system

Abstract

The system according to this embodiment aims to connect people who feel lonely through shared hobbies and interests. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a recommendation unit, and a counseling unit. The data collection unit collects user interest and activity history data. The analysis unit analyzes the data collected by the data collection unit. The recommendation unit recommends the most suitable groups and events based on the analysis results obtained by the analysis unit. The counseling unit provides expert counseling and support based on the groups and events recommended by the recommendation unit.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for people feeling lonely to connect through common hobbies and interests.

[0005] The system according to the embodiment aims to enable people feeling lonely to connect through common hobbies and interests.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, and a counseling unit. The data collection unit collects user interest and activity history data. The analysis unit analyzes the data collected by the data collection unit. The recommendation unit recommends the most suitable groups and events based on the analysis results obtained by the analysis unit. The counseling unit provides expert counseling and support based on the groups and events recommended by the recommendation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows people who feel lonely to connect with each other through shared hobbies and interests. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). (保留原标签,无需翻译)

[0015] (保留原标签,无需翻译) (保留原标签,无需翻译) 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. (保留原标签,无需翻译)

[0016] (保留原标签,无需翻译) (保留原标签,无需翻译) [First Embodiment] (保留原标签,无需翻译) FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment. (保留原标签,无需翻译)

[0017] (保留原标签,并翻译) (保留原标签,无需翻译) 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. (保留原标签,无需翻译)

[0018] (保留原标签,无需翻译) (保留原标签,无需翻译) 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). (保留原标签,无需翻译)

[0019] (保留原标签,无需翻译) (保留原标签,无需翻译)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 social engagement platform according to an embodiment of the present invention is an AI-based system aimed at connecting people who feel lonely through shared hobbies and interests. This system analyzes the user's preferences and activity history and recommends the most suitable groups and events. It also provides expert counseling and support. For example, the system collects data on the user's interests and activity history. Next, a generative AI analyzes this data and recommends the most suitable groups and events. Furthermore, if the user is feeling lonely, an AI counselor provides an initial diagnosis and ongoing support. This platform targets all people who feel lonely, including young people, the elderly, and middle-aged and older adults, and aims to alleviate loneliness through group activities with others who share common hobbies and interests, and to provide expert counseling and support. By having the generative AI analyze the user's activity history and interests, recommend the most suitable groups and events, and perform real-time sentiment analysis, users can more easily find groups and events that suit them and build new social connections. In addition, the AI ​​counselor's initial diagnosis and ongoing support can alleviate the user's loneliness and enhance their social presence. This allows social engagement platforms to help people who feel lonely connect with each other through shared hobbies and interests, and to provide professional counseling and support.

[0029] The social engagement platform according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, and a counseling unit. The data collection unit collects user interest and activity history data. For example, the data collection unit can collect user website browsing history, purchase history, and social media activity. The data collection unit can also collect information on events and groups that users have participated in. The analysis unit uses generative AI to analyze the data collected by the data collection unit. For example, the analysis unit can analyze the user's interests and activity history to identify what hobbies and interests the user has. The analysis unit can also analyze what groups and events are suitable for the user to participate in based on the user's activity history. The recommendation unit recommends the most suitable groups and events based on the analysis results obtained by the analysis unit. For example, the recommendation unit can recommend relevant groups and events based on the user's hobbies and interests. The recommendation unit can also recommend groups and events that the user has participated in in the past based on the user's activity history. The counseling unit provides expert counseling and support based on the groups and events recommended by the recommendation unit. The counseling department can, for example, provide an initial diagnosis and ongoing support if a user is experiencing feelings of loneliness. Furthermore, the counseling department can provide appropriate counseling and support based on the groups and events the user has participated in. Thus, the social engagement platform according to this embodiment can reduce feelings of loneliness by collecting and analyzing user interest and activity history data, recommending optimal groups and events, and providing expert counseling and support.

[0030] The data collection unit collects user interest and activity history data. For example, it can collect user website browsing history, purchase history, and social media activity. Specifically, website browsing history includes detailed data such as which pages the user visited, which links they clicked, and how long they spent on each page. Purchase history includes information such as which products the user purchased, purchase frequency, and payment method at the time of purchase. Social media activity data includes data such as which posts the user "liked" or commented on, which groups they belonged to, and which events they showed interest in. The data collection unit can also collect information on events and groups the user participated in. This includes which events the user attended, the date and time of the event, the location, a list of participants, and the activities at the event. Furthermore, the data collection unit can also collect data from online surveys and feedback conducted by users. This allows the data collection unit to comprehensively understand the user's diverse interests and activity history and build a detailed database. The collected data is encrypted to ensure security and privacy and stored on secure servers. This allows the data collection unit to collect a wealth of data while protecting user privacy, making it available for use by the analysis and recommendation units.

[0031] The analysis unit uses generative AI to analyze data collected by the data collection unit. For example, the analysis unit can analyze a user's interests and activity history to identify their hobbies and interests. Specifically, the generative AI uses natural language processing technology to extract a user's interests from their social media posts and website browsing history. For example, it can analyze the websites a user frequently visits and the content of articles they often read to identify topics they are interested in. The generative AI can also use clustering algorithms to group users' activity histories and identify groups of users with common interests. Furthermore, based on a user's activity history, the analysis unit can analyze what kinds of groups and events are suitable for them to participate in. For example, it can analyze evaluations and feedback on past events to identify what kinds of events a user prefers. This allows the analysis unit to gain a detailed understanding of the user's interests and provide foundational data for recommending the most suitable groups and events. In addition, the analysis unit can also analyze a user's psychological state and emotions. For example, it can analyze social media posts and survey responses to assess the level of loneliness and stress a user is currently experiencing. This allows the analysis unit to generate data that enables it to provide appropriate counseling and support tailored to the user's psychological state.

[0032] The recommendation department recommends the most suitable groups and events based on the analysis results obtained by the analysis department. For example, the recommendation department can recommend relevant groups and events based on the user's hobbies and interests. Specifically, it generates a list of relevant groups and events based on the user's interests and preferences analyzed by the generation AI and presents it to the user. For example, if the user is interested in music, it will recommend music-related events and groups. Also, if the user likes a particular genre of movies, it can recommend movie screenings and discussion groups related to that genre. Furthermore, the recommendation department can also recommend groups and events that the user has participated in in the past, based on the user's activity history. For example, it can analyze the evaluations and feedback of events the user has participated in in the past and recommend similar events. This gives the user the opportunity to participate again in events and groups they have enjoyed in the past. The recommendation department also takes into account the user's current psychological state and emotions to make the best recommendations. For example, if the user is feeling lonely, it will recommend events and groups that promote social interaction. Also, if the user is feeling stressed, it can recommend events and groups related to relaxation and mental health. This allows the recommendation team to provide optimal recommendations that take into account not only the user's interests and concerns, but also their psychological state and emotions, thereby increasing user satisfaction.

[0033] The Counseling Department provides expert counseling and support based on groups and events recommended by the Recommendation Department. For example, if a user is experiencing feelings of loneliness, the Counseling Department can conduct an initial assessment and provide ongoing support. Specifically, the Counseling Department has psychology professionals and counselors interact with users to assess their psychological state. The initial assessment evaluates the user's emotions, stress levels, and social connection status to create an appropriate counseling plan. Furthermore, the Counseling Department can provide appropriate counseling and support based on groups and events the user has participated in. For example, after a user participates in a particular event, they can share their experiences and impressions with a counselor and receive additional support as needed. This allows users to deepen their experiences gained through events and group activities and build better social connections. The Counseling Department can provide counseling in multiple formats, including online counseling and chat support. This allows users to receive counseling at their convenience. The Counseling Department can also continuously improve counseling plans and support content based on user feedback. This enables the Counseling Department to provide flexible and effective support tailored to user needs, reducing feelings of loneliness and strengthening social connections.

[0034] The support department can provide support to alleviate users' feelings of loneliness. For example, the support department can provide appropriate support when a user is feeling lonely. For example, the support department can provide professional counseling when a user is feeling lonely. The support department can also encourage users to participate in group activities or events when they are feeling lonely. Furthermore, the support department can recommend participation in online communities when a user is feeling lonely. In this way, the support department can enhance users' social presence by providing support to alleviate their feelings of loneliness. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the content of support to alleviate the user's feelings of loneliness into a generating AI and have the generating AI execute suggestions for support content.

[0035] The analysis unit can analyze a user's activity history and interests using a generative AI. For example, the analysis unit can input user activity history data into the generative AI, which can then analyze the user's interests and preferences. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit can use the generative AI to analyze user activity history data and identify what hobbies and interests the user has. The analysis unit can also use the generative AI to analyze, based on the user's activity history, what groups or events the user is likely to participate in. This improves the accuracy of the analysis of user activity history and interests by using a generative AI. 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 user activity history data into the generative AI and have the generative AI output the analysis results.

[0036] The recommendation unit can recommend the most suitable groups and events based on the analysis results using a generative AI. For example, the recommendation unit can input the analysis results obtained by the analysis unit into the generative AI, which can then recommend the most suitable groups and events. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The recommendation unit can use the generative AI to recommend groups and events related to the user's hobbies and interests based on the analysis results. The recommendation unit can also use the generative AI to recommend groups and events that the user has previously participated in, based on the user's activity history. This improves the accuracy of the recommendation unit's recommendation of the most suitable groups and events by using a generative AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the analysis results into the generative AI and have the generative AI output the recommendation results.

[0037] The counseling department can provide initial diagnosis and ongoing support when a user is experiencing loneliness. For example, the counseling department can conduct an initial diagnosis and assess the user's condition when a user is experiencing loneliness. The counseling department can also provide ongoing support when a user is experiencing loneliness. For example, the counseling department can provide support to alleviate the user's loneliness through regular follow-ups and online counseling. In this way, the counseling department can alleviate loneliness by providing initial diagnosis and ongoing support when a user is experiencing loneliness. Some or all of the above processes in the counseling department may be performed using AI, for example, or not using AI. For example, the counseling department can input the user's initial diagnosis data into a generating AI and have the generating AI output the diagnosis results.

[0038] The data collection unit can analyze the user's past activity history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from events the user has frequently participated in in the past. It can also collect data from apps and websites the user has preferred to use in the past. Furthermore, the data collection unit can select the optimal data collection method based on past surveys and feedback provided by the user. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past activity history. 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 the user's past activity history data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. It can also collect relevant data based on the user's current lifestyle (work, studies, family, etc.). Furthermore, the data collection unit can collect data related to communities and groups the user is currently participating in. This allows the data collection unit to collect more relevant data by filtering it based on the user's current lifestyle 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 data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the data filtering.

[0040] 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, the data collection unit can prioritize the collection of event information in the area where the user is currently located. The data collection unit can also collect information on nearby groups and communities based on the user's geographical location. Furthermore, if the user is traveling, the data collection unit can collect data related to the area they are traveling to. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. 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 into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics that the user frequently mentions on social media. It can also collect activity information from accounts and groups that the user follows. Furthermore, the data collection unit can collect information from online events and webinars that the user participates in. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. 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 the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a network analysis algorithm to data related to social engagement. It can also apply a clustering algorithm to data related to user interests. Furthermore, it can apply a time series analysis algorithm to data related to user activity history. By applying different analysis algorithms depending on the data category, the analysis unit can improve the accuracy of the analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, it can analyze data with a moderate submission date with appropriate priority. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into a generative AI and have the generative AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, it can analyze data with moderate relevance in an appropriate order. In this way, the analysis unit can perform 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, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0046] The recommendation system can adjust the level of detail of recommendations based on the importance of groups and events. For example, it can provide detailed recommendation information for highly important groups and events, and concise recommendation information for less important groups and events. Furthermore, it can provide recommendation information with an appropriate level of detail for moderately important groups and events. This allows the recommendation system to perform efficient recommendations by adjusting the level of detail based on the importance of groups and events. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input the importance of groups and events into a generative AI and have the generative AI adjust the level of detail of the recommendations.

[0047] The recommendation system can apply different recommendation algorithms depending on the category of the group or event during the recommendation process. For example, it can apply a network analysis algorithm to groups and events related to social engagement. It can also apply a clustering algorithm to groups and events related to the user's interests. Furthermore, it can apply a time-series analysis algorithm to groups and events related to the user's activity history. This improves recommendation accuracy by applying different recommendation algorithms depending on the category of the group or event. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input the categories of groups and events into a generative AI and have the generative AI apply the recommendation algorithm.

[0048] The recommendation system can determine the priority of recommendations based on the timing of the groups and events. For example, it can prioritize recommending groups and events that are scheduled to be held soon. It can also postpone recommending groups and events that are scheduled to be held far in the future. Furthermore, it can recommend groups and events that are scheduled to be held at a moderate pace with appropriate priority. This allows the recommendation system to perform recommendations efficiently by determining the priority of recommendations based on the timing of the groups and events. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input the timing of the groups and events into a generative AI and have the generative AI determine the recommendation priority.

[0049] The recommendation unit can adjust the order of recommendations based on the relevance of groups and events during the recommendation process. For example, the recommendation unit can prioritize recommending groups and events with high relevance. It can also postpone recommending groups and events with low relevance. Furthermore, it can recommend groups and events with moderate relevance in a suitable order. This allows the recommendation unit to perform efficient recommendations by adjusting the order of recommendations based on the relevance of groups and events. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation unit can input the relevance of groups and events into a generative AI and have the generative AI adjust the order of recommendations.

[0050] The counseling department can select the most suitable counseling method during a counseling session by referring to the user's past counseling history. For example, the counseling department can select the most suitable counseling method based on the content of counseling sessions the user has received in the past. Furthermore, the counseling department can prioritize selecting methods that were effective based on the user's past counseling history. In addition, the counseling department can analyze the user's past counseling history and propose new counseling methods. Thus, the counseling department can select the most suitable counseling method by referring to the user's past counseling history. Some or all of the above-described processes in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can input the user's past counseling history data into a generating AI and have the generating AI select the most suitable counseling method.

[0051] The counseling department can customize the counseling methods based on the user's current living situation during counseling sessions. For example, if the user is busy with work, the counseling department can provide a short and effective counseling method. Furthermore, if the user has family problems, the counseling department can provide a counseling method that takes the family environment into consideration. Additionally, if the user has health problems, the counseling department can provide a counseling method tailored to their health condition. This allows the counseling department to provide more appropriate counseling by customizing the counseling methods based on the user's current living situation. Some or all of the above-described processes in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can input the user's current living situation data into a generating AI and have the generating AI customize the counseling methods.

[0052] The counseling department can select the most suitable counseling method during a counseling session by considering the user's geographical location. For example, if the user lives in an urban area, the counseling department can provide online counseling. If the user lives in a rural area, the counseling department can provide telephone counseling. Furthermore, if the user lives overseas, the counseling department can provide a counseling method that takes time differences into account. In this way, the counseling department can select the most suitable counseling method by considering the user's geographical location. Some or all of the above processing in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can input the user's geographical location information into a generating AI and have the generating AI select the most suitable counseling method.

[0053] The counseling department can analyze a user's social media activity during counseling sessions and propose appropriate counseling methods. For example, the counseling department can provide counseling related to issues that the user frequently mentions on social media. It can also provide counseling that takes into account the opinions of experts the user follows. Furthermore, the counseling department can provide counseling based on the user's activities in online communities. This allows the counseling department to propose more appropriate counseling methods by analyzing the user's social media activity. Some or all of the above processing in the counseling department may be performed using AI, for example, or not. For example, the counseling department can input the user's social media activity data into a generating AI and have the generating AI propose counseling methods.

[0054] The emotion analysis unit can select the optimal analysis method by referring to the user's past emotional data during emotion analysis. For example, the emotion analysis unit can select the optimal analysis method based on data from past emotional analyses the user has received. Furthermore, the emotion analysis unit can prioritize selecting effective analysis methods from the user's past emotional data. In addition, the emotion analysis unit can analyze the user's past emotional data and propose new analysis methods. Thus, the emotion analysis unit can select the optimal analysis method by referring to the user's past emotional data. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's past emotional data into a generating AI and have the generating AI select the optimal analysis method.

[0055] The sentiment analysis unit can select the optimal analysis method by considering the user's geographical location information during sentiment analysis. For example, if the user lives in an urban area, the sentiment analysis unit can perform online sentiment analysis. If the user lives in a rural area, the sentiment analysis unit can also perform sentiment analysis by telephone. Furthermore, if the user lives overseas, the sentiment analysis unit can perform sentiment analysis while considering the time difference. In this way, the sentiment analysis unit can select the optimal analysis method by considering the user's geographical location information. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0056] The support unit can select the optimal support method by referring to the user's past support history during support. For example, the support unit can select the optimal support method based on the content of support the user has received in the past. Furthermore, the support unit can prioritize and select effective methods from the user's past support history. In addition, the support unit can analyze the user's past support history and propose new support methods. Thus, the support unit can select the optimal support method by referring to the user's past support history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past support history data into a generating AI and have the generating AI select the optimal support method.

[0057] The support unit can customize the means of support based on the user's current living situation. For example, if the user is busy with work, the support unit can provide effective support in a short amount of time. Furthermore, if the user has family problems, the support unit can provide support that takes their home environment into consideration. Additionally, if the user has health problems, the support unit can provide support tailored to their health condition. This allows the support unit to provide more appropriate support by customizing the means of support based on the user's current living situation. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI and have the generating AI customize the means of support.

[0058] The support department can select the optimal support method by considering the user's geographical location information during support. For example, if the user lives in an urban area, the support department can provide online support. If the user lives in a rural area, the support department can also provide telephone support. Furthermore, if the user lives overseas, the support department can provide support that takes time differences into account. In this way, the support department can select the optimal support method by considering the user's geographical location information. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, it can prioritize the collection of event information in the area where the user is currently located. The data collection unit can also collect information on nearby groups and communities based on the user's geographical location. Furthermore, if the user is traveling, the data collection unit can collect data related to the area they are traveling to. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. 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 into a generating AI and have the generating AI perform the collection of highly relevant data.

[0061] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data, a concise analysis on less important data, and an analysis of moderate importance on data of appropriate level of detail. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0062] The recommendation unit can adjust the level of detail of recommendations based on the importance of the group or event. For example, it can provide detailed recommendation information for highly important groups or events, concise recommendation information for less important groups or events, and recommendation information with an appropriate level of detail for moderately important groups or events. This allows the recommendation unit to perform efficient recommendations by adjusting the level of detail based on the importance of the group or event. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the importance of the group or event into a generative AI and have the generative AI adjust the level of detail of the recommendations.

[0063] The counseling department can select the most suitable counseling method during a counseling session by referring to the user's past counseling history. For example, it can select the most suitable counseling method based on the content of counseling sessions the user has received in the past. Furthermore, the counseling department can prioritize selecting methods that were effective based on the user's past counseling history. In addition, the counseling department can analyze the user's past counseling history and propose new counseling methods. Thus, the counseling department can select the most suitable counseling method by referring to the user's past counseling history. Some or all of the above processes in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can input the user's past counseling history data into a generating AI and have the generating AI select the most suitable counseling method.

[0064] The support unit can customize the means of support based on the user's current living situation. For example, if the user is busy with work, it can provide a quick and effective support method. The support unit can also provide support methods that take into account the user's home environment if the user is experiencing family problems. Furthermore, if the support unit is experiencing health problems, it can provide support methods tailored to the user's health condition. This allows the support unit to provide more appropriate support by customizing the means of support based on the user's current living situation. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI and have the generating AI customize the means of support.

[0065] The emotion analysis unit can select the optimal analysis method by referring to the user's past emotional data during emotion analysis. For example, it can select the optimal analysis method based on data from past emotion analyses the user has received. Furthermore, the emotion analysis unit can prioritize selecting effective analysis methods from the user's past emotional data. In addition, the emotion analysis unit can analyze the user's past emotional data and propose new analysis methods. Thus, the emotion analysis unit can select the optimal analysis method by referring to the user's past emotional data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's past emotional data into a generating AI and have the generating AI select the optimal analysis method.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects user interest and activity history data. For example, the data collection unit can collect the user's website browsing history, purchase history, and social media activity. The data collection unit can also collect information about events and groups the user has participated in. Step 2: The analysis unit uses generational AI to analyze the data collected by the collection unit. For example, the analysis unit can analyze the user's interests and activity history to identify what hobbies and interests the user has. The analysis unit can also analyze, based on the user's activity history, what kinds of groups or events the user is likely to participate in. Step 3: The recommendation unit recommends the most suitable groups and events based on the analysis results obtained by the analysis unit. For example, the recommendation unit can recommend relevant groups and events based on the user's hobbies and interests. It can also recommend groups and events that the user has previously participated in, based on the user's activity history. Step 4: The Counseling Department provides expert counseling and support based on groups and events recommended by the Recommendation Department. For example, the Counseling Department can provide an initial diagnosis and ongoing support if a user is experiencing feelings of loneliness. The Counseling Department can also provide appropriate counseling and support based on groups and events the user has participated in.

[0068] (Example of form 2) The social engagement platform according to an embodiment of the present invention is an AI-based system aimed at connecting people who feel lonely through shared hobbies and interests. This system analyzes the user's preferences and activity history and recommends the most suitable groups and events. It also provides expert counseling and support. For example, the system collects data on the user's interests and activity history. Next, a generative AI analyzes this data and recommends the most suitable groups and events. Furthermore, if the user is feeling lonely, an AI counselor provides an initial diagnosis and ongoing support. This platform targets all people who feel lonely, including young people, the elderly, and middle-aged and older adults, and aims to alleviate loneliness through group activities with others who share common hobbies and interests, and to provide expert counseling and support. By having the generative AI analyze the user's activity history and interests, recommend the most suitable groups and events, and perform real-time sentiment analysis, users can more easily find groups and events that suit them and build new social connections. In addition, the AI ​​counselor's initial diagnosis and ongoing support can alleviate the user's loneliness and enhance their social presence. This allows social engagement platforms to help people who feel lonely connect with each other through shared hobbies and interests, and to provide professional counseling and support.

[0069] The social engagement platform according to this embodiment comprises a data collection unit, an analysis unit, a recommendation unit, and a counseling unit. The data collection unit collects user interest and activity history data. For example, the data collection unit can collect user website browsing history, purchase history, and social media activity. The data collection unit can also collect information on events and groups that users have participated in. The analysis unit uses generative AI to analyze the data collected by the data collection unit. For example, the analysis unit can analyze the user's interests and activity history to identify what hobbies and interests the user has. The analysis unit can also analyze what groups and events are suitable for the user to participate in based on the user's activity history. The recommendation unit recommends the most suitable groups and events based on the analysis results obtained by the analysis unit. For example, the recommendation unit can recommend relevant groups and events based on the user's hobbies and interests. The recommendation unit can also recommend groups and events that the user has participated in in the past based on the user's activity history. The counseling unit provides expert counseling and support based on the groups and events recommended by the recommendation unit. The counseling department can, for example, provide an initial diagnosis and ongoing support if a user is experiencing feelings of loneliness. Furthermore, the counseling department can provide appropriate counseling and support based on the groups and events the user has participated in. Thus, the social engagement platform according to this embodiment can reduce feelings of loneliness by collecting and analyzing user interest and activity history data, recommending optimal groups and events, and providing expert counseling and support.

[0070] The data collection unit collects user interest and activity history data. For example, it can collect user website browsing history, purchase history, and social media activity. Specifically, website browsing history includes detailed data such as which pages the user visited, which links they clicked, and how long they spent on each page. Purchase history includes information such as which products the user purchased, purchase frequency, and payment method at the time of purchase. Social media activity data includes data such as which posts the user "liked" or commented on, which groups they belonged to, and which events they showed interest in. The data collection unit can also collect information on events and groups the user participated in. This includes which events the user attended, the date and time of the event, the location, a list of participants, and the activities at the event. Furthermore, the data collection unit can also collect data from online surveys and feedback conducted by users. This allows the data collection unit to comprehensively understand the user's diverse interests and activity history and build a detailed database. The collected data is encrypted to ensure security and privacy and stored on secure servers. This allows the data collection unit to collect a wealth of data while protecting user privacy, making it available for use by the analysis and recommendation units.

[0071] The analysis unit uses generative AI to analyze data collected by the data collection unit. For example, the analysis unit can analyze a user's interests and activity history to identify their hobbies and interests. Specifically, the generative AI uses natural language processing technology to extract a user's interests from their social media posts and website browsing history. For example, it can analyze the websites a user frequently visits and the content of articles they often read to identify topics they are interested in. The generative AI can also use clustering algorithms to group users' activity histories and identify groups of users with common interests. Furthermore, based on a user's activity history, the analysis unit can analyze what kinds of groups and events are suitable for them to participate in. For example, it can analyze evaluations and feedback on past events to identify what kinds of events a user prefers. This allows the analysis unit to gain a detailed understanding of the user's interests and provide foundational data for recommending the most suitable groups and events. In addition, the analysis unit can also analyze a user's psychological state and emotions. For example, it can analyze social media posts and survey responses to assess the level of loneliness and stress a user is currently experiencing. This allows the analysis unit to generate data that enables it to provide appropriate counseling and support tailored to the user's psychological state.

[0072] The recommendation department recommends the most suitable groups and events based on the analysis results obtained by the analysis department. For example, the recommendation department can recommend relevant groups and events based on the user's hobbies and interests. Specifically, it generates a list of relevant groups and events based on the user's interests and preferences analyzed by the generation AI and presents it to the user. For example, if the user is interested in music, it will recommend music-related events and groups. Also, if the user likes a particular genre of movies, it can recommend movie screenings and discussion groups related to that genre. Furthermore, the recommendation department can also recommend groups and events that the user has participated in in the past, based on the user's activity history. For example, it can analyze the evaluations and feedback of events the user has participated in in the past and recommend similar events. This gives the user the opportunity to participate again in events and groups they have enjoyed in the past. The recommendation department also takes into account the user's current psychological state and emotions to make the best recommendations. For example, if the user is feeling lonely, it will recommend events and groups that promote social interaction. Also, if the user is feeling stressed, it can recommend events and groups related to relaxation and mental health. This allows the recommendation team to provide optimal recommendations that take into account not only the user's interests and concerns, but also their psychological state and emotions, thereby increasing user satisfaction.

[0073] The Counseling Department provides expert counseling and support based on groups and events recommended by the Recommendation Department. For example, if a user is experiencing feelings of loneliness, the Counseling Department can conduct an initial assessment and provide ongoing support. Specifically, the Counseling Department has psychology professionals and counselors interact with users to assess their psychological state. The initial assessment evaluates the user's emotions, stress levels, and social connection status to create an appropriate counseling plan. Furthermore, the Counseling Department can provide appropriate counseling and support based on groups and events the user has participated in. For example, after a user participates in a particular event, they can share their experiences and impressions with a counselor and receive additional support as needed. This allows users to deepen their experiences gained through events and group activities and build better social connections. The Counseling Department can provide counseling in multiple formats, including online counseling and chat support. This allows users to receive counseling at their convenience. The Counseling Department can also continuously improve counseling plans and support content based on user feedback. This enables the Counseling Department to provide flexible and effective support tailored to user needs, reducing feelings of loneliness and strengthening social connections.

[0074] The emotion analysis unit can perform emotion analysis in real time. For example, the emotion analysis unit can analyze the user's facial expressions, voice, and text data to estimate the user's emotions in real time. For example, the emotion analysis unit can capture the user's facial expressions using a camera and estimate emotions using facial recognition technology. It can also record the user's voice using a microphone and estimate emotions using voice analysis technology. Furthermore, the emotion analysis unit can analyze text data entered by the user and estimate emotions using text analysis technology. As a result, the emotion analysis unit can perform emotion analysis in real time and respond appropriately to the user's emotions. 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-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input image data of the user captured by the camera into a generating AI, and have the generating AI perform the estimation of the user's emotions.

[0075] The support department can provide support to alleviate users' feelings of loneliness. For example, the support department can provide appropriate support when a user is feeling lonely. For example, the support department can provide professional counseling when a user is feeling lonely. The support department can also encourage users to participate in group activities or events when they are feeling lonely. Furthermore, the support department can recommend participation in online communities when a user is feeling lonely. In this way, the support department can enhance users' social presence by providing support to alleviate their feelings of loneliness. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the content of support to alleviate the user's feelings of loneliness into a generating AI and have the generating AI execute suggestions for support content.

[0076] The analysis unit can analyze a user's activity history and interests using a generative AI. For example, the analysis unit can input user activity history data into the generative AI, which can then analyze the user's interests and preferences. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The analysis unit can use the generative AI to analyze user activity history data and identify what hobbies and interests the user has. The analysis unit can also use the generative AI to analyze, based on the user's activity history, what groups or events the user is likely to participate in. This improves the accuracy of the analysis of user activity history and interests by using a generative AI. 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 user activity history data into the generative AI and have the generative AI output the analysis results.

[0077] The recommendation unit can recommend the most suitable groups and events based on the analysis results using a generative AI. For example, the recommendation unit can input the analysis results obtained by the analysis unit into the generative AI, which can then recommend the most suitable groups and events. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. The recommendation unit can use the generative AI to recommend groups and events related to the user's hobbies and interests based on the analysis results. The recommendation unit can also use the generative AI to recommend groups and events that the user has previously participated in, based on the user's activity history. This improves the accuracy of the recommendation unit's recommendation of the most suitable groups and events by using a generative AI. Some or all of the above processing in the recommendation unit may be performed using AI, for example, or without AI. For example, the recommendation unit can input the analysis results into the generative AI and have the generative AI output the recommendation results.

[0078] The counseling department can provide initial diagnosis and ongoing support when a user is experiencing loneliness. For example, the counseling department can conduct an initial diagnosis and assess the user's condition when a user is experiencing loneliness. The counseling department can also provide ongoing support when a user is experiencing loneliness. For example, the counseling department can provide support to alleviate the user's loneliness through regular follow-ups and online counseling. In this way, the counseling department can alleviate loneliness by providing initial diagnosis and ongoing support when a user is experiencing loneliness. Some or all of the above processes in the counseling department may be performed using AI, for example, or not using AI. For example, the counseling department can input the user's initial diagnosis data into a generating AI and have the generating AI output the diagnosis results.

[0079] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection and collect data when the user is relaxed. If the user is excited, the data collection unit can collect data in real time and immediately send it for analysis. Furthermore, if the user is tired, the data collection unit can temporarily stop data collection and resume it after the user has rested. This allows the data collection unit to collect more appropriate data by adjusting the timing of data collection according to the user's emotions. 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 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 have the generative AI adjust the timing of data collection.

[0080] The data collection unit can analyze the user's past activity history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from events the user has frequently participated in in the past. It can also collect data from apps and websites the user has preferred to use in the past. Furthermore, the data collection unit can select the optimal data collection method based on past surveys and feedback provided by the user. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past activity history. 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 the user's past activity history data into a generating AI and have the generating AI select the optimal data collection method.

[0081] The data collection unit can filter data based on the user's current lifestyle and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. It can also collect relevant data based on the user's current lifestyle (work, studies, family, etc.). Furthermore, the data collection unit can collect data related to communities and groups the user is currently participating in. This allows the data collection unit to collect more relevant data by filtering it based on the user's current lifestyle 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 data on the user's current lifestyle and areas of interest into a generating AI and have the generating AI perform the data filtering.

[0082] 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 feeling lonely, the data collection unit can prioritize collecting data related to social engagement. If the user is relaxed, the data collection unit can prioritize collecting data related to hobbies and interests. Furthermore, if the user is stressed, the data collection unit can prioritize collecting data that helps reduce stress. This allows the data collection unit to collect more appropriate data by prioritizing data according to the user's emotions. 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 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 have the generative AI determine the priority of the data.

[0083] 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, the data collection unit can prioritize the collection of event information in the area where the user is currently located. The data collection unit can also collect information on nearby groups and communities based on the user's geographical location. Furthermore, if the user is traveling, the data collection unit can collect data related to the area they are traveling to. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information. 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 into a generating AI and have the generating AI perform the collection of highly relevant data.

[0084] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to topics that the user frequently mentions on social media. It can also collect activity information from accounts and groups that the user follows. Furthermore, the data collection unit can collect information from online events and webinars that the user participates in. In this way, the data collection unit can collect relevant data by analyzing the user's social media activity. 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 the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, the analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. 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 without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the analysis.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. It can also perform a simplified analysis on data with low importance. Furthermore, it can perform an analysis with an appropriate level of detail on data with moderate importance. In this way, the analysis unit can perform efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a network analysis algorithm to data related to social engagement. It can also apply a clustering algorithm to data related to user interests. Furthermore, it can apply a time series analysis algorithm to data related to user activity history. By applying different analysis algorithms depending on the data category, the analysis unit can improve the accuracy of the analysis. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis. In this way, the analysis unit can provide more appropriate analysis results by adjusting the length of the analysis according to the user's emotions. 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 without AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the length of the analysis.

[0089] The analysis unit can determine the priority of analysis based on the data submission date during the analysis. For example, the analysis unit can prioritize the analysis of the most recent data. It can also postpone the analysis of older data. Furthermore, it can analyze data with a moderate submission date with appropriate priority. This allows the analysis unit to perform efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data submission date into a generative AI and have the generative AI determine the priority of analysis.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of data with high relevance. It can also postpone the analysis of data with low relevance. Furthermore, it can analyze data with moderate relevance in an appropriate order. In this way, the analysis unit can perform 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, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI adjust the order of analysis.

[0091] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, the recommendation system can provide detailed recommendations. If the user is in a hurry, it can provide concise and to-the-point recommendations. Furthermore, if the user is excited, it can provide visually appealing recommendations. In this way, the recommendation system can provide more appropriate recommendations by adjusting the way recommendations are presented according to the user's emotions. 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 recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0092] The recommendation system can adjust the level of detail of recommendations based on the importance of groups and events. For example, it can provide detailed recommendation information for highly important groups and events, and concise recommendation information for less important groups and events. Furthermore, it can provide recommendation information with an appropriate level of detail for moderately important groups and events. This allows the recommendation system to perform efficient recommendations by adjusting the level of detail based on the importance of groups and events. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input the importance of groups and events into a generative AI and have the generative AI adjust the level of detail of the recommendations.

[0093] The recommendation system can apply different recommendation algorithms depending on the category of the group or event during the recommendation process. For example, it can apply a network analysis algorithm to groups and events related to social engagement. It can also apply a clustering algorithm to groups and events related to the user's interests. Furthermore, it can apply a time-series analysis algorithm to groups and events related to the user's activity history. This improves recommendation accuracy by applying different recommendation algorithms depending on the category of the group or event. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not. For example, the recommendation system can input the categories of groups and events into a generative AI and have the generative AI apply the recommendation algorithm.

[0094] The recommendation system can estimate the user's emotions and adjust the length of recommendations based on those emotions. For example, if the user is in a hurry, the recommendation system can provide short, concise recommendations. If the user is relaxed, it can provide detailed recommendations. Furthermore, if the user is excited, it can provide visually appealing recommendations. By adjusting the length of recommendations according to the user's emotions, the recommendation system can provide more appropriate recommendations. 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 recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the length of recommendations.

[0095] The recommendation system can determine the priority of recommendations based on the timing of the groups and events. For example, it can prioritize recommending groups and events that are scheduled to be held soon. It can also postpone recommending groups and events that are scheduled to be held far in the future. Furthermore, it can recommend groups and events that are scheduled to be held at a moderate pace with appropriate priority. This allows the recommendation system to perform recommendations efficiently by determining the priority of recommendations based on the timing of the groups and events. Some or all of the above processing in the recommendation system may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation system can input the timing of the groups and events into a generative AI and have the generative AI determine the recommendation priority.

[0096] The recommendation unit can adjust the order of recommendations based on the relevance of groups and events during the recommendation process. For example, the recommendation unit can prioritize recommending groups and events with high relevance. It can also postpone recommending groups and events with low relevance. Furthermore, it can recommend groups and events with moderate relevance in a suitable order. This allows the recommendation unit to perform efficient recommendations by adjusting the order of recommendations based on the relevance of groups and events. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the recommendation unit can input the relevance of groups and events into a generative AI and have the generative AI adjust the order of recommendations.

[0097] The counseling unit can estimate the user's emotions and adjust the counseling method based on the estimated emotions. For example, if the user is tense, the counseling unit can provide a relaxing counseling method. If the user is relaxed, the counseling unit can provide detailed counseling. Furthermore, if the user is agitated, the counseling unit can provide a visually appealing counseling method. In this way, the counseling unit can provide more appropriate counseling by adjusting the counseling method according to the user's emotions. 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 counseling unit may be performed using AI, for example, or not using AI. For example, the counseling unit can input the user's emotion data into a generative AI and have the generative AI adjust the counseling method.

[0098] The counseling department can select the most suitable counseling method during a counseling session by referring to the user's past counseling history. For example, the counseling department can select the most suitable counseling method based on the content of counseling sessions the user has received in the past. Furthermore, the counseling department can prioritize selecting methods that were effective based on the user's past counseling history. In addition, the counseling department can analyze the user's past counseling history and propose new counseling methods. Thus, the counseling department can select the most suitable counseling method by referring to the user's past counseling history. Some or all of the above-described processes in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can input the user's past counseling history data into a generating AI and have the generating AI select the most suitable counseling method.

[0099] The counseling department can customize the counseling methods based on the user's current living situation during counseling sessions. For example, if the user is busy with work, the counseling department can provide a short and effective counseling method. Furthermore, if the user has family problems, the counseling department can provide a counseling method that takes the family environment into consideration. Additionally, if the user has health problems, the counseling department can provide a counseling method tailored to their health condition. This allows the counseling department to provide more appropriate counseling by customizing the counseling methods based on the user's current living situation. Some or all of the above-described processes in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can input the user's current living situation data into a generating AI and have the generating AI customize the counseling methods.

[0100] The counseling unit can estimate the user's emotions and determine counseling priorities based on those estimated emotions. For example, if the user is experiencing strong feelings of loneliness, the counseling unit can prioritize counseling. If the user is experiencing mild loneliness, the counseling unit can provide counseling with normal priority. Furthermore, if the user is not particularly lonely, the counseling unit can postpone counseling. This allows the counseling unit to provide more appropriate counseling by determining counseling priorities according to the user's emotions. 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 counseling unit may be performed using AI or not. For example, the counseling unit can input user emotion data into a generative AI and have the generative AI determine the counseling priorities.

[0101] The counseling department can select the most suitable counseling method during a counseling session by considering the user's geographical location. For example, if the user lives in an urban area, the counseling department can provide online counseling. If the user lives in a rural area, the counseling department can provide telephone counseling. Furthermore, if the user lives overseas, the counseling department can provide a counseling method that takes time differences into account. In this way, the counseling department can select the most suitable counseling method by considering the user's geographical location. Some or all of the above processing in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can input the user's geographical location information into a generating AI and have the generating AI select the most suitable counseling method.

[0102] The counseling department can analyze a user's social media activity during counseling sessions and propose appropriate counseling methods. For example, the counseling department can provide counseling related to issues that the user frequently mentions on social media. It can also provide counseling that takes into account the opinions of experts the user follows. Furthermore, the counseling department can provide counseling based on the user's activities in online communities. This allows the counseling department to propose more appropriate counseling methods by analyzing the user's social media activity. Some or all of the above processing in the counseling department may be performed using AI, for example, or not. For example, the counseling department can input the user's social media activity data into a generating AI and have the generating AI propose counseling methods.

[0103] The emotion analysis unit can estimate the user's emotions and adjust the emotion analysis method based on the estimated user emotions. For example, if the user is relaxed, the emotion analysis unit can perform a detailed emotion analysis. If the user is in a hurry, the emotion analysis unit can perform a concise emotion analysis. Furthermore, if the user is excited, the emotion analysis unit can perform a visually appealing emotion analysis. In this way, the emotion analysis unit can perform more appropriate emotion analysis by adjusting the emotion analysis method according to 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 emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the emotion analysis method.

[0104] The emotion analysis unit can select the optimal analysis method by referring to the user's past emotional data during emotion analysis. For example, the emotion analysis unit can select the optimal analysis method based on data from past emotional analyses the user has received. Furthermore, the emotion analysis unit can prioritize selecting effective analysis methods from the user's past emotional data. In addition, the emotion analysis unit can analyze the user's past emotional data and propose new analysis methods. Thus, the emotion analysis unit can select the optimal analysis method by referring to the user's past emotional data. Some or all of the above-described processes in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's past emotional data into a generating AI and have the generating AI select the optimal analysis method.

[0105] The emotion analysis unit can estimate the user's emotions and determine the priority of emotion analysis based on the estimated emotions. For example, if the user is feeling a strong sense of loneliness, the emotion analysis unit can prioritize emotion analysis. If the user is feeling a mild sense of loneliness, the emotion analysis unit can perform emotion analysis with the normal priority. Furthermore, if the user is not feeling particularly lonely, the emotion analysis unit can postpone the analysis. This allows the emotion analysis unit to perform more appropriate emotion analysis by determining the priority of emotion analysis according to 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 emotion analysis unit may be performed using AI, or not using AI. For example, the emotion analysis unit can input the user's emotion data into the generative AI and have the generative AI determine the priority of emotion analysis.

[0106] The sentiment analysis unit can select the optimal analysis method by considering the user's geographical location information during sentiment analysis. For example, if the user lives in an urban area, the sentiment analysis unit can perform online sentiment analysis. If the user lives in a rural area, the sentiment analysis unit can also perform sentiment analysis by telephone. Furthermore, if the user lives overseas, the sentiment analysis unit can perform sentiment analysis while considering the time difference. In this way, the sentiment analysis unit can select the optimal analysis method by considering the user's geographical location information. Some or all of the above processing in the sentiment analysis unit may be performed using AI, for example, or without AI. For example, the sentiment analysis unit can input the user's geographical location information into a generating AI and have the generating AI select the optimal analysis method.

[0107] The support unit can estimate the user's emotions and adjust its support methods based on the estimated emotions. For example, if the user is tense, the support unit can provide a relaxing support method. If the user is relaxed, the support unit can also provide detailed support. Furthermore, if the user is excited, the support unit can provide a visually appealing support method. In this way, the support unit can provide more appropriate support by adjusting its support methods according to the user's emotions. 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 support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the support methods.

[0108] The support unit can select the optimal support method by referring to the user's past support history during support. For example, the support unit can select the optimal support method based on the content of support the user has received in the past. Furthermore, the support unit can prioritize and select effective methods from the user's past support history. In addition, the support unit can analyze the user's past support history and propose new support methods. Thus, the support unit can select the optimal support method by referring to the user's past support history. Some or all of the above processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's past support history data into a generating AI and have the generating AI select the optimal support method.

[0109] The support unit can customize the means of support based on the user's current living situation. For example, if the user is busy with work, the support unit can provide effective support in a short amount of time. Furthermore, if the user has family problems, the support unit can provide support that takes their home environment into consideration. Additionally, if the user has health problems, the support unit can provide support tailored to their health condition. This allows the support unit to provide more appropriate support by customizing the means of support based on the user's current living situation. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI and have the generating AI customize the means of support.

[0110] The support unit can estimate the user's emotions and determine the priority of support based on the estimated emotions. For example, if the user is experiencing strong feelings of loneliness, the support unit can prioritize support. If the user is experiencing mild feelings of loneliness, the support unit can provide support with normal priority. Furthermore, if the user is not particularly feeling lonely, the support unit can postpone support. In this way, the support unit can provide more appropriate support by determining the priority of support according to the user's emotions. 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 support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI determine the priority of support.

[0111] The support department can select the optimal support method by considering the user's geographical location information during support. For example, if the user lives in an urban area, the support department can provide online support. If the user lives in a rural area, the support department can also provide telephone support. Furthermore, if the user lives overseas, the support department can provide support that takes time differences into account. In this way, the support department can select the optimal support method by considering the user's geographical location information. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the user's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is experiencing strong feelings of loneliness, emotion analysis can be prioritized. If the user is experiencing mild feelings of loneliness, emotion analysis can be performed with the normal priority. Furthermore, if the user is not experiencing any particular feelings of loneliness, it can be postponed. This allows the analysis unit to perform more appropriate analysis by determining the priority of analysis according to the user's emotions. 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-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI determine the priority of analysis.

[0114] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, it can prioritize the collection of event information in the area where the user is currently located. The data collection unit can also collect information on nearby groups and communities based on the user's geographical location. Furthermore, if the user is traveling, the data collection unit can collect data related to the area they are traveling to. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. 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 into a generating AI and have the generating AI perform the collection of highly relevant data.

[0115] The recommendation system can estimate the user's emotions and adjust the way recommendations are presented based on those emotions. For example, if the user is relaxed, it can provide detailed recommendations. If the user is in a hurry, it can provide concise recommendations that get straight to the point. Furthermore, if the user is excited, it can provide visually appealing recommendations. In this way, the recommendation system can provide more appropriate recommendations by adjusting the way recommendations are presented according to the user's emotions. 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 recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI adjust the way recommendations are presented.

[0116] The counseling unit can estimate the user's emotions and adjust the counseling method based on the estimated emotions. For example, if the user is tense, it can provide a relaxing counseling method. If the user is relaxed, it can provide detailed counseling. Furthermore, if the user is agitated, it can provide a visually appealing counseling method. In this way, the counseling unit can provide more appropriate counseling by adjusting the counseling method according to the user's emotions. 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 counseling unit may be performed using AI, for example, or not using AI. For example, the counseling unit can input user emotion data into a generative AI and have the generative AI adjust the counseling method.

[0117] The support unit can estimate the user's emotions and adjust its support methods based on those emotions. For example, if the user is tense, it can provide a relaxing support method. If the user is relaxed, it can provide more detailed support. Furthermore, if the user is excited, it can provide a visually appealing support method. In this way, the support unit can provide more appropriate support by adjusting its support methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 support unit may be performed using AI, for example, or without AI. For example, the support unit can input user emotion data into a generative AI and have the generative AI adjust the support methods.

[0118] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on highly important data, a concise analysis on less important data, and an analysis of moderate importance on data of appropriate level of detail. This allows the analysis unit to perform efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI adjust the level of detail of the analysis.

[0119] The recommendation unit can adjust the level of detail of recommendations based on the importance of the group or event. For example, it can provide detailed recommendation information for highly important groups or events, concise recommendation information for less important groups or events, and recommendation information with an appropriate level of detail for moderately important groups or events. This allows the recommendation unit to perform efficient recommendations by adjusting the level of detail based on the importance of the group or event. Some or all of the above processing in the recommendation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the recommendation unit can input the importance of the group or event into a generative AI and have the generative AI adjust the level of detail of the recommendations.

[0120] The counseling department can select the most suitable counseling method during a counseling session by referring to the user's past counseling history. For example, it can select the most suitable counseling method based on the content of counseling sessions the user has received in the past. Furthermore, the counseling department can prioritize selecting methods that were effective based on the user's past counseling history. In addition, the counseling department can analyze the user's past counseling history and propose new counseling methods. Thus, the counseling department can select the most suitable counseling method by referring to the user's past counseling history. Some or all of the above processes in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can input the user's past counseling history data into a generating AI and have the generating AI select the most suitable counseling method.

[0121] The support unit can customize the means of support based on the user's current living situation. For example, if the user is busy with work, it can provide a quick and effective support method. The support unit can also provide support methods that take into account the user's home environment if the user is experiencing family problems. Furthermore, if the support unit is experiencing health problems, it can provide support methods tailored to the user's health condition. This allows the support unit to provide more appropriate support by customizing the means of support based on the user's current living situation. Some or all of the above-described processes in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input the user's current living situation data into a generating AI and have the generating AI customize the means of support.

[0122] The emotion analysis unit can select the optimal analysis method by referring to the user's past emotional data during emotion analysis. For example, it can select the optimal analysis method based on data from past emotion analyses the user has received. Furthermore, the emotion analysis unit can prioritize selecting effective analysis methods from the user's past emotional data. In addition, the emotion analysis unit can analyze the user's past emotional data and propose new analysis methods. Thus, the emotion analysis unit can select the optimal analysis method by referring to the user's past emotional data. Some or all of the above processing in the emotion analysis unit may be performed using AI, for example, or without AI. For example, the emotion analysis unit can input the user's past emotional data into a generating AI and have the generating AI select the optimal analysis method.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects user interest and activity history data. For example, the data collection unit can collect the user's website browsing history, purchase history, and social media activity. The data collection unit can also collect information about events and groups the user has participated in. Step 2: The analysis unit uses generational AI to analyze the data collected by the collection unit. For example, the analysis unit can analyze the user's interests and activity history to identify what hobbies and interests the user has. The analysis unit can also analyze, based on the user's activity history, what kinds of groups or events the user is likely to participate in. Step 3: The recommendation unit recommends the most suitable groups and events based on the analysis results obtained by the analysis unit. For example, the recommendation unit can recommend relevant groups and events based on the user's hobbies and interests. It can also recommend groups and events that the user has previously participated in, based on the user's activity history. Step 4: The Counseling Department provides expert counseling and support based on groups and events recommended by the Recommendation Department. For example, the Counseling Department can provide an initial diagnosis and ongoing support if a user is experiencing feelings of loneliness. The Counseling Department can also provide appropriate counseling and support based on groups and events the user has participated in.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, counseling unit, emotion analysis unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user interest and activity history data using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The recommendation unit recommends the most suitable groups and events based on the results of the analysis unit, using the specific processing unit 290 of the data processing unit 12. The counseling unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides expert counseling and support. The emotion analysis unit analyzes the user's emotions in real time using the camera 42 and microphone 38B of the smart device 14. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides support to alleviate the user's feelings of loneliness. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, counseling unit, emotion analysis unit, and support unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user interest and activity history data using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The recommendation unit recommends the most suitable groups and events based on the results of the analysis unit, using the identification processing unit 290 of the data processing unit 12. The counseling unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides expert counseling and support. The emotion analysis unit analyzes the user's emotions in real time using the camera 42 and microphone 238 of the smart glasses 214. The support unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides support to alleviate the user's feelings of loneliness. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, counseling unit, emotion analysis unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user interest and activity history data using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generating AI. The recommendation unit recommends the most suitable groups and events based on the results of the analysis unit, using the specific processing unit 290 of the data processing unit 12. The counseling unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides expert counseling and support. The emotion analysis unit analyzes the user's emotions in real time using the camera 42 and microphone 238 of the headset terminal 314. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides support to alleviate the user's feelings of loneliness. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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.

[0176] 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.

[0177] Each of the multiple elements described above, including the data collection unit, analysis unit, recommendation unit, counseling unit, emotion analysis unit, and support unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user interest and activity history data using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data using generated AI. The recommendation unit recommends the most suitable groups and events based on the results of the analysis unit, using the specific processing unit 290 of the data processing unit 12. The counseling unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides expert counseling and support. The emotion analysis unit analyzes the user's emotions in real time using the camera 42 and microphone 238 of the robot 414. The support unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides support to alleviate the user's feelings of loneliness. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) A data collection unit that collects user interest and activity history data, An analysis unit analyzes the data collected by the aforementioned collection unit, A recommendation unit recommends the most suitable group or event based on the analysis results obtained by the aforementioned analysis unit, The system includes a counseling department that provides expert counseling and support based on groups and events recommended by the aforementioned recommendation department. A system characterized by the following features. (Note 2) It is equipped with an emotion analysis unit that performs real-time emotion analysis. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a support department that provides assistance to alleviate users' feelings of isolation. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We use generative AI to analyze users' activity history and interests. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, Using generative AI, we recommend the most suitable groups and events based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned counseling department, Provide initial diagnosis and ongoing support when users are experiencing feelings of loneliness. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection 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 activity history and select the optimal data collection method. 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 lifestyle 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 the collection of 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 users' 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, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, 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, During analysis, different analysis 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, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, 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 recommendation department, It estimates the user's emotions and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned recommendation department, When making recommendations, adjust the level of detail based on the importance of the group or event. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned recommendation department, When making recommendations, different recommendation algorithms are applied depending on the group or event category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned recommendation department, It estimates the user's sentiment and adjusts the length of recommendations based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned recommendation department, When making recommendations, priority will be determined based on the timing of the group or event. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned recommendation department, When making recommendations, the order of recommendations will be adjusted based on the relevance of the groups and events. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned counseling department, The system estimates the user's emotions and adjusts the counseling method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned counseling department, During counseling sessions, the system selects the most suitable counseling method by referring to the user's past counseling history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned counseling department, During counseling sessions, the counseling methods are customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned counseling department, The system estimates the user's emotions and determines counseling priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned counseling department, During counseling sessions, the most suitable counseling method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned counseling department, During counseling sessions, we analyze the user's social media activity and propose counseling methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned emotion analysis unit, The system estimates the user's emotions and adjusts the emotion analysis method based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned emotion analysis unit, During sentiment analysis, the system selects the optimal analysis method by referring to the user's past sentiment data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned emotion analysis unit, It estimates the user's emotions and determines the priority of emotion analysis based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned emotion analysis unit, When performing sentiment analysis, the optimal analysis method is selected by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned support unit is It estimates the user's emotions and adjusts the support method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned support unit is During support, the system will refer to the user's past support history to select the most appropriate support method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned support unit is During support, customize the support methods based on the user's current living situation. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned support unit is The system estimates the user's emotions and determines support priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned support unit is During support, the optimal support method will be selected considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0197] 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 data collection unit that collects user interest and activity history data, An analysis unit analyzes the data collected by the aforementioned collection unit, A recommendation unit recommends the most suitable group or event based on the analysis results obtained by the aforementioned analysis unit, The system includes a counseling department that provides expert counseling and support based on groups and events recommended by the aforementioned recommendation department. A system characterized by the following features.

2. It is equipped with an emotion analysis unit that performs real-time emotion analysis. The system according to feature 1.

3. It includes a support department that provides assistance to alleviate users' feelings of isolation. The system according to feature 1.

4. The aforementioned analysis unit, We use generative AI to analyze users' activity history and interests. The system according to feature 1.

5. The aforementioned recommendation department, Using generative AI, the system recommends the most suitable groups and events based on the analysis results. The system according to feature 1.

6. The aforementioned counseling department, Provide initial diagnosis and ongoing support when users are experiencing feelings of loneliness. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past activity history and select the optimal data collection method. The system according to feature 1.

9. The aforementioned collection unit is During data collection, filtering is performed based on the user's current lifestyle 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.

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