System

Abstract

An object of a system according to an embodiment is to appropriately evaluate a post content of a user and provide feedback.SOLUTION: A system includes a post analysis unit, an evaluation unit, and a feedback unit. The post analysis unit analyzes a post content of a user. The evaluation unit evaluates the content analyzed by the post analysis unit. The feedback unit feeds back a result evaluated by the evaluation unit to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that the content posted by users on social networks is not properly evaluated, making it difficult to satisfy users' desire for recognition.

[0005] The system according to the embodiment aims to appropriately evaluate the content posted by the user and provide feedback. [Means for solving the problem]

[0006] The system according to the embodiment includes a post analysis unit, an evaluation unit, and a feedback unit. The post analysis unit analyzes content posted by users. The evaluation unit evaluates the content analyzed by the post analysis unit. The feedback unit provides feedback to the user on the results of the evaluation by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately evaluate the content posted by the user and provide feedback. [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-expression evaluation system according to an embodiment of the present invention uses a generation AI to analyze content posted by users on social media, evaluate the content, and provide feedback on the evaluation results to the users. This allows the self-expression evaluation system to satisfy the users' desire for recognition, leading to mental stability and improved sociability.

[0029] A self-expression evaluation system according to an embodiment includes a post analysis unit, an evaluation unit, and a feedback unit. The post analysis unit analyzes the content posted by a user. For example, the post analysis unit analyzes the content posted by a user using text analysis technology. The post analysis unit can also analyze posted images using image recognition technology. The post analysis unit can also analyze the emotional tone of the posted content using sentiment analysis technology. For example, the post analysis unit analyzes the meaning of text using natural language processing technology to extract the subject and emotion of the posted content. Image recognition technology analyzes the content of posted images and identifies objects and scenes within the images. Sentiment analysis technology analyzes the emotional tone from the text and images and classifies the emotions into positive, negative, and neutral. The evaluation unit evaluates the content analyzed by the post analysis unit. For example, the evaluation unit gives a high rating to positive posts. The evaluation unit can also evaluate negative posts by indicating areas for improvement. The evaluation unit can also evaluate the content taking into account the emotional impact of the posted content. For example, the evaluation unit may give a high rating to posts that express positive emotions and provide an evaluation suggesting areas for improvement to posts that express negative emotions. The evaluation unit may use emotion analysis technology to evaluate the emotional impact of the content of the post and evaluate the post based on the evaluation results. The feedback unit may provide the evaluation results to the user as feedback. For example, the feedback unit may notify the user of the evaluation results as a text message. The feedback unit may also send the evaluation results as a notification to the user's device. The feedback unit may also provide the evaluation results to the user in the form of a report. For example, the feedback unit may notify the user of the evaluation results as a text message and explain the details of the evaluation. The notification may be sent to the user's smartphone or computer and can be viewed immediately. The report-style feedback is provided as a document summarizing the evaluation results in detail, which the user can refer to later. This allows the self-expression evaluation system according to the embodiment to satisfy the user's desire for recognition, stabilize their mental state, and improve their social skills. For example, knowing how their posts were evaluated may help the user gain confidence.Users can also find areas for self-improvement based on the evaluation results. Furthermore, by sharing the evaluation results with other users, communication can be promoted and socialization can be enhanced.

[0030] The post analysis unit can provide more personalized evaluations based on a user's past posting history and behavioral patterns. For example, the post analysis unit uses a generation AI to analyze a user's past posting history and understand the poster's preferences and tendencies. For example, it learns the characteristics of posts that have received many likes in the past and provides high ratings to new posts with similar characteristics. The post analysis unit also analyzes a user's behavioral patterns and provides evaluations taking into account factors such as posting frequency and time of day. For example, if content posted during a specific time period tends to receive many responses, it will provide high ratings to posts from that time period. The post analysis unit also provides personalized feedback based on a poster's past evaluation history. For example, a poster who has been rated "moving" in the past will receive feedback that emphasizes the moving elements. This allows for more personalized evaluations by taking into account a user's past posting history and behavioral patterns.

[0031] The post analysis unit also integrates data from other social media platforms, allowing for evaluations from a broader perspective. For example, the post analysis unit uses a generative AI to collect data from other social media platforms and analyze a user's overall online activity. For example, it integrates the content of posts from Twitter and Instagram to provide a comprehensive evaluation. The post analysis unit also evaluates a user's influence and popularity based on data from other social media platforms. For example, it may give high ratings to posts by users with many followers on multiple platforms. The post analysis unit also utilizes data from other social media platforms to evaluate the consistency and theme of a user's post content. For example, it may give high ratings to users who post on the same theme on multiple platforms. In this way, by integrating data from other social media platforms, evaluations can be made from a broader perspective.

[0032] The post analysis unit can refer to external news or trend information related to the post content and reflect it in the rating. For example, the post analysis unit uses a generation AI to collect external news sites and trend information and reflect information related to the post content in the rating. For example, it gives high ratings to posts related to the latest news or trends. The post analysis unit also analyzes whether the post content is related to current trends or topics and rates it based on that relevance. For example, it gives high ratings to posts that use popular hashtags. The post analysis unit also evaluates the social influence of the post content based on external news and trend information. For example, it gives high ratings to posts related to socially important themes. In this way, it is possible to reflect the rating by referring to external news and trend information related to the post content.

[0033] The feedback unit can customize the feedback content based on the user's past feedback history. For example, the feedback unit analyzes the user's past feedback history and customizes the feedback content based on that history. For example, for a user who has previously preferred positive feedback, the feedback unit provides feedback that emphasizes positive content. The feedback unit also analyzes what kind of feedback the user is likely to accept based on the feedback history and adjusts the feedback content based on the results. For example, for a user who prefers specific advice, the feedback unit provides feedback that includes specific areas for improvement. The feedback unit also takes the user's feedback history into consideration and provides feedback that is consistent with the past feedback content. For example, for a poster who has been rated "inspirational" in the past, the feedback unit provides similar inspiring feedback. In this way, the feedback content can be customized by taking the user's past feedback history into consideration.

[0034] The feedback unit can include specific improvements and advice for the next post in the feedback content. For example, the feedback unit includes specific improvements in the feedback content to indicate how the user can improve in their next post. For example, the feedback unit provides specific advice such as, "Next time, make the background brighter so that the photo stands out more." The feedback unit also includes advice for the next post in the feedback to promote user growth. For example, the feedback unit provides advice such as, "Next time, try writing more in detail about this topic." The feedback unit also indicates specific improvements in the feedback content to enable the user to understand how they can improve their post. For example, the feedback unit provides advice such as, "Next time, try to improve the composition to make the photo more appealing." In this way, by including specific improvements and advice for the next post in the feedback content, user growth can be promoted.

[0035] The feedback unit can provide the feedback content not only in text format but also in audio or video format. For example, the feedback unit provides the feedback content in audio format so that the user can hear and understand it. For example, the generation AI provides a function to read the feedback aloud. The feedback unit also provides the feedback content in video format to make it easier to understand visually. For example, the generation AI provides a function to explain the feedback content in video. The feedback unit also provides feedback in multiple formats, including text, audio, and video, so that the user can select the format that is easiest to understand. For example, the feedback content may be displayed in text and simultaneously explained in audio or video. In this way, providing the feedback content in not only text format but also audio or video format can deepen the user's understanding.

[0036] The feedback unit can share the feedback content with the user's friends and followers, thereby promoting feedback throughout the community. For example, the feedback unit can share the feedback content with the user's friends and followers, thereby promoting feedback throughout the community. For example, a function is provided in which the generation AI notifies the user's followers of the feedback content. The feedback unit can also share the feedback content to promote additional feedback from other users by sharing the feedback content. For example, a function is provided in which friends and followers can add comments to the feedback content. In order to promote feedback throughout the community, the feedback unit can also provide a function in which the feedback content is made public and other users can express their opinions on it. For example, a function is provided in which the feedback content is shared on social media. This allows the feedback content to be shared with the user's friends and followers, thereby promoting feedback throughout the community.

[0037] The evaluation unit can monitor the user's mental state and encourage them to consult a mental health professional if necessary. For example, the generation AI analyzes the content of the user's posts and monitors the user's mental state. For example, if negative emotions persist, the evaluation unit sends a notification encouraging the user to consult a mental health professional. The evaluation unit also evaluates the user's mental state and suggests consulting a mental health professional if necessary. For example, if stress or anxiety is increasing, the evaluation unit provides the contact information of a professional. The evaluation unit also suggests to the user when to consult a mental health professional based on the results of the mental state monitoring. For example, if negative posts continue for a long period of time, the evaluation unit recommends professional support. In this way, the user's mental state can be monitored and, if necessary, the user can be encouraged to consult a mental health professional, thereby providing mental support.

[0038] The evaluation unit can suggest activities for relaxation and stress relief to the user. For example, the generation AI evaluates the user's mental state and suggests activities for relaxation and stress relief. For example, it may recommend a meditation or yoga session. The evaluation unit may also analyze the content posted by the user and suggest relaxation activities if stress levels are high. For example, it may recommend a nature walk or listening to music. The evaluation unit may also suggest specific activities for stress relief to the user based on the evaluation results of the mental state. For example, it may recommend art therapy or massage. In this way, by suggesting activities for relaxation and stress relief to the user, it is possible to improve the user's mental stability.

[0039] The evaluation unit can suggest community activities and events to improve the user's mental state. For example, the generation AI evaluates the user's mental state and suggests community activities and events. For example, it introduces local volunteer activities and hobby circles. The evaluation unit also recommends participation in community activities and events to improve the user's mental state. For example, it suggests sporting events and cultural exchange events. The evaluation unit also builds a system that suggests community activities and events to the user based on the results of the mental state evaluation. For example, it introduces appropriate events based on the user's interests and concerns. In this way, the user's mental stability can be achieved by suggesting community activities and events to improve the user's mental state.

[0040] The evaluation unit can provide the user with information and resources related to mental health. For example, the generation AI evaluates the user's mental state and provides information and resources related to mental health. For example, it introduces stress management methods and counseling services. The evaluation unit also provides information related to mental health to improve the user's mental state. For example, it introduces relaxation techniques and self-care methods. The evaluation unit also builds a system that provides users with mental health resources based on the results of the mental state evaluation. For example, it introduces online counseling and mental health apps. In this way, the user can be provided with information and resources related to mental health, thereby providing them with mental support.

[0041] The evaluation unit can suggest specific actions to the user to promote interaction with other users. For example, the generation AI evaluates the content of the user's post and suggests specific actions to promote interaction with other users. For example, the evaluation unit makes a suggestion such as, "Try discussing this post with other users." The evaluation unit also analyzes the content of the user's post and suggests actions to promote interaction with other users. For example, the suggestion is, "Try exchanging opinions with other users on this topic." The evaluation unit also builds a system that suggests specific actions to the user to promote interaction with other users based on the evaluation made by the generation AI. For example, the suggestion is, "Try collaborating with other users on this post." In this way, by suggesting specific actions to the user to promote interaction with other users, the user's sociability can be improved.

[0042] The evaluation unit can provide users with training and resources to improve their communication skills. For example, the generation AI evaluates the content of a user's posts and provides training and resources to improve their communication skills. For example, the evaluation unit may introduce online courses or workshops. The evaluation unit also analyzes the content of a user's posts and provides specific advice to improve their communication skills. For example, the evaluation unit may provide advice such as, "It would be better if you provided more specific examples." The evaluation unit also builds a system that provides users with training and resources to improve their communication skills based on the evaluation performed by the generation AI. For example, the evaluation unit may introduce books or videos to improve communication skills. In this way, by providing users with training and resources to improve their communication skills, the user's sociability can be improved.

[0043] The evaluation unit can make suggestions to the user to promote interactions on other social media platforms. For example, the generation AI evaluates the content of the user's posts and makes suggestions to promote interactions on other social media platforms. For example, the evaluation unit makes a suggestion such as, "Try sharing this post on Twitter as well." The evaluation unit also analyzes the content of the user's posts and suggests specific actions to promote interactions on other social media platforms. For example, the evaluation unit makes a suggestion such as, "Try discussing this topic with your followers on Instagram." The evaluation unit also builds a system that makes suggestions to the user to promote interactions on other social media platforms based on the evaluation made by the generation AI. For example, the evaluation unit makes a suggestion such as, "Try sharing this post on LinkedIn to gather professional opinions." This makes it possible to improve the user's sociality by making suggestions to promote interactions on other social media platforms.

[0044] The evaluation unit can suggest offline social events and activities to the user. For example, the generation AI evaluates the content of the user's posts and suggests offline social events and activities. For example, it makes a suggestion such as, "Try attending a workshop on this topic." The evaluation unit also analyzes the content of the user's posts and suggests specific actions to promote offline interactions. For example, it makes a suggestion such as, "Try attending a seminar on this topic." The evaluation unit also builds a system that suggests offline social events and activities to the user based on the evaluation made by the generation AI. For example, it makes a suggestion such as, "Try attending a meetup on this topic and exchange opinions with other participants." In this way, by suggesting offline social events and activities to the user, it is possible to improve the user's sociability.

[0045] The evaluation unit can dynamically adjust the evaluation criteria when continuously evaluating a user's posts and provide feedback according to the user's growth. For example, the evaluation unit has a generation AI continuously evaluate a user's posts and dynamically adjust the evaluation criteria. For example, by tightening the evaluation criteria as the user grows, a higher level of feedback is provided. The evaluation unit also evaluates the user's growth and provides feedback according to that growth. For example, basic advice is provided in the initial stage, and more advanced advice is provided as the user grows. The evaluation unit also builds a system in which the generation AI continuously evaluates a user's posts and dynamically adjusts the evaluation criteria. For example, the evaluation criteria are adaptively changed as the content of the user's posts evolve. In this way, by continuously evaluating a user's posts and dynamically adjusting the evaluation criteria, feedback according to the user's growth can be provided.

[0046] When continuously evaluating a user's posts, the evaluation unit analyzes the evaluation results over time to visualize the user's growth and changes. For example, the evaluation unit has a generation AI continuously evaluate the user's posts and analyze the evaluation results over time. For example, the evaluation unit visualizes the growth by displaying changes in the content of the user's posts in a graph. The evaluation unit also analyzes the user's growth and changes over time and reflects the results in feedback. For example, it shows how the user has grown compared to past posts. The evaluation unit also builds a system in which the generation AI continuously evaluates the user's posts and analyzes the evaluation results over time. For example, it visualizes growth trends based on the user's posting history. In this way, by continuously evaluating the user's posts and analyzing the evaluation results over time, it is possible to visualize the user's growth and changes.

[0047] When continuously evaluating a user's posts, the evaluation unit can compare them with other users and provide a relative evaluation. For example, the generation AI continuously evaluates the user's posts and compares them with other users. For example, the evaluation is performed by comparing them with other users who have posted on the same topic. The evaluation unit also compares the content of the user's posts with other users and reflects the results in feedback. For example, it indicates areas where the user excels over other users and areas for improvement. The evaluation unit also builds a system in which the generation AI continuously evaluates the user's posts and compares them with other users. For example, it compares users who have posted in the same category and evaluates them. This makes it possible to provide a relative evaluation by comparing the user's posts with other users.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The self-expression evaluation system can further include a recommendation unit that recommends content related to a user's interests and concerns based on the content posted by the user. For example, if a user frequently posts about a particular theme, articles and videos related to that theme can be recommended. Also, if a user posts about a particular genre of music or movie, new or popular works related to that genre can be recommended. Furthermore, related online communities and forums can be recommended based on the content posted by the user. This allows users to easily find content that matches their interests and broaden the scope of self-expression.

[0050] The self-expression evaluation system may further include a learning support unit that provides learning resources to improve a user's skills and knowledge based on the user's posts. For example, if a user posts about a specific technology or skill, online courses and tutorials related to that technology or skill may be recommended. Also, if a user expresses interest in a specific field, books and research papers related to that field may be recommended. Furthermore, related workshops and seminars may be recommended based on the user's posts. This allows users to easily find resources to improve their skills and knowledge and promote self-development.

[0051] The self-expression evaluation system may further include a health management unit that monitors the user's health status and provides health management advice based on the user's posted content. For example, if the user posts about stress or fatigue, the system may suggest activities for relaxation and stress relief. If the user posts about diet and exercise, the system may recommend healthy meal plans and exercise programs. Furthermore, the system may recommend regular health checks and medical consultations based on the user's posted content. This allows the user to manage their health status and receive support for maintaining a healthy lifestyle.

[0052] The self-expression evaluation system may further include a career support unit that provides advice related to the user's career or occupation based on the user's posted content. For example, if the user posts about their career concerns or goals, career counseling and coaching resources may be recommended. Also, if the user expresses interest in a particular occupation or industry, job information and internship opportunities related to that occupation or industry may be provided. Furthermore, based on the user's posted content, courses for acquiring skills and qualifications to advance their career may be recommended. This allows the user to receive support in achieving their career goals.

[0053] The self-expression evaluation system can further include a lifestyle support unit that suggests activities related to the user's hobbies and lifestyle based on the content posted by the user. For example, if the user posts about a particular hobby, events and workshops related to that hobby can be recommended. Also, if the user posts about travel or outdoor activities, information on travel destinations and outdoor spots can be provided. Furthermore, based on the content posted by the user, activities that support lifestyle improvement or the discovery of new hobbies can be suggested. This allows the user to receive support for enriching their lifestyle.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The post analysis unit analyzes the content posted by the user. For example, the post analysis unit analyzes the content posted by the user using text analysis technology. The post analysis unit can also analyze posted images using image recognition technology. Furthermore, the post analysis unit can analyze the emotional tone of the posted content using sentiment analysis technology. Specifically, natural language processing technology is used to analyze the meaning of the text and extract the subject and emotion of the posted content. Image recognition technology analyzes the content of posted images and identifies objects and scenes within the images. Sentiment analysis technology analyzes the emotional tone from text and images and classifies emotions as positive, negative, or neutral. Step 2: The evaluation unit evaluates the content analyzed by the post analysis unit. For example, the evaluation unit gives a high rating to a positive post. It can also give an evaluation that indicates areas for improvement to a negative post. Furthermore, the evaluation unit can perform evaluations that take into account the emotional impact of the post content. Specifically, it uses emotion analysis technology to evaluate the emotional impact of the post content and performs evaluations based on the results. Step 3: The feedback unit provides the results of the evaluation by the evaluation unit to the user. For example, the feedback unit notifies the user of the evaluation results as a text message. The evaluation results can also be sent to the user's device as a notification. The evaluation results can also be provided to the user in report format. Specifically, the evaluation results are notified to the user as a text message, explaining the details of the evaluation. The notification is sent to the user's smartphone or computer and can be viewed immediately. Report-format feedback is provided as a document summarizing the evaluation results in detail, which the user can refer to later.

[0056] (Example 2) The self-expression evaluation system according to an embodiment of the present invention uses a generation AI to analyze content posted by users on social media, evaluate the content, and provide feedback on the evaluation results to the users. This allows the self-expression evaluation system to satisfy the users' desire for recognition, leading to mental stability and improved sociability.

[0057] A self-expression evaluation system according to an embodiment includes a post analysis unit, an evaluation unit, and a feedback unit. The post analysis unit analyzes the content posted by a user. For example, the post analysis unit analyzes the content posted by a user using text analysis technology. The post analysis unit can also analyze posted images using image recognition technology. The post analysis unit can also analyze the emotional tone of the posted content using sentiment analysis technology. For example, the post analysis unit analyzes the meaning of text using natural language processing technology to extract the subject and emotion of the posted content. Image recognition technology analyzes the content of posted images and identifies objects and scenes within the images. Sentiment analysis technology analyzes the emotional tone from the text and images and classifies the emotions into positive, negative, and neutral. The evaluation unit evaluates the content analyzed by the post analysis unit. For example, the evaluation unit gives a high rating to positive posts. The evaluation unit can also evaluate negative posts by indicating areas for improvement. The evaluation unit can also evaluate the content taking into account the emotional impact of the posted content. For example, the evaluation unit may give a high rating to posts that express positive emotions and provide an evaluation suggesting areas for improvement to posts that express negative emotions. The evaluation unit may use emotion analysis technology to evaluate the emotional impact of the content of the post and evaluate the post based on the evaluation results. The feedback unit may provide the evaluation results to the user as feedback. For example, the feedback unit may notify the user of the evaluation results as a text message. The feedback unit may also send the evaluation results as a notification to the user's device. The feedback unit may also provide the evaluation results to the user in the form of a report. For example, the feedback unit may notify the user of the evaluation results as a text message and explain the details of the evaluation. The notification may be sent to the user's smartphone or computer and can be viewed immediately. The report-style feedback is provided as a document summarizing the evaluation results in detail, which the user can refer to later. This allows the self-expression evaluation system according to the embodiment to satisfy the user's desire for recognition, stabilize their mental state, and improve their social skills. For example, knowing how their posts were evaluated may help the user gain confidence.Users can also find areas for self-improvement based on the evaluation results. Furthermore, by sharing the evaluation results with other users, communication can be promoted and socialization can be enhanced.

[0058] The post analysis unit can provide more personalized evaluations based on a user's past posting history and behavioral patterns. For example, the post analysis unit uses a generation AI to analyze a user's past posting history and understand the poster's preferences and tendencies. For example, it learns the characteristics of posts that have received many likes in the past and provides high ratings to new posts with similar characteristics. The post analysis unit also analyzes a user's behavioral patterns and provides evaluations taking into account factors such as posting frequency and time of day. For example, if content posted during a specific time period tends to receive many responses, it will provide high ratings to posts from that time period. The post analysis unit also provides personalized feedback based on a poster's past evaluation history. For example, a poster who has been rated "moving" in the past will receive feedback that emphasizes the moving elements. This allows for more personalized evaluations by taking into account a user's past posting history and behavioral patterns.

[0059] The evaluation unit can analyze the emotional tone or nuance of the posted content and perform an evaluation that takes emotional impact into consideration. For example, the evaluation unit uses a generation AI to analyze the emotional tone of the posted content and give a high rating to posts with positive emotions. For example, it gives a particularly high rating to posts that include expressions of joy or gratitude. The evaluation unit also analyzes the nuances of the posted content and performs an evaluation that takes emotional impact into consideration. For example, it performs an appropriate evaluation for posts that include humor or sarcasm after understanding those nuances. The evaluation unit also uses emotion analysis technology to measure the emotional intensity of the posted content and performs an evaluation according to that intensity. For example, it gives a high rating to very moving content and a low rating to content with little emotional impact. In this way, by analyzing the emotional tone and nuance of the posted content, it is possible to perform an evaluation that takes emotional impact into consideration.

[0060] The evaluation unit can estimate the user's emotion at the time of posting and perform evaluation based on that emotion. For example, when a user posts, the evaluation unit estimates the user's emotion in real time using a camera or microphone and performs evaluation based on that emotion. For example, if a user posts with a smile, the evaluation unit performs an evaluation that reflects that positive emotion. The evaluation unit also uses an emotion estimation function to analyze the user's emotion at the time of posting and provides feedback based on that emotion. For example, if a user posts with sadness, the evaluation unit provides encouraging feedback after understanding that emotion. The evaluation unit also estimates the user's emotion at the time of posting and adjusts the evaluation based on that emotion. For example, if a user posts with excitement, the evaluation unit performs a positive evaluation that reflects that excitement. In this way, by estimating the user's emotion at the time of posting, it is possible to perform evaluation based on that emotion.

[0061] The post analysis unit also integrates data from other social media platforms, allowing for evaluations from a broader perspective. For example, the post analysis unit uses a generative AI to collect data from other social media platforms and analyze a user's overall online activity. For example, it integrates the content of posts from Twitter and Instagram to provide a comprehensive evaluation. The post analysis unit also evaluates a user's influence and popularity based on data from other social media platforms. For example, it may give high ratings to posts by users with many followers on multiple platforms. The post analysis unit also utilizes data from other social media platforms to evaluate the consistency and theme of a user's post content. For example, it may give high ratings to users who post on the same theme on multiple platforms. In this way, by integrating data from other social media platforms, evaluations can be made from a broader perspective.

[0062] The post analysis unit can refer to external news or trend information related to the post content and reflect it in the rating. For example, the post analysis unit uses a generation AI to collect external news sites and trend information and reflect information related to the post content in the rating. For example, it gives high ratings to posts related to the latest news or trends. The post analysis unit also analyzes whether the post content is related to current trends or topics and rates it based on that relevance. For example, it gives high ratings to posts that use popular hashtags. The post analysis unit also evaluates the social influence of the post content based on external news and trend information. For example, it gives high ratings to posts related to socially important themes. In this way, it is possible to reflect the rating by referring to external news and trend information related to the post content.

[0063] The evaluation unit can collect other users' emotional reactions to the content posted by a user and make an evaluation based on those reactions. The evaluation unit, for example, uses an emotion estimation function to analyze what emotions other users have toward the post and makes an evaluation based on those emotional reactions. For example, it gives a high rating to a post in which many users have positive emotions. The evaluation unit also collects other users' emotional reaction data and adjusts the evaluation of the posted content based on that data. For example, it gives a low rating to a post in which many users have negative emotional reactions. The evaluation unit also uses the emotion estimation function to collect other users' emotional reactions in real time and make an evaluation based on those reactions. For example, it makes an evaluation based on the emotional reactions immediately after posting. In this way, by collecting other users' emotional reactions, it is possible to make an evaluation based on those reactions.

[0064] The feedback unit can customize the feedback content based on the user's past feedback history. For example, the feedback unit analyzes the user's past feedback history and customizes the feedback content based on that history. For example, for a user who has previously preferred positive feedback, the feedback unit provides feedback that emphasizes positive content. The feedback unit also analyzes what kind of feedback the user is likely to accept based on the feedback history and adjusts the feedback content based on the results. For example, for a user who prefers specific advice, the feedback unit provides feedback that includes specific areas for improvement. The feedback unit also takes the user's feedback history into consideration and provides feedback that is consistent with the past feedback content. For example, for a poster who has been rated "inspirational" in the past, the feedback unit provides similar inspiring feedback. In this way, the feedback content can be customized by taking the user's past feedback history into consideration.

[0065] The feedback unit can include specific improvements and advice for the next post in the feedback content. For example, the feedback unit includes specific improvements in the feedback content to indicate how the user can improve in their next post. For example, the feedback unit provides specific advice such as, "Next time, make the background brighter so that the photo stands out more." The feedback unit also includes advice for the next post in the feedback to promote user growth. For example, the feedback unit provides advice such as, "Next time, try writing more in detail about this topic." The feedback unit also indicates specific improvements in the feedback content to enable the user to understand how they can improve their post. For example, the feedback unit provides advice such as, "Next time, try to improve the composition to make the photo more appealing." In this way, by including specific improvements and advice for the next post in the feedback content, user growth can be promoted.

[0066] The feedback unit can use the emotion estimation function to estimate the emotion the user felt when receiving feedback and adjust the feedback content based on that emotion. For example, the feedback unit uses the emotion estimation function to analyze the emotion the user felt when receiving feedback in real time and adjust the feedback content based on that emotion. For example, if the user felt negative, the feedback unit provides encouraging feedback. The feedback unit also analyzes the user's emotional response and customizes the feedback content based on the results. For example, if the user felt positive, the feedback unit provides feedback that emphasizes that emotion. The feedback unit also uses the emotion estimation function to build a system that provides feedback according to the user's emotion. For example, if the user felt happy when receiving feedback, the feedback unit provides positive feedback that reflects that emotion. In this way, by estimating the emotion the user felt when receiving feedback and adjusting the feedback content based on that emotion, it is possible to provide appropriate feedback according to the user's emotion.

[0067] The feedback unit can provide the feedback content not only in text format but also in audio or video format. For example, the feedback unit provides the feedback content in audio format so that the user can hear and understand it. For example, the generation AI provides a function to read the feedback aloud. The feedback unit also provides the feedback content in video format to make it easier to understand visually. For example, the generation AI provides a function to explain the feedback content in video. The feedback unit also provides feedback in multiple formats, including text, audio, and video, so that the user can select the format that is easiest to understand. For example, the feedback content may be displayed in text and simultaneously explained in audio or video. In this way, providing the feedback content in not only text format but also audio or video format can deepen the user's understanding.

[0068] The feedback unit can share the feedback content with the user's friends and followers, thereby promoting feedback throughout the community. For example, the feedback unit can share the feedback content with the user's friends and followers, thereby promoting feedback throughout the community. For example, a function is provided in which the generation AI notifies the user's followers of the feedback content. The feedback unit can also share the feedback content to promote additional feedback from other users by sharing the feedback content. For example, a function is provided in which friends and followers can add comments to the feedback content. In order to promote feedback throughout the community, the feedback unit can also provide a function in which the feedback content is made public and other users can express their opinions on it. For example, a function is provided in which the feedback content is shared on social media. This allows the feedback content to be shared with the user's friends and followers, thereby promoting feedback throughout the community.

[0069] The feedback unit can use the emotion estimation function to collect the user's emotional reactions to the feedback content and continuously improve the feedback content based on the reactions. For example, the feedback unit can use the emotion estimation function to collect the user's emotional reactions to the feedback content in real time and continuously improve the feedback content based on the data. For example, the feedback unit can prioritize the adoption of feedback content in which the user has positive emotions. The feedback unit can also analyze the user's emotional reaction data and adjust the feedback content based on the results. For example, the feedback unit can modify feedback content that has a high number of negative emotional reactions. The feedback unit can also use the emotion estimation function to identify areas in the feedback content that need improvement and optimize the feedback content according to the user's emotional reactions. For example, the feedback unit can suggest modifying parts with low emotional scores. In this way, by collecting the user's emotional reactions to the feedback content and continuously improving the feedback content based on the reactions, more appropriate feedback can be provided.

[0070] The evaluation unit can monitor the user's mental state and encourage them to consult a mental health professional if necessary. For example, the generation AI analyzes the content of the user's posts and monitors the user's mental state. For example, if negative emotions persist, the evaluation unit sends a notification encouraging the user to consult a mental health professional. The evaluation unit also evaluates the user's mental state and suggests consulting a mental health professional if necessary. For example, if stress or anxiety is increasing, the evaluation unit provides the contact information of a professional. The evaluation unit also suggests to the user when to consult a mental health professional based on the results of the mental state monitoring. For example, if negative posts continue for a long period of time, the evaluation unit recommends professional support. In this way, the user's mental state can be monitored and, if necessary, the user can be encouraged to consult a mental health professional, thereby providing mental support.

[0071] The evaluation unit can suggest activities for relaxation and stress relief to the user. For example, the generation AI evaluates the user's mental state and suggests activities for relaxation and stress relief. For example, it may recommend a meditation or yoga session. The evaluation unit may also analyze the content posted by the user and suggest relaxation activities if stress levels are high. For example, it may recommend a nature walk or listening to music. The evaluation unit may also suggest specific activities for stress relief to the user based on the evaluation results of the mental state. For example, it may recommend art therapy or massage. In this way, by suggesting activities for relaxation and stress relief to the user, it is possible to improve the user's mental stability.

[0072] The evaluation unit can use the emotion estimation function to evaluate the user's mental state in real time and provide appropriate feedback. For example, the evaluation unit uses the emotion estimation function to evaluate the user's mental state in real time and provide feedback based on the evaluation result. For example, if the user is feeling stressed, the evaluation unit provides advice to relax. The evaluation unit also analyzes the user's emotions in real time and provides feedback according to the emotions. For example, if the user is feeling anxious, the evaluation unit sends a message that gives a sense of security. The evaluation unit also uses the emotion estimation function to build a system that evaluates the user's mental state and provides appropriate feedback based on the evaluation result. For example, if the user is feeling depressed, the evaluation unit sends an encouraging message. In this way, the user's mental state can be evaluated in real time and appropriate feedback can be provided based on the evaluation result, thereby providing psychological support to the user.

[0073] The evaluation unit can suggest community activities and events to improve the user's mental state. For example, the generation AI evaluates the user's mental state and suggests community activities and events. For example, it introduces local volunteer activities and hobby circles. The evaluation unit also recommends participation in community activities and events to improve the user's mental state. For example, it suggests sporting events and cultural exchange events. The evaluation unit also builds a system that suggests community activities and events to the user based on the results of the mental state evaluation. For example, it introduces appropriate events based on the user's interests and concerns. In this way, the user's mental stability can be achieved by suggesting community activities and events to improve the user's mental state.

[0074] The evaluation unit can provide the user with information and resources related to mental health. For example, the generation AI evaluates the user's mental state and provides information and resources related to mental health. For example, it introduces stress management methods and counseling services. The evaluation unit also provides information related to mental health to improve the user's mental state. For example, it introduces relaxation techniques and self-care methods. The evaluation unit also builds a system that provides users with mental health resources based on the results of the mental state evaluation. For example, it introduces online counseling and mental health apps. In this way, the user can be provided with information and resources related to mental health, thereby providing them with mental support.

[0075] The evaluation unit can use the emotion estimation function to provide feedback according to the user's mental state and provide mental support. For example, the evaluation unit uses the emotion estimation function to evaluate the user's mental state in real time and provide feedback based on the evaluation result. For example, if the user is feeling stressed, the evaluation unit provides advice to help the user relax. The evaluation unit also analyzes the user's emotions in real time and provides feedback according to the emotions. For example, if the user is feeling anxious, the evaluation unit sends a message that gives a sense of security. The evaluation unit also uses the emotion estimation function to build a system that evaluates the user's mental state and provides appropriate feedback based on the evaluation result. For example, if the user is feeling depressed, the evaluation unit sends an encouraging message. In this way, feedback according to the user's mental state is provided and mental support is provided, thereby achieving mental stability for the user.

[0076] The evaluation unit can suggest specific actions to the user to promote interaction with other users. For example, the generation AI evaluates the content of the user's post and suggests specific actions to promote interaction with other users. For example, the evaluation unit makes a suggestion such as, "Try discussing this post with other users." The evaluation unit also analyzes the content of the user's post and suggests actions to promote interaction with other users. For example, the suggestion is, "Try exchanging opinions with other users on this topic." The evaluation unit also builds a system that suggests specific actions to the user to promote interaction with other users based on the evaluation made by the generation AI. For example, the suggestion is, "Try collaborating with other users on this post." In this way, by suggesting specific actions to the user to promote interaction with other users, the user's sociability can be improved.

[0077] The evaluation unit can provide users with training and resources to improve their communication skills. For example, the generation AI evaluates the content of a user's posts and provides training and resources to improve their communication skills. For example, the evaluation unit may introduce online courses or workshops. The evaluation unit also analyzes the content of a user's posts and provides specific advice to improve their communication skills. For example, the evaluation unit may provide advice such as, "It would be better if you provided more specific examples." The evaluation unit also builds a system that provides users with training and resources to improve their communication skills based on the evaluation performed by the generation AI. For example, the evaluation unit may introduce books or videos to improve communication skills. In this way, by providing users with training and resources to improve their communication skills, the user's sociability can be improved.

[0078] The evaluation unit can use the emotion estimation function to evaluate the user's social emotions and provide feedback to improve sociability based on the emotions. For example, the evaluation unit uses the emotion estimation function to evaluate the user's social emotions in real time and provide feedback based on the evaluation result. For example, if the user feels lonely, the evaluation unit sends a message encouraging interaction with other users. The evaluation unit also analyzes the user's social emotions and provides feedback to improve sociability based on the emotions. For example, if the user feels anxious, the evaluation unit sends a message that gives a sense of security. The evaluation unit also uses the emotion estimation function to build a system that evaluates the user's social emotions and provides appropriate feedback based on the evaluation result. For example, if the user is depressed, the evaluation unit sends an encouraging message. In this way, the user's sociality can be improved by evaluating the user's social emotions and providing feedback to improve sociability based on the emotions.

[0079] The evaluation unit can make suggestions to the user to promote interactions on other social media platforms. For example, the generation AI evaluates the content of the user's posts and makes suggestions to promote interactions on other social media platforms. For example, the evaluation unit makes a suggestion such as, "Try sharing this post on Twitter as well." The evaluation unit also analyzes the content of the user's posts and suggests specific actions to promote interactions on other social media platforms. For example, the evaluation unit makes a suggestion such as, "Try discussing this topic with your followers on Instagram." The evaluation unit also builds a system that makes suggestions to the user to promote interactions on other social media platforms based on the evaluation made by the generation AI. For example, the evaluation unit makes a suggestion such as, "Try sharing this post on LinkedIn to gather professional opinions." This makes it possible to improve the user's sociality by making suggestions to promote interactions on other social media platforms.

[0080] The evaluation unit can suggest offline social events and activities to the user. For example, the generation AI evaluates the content of the user's posts and suggests offline social events and activities. For example, it makes a suggestion such as, "Try attending a workshop on this topic." The evaluation unit also analyzes the content of the user's posts and suggests specific actions to promote offline interactions. For example, it makes a suggestion such as, "Try attending a seminar on this topic." The evaluation unit also builds a system that suggests offline social events and activities to the user based on the evaluation made by the generation AI. For example, it makes a suggestion such as, "Try attending a meetup on this topic and exchange opinions with other participants." In this way, by suggesting offline social events and activities to the user, it is possible to improve the user's sociability.

[0081] The evaluation unit can use the emotion estimation function to evaluate the user's social emotions and provide feedback to promote interaction with other users based on the emotions. For example, the evaluation unit uses the emotion estimation function to evaluate the user's social emotions in real time and provide feedback based on the evaluation result. For example, if the user feels lonely, the evaluation unit sends a message encouraging interaction with other users. The evaluation unit also analyzes the user's social emotions and provides feedback to promote interaction with other users based on the emotions. For example, if the user feels anxious, the evaluation unit sends a message that gives a sense of security. The evaluation unit also uses the emotion estimation function to build a system that evaluates the user's social emotions and provides appropriate feedback based on the evaluation result. For example, if the user is depressed, the evaluation unit sends an encouraging message. This allows the user's sociality to be improved by evaluating the user's social emotions and providing feedback to promote interaction with other users based on the emotions.

[0082] The evaluation unit can dynamically adjust the evaluation criteria when continuously evaluating a user's posts and provide feedback according to the user's growth. For example, the evaluation unit has a generation AI continuously evaluate a user's posts and dynamically adjust the evaluation criteria. For example, by tightening the evaluation criteria as the user grows, a higher level of feedback is provided. The evaluation unit also evaluates the user's growth and provides feedback according to that growth. For example, basic advice is provided in the initial stage, and more advanced advice is provided as the user grows. The evaluation unit also builds a system in which the generation AI continuously evaluates a user's posts and dynamically adjusts the evaluation criteria. For example, the evaluation criteria are adaptively changed as the content of the user's posts evolve. In this way, by continuously evaluating a user's posts and dynamically adjusting the evaluation criteria, feedback according to the user's growth can be provided.

[0083] When continuously evaluating a user's posts, the evaluation unit analyzes the evaluation results over time to visualize the user's growth and changes. For example, the evaluation unit has a generation AI continuously evaluate the user's posts and analyze the evaluation results over time. For example, the evaluation unit visualizes the growth by displaying changes in the content of the user's posts in a graph. The evaluation unit also analyzes the user's growth and changes over time and reflects the results in feedback. For example, it shows how the user has grown compared to past posts. The evaluation unit also builds a system in which the generation AI continuously evaluates the user's posts and analyzes the evaluation results over time. For example, it visualizes growth trends based on the user's posting history. In this way, by continuously evaluating the user's posts and analyzing the evaluation results over time, it is possible to visualize the user's growth and changes.

[0084] The evaluation unit can use the emotion estimation function to continuously monitor changes in the user's emotions and provide feedback based on those changes. For example, the evaluation unit uses the emotion estimation function to continuously monitor changes in the user's emotions and provide feedback based on those changes. For example, if the user's emotions change to a positive one, the evaluation unit provides feedback praising the change. The evaluation unit also analyzes changes in the user's emotions and adjusts the feedback content based on those changes. For example, if negative emotions persist, the evaluation unit provides encouraging feedback. The evaluation unit also uses the emotion estimation function to continuously monitor changes in the user's emotions and builds a system that provides appropriate feedback based on those changes. For example, if the user's emotions are stable, the evaluation unit provides advice to maintain that stability. In this way, by continuously monitoring changes in the user's emotions and providing feedback based on those changes, it is possible to provide appropriate feedback according to the user's emotions.

[0085] When continuously evaluating a user's posts, the evaluation unit can compare them with other users and provide a relative evaluation. For example, the generation AI continuously evaluates the user's posts and compares them with other users. For example, the evaluation is performed by comparing them with other users who have posted on the same topic. The evaluation unit also compares the content of the user's posts with other users and reflects the results in feedback. For example, it indicates areas where the user excels over other users and areas for improvement. The evaluation unit also builds a system in which the generation AI continuously evaluates the user's posts and compares them with other users. For example, it compares users who have posted in the same category and evaluates them. This makes it possible to provide a relative evaluation by comparing the user's posts with other users.

[0086] The evaluation unit can use the emotion estimation function to continuously monitor changes in the user's emotions and provide feedback to promote interaction with other users based on those changes. For example, the evaluation unit can use the emotion estimation function to continuously monitor changes in the user's emotions and provide feedback to promote interaction with other users based on those changes. For example, if the user has positive emotions, the evaluation unit can send a message encouraging interaction with other users. The evaluation unit can also analyze changes in the user's emotions and provide feedback to promote interaction with other users based on those changes. For example, if the user feels lonely, the evaluation unit can send a message encouraging interaction with other users. The evaluation unit can also use the emotion estimation function to continuously monitor changes in the user's emotions and build a system that provides appropriate feedback based on those changes. For example, if the user feels anxious, the evaluation unit can send a message that gives a sense of security. This allows the user's sociability to be improved by continuously monitoring changes in the user's emotions and providing feedback to promote interaction with other users based on those changes.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The self-expression evaluation system can further include a recommendation unit that recommends content related to a user's interests and concerns based on the content posted by the user. For example, if a user frequently posts about a particular theme, articles and videos related to that theme can be recommended. Also, if a user posts about a particular genre of music or movie, new or popular works related to that genre can be recommended. Furthermore, related online communities and forums can be recommended based on the content posted by the user. This allows users to easily find content that matches their interests and broaden the scope of self-expression.

[0089] The self-expression evaluation system may further include a learning support unit that provides learning resources to improve a user's skills and knowledge based on the user's posts. For example, if a user posts about a specific technology or skill, online courses and tutorials related to that technology or skill may be recommended. Also, if a user expresses interest in a specific field, books and research papers related to that field may be recommended. Furthermore, related workshops and seminars may be recommended based on the user's posts. This allows users to easily find resources to improve their skills and knowledge and promote self-development.

[0090] The self-expression evaluation system may further include a health management unit that monitors the user's health status and provides health management advice based on the user's posted content. For example, if the user posts about stress or fatigue, the system may suggest activities for relaxation and stress relief. If the user posts about diet and exercise, the system may recommend healthy meal plans and exercise programs. Furthermore, the system may recommend regular health checks and medical consultations based on the user's posted content. This allows the user to manage their health status and receive support for maintaining a healthy lifestyle.

[0091] The self-expression evaluation system may further include a career support unit that provides advice related to the user's career or occupation based on the user's posted content. For example, if the user posts about their career concerns or goals, career counseling and coaching resources may be recommended. Also, if the user expresses interest in a particular occupation or industry, job information and internship opportunities related to that occupation or industry may be provided. Furthermore, based on the user's posted content, courses for acquiring skills and qualifications to advance their career may be recommended. This allows the user to receive support in achieving their career goals.

[0092] The self-expression evaluation system can further include a lifestyle support unit that suggests activities related to the user's hobbies and lifestyle based on the content posted by the user. For example, if the user posts about a particular hobby, events and workshops related to that hobby can be recommended. Also, if the user posts about travel or outdoor activities, information on travel destinations and outdoor spots can be provided. Furthermore, based on the content posted by the user, activities that support lifestyle improvement or the discovery of new hobbies can be suggested. This allows the user to receive support for enriching their lifestyle.

[0093] The evaluation unit can estimate a user's emotions, predict other users' emotional reactions to the user's posted content based on the estimated user emotions, and reflect the predicted results in the feedback. For example, if a user posts a content that expresses positive emotions, the evaluation unit can predict what emotional reactions the post will evoke in other users and provide feedback based on the predicted results. Also, if a user posts a content that expresses negative emotions, the evaluation unit can predict the impact the post will have on other users and provide appropriate feedback. Furthermore, the evaluation unit can analyze the relationship between the user's emotions and the emotional reactions of other users and adjust the feedback content based on the results. In this way, by predicting other users' emotional reactions based on the user's emotions and reflecting the predicted results in the feedback, more appropriate feedback can be provided.

[0094] The evaluation unit can estimate a user's emotions, predict other users' emotional reactions to the user's posted content based on the estimated user emotions, and reflect the predicted results in the feedback. For example, if a user posts a content that expresses positive emotions, the evaluation unit can predict what emotional reactions the post will evoke in other users and provide feedback based on the predicted results. Also, if a user posts a content that expresses negative emotions, the evaluation unit can predict the impact the post will have on other users and provide appropriate feedback. Furthermore, the evaluation unit can analyze the relationship between the user's emotions and the emotional reactions of other users and adjust the feedback content based on the results. In this way, by predicting other users' emotional reactions based on the user's emotions and reflecting the predicted results in the feedback, more appropriate feedback can be provided.

[0095] The evaluation unit can estimate a user's emotions, predict other users' emotional reactions to the user's posted content based on the estimated user emotions, and reflect the predicted results in the feedback. For example, if a user posts a content that expresses positive emotions, the evaluation unit can predict what emotional reactions the post will evoke in other users and provide feedback based on the predicted results. Also, if a user posts a content that expresses negative emotions, the evaluation unit can predict the impact the post will have on other users and provide appropriate feedback. Furthermore, the evaluation unit can analyze the relationship between the user's emotions and the emotional reactions of other users and adjust the feedback content based on the results. In this way, by predicting other users' emotional reactions based on the user's emotions and reflecting the predicted results in the feedback, more appropriate feedback can be provided.

[0096] The evaluation unit can estimate a user's emotions, predict other users' emotional reactions to the user's posted content based on the estimated user emotions, and reflect the predicted results in the feedback. For example, if a user posts a content that expresses positive emotions, the evaluation unit can predict what emotional reactions the post will evoke in other users and provide feedback based on the predicted results. Also, if a user posts a content that expresses negative emotions, the evaluation unit can predict the impact the post will have on other users and provide appropriate feedback. Furthermore, the evaluation unit can analyze the relationship between the user's emotions and the emotional reactions of other users and adjust the feedback content based on the results. In this way, by predicting other users' emotional reactions based on the user's emotions and reflecting the predicted results in the feedback, more appropriate feedback can be provided.

[0097] The evaluation unit can estimate a user's emotions, predict other users' emotional reactions to the user's posted content based on the estimated user emotions, and reflect the predicted results in the feedback. For example, if a user posts a content that expresses positive emotions, the evaluation unit can predict what emotional reactions the post will evoke in other users and provide feedback based on the predicted results. Also, if a user posts a content that expresses negative emotions, the evaluation unit can predict the impact the post will have on other users and provide appropriate feedback. Furthermore, the evaluation unit can analyze the relationship between the user's emotions and the emotional reactions of other users and adjust the feedback content based on the results. In this way, by predicting other users' emotional reactions based on the user's emotions and reflecting the predicted results in the feedback, more appropriate feedback can be provided.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The post analysis unit analyzes the content posted by the user. For example, the post analysis unit analyzes the content posted by the user using text analysis technology. The post analysis unit can also analyze posted images using image recognition technology. Furthermore, the post analysis unit can analyze the emotional tone of the posted content using sentiment analysis technology. Specifically, natural language processing technology is used to analyze the meaning of the text and extract the subject and emotion of the posted content. Image recognition technology analyzes the content of posted images and identifies objects and scenes within the images. Sentiment analysis technology analyzes the emotional tone from text and images and classifies emotions as positive, negative, or neutral. Step 2: The evaluation unit evaluates the content analyzed by the post analysis unit. For example, the evaluation unit gives a high rating to a positive post. It can also give an evaluation that indicates areas for improvement to a negative post. Furthermore, the evaluation unit can perform evaluations that take into account the emotional impact of the post content. Specifically, it uses emotion analysis technology to evaluate the emotional impact of the post content and performs evaluations based on the results. Step 3: The feedback unit provides the results of the evaluation by the evaluation unit to the user. For example, the feedback unit notifies the user of the evaluation results as a text message. The evaluation results can also be sent to the user's device as a notification. The evaluation results can also be provided to the user in report format. Specifically, the evaluation results are notified to the user as a text message, explaining the details of the evaluation. The notification is sent to the user's smartphone or computer and can be viewed immediately. Report-format feedback is provided as a document summarizing the evaluation results in detail, which the user can refer to later.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] The 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a post analysis unit that analyzes the content posted by users; an evaluation unit that evaluates the content analyzed by the post analysis unit; a feedback unit that feeds back the results of the evaluation by the evaluation unit to a user. A system characterized by:

2. The post analysis unit Provide more personalized evaluations based on users' past posting history and behavioral patterns 2. The system of claim 1.

3. The evaluation unit Analyze the emotional tone or nuance of posts and rate them based on their emotional impact 2. The system of claim 1.

4. The evaluation unit Estimate the user's emotions when posting and evaluate based on those emotions 2. The system of claim 1.

5. The post analysis unit Integrate data from other social media platforms to assess from a broader perspective 2. The system of claim 1.

6. The post analysis unit Refer to external news or trending information related to the post and incorporate it into the rating.

2. The system of claim 1.

Images