system

Abstract

The system according to this embodiment aims to resolve intergenerational issues by matching users with each other and providing necessary support. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a matching unit, a provision unit, and a reward unit. The reception unit accepts user registrations. The analysis unit analyzes the information received by the reception unit. The matching unit matches users based on the information analyzed by the analysis unit. The provision unit provides the necessary functions for users matched by the matching unit to provide support to each other. The reward unit awards point rewards when support is completed.

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 as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, matching and support between users for resolving inter-generational concerns are not sufficiently performed, and there is room for improvement.

[0005] The system according to an embodiment aims to match users with each other to resolve inter-generational concerns and provide necessary support.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a matching unit, a provision unit, and a reward unit. The reception unit accepts user registrations. The analysis unit analyzes the information received by the reception unit. The matching unit matches users based on the information analyzed by the analysis unit. The provision unit provides the necessary functions for users matched by the matching unit to provide support to each other. The reward unit awards points as rewards when support is completed. [Effects of the Invention]

[0007] The system according to this embodiment can match users with each other to resolve intergenerational issues and provide necessary support. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 Web platform according to an embodiment of the present invention is a system that utilizes generative AI to connect working-age and senior generations. In this system, users register on the Web platform, and working-age and senior users register their respective skills and consultation topics. The generative AI analyzes this information and matches users with each other. For example, younger generations with high IT literacy can support senior generations who are struggling with using smartphones. Senior generations can also provide knowledge of their social experience to younger generations. This platform provides multifaceted support and content, including smartphone support, health management, and entertainment. For example, regarding health management, senior generations can post health-related consultations, and working-age generations can provide advice. Regarding entertainment, users with common hobbies across generations can form communities and exchange information. Furthermore, the generative AI has a mechanism to evaluate user activities and award points as rewards. For example, users who provide support are awarded points, which can be received as rewards through an electronic payment system. In this way, support between users is promoted, and intergenerational interaction becomes more active. This platform is provided as a C2C service, and support is completed between users. Furthermore, it includes video call support, review and user rating features, which can improve user experience and satisfaction. It also contributes to reducing the burden on senior citizens working in stores and providing customer support. This system allows working generations and seniors to support and learn from each other, creating opportunities to improve society. It also provides a highly reliable community site, improving user experience and satisfaction, and enhancing brand value through the provision of new added value. In this way, the web platform can create opportunities for working generations and seniors to support and learn from each other, creating opportunities to improve society.

[0029] The Web platform according to this embodiment comprises a reception unit, an analysis unit, a matching unit, a provision unit, and a reward unit. The reception unit accepts user registrations. User registrations include information such as name, age, skills, and consultation content. The reception unit stores the information entered by the user in a database. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's skills and consultation content and generates data for appropriate matching. The analysis unit classifies the user's consultation content using natural language processing technology. The matching unit matches users based on the information analyzed by the analysis unit. The matching unit calculates the degree of match between users' skills and consultation content and performs optimal matching. The matching unit evaluates the compatibility between users using an AI algorithm. The provision unit provides the necessary functions for users matched by the matching unit to provide support to each other. The provision unit provides, for example, a chat function and a video call function. The provision unit provides, for example, tools for users to provide support. The rewards unit awards points as rewards when support is completed. The rewards unit, for example, awards points to users who have provided support, and enables them to receive those points as rewards through an electronic payment system. The rewards unit, for example, evaluates user activity and calculates points. As a result, the web platform according to the embodiment enables user registration, information analysis, user matching, provision of support functions, and awarding of point rewards.

[0030] The reception desk accepts user registrations. User registration includes information such as name, age, skills, and consultation details. The reception desk stores the information entered by users in a database. Specifically, users access the web platform and enter the required information in the registration form. In addition to basic information such as name and age, users are required to enter detailed information such as their skills, expertise, and the content of their consultation. This information is stored in a secure database and managed to protect user privacy. Furthermore, the reception desk also provides functions for users to update their registration information and enter additional information. For example, if a user acquires a new skill or their consultation details change, they can easily update their information. The reception desk also has functions to verify the accuracy of the information entered during user registration. For example, it verifies the email address and authenticates the phone number to ensure that the user is providing correct information. This allows the reception desk to collect reliable user information and improve the accuracy of subsequent analysis and matching.

[0031] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the user's skills and consultation content to generate data for appropriate matching. Specifically, it uses natural language processing technology to classify the user's consultation content. For example, it analyzes the text data of the consultation content entered by the user and extracts keywords and phrases. This allows it to automatically determine which category the consultation content belongs to. Furthermore, the analysis unit analyzes the user's skill information and evaluates the skill level and area of ​​expertise. For example, based on the skill information entered by the user, it identifies the level of skill proficiency and related fields. This allows it to understand the user's skill set in detail and generate basic data for appropriate matching. The analysis unit integrates this data and provides the information necessary for matching users. For example, it calculates the degree of match between the user's skills and consultation content and lists the best matching candidates. In addition, the analysis unit can continuously improve the accuracy of the matching algorithm by utilizing past matching data and user feedback. This allows the analysis unit to achieve highly accurate matching that meets user needs and improve the value of using the platform.

[0032] The matching unit matches users based on information analyzed by the analysis unit. For example, the matching unit calculates the degree of compatibility between users' skills and consultation topics to achieve optimal matching. Specifically, it uses an AI algorithm to evaluate the compatibility between users. For instance, it calculates a compatibility score based on users' skill sets and consultation topics, matching the most suitable users. The matching unit also considers users' past activity history and feedback to achieve more accurate matching. For example, it analyzes data from successful past matches and prioritizes matching users with similar conditions. Furthermore, the matching unit considers users' desired conditions and constraints. For example, it performs matching tailored to the needs of users who are only available during specific time slots or who require matching limited to specific regions. This allows the matching unit to respond to diverse user needs and provide optimal matching. In addition, the matching unit notifies users of the matching results and provides information to facilitate support after matching. For example, it provides contact information for matched users to communicate with each other and guides them through the support initiation process. This allows the matching function to support smooth communication between users and improve the platform user experience.

[0033] The service provider provides the necessary functions for users matched by the matching service provider to support each other. For example, the service provider provides chat and video call functions. Specifically, it provides chat rooms for real-time communication between users and interfaces for video calls. This allows users to provide effective support through text messages, voice, and video. Furthermore, the service provider also provides tools and resources for providing support. For example, by providing shared document and screen sharing functions, users can share information and proceed with support together. The service provider also has functions for managing the progress of support. For example, by recording the start and end times of support sessions and visualizing the progress, it enables users to provide support efficiently. Through these functions, the service provider can facilitate support activities between users. Furthermore, the service provider collects user feedback and uses it to improve the functions it provides. For example, it collects evaluations and comments from users after the end of support sessions to identify areas for improvement. In this way, the service provider can continue to provide functions that meet user needs and improve the value of the platform.

[0034] The rewards department awards points as rewards upon completion of support. For example, the rewards department awards points to users who have provided support, and allows them to receive these points as rewards through an electronic payment system. Specifically, when a support session ends, points are calculated based on the content and duration of the support and awarded to the user's account. Points are accumulated based on activity within the platform, and once a certain number of points are accumulated, they can be exchanged for rewards such as cash or gift cards through the electronic payment system. Furthermore, the rewards department evaluates user activity and sets criteria for awarding points. For example, it adjusts the amount of points awarded based on the quality of support and user ratings. This allows the rewards department to incentivize users to provide high-quality support. The rewards department also provides functions to manage point usage history and balances. Users can check their point accumulation status and usage history in their account and understand their reward receipt status. This allows the rewards department to provide users with a highly transparent rewards system and improve the reliability of the platform. In addition, the rewards department can improve the rewards system based on user feedback and increase user satisfaction.

[0035] The service provider includes a call unit that provides video call support. For example, the service provider provides a video call function, allowing users to provide support while seeing each other's faces. For example, the service provider optimizes the communication environment to improve the quality of video calls. For example, the service provider provides a screen sharing function during video calls, allowing users to provide support while sharing their screens. This enables smooth support between users by providing video call support.

[0036] The rewards section includes an evaluation section that conducts user evaluations upon completion of support. The rewards section provides, for example, a function that allows users who have received support to evaluate the users who provided that support. The rewards section awards points based on the evaluation results. The rewards section sets evaluation criteria such as the quality of support, speed of response, and friendliness. This allows for improvement of support quality through user evaluations.

[0037] The analysis unit analyzes the user's skills and consultation content. For example, the analysis unit analyzes the skills and consultation content registered by the user using natural language processing technology. For example, the analysis unit classifies the user's skills into categories and generates data for appropriate matching. For example, the analysis unit analyzes the consultation content using keyword extraction technology to understand the characteristics of the consultation content. As a result, appropriate matching becomes possible by analyzing the user's skills and consultation content.

[0038] The matching unit matches the most suitable users based on the analyzed information. For example, the matching unit calculates the degree of compatibility between users' skills and consultation topics to perform optimal matching. For example, the matching unit uses AI algorithms to evaluate the compatibility between users. For example, the matching unit refers to users' past matching history to suggest the most suitable match. This allows for effective support by matching the most suitable users.

[0039] The system includes a protection unit that provides security and privacy protection. For example, the protection unit encrypts user data to prevent unauthorized access by third parties. For example, the protection unit implements access control to manage user access rights to data. For example, the protection unit sets policies regarding the handling of personal information to protect user privacy. This ensures user safety through security and privacy protection.

[0040] The reception desk analyzes the user's past registration history and selects the optimal registration method. For example, the reception desk prioritizes suggesting registration methods that the user has frequently used in the past (voice, text, etc.). For example, the reception desk predicts and suggests registration methods to be used at specific times based on the user's past registration history. For example, the reception desk simplifies the registration process by automatically displaying information the user has entered in the past as suggestions. In this way, the optimal registration method can be suggested by analyzing the user's past registration history.

[0041] The registration department customizes registration fields based on the user's current interests during registration. For example, if a user is interested in health management, the department will prioritize displaying health-related registration fields. For example, if a user is interested in entertainment, the department will suggest entertainment-related registration fields. For example, the department will analyze the user's social media activity and customize relevant registration fields. This improves the user's registration experience by customizing registration fields based on the user's interests.

[0042] The reception desk prioritizes retrieving highly relevant information during registration, taking into account the user's geographical location. For example, if a user lives in a specific region, the reception desk prioritizes displaying information related to that region. For example, if a user is traveling, the reception desk suggests relevant information based on their current location. For example, the reception desk suggests the most suitable registration items based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to provide highly relevant information.

[0043] The reception desk analyzes the user's social media activity during registration and obtains relevant information. For example, the reception desk suggests relevant registration items based on information the user has shared on social media. For example, the reception desk analyzes the user's interests and preferences from their social media activity and suggests the most suitable registration items. For example, the reception desk automatically obtains relevant information based on the user's social media activity and simplifies the registration process. In this way, relevant information can be provided by analyzing the user's social media activity.

[0044] The analysis unit optimizes the analysis algorithm by referring to the user's past activity history during analysis. For example, the analysis unit prioritizes suggesting analysis methods that the user has frequently used in the past. For example, the analysis unit predicts and suggests analysis methods to be used during specific time periods based on the user's past activity history. For example, the analysis unit selects the optimal analysis algorithm based on the user's past activity history. In this way, the analysis algorithm can be optimized by referring to the user's past activity history.

[0045] The analysis unit applies different analysis methods depending on the user's skills and the category of the consultation topic during analysis. For example, if a user posts a consultation about IT literacy, the analysis unit applies an IT-related analysis method. For example, if a user posts a consultation about health management, the analysis unit applies a health-related analysis method. For example, if a user posts a consultation about entertainment, the analysis unit applies an entertainment-related analysis method. By applying analysis methods according to the user's skills and the content of the consultation, the accuracy of the analysis is improved.

[0046] The analysis unit determines the priority of analyses based on the user's submission timing. For example, if the user is in a hurry, the analysis unit will set a higher priority based on the submission timing. For example, if the user is relaxed, the analysis unit will adjust the priority of analyses based on the submission timing. For example, if the user submits during a specific time period, the analysis unit will determine the priority of analyses based on that time period. This allows for the rapid completion of analyses by determining the priority of analyses based on the user's submission timing.

[0047] The analysis unit improves the accuracy of its analysis by referring to relevant literature related to the user during the analysis process. For example, the analysis unit improves the accuracy of its analysis by referring to literature related to the content of the consultation posted by the user. For example, the analysis unit improves the accuracy of its analysis by referring to literature related to the user's skills. For example, the analysis unit improves the accuracy of its analysis by referring to literature related to the user's past activity history. In this way, the accuracy of the analysis is improved by referring to relevant literature related to the user.

[0048] The matching unit improves the accuracy of matching by considering the relationships between users during the matching process. For example, the matching unit improves the accuracy of matching by considering the relationships between users who have previously received support from others. For example, the matching unit improves the accuracy of matching by considering the relationships between users who have previously provided support to others. For example, the matching unit analyzes the relationships between users and proposes the optimal match. In this way, the accuracy of matching is improved by considering the relationships between users.

[0049] The matching unit considers user attribute information during the matching process. For example, the matching unit considers attribute information such as the user's age and gender to perform the optimal match. For example, the matching unit considers attribute information such as the user's occupation and hobbies to perform the optimal match. For example, the matching unit considers attribute information such as the user's place of residence and activity area to perform the optimal match. In this way, optimal matching becomes possible by considering the user's attribute information.

[0050] The matching unit considers the geographical distribution of users when matching. For example, if a user lives in a specific region, the matching unit prioritizes matching users related to that region. For example, if a user is traveling, the matching unit matches relevant users based on their current location. For example, the matching unit proposes the optimal match based on the geographical distribution of users. This makes optimal matching possible by considering the geographical distribution of users.

[0051] The matching unit improves the accuracy of matching by referring to the user's relevant literature during the matching process. For example, the matching unit improves the accuracy of matching by referring to literature related to the content of the consultation posted by the user. For example, the matching unit improves the accuracy of matching by referring to literature related to the user's skills. For example, the matching unit improves the accuracy of matching by referring to literature related to the user's past activity history. In this way, the accuracy of matching is improved by referring to the user's relevant literature.

[0052] The service provider selects the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider may prioritize suggesting service delivery methods that the user has frequently used in the past. For example, the service provider may predict and suggest service delivery methods to be used during specific time periods based on the user's past usage history. For example, the service provider selects the optimal service delivery method based on the user's past usage history. In this way, the service provider can suggest the optimal service delivery method by referring to the user's past usage history.

[0053] The service provider customizes the features offered based on the user's current needs at the time of delivery. For example, if the user is interested in health management, the service provider will prioritize providing health-related features. For example, if the user is interested in entertainment, the service provider will suggest entertainment-related features. The service provider analyzes the user's current needs and customizes the optimal features to be offered. By customizing the features offered based on the user's current needs, user satisfaction is improved.

[0054] The service provider selects the optimal delivery method when providing information, taking into account the user's geographical location. For example, if the user lives in a specific region, the service provider will prioritize providing information relevant to that region. For example, if the user is traveling, the service provider will suggest relevant information based on their current location. The service provider selects the optimal delivery method based on the user's geographical location. This allows the service provider to suggest the optimal delivery method by considering the user's geographical location.

[0055] The service provider analyzes the user's social media activity at the time of service delivery and proposes features to be provided. For example, the service provider proposes relevant features based on information shared by the user on social media. For example, the service provider analyzes the user's interests and preferences from their social media activity and proposes the most suitable features to be provided. For example, the service provider automatically acquires relevant information based on the user's social media activity and simplifies the service delivery procedure. This allows the service provider to propose relevant features by analyzing the user's social media activity.

[0056] The rewards department selects the optimal reward method when awarding rewards by referring to the user's past activity history. For example, the rewards department may prioritize suggesting reward methods that the user has frequently used in the past. For example, the rewards department may predict and suggest reward methods to be used during specific time periods based on the user's past activity history. For example, the rewards department selects the optimal reward method based on the user's past activity history. In this way, the optimal reward method can be suggested by referring to the user's past activity history.

[0057] The rewards department customizes reward methods based on the user's current activity level when awarding rewards. For example, if a user is interested in health management, the rewards department will prioritize providing health-related rewards. For example, if a user is interested in entertainment, the rewards department will suggest entertainment-related rewards. For example, the rewards department will analyze the user's current activity level and customize the most suitable reward method. By customizing reward methods based on the user's current activity level, user satisfaction is improved.

[0058] The rewards department selects the optimal reward method when awarding rewards, taking into account the user's geographical location. For example, if a user lives in a specific region, the rewards department will prioritize rewards related to that region. For example, if a user is traveling, the rewards department will suggest relevant rewards based on their current location. The rewards department selects the optimal reward method based on the user's geographical location. This allows the system to suggest the optimal reward method by considering the user's geographical location.

[0059] The rewards department analyzes the user's social media activity and proposes reward methods when awarding rewards. For example, the rewards department proposes relevant rewards based on information shared by the user on social media. For example, the rewards department analyzes the user's interests and preferences from their social media activity and proposes the most suitable reward method. For example, the rewards department automatically acquires relevant information based on the user's social media activity and simplifies the reward process. This allows the department to propose relevant rewards by analyzing the user's social media activity.

[0060] The call unit selects the optimal call method during a call by referring to the user's past call history. For example, the call unit may prioritize suggesting call methods that the user has frequently used in the past. For example, the call unit may predict and suggest a call method to be used during a specific time period based on the user's past call history. For example, the call unit selects the optimal call method based on the user's past call history. In this way, the optimal call method can be suggested by referring to the user's past call history.

[0061] The call unit selects the optimal call method during a call, taking into account the user's geographical location. For example, if the user lives in a specific region, the call unit prioritizes providing call methods relevant to that region. For example, if the user is traveling, the call unit suggests relevant call methods based on their current location. The call unit selects the optimal call method based on the user's geographical location, for example. This allows the system to suggest the optimal call method by considering the user's geographical location.

[0062] The evaluation unit selects the optimal evaluation method by referring to the user's past evaluation history during the evaluation process. For example, the evaluation unit prioritizes suggesting evaluation methods that the user has frequently used in the past. For example, the evaluation unit predicts and suggests evaluation methods to be used during specific time periods based on the user's past evaluation history. For example, the evaluation unit selects the optimal evaluation method based on the user's past evaluation history. In this way, the optimal evaluation method can be suggested by referring to the user's past evaluation history.

[0063] The evaluation unit selects the optimal evaluation method during evaluation, taking into account the user's geographical location information. For example, if the user lives in a specific region, the evaluation unit prioritizes providing evaluation methods relevant to that region. For example, if the user is traveling, the evaluation unit suggests relevant evaluation methods based on their current location. For example, the evaluation unit selects the optimal evaluation method based on the user's geographical location information. This allows the evaluation unit to suggest the optimal evaluation method by considering the user's geographical location information.

[0064] The protection unit selects the optimal protection method by referring to the user's past security history during protection. For example, the protection unit prioritizes suggesting security methods that the user has frequently used in the past. For example, the protection unit predicts and suggests security methods to be used during a specific time period based on the user's past security history. For example, the protection unit selects the optimal protection method based on the user's past security history. In this way, the optimal protection method can be suggested by referring to the user's past security history.

[0065] The protection unit customizes protection measures based on the user's current security status during protection. For example, if the user is interested in health management, the protection unit will prioritize providing health-related security measures. For example, if the user is interested in entertainment, the protection unit will suggest entertainment-related security measures. For example, the protection unit will analyze the user's current security status and customize the optimal protection measures. By customizing protection measures based on the user's current security status, user satisfaction is improved.

[0066] The protection unit selects the optimal protection method when protecting a user, taking into account the user's geographical location. For example, if the user lives in a specific region, the protection unit prioritizes providing protection methods relevant to that region. For example, if the user is traveling, the protection unit suggests relevant protection methods based on their current location. The protection unit selects the optimal protection method based on the user's geographical location. This allows the protection unit to suggest the optimal protection method by considering the user's geographical location.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The reception desk can analyze a user's past registration history and select the most suitable registration method. For example, it can prioritize suggesting registration methods that the user has frequently used in the past (voice, text, etc.). It can also predict and suggest registration methods that the user will use at specific times of day based on their past registration history. Furthermore, it can automatically display information the user has entered in the past as suggestions, simplifying the registration process. In this way, by analyzing a user's past registration history, the system can suggest the most suitable registration method.

[0069] The analysis unit can also optimize the analysis algorithm by referring to the user's past activity history during analysis. For example, it can prioritize suggesting analysis methods that the user has frequently used in the past. It can also predict and suggest analysis methods to be used during specific time periods based on the user's past activity history. Furthermore, it can select the optimal analysis algorithm based on the user's past activity history. In this way, the analysis algorithm can be optimized by referring to the user's past activity history.

[0070] The matching unit can also improve matching accuracy by considering user relationships during the matching process. For example, it can improve matching accuracy by considering the user's relationships with those who have previously provided support to them. It can also improve matching accuracy by considering the user's relationships with those to whom they have previously provided support. Furthermore, it can analyze user relationships and propose the optimal match. In this way, the accuracy of matching is improved by considering user relationships.

[0071] The service provider can also select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, it can prioritize suggesting service delivery methods that the user has frequently used in the past. It can also predict and suggest service delivery methods that the user will use at specific times of the day based on their past usage history. Furthermore, it can select the optimal service delivery method based on the user's past usage history. In this way, the optimal service delivery method can be suggested by referring to the user's past usage history.

[0072] The rewards department can also select the optimal reward method by referring to the user's past activity history when awarding rewards. For example, it can prioritize suggesting reward methods that the user has frequently used in the past. It can also predict and suggest reward methods that the user will use during specific time periods based on their past activity history. Furthermore, it can select the optimal reward method based on the user's past activity history. In this way, the optimal reward method can be suggested by referring to the user's past activity history.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The reception desk accepts user registrations. User registration includes information such as name, age, skills, and consultation details. The reception desk saves the information entered by the user to a database. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes, for example, the user's skills and consultation content, and generates data for appropriate matching. The analysis unit classifies the user's consultation content using, for example, natural language processing technology. Step 3: The matching unit matches users based on the information analyzed by the analysis unit. The matching unit calculates, for example, the degree of match between users' skills and consultation topics to perform the optimal matching. The matching unit also evaluates the compatibility between users using, for example, an AI algorithm. Step 4: The service provider provides the necessary functions for users matched by the matching service provider to support each other. For example, the service provider provides chat and video call functions. For example, the service provider provides tools for users to provide support. Step 5: The rewards department awards points as rewards when support is completed. For example, the rewards department awards points to the user who provided support and allows them to receive those points as rewards through an electronic payment system. For example, the rewards department evaluates the user's activity and calculates the points.

[0075] (Example of form 2) The Web platform according to an embodiment of the present invention is a system that utilizes generative AI to connect working-age and senior generations. In this system, users register on the Web platform, and working-age and senior users register their respective skills and consultation topics. The generative AI analyzes this information and matches users with each other. For example, younger generations with high IT literacy can support senior generations who are struggling with using smartphones. Senior generations can also provide knowledge of their social experience to younger generations. This platform provides multifaceted support and content, including smartphone support, health management, and entertainment. For example, regarding health management, senior generations can post health-related consultations, and working-age generations can provide advice. Regarding entertainment, users with common hobbies across generations can form communities and exchange information. Furthermore, the generative AI has a mechanism to evaluate user activities and award points as rewards. For example, users who provide support are awarded points, which can be received as rewards through an electronic payment system. In this way, support between users is promoted, and intergenerational interaction becomes more active. This platform is provided as a C2C service, and support is completed between users. Furthermore, it includes video call support, review and user rating features, which can improve user experience and satisfaction. It also contributes to reducing the burden on senior citizens working in stores and providing customer support. This system allows working generations and seniors to support and learn from each other, creating opportunities to improve society. It also provides a highly reliable community site, improving user experience and satisfaction, and enhancing brand value through the provision of new added value. In this way, the web platform can create opportunities for working generations and seniors to support and learn from each other, creating opportunities to improve society.

[0076] The Web platform according to this embodiment comprises a reception unit, an analysis unit, a matching unit, a provision unit, and a reward unit. The reception unit accepts user registrations. User registrations include information such as name, age, skills, and consultation content. The reception unit stores the information entered by the user in a database. The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes the user's skills and consultation content and generates data for appropriate matching. The analysis unit classifies the user's consultation content using natural language processing technology. The matching unit matches users based on the information analyzed by the analysis unit. The matching unit calculates the degree of match between users' skills and consultation content and performs optimal matching. The matching unit evaluates the compatibility between users using an AI algorithm. The provision unit provides the necessary functions for users matched by the matching unit to provide support to each other. The provision unit provides, for example, a chat function and a video call function. The provision unit provides, for example, tools for users to provide support. The rewards unit awards points as rewards when support is completed. The rewards unit, for example, awards points to users who have provided support, and enables them to receive those points as rewards through an electronic payment system. The rewards unit, for example, evaluates user activity and calculates points. As a result, the web platform according to the embodiment enables user registration, information analysis, user matching, provision of support functions, and awarding of point rewards.

[0077] The reception desk accepts user registrations. User registration includes information such as name, age, skills, and consultation details. The reception desk stores the information entered by users in a database. Specifically, users access the web platform and enter the required information in the registration form. In addition to basic information such as name and age, users are required to enter detailed information such as their skills, expertise, and the content of their consultation. This information is stored in a secure database and managed to protect user privacy. Furthermore, the reception desk also provides functions for users to update their registration information and enter additional information. For example, if a user acquires a new skill or their consultation details change, they can easily update their information. The reception desk also has functions to verify the accuracy of the information entered during user registration. For example, it verifies the email address and authenticates the phone number to ensure that the user is providing correct information. This allows the reception desk to collect reliable user information and improve the accuracy of subsequent analysis and matching.

[0078] The analysis unit analyzes the information received by the reception unit. For example, the analysis unit analyzes the user's skills and consultation content to generate data for appropriate matching. Specifically, it uses natural language processing technology to classify the user's consultation content. For example, it analyzes the text data of the consultation content entered by the user and extracts keywords and phrases. This allows it to automatically determine which category the consultation content belongs to. Furthermore, the analysis unit analyzes the user's skill information and evaluates the skill level and area of ​​expertise. For example, based on the skill information entered by the user, it identifies the level of skill proficiency and related fields. This allows it to understand the user's skill set in detail and generate basic data for appropriate matching. The analysis unit integrates this data and provides the information necessary for matching users. For example, it calculates the degree of match between the user's skills and consultation content and lists the best matching candidates. In addition, the analysis unit can continuously improve the accuracy of the matching algorithm by utilizing past matching data and user feedback. This allows the analysis unit to achieve highly accurate matching that meets user needs and improve the value of using the platform.

[0079] The matching unit matches users based on information analyzed by the analysis unit. For example, the matching unit calculates the degree of compatibility between users' skills and consultation topics to achieve optimal matching. Specifically, it uses an AI algorithm to evaluate the compatibility between users. For instance, it calculates a compatibility score based on users' skill sets and consultation topics, matching the most suitable users. The matching unit also considers users' past activity history and feedback to achieve more accurate matching. For example, it analyzes data from successful past matches and prioritizes matching users with similar conditions. Furthermore, the matching unit considers users' desired conditions and constraints. For example, it performs matching tailored to the needs of users who are only available during specific time slots or who require matching limited to specific regions. This allows the matching unit to respond to diverse user needs and provide optimal matching. In addition, the matching unit notifies users of the matching results and provides information to facilitate support after matching. For example, it provides contact information for matched users to communicate with each other and guides them through the support initiation process. This allows the matching function to support smooth communication between users and improve the platform user experience.

[0080] The service provider provides the necessary functions for users matched by the matching service provider to support each other. For example, the service provider provides chat and video call functions. Specifically, it provides chat rooms for real-time communication between users and interfaces for video calls. This allows users to provide effective support through text messages, voice, and video. Furthermore, the service provider also provides tools and resources for providing support. For example, by providing shared document and screen sharing functions, users can share information and proceed with support together. The service provider also has functions for managing the progress of support. For example, by recording the start and end times of support sessions and visualizing the progress, it enables users to provide support efficiently. Through these functions, the service provider can facilitate support activities between users. Furthermore, the service provider collects user feedback and uses it to improve the functions it provides. For example, it collects evaluations and comments from users after the end of support sessions to identify areas for improvement. In this way, the service provider can continue to provide functions that meet user needs and improve the value of the platform.

[0081] The rewards department awards points as rewards upon completion of support. For example, the rewards department awards points to users who have provided support, and allows them to receive these points as rewards through an electronic payment system. Specifically, when a support session ends, points are calculated based on the content and duration of the support and awarded to the user's account. Points are accumulated based on activity within the platform, and once a certain number of points are accumulated, they can be exchanged for rewards such as cash or gift cards through the electronic payment system. Furthermore, the rewards department evaluates user activity and sets criteria for awarding points. For example, it adjusts the amount of points awarded based on the quality of support and user ratings. This allows the rewards department to incentivize users to provide high-quality support. The rewards department also provides functions to manage point usage history and balances. Users can check their point accumulation status and usage history in their account and understand their reward receipt status. This allows the rewards department to provide users with a highly transparent rewards system and improve the reliability of the platform. In addition, the rewards department can improve the rewards system based on user feedback and increase user satisfaction.

[0082] The service provider includes a call unit that provides video call support. For example, the service provider provides a video call function, allowing users to provide support while seeing each other's faces. For example, the service provider optimizes the communication environment to improve the quality of video calls. For example, the service provider provides a screen sharing function during video calls, allowing users to provide support while sharing their screens. This enables smooth support between users by providing video call support.

[0083] The rewards section includes an evaluation section that conducts user evaluations upon completion of support. The rewards section provides, for example, a function that allows users who have received support to evaluate the users who provided that support. The rewards section awards points based on the evaluation results. The rewards section sets evaluation criteria such as the quality of support, speed of response, and friendliness. This allows for improvement of support quality through user evaluations.

[0084] The analysis unit analyzes the user's skills and consultation content. For example, the analysis unit analyzes the skills and consultation content registered by the user using natural language processing technology. For example, the analysis unit classifies the user's skills into categories and generates data for appropriate matching. For example, the analysis unit analyzes the consultation content using keyword extraction technology to understand the characteristics of the consultation content. As a result, appropriate matching becomes possible by analyzing the user's skills and consultation content.

[0085] The matching unit matches the most suitable users based on the analyzed information. For example, the matching unit calculates the degree of compatibility between users' skills and consultation topics to perform optimal matching. For example, the matching unit uses AI algorithms to evaluate the compatibility between users. For example, the matching unit refers to users' past matching history to suggest the most suitable match. This allows for effective support by matching the most suitable users.

[0086] The system includes a protection unit that provides security and privacy protection. For example, the protection unit encrypts user data to prevent unauthorized access by third parties. For example, the protection unit implements access control to manage user access rights to data. For example, the protection unit sets policies regarding the handling of personal information to protect user privacy. This ensures user safety through security and privacy protection.

[0087] The reception desk estimates the user's emotions and adjusts the content displayed on the registration form based on the estimated emotions. For example, if the user is nervous, the reception desk provides a simple and intuitive interface and simplifies the registration process. If the user is relaxed, the reception desk provides detailed input options and suggests a customizable registration method. If the user is in a hurry, the reception desk prioritizes voice input to allow for quick registration completion. This improves the user's registration experience by adjusting the content displayed on the registration form according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reception desk analyzes the user's past registration history and selects the optimal registration method. For example, the reception desk prioritizes suggesting registration methods that the user has frequently used in the past (voice, text, etc.). For example, the reception desk predicts and suggests registration methods to be used at specific times based on the user's past registration history. For example, the reception desk simplifies the registration process by automatically displaying information the user has entered in the past as suggestions. In this way, the optimal registration method can be suggested by analyzing the user's past registration history.

[0089] The registration department customizes registration fields based on the user's current interests during registration. For example, if a user is interested in health management, the department will prioritize displaying health-related registration fields. For example, if a user is interested in entertainment, the department will suggest entertainment-related registration fields. For example, the department will analyze the user's social media activity and customize relevant registration fields. This improves the user's registration experience by customizing registration fields based on the user's interests.

[0090] The reception desk estimates the user's emotions and determines registration priorities based on the estimated emotions. For example, if the user is stressed, the reception desk prioritizes displaying important registration items to allow for quick registration completion. For example, if the user is relaxed, the reception desk provides detailed registration items and suggests a customizable registration method. For example, if the user is in a hurry, the reception desk prioritizes displaying the most important registration items to allow for quick registration completion. This allows for quick registration completion by determining registration priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The reception desk prioritizes retrieving highly relevant information during registration, taking into account the user's geographical location. For example, if a user lives in a specific region, the reception desk prioritizes displaying information related to that region. For example, if a user is traveling, the reception desk suggests relevant information based on their current location. For example, the reception desk suggests the most suitable registration items based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to provide highly relevant information.

[0092] The reception desk analyzes the user's social media activity during registration and obtains relevant information. For example, the reception desk suggests relevant registration items based on information the user has shared on social media. For example, the reception desk analyzes the user's interests and preferences from their social media activity and suggests the most suitable registration items. For example, the reception desk automatically obtains relevant information based on the user's social media activity and simplifies the registration process. In this way, relevant information can be provided by analyzing the user's social media activity.

[0093] The analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and intuitive analysis method. If the user is relaxed, the analysis unit provides a detailed analysis method and proposes a customizable analysis method. If the user is in a hurry, the analysis unit ensures that the analysis is completed quickly. This improves the accuracy of the analysis by adjusting the analysis 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The analysis unit optimizes the analysis algorithm by referring to the user's past activity history during analysis. For example, the analysis unit prioritizes suggesting analysis methods that the user has frequently used in the past. For example, the analysis unit predicts and suggests analysis methods to be used during specific time periods based on the user's past activity history. For example, the analysis unit selects the optimal analysis algorithm based on the user's past activity history. In this way, the analysis algorithm can be optimized by referring to the user's past activity history.

[0095] The analysis unit applies different analysis methods depending on the user's skills and the category of the consultation topic during analysis. For example, if a user posts a consultation about IT literacy, the analysis unit applies an IT-related analysis method. For example, if a user posts a consultation about health management, the analysis unit applies a health-related analysis method. For example, if a user posts a consultation about entertainment, the analysis unit applies an entertainment-related analysis method. By applying analysis methods according to the user's skills and the content of the consultation, the accuracy of the analysis is improved.

[0096] The analysis unit estimates the user's emotions and adjusts the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, 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. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, the user's understanding is deepened. 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.

[0097] The analysis unit determines the priority of analyses based on the user's submission timing. For example, if the user is in a hurry, the analysis unit will set a higher priority based on the submission timing. For example, if the user is relaxed, the analysis unit will adjust the priority of analyses based on the submission timing. For example, if the user submits during a specific time period, the analysis unit will determine the priority of analyses based on that time period. This allows for the rapid completion of analyses by determining the priority of analyses based on the user's submission timing.

[0098] The analysis unit improves the accuracy of its analysis by referring to relevant literature related to the user during the analysis process. For example, the analysis unit improves the accuracy of its analysis by referring to literature related to the content of the consultation posted by the user. For example, the analysis unit improves the accuracy of its analysis by referring to literature related to the user's skills. For example, the analysis unit improves the accuracy of its analysis by referring to literature related to the user's past activity history. In this way, the accuracy of the analysis is improved by referring to relevant literature related to the user.

[0099] The matching unit estimates the user's emotions and adjusts the matching criteria based on the estimated emotions. For example, if the user is nervous, the matching unit provides simple and intuitive matching criteria. For example, if the user is relaxed, the matching unit provides detailed matching criteria and suggests a customizable matching method. For example, if the user is in a hurry, the matching unit enables quick matching. This allows for appropriate matching by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The matching unit improves the accuracy of matching by considering the relationships between users during the matching process. For example, the matching unit improves the accuracy of matching by considering the relationships between users who have previously received support from others. For example, the matching unit improves the accuracy of matching by considering the relationships between users who have previously provided support to others. For example, the matching unit analyzes the relationships between users and proposes the optimal match. In this way, the accuracy of matching is improved by considering the relationships between users.

[0101] The matching unit considers user attribute information during the matching process. For example, the matching unit considers attribute information such as the user's age and gender to perform the optimal match. For example, the matching unit considers attribute information such as the user's occupation and hobbies to perform the optimal match. For example, the matching unit considers attribute information such as the user's place of residence and activity area to perform the optimal match. In this way, optimal matching becomes possible by considering the user's attribute information.

[0102] The matching unit estimates the user's emotions and adjusts the order in which matching results are displayed based on the estimated emotions. For example, if the user is nervous, the matching unit provides a simple and highly visible display method. For example, if the user is relaxed, the matching unit provides a display method that includes detailed information. For example, if the user is in a hurry, the matching unit provides a display method that gets straight to the point. By adjusting the display order of matching results according to the user's emotions, user satisfaction is improved. 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.

[0103] The matching unit considers the geographical distribution of users when matching. For example, if a user lives in a specific region, the matching unit prioritizes matching users related to that region. For example, if a user is traveling, the matching unit matches relevant users based on their current location. For example, the matching unit proposes the optimal match based on the geographical distribution of users. This makes optimal matching possible by considering the geographical distribution of users.

[0104] The matching unit improves the accuracy of matching by referring to the user's relevant literature during the matching process. For example, the matching unit improves the accuracy of matching by referring to literature related to the content of the consultation posted by the user. For example, the matching unit improves the accuracy of matching by referring to literature related to the user's skills. For example, the matching unit improves the accuracy of matching by referring to literature related to the user's past activity history. In this way, the accuracy of matching is improved by referring to the user's relevant literature.

[0105] The service provider estimates the user's emotions and adjusts how the provided features are displayed based on the estimated emotions. For example, if the user is nervous, the service provider provides a simple and highly visible display. For example, if the user is relaxed, the service provider provides a display that includes detailed information. For example, if the user is in a hurry, the service provider provides a display that gets straight to the point. By adjusting how the provided features are displayed according to the user's emotions, user satisfaction is improved. 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.

[0106] The service provider selects the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, the service provider may prioritize suggesting service delivery methods that the user has frequently used in the past. For example, the service provider may predict and suggest service delivery methods to be used during specific time periods based on the user's past usage history. For example, the service provider selects the optimal service delivery method based on the user's past usage history. In this way, the service provider can suggest the optimal service delivery method by referring to the user's past usage history.

[0107] The service provider customizes the features offered based on the user's current needs at the time of delivery. For example, if the user is interested in health management, the service provider will prioritize providing health-related features. For example, if the user is interested in entertainment, the service provider will suggest entertainment-related features. The service provider analyzes the user's current needs and customizes the optimal features to be offered. By customizing the features offered based on the user's current needs, user satisfaction is improved.

[0108] The service provider estimates the user's emotions and prioritizes the features to be provided based on the estimated emotions. For example, if the user is stressed, the service provider prioritizes providing important features to ensure a quick response. For example, if the user is relaxed, the service provider provides detailed features and suggests customizable delivery methods. For example, if the user is in a hurry, the service provider prioritizes providing the most important features to ensure a quick response. This allows for a quick response by prioritizing the features to be provided according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The service provider selects the optimal delivery method when providing information, taking into account the user's geographical location. For example, if the user lives in a specific region, the service provider will prioritize providing information relevant to that region. For example, if the user is traveling, the service provider will suggest relevant information based on their current location. The service provider selects the optimal delivery method based on the user's geographical location. This allows the service provider to suggest the optimal delivery method by considering the user's geographical location.

[0110] The service provider analyzes the user's social media activity at the time of service delivery and proposes features to be provided. For example, the service provider proposes relevant features based on information shared by the user on social media. For example, the service provider analyzes the user's interests and preferences from their social media activity and proposes the most suitable features to be provided. For example, the service provider automatically acquires relevant information based on the user's social media activity and simplifies the service delivery procedure. This allows the service provider to propose relevant features by analyzing the user's social media activity.

[0111] The rewards unit estimates the user's emotions and adjusts the reward distribution method based on the estimated emotions. For example, if the user is nervous, the rewards unit provides a simple and intuitive reward distribution method. For example, if the user is relaxed, the rewards unit provides a detailed reward distribution method and suggests a customizable reward method. For example, if the user is in a hurry, the rewards unit enables quick reward distribution. This improves user satisfaction by adjusting the reward distribution method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The rewards department selects the optimal reward method when awarding rewards by referring to the user's past activity history. For example, the rewards department may prioritize suggesting reward methods that the user has frequently used in the past. For example, the rewards department may predict and suggest reward methods to be used during specific time periods based on the user's past activity history. For example, the rewards department selects the optimal reward method based on the user's past activity history. In this way, the optimal reward method can be suggested by referring to the user's past activity history.

[0113] The rewards department customizes reward methods based on the user's current activity level when awarding rewards. For example, if a user is interested in health management, the rewards department will prioritize providing health-related rewards. For example, if a user is interested in entertainment, the rewards department will suggest entertainment-related rewards. For example, the rewards department will analyze the user's current activity level and customize the most suitable reward method. By customizing reward methods based on the user's current activity level, user satisfaction is improved.

[0114] The rewards unit estimates the user's emotions and prioritizes rewards based on those emotions. For example, if the user is stressed, the rewards unit prioritizes important rewards to enable a quick response. For example, if the user is relaxed, the rewards unit provides detailed rewards and suggests customizable reward methods. For example, if the user is in a hurry, the rewards unit prioritizes the most important rewards to enable a quick response. This enables a quick response by prioritizing rewards according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The rewards department selects the optimal reward method when awarding rewards, taking into account the user's geographical location. For example, if a user lives in a specific region, the rewards department will prioritize rewards related to that region. For example, if a user is traveling, the rewards department will suggest relevant rewards based on their current location. The rewards department selects the optimal reward method based on the user's geographical location. This allows the system to suggest the optimal reward method by considering the user's geographical location.

[0116] The rewards department analyzes the user's social media activity and proposes reward methods when awarding rewards. For example, the rewards department proposes relevant rewards based on information shared by the user on social media. For example, the rewards department analyzes the user's interests and preferences from their social media activity and proposes the most suitable reward method. For example, the rewards department automatically acquires relevant information based on the user's social media activity and simplifies the reward process. This allows the department to propose relevant rewards by analyzing the user's social media activity.

[0117] The call unit estimates the user's emotions and adjusts the timing of the call's start based on the estimated emotions. For example, if the user is nervous, the call unit will start the call at a time when the user can relax. For example, if the user is relaxed, the call unit will provide a call that includes detailed explanations. For example, if the user is in a hurry, the call unit will enable the call to start quickly. By adjusting the timing of the call's start according to the user's emotions, user satisfaction is improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The call unit selects the optimal call method during a call by referring to the user's past call history. For example, the call unit may prioritize suggesting call methods that the user has frequently used in the past. For example, the call unit may predict and suggest a call method to be used during a specific time period based on the user's past call history. For example, the call unit selects the optimal call method based on the user's past call history. In this way, the optimal call method can be suggested by referring to the user's past call history.

[0119] The call function estimates the user's emotions and prioritizes calls based on those emotions. For example, if the user is stressed, the call function prioritizes important calls and ensures a quick response. For example, if the user is relaxed, the call function provides detailed calls and suggests customizable call methods. For example, if the user is in a hurry, the call function prioritizes the most important calls and ensures a quick response. This allows for a quick response by prioritizing calls according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The call unit selects the optimal call method during a call, taking into account the user's geographical location. For example, if the user lives in a specific region, the call unit prioritizes providing call methods relevant to that region. For example, if the user is traveling, the call unit suggests relevant call methods based on their current location. The call unit selects the optimal call method based on the user's geographical location, for example. This allows the system to suggest the optimal call method by considering the user's geographical location.

[0121] The evaluation unit estimates the user's emotions and adjusts the evaluation criteria based on the estimated emotions. For example, if the user is nervous, the evaluation unit provides simple and intuitive evaluation criteria. For example, if the user is relaxed, the evaluation unit provides detailed evaluation criteria and suggests a customizable evaluation method. For example, if the user is in a hurry, the evaluation unit enables the evaluation to be completed quickly. This improves user satisfaction by adjusting the evaluation criteria according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The evaluation unit selects the optimal evaluation method by referring to the user's past evaluation history during the evaluation process. For example, the evaluation unit prioritizes suggesting evaluation methods that the user has frequently used in the past. For example, the evaluation unit predicts and suggests evaluation methods to be used during specific time periods based on the user's past evaluation history. For example, the evaluation unit selects the optimal evaluation method based on the user's past evaluation history. In this way, the optimal evaluation method can be suggested by referring to the user's past evaluation history.

[0123] The evaluation unit estimates the user's emotions and determines the priority of evaluations based on the estimated emotions. For example, if the user is stressed, the evaluation unit prioritizes providing important evaluation items to enable a quick response. For example, if the user is relaxed, the evaluation unit provides detailed evaluation items and suggests a customizable evaluation method. For example, if the user is in a hurry, the evaluation unit prioritizes providing the most important evaluation items to enable a quick response. This enables a quick response by determining the priority of evaluations according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The evaluation unit selects the optimal evaluation method during evaluation, taking into account the user's geographical location information. For example, if the user lives in a specific region, the evaluation unit prioritizes providing evaluation methods relevant to that region. For example, if the user is traveling, the evaluation unit suggests relevant evaluation methods based on their current location. For example, the evaluation unit selects the optimal evaluation method based on the user's geographical location information. This allows the evaluation unit to suggest the optimal evaluation method by considering the user's geographical location information.

[0125] The protection unit estimates the user's emotions and adjusts security measures based on those emotions. For example, if the user is stressed, the protection unit provides simple and intuitive security measures. If the user is relaxed, the protection unit provides detailed security measures and suggests customizable security methods. If the user is in a hurry, the protection unit ensures that security measures are completed quickly. This improves user satisfaction by adjusting security measures according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The protection unit selects the optimal protection method by referring to the user's past security history during protection. For example, the protection unit prioritizes suggesting security methods that the user has frequently used in the past. For example, the protection unit predicts and suggests security methods to be used during a specific time period based on the user's past security history. For example, the protection unit selects the optimal protection method based on the user's past security history. In this way, the optimal protection method can be suggested by referring to the user's past security history.

[0127] The protection unit customizes protection measures based on the user's current security status during protection. For example, if the user is interested in health management, the protection unit will prioritize providing health-related security measures. For example, if the user is interested in entertainment, the protection unit will suggest entertainment-related security measures. For example, the protection unit will analyze the user's current security status and customize the optimal protection measures. By customizing protection measures based on the user's current security status, user satisfaction is improved.

[0128] The protection unit estimates the user's emotions and determines the priority of protection based on the estimated emotions. For example, if the user is stressed, the protection unit prioritizes providing important protection items and enables a quick response. For example, if the user is relaxed, the protection unit provides detailed protection items and suggests customizable protection methods. For example, if the user is in a hurry, the protection unit prioritizes providing the most important protection items and enables a quick response. This enables a quick response by determining the priority of protection according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0129] The protection unit selects the optimal protection method when protecting a user, taking into account the user's geographical location. For example, if the user lives in a specific region, the protection unit prioritizes providing protection methods relevant to that region. For example, if the user is traveling, the protection unit suggests relevant protection methods based on their current location. The protection unit selects the optimal protection method based on the user's geographical location. This allows the protection unit to suggest the optimal protection method by considering the user's geographical location.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] The reception desk can also estimate the user's emotions and adjust the content displayed on the registration form based on those emotions. For example, if the user is nervous, it can provide a simple and intuitive interface and simplify the registration process. If the user is relaxed, it can provide detailed input options and suggest a customizable registration method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick registration completion. In this way, the user's registration experience can be improved by adjusting the content displayed on the registration form according to the user's emotions.

[0132] The analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is nervous, it can provide a simple and intuitive analysis method. If the user is relaxed, it can provide a detailed analysis method and suggest a customizable analysis approach. Furthermore, if the user is in a hurry, it can complete the analysis quickly. By adjusting the analysis method according to the user's emotions, the accuracy of the analysis can be improved.

[0133] The matching unit can also estimate the user's emotions and adjust the matching criteria based on those emotions. For example, if the user is nervous, it can provide simple and intuitive matching criteria. If the user is relaxed, it can provide detailed matching criteria and suggest a customizable matching method. Furthermore, if the user is in a hurry, it can enable quick matching. By adjusting the matching criteria according to the user's emotions, appropriate matching becomes possible.

[0134] The service provider can also estimate the user's emotions and adjust how the features are displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. By adjusting how features are displayed according to the user's emotions, user satisfaction can be improved.

[0135] The rewards system can also estimate the user's emotions and adjust the reward distribution method based on those emotions. For example, if the user is stressed, it can provide a simple and intuitive reward distribution method. If the user is relaxed, it can provide a more detailed reward distribution method and even suggest customizable options. Furthermore, if the user is in a hurry, it can provide rewards quickly. By adjusting the reward distribution method according to the user's emotions, user satisfaction can be improved.

[0136] The reception desk can analyze a user's past registration history and select the most suitable registration method. For example, it can prioritize suggesting registration methods that the user has frequently used in the past (voice, text, etc.). It can also predict and suggest registration methods that the user will use at specific times of day based on their past registration history. Furthermore, it can automatically display information the user has entered in the past as suggestions, simplifying the registration process. In this way, by analyzing a user's past registration history, the system can suggest the most suitable registration method.

[0137] The analysis unit can also optimize the analysis algorithm by referring to the user's past activity history during analysis. For example, it can prioritize suggesting analysis methods that the user has frequently used in the past. It can also predict and suggest analysis methods to be used during specific time periods based on the user's past activity history. Furthermore, it can select the optimal analysis algorithm based on the user's past activity history. In this way, the analysis algorithm can be optimized by referring to the user's past activity history.

[0138] The matching unit can also improve matching accuracy by considering user relationships during the matching process. For example, it can improve matching accuracy by considering the user's relationships with those who have previously provided support to them. It can also improve matching accuracy by considering the user's relationships with those to whom they have previously provided support. Furthermore, it can analyze user relationships and propose the optimal match. In this way, the accuracy of matching is improved by considering user relationships.

[0139] The service provider can also select the optimal service delivery method by referring to the user's past usage history at the time of delivery. For example, it can prioritize suggesting service delivery methods that the user has frequently used in the past. It can also predict and suggest service delivery methods that the user will use at specific times of the day based on their past usage history. Furthermore, it can select the optimal service delivery method based on the user's past usage history. In this way, the optimal service delivery method can be suggested by referring to the user's past usage history.

[0140] The rewards department can also select the optimal reward method by referring to the user's past activity history when awarding rewards. For example, it can prioritize suggesting reward methods that the user has frequently used in the past. It can also predict and suggest reward methods that the user will use during specific time periods based on their past activity history. Furthermore, it can select the optimal reward method based on the user's past activity history. In this way, the optimal reward method can be suggested by referring to the user's past activity history.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The reception desk accepts user registrations. User registration includes information such as name, age, skills, and consultation details. The reception desk saves the information entered by the user to a database. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis unit analyzes, for example, the user's skills and consultation content, and generates data for appropriate matching. The analysis unit classifies the user's consultation content using, for example, natural language processing technology. Step 3: The matching unit matches users based on the information analyzed by the analysis unit. The matching unit calculates, for example, the degree of match between users' skills and consultation topics to perform the optimal matching. The matching unit also evaluates the compatibility between users using, for example, an AI algorithm. Step 4: The service provider provides the necessary functions for users matched by the matching service provider to support each other. For example, the service provider provides chat and video call functions. For example, the service provider provides tools for users to provide support. Step 5: The rewards department awards points as rewards when support is completed. For example, the rewards department awards points to the user who provided support and allows them to receive those points as rewards through an electronic payment system. For example, the rewards department evaluates the user's activity and calculates the points.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, provision unit, reward unit, and protection unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and stores user registration information in the database 24. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's skills and consultation content. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches users with each other. The provision unit is implemented by, for example, the control unit 46A of the smart device 14 and provides chat and video call functions. The reward unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates and awards point rewards. The protection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and encrypts user data and performs access control. 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.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, provision unit, reward unit, and protection unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and stores user registration information in the database 24. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's skills and consultation content. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and matches users with each other. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides chat and video call functions. The reward unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and calculates and awards point rewards. The protection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and encrypts user data and performs access control. 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.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, provision unit, reward unit, and protection 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 control unit 46A of the headset terminal 314 and stores user registration information in the database 24. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's skills and consultation content. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches users with each other. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides chat and video call functions. The reward unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates and awards point rewards. The protection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and encrypts user data and performs access control. 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.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] Each of the multiple elements described above, including the reception unit, analysis unit, matching unit, provision unit, reward unit, and protection 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 control unit 46A of the robot 414 and stores user registration information in the database 24. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's skills and consultation content. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and matches users with each other. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides chat and video call functions. The reward unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and calculates and awards point rewards. The protection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and encrypts user data and performs access control. 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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."

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] (Note 1) The reception desk that accepts user registrations, An analysis unit that analyzes the information received by the reception unit, A matching unit that matches users based on the information analyzed by the aforementioned analysis unit, A providing unit that provides the necessary functions when users matched by the matching unit provide support to each other, It includes a rewards section that awards points as a reward when support is completed. A system characterized by the following features. (Note 2) The aforementioned supply unit is, It features a voice call unit that supports video calls. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned compensation unit is, It includes an evaluation unit that conducts user evaluations upon completion of support. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze the user's skills and the content of their inquiries. The system described in Appendix 1, characterized by the features described herein. (Note 5) The matching unit is Based on the analyzed information, the system matches the most suitable users together. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned system, It is equipped with a protective section for security and privacy protection. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the content displayed on the registration form based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past registration history and select the optimal registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During registration, the registration fields are customized based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates user sentiment and determines registration priority based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During registration, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During registration, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to the user's past activity history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the user's skills and the category of the consultation content. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the analysis priority is determined based on when the user submitted their data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the system references relevant literature from the user to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) 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 20) The matching unit is During the matching process, the accuracy of the matching is improved by considering the relationships between users. The system described in Appendix 1, characterized by the features described herein. (Note 21) 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 22) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) 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 24) The matching unit is During the matching process, we improve the accuracy of the matching by referring to the user's relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the features it offers are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the service, 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 27) The aforementioned supply unit is, At the time of delivery, we customize the features provided based on the user's current needs. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the features to offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, We propose a feature that analyzes users' social media activity and provides relevant information upon launch. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned compensation unit is, The system estimates the user's emotions and adjusts the reward system based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned compensation unit is, When awarding rewards, the system will refer to the user's past activity history to select the most suitable reward method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned compensation unit is, When awarding rewards, customize the reward method based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned compensation unit is, The system estimates the user's emotions and prioritizes rewards based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned compensation unit is, When awarding rewards, the system will select the most appropriate reward method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned compensation unit is, When awarding rewards, we analyze the user's social media activity and suggest reward methods. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned communication unit is, It estimates the user's emotions and adjusts the timing of the call's initiation based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned communication unit is, During a call, the system selects the optimal calling method by referring to the user's past call history. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned communication unit is, It estimates the user's emotions and determines call priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned communication unit is, During a call, the system selects the optimal calling method by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 41) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The evaluation unit, During the evaluation process, the system selects the most suitable evaluation method by referring to the user's past evaluation history. The system described in Appendix 3, characterized by the features described herein. (Note 43) The evaluation unit, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 44) The evaluation unit, During evaluation, the optimal evaluation method is selected by considering the user's geographical location information. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned protective part is It estimates user sentiment and adjusts security measures based on the estimated user sentiment. The system described in Appendix 6, characterized by the features described herein. (Note 46) The aforementioned protective part is During protection, the system selects the optimal protection method by referring to the user's past security history. The system described in Appendix 6, characterized by the features described herein. (Note 47) The aforementioned protective part is During protection, customize the protection measures based on the user's current security status. The system described in Appendix 6, characterized by the features described herein. (Note 48) The aforementioned protective part is It estimates the user's emotions and determines the priority of protection based on the estimated user emotions. The system described in Appendix 6, characterized by the features described herein. (Note 49) The aforementioned protective part is When protecting, select the optimal protection method considering the user's geographical location information The system according to appended note 6, characterized in that it does so

Explanation of symbols

[0215] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset type terminal 414 Robot

Claims

1. The reception desk that accepts user registrations, An analysis unit that analyzes the information received by the reception unit, A matching unit that matches users based on the information analyzed by the aforementioned analysis unit, A providing unit that provides the necessary functions when users matched by the matching unit provide support to each other, It includes a rewards section that awards points as a reward when support is completed. A system characterized by the following features.

2. The aforementioned supply unit is, It features a voice call unit that supports video calls. The system according to feature 1.

3. The aforementioned compensation unit is, It includes an evaluation unit that conducts user evaluations upon completion of support. The system according to feature 1.

4. The aforementioned analysis unit, Analyze the user's skills and the content of their inquiries. The system according to feature 1.

5. The matching unit is Based on the analyzed information, the system matches the most suitable users together. The system according to feature 1.

6. The aforementioned system, It is equipped with a protective section for security and privacy protection. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the content displayed on the registration form based on those emotions. The system according to feature 1.

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

9. The aforementioned reception unit is During registration, the registration fields are customized based on the user's current interests and preferences. The system according to feature 1.

10. The aforementioned reception unit is The system estimates user sentiment and determines registration priority based on the estimated user sentiment. The system according to feature 1.

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