System

Abstract

An object of the system according to the embodiment is to accurately reproduce the voice of a target person and the expression of words.SOLUTION: A system includes a voice analysis unit, a word analysis unit, a personality diagnosis unit, and an expression generation unit. The voice analysis unit analyzes a voice of the target person. The word analysis unit analyzes the expression and content of the words of the target person analyzed by the voice analysis unit. The personality diagnosis unit takes in the personality diagnosis elements of the target person analyzed by the word analysis unit. The expression generating unit generates an expression based on the information acquired by the personality diagnosing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have difficulty accurately reproducing the target person's voice and verbal expressions, and there is room for improvement.

[0005] The system according to the embodiment aims to accurately reproduce the voice and verbal expressions of a target person. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice analysis unit, a word analysis unit, a personality assessment unit, and an expression generation unit. The voice analysis unit analyzes the voice of the target person. The word analysis unit analyzes the expressions and content of the target person's words analyzed by the voice analysis unit. The personality assessment unit incorporates the personality assessment elements of the target person analyzed by the word analysis unit. The expression generation unit generates an expression based on the information incorporated by the personality assessment unit. [Effects of the Invention]

[0007] The system according to the embodiment can accurately reproduce the voice and verbal expressions of the target person. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A system for filling the gaps in one's heart according to an embodiment of the present invention reproduces the voice of a loved one, a deceased relative, a great person, or a person one respects using AI voice to fill the gaps in one's heart. In this system, AI analyzes the target person's voice and reproduces the expression and content of the words in a way that matches the character. In this way, the system for filling the gaps in one's heart can faithfully reproduce the voice of a loved one, a deceased relative, a great person, or a person one respects, filling the gaps in one's heart.

[0029] A system for filling emotional gaps according to an embodiment includes a voice analysis unit, a word analysis unit, a personality assessment unit, and an expression generation unit. The voice analysis unit analyzes the voice of a target person. For example, the voice analysis unit collects voice data of the target person and analyzes the tone, rhythm, accent, etc. of the voice. The voice analysis unit can also extract acoustic features using voice recognition technology. For example, the voice analysis unit performs spectral analysis of the voice data to extract characteristic frequency components. The word analysis unit analyzes the verbal expressions and content of the target person analyzed by the voice analysis unit. For example, the word analysis unit collects essays and comments of the target person and analyzes their language and expression methods. The word analysis unit can also perform grammatical analysis and semantic analysis using natural language processing technology. For example, the word analysis unit performs morphological analysis of text data to analyze sentence structure. The personality assessment unit incorporates the personality assessment elements of the target person analyzed by the word analysis unit. For example, the personality assessment unit analyzes the personality assessment results and behavioral patterns of the target person and learns their characteristics. The personality assessment unit can also evaluate personality traits using psychological tests and behavioral analysis. For example, the personality assessment unit clusters personality assessment data and extracts specific personality traits. The expression generation unit generates expressions based on the information acquired by the personality assessment unit. For example, the expression generation unit reproduces the target person's words using natural language generation technology. The expression generation unit can also generate expressions tailored to specific scenarios using template-based generation. For example, the expression generation unit generates sentences based on templates and provides words tailored to specific situations. In this way, the system for filling emotional gaps according to the embodiment can fill emotional gaps by generating expressions based on the target person's voice, words, and personality assessment elements. For example, when a user feels a specific emotion, the system provides an encouraging or comforting message based on the target person's past statements. The system analyzes the user's emotional state in real time and, based on the results, selects the target person's past statements to generate an encouraging or comforting message. For example, if the user is sad, a comforting statement is selected. The system can use an emotion estimation function to provide an optimal message tailored to the user's emotional state.

[0030] The voice analysis unit can analyze emotional changes in the target person's voice and reproduce a voice that corresponds to a specific emotional state. For example, the voice analysis unit collects the target person's voice data and analyzes emotional changes. For example, it learns changes in voice tone and rhythm that correspond to emotional states such as joy, sadness, and anger, and inputs this into the generation AI. The voice analysis unit can also analyze changes in voice pitch, tempo, and strength to determine the emotional state. For example, the voice analysis unit analyzes voice data in the time domain and frequency domain to detect emotional changes. This makes it possible to reproduce a voice that corresponds to a specific emotional state.

[0031] The audio analysis unit can reproduce the characteristics of a target person's voice in different environmental sounds, providing a realistic audio experience. For example, the audio analysis unit collects the target person's voice data and analyzes the characteristics of the voice in different environmental sounds. For example, outdoor wind noise and indoor reverberation are taken into account and input into the generation AI. The audio analysis unit can also provide a realistic audio experience using 3D sound technology and binaural recording. For example, the audio analysis unit analyzes the positional information of the sound source and reproduces a three-dimensional sound effect. This makes it possible to reproduce the voice in different environmental sounds.

[0032] The language analysis unit can customize the target person's verbal expressions according to specific situations and scenarios, enabling more realistic dialogue. For example, in order to customize the target person's verbal expressions according to specific situations and scenarios, the language analysis unit collects past utterance data and learns the language used for each scenario. For example, expressions according to scenarios such as everyday conversations and business situations are input into the generative AI. The language analysis unit can also develop algorithms for generating natural responses and understanding context. For example, the language analysis unit analyzes dialogue data and generates responses appropriate for specific situations. This makes it possible to use words that are appropriate for specific situations and scenarios.

[0033] The personality diagnosis unit can generate voice and verbal expressions that emphasize specific personality traits based on the personality diagnosis results of the target person. The personality diagnosis unit, for example, collects the personality diagnosis results of the target person and generates voice and verbal expressions that emphasize specific personality traits. For example, to emphasize an extroverted personality, a bright and cheerful voice is generated. The personality diagnosis unit can also generate a calm voice to emphasize an introverted personality. For example, the personality diagnosis unit generates expressions that reflect specific personality traits based on the personality diagnosis data. This makes it possible to generate voice and verbal expressions that emphasize specific personality traits.

[0034] The expression generation unit can reconstruct the target person's past statements and writings according to a specific theme or topic, providing deep insight. The expression generation unit, for example, collects the target person's past statements and writings and reconstructs them according to a specific theme or topic. For example, statements related to political or philosophical topics are input into the generation AI. The expression generation unit can also use text mining technology to analyze past statements and writings and extract information related to a specific theme. For example, the expression generation unit searches for related documents from a database and reconstructs them. This makes it possible to provide deep insight according to a specific theme or topic.

[0035] The expression generation unit can reproduce the target person's past statements and writings in different media formats to provide a variety of content. For example, the expression generation unit collects the target person's past statements and writings and learns data for reproducing them in different media formats. For example, podcast or video content is input into the generation AI. The expression generation unit can also develop technology for generating audio content and video content. For example, the expression generation unit generates a variety of content using voice synthesis technology or video editing technology. This makes it possible to reproduce them in different media formats.

[0036] The expression generation unit can utilize the target person's past statements and writings as educational and research materials, thereby increasing their academic value. For example, the expression generation unit collects the target person's past statements and writings and learns data to use as educational and research materials. For example, statements from lectures and research papers are input into the generation AI. The expression generation unit can also develop specific ways to use them as educational and research materials. For example, the expression generation unit can use them as teaching materials or research data. This makes it possible to increase their academic value by using them as educational and research materials.

[0037] The expression generation unit can incorporate the target person's past statements and writings into social media and blog content to provide information to a wide range of users. The expression generation unit, for example, collects the target person's past statements and writings and learns data to apply to social media and blog content. For example, tweets and blog post statements are input into the generation AI. The expression generation unit can also develop specific methods for utilizing social media and blogs. For example, the expression generation unit generates audio content and text content to provide information to a wide range of users. This makes it possible to provide information to a wide range of users by incorporating it into social media and blog content.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The system may further include a health monitoring unit that monitors the user's health condition and provides a message according to the health condition. For example, the system may monitor the user's heart rate and blood pressure, and if an abnormality is detected, provide a message encouraging the user to relax in the voice of a specific person. Also, if the user is exercising, provide an encouraging message. Furthermore, the health monitoring unit may analyze the user's sleep condition and provide a message encouraging good quality sleep. For example, if the user has trouble falling asleep, provide a relaxing message in the voice of a specific person.

[0040] The system may further include a hobby and interest analysis unit that customizes the target person's voice and words based on the user's hobbies and interests. For example, if the user is interested in music, topics related to music can be provided in the target person's voice. Also, if the user is interested in sports, topics related to sports can be provided in the target person's voice. Furthermore, the hobby and interest analysis unit can analyze changes in the user's hobbies and interests and provide new topics at appropriate times. For example, if the user starts a new hobby, topics related to that hobby can be provided.

[0041] The system may further include a lifestyle rhythm analysis unit that customizes the target person's voice and words based on the user's lifestyle rhythm. For example, when the user wakes up in the morning, an encouraging message in the target person's voice can be provided. Also, before the user goes to bed at night, a relaxing message in the target person's voice can be provided. Furthermore, the lifestyle rhythm analysis unit can analyze changes in the user's lifestyle rhythm and provide messages at appropriate times. For example, if the user stays up late, a message encouraging the user to go to bed earlier can be provided.

[0042] The system may further include a learning analysis unit that analyzes the user's learning status and provides messages according to the learning status. For example, if the user is studying, an encouraging message can be provided in the target person's voice. Also, if the user is stuck in their studies, advice can be provided in the target person's voice. Furthermore, the learning analysis unit can analyze the user's learning progress and provide messages at appropriate times. For example, if the user is nervous before an exam, a message encouraging them to relax can be provided.

[0043] The system may further include a social relationship analysis unit that analyzes the user's social relationships and provides messages according to the social relationships. For example, if the user is having trouble with a friend, advice can be provided in the target person's voice. Also, if the user is having trouble with a family member, a message of comfort or encouragement can be provided in the target person's voice. Furthermore, the social relationship analysis unit can analyze changes in the user's social relationships and provide messages at appropriate times. For example, if the user makes a new friend, advice on how to deepen the relationship with that friend can be provided.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The voice analysis unit analyzes the target person's voice. For example, the voice analysis unit collects the target person's voice data and analyzes the tone, rhythm, accent, etc. of the voice. The voice analysis unit can also extract acoustic features using voice recognition technology. For example, the voice analysis unit performs spectral analysis on the voice data and extracts characteristic frequency components. Step 2: The language analysis unit analyzes the expressions and content of the target person's words analyzed by the speech analysis unit. For example, the language analysis unit collects the target person's essays and comments and analyzes their language and expression methods. The language analysis unit can also perform grammatical analysis and semantic analysis using natural language processing technology. For example, the language analysis unit performs morphological analysis of text data and analyzes sentence structure. Step 3: The personality assessment unit incorporates the personality assessment elements of the target person analyzed by the word analysis unit. For example, the personality assessment unit analyzes the personality assessment results and behavioral patterns of the target person and learns their characteristics. The personality assessment unit can also evaluate personality traits using psychological tests and behavioral analysis. For example, the personality assessment unit clusters the personality assessment data and extracts specific personality traits. Step 4: The expression generation unit generates expressions based on the information acquired by the personality assessment unit. For example, the expression generation unit uses natural language generation technology to reproduce the target person's words. The expression generation unit can also use template-based generation to generate expressions tailored to specific scenarios. For example, the expression generation unit generates sentences based on templates to provide words tailored to specific situations.

[0046] (Example 2) A system for filling the gaps in one's heart according to an embodiment of the present invention reproduces the voice of a loved one, a deceased relative, a great person, or a person one respects using AI voice to fill the gaps in one's heart. In this system, AI analyzes the target person's voice and reproduces the expression and content of the words in a way that matches the character. In this way, the system for filling the gaps in one's heart can faithfully reproduce the voice of a loved one, a deceased relative, a great person, or a person one respects, filling the gaps in one's heart.

[0047] A system for filling emotional gaps according to an embodiment includes a voice analysis unit, a word analysis unit, a personality assessment unit, and an expression generation unit. The voice analysis unit analyzes the voice of a target person. For example, the voice analysis unit collects voice data of the target person and analyzes the tone, rhythm, accent, etc. of the voice. The voice analysis unit can also extract acoustic features using voice recognition technology. For example, the voice analysis unit performs spectral analysis of the voice data to extract characteristic frequency components. The word analysis unit analyzes the verbal expressions and content of the target person analyzed by the voice analysis unit. For example, the word analysis unit collects essays and comments of the target person and analyzes their language and expression methods. The word analysis unit can also perform grammatical analysis and semantic analysis using natural language processing technology. For example, the word analysis unit performs morphological analysis of text data to analyze sentence structure. The personality assessment unit incorporates the personality assessment elements of the target person analyzed by the word analysis unit. For example, the personality assessment unit analyzes the personality assessment results and behavioral patterns of the target person and learns their characteristics. The personality assessment unit can also evaluate personality traits using psychological tests and behavioral analysis. For example, the personality assessment unit clusters personality assessment data and extracts specific personality traits. The expression generation unit generates expressions based on the information acquired by the personality assessment unit. For example, the expression generation unit reproduces the target person's words using natural language generation technology. The expression generation unit can also generate expressions tailored to specific scenarios using template-based generation. For example, the expression generation unit generates sentences based on templates and provides words tailored to specific situations. In this way, the system for filling emotional gaps according to the embodiment can fill emotional gaps by generating expressions based on the target person's voice, words, and personality assessment elements. For example, when a user feels a specific emotion, the system provides an encouraging or comforting message based on the target person's past statements. The system analyzes the user's emotional state in real time and, based on the results, selects the target person's past statements to generate an encouraging or comforting message. For example, if the user is sad, a comforting statement is selected. The system can use an emotion estimation function to provide an optimal message tailored to the user's emotional state.

[0048] The voice analysis unit can analyze emotional changes in the target person's voice and reproduce a voice that corresponds to a specific emotional state. For example, the voice analysis unit collects the target person's voice data and analyzes emotional changes. For example, it learns changes in voice tone and rhythm that correspond to emotional states such as joy, sadness, and anger, and inputs this into the generation AI. The voice analysis unit can also analyze changes in voice pitch, tempo, and strength to determine the emotional state. For example, the voice analysis unit analyzes voice data in the time domain and frequency domain to detect emotional changes. This makes it possible to reproduce a voice that corresponds to a specific emotional state.

[0049] The audio analysis unit can reproduce the characteristics of a target person's voice in different environmental sounds, providing a realistic audio experience. For example, the audio analysis unit collects the target person's voice data and analyzes the characteristics of the voice in different environmental sounds. For example, outdoor wind noise and indoor reverberation are taken into account and input into the generation AI. The audio analysis unit can also provide a realistic audio experience using 3D sound technology and binaural recording. For example, the audio analysis unit analyzes the positional information of the sound source and reproduces a three-dimensional sound effect. This makes it possible to reproduce the voice in different environmental sounds.

[0050] The language analysis unit can customize the target person's verbal expressions according to specific situations and scenarios, enabling more realistic dialogue. For example, in order to customize the target person's verbal expressions according to specific situations and scenarios, the language analysis unit collects past utterance data and learns the language used for each scenario. For example, expressions according to scenarios such as everyday conversations and business situations are input into the generative AI. The language analysis unit can also develop algorithms for generating natural responses and understanding context. For example, the language analysis unit analyzes dialogue data and generates responses appropriate for specific situations. This makes it possible to use words that are appropriate for specific situations and scenarios.

[0051] The personality diagnosis unit can generate voice and verbal expressions that emphasize specific personality traits based on the personality diagnosis results of the target person. The personality diagnosis unit, for example, collects the personality diagnosis results of the target person and generates voice and verbal expressions that emphasize specific personality traits. For example, to emphasize an extroverted personality, a bright and cheerful voice is generated. The personality diagnosis unit can also generate a calm voice to emphasize an introverted personality. For example, the personality diagnosis unit generates expressions that reflect specific personality traits based on the personality diagnosis data. This makes it possible to generate voice and verbal expressions that emphasize specific personality traits.

[0052] The expression generation unit can reconstruct the target person's past statements and writings according to a specific theme or topic, providing deep insight. The expression generation unit, for example, collects the target person's past statements and writings and reconstructs them according to a specific theme or topic. For example, statements related to political or philosophical topics are input into the generation AI. The expression generation unit can also use text mining technology to analyze past statements and writings and extract information related to a specific theme. For example, the expression generation unit searches for related documents from a database and reconstructs them. This makes it possible to provide deep insight according to a specific theme or topic.

[0053] The expression generation unit can reproduce the target person's past statements and writings in different media formats to provide a variety of content. For example, the expression generation unit collects the target person's past statements and writings and learns data for reproducing them in different media formats. For example, podcast or video content is input into the generation AI. The expression generation unit can also develop technology for generating audio content and video content. For example, the expression generation unit generates a variety of content using voice synthesis technology or video editing technology. This makes it possible to reproduce them in different media formats.

[0054] The expression generation unit can use the emotion estimation function to select a past statement of the target person that corresponds to the user's emotional state and provide the user with an optimal message. The expression generation unit, for example, analyzes the user's emotional state in real time and selects a past statement of the target person based on the results. For example, if the user is sad, it selects a comforting statement. The expression generation unit can also use the emotion estimation function to generate a message that corresponds to the user's emotional state. For example, the expression generation unit uses an emotion recognition algorithm to analyze the user's emotional state and provide an optimal message. This makes it possible to provide an optimal message that corresponds to the user's emotional state.

[0055] The expression generation unit can utilize the target person's past statements and writings as educational and research materials, thereby increasing their academic value. For example, the expression generation unit collects the target person's past statements and writings and learns data to use as educational and research materials. For example, statements from lectures and research papers are input into the generation AI. The expression generation unit can also develop specific ways to use them as educational and research materials. For example, the expression generation unit can use them as teaching materials or research data. This makes it possible to increase their academic value by using them as educational and research materials.

[0056] The expression generation unit can incorporate the target person's past statements and writings into social media and blog content to provide information to a wide range of users. The expression generation unit, for example, collects the target person's past statements and writings and learns data to apply to social media and blog content. For example, tweets and blog post statements are input into the generation AI. The expression generation unit can also develop specific methods for utilizing social media and blogs. For example, the expression generation unit generates audio content and text content to provide information to a wide range of users. This makes it possible to provide information to a wide range of users by incorporating it into social media and blog content.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The system may further include an emotion estimation unit that estimates the user's emotion and customizes the target person's voice and words based on the estimated emotion. For example, if the user is feeling stressed, the system can soften the target person's voice and provide a relaxing message. Alternatively, if the user is happy, the system can brighten the target person's voice and provide a message of empathy or congratulations. Furthermore, the emotion estimation unit can monitor the user's emotional changes in real time and provide messages at appropriate times. For example, if the user suddenly feels sad, the system can immediately provide a comforting message.

[0059] The system may further include a health monitoring unit that monitors the user's health condition and provides a message according to the health condition. For example, the system may monitor the user's heart rate and blood pressure, and if an abnormality is detected, provide a message encouraging the user to relax in the voice of a specific person. Also, if the user is exercising, provide an encouraging message. Furthermore, the health monitoring unit may analyze the user's sleep condition and provide a message encouraging good quality sleep. For example, if the user has trouble falling asleep, provide a relaxing message in the voice of a specific person.

[0060] The system may further include a hobby and interest analysis unit that customizes the target person's voice and words based on the user's hobbies and interests. For example, if the user is interested in music, topics related to music can be provided in the target person's voice. Also, if the user is interested in sports, topics related to sports can be provided in the target person's voice. Furthermore, the hobby and interest analysis unit can analyze changes in the user's hobbies and interests and provide new topics at appropriate times. For example, if the user starts a new hobby, topics related to that hobby can be provided.

[0061] The system may further include a lifestyle rhythm analysis unit that customizes the target person's voice and words based on the user's lifestyle rhythm. For example, when the user wakes up in the morning, an encouraging message in the target person's voice can be provided. Also, before the user goes to bed at night, a relaxing message in the target person's voice can be provided. Furthermore, the lifestyle rhythm analysis unit can analyze changes in the user's lifestyle rhythm and provide messages at appropriate times. For example, if the user stays up late, a message encouraging the user to go to bed earlier can be provided.

[0062] The system may further include an emotion estimation unit that estimates the user's emotion and customizes the target person's voice and words based on the estimated emotion. For example, if the user is feeling anxious, the system can calm the target person's voice and provide a reassuring message. Alternatively, if the user is excited, the system can provide a soothing message. Furthermore, the emotion estimation unit can monitor the user's emotional changes in real time and provide a message at an appropriate time. For example, if the user suddenly feels angry, the system can provide a message encouraging the user to quickly calm down.

[0063] The system may further include a learning analysis unit that analyzes the user's learning status and provides messages according to the learning status. For example, if the user is studying, an encouraging message can be provided in the target person's voice. Also, if the user is stuck in their studies, advice can be provided in the target person's voice. Furthermore, the learning analysis unit can analyze the user's learning progress and provide messages at appropriate times. For example, if the user is nervous before an exam, a message encouraging them to relax can be provided.

[0064] The system may further include an emotion estimation unit that estimates the user's emotion and customizes the target person's voice and words based on the estimated emotion. For example, if the user feels lonely, a message of comfort can be provided in the target person's voice. Alternatively, if the user feels happy, a message of sympathy or congratulations can be provided in the target person's voice. Furthermore, the emotion estimation unit can monitor the user's emotional changes in real time and provide messages at appropriate times. For example, if the user suddenly feels anxious, a reassuring message can be provided immediately.

[0065] The system may further include an emotion estimation unit that estimates the user's emotion and customizes the target person's voice and words based on the estimated emotion. For example, if the user is tired, a relaxing message can be provided in the target person's voice. Alternatively, if the user is excited, a soothing message can be provided in the target person's voice. Furthermore, the emotion estimation unit can monitor the user's emotional changes in real time and provide messages at appropriate times. For example, if the user suddenly feels sad, a comforting message can be provided immediately.

[0066] The system may further include a social relationship analysis unit that analyzes the user's social relationships and provides messages according to the social relationships. For example, if the user is having trouble with a friend, advice can be provided in the target person's voice. Also, if the user is having trouble with a family member, a message of comfort or encouragement can be provided in the target person's voice. Furthermore, the social relationship analysis unit can analyze changes in the user's social relationships and provide messages at appropriate times. For example, if the user makes a new friend, advice on how to deepen the relationship with that friend can be provided.

[0067] The system may further include an emotion estimation unit that estimates the user's emotion and customizes the target person's voice and words based on the estimated emotion. For example, if the user is nervous, a message in the target person's voice to relax can be provided. Also, if the user is depressed, a message in the target person's voice to encourage encouragement can be provided. Furthermore, the emotion estimation unit can monitor the user's emotional changes in real time and provide messages at appropriate times. For example, if the user suddenly feels joy, a message of sympathy or congratulations can be immediately provided.

[0068] The processing flow of the second embodiment will be briefly explained below.

[0069] Step 1: The voice analysis unit analyzes the target person's voice. For example, the voice analysis unit collects the target person's voice data and analyzes the tone, rhythm, accent, etc. of the voice. The voice analysis unit can also extract acoustic features using voice recognition technology. For example, the voice analysis unit performs spectral analysis on the voice data and extracts characteristic frequency components. Step 2: The language analysis unit analyzes the expressions and content of the target person's words analyzed by the speech analysis unit. For example, the language analysis unit collects the target person's essays and comments and analyzes their language and expression methods. The language analysis unit can also perform grammatical analysis and semantic analysis using natural language processing technology. For example, the language analysis unit performs morphological analysis of text data and analyzes sentence structure. Step 3: The personality assessment unit incorporates the personality assessment elements of the target person analyzed by the word analysis unit. For example, the personality assessment unit analyzes the personality assessment results and behavioral patterns of the target person and learns their characteristics. The personality assessment unit can also evaluate personality traits using psychological tests and behavioral analysis. For example, the personality assessment unit clusters the personality assessment data and extracts specific personality traits. Step 4: The expression generation unit generates expressions based on the information acquired by the personality assessment unit. For example, the expression generation unit uses natural language generation technology to reproduce the target person's words. The expression generation unit can also use template-based generation to generate expressions tailored to specific scenarios. For example, the expression generation unit generates sentences based on templates to provide words tailored to specific situations.

[0070] 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.

[0071] 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.

[0072] 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.

[0073] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0083] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0089] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0098] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0104] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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."

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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]

[0137] 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 voice analysis unit that analyzes the voice of the target person; a word analysis unit that analyzes the expressions and contents of the words of the target person analyzed by the voice analysis unit; a personality diagnosis unit that incorporates the personality diagnosis elements of the target person analyzed by the word analysis unit; an expression generation unit that generates an expression based on the information acquired by the personality diagnosis unit; A system characterized by:

2. The voice analysis unit The voice characteristics of the target person are reproduced with different environmental sounds to provide a realistic audio experience.

2. The system of claim 1.

3. The word analysis unit The target person's verbal expressions are customized to suit specific situations and scenarios, enabling more realistic dialogue.

2. The system of claim 1.

4. The personality diagnosis unit Based on the personality assessment results of the target person, voice and verbal expressions that emphasize specific personality traits are generated.

2. The system of claim 1.

5. The expression generation unit Reconstructing the target person's past statements and writings according to a specific theme or topic to provide deep insight 2. The system of claim 1.

6. The expression generation unit Selecting a past statement of the target person according to the user's emotional state and providing the user with an optimal message 2. The system of claim 1.

Images