System

Abstract

An object of the system according to the embodiment is to allow a user to objectively evaluate the content of his / her own statement or mail and to perform self-growth or self-counseling.SOLUTION: A system includes an analysis unit, an advice providing unit, and a feedback unit. The analysis unit analyzes the speech of the user and the content of the mail. The advice providing unit provides objective advice on the basis of a result analyzed by the analysis unit. The feedback unit supports self-growth and self-counseling of the user based on the advice provided by the advice providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult for users to objectively evaluate the content of their own comments and emails and to engage in self-improvement and self-counseling.

[0005] The system according to the embodiment aims to enable users to objectively evaluate the content of their own statements and emails, and to promote self-improvement and self-counseling. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an advice providing unit, and a feedback unit. The analysis unit analyzes the content of a user's comments and emails. The advice providing unit provides objective advice based on the results of the analysis by the analysis unit. The feedback unit supports the user's self-improvement and self-counseling based on the advice provided by the advice providing unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to objectively evaluate the content of his / her own comments and emails, and to engage in self-improvement and self-counseling. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The self-evaluation system according to an embodiment of the present invention uses a generation AI to analyze the content of a user's comments and emails and provide objective advice. This allows the self-evaluation system to support the user's self-improvement and self-counseling, and to discover new talents.

[0029] The self-assessment system according to the embodiment includes an analysis unit, an advice providing unit, and a feedback unit. The analysis unit analyzes the content of a user's statements or emails. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze the user's emotions using emotion analysis technology. The analysis unit can also extract important information using keyword extraction technology. The advice providing unit provides objective advice based on the results of the analysis by the analysis unit. For example, the advice providing unit provides psychological advice. The advice providing unit can also provide a guideline for action. The advice providing unit can also make problem-solving suggestions. The feedback unit supports the user's self-improvement and self-counseling based on the advice provided by the advice providing unit. For example, the feedback unit supports the user's goal setting. The feedback unit can also support progress management. The feedback unit can also provide a method of self-assessment. In this way, the self-assessment system according to the embodiment can support the user's self-improvement and self-counseling.

[0030] The analysis unit can analyze long-term trends by referencing the user's past comments and email history. For example, the analysis unit stores the user's past comments and email history in a database, and the generation AI refers to it to analyze long-term trends. For example, it identifies phrases and topics frequently used by the user and reflects them in the analysis results. The analysis unit also analyzes the user's communication style and patterns based on the user's past comments and email history. For example, it identifies how the user reacts in specific situations and reflects them in the analysis results. The analysis unit also tracks the user's growth and changes by referencing the user's past comments and email history and analyzing long-term trends. For example, it identifies how the user has improved their communication skills and reflects them in the analysis results. This makes it possible to track the user's growth and changes by referencing the user's past comments and email history and analyzing long-term trends.

[0031] The analysis unit can compare the user's statements and email content with data from other users and perform a relative evaluation. For example, the analysis unit compares the user's statements and email content with data from other users and performs a relative evaluation. For example, it evaluates the originality and persuasiveness of the user's statements by referring to other users' reactions and opinions on the same topic. The analysis unit also identifies strengths and weaknesses in the content of the user's statements and email content by comparing with data from other users. For example, it points out areas for improvement in the user's statements based on phrases and expressions frequently used by other users. The analysis unit also compares the user's statements and email content with data from other users and performs a relative evaluation to objectively evaluate the user's communication skills. For example, it evaluates the effectiveness and influence of the user's statements based on data from other users. In this way, the user's strengths and weaknesses can be objectively evaluated by comparing with data from other users and performing a relative evaluation.

[0032] The analysis unit can perform multimodal analysis by including audio data and video data in addition to the content of a user's utterances and emails. For example, the analysis unit can analyze audio data and video data in addition to the content of a user's utterances and emails. For example, the analysis unit can analyze the user's tone of voice and facial expressions to more accurately grasp their emotions and intentions. By performing multimodal analysis, the analysis unit can gain a deeper understanding of the content of a user's utterances and emails. For example, the analysis unit can analyze the intensity of the user's emotions from audio data and analyze the user's non-verbal communication from video data. By including audio data and video data in the analysis, the analysis unit can analyze the content of a user's utterances and emails more comprehensively. For example, the analysis unit can analyze the true meaning and emotions of the utterances based on the user's tone of voice and facial expressions. In this way, by including audio data and video data in the analysis, the content of a user's utterances and emails can be analyzed more comprehensively.

[0033] The analysis unit can also analyze statements and emails in different languages, allowing for analysis from an international perspective. The analysis unit, for example, analyzes the content of a user's statements and emails in different languages. For example, the analysis unit supports multiple languages, such as English, Japanese, and French, and performs analysis from an international perspective. Furthermore, by including statements and emails in different languages ​​in the analysis target, the analysis unit can evaluate the user's communication skills from multiple angles. For example, the analysis unit can analyze differences in expressions and nuances in different languages ​​to evaluate the user's multilingual ability. Furthermore, in order to perform analysis from an international perspective, the analysis unit can include statements and emails in different languages ​​in the analysis target. For example, the analysis unit can evaluate the diversity and adaptability of a user's statements based on data from users with different cultures and backgrounds. In this way, by including statements and emails in different languages ​​in the analysis target, the user's communication skills can be evaluated from multiple angles.

[0034] The advice providing unit can refer to the history of past advice and evaluate the effectiveness of the advice. For example, the advice providing unit stores the history of past advice in a database, and the generation AI refers to it to evaluate the effectiveness of the advice. For example, the current advice is adjusted based on the results of advice the user received in the past. The advice providing unit also tracks the user's growth and changes based on the advice history and evaluates the effectiveness of the advice. For example, it analyzes the results of advice the user received in the past and provides effective advice. The advice providing unit also refers to the history of past advice and evaluates the effectiveness of the advice, thereby providing more effective advice to the user. For example, the current advice is optimized based on the results of advice the user received in the past. In this way, more effective advice can be provided by referring to the history of past advice and evaluating the effectiveness of the advice.

[0035] The advice providing unit can compare the user's data with the data of other users and provide relative advice. The advice providing unit, for example, provides relative advice to the user by comparing with the data of other users. For example, specific advice is given to the user based on success stories of other users in the same situation. The advice providing unit also refers to the data of other users and relatively evaluates the user's strengths and weaknesses. For example, effective advice is provided to the user based on how other users solved problems. The advice providing unit also provides relative advice to the user by comparing with the data of other users. For example, specific areas for improvement are pointed out to the user based on the data of other users working on the same problem. In this way, by comparing with the data of other users and providing relative advice, the user's strengths and weaknesses can be objectively evaluated.

[0036] The advice providing unit can provide advice that is visually easy to understand by using visual feedback. The advice providing unit, for example, uses visual feedback to provide advice that is visually easy to understand to the user. For example, the advice providing unit visually shows the user's progress and areas for improvement by using graphs and charts. Furthermore, the advice providing unit uses visual feedback to enable the user to intuitively understand the advice. For example, the advice content is visually explained by using animations and infographics. Furthermore, the advice providing unit uses visual feedback to provide advice that is visually easy to understand to the user. For example, the user's actions and performance are visually shown and specific areas for improvement are pointed out. In this way, by using visual feedback, the advice can be intuitively understood by the user.

[0037] The advice providing unit can provide advice in different languages ​​and give advice from an international perspective. The advice providing unit, for example, provides advice in different languages ​​and gives advice from an international perspective. For example, the advice providing unit supports multiple languages ​​such as English, Japanese, and French and provides appropriate advice to the user. The advice providing unit also evaluates the user's multilingual ability by providing advice in different languages. For example, it analyzes differences in expressions and nuances in different languages ​​and provides appropriate advice to the user. The advice providing unit also provides advice in different languages ​​in order to give advice from an international perspective. For example, it provides appropriate advice to the user based on data of users with different cultures and backgrounds. In this way, by providing advice in different languages, it is possible to evaluate the user's multilingual ability and give advice from an international perspective.

[0038] The feedback unit can refer to the history of past feedback and evaluate the effectiveness of the feedback. For example, the feedback unit stores the history of past feedback in a database, and the generation AI refers to it to evaluate the effectiveness of the feedback. For example, the current feedback is adjusted based on the results of feedback the user received in the past. The feedback unit also tracks the growth and changes of the user based on the feedback history and evaluates the effectiveness of the feedback. For example, the feedback unit analyzes the results of feedback the user received in the past and provides effective feedback. The feedback unit also provides more effective feedback to the user by referring to the history of past feedback and evaluating the effectiveness of the feedback. For example, the current feedback is optimized based on the results of feedback the user received in the past. In this way, more effective feedback can be provided by referring to the history of past feedback and evaluating the effectiveness of the feedback.

[0039] The feedback unit can compare the user's data with the data of other users and provide relative feedback. The feedback unit, for example, provides relative feedback to the user by comparing with the data of other users. For example, specific feedback is provided to the user based on success stories of other users in the same situation. The feedback unit also refers to the data of other users and relatively evaluates the user's strengths and weaknesses. For example, effective feedback is provided to the user based on how other users solved a problem. The feedback unit also provides relative feedback to the user by comparing with the data of other users. For example, specific areas for improvement are pointed out to the user based on the data of other users working on the same problem. In this way, by comparing with the data of other users and providing relative feedback, the user's strengths and weaknesses can be objectively evaluated.

[0040] The feedback unit can provide visually easy-to-understand feedback using visual feedback. The feedback unit, for example, uses visual feedback to provide visually easy-to-understand feedback to the user. For example, the feedback unit visually shows the user's progress and areas for improvement using graphs and charts. Furthermore, the feedback unit uses visual feedback to enable the user to intuitively understand the feedback. For example, the feedback content is visually explained using animations and infographics. Furthermore, the feedback unit uses visual feedback to provide visually easy-to-understand feedback to the user. For example, the user's actions and performance are visually shown and specific areas for improvement are pointed out. In this way, the use of visual feedback can enable the user to intuitively understand the feedback.

[0041] The feedback unit may also provide feedback in different languages, enabling feedback from an international perspective. The feedback unit may provide feedback from an international perspective, for example, by providing feedback in different languages. For example, the feedback unit may support multiple languages, such as English, Japanese, and French, and provide appropriate feedback to the user. The feedback unit may also provide feedback in different languages ​​to evaluate the user's multilingual ability. For example, the feedback unit may analyze differences in expressions and nuances in different languages ​​and provide appropriate feedback to the user. The feedback unit may also provide feedback in different languages ​​to provide feedback from an international perspective. For example, the feedback unit may provide appropriate feedback to the user based on data on users with different cultures and backgrounds. In this way, the feedback unit may provide feedback in different languages ​​to evaluate the user's multilingual ability and provide feedback from an international perspective.

[0042] The feedback unit can refer to the history of past evaluations and evaluate the effectiveness of the evaluation. For example, the feedback unit stores the history of past evaluations in a database, and the generation AI refers to it to evaluate the effectiveness of the evaluation. For example, the current evaluation is adjusted based on the results of evaluations the user has received in the past. The feedback unit also tracks the user's growth and changes based on the evaluation history and evaluates the effectiveness of the evaluation. For example, it analyzes the results of evaluations the user has received in the past and provides an effective evaluation. The feedback unit also refers to the history of past evaluations and evaluates the effectiveness of the evaluation, thereby providing a more effective evaluation to the user. For example, the current evaluation is optimized based on the results of evaluations the user has received in the past. In this way, a more effective evaluation can be provided by referring to the history of past evaluations and evaluating the effectiveness of the evaluation.

[0043] The feedback unit can compare the user's data with the data of other users and perform a relative evaluation. The feedback unit, for example, performs a relative evaluation of the user by comparing with the data of other users. For example, the user's talent is evaluated based on success stories of other users in the same field. The feedback unit also refers to the data of other users and performs a relative evaluation of the user's strengths and weaknesses. For example, the user's talent is evaluated based on how other users demonstrate their talents. The feedback unit also performs a relative evaluation of the user by comparing with the data of other users. For example, the user's talent is evaluated based on the data of other users in the same field. In this way, by comparing with the data of other users and performing a relative evaluation, the user's strengths and weaknesses can be objectively evaluated.

[0044] The feedback unit can provide a visually easy-to-understand evaluation using visual feedback. The feedback unit, for example, uses visual feedback to provide a visually easy-to-understand evaluation to the user. For example, the evaluation results of the user's talent are visually shown using graphs or charts. Furthermore, the feedback unit uses visual feedback to enable the user to intuitively understand the evaluation. For example, the content of the evaluation is visually explained using animations or infographics. Furthermore, the feedback unit uses visual feedback to provide a visually easy-to-understand evaluation to the user. For example, the user's behavior or performance is visually shown to provide a specific evaluation of talent. In this way, by using visual feedback, the user can intuitively understand the evaluation.

[0045] The feedback unit also provides evaluations in different languages, allowing evaluation from an international perspective. The feedback unit, for example, provides evaluations in different languages, allowing evaluation from an international perspective. For example, the feedback unit supports multiple languages, such as English, Japanese, and French, and provides an appropriate evaluation to the user. The feedback unit also evaluates the user's multilingual ability by providing evaluations in different languages. For example, the feedback unit analyzes differences in expressions and nuances in different languages ​​and provides an appropriate evaluation to the user. The feedback unit also provides evaluations in different languages ​​in order to perform evaluations from an international perspective. For example, the feedback unit provides an appropriate evaluation to the user based on data of users with different cultures and backgrounds. In this way, by providing evaluations in different languages, the user's multilingual ability can be evaluated and evaluations can be performed from an international perspective.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit can include user behavioral data in the analysis targets in addition to the content of user comments and emails. For example, the analysis unit can analyze the user's web browsing history and app usage history to identify the user's interests and concerns. The analysis unit can also analyze the user's lifestyle and habits based on the user behavioral data. For example, the analysis unit can identify what activities the user engages in at what time of day and reflect this in the analysis results. By analyzing the user behavioral data, the analysis unit can identify the user's behavioral patterns and tendencies and provide more accurate advice. By including the user behavioral data in the analysis targets, the analysis unit can gain a deeper understanding of the user's interests, concerns, lifestyle, and habits, and provide more effective advice.

[0048] The analysis unit can include the user's physiological data in the analysis target in addition to the content of the user's comments and emails. For example, the analysis unit can analyze the user's heart rate and sleep data to identify the user's health condition and stress level. The analysis unit can also analyze the user's physical condition and health condition based on the physiological data. For example, the analysis unit can identify the time of day when the user is most likely to feel stressed and reflect this in the analysis results. The analysis unit can also identify the user's health condition and stress level by analyzing the user's physiological data, allowing for more accurate advice to be provided. By including the user's physiological data in the analysis target, the analysis unit can gain a deeper understanding of the user's health condition and stress level and provide more effective advice.

[0049] In addition to the content of a user's comments and emails, the analysis unit can include the user's social media data in the analysis target. For example, the analysis unit can analyze the user's social media posts and comments to identify the user's interests. The analysis unit can also analyze the user's communication style and network based on the social media data. For example, the analysis unit can identify the topics in which the user is interested and reflect this in the analysis results. By analyzing the user's social media data, the analysis unit can identify the user's interests, communication style, and network, and provide more accurate advice. In this way, by including the user's social media data in the analysis target, the user's interests, communication style, and network can be more deeply understood, and more effective advice can be provided.

[0050] The analysis unit can include the user's purchasing history in addition to the content of the user's comments and emails in the analysis target. For example, the analysis unit can analyze the user's online shopping history and in-store purchase history to identify the user's consumption trends and preferences. The analysis unit can also analyze the user's lifestyle and consumption patterns based on the purchase history. For example, the analysis unit can identify what products the user frequently purchases and reflect this in the analysis results. By analyzing the user's purchasing history, the analysis unit can identify the user's consumption trends and preferences and provide more accurate advice. By including the user's purchasing history in the analysis target, the analysis unit can gain a deeper understanding of the user's consumption trends, preferences, lifestyle, and consumption patterns, and provide more effective advice.

[0051] The analysis unit can include the user's location information data in the analysis target in addition to the content of the user's comments and emails. For example, the analysis unit can analyze the user's movement history and visited locations to identify the user's range of activity and movement patterns. The analysis unit can also analyze the user's lifestyle and movement patterns based on the location information data. For example, the analysis unit can identify the places the user frequently visits and reflect this in the analysis results. The analysis unit can also identify the user's range of activity and movement patterns by analyzing the user's location information data, allowing for more accurate advice to be provided. By including the user's location information data in the analysis target, the analysis unit can gain a deeper understanding of the user's range of activity, movement patterns, lifestyle, and movement patterns, and provide more effective advice.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The analysis unit analyzes the content of the user's comments and emails. For example, the analysis unit can use natural language processing technology to analyze text data and emotion analysis technology to analyze the user's emotions. It can also use keyword extraction technology to extract important information. Step 2: The advice provider provides objective advice based on the results of the analysis by the analyzer. For example, it can provide psychological advice, guidelines for action, and problem-solving suggestions. Step 3: The feedback unit supports the user's self-improvement and self-counseling based on the advice provided by the advice providing unit. For example, it can provide the user with methods for goal setting, progress management, and self-evaluation.

[0054] (Example 2) The self-evaluation system according to an embodiment of the present invention uses a generation AI to analyze the content of a user's comments and emails and provide objective advice. This allows the self-evaluation system to support the user's self-improvement and self-counseling, and to discover new talents.

[0055] The self-assessment system according to the embodiment includes an analysis unit, an advice providing unit, and a feedback unit. The analysis unit analyzes the content of a user's statements or emails. For example, the analysis unit analyzes text data using natural language processing technology. The analysis unit can also analyze the user's emotions using emotion analysis technology. The analysis unit can also extract important information using keyword extraction technology. The advice providing unit provides objective advice based on the results of the analysis by the analysis unit. For example, the advice providing unit provides psychological advice. The advice providing unit can also provide a guideline for action. The advice providing unit can also make problem-solving suggestions. The feedback unit supports the user's self-improvement and self-counseling based on the advice provided by the advice providing unit. For example, the feedback unit supports the user's goal setting. The feedback unit can also support progress management. The feedback unit can also provide a method of self-assessment. In this way, the self-assessment system according to the embodiment can support the user's self-improvement and self-counseling.

[0056] The analysis unit can use the emotion estimation function to track changes in the user's emotions and perform analysis based on the emotional fluctuations. The analysis unit uses the emotion estimation function to track changes in emotions, for example, when analyzing the content of a user's comments or emails. For example, when a user expresses anger or sadness, the intensity of that emotion is quantified and reflected in the analysis results. The analysis unit also uses the emotion estimation function to analyze emotional fluctuations from the content of the user's comments or emails and provide feedback based on the emotional changes. For example, if a user is feeling stressed, the analysis unit identifies the cause and provides appropriate advice. The analysis unit also uses the emotion estimation function to track changes in emotions in real time when analyzing the content of a user's comments or emails and performs analysis based on the emotional fluctuations. For example, when a user expresses positive emotions, the intensity of that emotion is quantified and reflected in the analysis results. In this way, by tracking changes in the user's emotions and performing analysis based on the emotional fluctuations, more accurate advice can be provided.

[0057] The analysis unit can analyze long-term trends by referencing the user's past comments and email history. For example, the analysis unit stores the user's past comments and email history in a database, and the generation AI refers to it to analyze long-term trends. For example, it identifies phrases and topics frequently used by the user and reflects them in the analysis results. The analysis unit also analyzes the user's communication style and patterns based on the user's past comments and email history. For example, it identifies how the user reacts in specific situations and reflects them in the analysis results. The analysis unit also tracks the user's growth and changes by referencing the user's past comments and email history and analyzing long-term trends. For example, it identifies how the user has improved their communication skills and reflects them in the analysis results. This makes it possible to track the user's growth and changes by referencing the user's past comments and email history and analyzing long-term trends.

[0058] The analysis unit can compare the user's statements and email content with data from other users and perform a relative evaluation. For example, the analysis unit compares the user's statements and email content with data from other users and performs a relative evaluation. For example, it evaluates the originality and persuasiveness of the user's statements by referring to other users' reactions and opinions on the same topic. The analysis unit also identifies strengths and weaknesses in the content of the user's statements and email content by comparing with data from other users. For example, it points out areas for improvement in the user's statements based on phrases and expressions frequently used by other users. The analysis unit also compares the user's statements and email content with data from other users and performs a relative evaluation to objectively evaluate the user's communication skills. For example, it evaluates the effectiveness and influence of the user's statements based on data from other users. In this way, the user's strengths and weaknesses can be objectively evaluated by comparing with data from other users and performing a relative evaluation.

[0059] The analysis unit can perform multimodal analysis by including audio data and video data in addition to the content of a user's utterances and emails. For example, the analysis unit can analyze audio data and video data in addition to the content of a user's utterances and emails. For example, the analysis unit can analyze the user's tone of voice and facial expressions to more accurately grasp their emotions and intentions. By performing multimodal analysis, the analysis unit can gain a deeper understanding of the content of a user's utterances and emails. For example, the analysis unit can analyze the intensity of the user's emotions from audio data and analyze the user's non-verbal communication from video data. By including audio data and video data in the analysis, the analysis unit can analyze the content of a user's utterances and emails more comprehensively. For example, the analysis unit can analyze the true meaning and emotions of the utterances based on the user's tone of voice and facial expressions. In this way, by including audio data and video data in the analysis, the content of a user's utterances and emails can be analyzed more comprehensively.

[0060] The analysis unit can also analyze statements and emails in different languages, allowing for analysis from an international perspective. The analysis unit, for example, analyzes the content of a user's statements and emails in different languages. For example, the analysis unit supports multiple languages, such as English, Japanese, and French, and performs analysis from an international perspective. Furthermore, by including statements and emails in different languages ​​in the analysis target, the analysis unit can evaluate the user's communication skills from multiple angles. For example, the analysis unit can analyze differences in expressions and nuances in different languages ​​to evaluate the user's multilingual ability. Furthermore, in order to perform analysis from an international perspective, the analysis unit can include statements and emails in different languages ​​in the analysis target. For example, the analysis unit can evaluate the diversity and adaptability of a user's statements based on data from users with different cultures and backgrounds. In this way, by including statements and emails in different languages ​​in the analysis target, the user's communication skills can be evaluated from multiple angles.

[0061] The analysis unit can use the emotion estimation function to analyze the user's emotions in real time and perform analysis according to changes in emotions. The analysis unit, for example, uses the emotion estimation function to analyze the content of the user's statements and emails in real time. For example, it tracks changes in emotions when the user makes statements and performs analysis according to those changes. Furthermore, by analyzing the user's emotions in real time, the analysis unit more accurately understands the true meaning and intention of the content of the statements and emails. For example, if the user is feeling stressed, it identifies the cause and provides appropriate advice. Furthermore, the analysis unit uses the emotion estimation function to analyze the user's emotions in real time and provide feedback according to changes in emotions. For example, when the user expresses positive emotions, the intensity of the emotion is quantified and reflected in the analysis results. In this way, the user's emotions can be analyzed in real time and analyzed according to changes in emotions, thereby providing more accurate advice.

[0062] The advice providing unit can provide advice according to the user's emotions using the emotion estimation function. The advice providing unit, for example, uses the emotion estimation function to provide advice according to the user's emotions. For example, if the user is feeling stressed, the advice providing unit provides advice that takes the user's emotions into consideration. The advice providing unit also analyzes the user's emotions in real time and provides advice based on the results. For example, if the user is expressing positive emotions, the advice providing unit provides advice that reinforces the emotions. The advice providing unit also uses the emotion estimation function to provide personalized advice according to the user's emotions. For example, if the user is expressing negative emotions, the advice providing unit provides advice that alleviates the emotions. In this way, more effective advice can be achieved by providing advice according to the user's emotions.

[0063] The advice providing unit can refer to the history of past advice and evaluate the effectiveness of the advice. For example, the advice providing unit stores the history of past advice in a database, and the generation AI refers to it to evaluate the effectiveness of the advice. For example, the current advice is adjusted based on the results of advice the user received in the past. The advice providing unit also tracks the user's growth and changes based on the advice history and evaluates the effectiveness of the advice. For example, it analyzes the results of advice the user received in the past and provides effective advice. The advice providing unit also refers to the history of past advice and evaluates the effectiveness of the advice, thereby providing more effective advice to the user. For example, the current advice is optimized based on the results of advice the user received in the past. In this way, more effective advice can be provided by referring to the history of past advice and evaluating the effectiveness of the advice.

[0064] The advice providing unit can compare the user's data with the data of other users and provide relative advice. The advice providing unit, for example, provides relative advice to the user by comparing with the data of other users. For example, specific advice is given to the user based on success stories of other users in the same situation. The advice providing unit also refers to the data of other users and relatively evaluates the user's strengths and weaknesses. For example, effective advice is provided to the user based on how other users solved problems. The advice providing unit also provides relative advice to the user by comparing with the data of other users. For example, specific areas for improvement are pointed out to the user based on the data of other users working on the same problem. In this way, by comparing with the data of other users and providing relative advice, the user's strengths and weaknesses can be objectively evaluated.

[0065] The advice providing unit can provide advice that is visually easy to understand by using visual feedback. The advice providing unit, for example, uses visual feedback to provide advice that is visually easy to understand to the user. For example, the advice providing unit visually shows the user's progress and areas for improvement by using graphs and charts. Furthermore, the advice providing unit uses visual feedback to enable the user to intuitively understand the advice. For example, the advice content is visually explained by using animations and infographics. Furthermore, the advice providing unit uses visual feedback to provide advice that is visually easy to understand to the user. For example, the user's actions and performance are visually shown and specific areas for improvement are pointed out. In this way, by using visual feedback, the advice can be intuitively understood by the user.

[0066] The advice providing unit can provide advice in different languages ​​and give advice from an international perspective. The advice providing unit, for example, provides advice in different languages ​​and gives advice from an international perspective. For example, the advice providing unit supports multiple languages ​​such as English, Japanese, and French and provides appropriate advice to the user. The advice providing unit also evaluates the user's multilingual ability by providing advice in different languages. For example, it analyzes differences in expressions and nuances in different languages ​​and provides appropriate advice to the user. The advice providing unit also provides advice in different languages ​​in order to give advice from an international perspective. For example, it provides appropriate advice to the user based on data of users with different cultures and backgrounds. In this way, by providing advice in different languages, it is possible to evaluate the user's multilingual ability and give advice from an international perspective.

[0067] The advice providing unit can provide advice in real time according to the user's emotions using the emotion estimation function. The advice providing unit, for example, uses the emotion estimation function to provide advice in real time according to the user's emotions. For example, if the user is feeling stressed, the advice providing unit provides advice that takes into consideration the user's emotions. The advice providing unit also analyzes the user's emotions in real time and provides advice based on the results. For example, if the user is expressing positive emotions, the advice providing unit provides advice that reinforces the emotions. The advice providing unit also uses the emotion estimation function to provide personalized advice in real time according to the user's emotions. For example, if the user is expressing negative emotions, the advice providing unit provides advice that alleviates the emotions. In this way, more effective advice can be achieved by providing advice in real time according to the user's emotions.

[0068] The feedback unit can provide feedback according to the user's emotions using the emotion estimation function. The feedback unit, for example, uses the emotion estimation function to provide feedback according to the user's emotions. For example, when the user performs self-evaluation, the feedback unit tracks changes in emotions and provides feedback based on the results. The feedback unit also analyzes the user's emotions in real time and provides feedback based on the results. For example, when the user expresses positive emotions, the feedback unit provides feedback that reinforces the emotions. The feedback unit also uses the emotion estimation function to provide personalized feedback according to the user's emotions. For example, when the user expresses negative emotions, the feedback unit provides feedback that alleviates the emotions. In this way, by providing feedback according to the user's emotions, self-development and self-counseling can be more effectively supported.

[0069] The feedback unit can refer to the history of past feedback and evaluate the effectiveness of the feedback. For example, the feedback unit stores the history of past feedback in a database, and the generation AI refers to it to evaluate the effectiveness of the feedback. For example, the current feedback is adjusted based on the results of feedback the user received in the past. The feedback unit also tracks the growth and changes of the user based on the feedback history and evaluates the effectiveness of the feedback. For example, the feedback unit analyzes the results of feedback the user received in the past and provides effective feedback. The feedback unit also provides more effective feedback to the user by referring to the history of past feedback and evaluating the effectiveness of the feedback. For example, the current feedback is optimized based on the results of feedback the user received in the past. In this way, more effective feedback can be provided by referring to the history of past feedback and evaluating the effectiveness of the feedback.

[0070] The feedback unit can compare the user's data with the data of other users and provide relative feedback. The feedback unit, for example, provides relative feedback to the user by comparing with the data of other users. For example, specific feedback is provided to the user based on success stories of other users in the same situation. The feedback unit also refers to the data of other users and relatively evaluates the user's strengths and weaknesses. For example, effective feedback is provided to the user based on how other users solved a problem. The feedback unit also provides relative feedback to the user by comparing with the data of other users. For example, specific areas for improvement are pointed out to the user based on the data of other users working on the same problem. In this way, by comparing with the data of other users and providing relative feedback, the user's strengths and weaknesses can be objectively evaluated.

[0071] The feedback unit can provide visually easy-to-understand feedback using visual feedback. The feedback unit, for example, uses visual feedback to provide visually easy-to-understand feedback to the user. For example, the feedback unit visually shows the user's progress and areas for improvement using graphs and charts. Furthermore, the feedback unit uses visual feedback to enable the user to intuitively understand the feedback. For example, the feedback content is visually explained using animations and infographics. Furthermore, the feedback unit uses visual feedback to provide visually easy-to-understand feedback to the user. For example, the user's actions and performance are visually shown and specific areas for improvement are pointed out. In this way, the use of visual feedback can enable the user to intuitively understand the feedback.

[0072] The feedback unit may also provide feedback in different languages, enabling feedback from an international perspective. The feedback unit may provide feedback from an international perspective, for example, by providing feedback in different languages. For example, the feedback unit may support multiple languages, such as English, Japanese, and French, and provide appropriate feedback to the user. The feedback unit may also provide feedback in different languages ​​to evaluate the user's multilingual ability. For example, the feedback unit may analyze differences in expressions and nuances in different languages ​​and provide appropriate feedback to the user. The feedback unit may also provide feedback in different languages ​​to provide feedback from an international perspective. For example, the feedback unit may provide appropriate feedback to the user based on data on users with different cultures and backgrounds. In this way, the feedback unit may provide feedback in different languages ​​to evaluate the user's multilingual ability and provide feedback from an international perspective.

[0073] The feedback unit can provide feedback according to the user's emotions in real time using the emotion estimation function. The feedback unit, for example, uses the emotion estimation function to provide feedback according to the user's emotions in real time. For example, if the user is feeling stressed, feedback that takes the emotion into consideration is provided. The feedback unit also analyzes the user's emotions in real time and provides feedback based on the results. For example, if the user is expressing positive emotions, feedback that reinforces the emotions is provided. The feedback unit also uses the emotion estimation function to provide personalized feedback according to the user's emotions in real time. For example, if the user is expressing negative emotions, feedback that alleviates the emotions is provided. In this way, by providing feedback according to the user's emotions in real time, self-development and self-counseling can be more effectively supported.

[0074] The feedback unit can use the emotion estimation function to evaluate the user's talents according to their emotions. The feedback unit, for example, uses the emotion estimation function to evaluate the user's talents according to their emotions. For example, if the user expresses positive emotions about a specific activity, the feedback unit evaluates the user's talents in that activity. The feedback unit also analyzes the user's emotions in real time and evaluates the user's talents based on the results. For example, if the user expresses positive emotions about a specific field, the feedback unit evaluates the user's talents in that field. The feedback unit also uses the emotion estimation function to evaluate the user's talents personalized according to their emotions. For example, if the user expresses positive emotions about a specific activity, the feedback unit evaluates the user's talents in that activity. In this way, by evaluating the user's talents according to their emotions, it is possible to discover new talents for the user.

[0075] The feedback unit can refer to the history of past evaluations and evaluate the effectiveness of the evaluation. For example, the feedback unit stores the history of past evaluations in a database, and the generation AI refers to it to evaluate the effectiveness of the evaluation. For example, the current evaluation is adjusted based on the results of evaluations the user has received in the past. The feedback unit also tracks the user's growth and changes based on the evaluation history and evaluates the effectiveness of the evaluation. For example, it analyzes the results of evaluations the user has received in the past and provides an effective evaluation. The feedback unit also refers to the history of past evaluations and evaluates the effectiveness of the evaluation, thereby providing a more effective evaluation to the user. For example, the current evaluation is optimized based on the results of evaluations the user has received in the past. In this way, a more effective evaluation can be provided by referring to the history of past evaluations and evaluating the effectiveness of the evaluation.

[0076] The feedback unit can compare the user's data with the data of other users and perform a relative evaluation. The feedback unit, for example, performs a relative evaluation of the user by comparing with the data of other users. For example, the user's talent is evaluated based on success stories of other users in the same field. The feedback unit also refers to the data of other users and performs a relative evaluation of the user's strengths and weaknesses. For example, the user's talent is evaluated based on how other users demonstrate their talents. The feedback unit also performs a relative evaluation of the user by comparing with the data of other users. For example, the user's talent is evaluated based on the data of other users in the same field. In this way, by comparing with the data of other users and performing a relative evaluation, the user's strengths and weaknesses can be objectively evaluated.

[0077] The feedback unit can provide a visually easy-to-understand evaluation using visual feedback. The feedback unit, for example, uses visual feedback to provide a visually easy-to-understand evaluation to the user. For example, the evaluation results of the user's talent are visually shown using graphs or charts. Furthermore, the feedback unit uses visual feedback to enable the user to intuitively understand the evaluation. For example, the content of the evaluation is visually explained using animations or infographics. Furthermore, the feedback unit uses visual feedback to provide a visually easy-to-understand evaluation to the user. For example, the user's behavior or performance is visually shown to provide a specific evaluation of talent. In this way, by using visual feedback, the user can intuitively understand the evaluation.

[0078] The feedback unit also provides evaluations in different languages, allowing evaluation from an international perspective. The feedback unit, for example, provides evaluations in different languages, allowing evaluation from an international perspective. For example, the feedback unit supports multiple languages, such as English, Japanese, and French, and provides an appropriate evaluation to the user. The feedback unit also evaluates the user's multilingual ability by providing evaluations in different languages. For example, the feedback unit analyzes differences in expressions and nuances in different languages ​​and provides an appropriate evaluation to the user. The feedback unit also provides evaluations in different languages ​​in order to perform evaluations from an international perspective. For example, the feedback unit provides an appropriate evaluation to the user based on data of users with different cultures and backgrounds. In this way, by providing evaluations in different languages, the user's multilingual ability can be evaluated and evaluations can be performed from an international perspective.

[0079] The feedback unit can use the emotion estimation function to provide an evaluation according to the user's emotion in real time. The feedback unit, for example, uses the emotion estimation function to provide an evaluation according to the user's emotion in real time. For example, if the user expresses positive emotion toward a specific activity, the feedback unit evaluates the user's talent in that activity. The feedback unit also analyzes the user's emotion in real time and provides an evaluation based on the results. For example, if the user expresses positive emotion in a specific field, the feedback unit evaluates the user's talent in that field. The feedback unit also uses the emotion estimation function to provide a personalized evaluation according to the user's emotion in real time. For example, if the user expresses positive emotion toward a specific activity, the feedback unit evaluates the user's talent in that activity. In this way, by providing an evaluation according to the user's emotion in real time, it is possible to discover new talents in the user.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The analysis unit can include user behavioral data in the analysis targets in addition to the content of user comments and emails. For example, the analysis unit can analyze the user's web browsing history and app usage history to identify the user's interests and concerns. The analysis unit can also analyze the user's lifestyle and habits based on the user behavioral data. For example, the analysis unit can identify what activities the user engages in at what time of day and reflect this in the analysis results. By analyzing the user behavioral data, the analysis unit can identify the user's behavioral patterns and tendencies and provide more accurate advice. By including the user behavioral data in the analysis targets, the analysis unit can gain a deeper understanding of the user's interests, concerns, lifestyle, and habits, and provide more effective advice.

[0082] The analysis unit can include the user's physiological data in the analysis target in addition to the content of the user's comments and emails. For example, the analysis unit can analyze the user's heart rate and sleep data to identify the user's health condition and stress level. The analysis unit can also analyze the user's physical condition and health condition based on the physiological data. For example, the analysis unit can identify the time of day when the user is most likely to feel stressed and reflect this in the analysis results. The analysis unit can also identify the user's health condition and stress level by analyzing the user's physiological data, allowing for more accurate advice to be provided. By including the user's physiological data in the analysis target, the analysis unit can gain a deeper understanding of the user's health condition and stress level and provide more effective advice.

[0083] In addition to the content of a user's comments and emails, the analysis unit can include the user's social media data in the analysis target. For example, the analysis unit can analyze the user's social media posts and comments to identify the user's interests. The analysis unit can also analyze the user's communication style and network based on the social media data. For example, the analysis unit can identify the topics in which the user is interested and reflect this in the analysis results. By analyzing the user's social media data, the analysis unit can identify the user's interests, communication style, and network, and provide more accurate advice. In this way, by including the user's social media data in the analysis target, the user's interests, communication style, and network can be more deeply understood, and more effective advice can be provided.

[0084] The analysis unit can include the user's purchasing history in addition to the content of the user's comments and emails in the analysis target. For example, the analysis unit can analyze the user's online shopping history and in-store purchase history to identify the user's consumption trends and preferences. The analysis unit can also analyze the user's lifestyle and consumption patterns based on the purchase history. For example, the analysis unit can identify what products the user frequently purchases and reflect this in the analysis results. By analyzing the user's purchasing history, the analysis unit can identify the user's consumption trends and preferences and provide more accurate advice. By including the user's purchasing history in the analysis target, the analysis unit can gain a deeper understanding of the user's consumption trends, preferences, lifestyle, and consumption patterns, and provide more effective advice.

[0085] The analysis unit can include the user's location information data in the analysis target in addition to the content of the user's comments and emails. For example, the analysis unit can analyze the user's movement history and visited locations to identify the user's range of activity and movement patterns. The analysis unit can also analyze the user's lifestyle and movement patterns based on the location information data. For example, the analysis unit can identify the places the user frequently visits and reflect this in the analysis results. The analysis unit can also identify the user's range of activity and movement patterns by analyzing the user's location information data, allowing for more accurate advice to be provided. By including the user's location information data in the analysis target, the analysis unit can gain a deeper understanding of the user's range of activity, movement patterns, lifestyle, and movement patterns, and provide more effective advice.

[0086] The analysis unit can estimate the user's emotions in addition to the content of the user's comments and emails, and perform analysis based on those emotions. For example, it tracks changes in the user's emotions when the user makes a comment and performs analysis based on those changes. The analysis unit also analyzes the user's emotions in real time to more accurately understand the true meaning and intention of the content of the comments and emails. For example, if the user is feeling stressed, it identifies the cause and provides appropriate advice. The analysis unit also uses an emotion estimation function to analyze the user's emotions in real time and provide feedback according to changes in emotion. For example, when the user expresses positive emotions, the intensity of the emotion is quantified and reflected in the analysis results. This allows the analysis of the user's emotions in real time and analysis based on changes in emotion to provide more accurate advice.

[0087] The advice providing unit can estimate the user's emotions and provide advice based on those emotions. For example, if the user is feeling stressed, the advice providing unit provides advice that takes those emotions into consideration. The advice providing unit also analyzes the user's emotions in real time and provides advice based on the results. For example, if the user is expressing positive emotions, the advice providing unit provides advice that reinforces those emotions. The advice providing unit also uses the emotion estimation function to provide personalized advice according to the user's emotions. For example, if the user is expressing negative emotions, the advice providing unit provides advice that alleviates those emotions. This makes it possible to provide more effective advice by providing advice that is according to the user's emotions.

[0088] The feedback unit can estimate the user's emotions and provide feedback based on those emotions. For example, when the user performs self-evaluation, the feedback unit tracks changes in emotions and provides feedback based on the results. The feedback unit also analyzes the user's emotions in real time and provides feedback based on the results. For example, if the user expresses positive emotions, the feedback unit provides feedback that reinforces those emotions. The feedback unit also uses the emotion estimation function to provide personalized feedback according to the user's emotions. For example, if the user expresses negative emotions, the feedback unit provides feedback that alleviates those emotions. In this way, by providing feedback according to the user's emotions, self-development and self-counseling can be more effectively supported.

[0089] The feedback unit can estimate the user's emotions and evaluate the user's talents based on those emotions. For example, if the user expresses positive emotions about a specific activity, the feedback unit evaluates the user's talents in that activity. The feedback unit also analyzes the user's emotions in real time and evaluates the user's talents based on the results. For example, if the user expresses positive emotions about a specific field, the feedback unit evaluates the user's talents in that field. The feedback unit also uses the emotion estimation function to perform a personalized talent evaluation based on the user's emotions. For example, if the user expresses positive emotions about a specific activity, the feedback unit evaluates the user's talents in that activity. This makes it possible to discover new talents for the user by evaluating the user's talents based on the user's emotions.

[0090] The feedback unit can estimate the user's emotions and provide an evaluation based on the emotions in real time. For example, if the user expresses positive emotions about a specific activity, the feedback unit evaluates the user's talent in that activity. The feedback unit can also analyze the user's emotions in real time and provide an evaluation based on the results. For example, if the user expresses positive emotions about a specific field, the feedback unit evaluates the user's talent in that field. The feedback unit can also use the emotion estimation function to provide a personalized evaluation based on the user's emotions in real time. For example, if the user expresses positive emotions about a specific activity, the feedback unit evaluates the user's talent in that activity. This makes it possible to discover new talents in the user by providing an evaluation based on the user's emotions in real time.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The analysis unit analyzes the content of the user's comments and emails. For example, the analysis unit can use natural language processing technology to analyze text data and emotion analysis technology to analyze the user's emotions. It can also use keyword extraction technology to extract important information. Step 2: The advice provider provides objective advice based on the results of the analysis by the analyzer. For example, it can provide psychological advice, guidelines for action, and problem-solving suggestions. Step 3: The feedback unit supports the user's self-improvement and self-counseling based on the advice provided by the advice providing unit. For example, it can provide the user with methods for goal setting, progress management, and self-evaluation.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 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.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The 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.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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]

[0160] 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. an analysis unit that analyzes the content of user comments and emails; an advice providing unit that provides objective advice based on the results of the analysis by the analysis unit; a feedback unit that supports the user's self-improvement and self-counseling based on the advice provided by the advice providing unit. A system characterized by:

2. The analysis unit Tracking changes in the user's emotions and performing analysis based on the emotional fluctuations 2. The system of claim 1.

3. The analysis unit Refer to the user's past comments and email history to analyze long-term trends 2. The system of claim 1.

4. The analysis unit The user's comments and email content are compared with other users' data to make a relative evaluation.

2. The system of claim 1.

5. The analysis unit In addition to the user's comments and email content, audio and video data are also included in the analysis, and multimodal analysis is performed.

2. The system of claim 1.

6. The analysis unit The analysis will also include statements and emails in different languages, and will be conducted from an international perspective.

2. The system of claim 1.

7. The analysis unit Analyzing the user's emotions in real time and performing analysis according to changes in the emotions 2. The system of claim 1.

8. The advice providing unit Providing advice according to the user's emotions 2. The system of claim 1.

Images