lifestyle habits suited to the environment. The data collection unit can also provide customized environmental advice based on the individual's preferences and health status. Furthermore, the generation unit can suggest environmental activities and measures based on the collected environmental data. This allows the digital clone to provide environmental advice using the individual's environmental data.

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

[0050] Step 1: The data collection unit collects an individual's past conversation data, diaries, social media posts, etc. For example, it collects data from text messages, voice calls, and video calls. It can also collect data from handwritten diaries, digital diaries, blogs, etc. It also collects data from social media posts such as Facebook, Twitter, and Instagram. Step 2: The analysis unit analyzes the collected data. For example, it performs text analysis to extract personal emotions and topics. It can also perform sentiment analysis to identify changes in personal emotions. It can also use topic modeling to identify personal interests. Step 3: The generator learns individual characteristics based on the analyzed data, such as personality traits, behavioral patterns, and emotional states. It also uses generative AI (e.g., text generation AI or multimodal generation AI) to reproduce the individual characteristics. Step 4: The response unit generates a response based on the user's input. For example, it can use natural language generation techniques to generate a response to the user's question. It can also use template-based response generation to provide canned responses.

[0051] (Example 2) The digital clone system according to an embodiment of the present invention uses AI technology to digitize an individual's will, thoughts, and experiences, and recreate a being that behaves like that individual. This allows the digital clone system to communicate with the deceased via a smartphone or dedicated device, and provide mental support.

[0052] The digital clone system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a response unit. The data collection unit collects an individual's past conversation data, diary entries, social media posts, and the like. For example, it collects data from text messages, voice calls, and video calls. It can also collect data from handwritten diaries, digital diaries, blogs, and other sources. It can also collect data from social media posts such as Facebook, Twitter, and Instagram. The analysis unit analyzes the collected data. For example, it performs text analysis to extract an individual's emotions and topics. It can also perform sentiment analysis to identify changes in an individual's emotions. It can also use topic modeling to identify an individual's interests. The generation unit learns individual characteristics based on the analyzed data. For example, it learns personality traits, behavioral patterns, and emotional states. It also uses generation AI (e.g., text generation AI or multimodal generation AI) to reproduce the individual's characteristics. The response unit generates responses based on user input. For example, it uses natural language generation technology to generate responses to user questions. It can also provide standard responses using template-based response generation. This allows the digital clone system according to the embodiment to learn the characteristics of an individual and generate responses based on input from the user.

[0053] The analysis unit can analyze an individual's emotional transitions and generate responses from the digital clone based on a specific emotional state. The analysis unit, for example, analyzes an individual's past conversation data or diary to identify emotional transitions. For example, it tracks emotional changes in response to specific events and generates responses based on that emotional state. The analysis unit also analyzes social media posts to learn an individual's emotional transitions. For example, it generates responses from the digital clone based on the emotional state during periods when there were many positive posts. The analysis unit also collects audio data and video messages to identify emotional transitions. For example, it analyzes changes in voice tone and facial expressions and generates responses based on that emotional state. This makes it possible to generate responses based on an individual's emotional transitions.

[0054] The analysis unit can learn an individual's non-verbal communication and reflect it in the digital clone. For example, the analysis unit analyzes an individual's video messages and learns facial expressions and gestures. For example, non-verbal communication such as smiles and hand movements is reflected in the digital clone. The analysis unit can also analyze past photos and videos to identify an individual's non-verbal communication patterns. For example, specific facial expressions and postures are reproduced in the digital clone. In addition, the analysis unit can collect an individual's movement data to learn non-verbal communication and reflect it in the digital clone. For example, the way they walk and hand movements are reproduced. This allows an individual's non-verbal communication to be reflected in the digital clone.

[0055] The analysis unit can use the emotion estimation function to estimate an individual's emotions and generate a digital clone based on those emotions. For example, the analysis unit can use the emotion estimation function to estimate emotions from an individual's past conversation data and generate a response of the digital clone based on those emotions. For example, emotions in response to a sad event can be reproduced. The analysis unit can also analyze audio data and identify the individual's emotions using the emotion estimation function. For example, emotions can be estimated from the tone and rhythm of the voice and a response can be generated based on those emotions. The analysis unit can also use the emotion estimation function to estimate emotions from an individual's video message and generate movements and facial expressions of the digital clone based on those emotions. For example, smiling and crying can be reproduced. In this way, the emotion estimation function can be used to generate a digital clone based on an individual's emotions.

[0056] The data collection unit collects an individual's voice data and video messages, and the generation unit can use these data to generate a digital clone. The data collection unit, for example, collects an individual's voice data and reflects it in the digital clone's voice responses. For example, it reproduces specific phrases and intonation. The data collection unit also analyzes the video messages and reflects it in the digital clone's movements and facial expressions. For example, it reproduces specific gestures and facial expressions. The data collection unit also integrates the voice data and video messages to more realistically reproduce the digital clone's responses. For example, it generates responses in which voice and movement are synchronized. This makes it possible to generate a digital clone using an individual's voice data and video messages.

[0057] The data collection unit collects personal data from different cultures and languages, and the generation unit can generate a multilingual digital clone. The data collection unit, for example, collects conversation data in different languages ​​to generate a multilingual digital clone. For example, it supports languages ​​such as English, Japanese, and French. The data collection unit also learns non-verbal communication patterns from different cultures and reflects them in the digital clone. For example, it reproduces gestures and facial expressions in a particular culture. The data collection unit also analyzes audio data and video messages in different languages ​​and reflects them in responses to generate a multilingual digital clone. For example, it enables conversations in multiple languages. This makes it possible to generate a multilingual digital clone using personal data from different cultures and languages.

[0058] The analysis unit uses the emotion estimation function to analyze the user's emotions in real time when generating a digital clone and can propose an optimal generation process. The analysis unit uses the emotion estimation function to analyze emotions in real time, for example, when the user generates a digital clone. For example, the analysis unit analyzes the user's facial expressions and voice to identify the user's emotional state. The analysis unit also proposes an optimal generation process based on the emotion estimation data according to the user's emotional state. For example, if positive emotions are strong, happy memories are prioritized. The analysis unit also collects the user's emotion estimation data in real time and dynamically adjusts the generation process. For example, the generation process is optimized according to changes in emotions. This makes it possible to propose an optimal generation process based on the user's emotions.

[0059] The response unit can analyze the user's past conversation history and generate responses to achieve more natural dialogue. For example, the response unit collects the user's past conversation history, and the generation AI analyzes it to generate responses to achieve natural dialogue. For example, it learns past conversation patterns and generates similar responses. The response unit also identifies the user's preferences and interests based on the conversation history and generates responses based on them. For example, it prioritizes topics that the user likes. The response unit also analyzes the past conversation history and learns the user's language and expression. For example, it reproduces specific phrases and expressions. This makes it possible to achieve natural dialogue based on the user's past conversation history.

[0060] The response unit can recreate specific memories and episodes of the deceased and use them in dialogue with the user. For example, the response unit collects the deceased's past memories and episodes, which the generation AI analyzes and recreates. For example, it recreates detailed memories of specific events. The response unit also generates dialogue with the user based on the deceased's episodes. For example, it can share memories of specific trips or events. The response unit also analyzes photos and videos to recreate the deceased's memories and episodes and use them in the dialogue. For example, it can recreate an episode related to a specific photo. This allows the deceased's specific memories and episodes to be recreated and used in dialogue with the user.

[0061] The response unit can use the emotion estimation function to generate an optimal response according to the user's emotional state. For example, the response unit uses the emotion estimation function to analyze the user's emotional state in real time and generate an optimal response according to that emotion. For example, when the user is sad, it provides words of comfort. The response unit also uses the generation AI to generate an optimal response based on the user's emotion estimation data. For example, when the user is happy, it provides words of empathy. The response unit also uses the emotion estimation function to identify the user's emotional state and generate a response based on that emotion. For example, when the user is angry, it provides a calm response. This makes it possible to generate an optimal response according to the user's emotional state.

[0062] The response unit can learn the user's lifestyle rhythm and start communication at the appropriate time. The response unit, for example, learns the user's lifestyle rhythm and builds a system in which the digital clone starts communication at the appropriate time. For example, it talks to the user when the user is relaxing. The response unit also analyzes the user's schedule data and the digital clone starts communication at the optimal time. For example, it selects a time when the user is not busy. The response unit also monitors the user's lifestyle rhythm in real time and the digital clone dynamically adjusts the timing of communication. For example, it talks to the user when the user is active. This allows communication to start at the appropriate time based on the user's lifestyle rhythm.

[0063] The response unit can use the emotion estimation function to analyze the emotions of the user when interacting with the digital clone in real time and adjust the content of the dialogue. The response unit, for example, uses the emotion estimation function to analyze the emotions of the user when interacting with the digital clone in real time. For example, it analyzes the user's facial expressions and voice to identify the emotional state. The response unit also dynamically adjusts the content of the dialogue by the digital clone based on the user's emotion estimation data. For example, it provides words of comfort when the user is sad. The response unit also uses the emotion estimation function to monitor the user's emotional state in real time and optimize the content of the dialogue. For example, it provides words of empathy when the user is happy. This makes it possible to adjust the content of the dialogue based on the user's emotions.

[0064] The response unit can analyze the user's psychological state and generate a response for providing appropriate mental support. The response unit, for example, builds a system that analyzes the user's psychological state and generates a response for providing appropriate mental support. For example, when the user is feeling stressed, the response unit suggests relaxation methods. The response unit also analyzes the user's past mental health data and provides individually customized mental support. For example, the response unit re-suggests support methods that have been effective for the user in the past. The response unit also monitors the user's psychological state in real time and provides appropriate mental support. For example, when the user is feeling anxious, the response unit provides reassuring words. In this way, appropriate mental support can be provided based on the user's psychological state.

[0065] The response unit can analyze the user's past mental health data and provide individually customized support. For example, the response unit collects the user's past mental health data, and the generation AI analyzes it to provide individually customized support. For example, it may suggest relaxation methods that have been effective in the past. The response unit also provides support for the user's specific problems and challenges based on the mental health data. For example, it may suggest measures to address stress factors that the user has experienced in the past. The response unit also analyzes the user's mental health data and creates an individually customized mental support plan. For example, it may set regular check-ins and reminders. This makes it possible to provide individually customized support based on the user's past mental health data.

[0066] The response unit can use the emotion estimation function to provide mental support based on the user's emotional state. For example, the response unit uses the emotion estimation function to analyze the user's emotional state in real time and provide mental support based on that emotion. For example, when the user is sad, it provides words of comfort. The response unit also uses the generation AI to provide optimal mental support based on the user's emotion estimation data. For example, when the user is happy, it provides words of empathy. The response unit also uses the emotion estimation function to identify the user's emotional state and provide mental support based on that emotion. For example, when the user is angry, it provides a calm response. This makes it possible to provide mental support based on the user's emotional state.

[0067] The response unit can provide advice regarding the user's daily life and enhance mental support. The response unit, for example, builds a system in which a digital clone provides advice regarding the user's daily life. For example, it makes suggestions about healthy eating and exercise. The response unit also analyzes the user's daily life data and the digital clone provides appropriate advice. For example, it makes suggestions to improve sleep quality. The response unit also enables the digital clone to provide advice regarding the user's daily life in real time and enhance mental support. For example, it suggests methods of stress management. This makes it possible to provide advice regarding the user's daily life and enhance mental support.

[0068] The response unit can suggest relaxation methods based on the user's hobbies and interests. The response unit, for example, analyzes the user's hobbies and interests to build a system in which the digital clone suggests relaxation methods. For example, the response unit suggests music or movies that the user likes. The response unit also allows the digital clone to suggest relaxation methods based on the user's past activity data. For example, the response unit re-suggests activities that the user enjoyed in the past. The response unit also allows the digital clone to suggest relaxation methods based on the user's hobbies and interests in real time. For example, the response unit makes suggestions for finding new hobbies or interests. This makes it possible to suggest relaxation methods based on the user's hobbies and interests.

[0069] The response unit can use the emotion estimation function to analyze the emotions of the user when receiving mental support in real time and adjust the support content. The response unit, for example, uses the emotion estimation function to analyze the emotions of the user when receiving mental support in real time. For example, it analyzes the user's facial expressions and voice to identify the emotional state. The response unit also dynamically adjusts the support content by the digital clone based on the user's emotion estimation data. For example, it provides words of comfort when the user is sad. The response unit also uses the emotion estimation function to monitor the user's emotional state in real time and optimize the support content. For example, it provides words of empathy when the user is happy. This makes it possible to adjust the content of mental support based on the user's emotions.

[0070] The generation unit can customize the digital clone generation process based on the user's life events. The generation unit, for example, collects the user's life event data and builds a system that customizes the digital clone generation process. For example, it reflects important events such as marriage and childbirth. The generation unit also customizes the digital clone's responses based on the user's life events. For example, it recreates memories related to specific events. The generation unit also dynamically adjusts the digital clone generation process based on the life event data. For example, it generates responses according to the user's life stage. This makes it possible to customize the digital clone generation process based on the user's life events.

[0071] The generation unit can learn the user's relationships with family and friends and provide more personalized services. For example, the generation unit collects relationship data with the user's family and friends, and the digital clone learns from this to provide personalized services. For example, memories related to specific family members and friends are recreated. The generation unit also generates appropriate responses based on the relationships with family and friends. For example, memories related to specific family events are shared. The generation unit also analyzes the relationship data with the user's family and friends, and the digital clone provides personalized services. For example, specific episodes related to family and friends are recreated. This allows personalized services to be provided based on the user's relationships with family and friends.

[0072] The generation unit can use the emotion estimation function to provide a value-added service based on the user's emotional state. For example, the generation unit uses the emotion estimation function to analyze the user's emotional state in real time and provide a value-added service based on the emotion. For example, when the user is sad, the generation unit provides words of comfort. The generation unit also provides an optimal value-added service for the digital clone based on the user's emotion estimation data. For example, when the user is happy, the generation unit provides words of empathy. The generation unit also uses the emotion estimation function to identify the user's emotional state and provide a value-added service based on the emotion. For example, when the user is angry, the generation unit provides a calm response. This makes it possible to provide a value-added service based on the user's emotional state.

[0073] The generation unit can learn the user's lifestyle habits and make suggestions for lifestyle improvements. The generation unit, for example, collects the user's lifestyle data, and the digital clone learns this data to build a system that makes suggestions for lifestyle improvements. For example, it makes suggestions for healthy eating and exercise. The generation unit also monitors the user's lifestyle habits in real time based on the lifestyle data, and makes appropriate suggestions for improvements. For example, it makes suggestions for improving sleep quality. The generation unit also learns the user's lifestyle habits, and the digital clone dynamically makes suggestions for lifestyle improvements. For example, it suggests methods of stress management. In this way, the user's lifestyle habits can be learned and suggestions for lifestyle improvements can be made.

[0074] The generation unit can use the emotion estimation function to analyze the emotions of the user when using the value-added service in real time and adjust the service content. The generation unit, for example, uses the emotion estimation function to analyze the emotions of the user when using the value-added service in real time. For example, the generation unit analyzes the user's facial expressions and voice to identify the user's emotional state. The generation unit also dynamically adjusts the service content through the digital clone based on the user's emotion estimation data. For example, when the user is sad, the generation unit provides words of comfort. The generation unit also uses the emotion estimation function to monitor the user's emotional state in real time and optimize the service content. For example, when the user is happy, the generation unit provides words of empathy. This makes it possible to adjust the content of the value-added service based on the user's emotions.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The data collection unit collects personal health and fitness data, and the generation unit can use this data to generate health advice for the digital clone. For example, the data collection unit can collect an individual's step count, heart rate, and sleep data to monitor their health status. The data collection unit can also collect food records and exercise history to suggest healthy lifestyle habits. Furthermore, the generation unit can provide individually customized fitness and meal plans based on the collected health data. This allows the digital clone to provide health advice using the individual's health data.

[0077] The analysis unit analyzes an individual's hobbies and interests, allowing the digital clone to make hobby-related suggestions to the user. For example, the analysis unit can collect data on an individual's past hobbies and suggest events and activities related to the hobbies. The analysis unit can also analyze an individual's interests and make suggestions for discovering new hobbies and interests. Furthermore, the analysis unit can provide information and news related to hobbies based on the data on an individual's hobbies. This allows the digital clone to make suggestions based on an individual's hobbies and interests.

[0078] The analysis unit can use the emotion estimation function to suggest music and videos based on the user's emotional state. For example, when the user wants to relax, relaxing music can be suggested. When the user wants to cheer up, energetic music and videos can be suggested. Furthermore, the analysis unit can analyze the user's emotional state in real time and provide content that is optimal for the user's emotions at that time. This allows music and videos to be suggested based on the user's emotional state.

[0079] The data collection unit collects travel data and sightseeing data of an individual, and the generation unit uses this data to enable the digital clone to propose a travel plan. For example, data on an individual's past travel history and tourist destinations is collected to propose the next travel destination. The data collection unit can also provide a customized travel plan based on the individual's preferences and interests. Furthermore, the generation unit can suggest activities and tourist spots during the trip based on the collected travel data. This allows the digital clone to propose a travel plan using the individual's travel data.

[0080] The analysis unit can use the emotion estimation function to provide mental health support based on the user's emotional state. For example, when the user is feeling stressed, it can suggest relaxation and stress relief methods. When the user is feeling anxious, it can provide reassuring words and advice. Furthermore, the analysis unit can analyze the user's emotional state in real time and provide mental health support that is optimal for the user's emotions at that time. This makes it possible to provide mental health support based on the user's emotional state.

[0081] The data collection unit collects individual learning data and educational data, and the generation unit uses this data to enable the digital clone to provide learning advice. For example, the data collection unit can collect an individual's past learning history and grade data and propose a learning plan. The data collection unit can also provide customized learning advice based on the individual's interests and goals. Furthermore, the generation unit can suggest learning activities and materials based on the collected learning data. This allows the digital clone to provide learning advice using the individual's learning data.

[0082] The analysis unit can use the emotion estimation function to provide feedback based on the user's emotional state. For example, when the user feels a sense of accomplishment, it can provide words of praise or encouragement. When the user feels frustrated, it can provide encouragement or advice. Furthermore, the analysis unit can analyze the user's emotional state in real time and provide feedback that is optimal for the user's emotions at that time. This makes it possible to provide feedback based on the user's emotional state.

[0083] The data collection unit collects individual purchasing data and consumption data, and the generation unit uses this data to enable the digital clone to provide purchasing advice. For example, the data collection unit can collect an individual's past purchasing history and consumption patterns and suggest the next purchase. The data collection unit can also provide customized purchasing advice based on the individual's preferences and budget. Furthermore, the generation unit can suggest activities and products to purchase based on the collected purchasing data. This allows the digital clone to provide purchasing advice using the individual's purchasing data.

[0084] The analysis unit can use the emotion estimation function to provide reminders based on the user's emotional state. For example, when the user is feeling stressed, a relaxation reminder can be provided. Also, when the user wants to improve their concentration, a reminder to maintain concentration can be provided. Furthermore, the analysis unit can analyze the user's emotional state in real time and provide a reminder that is optimal for the user's emotions at that time. This makes it possible to provide reminders based on the user's emotional state.

[0085] The data collection unit collects personal environmental data and weather data, and the generation unit uses this data to enable the digital clone to provide environmental advice. For example, data on the individual's living environment and weather can be collected to suggest lifestyle habits suited to the environment. The data collection unit can also provide customized environmental advice based on the individual's preferences and health status. Furthermore, the generation unit can suggest environmental activities and measures based on the collected environmental data. This allows the digital clone to provide environmental advice using the individual's environmental data.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The data collection unit collects an individual's past conversation data, diaries, social media posts, etc. For example, it collects data from text messages, voice calls, and video calls. It can also collect data from handwritten diaries, digital diaries, blogs, etc. It also collects data from social media posts such as Facebook, Twitter, and Instagram. Step 2: The analysis unit analyzes the collected data. For example, it performs text analysis to extract personal emotions and topics. It can also perform sentiment analysis to identify changes in personal emotions. It can also use topic modeling to identify personal interests. Step 3: The generator learns individual characteristics based on the analyzed data, such as personality traits, behavioral patterns, and emotional states. It also uses generative AI (e.g., text generation AI or multimodal generation AI) to reproduce the individual characteristics. Step 4: The response unit generates a response based on the user's input. For example, it can use natural language generation techniques to generate a response to the user's question. It can also use template-based response generation to provide canned responses.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0132] 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.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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 collection department collects past conversation data, diaries, social media posts, etc. from individuals. an analysis unit that analyzes the data collected by the data collection unit; a generation unit that learns personal characteristics based on the data analyzed by the analysis unit; a response unit that generates a response based on an input from a user. A system characterized by:

2. The analysis unit Analyzing an individual's emotional history and generating responses for the digital clone based on a specific emotional state 2. The system of claim 1.

3. The analysis unit Learn an individual's non-verbal communication and mirror it in their digital clone 2. The system of claim 1.

4. The analysis unit Estimate an individual's emotions and generate a digital clone based on those emotions 2. The system of claim 1.

5. The data collection unit Collect personal audio data and video messages, The generation unit These data are used to create a digital clone.

2. The system of claim 1.

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