system

Abstract

The system according to this embodiment aims to provide appropriate support for the user's concerns and interests. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a matching unit, and a communication unit. The reception unit receives input from the user regarding their concerns and interests. The analysis unit analyzes the information received by the reception unit. The matching unit automatically matches the user with other users who have similar experiences based on the information analyzed by the analysis unit. The communication unit facilitates communication between users who have been matched by the matching 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, performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 a user to obtain appropriate support for their worries and concerns.

[0005] The system according to the embodiment aims to provide appropriate support for the worries and concerns of the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a matching unit, and a communication unit. The reception unit receives input from the user regarding their concerns and interests. The analysis unit analyzes the information received by the reception unit. The matching unit automatically matches the user with other users who have similar experiences based on the information analyzed by the analysis unit. The communication unit facilitates communication between users who have been matched by the matching unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide appropriate support for the user's concerns 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The empathy matching AI according to an embodiment of the present invention is a support system that connects people in similar circumstances and promotes consultation and information sharing. The empathy matching AI analyzes the worries and interests entered by the user and automatically matches them with others who have similar experiences. This aims to reduce feelings of loneliness and provide appropriate support. First, the user enters their worries and interests. For example, "I have a lot of work stress" or "I have worries about childcare." This information is entered into the AI. Next, the AI ​​analyzes the entered information. The AI ​​understands the user's worries and interests and identifies other users who have similar experiences. For example, if there are other users who have worries about work stress, the AI ​​identifies those users. The AI ​​then automatically matches the identified users with each other. This connects people in similar circumstances and makes it easier for empathy and understanding to develop. For example, by matching users who have worries about childcare, they can consult with each other and share information. Furthermore, the matched users can receive online counseling and support. This provides appropriate support and is expected to improve mental health. For example, they can receive advice from experts through online counseling sessions. This system is beneficial for people who feel lonely, want to share their worries with someone, or seek connection with others in similar circumstances. For example, with the current increase in people feeling lonely and isolated due to the COVID-19 pandemic, this system is extremely useful. Furthermore, this system holds significant importance in the mental health market. The Japanese mental health market is worth approximately 150 billion yen, and this system could potentially capture 5% (approximately 5 billion yen) of that market. For instance, the increasing demand for support through online platforms is expected to drive the widespread use of this system. In this way, the Yoriso Matching AI aims to contribute to a richer society by reducing feelings of loneliness and providing appropriate support. For example, it can create communities where users can share their experiences and grow together. This allows the Yoriso Matching AI to efficiently analyze users' worries and interests, providing appropriate matching and communication.

[0029] The companion matching AI according to this embodiment comprises a reception unit, an analysis unit, a matching unit, and a communication unit. The reception unit receives input from the user regarding their worries and concerns. These worries and concerns include, but are not limited to, health issues, work-related worries, and hobbies. The reception unit accepts the user's worries and concerns in text format, for example. The reception unit can also accept the user's worries and concerns using voice input. For example, the reception unit can convert the user's voice input into text and accept it. Furthermore, the reception unit can estimate the user's emotions and adjust the input method for worries and concerns based on the estimated emotions. For example, if the user is feeling stressed, it can provide a simple interface and minimize the input procedure. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, but is not limited to, methods such as text analysis, sentiment analysis, and pattern recognition. The analysis unit, for example, analyzes the user's worries and concerns in detail and identifies other users with similar experiences. For example, text analysis is used to extract keywords related to the user's worries and interests, and other users with similar keywords are identified. The matching unit automatically matches the user with other users who have similar experiences based on the information analyzed by the analysis unit. Matching is performed based on, for example, a similarity scale or the algorithm used, but is not limited to such examples. The matching unit calculates the similarity of users' worries and interests using, for example, cosine similarity, and matches users with high similarity. The matching unit can also estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if a user is feeling stressed, it will be matched with other users who are also feeling stressed. The communication unit supports matching users in communicating with each other. Communication is performed by, for example, chat, video calls, forums, etc., but is not limited to such examples. The communication unit also supports matching users in receiving online counseling and support, for example, providing psychological counseling or technical support.Furthermore, the communication unit can estimate the user's emotions and adjust the communication method based on the estimated emotions. For example, if the user is nervous, it will communicate in a calm tone. As a result, the empathetic matching AI according to this embodiment can efficiently analyze the user's worries and interests and provide appropriate matching and communication.

[0030] The reception desk receives input from users regarding their worries and concerns. These concerns may include, but are not limited to, health issues, work-related worries, or hobbies. The reception desk can, for example, receive user input in text format. It can also accept user input via voice. For instance, it can convert user voice input into text for reception. Furthermore, the reception desk can estimate the user's emotions and adjust the input method based on that estimation. For example, if a user is stressed, it can provide a simple interface and minimize the input steps. The reception desk utilizes natural language processing technology to efficiently process user input. Specifically, it analyzes user-inputted text and voice data to extract keywords and important phrases. This allows for a quick understanding of user concerns and concerns, enabling appropriate responses. In the case of voice input, speech recognition technology is used to accurately convert user speech into text, minimizing misrecognition. Additionally, the reception desk can temporarily store user input and reuse it as needed. For example, by referring to previously entered concerns and interests, the system can respond in a way that suits the user's situation and needs. This ensures that users receive consistent support and improves reliability. The reception desk uses sentiment analysis technology to estimate the user's emotions. Specifically, it analyzes emotions from the user's text and voice data to estimate emotional states such as stress, joy, and sadness. This enables appropriate responses based on the user's emotions. For example, if the user is feeling stressed, the reception desk provides a simple interface and minimizes the input steps to reduce the user's burden. Conversely, if the user is feeling joy or excitement, it can encourage more detailed input to gain a deeper understanding of the user's interests. This allows the reception desk to respond flexibly to the user's emotions, contributing to an improved user experience.

[0031] The analysis unit analyzes the information received by the reception unit. Analysis is performed using methods such as text analysis, sentiment analysis, and pattern recognition, but is not limited to these examples. For instance, the analysis unit analyzes the user's concerns and interests in detail and identifies other users with similar experiences. For example, it uses text analysis to extract keywords related to the user's concerns and interests and identify other users with similar keywords. The analysis unit utilizes natural language processing techniques to analyze the user's input in detail. Specifically, it uses morphological analysis and contextual analysis to understand the intentions and emotions behind the user's concerns and interests. For example, if a user inputs "My work isn't going well," the analysis unit extracts the keywords "work" and "not going well" and searches for data on other users related to these keywords. Furthermore, it uses sentiment analysis techniques to estimate emotions from the user's input and understand emotional states such as stress and anxiety. This allows the analysis unit to understand the user's concerns and interests more deeply and respond appropriately. In addition, the analysis unit uses pattern recognition techniques to extract common patterns and trends from the user's input. For example, if multiple users have similar problems, the system can identify commonalities and pinpoint users with similar experiences. This allows the analytics unit to efficiently analyze users' problems and interests and support appropriate matching. The analytics unit can also analyze trends in users' problems and interests by utilizing past data and statistical information. For example, based on past data, it can understand the trends in problems at specific times or situations and formulate future countermeasures. This enables the analytics unit to handle not only real-time analysis but also long-term risk assessment and trend analysis, improving the reliability and security of the entire system.

[0032] The matching unit automatically matches users with similar experiences based on information analyzed by the analysis unit. Matching is performed based on, for example, a similarity measure or the algorithm used, but is not limited to such examples. The matching unit calculates the similarity of users' worries and concerns using, for example, cosine similarity, and matches users with high similarity. The matching unit can also estimate users' emotions and adjust the matching criteria based on the estimated emotions. For example, if a user is feeling stressed, it will be matched with users who are also feeling stressed. The matching unit uses a combination of multiple algorithms to evaluate the similarity of users' worries and concerns with high accuracy. Specifically, it evaluates the similarity between users from multiple angles using different similarity measures such as the Jaccard coefficient and Euclidean distance, in addition to cosine similarity. This enables more accurate matching. The matching unit also dynamically adjusts the matching criteria considering the user's emotional state. For example, if a user is feeling stressed, matching them with users who are also feeling stressed makes it easier to receive empathy and support. On the other hand, when users are relaxed, matching them with users who have different perspectives can encourage new discoveries and insights. Furthermore, the matching unit collects user feedback and continuously improves the accuracy of the matching algorithm. For example, it monitors user satisfaction and communication frequency after matching and adjusts the algorithm based on this data. This allows the matching unit to provide optimal matches that meet user needs and improve the overall effectiveness of the system. In addition, the matching unit anonymizes and encrypts data to protect user privacy. This allows users to use the system with peace of mind and improves its reliability.

[0033] The Communications Department supports matching users in communicating with each other. Communication can take place through methods such as chat, video calls, and forums, but is not limited to these. The Communications Department also supports matching users in receiving online counseling and support, such as providing psychological counseling or technical support. Furthermore, the Communications Department can estimate a user's emotions and adjust the communication method based on that estimation. For example, if a user is feeling anxious, the department will communicate in a calm tone. The Communications Department provides multiple communication methods to ensure smooth communication between users. Specifically, it offers a variety of methods such as text chat, voice calls, video calls, and forums, allowing users to choose the most suitable method according to their needs and circumstances. For example, text chat allows for real-time message exchange, while voice and video calls enable more direct communication. Forums allow multiple users to participate in exchanging opinions and sharing information. In addition, the Communications Department monitors users' emotional states in real time and provides appropriate support. For example, if a user is feeling anxious, the system will automatically generate messages in a calm tone to help the user relax. Furthermore, when a user is excited, the system provides proactive responses, enabling communication that is sensitive to the user's emotions. This allows users to communicate with confidence and improves trust. The communications department encrypts and anonymizes communication content to protect user privacy. This allows users to use the system with confidence and improves trust. The communications department also collects user feedback and continuously improves communication methods and support. For example, it monitors user satisfaction and usage patterns and uses this data to improve the system. In this way, the communications department can provide optimal support to users and improve the overall effectiveness of the system.

[0034] The analysis unit can analyze the user's concerns and interests in detail. For example, the analysis unit can extract keywords related to the user's concerns and interests using text analysis. For example, the analysis unit can analyze the user's input using natural language processing technology and identify important keywords. The analysis unit can also estimate the user's emotions using sentiment analysis. For example, the analysis unit can estimate emotions from the user's input and calculate an emotion score. The analysis unit can also identify patterns in the user's concerns and interests using pattern recognition technology. For example, the analysis unit can learn patterns in the user's concerns and interests based on past data and apply them to new input. This allows for more appropriate matching by analyzing the user's concerns and interests in detail. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's input into a generating AI and output the analysis results to the generating AI.

[0035] The matching unit can identify other users with similar experiences based on the information analyzed by the analysis unit. For example, the matching unit can calculate the similarity of users' concerns and interests using cosine similarity. Alternatively, it can vectorize the user's input and calculate its similarity to other users' inputs. The matching unit can also calculate the similarity of users' concerns and interests using the Jaccard coefficient. For example, it can treat the user's input as a set and calculate the proportion of common elements with other users' inputs. Furthermore, the matching unit can calculate the similarity of users' concerns and interests using TF-IDF. For example, it can evaluate the importance of the user's input and calculate its similarity to other users' inputs. This allows for more appropriate matching by identifying users with similar experiences. Some or all of the above processing in the matching unit may be performed using AI, or without AI. For example, the matching unit can input the information analyzed by the analysis unit into a generating AI and output the matching results to the generating AI.

[0036] The communication department can support matched users in receiving online counseling and support. For example, the communication department can provide a chat function to support real-time message exchange between users. For example, it can provide text chat and voice chat, allowing users to communicate freely. The communication department can also provide a video call function to support face-to-face communication between users. For example, it can enable more intimate communication between users through video calls. Furthermore, the communication department can provide a forum function to support information sharing between users. For example, it can provide forums on specific topics, allowing users to exchange opinions. This can lead to improved mental health for users by supporting them in receiving online counseling and support. Some or all of the above-described processes in the communication department may be performed using AI, or not. For example, the communication department can input the content of user communication into a generating AI and have the generating AI output appropriate support.

[0037] The service provider can provide analysis results. For example, the service provider can provide analysis results in report format. For example, the service provider can generate a report detailing the analysis results regarding the user's concerns and interests and provide it to the user. The service provider can also display analysis results in graph form. For example, the service provider can graph the analysis results of the user's concerns and interests and provide them in a visually easy-to-understand format. The service provider can also notify users of the analysis results. For example, the service provider can send the analysis results to the user via email or push notification. By providing the analysis results, the service provider can provide users with appropriate information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI determine how to provide the analysis results.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input format. For example, the reception desk can automatically display as suggestions the user has frequently entered their concerns or interests in the past. For example, the reception desk can prioritize suggesting input methods the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest concerns or interests used during specific time periods based on the user's past input history. For example, the reception desk can suggest similar content based on what the user has entered during specific time periods in the past. In this way, by analyzing past input history, the reception desk can suggest the optimal input format for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI suggest the optimal input format.

[0039] The reception unit can filter input content based on the user's current living situation and areas of interest when the user enters their concerns or interests. For example, when the user enters their current living situation, the reception unit will prioritize displaying relevant concerns or interests. For example, the reception unit will automatically suggest relevant topics based on the user's areas of interest. The reception unit can also filter input content according to the user's living situation and provide appropriate options. For example, when the user enters their current living situation, the reception unit will prioritize displaying relevant concerns or interests. This allows for more appropriate input by filtering input content based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering of the input content.

[0040] The reception desk can prioritize accepting highly relevant input content when users input their concerns or interests, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk will prioritize displaying concerns and interests related to that region. For instance, the reception desk may prompt the user to input region-specific issues based on their current location. The reception desk can also automatically suggest relevant topics, taking into account the user's geographical location. For example, the reception desk may suggest relevant topics based on the user's geographical location. This allows the reception desk to prioritize accepting highly relevant input content by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk may input the user's geographical location into a generating AI and have the generating AI suggest highly relevant input content.

[0041] The reception unit can analyze the user's social media activity when they input their concerns or interests, and accept relevant input content. For example, the reception unit can analyze the user's social media posts and automatically suggest relevant concerns or interests. For example, the reception unit can filter appropriate input content based on the user's social media activity history. The reception unit can also prioritize displaying relevant topics based on the user's areas of interest on social media. For example, the reception unit can suggest relevant topics based on the user's areas of interest on social media. In this way, by analyzing the user's social media activity, it can accept relevant input content. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input content.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's concerns and interests. For example, the analysis unit can perform a detailed analysis on concerns and interests of high importance, and a concise analysis on concerns and interests of low importance. The analysis unit can also adjust the level of detail of the analysis based on the importance level specified by the user. For example, the analysis unit can adjust the level of detail of the analysis based on the importance level specified by the user. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the concerns and interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the user's concerns and interests into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of the problem or concern during analysis. For example, the analysis unit can apply a specialized analysis algorithm to work-related problems. For example, the analysis unit can apply a childcare-specific analysis algorithm to childcare-related problems. The analysis unit can also apply a health-specific analysis algorithm to health-related problems. For example, the analysis unit can apply a health-specific analysis algorithm to health-related problems. By applying different analysis algorithms depending on the category of the problem or concern, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's problem or concern category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on when the user's concerns and interests were submitted. For example, the analysis unit may prioritize analyzing recently submitted concerns and interests. For example, it may postpone the analysis of older concerns and interests. The analysis unit can also automatically adjust the priority of analysis based on the submission date. For example, the analysis unit can automatically adjust the priority of analysis based on the submission date. This allows for more appropriate analysis by determining the priority of analysis based on the submission date of concerns and interests. 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 concerns and interest submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the problems and concerns during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant problems and concerns. For example, the analysis unit may postpone the analysis of less relevant problems and concerns. The analysis unit can also automatically adjust the order of analysis based on relevance. For example, the analysis unit can automatically adjust the order of analysis based on relevance. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the problems and concerns. 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 relevance data of the user's problems and concerns into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0046] The matching unit can improve the accuracy of matching by considering the interrelationships of users' worries and interests. For example, the matching unit can match users who have work-related worries with each other. For example, the matching unit can match users who have childcare-related worries with each other. The matching unit can also match users who have health-related worries with each other. For example, the matching unit can match users who have health-related worries with each other. This allows for more appropriate matching by considering the interrelationships of users' worries and interests. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships of users' worries and interests into a generating AI and have the generating AI perform the task of improving the accuracy of matching.

[0047] The matching unit can perform matching while considering user attribute information. For example, the matching unit can match users of similar ages. For example, the matching unit can match users of the same gender. The matching unit can also match users who live close to each other. For example, the matching unit can match users who live close to each other. By considering user attribute information, more appropriate matching becomes possible. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user attribute information data into a generating AI and have the generating AI perform the matching.

[0048] The matching unit can perform matching while considering the geographical distribution of users. For example, the matching unit can match users who live in the same area. For example, the matching unit can match users who are geographically close to each other. The matching unit can also match users who are geographically related to each other. For example, the matching unit can match users who are geographically related to each other. This allows for more appropriate matching by considering the geographical distribution of users. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user geographical distribution data into a generating AI and have the generating AI perform the matching.

[0049] The matching unit can improve the accuracy of matching by referring to related literature during the matching process. For example, the matching unit can match users with the same problems based on related literature. For example, the matching unit can match users with the same interests based on related literature. The matching unit can also match users with the same experiences based on related literature. For example, the matching unit can match users with the same experiences based on related literature. This makes it possible to perform more appropriate matching by referring to related literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input related literature data into a generating AI and have the generating AI perform the task of improving the accuracy of matching.

[0050] The communication department can select the optimal method of communication by referring to the user's past communication history. For example, the communication department may prioritize suggesting communication methods that the user has preferred in the past (such as chat or video calls). For example, the communication department may select the most effective communication method from the user's past communication history. The communication department can also suggest the optimal method by referring to the communication style the user has used in the past (such as formal or casual). For example, the communication department may suggest the optimal method by referring to the communication style the user has used in the past. This allows for the selection of a more appropriate communication method by referring to the user's past communication history. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department may input the user's past communication history data into a generating AI and have the generating AI select the optimal communication method.

[0051] The communication unit can customize communication methods based on the user's current lifestyle during communication. For example, if the user is busy, the communication unit will suggest a short communication method. For example, if the user is relaxed, the communication unit will suggest a longer communication method. The communication unit can also customize the optimal communication method according to the user's lifestyle. For example, the communication unit customizes the optimal communication method according to the user's lifestyle. This allows for more appropriate communication by customizing the communication method according to the user's lifestyle. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of communication methods.

[0052] The communication unit can select the optimal communication method during communication, taking into account the user's geographical location information. For example, if the user is in a specific region, the communication unit can suggest a communication method suitable for that region. For example, the communication unit can select the optimal communication method based on the user's current location. The communication unit can also suggest relevant topics, taking into account the user's geographical location information. For example, the communication unit can suggest relevant topics based on the user's geographical location information. This allows for the selection of a more appropriate communication method by considering the user's geographical location information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal communication method.

[0053] The communications department can analyze a user's social media activity and suggest communication methods during communication. For example, the communications department can analyze the content of a user's social media posts and suggest the most suitable communication method. For example, the communications department can select an appropriate communication method based on a user's social media activity history. The communications department can also suggest relevant topics based on a user's areas of interest on social media. For example, the communications department can suggest relevant topics based on a user's areas of interest on social media. This allows for the suggestion of more appropriate communication methods by analyzing the user's social media activity. Some or all of the above processing in the communications department may be performed using AI, for example, or not using AI. For example, the communications department can input user social media activity data into a generating AI and have the generating AI suggest the most suitable communication method.

[0054] The service provider can select the optimal delivery method by referring to the user's past usage history when providing analysis results. For example, the service provider can prioritize suggesting delivery methods that the user has preferred in the past (text, video, etc.). For example, the service provider can select the most effective delivery method from the user's past usage history. The service provider can also suggest the optimal method by referring to the delivery style the user has used in the past (formal, casual, etc.). For example, the service provider can suggest the optimal method by referring to the delivery style the user has used in the past. This allows for the selection of a more appropriate delivery method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0055] The service provider can select the optimal service delivery method when providing analysis results, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a service delivery method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a service delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible service delivery method. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible service delivery method. This allows for the selection of a more appropriate service delivery method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user device information data into a generating AI and have the generating AI select the optimal service delivery method.

[0056] The service provider can provide analysis results in multiple languages ​​according to the user's language settings. For example, the service provider can automatically set the language of the analysis results based on the language settings of the user's device. For example, the service provider can provide a language switching function if the user uses multiple languages. The service provider can also provide analysis results in a language selected by the user. For example, the service provider can provide analysis results in a language selected by the user. This enables more appropriate service provision by providing multilingual support according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI perform the multilingual provision.

[0057] The service provider can provide analysis results by referring to the user's calendar information and making suggestions based on their schedule. For example, the service provider can refer to the schedule registered in the user's calendar and automatically provide analysis results. For example, the service provider can suggest analysis results related to a specific event based on the user's calendar information. The service provider can also provide optimal analysis results tailored to the schedule based on the user's calendar information. For example, the service provider can provide optimal analysis results tailored to the schedule based on the user's calendar information. This makes it possible to make more appropriate suggestions by referring to the user's calendar information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's calendar information data into a generating AI and have the generating AI execute suggestions based on the schedule.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The reception desk can analyze the user's past input history when they enter their concerns and interests, and suggest the most suitable input format. For example, it can automatically display concerns and interests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest concerns and interests that the user will use at specific times of the day based on their past input history. In this way, by analyzing past input history, the system can suggest the most suitable input format for the user.

[0060] The reception desk can prioritize receiving highly relevant input content by considering the user's geographical location. For example, if a user lives in a specific region, it will prioritize displaying problems and interests related to that region. It can also prompt users to input region-specific issues based on their current location. Furthermore, it can automatically suggest relevant topics by considering the user's geographical location. In this way, by considering the user's geographical location, it can prioritize receiving highly relevant input content.

[0061] The analysis unit can apply different analysis algorithms depending on the category of the problem or concern. For example, a specialized analysis algorithm can be applied to work-related problems. Similarly, a childcare-specific analysis algorithm can be applied to childcare-related problems. Furthermore, a health-specific analysis algorithm can be applied to health-related problems. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the problem or concern.

[0062] The matching function can perform matching while considering user attribute information. For example, it can match users of similar ages, users of the same gender, and users who live close to each other. By considering user attribute information, more appropriate matching becomes possible.

[0063] The communications department can analyze users' social media activity and suggest appropriate communication methods. For example, it can analyze the content of users' social media posts and suggest the most suitable communication method. It can also select appropriate communication methods based on users' social media activity history. Furthermore, it can suggest relevant topics based on users' areas of interest on social media. In this way, by analyzing users' social media activity, it can suggest more appropriate communication methods.

[0064] The service provider can refer to the user's calendar information to provide schedule-based suggestions. For example, it can refer to the appointments registered in the user's calendar and automatically provide analysis results. It can also suggest analysis results related to specific events based on the user's calendar information. Furthermore, it can provide optimal analysis results tailored to the user's schedule based on the user's calendar information. This allows for more appropriate suggestions by referring to the user's calendar information.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives input from the user regarding their concerns and interests. These concerns and interests may include health issues, work-related problems, and hobbies. The reception desk can receive user concerns and interests using text or voice input. Furthermore, the reception desk can estimate the user's emotions and adjust the input method based on the estimated emotions. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and pattern recognition. The analysis unit analyzes the user's concerns and interests in detail and identifies other users with similar experiences. Step 3: The matching unit automatically matches users with similar experiences based on the information analyzed by the analysis unit. Matching is performed based on the similarity measure and the algorithm used. For example, cosine similarity is used to calculate the similarity of users' worries and interests, and users with high similarity are matched together. It is also possible to estimate the user's emotions and adjust the matching criteria based on the estimated emotions. Step 4: The communications department supports matching users in communicating with each other. Communication takes place through methods such as chat, video calls, and forums. The communications department also supports matching users in receiving online counseling and support. It can also estimate users' emotions and adjust the communication method based on those estimated emotions.

[0067] (Example of form 2) The empathy matching AI according to an embodiment of the present invention is a support system that connects people in similar circumstances and promotes consultation and information sharing. The empathy matching AI analyzes the worries and interests entered by the user and automatically matches them with others who have similar experiences. This aims to reduce feelings of loneliness and provide appropriate support. First, the user enters their worries and interests. For example, "I have a lot of work stress" or "I have worries about childcare." This information is entered into the AI. Next, the AI ​​analyzes the entered information. The AI ​​understands the user's worries and interests and identifies other users who have similar experiences. For example, if there are other users who have worries about work stress, the AI ​​identifies those users. The AI ​​then automatically matches the identified users with each other. This connects people in similar circumstances and makes it easier for empathy and understanding to develop. For example, by matching users who have worries about childcare, they can consult with each other and share information. Furthermore, the matched users can receive online counseling and support. This provides appropriate support and is expected to improve mental health. For example, they can receive advice from experts through online counseling sessions. This system is beneficial for people who feel lonely, want to share their worries with someone, or seek connection with others in similar circumstances. For example, with the current increase in people feeling lonely and isolated due to the COVID-19 pandemic, this system is extremely useful. Furthermore, this system holds significant importance in the mental health market. The Japanese mental health market is worth approximately 150 billion yen, and this system could potentially capture 5% (approximately 5 billion yen) of that market. For instance, the increasing demand for support through online platforms is expected to drive the widespread use of this system. In this way, the Yoriso Matching AI aims to contribute to a richer society by reducing feelings of loneliness and providing appropriate support. For example, it can create communities where users can share their experiences and grow together. This allows the Yoriso Matching AI to efficiently analyze users' worries and interests, providing appropriate matching and communication.

[0068] The companion matching AI according to this embodiment comprises a reception unit, an analysis unit, a matching unit, and a communication unit. The reception unit receives input from the user regarding their worries and concerns. These worries and concerns include, but are not limited to, health issues, work-related worries, and hobbies. The reception unit accepts the user's worries and concerns in text format, for example. The reception unit can also accept the user's worries and concerns using voice input. For example, the reception unit can convert the user's voice input into text and accept it. Furthermore, the reception unit can estimate the user's emotions and adjust the input method for worries and concerns based on the estimated emotions. For example, if the user is feeling stressed, it can provide a simple interface and minimize the input procedure. The analysis unit analyzes the information received by the reception unit. The analysis is performed by, but is not limited to, methods such as text analysis, sentiment analysis, and pattern recognition. The analysis unit, for example, analyzes the user's worries and concerns in detail and identifies other users with similar experiences. For example, text analysis is used to extract keywords related to the user's worries and interests, and other users with similar keywords are identified. The matching unit automatically matches the user with other users who have similar experiences based on the information analyzed by the analysis unit. Matching is performed based on, for example, a similarity scale or the algorithm used, but is not limited to such examples. The matching unit calculates the similarity of users' worries and interests using, for example, cosine similarity, and matches users with high similarity. The matching unit can also estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if a user is feeling stressed, it will be matched with other users who are also feeling stressed. The communication unit supports matching users in communicating with each other. Communication is performed by, for example, chat, video calls, forums, etc., but is not limited to such examples. The communication unit also supports matching users in receiving online counseling and support, for example, providing psychological counseling or technical support.Furthermore, the communication unit can estimate the user's emotions and adjust the communication method based on the estimated emotions. For example, if the user is nervous, it will communicate in a calm tone. As a result, the empathetic matching AI according to this embodiment can efficiently analyze the user's worries and interests and provide appropriate matching and communication.

[0069] The reception desk receives input from users regarding their worries and concerns. These concerns may include, but are not limited to, health issues, work-related worries, or hobbies. The reception desk can, for example, receive user input in text format. It can also accept user input via voice. For instance, it can convert user voice input into text for reception. Furthermore, the reception desk can estimate the user's emotions and adjust the input method based on that estimation. For example, if a user is stressed, it can provide a simple interface and minimize the input steps. The reception desk utilizes natural language processing technology to efficiently process user input. Specifically, it analyzes user-inputted text and voice data to extract keywords and important phrases. This allows for a quick understanding of user concerns and concerns, enabling appropriate responses. In the case of voice input, speech recognition technology is used to accurately convert user speech into text, minimizing misrecognition. Additionally, the reception desk can temporarily store user input and reuse it as needed. For example, by referring to previously entered concerns and interests, the system can respond in a way that suits the user's situation and needs. This ensures that users receive consistent support and improves reliability. The reception desk uses sentiment analysis technology to estimate the user's emotions. Specifically, it analyzes emotions from the user's text and voice data to estimate emotional states such as stress, joy, and sadness. This enables appropriate responses based on the user's emotions. For example, if the user is feeling stressed, the reception desk provides a simple interface and minimizes the input steps to reduce the user's burden. Conversely, if the user is feeling joy or excitement, it can encourage more detailed input to gain a deeper understanding of the user's interests. This allows the reception desk to respond flexibly to the user's emotions, contributing to an improved user experience.

[0070] The analysis unit analyzes the information received by the reception unit. Analysis is performed using methods such as text analysis, sentiment analysis, and pattern recognition, but is not limited to these examples. For instance, the analysis unit analyzes the user's concerns and interests in detail and identifies other users with similar experiences. For example, it uses text analysis to extract keywords related to the user's concerns and interests and identify other users with similar keywords. The analysis unit utilizes natural language processing techniques to analyze the user's input in detail. Specifically, it uses morphological analysis and contextual analysis to understand the intentions and emotions behind the user's concerns and interests. For example, if a user inputs "My work isn't going well," the analysis unit extracts the keywords "work" and "not going well" and searches for data on other users related to these keywords. Furthermore, it uses sentiment analysis techniques to estimate emotions from the user's input and understand emotional states such as stress and anxiety. This allows the analysis unit to understand the user's concerns and interests more deeply and respond appropriately. In addition, the analysis unit uses pattern recognition techniques to extract common patterns and trends from the user's input. For example, if multiple users have similar problems, the system can identify commonalities and pinpoint users with similar experiences. This allows the analytics unit to efficiently analyze users' problems and interests and support appropriate matching. The analytics unit can also analyze trends in users' problems and interests by utilizing past data and statistical information. For example, based on past data, it can understand the trends in problems at specific times or situations and formulate future countermeasures. This enables the analytics unit to handle not only real-time analysis but also long-term risk assessment and trend analysis, improving the reliability and security of the entire system.

[0071] The matching unit automatically matches users with similar experiences based on information analyzed by the analysis unit. Matching is performed based on, for example, a similarity measure or the algorithm used, but is not limited to such examples. The matching unit calculates the similarity of users' worries and concerns using, for example, cosine similarity, and matches users with high similarity. The matching unit can also estimate users' emotions and adjust the matching criteria based on the estimated emotions. For example, if a user is feeling stressed, it will be matched with users who are also feeling stressed. The matching unit uses a combination of multiple algorithms to evaluate the similarity of users' worries and concerns with high accuracy. Specifically, it evaluates the similarity between users from multiple angles using different similarity measures such as the Jaccard coefficient and Euclidean distance, in addition to cosine similarity. This enables more accurate matching. The matching unit also dynamically adjusts the matching criteria considering the user's emotional state. For example, if a user is feeling stressed, matching them with users who are also feeling stressed makes it easier to receive empathy and support. On the other hand, when users are relaxed, matching them with users who have different perspectives can encourage new discoveries and insights. Furthermore, the matching unit collects user feedback and continuously improves the accuracy of the matching algorithm. For example, it monitors user satisfaction and communication frequency after matching and adjusts the algorithm based on this data. This allows the matching unit to provide optimal matches that meet user needs and improve the overall effectiveness of the system. In addition, the matching unit anonymizes and encrypts data to protect user privacy. This allows users to use the system with peace of mind and improves its reliability.

[0072] The Communications Department supports matching users in communicating with each other. Communication can take place through methods such as chat, video calls, and forums, but is not limited to these. The Communications Department also supports matching users in receiving online counseling and support, such as providing psychological counseling or technical support. Furthermore, the Communications Department can estimate a user's emotions and adjust the communication method based on that estimation. For example, if a user is feeling anxious, the department will communicate in a calm tone. The Communications Department provides multiple communication methods to ensure smooth communication between users. Specifically, it offers a variety of methods such as text chat, voice calls, video calls, and forums, allowing users to choose the most suitable method according to their needs and circumstances. For example, text chat allows for real-time message exchange, while voice and video calls enable more direct communication. Forums allow multiple users to participate in exchanging opinions and sharing information. In addition, the Communications Department monitors users' emotional states in real time and provides appropriate support. For example, if a user is feeling anxious, the system will automatically generate messages in a calm tone to help the user relax. Furthermore, when a user is excited, the system provides proactive responses, enabling communication that is sensitive to the user's emotions. This allows users to communicate with confidence and improves trust. The communications department encrypts and anonymizes communication content to protect user privacy. This allows users to use the system with confidence and improves trust. The communications department also collects user feedback and continuously improves communication methods and support. For example, it monitors user satisfaction and usage patterns and uses this data to improve the system. In this way, the communications department can provide optimal support to users and improve the overall effectiveness of the system.

[0073] The analysis unit can analyze the user's concerns and interests in detail. For example, the analysis unit can extract keywords related to the user's concerns and interests using text analysis. For example, the analysis unit can analyze the user's input using natural language processing technology and identify important keywords. The analysis unit can also estimate the user's emotions using sentiment analysis. For example, the analysis unit can estimate emotions from the user's input and calculate an emotion score. The analysis unit can also identify patterns in the user's concerns and interests using pattern recognition technology. For example, the analysis unit can learn patterns in the user's concerns and interests based on past data and apply them to new input. This allows for more appropriate matching by analyzing the user's concerns and interests in detail. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's input into a generating AI and output the analysis results to the generating AI.

[0074] The matching unit can identify other users with similar experiences based on the information analyzed by the analysis unit. For example, the matching unit can calculate the similarity of users' concerns and interests using cosine similarity. Alternatively, it can vectorize the user's input and calculate its similarity to other users' inputs. The matching unit can also calculate the similarity of users' concerns and interests using the Jaccard coefficient. For example, it can treat the user's input as a set and calculate the proportion of common elements with other users' inputs. Furthermore, the matching unit can calculate the similarity of users' concerns and interests using TF-IDF. For example, it can evaluate the importance of the user's input and calculate its similarity to other users' inputs. This allows for more appropriate matching by identifying users with similar experiences. Some or all of the above processing in the matching unit may be performed using AI, or without AI. For example, the matching unit can input the information analyzed by the analysis unit into a generating AI and output the matching results to the generating AI.

[0075] The communication department can support matched users in receiving online counseling and support. For example, the communication department can provide a chat function to support real-time message exchange between users. For example, it can provide text chat and voice chat, allowing users to communicate freely. The communication department can also provide a video call function to support face-to-face communication between users. For example, it can enable more intimate communication between users through video calls. Furthermore, the communication department can provide a forum function to support information sharing between users. For example, it can provide forums on specific topics, allowing users to exchange opinions. This can lead to improved mental health for users by supporting them in receiving online counseling and support. Some or all of the above-described processes in the communication department may be performed using AI, or not. For example, the communication department can input the content of user communication into a generating AI and have the generating AI output appropriate support.

[0076] The service provider can provide analysis results. For example, the service provider can provide analysis results in report format. For example, the service provider can generate a report detailing the analysis results regarding the user's concerns and interests and provide it to the user. The service provider can also display analysis results in graph form. For example, the service provider can graph the analysis results of the user's concerns and interests and provide them in a visually easy-to-understand format. The service provider can also notify users of the analysis results. For example, the service provider can send the analysis results to the user via email or push notification. By providing the analysis results, the service provider can provide users with appropriate information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the analysis results into a generating AI and have the generating AI determine how to provide the analysis results.

[0077] The reception desk can estimate the user's emotions and adjust the input method for worries and concerns based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input if the user is in a hurry, allowing for quick input of worries and concerns. For example, the reception desk can convert the user's voice input into text and process it quickly. This allows for more appropriate input by adjusting the input method 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 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI adjust the input method based on the emotion.

[0078] The reception desk can analyze the user's past input history and suggest the optimal input format. For example, the reception desk can automatically display as suggestions the user has frequently entered their concerns or interests in the past. For example, the reception desk can prioritize suggesting input methods the user has used in the past (voice, text, etc.). The reception desk can also predict and suggest concerns or interests used during specific time periods based on the user's past input history. For example, the reception desk can suggest similar content based on what the user has entered during specific time periods in the past. In this way, by analyzing past input history, the reception desk can suggest the optimal input format for the user. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history into a generating AI and have the generating AI suggest the optimal input format.

[0079] The reception unit can filter input content based on the user's current living situation and areas of interest when the user enters their concerns or interests. For example, when the user enters their current living situation, the reception unit will prioritize displaying relevant concerns or interests. For example, the reception unit will automatically suggest relevant topics based on the user's areas of interest. The reception unit can also filter input content according to the user's living situation and provide appropriate options. For example, when the user enters their current living situation, the reception unit will prioritize displaying relevant concerns or interests. This allows for more appropriate input by filtering input content based on the user's living situation and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input data on the user's living situation and areas of interest into a generating AI and have the generating AI perform the filtering of the input content.

[0080] The reception desk can estimate the user's emotions and prioritize input based on those emotions. For example, if the user is nervous, the reception desk may prioritize inputting important worries or concerns. For example, if the user is relaxed, the reception desk may prioritize inputting detailed information. The reception desk can also prioritize concise input if the user is in a hurry. For example, if the user is in a hurry, the reception desk may prioritize voice input to allow for quick input of worries or concerns. This allows for more appropriate input by prioritizing input 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI determine the priority of input.

[0081] The reception desk can prioritize accepting highly relevant input content when users input their concerns or interests, taking into account their geographical location. For example, if a user lives in a specific region, the reception desk will prioritize displaying concerns and interests related to that region. For instance, the reception desk may prompt the user to input region-specific issues based on their current location. The reception desk can also automatically suggest relevant topics, taking into account the user's geographical location. For example, the reception desk may suggest relevant topics based on the user's geographical location. This allows the reception desk to prioritize accepting highly relevant input content by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk may input the user's geographical location into a generating AI and have the generating AI suggest highly relevant input content.

[0082] The reception unit can analyze the user's social media activity when they input their concerns or interests, and accept relevant input content. For example, the reception unit can analyze the user's social media posts and automatically suggest relevant concerns or interests. For example, the reception unit can filter appropriate input content based on the user's social media activity history. The reception unit can also prioritize displaying relevant topics based on the user's areas of interest on social media. For example, the reception unit can suggest relevant topics based on the user's areas of interest on social media. In this way, by analyzing the user's social media activity, it can accept relevant input content. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input content.

[0083] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can improve the accuracy of the analysis and provide more specific advice. For example, if the user is relaxed, the analysis unit can adjust the accuracy of the analysis and provide broader advice. The analysis unit can also perform a quick analysis and provide concise advice if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can adjust the accuracy of the analysis and perform a quick analysis. This allows for more appropriate analysis by adjusting the accuracy 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 perform the adjustment of the analysis accuracy.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the user's concerns and interests. For example, the analysis unit can perform a detailed analysis on concerns and interests of high importance, and a concise analysis on concerns and interests of low importance. The analysis unit can also adjust the level of detail of the analysis based on the importance level specified by the user. For example, the analysis unit can adjust the level of detail of the analysis based on the importance level specified by the user. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the concerns and interests. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance data of the user's concerns and interests into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of the problem or concern during analysis. For example, the analysis unit can apply a specialized analysis algorithm to work-related problems. For example, the analysis unit can apply a childcare-specific analysis algorithm to childcare-related problems. The analysis unit can also apply a health-specific analysis algorithm to health-related problems. For example, the analysis unit can apply a health-specific analysis algorithm to health-related problems. By applying different analysis algorithms depending on the category of the problem or concern, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's problem or concern category data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. The analysis unit can also provide a concise display method if the user is in a hurry. For example, if the analysis unit provides a concise display method if the user is in a hurry, the analysis unit provides a concise display method. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0087] The analysis unit can determine the priority of analysis based on when the user's concerns and interests were submitted. For example, the analysis unit may prioritize analyzing recently submitted concerns and interests. For example, it may postpone the analysis of older concerns and interests. The analysis unit can also automatically adjust the priority of analysis based on the submission date. For example, the analysis unit can automatically adjust the priority of analysis based on the submission date. This allows for more appropriate analysis by determining the priority of analysis based on the submission date of concerns and interests. 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 concerns and interest submission date data into a generating AI and have the generating AI determine the priority of analysis.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the problems and concerns during the analysis process. For example, the analysis unit may prioritize the analysis of highly relevant problems and concerns. For example, the analysis unit may postpone the analysis of less relevant problems and concerns. The analysis unit can also automatically adjust the order of analysis based on relevance. For example, the analysis unit can automatically adjust the order of analysis based on relevance. This allows for more appropriate analysis by adjusting the order of analysis based on the relevance of the problems and concerns. 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 relevance data of the user's problems and concerns into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0089] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if a user is feeling stressed, the matching unit will match them with other users who are also feeling stressed. For example, if a user is relaxed, the matching unit will match them with other relaxed users. The matching unit can also perform a quick match if the user is in a hurry. For example, if a user is in a hurry, the matching unit will perform a quick match. By adjusting the matching criteria according to the user's emotions, a more appropriate match can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI, or not using AI. For example, the matching unit can input user emotion data into a generative AI and have the generative AI adjust the matching criteria.

[0090] The matching unit can improve the accuracy of matching by considering the interrelationships of users' worries and interests. For example, the matching unit can match users who have work-related worries with each other. For example, the matching unit can match users who have childcare-related worries with each other. The matching unit can also match users who have health-related worries with each other. For example, the matching unit can match users who have health-related worries with each other. This allows for more appropriate matching by considering the interrelationships of users' worries and interests. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input data on the interrelationships of users' worries and interests into a generating AI and have the generating AI perform the task of improving the accuracy of matching.

[0091] The matching unit can perform matching while considering user attribute information. For example, the matching unit can match users of similar ages. For example, the matching unit can match users of the same gender. The matching unit can also match users who live close to each other. For example, the matching unit can match users who live close to each other. By considering user attribute information, more appropriate matching becomes possible. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user attribute information data into a generating AI and have the generating AI perform the matching.

[0092] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated emotions. For example, if the user is nervous, the matching unit can provide a simple and highly visible display method. For example, if the user is relaxed, the matching unit can provide a display method that includes detailed information. The matching unit can also provide a concise display method if the user is in a hurry. For example, if the matching unit is in a hurry, the matching unit can provide a concise display method. By adjusting the display order of the matching results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user emotion data into the generative AI and have the generative AI adjust the display order of the matching results.

[0093] The matching unit can perform matching while considering the geographical distribution of users. For example, the matching unit can match users who live in the same area. For example, the matching unit can match users who are geographically close to each other. The matching unit can also match users who are geographically related to each other. For example, the matching unit can match users who are geographically related to each other. This allows for more appropriate matching by considering the geographical distribution of users. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input user geographical distribution data into a generating AI and have the generating AI perform the matching.

[0094] The matching unit can improve the accuracy of matching by referring to related literature during the matching process. For example, the matching unit can match users with the same problems based on related literature. For example, the matching unit can match users with the same interests based on related literature. The matching unit can also match users with the same experiences based on related literature. For example, the matching unit can match users with the same experiences based on related literature. This makes it possible to perform more appropriate matching by referring to related literature. Some or all of the above processing in the matching unit may be performed using AI, for example, or without AI. For example, the matching unit can input related literature data into a generating AI and have the generating AI perform the task of improving the accuracy of matching.

[0095] The communication unit can estimate the user's emotions and adjust its communication style based on those emotions. For example, if the user is tense, the communication unit will communicate in a calm tone. For example, if the user is relaxed, the communication unit will communicate in a cheerful tone. The communication unit can also communicate quickly and concisely if the user is in a hurry. For example, if the communication unit is in a hurry, the communication unit will communicate quickly and concisely. By adjusting the communication style according to the user's emotions, more appropriate communication becomes possible. 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 communication unit may be performed using AI, or not using AI. For example, the communication unit can input user emotion data into the generative AI and have the generative AI adjust the communication style.

[0096] The communication department can select the optimal method of communication by referring to the user's past communication history. For example, the communication department may prioritize suggesting communication methods that the user has preferred in the past (such as chat or video calls). For example, the communication department may select the most effective communication method from the user's past communication history. The communication department can also suggest the optimal method by referring to the communication style the user has used in the past (such as formal or casual). For example, the communication department may suggest the optimal method by referring to the communication style the user has used in the past. This allows for the selection of a more appropriate communication method by referring to the user's past communication history. Some or all of the above processing in the communication department may be performed using AI, for example, or not using AI. For example, the communication department may input the user's past communication history data into a generating AI and have the generating AI select the optimal communication method.

[0097] The communication unit can customize communication methods based on the user's current lifestyle during communication. For example, if the user is busy, the communication unit will suggest a short communication method. For example, if the user is relaxed, the communication unit will suggest a longer communication method. The communication unit can also customize the optimal communication method according to the user's lifestyle. For example, the communication unit customizes the optimal communication method according to the user's lifestyle. This allows for more appropriate communication by customizing the communication method according to the user's lifestyle. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input user lifestyle data into a generating AI and have the generating AI perform the customization of communication methods.

[0098] The communication unit can estimate the user's emotions and determine communication priorities based on those estimated emotions. For example, if the user is tense, the communication unit will prioritize important communication. For example, if the user is relaxed, the communication unit will provide detailed communication. The communication unit can also prioritize concise communication if the user is in a hurry. For example, if the communication unit is in a hurry, the communication unit will prioritize concise communication. This allows for more appropriate communication by determining communication priorities 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 communication unit may be performed using AI, or not using AI. For example, the communication unit can input user emotion data into a generative AI and have the generative AI determine communication priorities.

[0099] The communication unit can select the optimal communication method during communication, taking into account the user's geographical location information. For example, if the user is in a specific region, the communication unit can suggest a communication method suitable for that region. For example, the communication unit can select the optimal communication method based on the user's current location. The communication unit can also suggest relevant topics, taking into account the user's geographical location information. For example, the communication unit can suggest relevant topics based on the user's geographical location information. This allows for the selection of a more appropriate communication method by considering the user's geographical location information. Some or all of the above processing in the communication unit may be performed using AI, for example, or without AI. For example, the communication unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal communication method.

[0100] The communications department can analyze a user's social media activity and suggest communication methods during communication. For example, the communications department can analyze the content of a user's social media posts and suggest the most suitable communication method. For example, the communications department can select an appropriate communication method based on a user's social media activity history. The communications department can also suggest relevant topics based on a user's areas of interest on social media. For example, the communications department can suggest relevant topics based on a user's areas of interest on social media. This allows for the suggestion of more appropriate communication methods by analyzing the user's social media activity. Some or all of the above processing in the communications department may be performed using AI, for example, or not using AI. For example, the communications department can input user social media activity data into a generating AI and have the generating AI suggest the most suitable communication method.

[0101] The service provider can estimate the user's emotions and adjust the method of providing analysis results based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and easy-to-understand method of delivery. For example, if the user is relaxed, the service provider can provide a method of delivery that includes detailed information. The service provider can also provide a concise method of delivery if the user is in a hurry. For example, if the service provider is in a hurry, the service provider can provide a concise method of delivery. By adjusting the method of providing analysis results according to the user's emotions, more appropriate delivery becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the method of providing analysis results.

[0102] The service provider can select the optimal delivery method by referring to the user's past usage history when providing analysis results. For example, the service provider can prioritize suggesting delivery methods that the user has preferred in the past (text, video, etc.). For example, the service provider can select the most effective delivery method from the user's past usage history. The service provider can also suggest the optimal method by referring to the delivery style the user has used in the past (formal, casual, etc.). For example, the service provider can suggest the optimal method by referring to the delivery style the user has used in the past. This allows for the selection of a more appropriate delivery method by referring to the user's past usage history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past usage history data into a generating AI and have the generating AI select the optimal delivery method.

[0103] The service provider can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is tense, the service provider will prioritize providing important analysis results. For example, if the user is relaxed, the service provider will provide detailed analysis results. The service provider can also prioritize providing concise analysis results if the user is in a hurry. For example, if the service provider is in a hurry, the service provider will prioritize providing concise analysis results. This allows for more appropriate delivery by determining the priority of analysis results 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0104] The service provider can select the optimal service delivery method when providing analysis results, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a service delivery method that matches the screen size. For example, if the user is using a tablet, the service provider can provide a service delivery method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible service delivery method. For example, if the user is using a smartwatch, the service provider can provide a concise and highly visible service delivery method. This allows for the selection of a more appropriate service delivery method by considering the user's device information. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input user device information data into a generating AI and have the generating AI select the optimal service delivery method.

[0105] The service provider can provide analysis results in multiple languages ​​according to the user's language settings. For example, the service provider can automatically set the language of the analysis results based on the language settings of the user's device. For example, the service provider can provide a language switching function if the user uses multiple languages. The service provider can also provide analysis results in a language selected by the user. For example, the service provider can provide analysis results in a language selected by the user. This enables more appropriate service provision by providing multilingual support according to the user's language settings. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's language setting data into a generating AI and have the generating AI perform the multilingual provision.

[0106] The service provider can provide analysis results by referring to the user's calendar information and making suggestions based on their schedule. For example, the service provider can refer to the schedule registered in the user's calendar and automatically provide analysis results. For example, the service provider can suggest analysis results related to a specific event based on the user's calendar information. The service provider can also provide optimal analysis results tailored to the schedule based on the user's calendar information. For example, the service provider can provide optimal analysis results tailored to the schedule based on the user's calendar information. This makes it possible to make more appropriate suggestions by referring to the user's calendar information. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's calendar information data into a generating AI and have the generating AI execute suggestions based on the schedule.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The reception desk can analyze the user's past input history when they enter their concerns and interests, and suggest the most suitable input format. For example, it can automatically display concerns and interests that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest concerns and interests that the user will use at specific times of the day based on their past input history. In this way, by analyzing past input history, the system can suggest the most suitable input format for the user.

[0109] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on those emotions. For example, if the user is stressed, the accuracy of the analysis can be increased to provide more specific advice. If the user is relaxed, the accuracy of the analysis can be adjusted to provide broader advice. Furthermore, if the user is in a hurry, the analysis can be performed quickly to provide concise advice. In this way, adjusting the accuracy of the analysis according to the user's emotions enables more appropriate analysis.

[0110] The matching unit can estimate the user's emotions and adjust the matching criteria based on those emotions. For example, if a user is feeling stressed, it will be matched with other users who are also feeling stressed. If a user is relaxed, it can be matched with other relaxed users. Furthermore, if a user is in a hurry, it can perform a quick match. By adjusting the matching criteria according to the user's emotions, more appropriate matching becomes possible.

[0111] The communication department can estimate the user's emotions and adjust its communication style based on those emotions. For example, if the user is nervous, it can communicate in a calm tone. If the user is relaxed, it can communicate in a cheerful tone. Furthermore, if the user is in a hurry, it can communicate quickly and concisely. By adjusting the communication style according to the user's emotions, more appropriate communication becomes possible.

[0112] The service provider can estimate the user's emotions and adjust the method of delivering the analysis results based on those emotions. For example, if the user is nervous, a simple and highly visual presentation method can be provided. If the user is relaxed, a presentation method including detailed information can be provided. Furthermore, if the user is in a hurry, a presentation method that gets straight to the point can be provided. By adjusting the method of delivering the analysis results according to the user's emotions, a more appropriate presentation becomes possible.

[0113] The reception desk can prioritize receiving highly relevant input content by considering the user's geographical location. For example, if a user lives in a specific region, it will prioritize displaying problems and interests related to that region. It can also prompt users to input region-specific issues based on their current location. Furthermore, it can automatically suggest relevant topics by considering the user's geographical location. In this way, by considering the user's geographical location, it can prioritize receiving highly relevant input content.

[0114] The analysis unit can apply different analysis algorithms depending on the category of the problem or concern. For example, a specialized analysis algorithm can be applied to work-related problems. Similarly, a childcare-specific analysis algorithm can be applied to childcare-related problems. Furthermore, a health-specific analysis algorithm can be applied to health-related problems. This allows for more appropriate analysis by applying different analysis algorithms depending on the category of the problem or concern.

[0115] The matching function can perform matching while considering user attribute information. For example, it can match users of similar ages, users of the same gender, and users who live close to each other. By considering user attribute information, more appropriate matching becomes possible.

[0116] The communications department can analyze users' social media activity and suggest appropriate communication methods. For example, it can analyze the content of users' social media posts and suggest the most suitable communication method. It can also select appropriate communication methods based on users' social media activity history. Furthermore, it can suggest relevant topics based on users' areas of interest on social media. In this way, by analyzing users' social media activity, it can suggest more appropriate communication methods.

[0117] The service provider can refer to the user's calendar information to provide schedule-based suggestions. For example, it can refer to the appointments registered in the user's calendar and automatically provide analysis results. It can also suggest analysis results related to specific events based on the user's calendar information. Furthermore, it can provide optimal analysis results tailored to the user's schedule based on the user's calendar information. This allows for more appropriate suggestions by referring to the user's calendar information.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception desk receives input from the user regarding their concerns and interests. These concerns and interests may include health issues, work-related problems, and hobbies. The reception desk can receive user concerns and interests using text or voice input. Furthermore, the reception desk can estimate the user's emotions and adjust the input method based on the estimated emotions. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using methods such as text analysis, sentiment analysis, and pattern recognition. The analysis unit analyzes the user's concerns and interests in detail and identifies other users with similar experiences. Step 3: The matching unit automatically matches users with similar experiences based on the information analyzed by the analysis unit. Matching is performed based on the similarity measure and the algorithm used. For example, cosine similarity is used to calculate the similarity of users' worries and interests, and users with high similarity are matched together. It is also possible to estimate the user's emotions and adjust the matching criteria based on the estimated emotions. Step 4: The communications department supports matching users in communicating with each other. Communication takes place through methods such as chat, video calls, and forums. The communications department also supports matching users in receiving online counseling and support. It can also estimate users' emotions and adjust the communication method based on those estimated emotions.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, communication unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the user's concerns and interests in text or voice. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The communication unit is implemented by the output device 40 of the smart device 14 and supports the matched users in chatting or video calling. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the user with the analysis results in report format or graph display. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, communication unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the user's concerns and interests by voice. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The matching unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The communication unit is implemented by the speaker 240 of the smart glasses 214 and supports voice communication between matched users. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides the user with the analysis results in report format or graph display. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, communication unit, and provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the user's concerns and interests by voice. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The communication unit is implemented by, for example, the display 343 of the headset terminal 314 and supports video calls between matched users. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the user with the analysis results in report format or graph display. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, communication unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the user's concerns and interests by voice. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the received information. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches users based on the analysis results. The communication unit is implemented by, for example, the speaker 240 of the robot 414 and supports voice communication between matched users. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the user with the analysis results in report format or graph display. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] (Note 1) A reception desk that accepts user input about their concerns and interests, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a matching unit automatically matches users with similar experiences. The system includes a communication unit that enables users matched by the matching unit to communicate with each other. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze users' concerns and interests in detail. The system described in Appendix 1, characterized by the features described herein. (Note 3) The matching unit is Based on the information analyzed by the analysis unit, other users with similar experiences are identified. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned communications department, This service supports matching users in receiving online counseling and support from each other. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a unit that provides analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for worries and concerns based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users input their worries or interests, the system filters the input based on their current living situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and prioritizes input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input their concerns or interests, the system prioritizes accepting input that is highly relevant, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input their concerns or interests, the system analyzes their social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of the concerns and interests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of concerns or interests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the concerns and interests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the order of analysis is adjusted based on the relationships between concerns and interests. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is To improve matching accuracy, we consider the interrelationships between users' concerns and interests during the matching process. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is During the matching process, user attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is It estimates the user's emotions and adjusts the display order of matching results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is During the matching process, the geographical distribution of users is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is During the matching process, we refer to relevant literature to improve the accuracy of the matching. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned communications department, It estimates the user's emotions and adjusts the communication method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned communications department, During communication, the system selects the optimal method by referring to the user's past communication history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned communications department, During communication, the communication method is customized based on the user's current life situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned communications department, It estimates the user's emotions and determines communication priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned communications department, When communicating, the system selects the optimal communication method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned communications department, During communication, we analyze the user's social media activity and suggest communication methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We estimate the user's emotions and adjust the method of providing analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing analysis results, the optimal delivery method is selected by referring to the user's past usage history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing analysis results, the optimal delivery method will be selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing analysis results, we will offer multilingual support according to the user's language settings. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing analysis results, we will refer to the user's calendar information to make suggestions based on their schedule. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0192] 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 reception desk that accepts user input about their concerns and interests, An analysis unit that analyzes the information received by the reception unit, Based on the information analyzed by the aforementioned analysis unit, a matching unit automatically matches users with similar experiences. The system includes a communication unit that enables users matched by the matching unit to communicate with each other. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze users' concerns and interests in detail. The system according to feature 1.

3. The matching unit is Based on the information analyzed by the aforementioned analysis unit, other users with similar experiences are identified. The system according to feature 1.

4. The aforementioned communications department, This service supports matching users in receiving online counseling and support from each other. The system according to feature 1.

5. It includes a unit that provides analysis results. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for worries and concerns based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system according to feature 1.

8. The aforementioned reception unit is When users input their worries or interests, the system filters the input based on their current living situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When users input their concerns or interests, the system prioritizes accepting input that is highly relevant, taking into account their geographical location. The system according to feature 1.

Images