system

Abstract

The system according to this embodiment aims to perform sentiment analysis based on the user's behavior history and speech content, and to provide positive feedback and a virtual reality environment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a suggestion unit, a provision unit, and a re-experience unit. The collection unit collects the user's behavior history and speech content. The analysis unit performs sentiment analysis on the data collected by the collection unit. The suggestion unit makes suggestions to praise the user based on the analysis results obtained by the analysis unit. The provision unit provides a virtual reality environment based on the content suggested by the suggestion unit. The re-experience unit records the user's success experiences and goal achievements in the environment provided by the provision unit and allows the user to re-experience them.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, sentiment analysis based on a user's action history and utterance content and the provision of positive feedback have not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to perform sentiment analysis based on a user's action history and utterance content and provide positive feedback and a virtual reality environment.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a provision unit, and a re-experience unit. The collection unit collects the user's behavior history and utterances. The analysis unit performs sentiment analysis on the data collected by the collection unit. The suggestion unit makes suggestions to praise the user based on the analysis results obtained by the analysis unit. The provision unit provides a virtual reality environment based on the content suggested by the suggestion unit. The re-experience unit records the user's successful experiences and goal achievements in the environment provided by the provision unit and allows the user to re-experience them. [Effects of the Invention]

[0007] The system according to this embodiment can perform sentiment analysis based on the user's behavior history and speech content, and provide positive feedback and a virtual reality environment. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The positive support system according to an embodiment of the present invention is a system aimed at enabling users to live their daily lives positively. This system provides a function in which AI automatically analyzes the emotions of the user's behavior history and speech content and spontaneously praises their good points. The AI ​​also suggests specific areas for improvement to the user. Furthermore, it provides an environment in which the user can build self-esteem and experience growth using virtual reality. In addition, it provides a function to record the user's success experiences and goal achievements and allow them to re-experience these positive experiences. For example, it collects the user's behavior history and speech content. For example, it collects content posted by the user on social media and daily conversations. Next, the AI ​​performs an emotional analysis on the collected data. The AI ​​extracts positive elements from the user's statements and actions and spontaneously praises the user's good points. For example, it provides feedback such as, "Your statements are very positive and wonderful." Furthermore, the AI ​​suggests specific areas for improvement from the user's actions and statements. For example, it provides advice such as, "It would be good if you actively expressed your opinions more." This allows the user to gain hints for self-improvement. Next, it provides an environment in which the user can build self-esteem and experience growth using virtual reality. Users can visually confirm their growth in a VR space, thereby boosting their self-esteem. For example, they can relive their achieved goals and successes in the VR space. Finally, the system records users' successes and goal achievements and provides a function to relive these positive experiences. By reflecting on past successes, users can further enhance their self-esteem. For example, by re-experiencing the results of projects they have completed, they can gain confidence. In this way, the positive support system utilizes AI and virtual reality to boost users' self-esteem and help them live more positive lives.

[0029] The positive support system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a provision unit, and a re-experience unit. The collection unit collects the user's behavioral history and speech content. For example, the collection unit collects content posted by the user on social media and everyday conversations. The collection unit can also collect, for example, website browsing history and app usage history. The collection unit can also collect, for example, the user's speech content using speech recognition technology. The analysis unit performs sentiment analysis on the data collected by the collection unit. For example, the analysis unit analyzes the user's statements and actions using natural language processing technology and classifies the emotions. For example, the analysis unit learns criteria for classifying emotions using a machine learning algorithm and extracts positive elements. For example, the analysis unit can also extract positive keywords based on the results of the sentiment analysis. The suggestion unit makes suggestions to praise the user based on the analysis results obtained by the analysis unit. For example, the suggestion unit provides feedback to the user regarding their statements and actions, such as "Your statements are very positive and wonderful." The proposal department, for example, proposes specific areas for improvement based on the user's actions and statements. The proposal department provides advice such as, for example, "It would be good to express your opinions more actively." The service provider provides a virtual reality environment based on the content proposed by the proposal department. The service provider provides a VR space where the user can build self-esteem and experience growth. The service provider allows the user to re-experience achieved goals and successes in the VR space. The service provider provides a function to record the user's successes and goal achievements and allow them to re-experience these positive experiences. The re-experience department records the user's successes and goal achievements in the environment provided by the service provider and allows them to re-experience them. The re-experience department allows the user to gain confidence by re-experience the results of projects they have achieved. The re-experience department allows the user to further enhance their self-esteem by reflecting on their successes. As a result, the positive support system according to this embodiment can help users enhance their self-esteem and live positive days.

[0030] The data collection unit collects user behavior history and speech content. For example, it collects content posted by users on social media and everyday conversations. Specifically, social media posts are collected as text data, serving as foundational data for analyzing user emotions and intentions. Everyday conversations are converted into text data using speech recognition technology and similarly used for analysis. The data collection unit can also collect website browsing history and app usage history, for example. This allows for an understanding of what information users are interested in and what behavioral patterns they have. The data collection unit can also collect user speech content using speech recognition technology, for example. Speech recognition technology converts user speech into text in real time, and this data is used for analyzing emotions and intentions. As a result, the data collection unit can comprehensively collect diverse user behavior and speech content, providing a rich dataset for analysis. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department performs sentiment analysis on the data collected by the data collection department. For example, the analysis department uses natural language processing (NLP) to analyze user statements and actions and classify emotions. Specifically, it uses NLP to classify emotions such as positive, negative, and neutral from user statements and actions. The analysis department also uses machine learning algorithms to learn emotion classification criteria and extract positive elements. Machine learning algorithms learn emotion patterns based on large amounts of data and can classify emotions with high accuracy even for new data. The analysis department can also extract positive keywords based on the results of the sentiment analysis. This allows for the identification of positive elements from user statements and actions, providing foundational data to enhance user self-esteem. Furthermore, the analysis department can utilize historical data and statistical information to analyze long-term emotional fluctuations and trends. For example, it can analyze emotional fluctuations over a specific period based on users' past statements and actions to identify positive and negative changes. Additionally, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to handle not only real-time sentiment analysis but also long-term sentiment fluctuations and anomaly detection, improving the overall reliability and security of the system.

[0032] The Suggestion Department makes suggestions to praise users based on the analysis results obtained by the Analysis Department. For example, the Suggestion Department provides feedback such as, "Your comments are very positive and wonderful," in response to a user's statements or actions. Specifically, it generates appropriate feedback for users based on positive keywords and sentiment analysis results provided by the Analysis Department. The Suggestion Department also suggests specific areas for improvement based on the user's actions and statements. For example, it might offer advice such as, "It would be good to express your opinions more actively." This allows users to receive specific feedback on their actions and statements and gain guidance for self-improvement. Furthermore, the Suggestion Department can also provide advice for setting long-term goals and growth based on the user's past actions and statements. For example, it can set new goals based on goals and successes the user has achieved in the past and provide specific advice for achieving them. In addition, the Suggestion Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the Suggestion Department to provide users with appropriate and effective feedback and support them in increasing their self-esteem.

[0033] The service provider will provide a virtual reality environment based on the proposals submitted by the proposal team. For example, the service provider will provide a VR space where users can build self-esteem and experience growth. Specifically, users can re-experience goals they have achieved and successes they have had in the VR space. This allows users to visually reaffirm their successes and boost their self-esteem. The service provider will also provide a function to record users' successes and goal achievements and allow them to re-experience these positive experiences. This allows users to reflect on past successes and further enhance their self-esteem. Furthermore, the service provider can collect user feedback and continuously improve the content and functions of the VR space. For example, based on feedback on what users experienced in the VR space, new scenarios and functions can be added to enrich the user experience. The service provider can also facilitate interaction and cooperation among users by allowing multiple users to experience the VR space simultaneously. In this way, the service provider can provide users with a wealth of experiences to boost their self-esteem and help them live more positive lives.

[0034] The Re-experience Department records and allows users to re-experience their successes and goal achievements within the environment provided by the Service Provider Department. For example, the Re-experience Department can help users gain confidence by allowing them to re-experience the results of projects they have achieved. Specifically, it can recreate projects and goals that users have achieved in the past in a VR space and allow them to re-experience those successes. For example, the Re-experience Department can further enhance users' self-esteem by allowing them to reflect on their successes. This allows users to visually reconfirm past successes and gain confidence. Furthermore, the Re-experience Department can collect user feedback and continuously improve the content and methods of the re-experience. For example, based on feedback on the content of the user's re-experience, new scenarios and functions can be added to enrich the user experience. The Re-experience Department can also promote interaction and cooperation among users by allowing multiple users to re-experience simultaneously. In this way, the Re-experience Department can provide users with a wealth of experiences to enhance their self-esteem and help them live more positive lives.

[0035] The data collection unit can collect users' social media posts and daily conversations. For example, the data collection unit can collect content posted by users on social media. For example, the data collection unit can also collect users' daily conversations using speech recognition technology. For example, the data collection unit can also collect text data from chat applications used by users. This allows for more detailed data to be obtained by collecting users' social media posts and daily conversations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into AI, and the AI ​​can automatically collect the data.

[0036] The analysis unit can perform sentiment analysis on the collected data and extract positive elements. For example, the analysis unit can use natural language processing technology to analyze user statements and actions and classify emotions. The analysis unit can also use machine learning algorithms to learn emotion classification criteria and extract positive elements. The analysis unit can also extract positive keywords based on the results of the sentiment analysis. In this way, by performing sentiment analysis on the collected data and extracting positive elements, the positive aspects of the user can be highlighted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, which can then automatically perform sentiment analysis.

[0037] The suggestion department can propose specific areas for improvement based on the user's actions and statements. For example, the suggestion department can provide advice such as, "It would be good to express your opinions more actively" in response to the user's actions and statements. The suggestion department can also extract specific areas for improvement from the user's actions and statements and propose them. For example, the suggestion department can analyze the user's behavior history and propose areas for improvement. In this way, by proposing specific areas for improvement based on the user's actions and statements, it is possible to promote self-improvement by the user. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input user behavior and statement data into AI, and the AI ​​can automatically propose areas for improvement.

[0038] The service provider can provide a virtual reality environment in which users can build self-esteem and experience growth. For example, the service provider can allow users to re-experience goals they have achieved and successes in a VR space. The service provider can also provide a VR space in which users can build self-esteem and experience growth. For example, the service provider can record users' successes and goal achievements and provide a function to allow users to re-experience these positive experiences. In this way, by providing a virtual reality environment in which users can build self-esteem and experience growth, the service provider can enhance users' self-esteem. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user success experience data into AI, and the AI ​​can automatically generate a VR environment.

[0039] The re-experience unit can record and allow users to re-experience their successes and goal achievements. For example, the re-experience unit can help users gain confidence by allowing them to re-experience the results of projects they have completed. For example, the re-experience unit can further enhance users' self-esteem by allowing them to reflect on their successes. The re-experience unit can also provide a function to record users' successes and goal achievements and allow them to re-experience these positive experiences. This allows users to further enhance their self-esteem by recording and allowing them to re-experience their successes and goal achievements. Some or all of the above processes in the re-experience unit may be performed using AI, for example, or not using AI. For example, the re-experience unit can input user success data into AI, which can then automatically generate re-experience scenarios.

[0040] The data collection unit can analyze the user's past behavior history and select the optimal collection timing. For example, if the user was active during a specific time period in the past, the data collection unit will collect data during that time period. For example, if the user took a specific action on a specific day of the week in the past, the data collection unit can also collect data on that day of the week. For example, if the user took a positive action after a specific event in the past, the data collection unit can also collect data after that event. This allows for efficient data collection by analyzing the user's past behavior history and selecting the optimal collection timing. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI, which can then automatically select the optimal collection timing.

[0041] The data collection unit can filter data based on the user's current activities and areas of interest during collection. For example, if the user is currently interested in sports, the data collection unit will prioritize collecting sports-related data. For example, if the user is currently interested in reading, the data collection unit can also prioritize collecting reading-related data. For example, if the user is currently interested in travel, the data collection unit can also prioritize collecting travel-related data. This allows for the collection of highly relevant data by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest data into a generating AI, which can then automatically perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is participating in a specific event, the data collection unit can prioritize the collection of data related to that event. By prioritizing the collection of highly relevant data while considering the user's geographical location information, more appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI, which can then automatically prioritize the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if a user frequently posts about a particular topic, the data collection unit can collect data related to that topic. For example, if a user uses a particular hashtag, the data collection unit can also collect data related to that hashtag. For example, if a user participates in a particular group or community, the data collection unit can also collect data related to that group or community. This allows for the collection of more appropriate data by analyzing the user's social media activity and collecting relevant data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then automatically collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with an appropriate level of detail on data of moderate importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, which can then automatically adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit can also apply a speech recognition algorithm to audio data. This allows for more appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can automatically apply an appropriate analysis algorithm.

[0046] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department may prioritize the analysis of the most recent data. The analysis department may also prioritize the analysis of the most recent data while referring to past data. The analysis department may also prioritize the analysis of data submitted within a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI, and the generating AI can automatically determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may also analyze data with low relevance last. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then automatically adjust the order of analysis.

[0048] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department may prioritize the analysis of the most recent data. The analysis department may also prioritize the analysis of the most recent data while referring to past data. The analysis department may also prioritize the analysis of data submitted within a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI, and the generating AI can automatically determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may also analyze data with low relevance last. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then automatically adjust the order of analysis.

[0050] The suggestion unit can adjust the level of detail of its suggestions based on the user's behavior history. For example, if the user has previously preferred detailed suggestions, the suggestion unit will provide detailed suggestions. If the user has previously preferred concise suggestions, the suggestion unit can also provide concise suggestions. The suggestion unit can also provide suggestions with an appropriate level of detail based on the user's behavior history. By adjusting the level of detail of suggestions based on the user's behavior history, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user behavior history data into a generating AI, which can then automatically adjust the level of detail of the suggestions.

[0051] The suggestion unit can apply different suggestion algorithms depending on the user's area of ​​interest when making suggestions. For example, if the user is interested in sports, the suggestion unit will make sports-related suggestions. If the user is interested in reading, the suggestion unit can also make reading-related suggestions. If the user is interested in travel, the suggestion unit can also make travel-related suggestions. By applying different suggestion algorithms according to the user's area of ​​interest, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user area of ​​interest data into a generating AI, and the generating AI can automatically apply an appropriate suggestion algorithm.

[0052] The suggestion unit can prioritize suggestions based on the user's past successes. For example, the suggestion unit may prioritize suggesting methods that have worked for the user in the past. The suggestion unit may also avoid suggesting methods that have failed for the user in the past. The suggestion unit may also make optimal suggestions by referring to the user's past successes. This allows for more appropriate suggestions to be made by prioritizing suggestions based on the user's past successes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past success data into a generating AI, which can then automatically determine the priority of suggestions.

[0053] The suggestion unit can adjust the order of suggestions based on user relevance. For example, the suggestion unit may prioritize suggestions with high relevance. It may also choose to present suggestions with moderate relevance next, or suggest suggestions with low relevance last. By adjusting the order of suggestions based on user relevance, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input user relevance data into a generating AI, which can then automatically adjust the order of suggestions.

[0054] The service provider can select the optimal environment by referring to the user's past experiences at the time of service provision. For example, the service provider can recreate an environment in which the user was able to relax in the past. For example, the service provider can recreate an environment in which the user was pleased in the past. For example, the service provider can recreate an environment in which the user was comforted in the past. By doing so, by selecting the optimal environment by referring to the user's past experiences, a more appropriate environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past experience data into a generating AI, and the generating AI can automatically select the optimal environment.

[0055] The service provider can customize the environment based on the user's current living situation at the time of delivery. For example, if the user is stressed at work, the service provider can provide a relaxing office environment. For example, if the user is relaxing at home, the service provider can provide a home-like environment. For example, if the user is traveling, the service provider can provide an environment related to the travel destination. In this way, a more appropriate environment can be provided by customizing the environment based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current living situation data into a generating AI, and the generating AI can automatically customize the environment.

[0056] The service provider can select the optimal environment by considering the user's geographical location information at the time of service provision. For example, if the user is in a specific region, the service provider can provide an environment related to that region. For example, if the user is traveling, the service provider can also provide an environment related to the travel destination. For example, if the user is participating in a specific event, the service provider can also provide an environment related to that event. By selecting the optimal environment by considering the user's geographical location information, a more appropriate environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI, and the generating AI can automatically select the optimal environment.

[0057] The service provider can analyze the user's social media activity and suggest an environment at the time of provision. For example, if the user frequently posts about a particular topic, the service provider can provide an environment related to that topic. For example, if the user uses a particular hashtag, the service provider can also provide an environment related to that hashtag. For example, if the user participates in a particular group or community, the service provider can also provide an environment related to that group or community. In this way, by analyzing the user's social media activity and suggesting an environment, a more appropriate environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI, and the generating AI can automatically suggest an environment.

[0058] The re-experience unit can analyze the user's past successes during a re-experience to select the optimal method for that experience. For example, the re-experience unit can allow the user to re-experience a project they previously succeeded in. For example, the re-experience unit can allow the user to re-experience goals they previously achieved. For example, the re-experience unit can allow the user to re-experience awards and accolades they previously received. By analyzing the user's past successes and selecting the optimal method for that experience, a more appropriate re-experience can be provided. Some or all of the above-described processes in the re-experience unit may be performed using AI, for example, or without AI. For example, the re-experience unit can input the user's past success data into a generating AI, which can then automatically select the optimal method for that experience.

[0059] The re-experience unit can customize the means of re-experience based on the user's current living situation. For example, if the user is stressed at work, the re-experience unit can provide a relaxing re-experience method. For example, if the user is relaxing at home, the re-experience unit can also provide a home-like re-experience method. For example, if the user is traveling, the re-experience unit can also provide a re-experience method related to the travel destination. This allows for a more appropriate re-experience to be provided by customizing the means of re-experience based on the user's current living situation. Some or all of the above processing in the re-experience unit may be performed using AI, for example, or without AI. For example, the re-experience unit can input the user's current living situation data into a generating AI, which can then automatically customize the means of re-experience.

[0060] The re-experience unit can select the optimal re-experience method by considering the user's geographical location information during the re-experience process. For example, if the user is in a specific region, the re-experience unit can provide a re-experience method related to that region. For example, if the user is traveling, the re-experience unit can also provide a re-experience method related to the travel destination. For example, if the user is participating in a specific event, the re-experience unit can also provide a re-experience method related to that event. By selecting the optimal re-experience method by considering the user's geographical location information, a more appropriate re-experience can be provided. Some or all of the above processing in the re-experience unit may be performed using AI, for example, or without AI. For example, the re-experience unit can input the user's geographical location data into a generating AI, which can then automatically select the optimal re-experience method.

[0061] The re-experience unit can analyze the user's social media activity during a re-experience and suggest ways to re-experience it. For example, if the user frequently posts about a particular topic, the re-experience unit can provide re-experience methods related to that topic. For example, if the user uses a particular hashtag, the re-experience unit can also provide re-experience methods related to that hashtag. For example, if the user participates in a particular group or community, the re-experience unit can also provide re-experience methods related to that group or community. By analyzing the user's social media activity and suggesting ways to re-experience it, a more appropriate re-experience can be provided. Some or all of the above processing in the re-experience unit may be performed using AI, for example, or without AI. For example, the re-experience unit can input the user's social media activity data into a generating AI, and the generating AI can automatically suggest ways to re-experience it.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] A positive support system can include a learning unit that learns the user's preferences and tendencies based on the user's behavioral history. The learning unit can, for example, learn activities the user has enjoyed in the past and suggest similar activities. The learning unit can also, for example, learn activities the user has avoided in the past and avoid suggesting them. The learning unit can also, for example, analyze the user's behavioral patterns and make suggestions at the optimal time. This allows for more appropriate support to be provided by making suggestions based on the user's preferences and tendencies. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral history data into a generating AI, which can then automatically perform the learning.

[0064] A positive support system may include an estimation unit that estimates the user's interests and concerns based on the user's behavioral history and spoken content. For example, the estimation unit may estimate interests based on activities in which the user has spent a lot of time in the past. The estimation unit may also estimate interests based on topics that the user frequently talks about. The estimation unit may also estimate interests based on accounts that the user follows on social media. This allows for more appropriate support to be provided by making suggestions based on the user's interests and concerns. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit may input the user's behavioral history data into a generating AI, which can then automatically estimate interests and concerns.

[0065] The positive support system may include a suggestion unit that makes suggestions to improve the user's lifestyle based on the user's behavioral history. For example, the suggestion unit may suggest similar activities based on the user's past healthy activities. For example, the suggestion unit may also suggest avoiding unhealthy activities based on the user's past avoidance of those activities. For example, the suggestion unit may analyze the user's behavioral patterns and suggest healthy activities at the optimal time. This allows for more appropriate support by making suggestions to improve the user's lifestyle. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input the user's behavioral history data into a generating AI, which can then automatically make suggestions.

[0066] A positive support system may include a planning unit that creates a plan to help the user achieve their goals based on the user's behavioral history. The planning unit may, for example, set similar goals based on goals the user has achieved in the past. The planning unit may also, for example, create a plan to avoid goals that the user has failed to achieve in the past. The planning unit may also, for example, analyze the user's behavioral patterns and create a plan for achieving goals at the optimal timing. This allows for the provision of more appropriate support by creating a plan to help the user achieve their goals. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit may input the user's behavioral history data into a generating AI, and the generating AI may automatically create a plan.

[0067] The positive support system may include a material provision unit that provides learning materials to support the user's learning based on the user's behavioral history. For example, the material provision unit may provide similar materials based on what the user has learned in the past. For example, the material provision unit may also provide materials to help the user overcome areas where they have struggled in the past. For example, the material provision unit may analyze the user's learning patterns and provide materials at the optimal time. This allows for more appropriate support by providing learning materials to assist the user's learning. Some or all of the above-described processes in the material provision unit may be performed using AI, for example, or without AI. For example, the material provision unit may input the user's behavioral history data into a generating AI, which can then automatically provide learning materials.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects the user's behavioral history and spoken content. For example, the data collection unit collects content from social media posts, daily conversations, website browsing history, app usage history, and spoken content using speech recognition technology. Step 2: The analysis unit performs sentiment analysis on the data collected by the collection unit. For example, it uses natural language processing techniques and machine learning algorithms to analyze users' statements and actions, classify their emotions, and extract positive elements and keywords. Step 3: The proposal team makes suggestions that praise the user based on the analysis results obtained by the analysis team. For example, they might provide feedback on the user's statements or actions such as, "Your comments are very positive and wonderful," or suggest specific areas for improvement. Step 4: The provisioning department provides a virtual reality environment based on the proposal submitted by the proposal department. For example, they might provide a VR space where users can build self-esteem and experience growth, or a VR space where they can relive achieved goals and successes. Step 5: The Re-experience Department records and allows users to re-experience their successes and goal achievements within the environment provided by the Delivery Department. For example, by allowing users to re-experience the results of projects they have achieved, they can gain confidence and further enhance their self-esteem.

[0070] (Example of form 2) The positive support system according to an embodiment of the present invention is a system aimed at enabling users to live their daily lives positively. This system provides a function in which AI automatically analyzes the emotions of the user's behavior history and speech content and spontaneously praises their good points. The AI ​​also suggests specific areas for improvement to the user. Furthermore, it provides an environment in which the user can build self-esteem and experience growth using virtual reality. In addition, it provides a function to record the user's success experiences and goal achievements and allow them to re-experience these positive experiences. For example, it collects the user's behavior history and speech content. For example, it collects content posted by the user on social media and daily conversations. Next, the AI ​​performs an emotional analysis on the collected data. The AI ​​extracts positive elements from the user's statements and actions and spontaneously praises the user's good points. For example, it provides feedback such as, "Your statements are very positive and wonderful." Furthermore, the AI ​​suggests specific areas for improvement from the user's actions and statements. For example, it provides advice such as, "It would be good if you actively expressed your opinions more." This allows the user to gain hints for self-improvement. Next, it provides an environment in which the user can build self-esteem and experience growth using virtual reality. Users can visually confirm their growth in a VR space, thereby boosting their self-esteem. For example, they can relive their achieved goals and successes in the VR space. Finally, the system records users' successes and goal achievements and provides a function to relive these positive experiences. By reflecting on past successes, users can further enhance their self-esteem. For example, by re-experiencing the results of projects they have completed, they can gain confidence. In this way, the positive support system utilizes AI and virtual reality to boost users' self-esteem and help them live more positive lives.

[0071] The positive support system according to this embodiment comprises a collection unit, an analysis unit, a suggestion unit, a provision unit, and a re-experience unit. The collection unit collects the user's behavioral history and speech content. For example, the collection unit collects content posted by the user on social media and everyday conversations. The collection unit can also collect, for example, website browsing history and app usage history. The collection unit can also collect, for example, the user's speech content using speech recognition technology. The analysis unit performs sentiment analysis on the data collected by the collection unit. For example, the analysis unit analyzes the user's statements and actions using natural language processing technology and classifies the emotions. For example, the analysis unit learns criteria for classifying emotions using a machine learning algorithm and extracts positive elements. For example, the analysis unit can also extract positive keywords based on the results of the sentiment analysis. The suggestion unit makes suggestions to praise the user based on the analysis results obtained by the analysis unit. For example, the suggestion unit provides feedback to the user regarding their statements and actions, such as "Your statements are very positive and wonderful." The proposal department, for example, proposes specific areas for improvement based on the user's actions and statements. The proposal department provides advice such as, for example, "It would be good to express your opinions more actively." The service provider provides a virtual reality environment based on the content proposed by the proposal department. The service provider provides a VR space where the user can build self-esteem and experience growth. The service provider allows the user to re-experience achieved goals and successes in the VR space. The service provider provides a function to record the user's successes and goal achievements and allow them to re-experience these positive experiences. The re-experience department records the user's successes and goal achievements in the environment provided by the service provider and allows them to re-experience them. The re-experience department allows the user to gain confidence by re-experience the results of projects they have achieved. The re-experience department allows the user to further enhance their self-esteem by reflecting on their successes. As a result, the positive support system according to this embodiment can help users enhance their self-esteem and live positive days.

[0072] The data collection unit collects user behavior history and speech content. For example, it collects content posted by users on social media and everyday conversations. Specifically, social media posts are collected as text data, serving as foundational data for analyzing user emotions and intentions. Everyday conversations are converted into text data using speech recognition technology and similarly used for analysis. The data collection unit can also collect website browsing history and app usage history, for example. This allows for an understanding of what information users are interested in and what behavioral patterns they have. The data collection unit can also collect user speech content using speech recognition technology, for example. Speech recognition technology converts user speech into text in real time, and this data is used for analyzing emotions and intentions. As a result, the data collection unit can comprehensively collect diverse user behavior and speech content, providing a rich dataset for analysis. Furthermore, the data collection unit can centrally manage this data and collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0073] The analysis department performs sentiment analysis on the data collected by the data collection department. For example, the analysis department uses natural language processing (NLP) to analyze user statements and actions and classify emotions. Specifically, it uses NLP to classify emotions such as positive, negative, and neutral from user statements and actions. The analysis department also uses machine learning algorithms to learn emotion classification criteria and extract positive elements. Machine learning algorithms learn emotion patterns based on large amounts of data and can classify emotions with high accuracy even for new data. The analysis department can also extract positive keywords based on the results of the sentiment analysis. This allows for the identification of positive elements from user statements and actions, providing foundational data to enhance user self-esteem. Furthermore, the analysis department can utilize historical data and statistical information to analyze long-term emotional fluctuations and trends. For example, it can analyze emotional fluctuations over a specific period based on users' past statements and actions to identify positive and negative changes. Additionally, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to handle not only real-time sentiment analysis but also long-term sentiment fluctuations and anomaly detection, improving the overall reliability and security of the system.

[0074] The Suggestion Department makes suggestions to praise users based on the analysis results obtained by the Analysis Department. For example, the Suggestion Department provides feedback such as, "Your comments are very positive and wonderful," in response to a user's statements or actions. Specifically, it generates appropriate feedback for users based on positive keywords and sentiment analysis results provided by the Analysis Department. The Suggestion Department also suggests specific areas for improvement based on the user's actions and statements. For example, it might offer advice such as, "It would be good to express your opinions more actively." This allows users to receive specific feedback on their actions and statements and gain guidance for self-improvement. Furthermore, the Suggestion Department can also provide advice for setting long-term goals and growth based on the user's past actions and statements. For example, it can set new goals based on goals and successes the user has achieved in the past and provide specific advice for achieving them. In addition, the Suggestion Department can collect user feedback and continuously improve the accuracy and effectiveness of its suggestions. This allows the Suggestion Department to provide users with appropriate and effective feedback and support them in increasing their self-esteem.

[0075] The service provider will provide a virtual reality environment based on the proposals submitted by the proposal team. For example, the service provider will provide a VR space where users can build self-esteem and experience growth. Specifically, users can re-experience goals they have achieved and successes they have had in the VR space. This allows users to visually reaffirm their successes and boost their self-esteem. The service provider will also provide a function to record users' successes and goal achievements and allow them to re-experience these positive experiences. This allows users to reflect on past successes and further enhance their self-esteem. Furthermore, the service provider can collect user feedback and continuously improve the content and functions of the VR space. For example, based on feedback on what users experienced in the VR space, new scenarios and functions can be added to enrich the user experience. The service provider can also facilitate interaction and cooperation among users by allowing multiple users to experience the VR space simultaneously. In this way, the service provider can provide users with a wealth of experiences to boost their self-esteem and help them live more positive lives.

[0076] The Re-experience Department records and allows users to re-experience their successes and goal achievements within the environment provided by the Service Provider Department. For example, the Re-experience Department can help users gain confidence by allowing them to re-experience the results of projects they have achieved. Specifically, it can recreate projects and goals that users have achieved in the past in a VR space and allow them to re-experience those successes. For example, the Re-experience Department can further enhance users' self-esteem by allowing them to reflect on their successes. This allows users to visually reconfirm past successes and gain confidence. Furthermore, the Re-experience Department can collect user feedback and continuously improve the content and methods of the re-experience. For example, based on feedback on the content of the user's re-experience, new scenarios and functions can be added to enrich the user experience. The Re-experience Department can also promote interaction and cooperation among users by allowing multiple users to re-experience simultaneously. In this way, the Re-experience Department can provide users with a wealth of experiences to enhance their self-esteem and help them live more positive lives.

[0077] The data collection unit can collect users' social media posts and daily conversations. For example, the data collection unit can collect content posted by users on social media. For example, the data collection unit can also collect users' daily conversations using speech recognition technology. For example, the data collection unit can also collect text data from chat applications used by users. This allows for more detailed data to be obtained by collecting users' social media posts and daily conversations. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into AI, and the AI ​​can automatically collect the data.

[0078] The analysis unit can perform sentiment analysis on the collected data and extract positive elements. For example, the analysis unit can use natural language processing technology to analyze user statements and actions and classify emotions. The analysis unit can also use machine learning algorithms to learn emotion classification criteria and extract positive elements. The analysis unit can also extract positive keywords based on the results of the sentiment analysis. In this way, by performing sentiment analysis on the collected data and extracting positive elements, the positive aspects of the user can be highlighted. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the collected data into AI, which can then automatically perform sentiment analysis.

[0079] The suggestion department can propose specific areas for improvement based on the user's actions and statements. For example, the suggestion department can provide advice such as, "It would be good to express your opinions more actively" in response to the user's actions and statements. The suggestion department can also extract specific areas for improvement from the user's actions and statements and propose them. For example, the suggestion department can analyze the user's behavior history and propose areas for improvement. In this way, by proposing specific areas for improvement based on the user's actions and statements, it is possible to promote self-improvement by the user. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input user behavior and statement data into AI, and the AI ​​can automatically propose areas for improvement.

[0080] The service provider can provide a virtual reality environment in which users can build self-esteem and experience growth. For example, the service provider can allow users to re-experience goals they have achieved and successes in a VR space. The service provider can also provide a VR space in which users can build self-esteem and experience growth. For example, the service provider can record users' successes and goal achievements and provide a function to allow users to re-experience these positive experiences. In this way, by providing a virtual reality environment in which users can build self-esteem and experience growth, the service provider can enhance users' self-esteem. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user success experience data into AI, and the AI ​​can automatically generate a VR environment.

[0081] The re-experience unit can record and allow users to re-experience their successes and goal achievements. For example, the re-experience unit can help users gain confidence by allowing them to re-experience the results of projects they have completed. For example, the re-experience unit can further enhance users' self-esteem by allowing them to reflect on their successes. The re-experience unit can also provide a function to record users' successes and goal achievements and allow them to re-experience these positive experiences. This allows users to further enhance their self-esteem by recording and allowing them to re-experience their successes and goal achievements. Some or all of the above processes in the re-experience unit may be performed using AI, for example, or not using AI. For example, the re-experience unit can input user success data into AI, which can then automatically generate re-experience scenarios.

[0082] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting data that promotes relaxation. For example, if the user is happy, the data collection unit may also collect positive data that further enhances that emotion. For example, if the user is sad, the data collection unit may also collect data that includes comforting and encouraging content. By adjusting the types of data collected based on the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and adjust the types of data that the generative AI automatically collects.

[0083] The data collection unit can analyze the user's past behavior history and select the optimal collection timing. For example, if the user was active during a specific time period in the past, the data collection unit will collect data during that time period. For example, if the user took a specific action on a specific day of the week in the past, the data collection unit can also collect data on that day of the week. For example, if the user took a positive action after a specific event in the past, the data collection unit can also collect data after that event. This allows for efficient data collection by analyzing the user's past behavior history and selecting the optimal collection timing. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history data into a generating AI, which can then automatically select the optimal collection timing.

[0084] The data collection unit can filter data based on the user's current activities and areas of interest during collection. For example, if the user is currently interested in sports, the data collection unit will prioritize collecting sports-related data. For example, if the user is currently interested in reading, the data collection unit can also prioritize collecting reading-related data. For example, if the user is currently interested in travel, the data collection unit can also prioritize collecting travel-related data. This allows for the collection of highly relevant data by filtering based on the user's current activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest data into a generating AI, which can then automatically perform the filtering.

[0085] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data that promotes relaxation. For example, if the user is happy, the data collection unit may prioritize collecting positive data that further enhances that emotion. For example, if the user is sad, the data collection unit may prioritize collecting data that contains comforting or encouraging content. By prioritizing the data to collect based on the user's emotions, more appropriate data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can automatically determine the priority of data to collect.

[0086] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is participating in a specific event, the data collection unit can prioritize the collection of data related to that event. By prioritizing the collection of highly relevant data while considering the user's geographical location information, more appropriate data can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI, which can then automatically prioritize the collection of highly relevant data.

[0087] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, if a user frequently posts about a particular topic, the data collection unit can collect data related to that topic. For example, if a user uses a particular hashtag, the data collection unit can also collect data related to that hashtag. For example, if a user participates in a particular group or community, the data collection unit can also collect data related to that group or community. This allows for the collection of more appropriate data by analyzing the user's social media activity and collecting relevant data. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then automatically collect relevant data.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing data that helps reduce stress. For example, if the user is happy, the analysis unit may prioritize analyzing data that further enhances that emotion. For example, if the user is sad, the analysis unit may prioritize analyzing data that provides comfort or encouragement. This allows for more appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then automatically adjust the analysis method.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on highly important data. For example, the analysis unit can perform a simplified analysis on less important data. For example, the analysis unit can perform an analysis with an appropriate level of detail on data of moderate importance. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, which can then automatically adjust the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit can also apply a speech recognition algorithm to audio data. This allows for more appropriate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can automatically apply an appropriate analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing data that helps reduce stress. For example, if the user is happy, the analysis unit may prioritize analyzing data that further enhances that emotion. For example, if the user is sad, the analysis unit may prioritize analyzing data that offers comfort or encouragement. By determining the priority of analysis based on the user's emotions, more appropriate data can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can automatically determine the priority of analysis.

[0092] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department may prioritize the analysis of the most recent data. The analysis department may also prioritize the analysis of the most recent data while referring to past data. The analysis department may also prioritize the analysis of data submitted within a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI, and the generating AI can automatically determine the priority of analysis.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may also analyze data with low relevance last. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then automatically adjust the order of analysis.

[0094] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing data that helps reduce stress. For example, if the user is happy, the analysis unit may prioritize analyzing data that further enhances that emotion. For example, if the user is sad, the analysis unit may prioritize analyzing data that offers comfort or encouragement. By determining the priority of analysis based on the user's emotions, more appropriate data can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, and the generative AI can automatically determine the priority of analysis.

[0095] The analysis department can determine the priority of analysis based on the data submission date. For example, the analysis department may prioritize the analysis of the most recent data. The analysis department may also prioritize the analysis of the most recent data while referring to past data. The analysis department may also prioritize the analysis of data submitted within a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data submission date. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the data submission date into a generating AI, and the generating AI can automatically determine the priority of analysis.

[0096] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may also analyze data with low relevance last. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then automatically adjust the order of analysis.

[0097] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will present suggestions in gentle language. If the user is happy, the suggestion unit may present suggestions in cheerful language. If the user is sad, the suggestion unit may present suggestions that include words of comfort. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then automatically adjust the way suggestions are presented.

[0098] The suggestion unit can adjust the level of detail of its suggestions based on the user's behavior history. For example, if the user has previously preferred detailed suggestions, the suggestion unit will provide detailed suggestions. If the user has previously preferred concise suggestions, the suggestion unit can also provide concise suggestions. The suggestion unit can also provide suggestions with an appropriate level of detail based on the user's behavior history. By adjusting the level of detail of suggestions based on the user's behavior history, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user behavior history data into a generating AI, which can then automatically adjust the level of detail of the suggestions.

[0099] The suggestion unit can apply different suggestion algorithms depending on the user's area of ​​interest when making suggestions. For example, if the user is interested in sports, the suggestion unit will make sports-related suggestions. If the user is interested in reading, the suggestion unit can also make reading-related suggestions. If the user is interested in travel, the suggestion unit can also make travel-related suggestions. By applying different suggestion algorithms according to the user's area of ​​interest, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user area of ​​interest data into a generating AI, and the generating AI can automatically apply an appropriate suggestion algorithm.

[0100] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can make short, concise suggestions. If the user is happy, the suggestion unit can make detailed suggestions. If the user is sad, the suggestion unit can make comforting suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then automatically adjust the length of the suggestions.

[0101] The suggestion unit can prioritize suggestions based on the user's past successes. For example, the suggestion unit may prioritize suggesting methods that have worked for the user in the past. The suggestion unit may also avoid suggesting methods that have failed for the user in the past. The suggestion unit may also make optimal suggestions by referring to the user's past successes. This allows for more appropriate suggestions to be made by prioritizing suggestions based on the user's past successes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's past success data into a generating AI, which can then automatically determine the priority of suggestions.

[0102] The suggestion unit can adjust the order of suggestions based on user relevance. For example, the suggestion unit may prioritize suggestions with high relevance. It may also choose to present suggestions with moderate relevance next, or suggest suggestions with low relevance last. By adjusting the order of suggestions based on user relevance, more appropriate suggestions can be made. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input user relevance data into a generating AI, which can then automatically adjust the order of suggestions.

[0103] The service provider can estimate the user's emotions and adjust the way the virtual reality environment is provided based on the estimated emotions. For example, if the user is stressed, the service provider can provide a relaxing environment. For example, if the user is happy, the service provider can also provide an environment that further enhances that emotion. For example, if the user is sad, the service provider can also provide a comforting and encouraging environment. In this way, by adjusting the way the virtual reality environment is provided based on the user's emotions, a more appropriate environment can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can automatically adjust the way the virtual reality environment is provided.

[0104] The service provider can select the optimal environment by referring to the user's past experiences at the time of service provision. For example, the service provider can recreate an environment in which the user was able to relax in the past. For example, the service provider can recreate an environment in which the user was pleased in the past. For example, the service provider can recreate an environment in which the user was comforted in the past. By doing so, by selecting the optimal environment by referring to the user's past experiences, a more appropriate environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past experience data into a generating AI, and the generating AI can automatically select the optimal environment.

[0105] The service provider can customize the environment based on the user's current living situation at the time of delivery. For example, if the user is stressed at work, the service provider can provide a relaxing office environment. For example, if the user is relaxing at home, the service provider can provide a home-like environment. For example, if the user is traveling, the service provider can provide an environment related to the travel destination. In this way, a more appropriate environment can be provided by customizing the environment based on the user's current living situation. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current living situation data into a generating AI, and the generating AI can automatically customize the environment.

[0106] The service provider can estimate the user's emotions and determine the priority of the environment to provide based on the estimated emotions. For example, if the user is stressed, the service provider may prioritize providing a relaxing environment. For example, if the user is happy, the service provider may prioritize providing an environment that further enhances that emotion. For example, if the user is sad, the service provider may prioritize providing a comforting or encouraging environment. In this way, by determining the priority of the environment to provide based on the user's emotions, a more appropriate environment can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can automatically determine the priority of the environment to provide.

[0107] The service provider can select the optimal environment by considering the user's geographical location information at the time of service provision. For example, if the user is in a specific region, the service provider can provide an environment related to that region. For example, if the user is traveling, the service provider can also provide an environment related to the travel destination. For example, if the user is participating in a specific event, the service provider can also provide an environment related to that event. By selecting the optimal environment by considering the user's geographical location information, a more appropriate environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI, and the generating AI can automatically select the optimal environment.

[0108] The service provider can analyze the user's social media activity and suggest an environment at the time of provision. For example, if the user frequently posts about a particular topic, the service provider can provide an environment related to that topic. For example, if the user uses a particular hashtag, the service provider can also provide an environment related to that hashtag. For example, if the user participates in a particular group or community, the service provider can also provide an environment related to that group or community. In this way, by analyzing the user's social media activity and suggesting an environment, a more appropriate environment can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI, and the generating AI can automatically suggest an environment.

[0109] The re-experience unit can estimate the user's emotions and adjust the re-experience method based on the estimated user emotions. For example, if the user is feeling stressed, the re-experience unit can provide a relaxing re-experience method. For example, if the user is happy, the re-experience unit can also provide a re-experience method that further enhances that emotion. For example, if the user is sad, the re-experience unit can also provide a comforting or encouraging re-experience method. In this way, by adjusting the re-experience method based on the user's emotions, a more appropriate re-experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the re-experience unit may be performed using AI, for example, or without AI. For example, the re-experience unit can input the user's emotion data into the generative AI, and the generative AI can automatically adjust the re-experience method.

[0110] The re-experience unit can analyze the user's past successes during a re-experience to select the optimal method for that experience. For example, the re-experience unit can allow the user to re-experience a project they previously succeeded in. For example, the re-experience unit can allow the user to re-experience goals they previously achieved. For example, the re-experience unit can allow the user to re-experience awards and accolades they previously received. By analyzing the user's past successes and selecting the optimal method for that experience, a more appropriate re-experience can be provided. Some or all of the above-described processes in the re-experience unit may be performed using AI, for example, or without AI. For example, the re-experience unit can input the user's past success data into a generating AI, which can then automatically select the optimal method for that experience.

[0111] The re-experience unit can customize the means of re-experience based on the user's current living situation. For example, if the user is stressed at work, the re-experience unit can provide a relaxing re-experience method. For example, if the user is relaxing at home, the re-experience unit can also provide a home-like re-experience method. For example, if the user is traveling, the re-experience unit can also provide a re-experience method related to the travel destination. This allows for a more appropriate re-experience to be provided by customizing the means of re-experience based on the user's current living situation. Some or all of the above processing in the re-experience unit may be performed using AI, for example, or without AI. For example, the re-experience unit can input the user's current living situation data into a generating AI, which can then automatically customize the means of re-experience.

[0112] The re-experience unit can estimate the user's emotions and determine the priority of re-experiences based on the estimated emotions. For example, if the user is stressed, the re-experience unit may prioritize providing relaxing re-experiences. If the user is happy, the re-experience unit may also prioritize providing re-experiences that further enhance that emotion. If the user is sad, the re-experience unit may also prioritize providing comforting or encouraging re-experiences. In this way, by determining the priority of re-experiences based on the user's emotions, a more appropriate re-experience can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the re-experience unit may be performed using AI, for example, or not using AI. For example, the re-experience unit can input the user's emotion data into a generative AI, which can then automatically determine the priority of re-experiences.

[0113] The re-experience unit can select the optimal re-experience method by considering the user's geographical location information during the re-experience process. For example, if the user is in a specific region, the re-experience unit can provide a re-experience method related to that region. For example, if the user is traveling, the re-experience unit can also provide a re-experience method related to the travel destination. For example, if the user is participating in a specific event, the re-experience unit can also provide a re-experience method related to that event. By selecting the optimal re-experience method by considering the user's geographical location information, a more appropriate re-experience can be provided. Some or all of the above processing in the re-experience unit may be performed using AI, for example, or without AI. For example, the re-experience unit can input the user's geographical location data into a generating AI, which can then automatically select the optimal re-experience method.

[0114] The re-experience unit can analyze the user's social media activity during a re-experience and suggest ways to re-experience it. For example, if the user frequently posts about a particular topic, the re-experience unit can provide re-experience methods related to that topic. For example, if the user uses a particular hashtag, the re-experience unit can also provide re-experience methods related to that hashtag. For example, if the user participates in a particular group or community, the re-experience unit can also provide re-experience methods related to that group or community. By analyzing the user's social media activity and suggesting ways to re-experience it, a more appropriate re-experience can be provided. Some or all of the above processing in the re-experience unit may be performed using AI, for example, or without AI. For example, the re-experience unit can input the user's social media activity data into a generating AI, and the generating AI can automatically suggest ways to re-experience it.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] A positive support system may include a prediction unit that estimates the user's emotions and predicts the user's behavior based on the estimated emotions. For example, if the user is feeling stressed, the prediction unit may predict actions to reduce stress. If the user is happy, the prediction unit may also predict actions to maintain that emotion. If the user is sad, the prediction unit may also predict comforting or encouraging actions. This allows for more appropriate support to be provided by predicting behavior based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input the user's emotion data into the generative AI, which can then automatically predict behavior.

[0117] A positive support system can include a learning unit that learns the user's preferences and tendencies based on the user's behavioral history. The learning unit can, for example, learn activities the user has enjoyed in the past and suggest similar activities. The learning unit can also, for example, learn activities the user has avoided in the past and avoid suggesting them. The learning unit can also, for example, analyze the user's behavioral patterns and make suggestions at the optimal time. This allows for more appropriate support to be provided by making suggestions based on the user's preferences and tendencies. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's behavioral history data into a generating AI, which can then automatically perform the learning.

[0118] A positive support system may include a health monitoring unit that estimates the user's emotions and monitors the user's health status based on the estimated emotions. For example, the health monitoring unit may monitor the stress level if the user is feeling stressed. For example, if the user is happy, the health monitoring unit may also monitor the impact of that emotion on their health. For example, if the user is sad, the health monitoring unit may also monitor the impact of emotional changes on their health. This allows for more appropriate health management by monitoring the user's health status based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the health monitoring unit may be performed using AI, for example, or without AI. For example, the health monitoring unit can input the user's emotion data into the generative AI, which can then automatically monitor the user's health status.

[0119] A positive support system may include an estimation unit that estimates the user's interests and concerns based on the user's behavioral history and spoken content. For example, the estimation unit may estimate interests based on activities in which the user has spent a lot of time in the past. The estimation unit may also estimate interests based on topics that the user frequently talks about. The estimation unit may also estimate interests based on accounts that the user follows on social media. This allows for more appropriate support to be provided by making suggestions based on the user's interests and concerns. Some or all of the above processing in the estimation unit may be performed using AI, for example, or without AI. For example, the estimation unit may input the user's behavioral history data into a generating AI, which can then automatically estimate interests and concerns.

[0120] A positive support system may include an evaluation unit that estimates the user's emotions and evaluates the user's stress level based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit quantifies and evaluates the stress level. For example, if the user is happy, the evaluation unit can also evaluate the impact of that emotion on the stress level. For example, if the user is sad, the evaluation unit can also evaluate the impact of that emotion on the stress level. This allows for more appropriate stress management by evaluating the stress level based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's emotion data into the generative AI, which can then automatically evaluate the stress level.

[0121] The positive support system may include a suggestion unit that makes suggestions to improve the user's lifestyle based on the user's behavioral history. For example, the suggestion unit may suggest similar activities based on the user's past healthy activities. For example, the suggestion unit may also suggest avoiding unhealthy activities based on the user's past avoidance of those activities. For example, the suggestion unit may analyze the user's behavioral patterns and suggest healthy activities at the optimal time. This allows for more appropriate support by making suggestions to improve the user's lifestyle. Some or all of the above-described processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit may input the user's behavioral history data into a generating AI, which can then automatically make suggestions.

[0122] A positive support system may include a feedback unit that estimates the user's emotions and provides feedback to improve the user's motivation based on the estimated emotions. For example, the feedback unit may provide words of encouragement if the user is feeling stressed. For example, if the user is happy, the feedback unit may also provide feedback that further enhances that emotion. For example, if the user is sad, the feedback unit may also provide words of comfort. This allows for more appropriate support by providing feedback to improve motivation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input the user's emotion data into the generative AI, and the generative AI can automatically provide feedback.

[0123] A positive support system may include a planning unit that creates a plan to help the user achieve their goals based on the user's behavioral history. The planning unit may, for example, set similar goals based on goals the user has achieved in the past. The planning unit may also, for example, create a plan to avoid goals that the user has failed to achieve in the past. The planning unit may also, for example, analyze the user's behavioral patterns and create a plan for achieving goals at the optimal timing. This allows for the provision of more appropriate support by creating a plan to help the user achieve their goals. Some or all of the above processes in the planning unit may be performed using AI, for example, or not using AI. For example, the planning unit may input the user's behavioral history data into a generating AI, and the generating AI may automatically create a plan.

[0124] A positive support system may include an advice unit that estimates the user's emotions and provides advice to support the user's communication based on the estimated emotions. For example, if the user is feeling stressed, the advice unit may suggest ways to communicate in a way that helps them relax. For example, if the user is happy, the advice unit may also suggest ways to communicate in a way that helps them share that emotion. For example, if the user is sad, the advice unit may also suggest ways to communicate in a way that offers comfort and encouragement. This allows for more appropriate support by providing advice to support communication based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's emotion data into the generative AI, and the generative AI can automatically provide advice.

[0125] The positive support system may include a material provision unit that provides learning materials to support the user's learning based on the user's behavioral history. For example, the material provision unit may provide similar materials based on what the user has learned in the past. For example, the material provision unit may also provide materials to help the user overcome areas where they have struggled in the past. For example, the material provision unit may analyze the user's learning patterns and provide materials at the optimal time. This allows for more appropriate support by providing learning materials to assist the user's learning. Some or all of the above-described processes in the material provision unit may be performed using AI, for example, or without AI. For example, the material provision unit may input the user's behavioral history data into a generating AI, which can then automatically provide learning materials.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The data collection unit collects the user's behavioral history and spoken content. For example, the data collection unit collects content from social media posts, daily conversations, website browsing history, app usage history, and spoken content using speech recognition technology. Step 2: The analysis unit performs sentiment analysis on the data collected by the collection unit. For example, it uses natural language processing techniques and machine learning algorithms to analyze users' statements and actions, classify their emotions, and extract positive elements and keywords. Step 3: The proposal team makes suggestions that praise the user based on the analysis results obtained by the analysis team. For example, they might provide feedback on the user's statements or actions such as, "Your comments are very positive and wonderful," or suggest specific areas for improvement. Step 4: The provisioning department provides a virtual reality environment based on the proposal submitted by the proposal department. For example, they might provide a VR space where users can build self-esteem and experience growth, or a VR space where they can relive achieved goals and successes. Step 5: The Re-experience Department records and allows users to re-experience their successes and goal achievements within the environment provided by the Delivery Department. For example, by allowing users to re-experience the results of projects they have achieved, they can gain confidence and further enhance their self-esteem.

[0128] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and re-experience unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects the user's behavior history and speech content using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs sentiment analysis on the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions to praise the user. The provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides a virtual reality environment. The re-experience unit is implemented in the specific processing unit 46A of the smart device 14 and records the user's success experiences and goal achievements and allows them to re-experience them. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, suggestion unit, provision unit, and re-experience unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects the user's behavior history and speech content using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12 and performs sentiment analysis on the collected data. The suggestion unit is implemented in the identification processing unit 290 of the data processing unit 12 and makes suggestions to praise the user. The provision unit is implemented in the control unit 46A of the smart glasses 214 and provides a virtual reality environment. The re-experience unit is implemented in the control unit 46A of the smart glasses 214 and records the user's successful experiences and goal achievements and allows them to re-experience them. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and re-experience unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects the user's behavior history and speech content using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs sentiment analysis on the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions to praise the user. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides a virtual reality environment. The re-experience unit is implemented in the control unit 46A of the headset terminal 314 and records the user's success experiences and goal achievements and allows them to re-experience them. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 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.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, and re-experience unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects the user's behavior history and speech content using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and performs sentiment analysis on the collected data. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and makes suggestions to praise the user. The provision unit is implemented in the control unit 46A of the robot 414 and provides a virtual reality environment. The re-experience unit is implemented in the control unit 46A of the robot 414 and records the user's successful experiences and goal achievements and allows them to re-experience them. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0181] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) A collection unit that collects user behavior history and speech content, An analysis unit performs sentiment analysis on the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit makes suggestions to praise the user, A provisioning unit that provides a virtual reality environment based on the content proposed by the aforementioned proposal unit, The system includes a re-experience unit that records the user's success experiences and goal achievements in the environment provided by the aforementioned provision unit, and allows the user to re-experience them. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collects users' social media posts and everyday conversations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed for sentiment, and positive elements are extracted. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose specific areas for improvement based on user behavior and comments. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide a virtual reality environment where users can build self-esteem and experience personal growth. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned re-experience section is, Record and allow users to relive their success stories and goal achievements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past behavior history to select the optimal timing for data collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During data collection, filtering is performed based on the user's current activity and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is We estimate the user's emotions and prioritize the analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, adjust the level of detail in the suggestions based on the user's behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the user's area of ​​interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, prioritize them based on the user's past successes. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the virtual reality environment is delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal environment is selected by referring to the user's past experiences. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the environment is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the environment to provide based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, the optimal environment is selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and suggest an environment that suits them. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned re-experience section is, It estimates the user's emotions and adjusts the re-experience method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned re-experience section is, During a re-experience, the system analyzes the user's past successful experiences to select the optimal method for re-experience. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned re-experience section is, During a re-experience, the means of re-experience are customized based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned re-experience section is, It estimates the user's emotions and determines the priority of re-experiences based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned re-experience section is, When re-experiencing the product, the optimal re-experiencing method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned re-experience section is, During the re-experience process, we analyze the user's social media activity and propose ways to facilitate that re-experience. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects user behavior history and speech content, An analysis unit performs sentiment analysis on the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit makes suggestions to praise the user, A provisioning unit that provides a virtual reality environment based on the content proposed by the aforementioned proposal unit, The system includes a re-experience unit that records the user's success experiences and goal achievements in the environment provided by the aforementioned provision unit, and allows the user to re-experience them. A system characterized by the following features.

2. The aforementioned collection unit is Collects users' social media posts and everyday conversations. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed for sentiment, and positive elements are extracted. The system according to feature 1.

4. The aforementioned proposal section is, We propose specific areas for improvement based on user behavior and comments. The system according to feature 1.

5. The aforementioned supply unit is, We provide a virtual reality environment where users can build self-esteem and experience personal growth. The system according to feature 1.

6. The aforementioned re-experience section is, Record and allow users to relive their success stories and goal achievements. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past behavior history to select the optimal timing for data collection. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current activity and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

Images