System

Abstract

An object of the system according to the embodiment is to enable a person who knows a stranger or is not good at a presentation to communicate with confidence.SOLUTION: A system includes a voice data collection part, a personality analysis part, a virtual avatar generation part, a presentation execution part, and a support provision part. The voice data collection unit collects voice data of a user. The personality analysis unit analyzes the voice data collected by the voice data collection unit. The virtual avatar generation unit generates a virtual avatar based on the result analyzed by the personality analysis unit. A presentation execution part performs presentation through the virtual avatar generated by the virtual avatar generation part. The support providing unit provides support during the presentation performed by the presentation execution unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult for people who are shy or uncomfortable with presentations to communicate with confidence.

[0005] The system according to the embodiment aims to enable people who are shy or uncomfortable with presentations to communicate with confidence. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice data collection unit, a personality analysis unit, a virtual avatar generation unit, a presentation execution unit, and a support provision unit. The voice data collection unit collects voice data of a user. The personality analysis unit analyzes the voice data collected by the voice data collection unit. The virtual avatar generation unit generates a virtual avatar based on the results of the analysis by the personality analysis unit. The presentation execution unit gives a presentation through the virtual avatar generated by the virtual avatar generation unit. The support provision unit provides support during the presentation given by the presentation execution unit. [Effects of the Invention]

[0007] The system according to the embodiment allows people who are shy or uncomfortable with presentations to communicate with confidence. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Virtual Pitch Partner system according to an embodiment of the present invention analyzes a user's voice data and personality, generates a virtual avatar, makes presentations, and provides invisible support, allowing users to communicate naturally.

[0029] The Virtual Pitch Partner system according to the embodiment includes a voice data collection unit, a personality analysis unit, a virtual alter-ego generation unit, a presentation execution unit, and a support provision unit. The voice data collection unit collects voice data from a user. For example, the voice data collection unit records the user's voice using a microphone. The voice data collection unit can also collect voice data using a microphone on a smartphone or a computer. The voice data collection unit can also collect voice data by uploading voice files. The personality analysis unit analyzes the voice data collected by the voice data collection unit. For example, the personality analysis unit analyzes the tone and rhythm of the voice data to estimate the user's personality traits. The personality analysis unit can also analyze the content of the voice data to diagnose the user's personality. The personality analysis unit can also analyze the user's past speech history to track personality changes and growth. The virtual alter-ego generation unit generates a virtual alter-ego based on the results of the analysis by the personality analysis unit. For example, the virtual alter-ego generation unit sets an optimal character based on the user's personality traits. The virtual avatar generation unit can also generate a virtual avatar by taking into account the user's past experiences of success and failure. The virtual avatar generation unit can also create a character that reflects the user's goals and hopes and increases their motivation. The presentation execution unit gives a presentation through the virtual avatar generated by the virtual avatar generation unit. For example, the presentation execution unit causes the virtual avatar to speak with confidence based on the presentation content entered by the user. The presentation execution unit can also monitor the user's emotional state in real time while the presentation is in progress and provide support at an appropriate time. The presentation execution unit can also use an emotion estimation function to cause the virtual avatar to provide appropriate feedback according to the user's emotional state during the presentation. The support provision unit provides invisible support during a presentation given by the presentation execution unit. For example, the support provision unit can monitor the user's state of tension in real time and provide advice on how to relax.The support providing unit can also analyze the content of the user's comments in real time and suggest what to say at the appropriate time. The support providing unit can also use the emotion estimation function to allow the virtual avatar to provide appropriate support according to the user's emotional state. This allows the Virtual Pitch Partner system according to the embodiment to allow users to communicate naturally. For example, users can give presentations with confidence, enabling more effective communication in sales activities.

[0030] The personality analysis unit can analyze the speech history and track personality changes and growth. The personality analysis unit, for example, collects the user's past speech history and performs text analysis. For example, it extracts keywords and phrases from the speech content and tracks personality changes and growth. The personality analysis unit can also analyze the user's speech history and evaluate personality changes. For example, it analyzes changes in speech content and tone and evaluates personality changes. The personality analysis unit can also analyze the user's speech history and evaluate personality growth. For example, it analyzes positive changes in speech content and skill improvements and evaluates personality growth. This makes it possible to analyze the user's past speech history and track personality changes and growth, enabling more personalized support.

[0031] The virtual alter-ego generation unit can set an optimal character by taking into account the user's experiences of success and failure. The virtual alter-ego generation unit, for example, collects the user's past experiences of success and failure and reflects them in the character setting of the virtual alter-ego. For example, if the user has had many successful experiences, a confident character is set. The virtual alter-ego generation unit can also analyze the user's experiences of success and failure and set the character's behavior pattern. For example, a proactive behavior pattern is set based on the user's experiences of success, and a cautious behavior pattern is set based on the user's experiences of failure. The virtual alter-ego generation unit can also set the character's speaking style and attitude by taking into account the user's experiences of success and failure. For example, a confident speaking style is set based on the user's experiences of success, and a humble attitude is set based on the user's experiences of failure. In this way, the optimal character is set by taking into account the user's past experiences of success and failure, and the optimal virtual alter-ego can be provided for the user.

[0032] The virtual alter-ego generation unit can set a character that reflects the user's goals and aspirations and enhances motivation. The virtual alter-ego generation unit, for example, collects the user's goals and aspirations and reflects them in the character settings for the virtual alter-ego. For example, a character that enhances motivation is set according to the goals the user wants to achieve. The virtual alter-ego generation unit can also analyze the user's goals and aspirations and set the character's behavior pattern. For example, it can set a proactive behavior pattern toward achieving the goal and set a positive attitude based on the aspirations. The virtual alter-ego generation unit can also set the character's speaking style and attitude to reflect the user's goals and aspirations. For example, it can set a confident speaking style toward achieving the goal and set a positive attitude based on the aspirations. In this way, by setting a character that reflects the user's goals and aspirations and enhances motivation, it is possible to support the user in achieving their goals.

[0033] The support providing unit can analyze the content of utterances in real time and suggest what to say at an appropriate time. The support providing unit, for example, builds a system that analyzes the content of utterances made by a user in real time. For example, the support providing unit converts the content of utterances into text using speech recognition technology and analyzes it. The support providing unit can also develop an algorithm for analyzing the content of utterances in real time and suggesting what to say at an appropriate time. For example, the support providing unit analyzes the content of utterances made by a user and suggests what to say next. The support providing unit can also provide an interface for analyzing the content of utterances in real time and suggesting what to say at an appropriate time. For example, a notification is displayed when the user needs what to say next. In this way, the user can communicate more effectively by analyzing the content of utterances made by a user in real time and suggesting what to say at an appropriate time.

[0034] The support providing unit can add a function to switch invisible support according to different situations. For example, the support providing unit provides situation-specific settings in order to switch invisible support according to different situations. For example, it can allow the user to select settings for business meetings and settings for casual conversations. The support providing unit can also develop an algorithm for switching invisible support according to different situations. For example, it can automatically switch support content according to the situation. The support providing unit can also provide an interface for switching invisible support according to different situations. For example, it can allow the user to select support content according to the situation. In this way, the user can receive more appropriate support by switching invisible support according to different situations.

[0035] The virtual avatar generation unit can analyze a communication style in detail and suggest optimal speaking styles and content. The virtual avatar generation unit, for example, collects and analyzes past speech content and tone to analyze a user's communication style in detail. For example, it identifies phrases and speech patterns frequently used by the user. The virtual avatar generation unit can also develop an algorithm for analyzing a communication style in detail and suggesting optimal speaking styles and content. For example, it can suggest optimal speaking styles and content based on the user's speech content and tone. The virtual avatar generation unit can also provide an interface for analyzing a communication style in detail and suggesting optimal speaking styles and content. For example, it allows the user to select speaking styles and content. In this way, the user's communication style can be analyzed in detail and optimal speaking styles and content can be suggested, allowing the user to communicate more naturally.

[0036] The virtual avatar generation unit can analyze communication history and support natural communication. The virtual avatar generation unit, for example, collects and analyzes the user's past communication history. For example, it analyzes the content, tone, and speaking patterns of what is said to support natural communication. The virtual avatar generation unit can also analyze the communication history and develop an algorithm to support natural communication. For example, it can suggest an optimal communication method based on the content and tone of what is said by the user. The virtual avatar generation unit can also analyze the communication history and provide an interface to support natural communication. For example, it can allow the user to select a communication method. In this way, by analyzing the user's past communication history and supporting natural communication, the user can communicate with more confidence.

[0037] The virtual avatar generation unit can add a function for switching natural communication depending on different situations. For example, the virtual avatar generation unit provides settings for each situation in order to switch natural communication depending on different situations. For example, settings for a business meeting and settings for a casual conversation can be selected. The virtual avatar generation unit can also develop an algorithm for switching natural communication depending on different situations. For example, the virtual avatar generation unit can automatically switch communication methods depending on the situation. The virtual avatar generation unit can also provide an interface for switching natural communication depending on different situations. For example, the virtual avatar generation unit can allow the user to select a communication method depending on the situation. This allows the user to communicate more appropriately by switching natural communication depending on different situations.

[0038] The virtual avatar generation unit can adapt natural communication to different languages ​​and cultures, supporting international communication. For example, the virtual avatar generation unit uses multilingual voice synthesis technology to adapt natural communication to different languages. For example, it can enable communication in multiple languages, such as English, French, and Chinese. The virtual avatar generation unit can also develop algorithms to adapt natural communication to different cultures. For example, it can provide a communication method that takes cultural customs and values ​​into consideration. The virtual avatar generation unit can also provide an interface to adapt natural communication to different languages ​​and cultures. For example, it can allow the user to select a communication method according to the language or culture. This allows natural communication to be adapted to different languages ​​and cultures, supporting international communication.

[0039] The personality analysis unit can compare the voice and personality analysis results with other users to identify similarities and differences. The personality analysis unit, for example, stores the user's voice and personality analysis results in a database and compares them with other users. For example, it analyzes similarities and differences based on voice features and personality traits. The personality analysis unit can also develop an algorithm for comparing the voice and personality analysis results with other users to identify similarities and differences. For example, it calculates the similarity of voice features and personality traits. The personality analysis unit can also provide an interface for comparing the voice and personality analysis results with other users to identify similarities and differences. For example, it allows the user to check similarities and differences with other users. This makes it possible to identify similarities and differences by comparing the user's voice and personality analysis results with other users, thereby enabling more personalized support.

[0040] The personality analysis unit can compare the voice and personality analysis results in different situations to understand personality traits according to the situation. For example, the personality analysis unit compares the user's voice and personality analysis results in work and private situations. For example, it analyzes voice data during work and voice data during private time separately. The personality analysis unit can also compare the voice and personality analysis results in different situations to develop an algorithm for understanding personality traits according to the situation. For example, it analyzes the differences in personality traits during work and private time. The personality analysis unit can also provide an interface for comparing the voice and personality analysis results in different situations to understand personality traits according to the situation. For example, it allows the user to check personality traits for each situation. In this way, by comparing the user's voice and personality analysis results in different situations, personality traits according to the situation can be understood and more appropriate support can be provided.

[0041] The personality analysis unit can also analyze background sounds and environmental sounds when collecting voice data and estimate the living environment. For example, the personality analysis unit extracts acoustic features to analyze background sounds and environmental sounds when collecting voice data. For example, it analyzes the type and volume of background sounds to estimate the user's living environment. The personality analysis unit can also develop an algorithm for analyzing background sounds and environmental sounds when collecting voice data and estimating the living environment. For example, it estimates the living environment based on the type and volume of background sounds. The personality analysis unit can also provide an interface for analyzing background sounds and environmental sounds when collecting voice data and estimating the living environment. For example, it allows the user to check the estimated results of the living environment. In this way, by analyzing background sounds and environmental sounds when collecting voice data, the user's living environment can be estimated and more appropriate support can be provided.

[0042] The personality analysis unit can also take social media activities and online behavior into consideration when collecting information about personality. For example, the personality analysis unit collects a user's social media activities and online behavior and analyzes them as information about personality. For example, it analyzes the content of posts and patterns of likes. The personality analysis unit can also develop an algorithm for taking social media activities and online behavior into consideration when collecting information about personality. For example, it can analyze the content of social media posts and patterns of online behavior. The personality analysis unit can also provide an interface for taking social media activities and online behavior into consideration when collecting information about personality. For example, it can allow the user to check the analysis results of social media activities and online behavior. This allows more accurate personality diagnosis by taking social media activities and online behavior into consideration when collecting information about personality.

[0043] The personality analysis unit can collect voice data and personality information at different times of the day and in different situations, and grasp personality traits according to the time of day and the situation. The personality analysis unit, for example, collects and analyzes voice data and personality information at different times of the day and in different situations. For example, it compares voice data from the morning and evening to evaluate personality traits for each time of day. The personality analysis unit can also collect voice data and personality information at different times of the day and in different situations, and develop an algorithm for grasping personality traits according to the time of day and the situation. For example, it analyzes differences in personality traits according to the time of day and the situation. The personality analysis unit can also collect voice data and personality information at different times of the day and in different situations, and provide an interface for grasping personality traits according to the time of day and the situation. For example, it allows the user to check personality traits according to the time of day and the situation. This enables more accurate personality diagnosis by collecting voice data and personality information at different times of the day and in different situations, and grasping personality traits according to the time of day and the situation.

[0044] The virtual alter-ego generation unit can customize the appearance and tone of voice of the virtual alter-ego to suit the user's preferences. The virtual alter-ego generation unit, for example, provides the user with options for customizing the appearance of the virtual alter-ego to suit the user's preferences. For example, it provides an interface that allows the user to select hairstyle, clothing, and facial features. The virtual alter-ego generation unit can also develop an algorithm for customizing the tone of voice of the virtual alter-ego to suit the user's preferences. For example, it can adjust the pitch, intensity, and emotional expression. The virtual alter-ego generation unit can also provide an interface for customizing the appearance and tone of voice of the virtual alter-ego to suit the user's preferences. For example, it allows the user to select the appearance and tone of voice. In this way, the appearance and tone of voice of the virtual alter-ego can be customized to suit the user's preferences, thereby providing a virtual alter-ego that is more familiar to the user.

[0045] The virtual alter-ego generation unit can optimize the behavior pattern of the virtual alter-ego based on the user's behavior history. The virtual alter-ego generation unit, for example, collects the user's past behavior history and optimizes the virtual alter-ego's behavior pattern. For example, it analyzes the user's statements and behavior patterns and reflects them in the virtual alter-ego. The virtual alter-ego generation unit can also develop an algorithm for optimizing the virtual alter-ego's behavior pattern based on the user's behavior history. For example, it performs predictions and behavior simulations based on the past behavior history. The virtual alter-ego generation unit can also provide an interface for optimizing the virtual alter-ego's behavior pattern based on the user's behavior history. For example, it allows the user to select a behavior pattern. In this way, the virtual alter-ego's behavior pattern can be optimized based on the user's past behavior history, thereby providing a more suitable virtual alter-ego for the user.

[0046] The virtual alter-ego generation unit can add a function for switching the virtual alter-ego according to different situations. For example, the virtual alter-ego generation unit provides settings for each situation in order to switch the virtual alter-ego according to different situations. For example, it can allow the user to select settings for a business meeting and settings for a casual conversation. The virtual alter-ego generation unit can also develop an algorithm for switching the virtual alter-ego according to different situations. For example, it can automatically switch the behavior or speaking style of the virtual alter-ego according to the situation. The virtual alter-ego generation unit can also provide an interface for switching the virtual alter-ego according to different situations. For example, it can allow the user to select settings for the virtual alter-ego according to the situation. This allows the user to receive more appropriate support by switching the virtual alter-ego according to different situations.

[0047] The virtual avatar generation unit can customize the virtual avatar according to different industries or occupations to provide specialized support. For example, the virtual avatar generation unit reflects industry-specific knowledge and skills in order to customize the virtual avatar according to different industries or occupations. For example, a virtual avatar for the medical industry incorporates medical terminology and procedures. The virtual avatar generation unit can also develop algorithms for customizing the virtual avatar according to different industries or occupations. For example, specialized support according to the industry or occupation is provided. The virtual avatar generation unit can also provide an interface for customizing the virtual avatar according to different industries or occupations. For example, the user can select virtual avatar settings according to the industry or occupation. This allows the user to receive more specialized support by customizing the virtual avatar according to different industries or occupations.

[0048] The virtual avatar generation unit can adapt the virtual avatar to different age groups and genders, thereby making it suitable for a wide range of users. For example, the virtual avatar generation unit incorporates language and behavior patterns appropriate for different ages to adapt the virtual avatar to different age groups. For example, casual language is used for younger people, and polite language is used for older people. The virtual avatar generation unit can also develop an algorithm for adapting the virtual avatar to different age groups and genders. For example, it can provide a communication method appropriate for age and gender. The virtual avatar generation unit can also provide an interface for adapting the virtual avatar to different age groups and genders. For example, it can allow the user to select settings for the virtual avatar appropriate for age group and gender. This allows the virtual avatar to adapt to a wide range of users by adapting the virtual avatar to different age groups and genders.

[0049] The presentation execution unit can customize the content of a presentation for different industries or occupations and provide professional support. For example, the presentation execution unit reflects industry-specific knowledge and skills to customize the content of a presentation for different industries or occupations. For example, medical terminology and procedures are incorporated into a presentation for the medical industry. The presentation execution unit can also develop an algorithm for customizing the content of a presentation for different industries or occupations. For example, professional support is provided for different industries or occupations. The presentation execution unit can also provide an interface for customizing the content of a presentation for different industries or occupations. For example, the user can select presentation settings according to the industry or occupation. This allows the user to receive more professional support by customizing the content of the presentation for different industries or occupations.

[0050] The presentation execution unit can adapt the content of a presentation to different languages ​​and cultures, supporting international communication. For example, the presentation execution unit uses multilingual speech synthesis technology to adapt the content of a presentation to different languages. For example, it can make it possible to give presentations in multiple languages, such as English, French, and Chinese. The presentation execution unit can also develop algorithms to adapt the content of a presentation to different cultures. For example, it can provide a presentation method that takes cultural customs and values ​​into consideration. The presentation execution unit can also provide an interface to adapt the content of a presentation to different languages ​​and cultures. For example, it can allow a user to select a presentation method appropriate for the language or culture. This allows international communication to be supported by adapting the content of a presentation to different languages ​​and cultures.

[0051] The support providing unit can analyze the content of utterances in real time and suggest what to say at an appropriate time. The support providing unit, for example, builds a system that analyzes the content of utterances made by a user in real time. For example, the support providing unit converts the content of utterances into text using speech recognition technology and analyzes it. The support providing unit can also develop an algorithm for analyzing the content of utterances in real time and suggesting what to say at an appropriate time. For example, the support providing unit analyzes the content of utterances made by a user and suggests what to say next. The support providing unit can also provide an interface for analyzing the content of utterances in real time and suggesting what to say at an appropriate time. For example, a notification is displayed when the user needs what to say next. In this way, the user can communicate more effectively by analyzing the content of utterances made by a user in real time and suggesting what to say at an appropriate time.

[0052] The support providing unit can add a function to switch invisible support according to different situations. For example, the support providing unit provides situation-specific settings in order to switch invisible support according to different situations. For example, it can allow the user to select settings for business meetings and settings for casual conversations. The support providing unit can also develop an algorithm for switching invisible support according to different situations. For example, it can automatically switch support content according to the situation. The support providing unit can also provide an interface for switching invisible support according to different situations. For example, it can allow the user to select support content according to the situation. In this way, the user can receive more appropriate support by switching invisible support according to different situations.

[0053] The support providing unit can adapt the invisible support to different languages ​​and cultures to support international communication. For example, the support providing unit uses multilingual speech synthesis technology to adapt the invisible support to different languages. For example, it can provide support in multiple languages ​​such as English, French, and Chinese. The support providing unit can also develop algorithms to adapt the invisible support to different cultures. For example, it can provide a support method that takes cultural customs and values ​​into consideration. The support providing unit can also provide an interface to adapt the invisible support to different languages ​​and cultures. For example, it can allow the user to select a support method according to the language or culture. In this way, it is possible to support international communication by adapting the invisible support to different languages ​​and cultures.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The VirtualPitch Partner system can also include a gesture recognition unit that recognizes user gestures and supports the progress of the presentation. For example, the gesture recognition unit can detect a user raising their hand and issue an instruction to proceed to the next slide. The gesture recognition unit can also detect a user pointing their finger to emphasize a particular point. The gesture recognition unit can also detect a user waving their hand to indicate the end of the presentation. This allows the user to control the presentation using natural gestures, allowing for a smoother progress.

[0056] The VirtualPitch Partner system can also include an eye-tracking unit that tracks the user's gaze and adjusts the content of the presentation based on the user's gaze. For example, if the user looks at a particular slide for a long time, the eye-tracking unit can explain the content of that slide in detail. The eye-tracking unit can also move on to the next topic when the user moves their gaze. The eye-tracking unit can also display information related to a specific object when the user directs their gaze at that object. This allows the user to control the presentation using only their gaze, making it more intuitive.

[0057] The Virtual Pitch Partner system can further include a body temperature monitoring unit that monitors the user's body temperature and adjusts the progress of the presentation based on changes in body temperature. For example, if the user's body temperature rises, the body temperature monitoring unit can suggest relaxation techniques to relieve tension. If the user's body temperature drops, the body temperature monitoring unit can also provide advice on how to improve concentration. If the user's body temperature remains stable, the body temperature monitoring unit can also provide support to ensure the smooth progress of the presentation. This allows the user to receive support according to changes in body temperature, enabling a more effective presentation.

[0058] The Virtual Pitch Partner system can also include a gait analysis unit that analyzes the user's walking pattern and supports the progress of the presentation. For example, the gait analysis unit can analyze the user's walking pattern on stage and suggest the optimal movement route. The gait analysis unit can also emphasize the presentation content at a specific location if the user stops at that location. The gait analysis unit can also provide immediate support if the user loses balance while walking. This allows the user to receive support based on their walking pattern, allowing them to deliver their presentation with greater confidence.

[0059] The Virtual Pitch Partner system may further include an outfit recognition unit that recognizes a user's outfit and adjusts the content and style of the presentation. For example, if the user is wearing formal attire, the outfit recognition unit may make the tone of the presentation more professional. Alternatively, if the user is wearing casual attire, the outfit recognition unit may make the tone of the presentation more relaxed. The outfit recognition unit may also adjust the visual style of the presentation to match the user's outfit. This allows the user to give an optimal presentation based on their outfit, creating a more consistent impression.

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

[0061] Step 1: The voice data collection unit collects voice data of the user. For example, the voice data collection unit may record the user's voice using a microphone. Voice data may also be collected using the microphone of a smartphone or computer. Voice data may also be collected by uploading a voice file. Step 2: The personality analysis unit analyzes the voice data collected by the voice data collection unit. For example, it analyzes the tone and rhythm of the voice data to estimate the user's personality traits. It can also analyze the content of the voice data to diagnose the user's personality. It can also analyze the user's past speech history to track changes and growth in personality. Step 3: The virtual avatar generation unit generates a virtual avatar based on the results of the analysis by the personality analysis unit. For example, it sets an optimal character based on the user's personality traits. It can also generate a virtual avatar taking into account the user's past experiences of success and failure. It can also set a character that reflects the user's goals and hopes and increases their motivation. Step 4: The presentation execution unit gives a presentation through the virtual avatar generated by the virtual avatar generation unit. For example, the virtual avatar speaks confidently based on the presentation content entered by the user. It can also monitor the user's emotional state in real time while the presentation is in progress and provide support at the appropriate time. Furthermore, using an emotion estimation function, the virtual avatar can provide appropriate feedback depending on the user's emotional state during the presentation. Step 5: The support provider provides invisible support during the presentation given by the presentation execution unit. For example, it can monitor the user's state of tension in real time and provide advice on how to relax. It can also analyze the content of the user's speech in real time and suggest what to say at the appropriate time. Furthermore, it is possible for the virtual avatar to provide appropriate support depending on the user's emotional state using an emotion estimation function.

[0062] (Example 2) The Virtual Pitch Partner system according to an embodiment of the present invention analyzes a user's voice data and personality, generates a virtual avatar, makes presentations, and provides invisible support, allowing users to communicate naturally.

[0063] The Virtual Pitch Partner system according to the embodiment includes a voice data collection unit, a personality analysis unit, a virtual alter-ego generation unit, a presentation execution unit, and a support provision unit. The voice data collection unit collects voice data from a user. For example, the voice data collection unit records the user's voice using a microphone. The voice data collection unit can also collect voice data using a microphone on a smartphone or a computer. The voice data collection unit can also collect voice data by uploading voice files. The personality analysis unit analyzes the voice data collected by the voice data collection unit. For example, the personality analysis unit analyzes the tone and rhythm of the voice data to estimate the user's personality traits. The personality analysis unit can also analyze the content of the voice data to diagnose the user's personality. The personality analysis unit can also analyze the user's past speech history to track personality changes and growth. The virtual alter-ego generation unit generates a virtual alter-ego based on the results of the analysis by the personality analysis unit. For example, the virtual alter-ego generation unit sets an optimal character based on the user's personality traits. The virtual avatar generation unit can also generate a virtual avatar by taking into account the user's past experiences of success and failure. The virtual avatar generation unit can also create a character that reflects the user's goals and hopes and increases their motivation. The presentation execution unit gives a presentation through the virtual avatar generated by the virtual avatar generation unit. For example, the presentation execution unit causes the virtual avatar to speak with confidence based on the presentation content entered by the user. The presentation execution unit can also monitor the user's emotional state in real time while the presentation is in progress and provide support at an appropriate time. The presentation execution unit can also use an emotion estimation function to cause the virtual avatar to provide appropriate feedback according to the user's emotional state during the presentation. The support provision unit provides invisible support during a presentation given by the presentation execution unit. For example, the support provision unit can monitor the user's state of tension in real time and provide advice on how to relax.The support providing unit can also analyze the content of the user's comments in real time and suggest what to say at the appropriate time. The support providing unit can also use the emotion estimation function to allow the virtual avatar to provide appropriate support according to the user's emotional state. This allows the Virtual Pitch Partner system according to the embodiment to allow users to communicate naturally. For example, users can give presentations with confidence, enabling more effective communication in sales activities.

[0064] The personality analysis unit can analyze the speech history and track personality changes and growth. The personality analysis unit, for example, collects the user's past speech history and performs text analysis. For example, it extracts keywords and phrases from the speech content and tracks personality changes and growth. The personality analysis unit can also analyze the user's speech history and evaluate personality changes. For example, it analyzes changes in speech content and tone and evaluates personality changes. The personality analysis unit can also analyze the user's speech history and evaluate personality growth. For example, it analyzes positive changes in speech content and skill improvements and evaluates personality growth. This makes it possible to analyze the user's past speech history and track personality changes and growth, enabling more personalized support.

[0065] The personality analysis unit can use the emotion estimation function to analyze the emotional state in real time and reflect it in the personality diagnosis result. The personality analysis unit can use the emotion estimation function to analyze the emotional state in real time from the user's voice data. For example, it can analyze the tone and rhythm of the voice and calculate an emotion score. The personality analysis unit can also use the emotion estimation function to analyze the emotional state in real time from the user's facial expression data. For example, it can analyze changes in facial expressions and calculate an emotion score. The personality analysis unit can also use the emotion estimation function to analyze the emotional state in real time from the user's biometric data. For example, it can analyze the heart rate and electrodermal activity and calculate an emotion score. In this way, the emotion estimation function can be used to analyze the user's emotional state in real time and reflect it in the personality diagnosis result, enabling a more accurate personality diagnosis.

[0066] The virtual alter-ego generation unit can set an optimal character by taking into account the user's experiences of success and failure. The virtual alter-ego generation unit, for example, collects the user's past experiences of success and failure and reflects them in the character setting of the virtual alter-ego. For example, if the user has had many successful experiences, a confident character is set. The virtual alter-ego generation unit can also analyze the user's experiences of success and failure and set the character's behavior pattern. For example, a proactive behavior pattern is set based on the user's experiences of success, and a cautious behavior pattern is set based on the user's experiences of failure. The virtual alter-ego generation unit can also set the character's speaking style and attitude by taking into account the user's experiences of success and failure. For example, a confident speaking style is set based on the user's experiences of success, and a humble attitude is set based on the user's experiences of failure. In this way, the optimal character is set by taking into account the user's past experiences of success and failure, and the optimal virtual alter-ego can be provided for the user.

[0067] The virtual alter-ego generation unit can set a character that reflects the user's goals and aspirations and enhances motivation. The virtual alter-ego generation unit, for example, collects the user's goals and aspirations and reflects them in the character settings for the virtual alter-ego. For example, a character that enhances motivation is set according to the goals the user wants to achieve. The virtual alter-ego generation unit can also analyze the user's goals and aspirations and set the character's behavior pattern. For example, it can set a proactive behavior pattern toward achieving the goal and set a positive attitude based on the aspirations. The virtual alter-ego generation unit can also set the character's speaking style and attitude to reflect the user's goals and aspirations. For example, it can set a confident speaking style toward achieving the goal and set a positive attitude based on the aspirations. In this way, by setting a character that reflects the user's goals and aspirations and enhances motivation, it is possible to support the user in achieving their goals.

[0068] The presentation execution unit can monitor the emotional state in real time while the presentation is in progress and provide support at an appropriate time. For example, the presentation execution unit builds a system that monitors the emotional state of a user in real time while the presentation is in progress. For example, the system analyzes the user's facial expressions and voice and calculates an emotional score. The presentation execution unit can also develop an algorithm for monitoring the emotional state in real time and providing support at an appropriate time. For example, support is provided when the user's emotional score exceeds a certain threshold. The presentation execution unit can also provide an interface for monitoring the emotional state in real time and providing support at an appropriate time. For example, a notification is displayed when the user needs support. In this way, the system can monitor the user's emotional state in real time while the presentation is in progress and provide support at an appropriate time, allowing the user to give a presentation with greater confidence.

[0069] The presentation execution unit can use the emotion estimation function to enable the virtual avatar to provide appropriate feedback according to the emotional state of the user during the presentation. The presentation execution unit, for example, uses the emotion estimation function to monitor the emotional state of the user during the presentation in real time. For example, the presentation execution unit can analyze the user's facial expressions and voice and calculate an emotion score. The presentation execution unit can also use the emotion estimation function to develop an algorithm for the virtual avatar to provide appropriate feedback according to the emotional state during the presentation. For example, feedback is provided when the user's emotion score exceeds a certain threshold. The presentation execution unit can also use the emotion estimation function to provide an interface for the virtual avatar to provide appropriate feedback according to the emotional state during the presentation. For example, a notification is displayed when the user needs feedback. This allows the virtual avatar to provide appropriate feedback according to the user's emotional state during the presentation using the emotion estimation function, allowing the user to give a more effective presentation.

[0070] The support providing unit can monitor the state of tension in real time and provide advice on how to relax. The support providing unit, for example, builds a system that monitors the user's state of tension in real time. For example, it analyzes the user's facial expressions and voice and calculates a tension score. The support providing unit can also develop an algorithm for monitoring the state of tension in real time and providing advice on how to relax. For example, it provides advice when the user's tension score exceeds a certain threshold. The support providing unit can also provide an interface for monitoring the state of tension in real time and providing advice on how to relax. For example, it displays a notification when the user needs advice on how to relax. In this way, the user's state of tension can be monitored in real time and advice on how to relax can be provided, allowing the user to communicate in a more relaxed manner.

[0071] The support providing unit can analyze the content of utterances in real time and suggest what to say at an appropriate time. The support providing unit, for example, builds a system that analyzes the content of utterances made by a user in real time. For example, the support providing unit converts the content of utterances into text using speech recognition technology and analyzes it. The support providing unit can also develop an algorithm for analyzing the content of utterances in real time and suggesting what to say at an appropriate time. For example, the support providing unit analyzes the content of utterances made by a user and suggests what to say next. The support providing unit can also provide an interface for analyzing the content of utterances in real time and suggesting what to say at an appropriate time. For example, a notification is displayed when the user needs what to say next. In this way, the user can communicate more effectively by analyzing the content of utterances made by a user in real time and suggesting what to say at an appropriate time.

[0072] The support providing unit can use the emotion estimation function to enable the virtual alter ego to provide appropriate support according to the user's emotional state. The support providing unit can, for example, use the emotion estimation function to build a system that monitors the user's emotional state in real time. For example, the support providing unit can analyze the user's facial expressions and voice and calculate an emotion score. The support providing unit can also use the emotion estimation function to develop an algorithm for the virtual alter ego to provide appropriate support according to the user's emotional state. For example, support is provided when the user's emotion score exceeds a certain threshold. The support providing unit can also use the emotion estimation function to provide an interface for the virtual alter ego to provide appropriate support according to the user's emotional state. For example, a notification is displayed when the user needs support. This allows the user to communicate more effectively by using the emotion estimation function to enable the virtual alter ego to provide appropriate support according to the user's emotional state.

[0073] The support providing unit can add a function to switch invisible support according to different situations. For example, the support providing unit provides situation-specific settings in order to switch invisible support according to different situations. For example, it can allow the user to select settings for business meetings and settings for casual conversations. The support providing unit can also develop an algorithm for switching invisible support according to different situations. For example, it can automatically switch support content according to the situation. The support providing unit can also provide an interface for switching invisible support according to different situations. For example, it can allow the user to select support content according to the situation. In this way, the user can receive more appropriate support by switching invisible support according to different situations.

[0074] The support providing unit can add a function that uses the emotion estimation function to allow the virtual avatar to provide appropriate advice according to the user's emotional state. The support providing unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional state in real time. For example, the support providing unit can analyze the user's facial expressions and voice and calculate an emotion score. The support providing unit can also use the emotion estimation function to develop an algorithm that allows the virtual avatar to provide appropriate advice according to the user's emotional state. For example, advice is provided when the user's emotion score exceeds a certain threshold. The support providing unit can also use the emotion estimation function to provide an interface that allows the virtual avatar to provide appropriate advice according to the user's emotional state. For example, a notification is displayed when the user needs advice. This allows the user to communicate more effectively by using the emotion estimation function to allow the virtual avatar to provide appropriate advice according to the user's emotional state.

[0075] The virtual avatar generation unit can analyze a communication style in detail and suggest optimal speaking styles and content. The virtual avatar generation unit, for example, collects and analyzes past speech content and tone to analyze a user's communication style in detail. For example, it identifies phrases and speech patterns frequently used by the user. The virtual avatar generation unit can also develop an algorithm for analyzing a communication style in detail and suggesting optimal speaking styles and content. For example, it can suggest optimal speaking styles and content based on the user's speech content and tone. The virtual avatar generation unit can also provide an interface for analyzing a communication style in detail and suggesting optimal speaking styles and content. For example, it allows the user to select speaking styles and content. In this way, the user's communication style can be analyzed in detail and optimal speaking styles and content can be suggested, allowing the user to communicate more naturally.

[0076] The virtual avatar generation unit can analyze communication history and support natural communication. The virtual avatar generation unit, for example, collects and analyzes the user's past communication history. For example, it analyzes the content, tone, and speaking patterns of what is said to support natural communication. The virtual avatar generation unit can also analyze the communication history and develop an algorithm to support natural communication. For example, it can suggest an optimal communication method based on the content and tone of what is said by the user. The virtual avatar generation unit can also analyze the communication history and provide an interface to support natural communication. For example, it can allow the user to select a communication method. In this way, by analyzing the user's past communication history and supporting natural communication, the user can communicate with more confidence.

[0077] The virtual avatar generation unit can use the emotion estimation function to enable the virtual avatar to provide appropriate feedback according to the emotional state. The virtual avatar generation unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional state in real time. For example, the virtual avatar generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The virtual avatar generation unit can also use the emotion estimation function to develop an algorithm for the virtual avatar to provide appropriate feedback according to the emotional state. For example, feedback is provided when the user's emotion score exceeds a certain threshold. The virtual avatar generation unit can also use the emotion estimation function to provide an interface for the virtual avatar to provide appropriate feedback according to the emotional state. For example, a notification is displayed when the user needs feedback. This allows the virtual avatar to provide appropriate feedback according to the user's emotional state using the emotion estimation function, allowing the user to communicate more naturally.

[0078] The virtual avatar generation unit can add a function for switching natural communication depending on different situations. For example, the virtual avatar generation unit provides settings for each situation in order to switch natural communication depending on different situations. For example, settings for a business meeting and settings for a casual conversation can be selected. The virtual avatar generation unit can also develop an algorithm for switching natural communication depending on different situations. For example, the virtual avatar generation unit can automatically switch communication methods depending on the situation. The virtual avatar generation unit can also provide an interface for switching natural communication depending on different situations. For example, the virtual avatar generation unit can allow the user to select a communication method depending on the situation. This allows the user to communicate more appropriately by switching natural communication depending on different situations.

[0079] The virtual avatar generation unit can adapt natural communication to different languages ​​and cultures, supporting international communication. For example, the virtual avatar generation unit uses multilingual voice synthesis technology to adapt natural communication to different languages. For example, it can enable communication in multiple languages, such as English, French, and Chinese. The virtual avatar generation unit can also develop algorithms to adapt natural communication to different cultures. For example, it can provide a communication method that takes cultural customs and values ​​into consideration. The virtual avatar generation unit can also provide an interface to adapt natural communication to different languages ​​and cultures. For example, it can allow the user to select a communication method according to the language or culture. This allows natural communication to be adapted to different languages ​​and cultures, supporting international communication.

[0080] The personality analysis unit can compare the voice and personality analysis results with other users to identify similarities and differences. The personality analysis unit, for example, stores the user's voice and personality analysis results in a database and compares them with other users. For example, it analyzes similarities and differences based on voice features and personality traits. The personality analysis unit can also develop an algorithm for comparing the voice and personality analysis results with other users to identify similarities and differences. For example, it calculates the similarity of voice features and personality traits. The personality analysis unit can also provide an interface for comparing the voice and personality analysis results with other users to identify similarities and differences. For example, it allows the user to check similarities and differences with other users. This makes it possible to identify similarities and differences by comparing the user's voice and personality analysis results with other users, thereby enabling more personalized support.

[0081] The personality analysis unit can compare the voice and personality analysis results in different situations to understand personality traits according to the situation. For example, the personality analysis unit compares the user's voice and personality analysis results in work and private situations. For example, it analyzes voice data during work and voice data during private time separately. The personality analysis unit can also compare the voice and personality analysis results in different situations to develop an algorithm for understanding personality traits according to the situation. For example, it analyzes the differences in personality traits during work and private time. The personality analysis unit can also provide an interface for comparing the voice and personality analysis results in different situations to understand personality traits according to the situation. For example, it allows the user to check personality traits for each situation. In this way, by comparing the user's voice and personality analysis results in different situations, personality traits according to the situation can be understood and more appropriate support can be provided.

[0082] The personality analysis unit can use the emotion estimation function to analyze the emotional state and suggest an optimal personality diagnostic test. For example, the personality analysis unit can use the emotion estimation function to analyze the emotional state from the user's voice data and suggest an optimal personality diagnostic test based on the results. For example, if the emotion score is high, the personality analysis unit can suggest a test that evaluates positive personality traits. The personality analysis unit can also use the emotion estimation function to analyze the emotional state from the user's facial expression data and suggest an optimal personality diagnostic test based on the results. For example, the emotion estimation function can analyze changes in facial expressions and calculate an emotion score. The personality analysis unit can also use the emotion estimation function to analyze the emotional state from the user's biometric data and suggest an optimal personality diagnostic test based on the results. For example, the emotion estimation function can analyze the heart rate or electrodermal activity and calculate an emotion score. This enables more accurate personality diagnosis by using the emotion estimation function to analyze the user's emotional state and suggest an optimal personality diagnostic test.

[0083] The personality analysis unit can also analyze background sounds and environmental sounds when collecting voice data and estimate the living environment. For example, the personality analysis unit extracts acoustic features to analyze background sounds and environmental sounds when collecting voice data. For example, it analyzes the type and volume of background sounds to estimate the user's living environment. The personality analysis unit can also develop an algorithm for analyzing background sounds and environmental sounds when collecting voice data and estimating the living environment. For example, it estimates the living environment based on the type and volume of background sounds. The personality analysis unit can also provide an interface for analyzing background sounds and environmental sounds when collecting voice data and estimating the living environment. For example, it allows the user to check the estimated results of the living environment. In this way, by analyzing background sounds and environmental sounds when collecting voice data, the user's living environment can be estimated and more appropriate support can be provided.

[0084] The personality analysis unit can also take social media activities and online behavior into consideration when collecting information about personality. For example, the personality analysis unit collects a user's social media activities and online behavior and analyzes them as information about personality. For example, it analyzes the content of posts and patterns of likes. The personality analysis unit can also develop an algorithm for taking social media activities and online behavior into consideration when collecting information about personality. For example, it can analyze the content of social media posts and patterns of online behavior. The personality analysis unit can also provide an interface for taking social media activities and online behavior into consideration when collecting information about personality. For example, it can allow the user to check the analysis results of social media activities and online behavior. This allows more accurate personality diagnosis by taking social media activities and online behavior into consideration when collecting information about personality.

[0085] The personality analysis unit can use the emotion estimation function to track emotional fluctuations from voice data in real time and reflect them in the personality analysis. The personality analysis unit, for example, uses the emotion estimation function to track emotional fluctuations of a user from voice data in real time. For example, it analyzes the tone and rhythm of the voice and calculates an emotion score. The personality analysis unit can also use the emotion estimation function to develop an algorithm for tracking emotional fluctuations from voice data in real time and reflecting them in the personality analysis. For example, it analyzes fluctuations in the emotion score and reflects them in the personality analysis. The personality analysis unit can also use the emotion estimation function to provide an interface for tracking emotional fluctuations from voice data in real time and reflecting them in the personality analysis. For example, it allows the user to check emotional fluctuations. This enables more accurate personality diagnosis by using the emotion estimation function to track emotional fluctuations from voice data in real time and reflecting them in the personality analysis.

[0086] The personality analysis unit can collect voice data and personality information at different times of the day and in different situations, and grasp personality traits according to the time of day and the situation. The personality analysis unit, for example, collects and analyzes voice data and personality information at different times of the day and in different situations. For example, it compares voice data from the morning and evening to evaluate personality traits for each time of day. The personality analysis unit can also collect voice data and personality information at different times of the day and in different situations, and develop an algorithm for grasping personality traits according to the time of day and the situation. For example, it analyzes differences in personality traits according to the time of day and the situation. The personality analysis unit can also collect voice data and personality information at different times of the day and in different situations, and provide an interface for grasping personality traits according to the time of day and the situation. For example, it allows the user to check personality traits according to the time of day and the situation. This enables more accurate personality diagnosis by collecting voice data and personality information at different times of the day and in different situations, and grasping personality traits according to the time of day and the situation.

[0087] The personality analysis unit can use the emotion estimation function to analyze the emotional state from the voice data and suggest an optimal personality diagnostic test. For example, the personality analysis unit can use the emotion estimation function to analyze the user's emotional state from the voice data and suggest an optimal personality diagnostic test based on the results. For example, if the emotion score is high, a test that evaluates positive personality traits can be suggested. The personality analysis unit can also use the emotion estimation function to analyze the emotional state from the voice data and develop an algorithm for suggesting an optimal personality diagnostic test based on the results. For example, the personality analysis unit can select a personality diagnostic test based on the emotion score. The personality analysis unit can also use the emotion estimation function to analyze the emotional state from the voice data and provide an interface for suggesting an optimal personality diagnostic test based on the results. For example, the personality analysis unit can allow the user to select a personality diagnostic test. This enables a more accurate personality diagnosis by using the emotion estimation function to analyze the user's emotional state from the voice data and suggesting an optimal personality diagnostic test.

[0088] The virtual alter-ego generation unit can customize the appearance and tone of voice of the virtual alter-ego to suit the user's preferences. The virtual alter-ego generation unit, for example, provides the user with options for customizing the appearance of the virtual alter-ego to suit the user's preferences. For example, it provides an interface that allows the user to select hairstyle, clothing, and facial features. The virtual alter-ego generation unit can also develop an algorithm for customizing the tone of voice of the virtual alter-ego to suit the user's preferences. For example, it can adjust the pitch, intensity, and emotional expression. The virtual alter-ego generation unit can also provide an interface for customizing the appearance and tone of voice of the virtual alter-ego to suit the user's preferences. For example, it allows the user to select the appearance and tone of voice. In this way, the appearance and tone of voice of the virtual alter-ego can be customized to suit the user's preferences, thereby providing a virtual alter-ego that is more familiar to the user.

[0089] The virtual alter-ego generation unit can optimize the behavior pattern of the virtual alter-ego based on the user's behavior history. The virtual alter-ego generation unit, for example, collects the user's past behavior history and optimizes the virtual alter-ego's behavior pattern. For example, it analyzes the user's statements and behavior patterns and reflects them in the virtual alter-ego. The virtual alter-ego generation unit can also develop an algorithm for optimizing the virtual alter-ego's behavior pattern based on the user's behavior history. For example, it performs predictions and behavior simulations based on the past behavior history. The virtual alter-ego generation unit can also provide an interface for optimizing the virtual alter-ego's behavior pattern based on the user's behavior history. For example, it allows the user to select a behavior pattern. In this way, the virtual alter-ego's behavior pattern can be optimized based on the user's past behavior history, thereby providing a more suitable virtual alter-ego for the user.

[0090] The virtual alter-ego generation unit can use the emotion estimation function to design the virtual alter-ego to show an appropriate reaction depending on the emotional state. For example, the virtual alter-ego generation unit uses the emotion estimation function to design the virtual alter-ego to show an appropriate reaction depending on the user's emotional state. For example, if the user is nervous, the virtual alter-ego provides advice to help the user relax. The virtual alter-ego generation unit can also use the emotion estimation function to develop an algorithm for the virtual alter-ego to show an appropriate reaction depending on the emotional state. For example, the reaction is selected based on an emotion score. The virtual alter-ego generation unit can also use the emotion estimation function to provide an interface for the virtual alter-ego to show an appropriate reaction depending on the emotional state. For example, the virtual alter-ego generation unit allows the user to select a reaction. In this way, by using the emotion estimation function to design the virtual alter-ego to show an appropriate reaction depending on the user's emotional state, the user can communicate more effectively.

[0091] The virtual alter-ego generation unit can add a function for switching the virtual alter-ego according to different situations. For example, the virtual alter-ego generation unit provides settings for each situation in order to switch the virtual alter-ego according to different situations. For example, it can allow the user to select settings for a business meeting and settings for a casual conversation. The virtual alter-ego generation unit can also develop an algorithm for switching the virtual alter-ego according to different situations. For example, it can automatically switch the behavior or speaking style of the virtual alter-ego according to the situation. The virtual alter-ego generation unit can also provide an interface for switching the virtual alter-ego according to different situations. For example, it can allow the user to select settings for the virtual alter-ego according to the situation. This allows the user to receive more appropriate support by switching the virtual alter-ego according to different situations.

[0092] The virtual avatar generation unit can use the emotion estimation function to add a function that allows the virtual avatar to provide appropriate advice according to the emotional state of the user. For example, the virtual avatar generation unit can use the emotion estimation function to add a function that allows the virtual avatar to provide appropriate advice according to the emotional state of the user. For example, if the user is feeling stressed, the virtual avatar generation unit can provide advice to relax. The virtual avatar generation unit can also use the emotion estimation function to develop an algorithm for the virtual avatar to provide appropriate advice according to the emotional state. For example, the advice can be selected based on an emotion score. The virtual avatar generation unit can also use the emotion estimation function to provide an interface that allows the virtual avatar to provide appropriate advice according to the emotional state. For example, the virtual avatar generation unit can allow the user to select advice. This allows the virtual avatar to use the emotion estimation function to provide appropriate advice according to the user's emotional state, allowing the user to communicate more effectively.

[0093] The virtual avatar generation unit can customize the virtual avatar according to different industries or occupations to provide specialized support. For example, the virtual avatar generation unit reflects industry-specific knowledge and skills in order to customize the virtual avatar according to different industries or occupations. For example, a virtual avatar for the medical industry incorporates medical terminology and procedures. The virtual avatar generation unit can also develop algorithms for customizing the virtual avatar according to different industries or occupations. For example, specialized support according to the industry or occupation is provided. The virtual avatar generation unit can also provide an interface for customizing the virtual avatar according to different industries or occupations. For example, the user can select virtual avatar settings according to the industry or occupation. This allows the user to receive more specialized support by customizing the virtual avatar according to different industries or occupations.

[0094] The virtual avatar generation unit can adapt the virtual avatar to different age groups and genders, thereby making it suitable for a wide range of users. For example, the virtual avatar generation unit incorporates language and behavior patterns appropriate for different ages to adapt the virtual avatar to different age groups. For example, casual language is used for younger people, and polite language is used for older people. The virtual avatar generation unit can also develop an algorithm for adapting the virtual avatar to different age groups and genders. For example, it can provide a communication method appropriate for age and gender. The virtual avatar generation unit can also provide an interface for adapting the virtual avatar to different age groups and genders. For example, it can allow the user to select settings for the virtual avatar appropriate for age group and gender. This allows the virtual avatar to adapt to a wide range of users by adapting the virtual avatar to different age groups and genders.

[0095] The virtual avatar generation unit can use the emotion estimation function to add a function that allows the virtual avatar to provide appropriate advice according to the emotional state of the user. For example, the virtual avatar generation unit can use the emotion estimation function to add a function that allows the virtual avatar to provide appropriate advice according to the emotional state of the user. For example, if the user is feeling stressed, the virtual avatar generation unit can provide advice to relax. The virtual avatar generation unit can also use the emotion estimation function to develop an algorithm for the virtual avatar to provide appropriate advice according to the emotional state. For example, the advice can be selected based on an emotion score. The virtual avatar generation unit can also use the emotion estimation function to provide an interface that allows the virtual avatar to provide appropriate advice according to the emotional state. For example, the virtual avatar generation unit can allow the user to select advice. This allows the virtual avatar to use the emotion estimation function to provide appropriate advice according to the user's emotional state, allowing the user to communicate more effectively.

[0096] The presentation execution unit can customize the content of a presentation for different industries or occupations and provide professional support. For example, the presentation execution unit reflects industry-specific knowledge and skills to customize the content of a presentation for different industries or occupations. For example, medical terminology and procedures are incorporated into a presentation for the medical industry. The presentation execution unit can also develop an algorithm for customizing the content of a presentation for different industries or occupations. For example, professional support is provided for different industries or occupations. The presentation execution unit can also provide an interface for customizing the content of a presentation for different industries or occupations. For example, the user can select presentation settings according to the industry or occupation. This allows the user to receive more professional support by customizing the content of the presentation for different industries or occupations.

[0097] The presentation execution unit can adapt the content of a presentation to different languages ​​and cultures, supporting international communication. For example, the presentation execution unit uses multilingual speech synthesis technology to adapt the content of a presentation to different languages. For example, it can make it possible to give presentations in multiple languages, such as English, French, and Chinese. The presentation execution unit can also develop algorithms to adapt the content of a presentation to different cultures. For example, it can provide a presentation method that takes cultural customs and values ​​into consideration. The presentation execution unit can also provide an interface to adapt the content of a presentation to different languages ​​and cultures. For example, it can allow a user to select a presentation method appropriate for the language or culture. This allows international communication to be supported by adapting the content of a presentation to different languages ​​and cultures.

[0098] The support providing unit can monitor the state of tension in real time and provide advice on how to relax. The support providing unit, for example, builds a system that monitors the user's state of tension in real time. For example, it analyzes the user's facial expressions and voice and calculates a tension score. The support providing unit can also develop an algorithm for monitoring the state of tension in real time and providing advice on how to relax. For example, it provides advice when the user's tension score exceeds a certain threshold. The support providing unit can also provide an interface for monitoring the state of tension in real time and providing advice on how to relax. For example, it displays a notification when the user needs advice on how to relax. In this way, the user's state of tension can be monitored in real time and advice on how to relax can be provided, allowing the user to communicate in a more relaxed manner.

[0099] The support providing unit can analyze the content of utterances in real time and suggest what to say at an appropriate time. The support providing unit, for example, builds a system that analyzes the content of utterances made by a user in real time. For example, the support providing unit converts the content of utterances into text using speech recognition technology and analyzes it. The support providing unit can also develop an algorithm for analyzing the content of utterances in real time and suggesting what to say at an appropriate time. For example, the support providing unit analyzes the content of utterances made by a user and suggests what to say next. The support providing unit can also provide an interface for analyzing the content of utterances in real time and suggesting what to say at an appropriate time. For example, a notification is displayed when the user needs what to say next. In this way, the user can communicate more effectively by analyzing the content of utterances made by a user in real time and suggesting what to say at an appropriate time.

[0100] The support providing unit can use the emotion estimation function to enable the virtual alter ego to provide appropriate support according to the user's emotional state. The support providing unit can, for example, use the emotion estimation function to build a system that monitors the user's emotional state in real time. For example, the support providing unit can analyze the user's facial expressions and voice and calculate an emotion score. The support providing unit can also use the emotion estimation function to develop an algorithm for the virtual alter ego to provide appropriate support according to the user's emotional state. For example, support is provided when the user's emotion score exceeds a certain threshold. The support providing unit can also use the emotion estimation function to provide an interface for the virtual alter ego to provide appropriate support according to the user's emotional state. For example, a notification is displayed when the user needs support. This allows the user to communicate more effectively by using the emotion estimation function to enable the virtual alter ego to provide appropriate support according to the user's emotional state.

[0101] The support providing unit can add a function to switch invisible support according to different situations. For example, the support providing unit provides situation-specific settings in order to switch invisible support according to different situations. For example, it can allow the user to select settings for business meetings and settings for casual conversations. The support providing unit can also develop an algorithm for switching invisible support according to different situations. For example, it can automatically switch support content according to the situation. The support providing unit can also provide an interface for switching invisible support according to different situations. For example, it can allow the user to select support content according to the situation. In this way, the user can receive more appropriate support by switching invisible support according to different situations.

[0102] The support providing unit can adapt the invisible support to different languages ​​and cultures to support international communication. For example, the support providing unit uses multilingual speech synthesis technology to adapt the invisible support to different languages. For example, it can provide support in multiple languages ​​such as English, French, and Chinese. The support providing unit can also develop algorithms to adapt the invisible support to different cultures. For example, it can provide a support method that takes cultural customs and values ​​into consideration. The support providing unit can also provide an interface to adapt the invisible support to different languages ​​and cultures. For example, it can allow the user to select a support method according to the language or culture. In this way, it is possible to support international communication by adapting the invisible support to different languages ​​and cultures.

[0103] The support providing unit can add a function that uses the emotion estimation function to allow the virtual avatar to provide appropriate advice according to the user's emotional state. The support providing unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional state in real time. For example, the support providing unit can analyze the user's facial expressions and voice and calculate an emotion score. The support providing unit can also use the emotion estimation function to develop an algorithm that allows the virtual avatar to provide appropriate advice according to the user's emotional state. For example, advice is provided when the user's emotion score exceeds a certain threshold. The support providing unit can also use the emotion estimation function to provide an interface that allows the virtual avatar to provide appropriate advice according to the user's emotional state. For example, a notification is displayed when the user needs advice. This allows the user to communicate more effectively by using the emotion estimation function to allow the virtual avatar to provide appropriate advice according to the user's emotional state.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The VirtualPitch Partner system can also include a gesture recognition unit that recognizes user gestures and supports the progress of the presentation. For example, the gesture recognition unit can detect a user raising their hand and issue an instruction to proceed to the next slide. The gesture recognition unit can also detect a user pointing their finger to emphasize a particular point. The gesture recognition unit can also detect a user waving their hand to indicate the end of the presentation. This allows the user to control the presentation using natural gestures, allowing for a smoother progress.

[0106] The VirtualPitch Partner system can also include an eye-tracking unit that tracks the user's gaze and adjusts the content of the presentation based on the user's gaze. For example, if the user looks at a particular slide for a long time, the eye-tracking unit can explain the content of that slide in detail. The eye-tracking unit can also move on to the next topic when the user moves their gaze. The eye-tracking unit can also display information related to a specific object when the user directs their gaze at that object. This allows the user to control the presentation using only their gaze, making it more intuitive.

[0107] The Virtual Pitch Partner system can further include a body temperature monitoring unit that monitors the user's body temperature and adjusts the progress of the presentation based on changes in body temperature. For example, if the user's body temperature rises, the body temperature monitoring unit can suggest relaxation techniques to relieve tension. If the user's body temperature drops, the body temperature monitoring unit can also provide advice on how to improve concentration. If the user's body temperature remains stable, the body temperature monitoring unit can also provide support to ensure the smooth progress of the presentation. This allows the user to receive support according to changes in body temperature, enabling a more effective presentation.

[0108] The Virtual Pitch Partner system can also include a gait analysis unit that analyzes the user's walking pattern and supports the progress of the presentation. For example, the gait analysis unit can analyze the user's walking pattern on stage and suggest the optimal movement route. The gait analysis unit can also emphasize the presentation content at a specific location if the user stops at that location. The gait analysis unit can also provide immediate support if the user loses balance while walking. This allows the user to receive support based on their walking pattern, allowing them to deliver their presentation with greater confidence.

[0109] The Virtual Pitch Partner system may further include an outfit recognition unit that recognizes a user's outfit and adjusts the content and style of the presentation. For example, if the user is wearing formal attire, the outfit recognition unit may make the tone of the presentation more professional. Alternatively, if the user is wearing casual attire, the outfit recognition unit may make the tone of the presentation more relaxed. The outfit recognition unit may also adjust the visual style of the presentation to match the user's outfit. This allows the user to give an optimal presentation based on their outfit, creating a more consistent impression.

[0110] The Virtual Pitch Partner system can estimate a user's emotions and adjust the content of their presentation based on the estimated emotions. For example, if the user is nervous, it can provide advice on how to relax. If the user is confident, it can provide support to help the presentation proceed smoothly. If the user is tired, it can suggest taking a break. This allows users to receive support based on their emotional state, enabling more effective presentations.

[0111] The Virtual Pitch Partner system can estimate the user's emotions and adjust the facial expression of the virtual avatar based on the estimated emotions. For example, if the user is happy, the virtual avatar will also smile. If the user is surprised, the virtual avatar can also have a surprised expression. If the user is sad, the virtual avatar can also have a sad expression. This allows the virtual avatar to empathize with the user's emotions, enabling more natural communication.

[0112] The Virtual Pitch Partner system can estimate a user's emotions and adjust the content of presentation slides based on the estimated emotions. For example, if a user is nervous, the content of the slides can be made simpler. If a user is confident, detailed information can be provided. If a user is tired, visually stimulating slides can be provided. This allows users to receive slide content that matches their emotional state, enabling more effective presentations.

[0113] The Virtual Pitch Partner system can estimate a user's emotions and adjust the tone of their presentation based on the estimated emotions. For example, if the user is nervous, the system can calm their tone. If the user is confident, the system can strengthen their tone. If the user is tired, the system can refresh their tone. This allows users to receive a tone of voice that matches their emotional state, enabling more effective presentations.

[0114] The Virtual Pitch Partner system can estimate a user's emotions and adjust the presentation speed based on the estimated emotions. For example, if the user is nervous, the system can slow down the speed. If the user is confident, the system can speed up the speed. If the user is tired, the system can keep the speed constant. This allows users to give presentations at a speed that suits their emotional state, resulting in more effective presentations.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The voice data collection unit collects voice data of the user. For example, the voice data collection unit may record the user's voice using a microphone. Voice data may also be collected using the microphone of a smartphone or computer. Voice data may also be collected by uploading a voice file. Step 2: The personality analysis unit analyzes the voice data collected by the voice data collection unit. For example, it analyzes the tone and rhythm of the voice data to estimate the user's personality traits. It can also analyze the content of the voice data to diagnose the user's personality. It can also analyze the user's past speech history to track changes and growth in personality. Step 3: The virtual avatar generation unit generates a virtual avatar based on the results of the analysis by the personality analysis unit. For example, it sets an optimal character based on the user's personality traits. It can also generate a virtual avatar taking into account the user's past experiences of success and failure. It can also set a character that reflects the user's goals and hopes and increases their motivation. Step 4: The presentation execution unit gives a presentation through the virtual avatar generated by the virtual avatar generation unit. For example, the virtual avatar speaks confidently based on the presentation content entered by the user. It can also monitor the user's emotional state in real time while the presentation is in progress and provide support at the appropriate time. Furthermore, using an emotion estimation function, the virtual avatar can provide appropriate feedback depending on the user's emotional state during the presentation. Step 5: The support provider provides invisible support during the presentation given by the presentation execution unit. For example, it can monitor the user's state of tension in real time and provide advice on how to relax. It can also analyze the content of the user's speech in real time and suggest what to say at the appropriate time. Furthermore, it is possible for the virtual avatar to provide appropriate support depending on the user's emotional state using an emotion estimation function.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0136] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice data collection unit that collects voice data of a user; a personality analysis unit that analyzes the voice data collected by the voice data collection unit; a virtual alter-ego generation unit that generates a virtual alter-ego based on the results of the analysis by the personality analysis unit; a presentation execution unit that performs a presentation using the virtual avatar generated by the virtual avatar generation unit; a support providing unit that provides support during the presentation performed by the presentation executing unit. A system characterized by:

2. The personality analysis unit The tone and rhythm of the voice data are analyzed in detail to estimate the stress level or emotional state. The system of claim 1 .

3. The virtual alter ego generation unit Considering successful and unsuccessful experiences, choose the best character The system of claim 1 .

4. The support providing unit Monitors tension levels in real time and provides relaxation advice The system of claim 1 .

5. The personality analysis unit Using emotion estimation function, we analyze your emotional state and suggest the best personality test The system of claim 1 .

6. The virtual alter ego generation unit Using emotion estimation functionality, we design virtual avatars to respond appropriately to their emotional state. The system of claim 1 .

7. The presentation execution unit Customize the content of the presentations for different industries and job roles and provide professional support The system of claim 1 .

8. The support providing unit Add a function that uses emotion estimation to allow the virtual avatar to provide appropriate advice depending on the user's emotional state. The system of claim 1 .

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