System

Abstract

An object of a system according to an embodiment is to propose an optimal laughter style based on the personality and interest of a user.SOLUTION: A system according to an embodiment includes a characteristic analyzer, a style suggester, and a training suggester. The characteristic analysis unit analyzes the personality and interest of the user. The style proposing unit proposes a laughter style based on the characteristics of the user analyzed by the characteristic analyzing unit. The training suggestion unit suggests training based on the laughter style suggested by the style suggestion unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to suggest the optimal comedy style based on an individual's personality and interests.

[0005] The system according to the embodiment aims to suggest the most suitable comedy style based on the user's personality and interests. [Means for solving the problem]

[0006] The system according to the embodiment includes a characteristic analysis unit, a style suggestion unit, and a training suggestion unit. The characteristic analysis unit analyzes a user's personality and interests. The style suggestion unit suggests a comedy style based on the user's characteristics analyzed by the characteristic analysis unit. The training suggestion unit suggests training based on the comedy style suggested by the style suggestion unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable comedy style based on the user's personality and interests. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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) The Comedy Maker AI system according to an embodiment of the present invention uses generative AI to create unique characters based on an individual's personality and interests, and then suggests a humor style that matches that character. This allows the Comedy Maker AI system to suggest humor styles based on the user's personality and interests, and provide training to help them achieve their ideal level of humor.

[0029] The comedy maker AI system according to the embodiment includes a characteristic analysis unit, a style suggestion unit, and a training suggestion unit. The characteristic analysis unit analyzes a user's personality and interests. For example, the characteristic analysis unit analyzes the user's personality and interests through dialogue with the user using a generative AI. The characteristic analysis unit can also grasp the user's characteristics through psychological tests and questionnaires. The characteristic analysis unit can also analyze the user's behavioral history and track changes in interests. The style suggestion unit suggests a comedy style based on the user's characteristics analyzed by the characteristic analysis unit. For example, if the user has a bright and sociable personality, the style suggestion unit suggests a bright and energetic comedian's style. If the user has an intelligent and calm personality, the style suggestion unit suggests a comedian's style with intellectual humor. The style suggestion unit can also select a humor style that best suits the user's characteristics and provide specific examples. The training suggestion unit suggests training based on the comedy style suggested by the style suggestion unit. For example, if a user wants to improve their comedic skills, the training suggestion unit suggests specific comedic patterns and practice methods. Furthermore, if the user wants to improve their tsukkomi skills, the training suggestion unit suggests appropriate tsukkomi timing and phrases. The training suggestion unit also provides a training plan tailored to the user's goals, helping the user become a more entertaining person by leveraging their individuality. This allows the comedy maker AI system according to the embodiment to suggest a humor style based on the user's personality and interests, and provide training to help the user achieve their ideal level of humor. For example, by leveraging their own characteristics to create a unique character, users can enjoy communicating with friends and family more. It is also expected that interpersonal relationships at work and school will be smoother, enriching the user's overall life.

[0030] The characteristic analysis unit can analyze a user's interaction history over the long term and track changes in personality and interests over time. For example, the characteristic analysis unit collects interaction history with the user over a period of several months to several years and analyzes changes in personality and interests over time. For example, it tracks how the topics in which the user was interested at a particular time changed. This makes it possible to track changes in the user's personality and interests over the long term and perform more accurate characteristic analysis.

[0031] The characteristic analysis unit can analyze non-verbal communication and perform a deeper characteristic analysis. For example, the characteristic analysis unit captures the user's facial expressions with a camera and extracts emotions and personality traits using facial expression analysis technology. For example, it analyzes the frequency and intensity of smiles to identify the user's positive personality traits. The characteristic analysis unit can also analyze the tone of the user's voice to extract emotions and personality traits. For example, it analyzes the tone and speed of the voice to understand the user's emotional state. This enables a deeper characteristic analysis by analyzing the user's non-verbal communication.

[0032] The characteristic analysis unit can integrate the user's social media activities and online behavior history to perform a more comprehensive characteristic analysis. The characteristic analysis unit, for example, analyzes the user's social media activities and extracts personality and interests from the content posted and reactions. For example, it analyzes the content the user frequently shares and the trends in comments. The characteristic analysis unit can also analyze the user's online behavior history and identify interests from website browsing history and search history. For example, it analyzes the websites the user frequently visits and search keywords. In this way, a more comprehensive characteristic analysis can be performed by integrating the user's social media activities and online behavior history.

[0033] The characteristic analysis unit can compare the results of a user's characteristic analysis with other users and identify user groups with similar characteristics. The characteristic analysis unit, for example, stores the results of a user's characteristic analysis in a database and develops an algorithm for comparing the results with other users. For example, it can identify user groups based on similarities in personality and interests. The characteristic analysis unit can also use a clustering algorithm to identify user groups with similar characteristics. For example, it can group users based on patterns of personality traits and interests. By identifying user groups with similar characteristics, it is possible to understand the trends and interests of the entire group.

[0034] The characteristic analysis unit can analyze the content of a user's dialogue using natural language processing technology and extract latent interests and personality traits. For example, the characteristic analysis unit analyzes the content of a dialogue with a user using natural language processing technology and extracts latent interests and personality traits from keywords and phrases. For example, it analyzes words and topics frequently used by the user. The characteristic analysis unit can also identify latent interests from the content of a user's dialogue using topic modeling. For example, it extracts topics that appear frequently in the content of the dialogue. In this way, it is possible to extract latent interests and personality traits by analyzing the content of a user's dialogue.

[0035] The characteristic analysis unit can learn the user's dialogue patterns using a machine learning model and dynamically improve the analysis accuracy as the dialogue progresses. The characteristic analysis unit, for example, builds a system that learns the user's dialogue patterns using a machine learning model and dynamically improves the analysis accuracy as the dialogue progresses. For example, the model is updated based on the user's reactions during the dialogue. The characteristic analysis unit can also use a neural network to learn the user's dialogue patterns and improve the analysis accuracy as the dialogue progresses. For example, the frequency of the user's utterances and the timing of responses are analyzed. This allows for dynamic improvement of the analysis accuracy as the dialogue progresses, enabling more accurate characteristic analysis.

[0036] The characteristic analysis unit can introduce voice recognition technology into the dialogue with the user and analyze personality and interests from the voice data. For example, the characteristic analysis unit introduces voice recognition technology into the dialogue with the user and builds a system that analyzes the voice data to identify personality and interests. For example, it analyzes the tone and speed of the user's voice. The characteristic analysis unit can also use voice recognition technology to convert the content of the user's dialogue into text data and analyze personality and interests based on that data. For example, it converts voice data into text data and analyzes it using natural language processing technology. This makes it possible to analyze characteristics from more diverse angles by analyzing the voice data.

[0037] The characteristic analysis unit can share the content of the dialogue with the user with other generative AIs and integrate the analysis results from different perspectives. For example, the characteristic analysis unit can build a system that shares the content of the dialogue with the user with other generative AIs and integrates the analysis results from different perspectives. For example, multiple AIs can cooperate to analyze the user's personality and interests. The characteristic analysis unit can also use a generative AI to analyze the content of the user's dialogue and share the results with other generative AIs. For example, different generative AIs can analyze the user's characteristics from their own perspectives and integrate the results. This enables more accurate characteristic analysis by integrating the analysis results from different perspectives.

[0038] The style suggestion unit can propose a humor style suited to a culture by taking into account the user's cultural background and linguistic characteristics. The style suggestion unit, for example, analyzes the user's cultural background and builds a system that proposes a humor style suited to that culture. For example, the humor style is selected based on the culture of the user's country or region. The style suggestion unit can also analyze the user's linguistic characteristics and propose a humor style suited to that language. For example, the humor style is selected based on the user's native language or the characteristics of the language used. In this way, a more appropriate humor style can be proposed by taking into account the cultural background and linguistic characteristics.

[0039] The style suggestion unit can present samples of different humor styles to the user and select an optimal style based on the user's reaction. The style suggestion unit, for example, constructs a system that presents samples of different humor styles to the user and selects an optimal style based on the reaction. For example, the system presents multiple jokes or patterns and analyzes the user's reaction. The style suggestion unit can also dynamically change the humor style based on the user's reaction and provide an optimal style. This allows samples of different humor styles to be presented and an optimal style to be selected based on the user's reaction.

[0040] The style suggestion unit can incorporate feedback from other users into the user's humor style proposals to improve the accuracy of the proposals. For example, the style suggestion unit collects feedback from other users on the user's humor style proposals and builds a system that improves the accuracy of the proposals based on that data. For example, the system analyzes the ratings and comments of other users. The style suggestion unit can also dynamically change the humor style based on the feedback from other users to provide an optimal style. In this way, the accuracy of the proposals can be improved by incorporating feedback from other users.

[0041] The style suggestion unit can analyze the styles of famous comedians of the past based on the user's characteristics and suggest an optimal style. The style suggestion unit, for example, builds a system that analyzes the styles of famous comedians of the past based on the user's characteristics and suggests an optimal style. For example, it selects a comedian's style that matches the user's personality and interests. The style suggestion unit can also analyze performance data of comedians of the past and suggest an optimal style to the user. In this way, the optimal style can be suggested by analyzing the styles of famous comedians of the past.

[0042] The style suggestion unit can present samples of different comedy styles to the user and select the optimal style based on the user's reaction. The style suggestion unit, for example, constructs a system that presents samples of different comedy styles to the user and selects the optimal style based on the reaction. For example, the system presents performances by multiple comedians and analyzes the user's reaction. The style suggestion unit can also dynamically change the comedy style based on the user's reaction and provide the optimal style. This allows samples of different comedy styles to be presented and the optimal style to be selected based on the user's reaction.

[0043] The style suggestion unit can incorporate feedback from other users into comedy style suggestions based on the user's characteristics, thereby improving the accuracy of the suggestions. For example, the style suggestion unit collects feedback from other users regarding comedy style suggestions based on the user's characteristics, and builds a system that improves the accuracy of the suggestions based on that data. For example, it analyzes the ratings and comments of other users. The style suggestion unit can also dynamically change the comedy style based on the feedback from other users and provide an optimal style. In this way, by incorporating feedback from other users, the accuracy of the suggestions can be improved.

[0044] The training suggestion unit can analyze the styles of famous comedians of the past based on the user's characteristics and suggest an optimal style. The training suggestion unit, for example, builds a system that analyzes the styles of famous comedians of the past based on the user's characteristics and suggests an optimal style. For example, it selects a comedian's style that matches the user's personality and interests. The training suggestion unit can also analyze performance data of comedians of the past and suggest an optimal style to the user. In this way, the optimal style can be suggested by analyzing the styles of famous comedians of the past.

[0045] The training suggestion unit can use a generation AI to automatically generate patterns of funny jokes and tsukkomi that match the user's characteristics and suggest them to the user. The training suggestion unit, for example, builds a system in which a generation AI automatically generates patterns of funny jokes and tsukkomi that match the user's characteristics and suggests them to the user. For example, it generates patterns of funny jokes and tsukkomi based on the user's personality and interests. The training suggestion unit can also use the generation AI to dynamically generate and suggest patterns of funny jokes and tsukkomi that are optimal for the user's characteristics. This allows the generation AI to automatically generate and suggest patterns of funny jokes and tsukkomi that are optimal for the user.

[0046] The training suggestion unit can present samples of different comedy styles to the user and select the optimal style based on the user's reaction. The training suggestion unit, for example, constructs a system that presents samples of different comedy styles to the user and selects the optimal style based on the reaction. For example, the system presents performances by multiple comedians and analyzes the user's reaction. The training suggestion unit can also dynamically change the comedy style based on the user's reaction and provide the optimal style. This allows samples of different comedy styles to be presented and the optimal style to be selected based on the user's reaction.

[0047] The training suggestion unit can incorporate feedback from other users into the user's humor style proposals to improve the accuracy of the proposals. For example, the training suggestion unit collects feedback from other users on the user's humor style proposals and builds a system that improves the accuracy of the proposals based on that data. For example, the training suggestion unit analyzes the ratings and comments of other users. The training suggestion unit can also dynamically change the humor style based on the feedback from other users to provide an optimal style. In this way, by incorporating feedback from other users, the accuracy of the proposals can be improved.

[0048] The training suggestion unit can analyze the styles of famous comedians of the past based on the user's characteristics and suggest an optimal style. The training suggestion unit, for example, builds a system that analyzes the styles of famous comedians of the past based on the user's characteristics and suggests an optimal style. For example, it selects a comedian's style that matches the user's personality and interests. The training suggestion unit can also analyze performance data of comedians of the past and suggest an optimal style to the user. In this way, the optimal style can be suggested by analyzing the styles of famous comedians of the past.

[0049] The training suggestion unit can use a generation AI to automatically generate patterns of funny jokes and tsukkomi that match the user's characteristics and suggest them to the user. The training suggestion unit, for example, builds a system in which a generation AI automatically generates patterns of funny jokes and tsukkomi that match the user's characteristics and suggests them to the user. For example, it generates patterns of funny jokes and tsukkomi based on the user's personality and interests. The training suggestion unit can also use the generation AI to dynamically generate and suggest patterns of funny jokes and tsukkomi that are optimal for the user's characteristics. This allows the generation AI to automatically generate and suggest patterns of funny jokes and tsukkomi that are optimal for the user.

[0050] The training suggestion unit can present samples of different comedy styles to the user and select the optimal style based on the user's reaction. The training suggestion unit, for example, constructs a system that presents samples of different comedy styles to the user and selects the optimal style based on the reaction. For example, the system presents performances by multiple comedians and analyzes the user's reaction. The training suggestion unit can also dynamically change the comedy style based on the user's reaction and provide the optimal style. This allows samples of different comedy styles to be presented and the optimal style to be selected based on the user's reaction.

[0051] The training suggestion unit can incorporate feedback from other users into the user's humor style proposals to improve the accuracy of the proposals. For example, the training suggestion unit collects feedback from other users on the user's humor style proposals and builds a system that improves the accuracy of the proposals based on that data. For example, the training suggestion unit analyzes the ratings and comments of other users. The training suggestion unit can also dynamically change the humor style based on the feedback from other users to provide an optimal style. In this way, by incorporating feedback from other users, the accuracy of the proposals can be improved.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The characteristic analysis unit can not only analyze the user's hobbies and interests, but also analyze the user's lifestyle habits and daily behavior patterns. For example, it can analyze what time of day the user is most active and where they relax. The characteristic analysis unit can also analyze the user's eating and exercise habits to understand their health condition and stress level. This allows for a comprehensive understanding of the user's entire lifestyle and more accurate characteristic analysis.

[0054] The characteristic analysis unit can not only analyze the user's dialogue history over the long term, but also analyze the characteristics of the user's dialogue partners. For example, it can analyze the type of people the user most frequently talks to, and the personality and interests of the dialogue partners. The characteristic analysis unit can also analyze the user's relationships with their dialogue partners, such as friends, family, and colleagues. This makes it possible to perform characteristic analysis that takes into account the user's relationships with their dialogue partners.

[0055] The characteristic analysis unit can not only analyze non-verbal communication, but also the user's physical movements and posture. For example, the user's gestures and posture can be captured with a camera and motion analysis technology can be used to extract personality traits. The characteristic analysis unit can also analyze the user's walking and standing style to understand their personality and emotional state. This allows for deeper characteristic analysis by analyzing the user's physical movements and posture.

[0056] The characteristic analysis unit not only integrates a user's social media activities and online behavior history, but can also analyze the user's offline activities. For example, it analyzes the events the user participates in and their hobbies to extract information about their personality and interests. The characteristic analysis unit can also analyze the user's offline friendships and understand their relationships with friends and acquaintances. This allows for a more comprehensive characteristic analysis by integrating the user's online and offline activities.

[0057] The characteristic analysis unit can not only compare the results of the user's characteristic analysis with other users, but also suggest optimal communication partners based on the user's characteristics. For example, it can match users with similar personalities and interests and provide communication opportunities. The characteristic analysis unit can also suggest group activities and events based on the user's characteristics. This can strengthen the user's social connections by suggesting optimal communication partners and activities based on the user's characteristics.

[0058] The characteristic analysis unit not only analyzes the content of a user's dialogue using natural language processing technology, but also takes into account the context and background information of the user's dialogue. For example, it analyzes the situation in which the user is having a dialogue, as well as the purpose and intention of the dialogue. The characteristic analysis unit can also analyze the context of the user's dialogue and grasp the flow of the dialogue and changes in topic. This allows for more accurate characteristic analysis by taking into account the context and background information of the user's dialogue.

[0059] The characteristic analysis unit not only learns the user's dialogue patterns using a machine learning model, but can also analyze the user's dialogue style and communication habits. For example, it analyzes the dialogue progression pattern to determine the user's preferred language and expressions. The characteristic analysis unit can also analyze the user's dialogue style and understand the user's role and position in the dialogue. This allows for more accurate characteristic analysis by analyzing the user's dialogue style and communication habits.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The characteristic analysis unit analyzes the user's personality and interests. For example, it uses generative AI to analyze personality and interests through dialogue with the user. It can also understand the user's characteristics through psychological tests and questionnaires. It can also analyze the user's behavioral history and track changes in interests. Step 2: The style suggestion unit suggests a comedy style based on the user's characteristics analyzed by the characteristic analysis unit. For example, if the user has a bright and sociable personality, the unit suggests a cheerful and lively comedian's style, and if the user has an intelligent and calm personality, the unit suggests a comedian's style with intellectual humor. Furthermore, the unit can select the humor style that best suits the user's characteristics and provide specific examples. Step 3: The training suggestion unit suggests training based on the comedy style suggested by the style suggestion unit. For example, if a user wants to improve their boke (comedy) skills, the training suggestion unit suggests specific boke patterns and practice methods, and if a user wants to improve their tsukkomi (comedy) skills, the training suggestion unit suggests appropriate tsukkomi timing and phrases. The training suggestion unit also provides a training plan tailored to the user's goals, helping the user to become a more entertaining person by making the most of their individuality.

[0062] (Example 2) The Comedy Maker AI system according to an embodiment of the present invention uses generative AI to create unique characters based on an individual's personality and interests, and then suggests a humor style that matches that character. This allows the Comedy Maker AI system to suggest humor styles based on the user's personality and interests, and provide training to help them achieve their ideal level of humor.

[0063] The comedy maker AI system according to the embodiment includes a characteristic analysis unit, a style suggestion unit, and a training suggestion unit. The characteristic analysis unit analyzes a user's personality and interests. For example, the characteristic analysis unit analyzes the user's personality and interests through dialogue with the user using a generative AI. The characteristic analysis unit can also grasp the user's characteristics through psychological tests and questionnaires. The characteristic analysis unit can also analyze the user's behavioral history and track changes in interests. The style suggestion unit suggests a comedy style based on the user's characteristics analyzed by the characteristic analysis unit. For example, if the user has a bright and sociable personality, the style suggestion unit suggests a bright and energetic comedian's style. If the user has an intelligent and calm personality, the style suggestion unit suggests a comedian's style with intellectual humor. The style suggestion unit can also select a humor style that best suits the user's characteristics and provide specific examples. The training suggestion unit suggests training based on the comedy style suggested by the style suggestion unit. For example, if a user wants to improve their comedic skills, the training suggestion unit suggests specific comedic patterns and practice methods. Furthermore, if the user wants to improve their tsukkomi skills, the training suggestion unit suggests appropriate tsukkomi timing and phrases. The training suggestion unit also provides a training plan tailored to the user's goals, helping the user become a more entertaining person by leveraging their individuality. This allows the comedy maker AI system according to the embodiment to suggest a humor style based on the user's personality and interests, and provide training to help the user achieve their ideal level of humor. For example, by leveraging their own characteristics to create a unique character, users can enjoy communicating with friends and family more. It is also expected that interpersonal relationships at work and school will be smoother, enriching the user's overall life.

[0064] The characteristic analysis unit can analyze a user's interaction history over the long term and track changes in personality and interests over time. For example, the characteristic analysis unit collects interaction history with the user over a period of several months to several years and analyzes changes in personality and interests over time. For example, it tracks how the topics in which the user was interested at a particular time changed. This makes it possible to track changes in the user's personality and interests over the long term and perform more accurate characteristic analysis.

[0065] The characteristic analysis unit can analyze non-verbal communication and perform a deeper characteristic analysis. For example, the characteristic analysis unit captures the user's facial expressions with a camera and extracts emotions and personality traits using facial expression analysis technology. For example, it analyzes the frequency and intensity of smiles to identify the user's positive personality traits. The characteristic analysis unit can also analyze the tone of the user's voice to extract emotions and personality traits. For example, it analyzes the tone and speed of the voice to understand the user's emotional state. This enables a deeper characteristic analysis by analyzing the user's non-verbal communication.

[0066] The characteristic analysis unit can analyze the user's emotional state in real time using the emotion estimation function and perform characteristic analysis according to the emotion at any given time. The characteristic analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and perform characteristic analysis based on that data. For example, it compares the content of conversations when the user is happy and when the user is sad. The characteristic analysis unit can also monitor the user's emotional state in real time and perform characteristic analysis according to the emotion at any given time. For example, it analyzes the behavioral patterns when the user is stressed and when the user is relaxed. This makes it possible to analyze the user's emotional state in real time and perform characteristic analysis according to the emotion at any given time.

[0067] The characteristic analysis unit can integrate the user's social media activities and online behavior history to perform a more comprehensive characteristic analysis. The characteristic analysis unit, for example, analyzes the user's social media activities and extracts personality and interests from the content posted and reactions. For example, it analyzes the content the user frequently shares and the trends in comments. The characteristic analysis unit can also analyze the user's online behavior history and identify interests from website browsing history and search history. For example, it analyzes the websites the user frequently visits and search keywords. In this way, a more comprehensive characteristic analysis can be performed by integrating the user's social media activities and online behavior history.

[0068] The characteristic analysis unit can compare the results of a user's characteristic analysis with other users and identify user groups with similar characteristics. The characteristic analysis unit, for example, stores the results of a user's characteristic analysis in a database and develops an algorithm for comparing the results with other users. For example, it can identify user groups based on similarities in personality and interests. The characteristic analysis unit can also use a clustering algorithm to identify user groups with similar characteristics. For example, it can group users based on patterns of personality traits and interests. By identifying user groups with similar characteristics, it is possible to understand the trends and interests of the entire group.

[0069] The characteristic analysis unit can analyze the content of a user's dialogue using natural language processing technology and extract latent interests and personality traits. For example, the characteristic analysis unit analyzes the content of a dialogue with a user using natural language processing technology and extracts latent interests and personality traits from keywords and phrases. For example, it analyzes words and topics frequently used by the user. The characteristic analysis unit can also identify latent interests from the content of a user's dialogue using topic modeling. For example, it extracts topics that appear frequently in the content of the dialogue. In this way, it is possible to extract latent interests and personality traits by analyzing the content of a user's dialogue.

[0070] The characteristic analysis unit can learn the user's dialogue patterns using a machine learning model and dynamically improve the analysis accuracy as the dialogue progresses. The characteristic analysis unit, for example, builds a system that learns the user's dialogue patterns using a machine learning model and dynamically improves the analysis accuracy as the dialogue progresses. For example, the model is updated based on the user's reactions during the dialogue. The characteristic analysis unit can also use a neural network to learn the user's dialogue patterns and improve the analysis accuracy as the dialogue progresses. For example, the frequency of the user's utterances and the timing of responses are analyzed. This allows for dynamic improvement of the analysis accuracy as the dialogue progresses, enabling more accurate characteristic analysis.

[0071] The characteristic analysis unit can use the emotion estimation function to monitor the user's emotional changes during a conversation in real time and perform personality analysis based on the emotions. The characteristic analysis unit, for example, uses the emotion estimation function to monitor the user's emotional changes during a conversation in real time and build a system that performs personality analysis based on the data. For example, the user's emotion score is analyzed in real time. The characteristic analysis unit can also monitor the user's emotional changes in real time and perform personality analysis based on the emotions at any given time. For example, the content of the conversation when the user is relaxed and when the user is nervous can be compared. This makes it possible to monitor emotional changes during a conversation in real time and perform personality analysis based on emotions.

[0072] The characteristic analysis unit can introduce voice recognition technology into the dialogue with the user and analyze personality and interests from the voice data. For example, the characteristic analysis unit introduces voice recognition technology into the dialogue with the user and builds a system that analyzes the voice data to identify personality and interests. For example, it analyzes the tone and speed of the user's voice. The characteristic analysis unit can also use voice recognition technology to convert the content of the user's dialogue into text data and analyze personality and interests based on that data. For example, it converts voice data into text data and analyzes it using natural language processing technology. This makes it possible to analyze characteristics from more diverse angles by analyzing the voice data.

[0073] The characteristic analysis unit can share the content of the dialogue with the user with other generative AIs and integrate the analysis results from different perspectives. For example, the characteristic analysis unit can build a system that shares the content of the dialogue with the user with other generative AIs and integrates the analysis results from different perspectives. For example, multiple AIs can cooperate to analyze the user's personality and interests. The characteristic analysis unit can also use a generative AI to analyze the content of the user's dialogue and share the results with other generative AIs. For example, different generative AIs can analyze the user's characteristics from their own perspectives and integrate the results. This enables more accurate characteristic analysis by integrating the analysis results from different perspectives.

[0074] The characteristic analysis unit uses the emotion estimation function to adjust the dialogue content according to the user's emotions during the dialogue, thereby achieving more accurate personality analysis. The characteristic analysis unit, for example, uses the emotion estimation function to build a system that adjusts the dialogue content according to the user's emotions during the dialogue. For example, dialogue content appropriate for when the user is relaxed is provided. The characteristic analysis unit can also dynamically change the dialogue content according to the user's emotional state to perform more accurate personality analysis. For example, when the user is feeling stressed, dialogue content that relaxes the user is provided, improving the accuracy of the personality analysis. In this way, more accurate personality analysis is possible by adjusting the dialogue content according to the user's emotions.

[0075] The style suggestion unit can propose a humor style suited to a culture by taking into account the user's cultural background and linguistic characteristics. The style suggestion unit, for example, analyzes the user's cultural background and builds a system that proposes a humor style suited to that culture. For example, the humor style is selected based on the culture of the user's country or region. The style suggestion unit can also analyze the user's linguistic characteristics and propose a humor style suited to that language. For example, the humor style is selected based on the user's native language or the characteristics of the language used. In this way, a more appropriate humor style can be proposed by taking into account the cultural background and linguistic characteristics.

[0076] The style suggestion unit can use the emotion estimation function to adjust the humor style in real time according to the user's emotional state. For example, the style suggestion unit uses the emotion estimation function to build a system that adjusts the humor style in real time according to the user's emotional state. For example, the style suggestion unit can suggest light jokes when the user is relaxed, and suggest a humor style that relaxes the user when the user is nervous. The style suggestion unit can also dynamically change the humor style according to the user's emotional state to provide an optimal humor style. This allows the humor style to be adjusted in real time according to the user's emotional state.

[0077] The style suggestion unit can present samples of different humor styles to the user and select an optimal style based on the user's reaction. The style suggestion unit, for example, constructs a system that presents samples of different humor styles to the user and selects an optimal style based on the reaction. For example, the system presents multiple jokes or patterns and analyzes the user's reaction. The style suggestion unit can also dynamically change the humor style based on the user's reaction and provide an optimal style. This allows samples of different humor styles to be presented and an optimal style to be selected based on the user's reaction.

[0078] The style suggestion unit can incorporate feedback from other users into the user's humor style proposals to improve the accuracy of the proposals. For example, the style suggestion unit collects feedback from other users on the user's humor style proposals and builds a system that improves the accuracy of the proposals based on that data. For example, the system analyzes the ratings and comments of other users. The style suggestion unit can also dynamically change the humor style based on the feedback from other users to provide an optimal style. In this way, the accuracy of the proposals can be improved by incorporating feedback from other users.

[0079] The style suggestion unit uses the emotion estimation function to suggest a humor style based on the user's emotion, thereby making the user's emotion positive. The style suggestion unit, for example, uses the emotion estimation function to build a system that suggests a humor style based on the user's emotion. For example, the style suggestion unit preferentially suggests a humor style that indicates a positive emotion for the user. The style suggestion unit can also dynamically change the humor style according to the user's emotional state to make the user's emotion positive. In this way, by suggesting a humor style based on the user's emotion, the user's emotion can be made positive.

[0080] The style suggestion unit can analyze the styles of famous comedians of the past based on the user's characteristics and suggest an optimal style. The style suggestion unit, for example, builds a system that analyzes the styles of famous comedians of the past based on the user's characteristics and suggests an optimal style. For example, it selects a comedian's style that matches the user's personality and interests. The style suggestion unit can also analyze performance data of comedians of the past and suggest an optimal style to the user. In this way, the optimal style can be suggested by analyzing the styles of famous comedians of the past.

[0081] The style suggestion unit can use the emotion estimation function to adjust the boke / tsukkomi patterns based on the user's emotions in real time. The style suggestion unit, for example, uses the emotion estimation function to build a system that adjusts the boke / tsukkomi patterns based on the user's emotions in real time. For example, it can suggest light boke when the user is relaxed, and suggest tsukkomi that will relax the user when the user is tense. The style suggestion unit can also dynamically change the boke / tsukkomi patterns according to the user's emotional state and provide the optimal pattern. This makes it possible to adjust the boke / tsukkomi patterns based on the user's emotions in real time.

[0082] The style suggestion unit can present samples of different comedy styles to the user and select the optimal style based on the user's reaction. The style suggestion unit, for example, constructs a system that presents samples of different comedy styles to the user and selects the optimal style based on the reaction. For example, the system presents performances by multiple comedians and analyzes the user's reaction. The style suggestion unit can also dynamically change the comedy style based on the user's reaction and provide the optimal style. This allows samples of different comedy styles to be presented and the optimal style to be selected based on the user's reaction.

[0083] The style suggestion unit can incorporate feedback from other users into comedy style suggestions based on the user's characteristics, thereby improving the accuracy of the suggestions. For example, the style suggestion unit collects feedback from other users regarding comedy style suggestions based on the user's characteristics, and builds a system that improves the accuracy of the suggestions based on that data. For example, it analyzes the ratings and comments of other users. The style suggestion unit can also dynamically change the comedy style based on the feedback from other users and provide an optimal style. In this way, by incorporating feedback from other users, the accuracy of the suggestions can be improved.

[0084] The style suggestion unit uses the emotion estimation function to suggest a comedy style based on the user's emotion, thereby making the user's emotion positive. The style suggestion unit, for example, uses the emotion estimation function to build a system that suggests a comedy style based on the user's emotion. For example, the style suggestion unit preferentially suggests a comedy style that indicates a positive emotion for the user. The style suggestion unit can also dynamically change the comedy style according to the user's emotional state to make the user's emotion positive. In this way, the user's emotion can be made positive by suggesting a comedy style based on the user's emotion.

[0085] The training suggestion unit can analyze the styles of famous comedians of the past based on the user's characteristics and suggest an optimal style. The training suggestion unit, for example, builds a system that analyzes the styles of famous comedians of the past based on the user's characteristics and suggests an optimal style. For example, it selects a comedian's style that matches the user's personality and interests. The training suggestion unit can also analyze performance data of comedians of the past and suggest an optimal style to the user. In this way, the optimal style can be suggested by analyzing the styles of famous comedians of the past.

[0086] The training suggestion unit can use a generation AI to automatically generate patterns of funny jokes and tsukkomi that match the user's characteristics and suggest them to the user. The training suggestion unit, for example, builds a system in which a generation AI automatically generates patterns of funny jokes and tsukkomi that match the user's characteristics and suggests them to the user. For example, it generates patterns of funny jokes and tsukkomi based on the user's personality and interests. The training suggestion unit can also use the generation AI to dynamically generate and suggest patterns of funny jokes and tsukkomi that are optimal for the user's characteristics. This allows the generation AI to automatically generate and suggest patterns of funny jokes and tsukkomi that are optimal for the user.

[0087] The training suggestion unit can present samples of different comedy styles to the user and select the optimal style based on the user's reaction. The training suggestion unit, for example, constructs a system that presents samples of different comedy styles to the user and selects the optimal style based on the reaction. For example, the system presents performances by multiple comedians and analyzes the user's reaction. The training suggestion unit can also dynamically change the comedy style based on the user's reaction and provide the optimal style. This allows samples of different comedy styles to be presented and the optimal style to be selected based on the user's reaction.

[0088] The training suggestion unit can incorporate feedback from other users into the user's humor style proposals to improve the accuracy of the proposals. For example, the training suggestion unit collects feedback from other users on the user's humor style proposals and builds a system that improves the accuracy of the proposals based on that data. For example, the training suggestion unit analyzes the ratings and comments of other users. The training suggestion unit can also dynamically change the humor style based on the feedback from other users to provide an optimal style. In this way, by incorporating feedback from other users, the accuracy of the proposals can be improved.

[0089] The training suggestion unit uses the emotion estimation function to suggest a humor style based on the user's emotions, thereby making the user's emotions more positive. The training suggestion unit, for example, uses the emotion estimation function to build a system that suggests a humor style based on the user's emotions. For example, the training suggestion unit preferentially suggests a humor style that indicates a positive emotion for the user. The training suggestion unit can also dynamically change the humor style depending on the user's emotional state to make the user's emotions more positive. In this way, by suggesting a humor style based on the user's emotions, the user's emotions can be made more positive.

[0090] The training suggestion unit can analyze the styles of famous comedians of the past based on the user's characteristics and suggest an optimal style. The training suggestion unit, for example, builds a system that analyzes the styles of famous comedians of the past based on the user's characteristics and suggests an optimal style. For example, it selects a comedian's style that matches the user's personality and interests. The training suggestion unit can also analyze performance data of comedians of the past and suggest an optimal style to the user. In this way, the optimal style can be suggested by analyzing the styles of famous comedians of the past.

[0091] The training suggestion unit can use a generation AI to automatically generate patterns of funny jokes and tsukkomi that match the user's characteristics and suggest them to the user. The training suggestion unit, for example, builds a system in which a generation AI automatically generates patterns of funny jokes and tsukkomi that match the user's characteristics and suggests them to the user. For example, it generates patterns of funny jokes and tsukkomi based on the user's personality and interests. The training suggestion unit can also use the generation AI to dynamically generate and suggest patterns of funny jokes and tsukkomi that are optimal for the user's characteristics. This allows the generation AI to automatically generate and suggest patterns of funny jokes and tsukkomi that are optimal for the user.

[0092] The training suggestion unit can present samples of different comedy styles to the user and select the optimal style based on the user's reaction. The training suggestion unit, for example, constructs a system that presents samples of different comedy styles to the user and selects the optimal style based on the reaction. For example, the system presents performances by multiple comedians and analyzes the user's reaction. The training suggestion unit can also dynamically change the comedy style based on the user's reaction and provide the optimal style. This allows samples of different comedy styles to be presented and the optimal style to be selected based on the user's reaction.

[0093] The training suggestion unit can incorporate feedback from other users into the user's humor style proposals to improve the accuracy of the proposals. For example, the training suggestion unit collects feedback from other users on the user's humor style proposals and builds a system that improves the accuracy of the proposals based on that data. For example, the training suggestion unit analyzes the ratings and comments of other users. The training suggestion unit can also dynamically change the humor style based on the feedback from other users to provide an optimal style. In this way, by incorporating feedback from other users, the accuracy of the proposals can be improved.

[0094] The training suggestion unit uses the emotion estimation function to suggest a humor style based on the user's emotions, thereby making the user's emotions more positive. The training suggestion unit, for example, uses the emotion estimation function to build a system that suggests a humor style based on the user's emotions. For example, the training suggestion unit preferentially suggests a humor style that indicates a positive emotion for the user. The training suggestion unit can also dynamically change the humor style depending on the user's emotional state to make the user's emotions more positive. In this way, by suggesting a humor style based on the user's emotions, the user's emotions can be made more positive.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The characteristic analysis unit can not only analyze the user's hobbies and interests, but also analyze the user's lifestyle habits and daily behavior patterns. For example, it can analyze what time of day the user is most active and where they relax. The characteristic analysis unit can also analyze the user's eating and exercise habits to understand their health condition and stress level. This allows for a comprehensive understanding of the user's entire lifestyle and more accurate characteristic analysis.

[0097] The characteristic analysis unit can not only analyze the user's dialogue history over the long term, but also analyze the characteristics of the user's dialogue partners. For example, it can analyze the type of people the user most frequently talks to, and the personality and interests of the dialogue partners. The characteristic analysis unit can also analyze the user's relationships with their dialogue partners, such as friends, family, and colleagues. This makes it possible to perform characteristic analysis that takes into account the user's relationships with their dialogue partners.

[0098] The characteristic analysis unit can not only analyze non-verbal communication, but also the user's physical movements and posture. For example, the user's gestures and posture can be captured with a camera and motion analysis technology can be used to extract personality traits. The characteristic analysis unit can also analyze the user's walking and standing style to understand their personality and emotional state. This allows for deeper characteristic analysis by analyzing the user's physical movements and posture.

[0099] The characteristic analysis unit uses the emotion estimation function to not only analyze the user's emotional state in real time, but also track the user's emotional change patterns over the long term. For example, it analyzes the emotions a user feels in specific situations and identifies those patterns. The characteristic analysis unit can also analyze the user's emotional changes over time and predict emotional fluctuations. This allows for more accurate characteristic analysis by tracking the user's emotional change patterns over the long term.

[0100] The characteristic analysis unit not only integrates a user's social media activities and online behavior history, but can also analyze the user's offline activities. For example, it analyzes the events the user participates in and their hobbies to extract information about their personality and interests. The characteristic analysis unit can also analyze the user's offline friendships and understand their relationships with friends and acquaintances. This allows for a more comprehensive characteristic analysis by integrating the user's online and offline activities.

[0101] The characteristic analysis unit can not only compare the results of the user's characteristic analysis with other users, but also suggest optimal communication partners based on the user's characteristics. For example, it can match users with similar personalities and interests and provide communication opportunities. The characteristic analysis unit can also suggest group activities and events based on the user's characteristics. This can strengthen the user's social connections by suggesting optimal communication partners and activities based on the user's characteristics.

[0102] The characteristic analysis unit not only analyzes the content of a user's dialogue using natural language processing technology, but also takes into account the context and background information of the user's dialogue. For example, it analyzes the situation in which the user is having a dialogue, as well as the purpose and intention of the dialogue. The characteristic analysis unit can also analyze the context of the user's dialogue and grasp the flow of the dialogue and changes in topic. This allows for more accurate characteristic analysis by taking into account the context and background information of the user's dialogue.

[0103] The characteristic analysis unit not only learns the user's dialogue patterns using a machine learning model, but can also analyze the user's dialogue style and communication habits. For example, it analyzes the dialogue progression pattern to determine the user's preferred language and expressions. The characteristic analysis unit can also analyze the user's dialogue style and understand the user's role and position in the dialogue. This allows for more accurate characteristic analysis by analyzing the user's dialogue style and communication habits.

[0104] The characteristic analysis unit not only uses the emotion estimation function to monitor the user's emotional changes during a conversation in real time, but also adjusts the content of the conversation in response to the user's emotional changes. For example, when the user is feeling stressed, it provides conversation content that relaxes the user, and when the user is happy, it provides conversation content that further enhances that emotion. The characteristic analysis unit can also dynamically change the topic and progress of the conversation in response to changes in the user's emotions. This allows for more accurate characteristic analysis by adjusting the content of the conversation in response to changes in the user's emotions.

[0105] The characteristic analysis unit not only applies voice recognition technology to dialogue with the user, but can also estimate emotions and stress levels from the user's voice data. For example, it analyzes the tone, pitch, and speed of the user's voice to understand their emotional state and stress level. The characteristic analysis unit can also analyze the user's voice data and track changes in emotions during the dialogue. This allows for a more multifaceted analysis of the user's emotions and stress level by analyzing the voice data.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The characteristic analysis unit analyzes the user's personality and interests. For example, it uses generative AI to analyze personality and interests through dialogue with the user. It can also understand the user's characteristics through psychological tests and questionnaires. It can also analyze the user's behavioral history and track changes in interests. Step 2: The style suggestion unit suggests a comedy style based on the user's characteristics analyzed by the characteristic analysis unit. For example, if the user has a bright and sociable personality, the unit suggests a cheerful and lively comedian's style, and if the user has an intelligent and calm personality, the unit suggests a comedian's style with intellectual humor. Furthermore, the unit can select the humor style that best suits the user's characteristics and provide specific examples. Step 3: The training suggestion unit suggests training based on the comedy style suggested by the style suggestion unit. For example, if a user wants to improve their boke (comedy) skills, the training suggestion unit suggests specific boke patterns and practice methods, and if a user wants to improve their tsukkomi (comedy) skills, the training suggestion unit suggests appropriate tsukkomi timing and phrases. The training suggestion unit also provides a training plan tailored to the user's goals, helping the user to become a more entertaining person by making the most of their individuality.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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]

[0175] 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 characteristic analysis section that analyzes the user's personality and interests; a style suggestion unit that suggests a comedy style based on the user's characteristics analyzed by the characteristic analysis unit; a training suggestion unit that suggests training based on the comedy style suggested by the style suggestion unit. A system characterized by:

2. The characteristic analysis unit Analyze the user's non-verbal communication to perform a deeper characterization 2. The system of claim 1.

3. The characteristic analysis unit Integrate the user's social media activity or online behavior history to conduct a more comprehensive characterization 2. The system of claim 1.

4. The style suggestion unit Presenting samples of different humor styles to the user and selecting the most suitable style based on the user's response.

2. The system of claim 1.

5. The training suggestion unit Based on the characteristics of the user, the styles of famous comedians from the past are analyzed and the most suitable style is suggested.

2. The system of claim 1.

6. The characteristic analysis unit Using the emotion estimation function, the emotional state of the user is analyzed in real time, and characteristics are analyzed according to the emotions at each moment.

2. The system of claim 1.

7. The style suggestion unit Using emotion estimation capabilities to adjust the humor style in real time according to the user's emotional state.

2. The system of claim 1.

8. The training suggestion unit Using an emotion estimation function, a humor style is suggested based on the emotion of the user, and the emotion of the user is made positive.

2. The system of claim 1.

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