system

Abstract

The system according to this embodiment aims to propose the most suitable match based on the user's profile information. [Solution] The system according to the embodiment comprises a registration unit, an analysis unit, and a proposal unit. The registration unit registers the user's profile information. The analysis unit analyzes the information registered by the registration unit. The proposal unit proposes matching based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, there is a problem that it is difficult for a user to find an optimal matching based on their own profile information, and an efficient matching has not been realized.

[0005] The system according to the embodiment aims to propose an optimal matching based on the profile information of the user.

Means for Solving the Problems

[0006] The system according to the embodiment includes a registration unit, an analysis unit, and a proposal unit. The registration unit registers the profile information of the user. The analysis unit analyzes the information registered by the registration unit. The proposal unit proposes a matching based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable match based on the user's profile information. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 matching suggestion system according to an embodiment of the present invention is a system in which a user registers information such as their profile, interests, and skills, and an AI analyzes this information to suggest matching the user with other users who have similar interests and goals. In the matching suggestion system, the user registers information such as their profile, interests, and skills. For example, the user inputs information such as hobbies, activity range, personality, preferences, skills, experience, and industry knowledge. This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​identifies other users who have similar interests and goals, taking into account the user's hobbies, activity range, personality, preferences, skills, experience, and industry knowledge. For example, it can find people who not only share the same hobbies but also have compatible values ​​and personalities. In the case of friends, the AI ​​not only finds people with similar hobbies and activity ranges but also considers factors such as personality and preferences when making a match. This supports encounters with people who not only share the same hobbies but also have compatible values ​​and personalities. On the other hand, in the case of colleagues, the AI ​​suggests partners or team members who can build a mutually complementary relationship based on skills, experience, and industry knowledge. For example, it can find people who share common work goals and visions. The advantage of this service is that it saves users the trouble of searching themselves. Because the AI ​​analyzes large amounts of data and suggests optimal matches, users can find ideal friends or work partners more efficiently. Furthermore, as the AI ​​evolves, it learns from accumulating user responses and evaluations, enabling it to make better suggestions. By users evaluating their relationships with suggested partners and colleagues, the AI's performance improves, allowing it to provide services tailored to individual preferences and needs. For example, by evaluating the relationships with suggested friends or work partners, the AI ​​learns from these evaluations and incorporates them into future suggestions. This allows users to receive more accurate matches. In this way, AI-powered matching services can support users in efficiently finding ideal friends and work partners, improving user satisfaction and work motivation. This enables the matching suggestion system to efficiently register, analyze, and suggest matches based on user profile information.

[0029] The matching suggestion system according to the embodiment comprises a registration unit, an analysis unit, and a suggestion unit. The registration unit registers the user's profile information. The user's profile information includes, but is not limited to, name, age, occupation, and hobbies. The registration unit stores the information entered by the user in a database, for example. The registration unit can also overwrite existing information when the user updates their profile information. For example, if the user adds a new hobby, the registration unit adds that information to the database. The analysis unit analyzes the information registered by the registration unit. The analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit identifies the user's interests and goals based on information such as the user's hobbies, range of activities, personality, and preferences. The analysis unit can also identify the user's areas of expertise and strengths based on information such as the user's skills, experience, and industry knowledge. For example, the analysis unit analyzes the user's work history and skill set to identify the fields in which the user can excel. The suggestion unit proposes matches based on the analysis results obtained by the analysis unit. Suggestions are made based on criteria such as shared hobbies or complementary skills, but are not limited to such examples. For instance, the suggestion unit may suggest other users who share the same hobbies as the user. The suggestion unit may also suggest partners or team members who complement the user's skills and experience. For example, the suggestion unit may suggest other users who possess skills that the user lacks. This enables the matching suggestion system according to the embodiment to efficiently register, analyze, and suggest matching information for users. Some or all of the above-described processes in the registration unit, analysis unit, and suggestion unit may be performed using AI, or not, for example. For instance, the registration unit inputs information entered by the user into the AI, and the AI ​​stores the information in a database. The analysis unit can analyze the user's information using AI, and the suggestion unit can suggest matches using AI.

[0030] The registration section registers user profile information. This profile information includes, but is not limited to, name, age, occupation, and hobbies. The registration section also stores user-entered information in a database. Specifically, information entered by users through web forms or applications is stored in a secure database. The database is encrypted to protect user privacy and can only be viewed and edited by authorized users. Furthermore, the registration section can overwrite existing information when users update their profile. For example, if a user adds a new hobby, that information is added to the database. This ensures that users always have the most up-to-date information, enabling accurate matching. Additionally, the registration section handles user requests to delete their profile information, providing a function to completely delete data upon user request. This allows users to freely manage their own information and use the system with peace of mind.

[0031] The Analysis Department analyzes information registered by the Registration Department. This analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, statistical analysis aggregates user age groups, occupational distribution, and hobby trends to understand overall trends. When using machine learning algorithms, it identifies user interests and goals based on information such as hobbies, activity ranges, personality, and preferences. For example, clustering algorithms can be used to group users with similar hobbies and interests. The Analysis Department can also identify users' areas of expertise and strengths based on information such as skills, experience, and industry knowledge. For example, natural language processing technology can be used to analyze a user's work history and skill set to identify areas where they can excel. Furthermore, the Analysis Department can learn user preferences and behavioral patterns based on past behavioral history and feedback, enabling more accurate analysis. This allows the Analysis Department to comprehensively analyze diverse user information and provide a foundation for making optimal matching suggestions for individual users.

[0032] The Proposal Department proposes matches based on the analysis results obtained by the Analysis Department. These proposals are based on criteria such as shared hobbies and complementary skills, but are not limited to these examples. Specifically, the Proposal Department can suggest other users with similar hobbies. For example, a user who enjoys outdoor activities can be suggested other users who also enjoy outdoor activities. The Proposal Department can also suggest partners or team members who complement the user's skills and experience. For example, a user with programming skills can be suggested a user with design skills, enabling them to collaborate on projects. Furthermore, the Proposal Department can use AI to make these matching suggestions. The AI ​​learns from the user's profile information and past matching history to build an algorithm that proposes the optimal match. This allows the Proposal Department to provide highly accurate matching suggestions that meet the diverse needs of users. The Proposal Department can also collect user feedback and continuously improve the accuracy of its suggestion algorithm. This allows the Proposal Department to always utilize the latest information and technology to provide users with the best possible matching suggestions.

[0033] The suggestion unit can perform matching by considering factors such as hobbies, activity range, personality, and preferences. For example, based on the user's hobbies and activity range, the suggestion unit can suggest other users with similar hobbies. For example, if the user enjoys outdoor activities, the suggestion unit can suggest other users who also enjoy outdoor activities. The suggestion unit can also suggest compatible users based on the user's personality and preferences. For example, if the user has an introverted personality, the suggestion unit can suggest other users with a similar introverted personality. The suggestion unit can also suggest users with common interests based on the user's preferences. For example, if the user enjoys a particular music genre, the suggestion unit can suggest other users who enjoy the same music genre. This enables matching based on the user's hobbies and personality. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input information about the user's hobbies and personality into an AI, which can then suggest matches.

[0034] The proposal team can suggest partners and team members who can build mutually complementary relationships based on skills, experience, and industry knowledge. For example, based on the user's skills and experience, the proposal team can suggest other users with complementary skills. For instance, if the user has programming skills, the proposal team can suggest other users with design skills. The proposal team can also suggest other users working in the same industry based on the user's industry knowledge. For example, if the user works in the IT industry, the proposal team can suggest other users working in the same IT industry. The proposal team can also suggest other users with similar experience based on the user's experience. For example, if the user has project management experience, the proposal team can suggest other users with project management experience. This makes it possible to suggest partners and team members based on skills and experience. Some or all of the above processes in the proposal team may be performed using AI, or not. For example, the proposal team can input information about the user's skills and experience into an AI, which can then suggest matches.

[0035] The suggestion department can find individuals who share common work goals and visions. For example, based on a user's work goals and vision, the suggestion department can suggest other users with the same goals and visions. For instance, if a user's goal is to successfully complete a specific project, the suggestion department can suggest other users with the same goal. The suggestion department can also suggest other users with a shared vision based on the user's vision. For example, if a user aims for sustainable development, the suggestion department can suggest other users with the same vision. This makes it possible to suggest individuals who share common work goals and visions. Some or all of the above processes in the suggestion department may be performed using AI, or not. For example, the suggestion department can input information about the user's work goals and visions into an AI, which can then suggest matches.

[0036] The suggestion unit can learn from user reactions and evaluations and reflect them in future suggestions. For example, the suggestion unit learns from user evaluations of relationships with suggested partners or colleagues. For example, the suggestion unit evaluates a user's relationship with a suggested friend and uses that evaluation to improve future suggestions. The suggestion unit can also improve the accuracy of suggestions based on user reactions. For example, the suggestion unit evaluates a user's relationship with a suggested colleague or partner and uses that evaluation to improve future suggestions. In this way, the accuracy of suggestions improves by learning from user reactions and evaluations. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input user evaluation data into AI, which can learn the evaluations and reflect them in future suggestions.

[0037] The registration unit can analyze the user's past registration information and suggest the optimal input format. For example, the registration unit can automatically display information that the user has frequently entered in the past as a suggestion. For example, the registration unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The registration unit can also predict and suggest information that the user will use at a specific time of day based on the user's past registration information. For example, the registration unit can suggest information related to the same time period based on information the user has entered at a specific time of day in the past. This makes it possible to suggest the optimal input format based on past registration information. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's past registration information into AI, and the AI ​​can suggest the optimal input format.

[0038] The registration unit can customize input fields based on the user's current lifestyle and areas of interest when registering profile information. For example, the registration unit can automatically display relevant input fields based on the user's current occupation or hobbies. For example, the registration unit can customize input fields based on the user's areas of interest and prioritize the input of relevant information. The registration unit can also suggest appropriate input fields according to the user's lifestyle (e.g., student, working professional, retired). For example, if the user is a student, the registration unit will suggest input fields related to academics. This allows for the customization of input fields based on lifestyle and areas of interest. Some or all of the above processes in the registration unit may be performed using AI, or not. For example, the registration unit can input information about the user's lifestyle and areas of interest into the AI, which can then customize the input fields.

[0039] The registration unit can prioritize inputting highly relevant information by considering the user's geographical location when registering profile information. For example, the registration unit can prioritize inputting region-related information based on the user's current location. For example, if the user is traveling, the registration unit can prioritize inputting information related to their travel destination. The registration unit can also input information related to local events and activities based on the user's place of residence. For example, if the user lives in a specific region, the registration unit can prioritize inputting event information for that region. This enables the input of highly relevant information based on geographical location information. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's geographical location information into AI, which can then prioritize inputting highly relevant information.

[0040] The registration unit can analyze a user's social media activity and automatically input relevant information when registering profile information. For example, the registration unit can analyze the content of a user's social media posts and automatically input information related to their hobbies and areas of interest. For example, the registration unit can analyze a user's social media friendships and input information related to friends with similar hobbies and activities. The registration unit can also refer to a user's social media profile information and automatically input basic information. For example, the registration unit can automatically input basic information such as name and age based on the user's social media profile information. This enables the automatic input of information based on social media activity. Some or all of the above processing in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input data on the user's social media activity into AI, and the AI ​​can automatically input relevant information.

[0041] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavioral history. For example, the analysis unit can improve the accuracy of its analysis by identifying areas of interest based on the user's past behavioral history. For example, the analysis unit can improve the accuracy of its analysis by analyzing the user's past behavioral patterns and predicting future behavior. Furthermore, the analysis unit can improve the accuracy of its analysis by predicting behavior in specific time periods or situations based on the user's past behavioral history. For example, the analysis unit can predict behavior related to the same time period based on the actions the user has taken in the past during a specific time period. This makes it possible to improve the accuracy of analysis based on past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavioral history into AI, which can then improve the accuracy of its analysis.

[0042] The analysis unit can apply different analysis methods depending on the categories of the user's profile information during analysis. For example, the analysis unit can select an appropriate analysis method based on the user's hobbies and areas of interest. For example, the analysis unit can apply a specialized analysis method based on the user's skills and experience. The analysis unit can also apply a personalized analysis method based on the user's personality and preferences. For example, if the user has an introverted personality, the analysis unit will apply an analysis method suitable for an introverted personality. This makes it possible to apply analysis methods according to the categories of the profile information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the user's profile information into the AI, and the AI ​​can apply different analysis methods.

[0043] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information. For example, the analysis unit can prioritize analyzing region-related information based on the user's current location. For example, if the user is traveling, the analysis unit will prioritize analyzing information related to their travel destination. The analysis unit can also analyze information related to local events and activities based on the user's place of residence. For example, if the user lives in a specific region, the analysis unit will prioritize analyzing event information for that region. This makes it possible to improve the accuracy of geographical location-based analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into AI, which can then improve the accuracy of the analysis.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by identifying areas of interest based on the literature the user has read in the past. For example, the analysis unit can improve the accuracy of its analysis by analyzing the user's relevant literature and predicting future behavior. Furthermore, the analysis unit can improve the accuracy of its analysis by predicting behavior in specific time periods or situations based on the user's relevant literature. For example, the analysis unit predicts relevant behavior in the same time period based on the literature the user has read in the past. This makes it possible to improve the accuracy of analysis based on relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature into AI, which can then improve the accuracy of its analysis.

[0045] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past matching history. For example, the suggestion unit can identify areas of interest based on the user's past matching history to improve the accuracy of its suggestions. For example, the suggestion unit can predict the user's behavior at specific times or in specific situations based on their past matching history to improve the accuracy of its suggestions. The suggestion unit can also analyze the user's past matching history to make the most efficient suggestions. For example, the suggestion unit can suggest other users with the same patterns as the user's past successful matching patterns. This makes it possible to improve the accuracy of suggestions based on past matching history. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's past matching history into AI, which can then improve the accuracy of its suggestions.

[0046] The suggestion unit can apply different suggestion algorithms depending on the user's profile information categories when making suggestions. For example, the suggestion unit can select an appropriate suggestion algorithm based on the user's hobbies and areas of interest. For example, the suggestion unit can apply a specialized suggestion algorithm based on the user's skills and experience. The suggestion unit can also apply a personalized suggestion algorithm based on the user's personality and preferences. For example, if the user has an introverted personality, the suggestion unit can apply a suggestion algorithm suitable for an introverted personality. This makes it possible to apply suggestion algorithms according to the profile information categories. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's profile information categories into the AI, and the AI ​​can apply different suggestion algorithms.

[0047] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can prioritize suggestions related to the user's region based on the user's current location. For example, if the user is traveling, the suggestion unit can prioritize suggestions related to the travel destination. The suggestion unit can also make suggestions related to local events and activities based on the user's place of residence. For example, if the user lives in a specific region, the suggestion unit can prioritize suggesting event information for that region. This enables optimal suggestions based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location information into AI, which can then make optimal suggestions.

[0048] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can analyze the content of the user's social media posts and make suggestions related to their hobbies and areas of interest. For example, the suggestion unit can analyze the user's social media friendships and make suggestions related to friends with similar hobbies and activities. The suggestion unit can also refer to the user's social media profile information and make relevant suggestions. For example, the suggestion unit can suggest other users with similar hobbies based on the user's social media profile information. This makes it possible to make relevant suggestions based on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the user's social media activity into AI, and the AI ​​can make relevant suggestions.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The suggestion unit can analyze a user's past matching history to improve the accuracy of its suggestions. For example, it can suggest other users with similar patterns to those of successful matches the user has had in the past. For example, it can predict a user's behavior at specific times or in specific situations based on their past matching history to improve the accuracy of its suggestions. The suggestion unit can also identify a user's interests and areas of concern based on their past matching history to improve the accuracy of its suggestions. For example, it can suggest other users with similar patterns to those of successful matches the user has had in the past. This makes it possible to improve the accuracy of suggestions based on past matching history. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's past matching history into an AI, which can then improve the accuracy of its suggestions.

[0051] The suggestion unit can apply different suggestion algorithms depending on the categories of the user's profile information. For example, the suggestion unit can select an appropriate suggestion algorithm based on the user's hobbies and areas of interest. For example, the suggestion unit can apply a specialized suggestion algorithm based on the user's skills and experience. The suggestion unit can also apply a personalized suggestion algorithm based on the user's personality and preferences. For example, if the user has an introverted personality, the suggestion unit can apply a suggestion algorithm suited to an introverted personality. This makes it possible to apply suggestion algorithms according to the categories of the profile information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the categories of the user's profile information into the AI, and the AI ​​can apply different suggestion algorithms.

[0052] The suggestion unit can make optimal suggestions by taking into account the user's geographical location. For example, the suggestion unit can prioritize suggestions related to the user's region based on the user's current location. For example, if the user is traveling, the suggestion unit can prioritize suggestions related to the travel destination. The suggestion unit can also make suggestions related to local events and activities based on the user's place of residence. For example, if the user lives in a specific region, the suggestion unit can prioritize suggesting event information for that region. This enables optimal suggestions based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into AI, which can then make optimal suggestions.

[0053] The suggestion unit can analyze a user's social media activity and make relevant suggestions. For example, the suggestion unit can analyze a user's social media posts and make suggestions related to their hobbies and areas of interest. For example, the suggestion unit can analyze a user's social media friendships and make suggestions related to friends with similar hobbies and activities. The suggestion unit can also refer to a user's social media profile information and make relevant suggestions. For example, based on a user's social media profile information, the suggestion unit can suggest other users with similar hobbies. This enables relevant suggestions based on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input data on the user's social media activity into an AI, which can then make relevant suggestions.

[0054] The suggestion unit can customize suggestions based on the user's current lifestyle and areas of interest. For example, the suggestion unit can automatically display relevant suggestions based on the user's current occupation or hobbies. For example, the suggestion unit can customize suggestions based on the user's areas of interest and prioritize relevant information. The suggestion unit can also provide appropriate suggestions according to the user's lifestyle (e.g., student, working professional, retired). For example, if the user is a student, the suggestion unit will provide suggestions related to academics. This allows for the customization of suggestions based on lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input information about the user's lifestyle and areas of interest into the AI, which can then customize the suggestions.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The registration unit registers the user's profile information. This profile information includes name, age, occupation, hobbies, etc. The registration unit saves the information entered by the user to a database and can overwrite existing information when the user updates their profile. For example, if a user adds a new hobby, that information is added to the database. Step 2: The analysis department analyzes the information registered by the registration department. The analysis is performed using statistical analysis and machine learning algorithms. For example, it identifies the user's interests and goals based on information such as the user's hobbies, range of activities, personality, and preferences. It can also identify the user's areas of expertise and strengths based on information such as the user's skills, experience, and industry knowledge. Step 3: The proposal team proposes matches based on the analysis results obtained by the analysis team. Proposals are made based on criteria such as common hobbies and complementary skills. For example, they may propose other users who share the same hobbies as the user, or partners or team members who complement the user's skills and experience.

[0057] (Example of form 2) The matching suggestion system according to an embodiment of the present invention is a system in which a user registers information such as their profile, interests, and skills, and an AI analyzes this information to suggest matching the user with other users who have similar interests and goals. In the matching suggestion system, the user registers information such as their profile, interests, and skills. For example, the user inputs information such as hobbies, activity range, personality, preferences, skills, experience, and industry knowledge. This information is input to the AI. Next, the AI ​​analyzes the input information. The AI ​​identifies other users who have similar interests and goals, taking into account the user's hobbies, activity range, personality, preferences, skills, experience, and industry knowledge. For example, it can find people who not only share the same hobbies but also have compatible values ​​and personalities. In the case of friends, the AI ​​not only finds people with similar hobbies and activity ranges but also considers factors such as personality and preferences when making a match. This supports encounters with people who not only share the same hobbies but also have compatible values ​​and personalities. On the other hand, in the case of colleagues, the AI ​​suggests partners or team members who can build a mutually complementary relationship based on skills, experience, and industry knowledge. For example, it can find people who share common work goals and visions. The advantage of this service is that it saves users the trouble of searching themselves. Because the AI ​​analyzes large amounts of data and suggests optimal matches, users can find ideal friends or work partners more efficiently. Furthermore, as the AI ​​evolves, it learns from accumulating user responses and evaluations, enabling it to make better suggestions. By users evaluating their relationships with suggested partners and colleagues, the AI's performance improves, allowing it to provide services tailored to individual preferences and needs. For example, by evaluating the relationships with suggested friends or work partners, the AI ​​learns from these evaluations and incorporates them into future suggestions. This allows users to receive more accurate matches. In this way, AI-powered matching services can support users in efficiently finding ideal friends and work partners, improving user satisfaction and work motivation. This enables the matching suggestion system to efficiently register, analyze, and suggest matches based on user profile information.

[0058] The matching suggestion system according to the embodiment comprises a registration unit, an analysis unit, and a suggestion unit. The registration unit registers the user's profile information. The user's profile information includes, but is not limited to, name, age, occupation, and hobbies. The registration unit stores the information entered by the user in a database, for example. The registration unit can also overwrite existing information when the user updates their profile information. For example, if the user adds a new hobby, the registration unit adds that information to the database. The analysis unit analyzes the information registered by the registration unit. The analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. For example, the analysis unit identifies the user's interests and goals based on information such as the user's hobbies, range of activities, personality, and preferences. The analysis unit can also identify the user's areas of expertise and strengths based on information such as the user's skills, experience, and industry knowledge. For example, the analysis unit analyzes the user's work history and skill set to identify the fields in which the user can excel. The suggestion unit proposes matches based on the analysis results obtained by the analysis unit. Suggestions are made based on criteria such as shared hobbies or complementary skills, but are not limited to such examples. For instance, the suggestion unit may suggest other users who share the same hobbies as the user. The suggestion unit may also suggest partners or team members who complement the user's skills and experience. For example, the suggestion unit may suggest other users who possess skills that the user lacks. This enables the matching suggestion system according to the embodiment to efficiently register, analyze, and suggest matching information for users. Some or all of the above-described processes in the registration unit, analysis unit, and suggestion unit may be performed using AI, or not, for example. For instance, the registration unit inputs information entered by the user into the AI, and the AI ​​stores the information in a database. The analysis unit can analyze the user's information using AI, and the suggestion unit can suggest matches using AI.

[0059] The registration section registers user profile information. This profile information includes, but is not limited to, name, age, occupation, and hobbies. The registration section also stores user-entered information in a database. Specifically, information entered by users through web forms or applications is stored in a secure database. The database is encrypted to protect user privacy and can only be viewed and edited by authorized users. Furthermore, the registration section can overwrite existing information when users update their profile. For example, if a user adds a new hobby, that information is added to the database. This ensures that users always have the most up-to-date information, enabling accurate matching. Additionally, the registration section handles user requests to delete their profile information, providing a function to completely delete data upon user request. This allows users to freely manage their own information and use the system with peace of mind.

[0060] The Analysis Department analyzes information registered by the Registration Department. This analysis is performed using, but is not limited to, statistical analysis or machine learning algorithms. Specifically, statistical analysis aggregates user age groups, occupational distribution, and hobby trends to understand overall trends. When using machine learning algorithms, it identifies user interests and goals based on information such as hobbies, activity ranges, personality, and preferences. For example, clustering algorithms can be used to group users with similar hobbies and interests. The Analysis Department can also identify users' areas of expertise and strengths based on information such as skills, experience, and industry knowledge. For example, natural language processing technology can be used to analyze a user's work history and skill set to identify areas where they can excel. Furthermore, the Analysis Department can learn user preferences and behavioral patterns based on past behavioral history and feedback, enabling more accurate analysis. This allows the Analysis Department to comprehensively analyze diverse user information and provide a foundation for making optimal matching suggestions for individual users.

[0061] The Proposal Department proposes matches based on the analysis results obtained by the Analysis Department. These proposals are based on criteria such as shared hobbies and complementary skills, but are not limited to these examples. Specifically, the Proposal Department can suggest other users with similar hobbies. For example, a user who enjoys outdoor activities can be suggested other users who also enjoy outdoor activities. The Proposal Department can also suggest partners or team members who complement the user's skills and experience. For example, a user with programming skills can be suggested a user with design skills, enabling them to collaborate on projects. Furthermore, the Proposal Department can use AI to make these matching suggestions. The AI ​​learns from the user's profile information and past matching history to build an algorithm that proposes the optimal match. This allows the Proposal Department to provide highly accurate matching suggestions that meet the diverse needs of users. The Proposal Department can also collect user feedback and continuously improve the accuracy of its suggestion algorithm. This allows the Proposal Department to always utilize the latest information and technology to provide users with the best possible matching suggestions.

[0062] The suggestion unit can perform matching by considering factors such as hobbies, activity range, personality, and preferences. For example, based on the user's hobbies and activity range, the suggestion unit can suggest other users with similar hobbies. For example, if the user enjoys outdoor activities, the suggestion unit can suggest other users who also enjoy outdoor activities. The suggestion unit can also suggest compatible users based on the user's personality and preferences. For example, if the user has an introverted personality, the suggestion unit can suggest other users with a similar introverted personality. The suggestion unit can also suggest users with common interests based on the user's preferences. For example, if the user enjoys a particular music genre, the suggestion unit can suggest other users who enjoy the same music genre. This enables matching based on the user's hobbies and personality. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input information about the user's hobbies and personality into an AI, which can then suggest matches.

[0063] The proposal team can suggest partners and team members who can build mutually complementary relationships based on skills, experience, and industry knowledge. For example, based on the user's skills and experience, the proposal team can suggest other users with complementary skills. For instance, if the user has programming skills, the proposal team can suggest other users with design skills. The proposal team can also suggest other users working in the same industry based on the user's industry knowledge. For example, if the user works in the IT industry, the proposal team can suggest other users working in the same IT industry. The proposal team can also suggest other users with similar experience based on the user's experience. For example, if the user has project management experience, the proposal team can suggest other users with project management experience. This makes it possible to suggest partners and team members based on skills and experience. Some or all of the above processes in the proposal team may be performed using AI, or not. For example, the proposal team can input information about the user's skills and experience into an AI, which can then suggest matches.

[0064] The suggestion department can find individuals who share common work goals and visions. For example, based on a user's work goals and vision, the suggestion department can suggest other users with the same goals and visions. For instance, if a user's goal is to successfully complete a specific project, the suggestion department can suggest other users with the same goal. The suggestion department can also suggest other users with a shared vision based on the user's vision. For example, if a user aims for sustainable development, the suggestion department can suggest other users with the same vision. This makes it possible to suggest individuals who share common work goals and visions. Some or all of the above processes in the suggestion department may be performed using AI, or not. For example, the suggestion department can input information about the user's work goals and visions into an AI, which can then suggest matches.

[0065] The suggestion unit can learn from user reactions and evaluations and reflect them in future suggestions. For example, the suggestion unit learns from user evaluations of relationships with suggested partners or colleagues. For example, the suggestion unit evaluates a user's relationship with a suggested friend and uses that evaluation to improve future suggestions. The suggestion unit can also improve the accuracy of suggestions based on user reactions. For example, the suggestion unit evaluates a user's relationship with a suggested colleague or partner and uses that evaluation to improve future suggestions. In this way, the accuracy of suggestions improves by learning from user reactions and evaluations. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input user evaluation data into AI, which can learn the evaluations and reflect them in future suggestions.

[0066] The registration unit can estimate the user's emotions and adjust the method of inputting profile information based on the estimated emotions. For example, if the user is stressed, the registration unit can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the registration unit can provide detailed input options and suggest a customizable input method. The registration unit can also prioritize voice input if the user is in a hurry, allowing for quick input of profile information. For example, when the user inputs profile information using voice input, the registration unit can use speech recognition technology to convert it into text data. This allows for adjustment of the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not using AI. For example, the registration unit can input the user's emotion data into an AI, which can estimate the emotions and adjust the input method.

[0067] The registration unit can analyze the user's past registration information and suggest the optimal input format. For example, the registration unit can automatically display information that the user has frequently entered in the past as a suggestion. For example, the registration unit can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The registration unit can also predict and suggest information that the user will use at a specific time of day based on the user's past registration information. For example, the registration unit can suggest information related to the same time period based on information the user has entered at a specific time of day in the past. This makes it possible to suggest the optimal input format based on past registration information. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's past registration information into AI, and the AI ​​can suggest the optimal input format.

[0068] The registration unit can customize input fields based on the user's current lifestyle and areas of interest when registering profile information. For example, the registration unit can automatically display relevant input fields based on the user's current occupation or hobbies. For example, the registration unit can customize input fields based on the user's areas of interest and prioritize the input of relevant information. The registration unit can also suggest appropriate input fields according to the user's lifestyle (e.g., student, working professional, retired). For example, if the user is a student, the registration unit will suggest input fields related to academics. This allows for the customization of input fields based on lifestyle and areas of interest. Some or all of the above processes in the registration unit may be performed using AI, or not. For example, the registration unit can input information about the user's lifestyle and areas of interest into the AI, which can then customize the input fields.

[0069] The registration unit can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is stressed, the registration unit will prioritize the input of important information and postpone other information. For example, if the user is relaxed, the registration unit will prompt the user to enter detailed information. Also, if the user is in a hurry, the registration unit can ask the user to enter only the most important information. For example, if the user is in a hurry, the registration unit will use voice input to quickly enter important information. This makes it possible to determine the priority of information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the registration unit may be performed using AI or not using AI. For example, the registration unit can input user emotion data into an AI, which can estimate the emotions and determine the priority of the information to be entered.

[0070] The registration unit can prioritize inputting highly relevant information by considering the user's geographical location when registering profile information. For example, the registration unit can prioritize inputting region-related information based on the user's current location. For example, if the user is traveling, the registration unit can prioritize inputting information related to their travel destination. The registration unit can also input information related to local events and activities based on the user's place of residence. For example, if the user lives in a specific region, the registration unit can prioritize inputting event information for that region. This enables the input of highly relevant information based on geographical location information. Some or all of the above processing in the registration unit may be performed using AI, for example, or not using AI. For example, the registration unit can input the user's geographical location information into AI, which can then prioritize inputting highly relevant information.

[0071] The registration unit can analyze a user's social media activity and automatically input relevant information when registering profile information. For example, the registration unit can analyze the content of a user's social media posts and automatically input information related to their hobbies and areas of interest. For example, the registration unit can analyze a user's social media friendships and input information related to friends with similar hobbies and activities. The registration unit can also refer to a user's social media profile information and automatically input basic information. For example, the registration unit can automatically input basic information such as name and age based on the user's social media profile information. This enables the automatic input of information based on social media activity. Some or all of the above processing in the registration unit may be performed using AI, for example, or not. For example, the registration unit can input data on the user's social media activity into AI, and the AI ​​can automatically input relevant information.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more information. For example, if the user is stressed, the analysis unit can perform a concise analysis and provide only the essential information. The analysis unit can also perform a rapid analysis and prioritize providing the most important information if the user is in a hurry. For example, if the user is in a hurry, the analysis unit can provide a concise report and highlight only the key points. This allows for the adjustment of the analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into AI, which can estimate emotions and adjust the analysis algorithm.

[0073] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavioral history. For example, the analysis unit can improve the accuracy of its analysis by identifying areas of interest based on the user's past behavioral history. For example, the analysis unit can improve the accuracy of its analysis by analyzing the user's past behavioral patterns and predicting future behavior. Furthermore, the analysis unit can improve the accuracy of its analysis by predicting behavior in specific time periods or situations based on the user's past behavioral history. For example, the analysis unit can predict behavior related to the same time period based on the actions the user has taken in the past during a specific time period. This makes it possible to improve the accuracy of analysis based on past behavioral history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past behavioral history into AI, which can then improve the accuracy of its analysis.

[0074] The analysis unit can apply different analysis methods depending on the categories of the user's profile information during analysis. For example, the analysis unit can select an appropriate analysis method based on the user's hobbies and areas of interest. For example, the analysis unit can apply a specialized analysis method based on the user's skills and experience. The analysis unit can also apply a personalized analysis method based on the user's personality and preferences. For example, if the user has an introverted personality, the analysis unit will apply an analysis method suitable for an introverted personality. This makes it possible to apply analysis methods according to the categories of the profile information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the categories of the user's profile information into the AI, and the AI ​​can apply different analysis methods.

[0075] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple and easy-to-read display. For example, if the user is relaxed, the analysis unit provides a display that includes detailed information. The analysis unit can also provide a concise display if the user is in a hurry. For example, if the user is in a hurry, the analysis unit provides a brief report, highlighting only the important points. This allows for adjustment of how the analysis results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into AI, which can estimate emotions and adjust how the analysis results are displayed.

[0076] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information. For example, the analysis unit can prioritize analyzing region-related information based on the user's current location. For example, if the user is traveling, the analysis unit will prioritize analyzing information related to their travel destination. The analysis unit can also analyze information related to local events and activities based on the user's place of residence. For example, if the user lives in a specific region, the analysis unit will prioritize analyzing event information for that region. This makes it possible to improve the accuracy of geographical location-based analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into AI, which can then improve the accuracy of the analysis.

[0077] The analysis unit can improve the accuracy of its analysis by referring to the user's relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by identifying areas of interest based on the literature the user has read in the past. For example, the analysis unit can improve the accuracy of its analysis by analyzing the user's relevant literature and predicting future behavior. Furthermore, the analysis unit can improve the accuracy of its analysis by predicting behavior in specific time periods or situations based on the user's relevant literature. For example, the analysis unit predicts relevant behavior in the same time period based on the literature the user has read in the past. This makes it possible to improve the accuracy of analysis based on relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's relevant literature into AI, which can then improve the accuracy of its analysis.

[0078] The suggestion section can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is tense, the suggestion section may provide a simple and highly visible presentation. For example, if the user is relaxed, the suggestion section may provide a presentation that includes detailed information. The suggestion section may also provide a concise presentation if the user is in a hurry. For example, if the user is in a hurry, the suggestion section may provide a brief report, highlighting only the important points. This allows for adjustment of the presentation of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input user emotion data into an AI, which can estimate the emotion and adjust the presentation of suggestions.

[0079] The suggestion unit can improve the accuracy of its suggestions by referring to the user's past matching history. For example, the suggestion unit can identify areas of interest based on the user's past matching history to improve the accuracy of its suggestions. For example, the suggestion unit can predict the user's behavior at specific times or in specific situations based on their past matching history to improve the accuracy of its suggestions. The suggestion unit can also analyze the user's past matching history to make the most efficient suggestions. For example, the suggestion unit can suggest other users with the same patterns as the user's past successful matching patterns. This makes it possible to improve the accuracy of suggestions based on past matching history. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's past matching history into AI, which can then improve the accuracy of its suggestions.

[0080] The suggestion unit can apply different suggestion algorithms depending on the user's profile information categories when making suggestions. For example, the suggestion unit can select an appropriate suggestion algorithm based on the user's hobbies and areas of interest. For example, the suggestion unit can apply a specialized suggestion algorithm based on the user's skills and experience. The suggestion unit can also apply a personalized suggestion algorithm based on the user's personality and preferences. For example, if the user has an introverted personality, the suggestion unit can apply a suggestion algorithm suitable for an introverted personality. This makes it possible to apply suggestion algorithms according to the profile information categories. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's profile information categories into the AI, and the AI ​​can apply different suggestion algorithms.

[0081] The suggestion section can estimate the user's emotions and prioritize suggestions based on those emotions. For example, if the user is stressed, the suggestion section will prioritize important suggestions and postpone others. For example, if the user is relaxed, the suggestion section will encourage more detailed suggestions. Furthermore, if the user is in a hurry, the suggestion section can provide only the most important suggestions. For example, if the user is in a hurry, the suggestion section will provide a concise report, highlighting only the key points. This allows for the prioritization of suggestions 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. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input user emotion data into an AI, which can estimate the emotions and determine the priority of suggestions.

[0082] The suggestion unit can make optimal suggestions by considering the user's geographical location information. For example, the suggestion unit can prioritize suggestions related to the user's region based on the user's current location. For example, if the user is traveling, the suggestion unit can prioritize suggestions related to the travel destination. The suggestion unit can also make suggestions related to local events and activities based on the user's place of residence. For example, if the user lives in a specific region, the suggestion unit can prioritize suggesting event information for that region. This enables optimal suggestions based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's geographical location information into AI, which can then make optimal suggestions.

[0083] The suggestion unit can analyze the user's social media activity and make relevant suggestions when making suggestions. For example, the suggestion unit can analyze the content of the user's social media posts and make suggestions related to their hobbies and areas of interest. For example, the suggestion unit can analyze the user's social media friendships and make suggestions related to friends with similar hobbies and activities. The suggestion unit can also refer to the user's social media profile information and make relevant suggestions. For example, the suggestion unit can suggest other users with similar hobbies based on the user's social media profile information. This makes it possible to make relevant suggestions based on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input data on the user's social media activity into AI, and the AI ​​can make relevant suggestions.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can select a time to provide detailed suggestions. For example, if the user is stressed, the suggestion unit can refrain from making suggestions and wait until the user is relaxed. The suggestion unit can also provide concise suggestions quickly if the user is in a hurry. For example, if the user is in a hurry, the suggestion unit can provide suggestions that emphasize only the important points. This allows for adjustment of the timing of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can estimate the emotions and adjust the timing of suggestions.

[0086] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. For example, if the user is stressed, the suggestion unit will provide concise and to-the-point suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions containing only the most important information. For example, if the user is in a hurry, the suggestion unit will provide a concise report highlighting only the key points. This allows for the adjustment of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can estimate the emotions and adjust the suggestions.

[0087] The suggestion unit can estimate the user's emotions and adjust the frequency of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit can increase the frequency of suggestions. For example, if the user is stressed, the suggestion unit can decrease the frequency of suggestions and make suggestions when the user is relaxed. The suggestion unit can also make only important suggestions if the user is in a hurry. For example, if the user is in a hurry, the suggestion unit can quickly make concise suggestions. This makes it possible to adjust the frequency of suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into AI, which can estimate emotions and adjust the frequency of suggestions.

[0088] The suggestion section can estimate the user's emotions and adjust the format of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion section may provide suggestions in a detailed report format. For example, if the user is stressed, the suggestion section may provide suggestions in a concise bulleted list format. Furthermore, if the user is in a hurry, the suggestion section may provide suggestions in a short, to-the-point message format. For example, if the user is in a hurry, the suggestion section may provide a short message highlighting only the important points. This allows for adjustment of the suggestion format according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input user emotion data into an AI, which can estimate the emotion and adjust the suggestion format.

[0089] The suggestion unit can estimate the user's emotions and personalize the content of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide suggestions with detailed information. For example, if the user is stressed, the suggestion unit will provide concise and to-the-point suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions containing only the most important information. For example, if the user is in a hurry, the suggestion unit will provide a concise report highlighting only the key points. This enables the personalization of suggestions 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. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI, which can estimate the emotions and personalize the suggestions.

[0090] The suggestion unit can analyze a user's past matching history to improve the accuracy of its suggestions. For example, it can suggest other users with similar patterns to those of successful matches the user has had in the past. For example, it can predict a user's behavior at specific times or in specific situations based on their past matching history to improve the accuracy of its suggestions. The suggestion unit can also identify a user's interests and areas of concern based on their past matching history to improve the accuracy of its suggestions. For example, it can suggest other users with similar patterns to those of successful matches the user has had in the past. This makes it possible to improve the accuracy of suggestions based on past matching history. Some or all of the above processes in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the user's past matching history into an AI, which can then improve the accuracy of its suggestions.

[0091] The suggestion unit can apply different suggestion algorithms depending on the categories of the user's profile information. For example, the suggestion unit can select an appropriate suggestion algorithm based on the user's hobbies and areas of interest. For example, the suggestion unit can apply a specialized suggestion algorithm based on the user's skills and experience. The suggestion unit can also apply a personalized suggestion algorithm based on the user's personality and preferences. For example, if the user has an introverted personality, the suggestion unit can apply a suggestion algorithm suited to an introverted personality. This makes it possible to apply suggestion algorithms according to the categories of the profile information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the categories of the user's profile information into the AI, and the AI ​​can apply different suggestion algorithms.

[0092] The suggestion unit can make optimal suggestions by taking into account the user's geographical location. For example, the suggestion unit can prioritize suggestions related to the user's region based on the user's current location. For example, if the user is traveling, the suggestion unit can prioritize suggestions related to the travel destination. The suggestion unit can also make suggestions related to local events and activities based on the user's place of residence. For example, if the user lives in a specific region, the suggestion unit can prioritize suggesting event information for that region. This enables optimal suggestions based on geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's geographical location information into AI, which can then make optimal suggestions.

[0093] The suggestion unit can analyze a user's social media activity and make relevant suggestions. For example, the suggestion unit can analyze a user's social media posts and make suggestions related to their hobbies and areas of interest. For example, the suggestion unit can analyze a user's social media friendships and make suggestions related to friends with similar hobbies and activities. The suggestion unit can also refer to a user's social media profile information and make relevant suggestions. For example, based on a user's social media profile information, the suggestion unit can suggest other users with similar hobbies. This enables relevant suggestions based on social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input data on the user's social media activity into an AI, which can then make relevant suggestions.

[0094] The suggestion unit can customize suggestions based on the user's current lifestyle and areas of interest. For example, the suggestion unit can automatically display relevant suggestions based on the user's current occupation or hobbies. For example, the suggestion unit can customize suggestions based on the user's areas of interest and prioritize relevant information. The suggestion unit can also provide appropriate suggestions according to the user's lifestyle (e.g., student, working professional, retired). For example, if the user is a student, the suggestion unit will provide suggestions related to academics. This allows for the customization of suggestions based on lifestyle and areas of interest. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input information about the user's lifestyle and areas of interest into the AI, which can then customize the suggestions.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The registration unit registers the user's profile information. This profile information includes name, age, occupation, hobbies, etc. The registration unit saves the information entered by the user to a database and can overwrite existing information when the user updates their profile. For example, if a user adds a new hobby, that information is added to the database. Step 2: The analysis department analyzes the information registered by the registration department. The analysis is performed using statistical analysis and machine learning algorithms. For example, it identifies the user's interests and goals based on information such as the user's hobbies, range of activities, personality, and preferences. It can also identify the user's areas of expertise and strengths based on information such as the user's skills, experience, and industry knowledge. Step 3: The proposal team proposes matches based on the analysis results obtained by the analysis team. Proposals are made based on criteria such as common hobbies and complementary skills. For example, they may propose other users who share the same hobbies as the user, or partners or team members who complement the user's skills and experience.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the registration unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart device 14 and stores the user's entered profile information in the database 24. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the registered information using statistical analysis and machine learning algorithms. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a match based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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 (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 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the registration unit, analysis unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the smart glasses 214 and stores the user's entered profile information in the database 24. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the registered information using statistical analysis and machine learning algorithms. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a match based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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 (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 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the registration unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the headset terminal 314 and stores the user's entered profile information in the database 24. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the registered information using statistical analysis and machine learning algorithms. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a match based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0147] 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 (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 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.

[0148] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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 external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0149] Each of the multiple elements described above, including the registration unit, analysis unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the registration unit is implemented by the control unit 46A of the robot 414 and stores the user-entered profile information in the database 24. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the registered information using statistical analysis and machine learning algorithms. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12 and proposes a match based on the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A registration section for registering user profile information, An analysis unit analyzes the information registered by the registration unit, The system includes a proposal unit that proposes a match based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned proposal section is, Matching is done by considering factors such as hobbies, activities, personality, and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose partners and team members who can build mutually complementary relationships based on their skills, experience, and industry knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Find someone who shares your work goals and vision. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Learn from user reactions and feedback, and incorporate them into future suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned registration unit is It estimates the user's emotions and adjusts how profile information is entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned registration unit is It analyzes the user's past registration information and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned registration unit is When registering profile information, input fields are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned registration unit is It estimates the user's emotions and prioritizes the information to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned registration unit is When registering profile information, the system prioritizes input of highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned registration unit is When registering profile information, the system analyzes the user's social media activity and automatically fills in relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis algorithm based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, we improve the accuracy of the analysis by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical methods are applied depending on the category of the user's profile information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During analysis, the accuracy of the analysis is improved by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, we refer to relevant user literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making suggestions, we improve the accuracy of suggestions by referring to the user's past matching history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the category of the user's profile information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and make relevant suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A registration section for registering user profile information, An analysis unit analyzes the information registered by the registration unit, The system includes a proposal unit that proposes a match based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned proposal section is, Matching is done by considering factors such as hobbies, activities, personality, and preferences. The system according to feature 1.

3. The aforementioned proposal section is, We propose partners and team members who can build mutually complementary relationships based on their skills, experience, and industry knowledge. The system according to feature 1.

4. The aforementioned proposal section is, Find someone who shares your work goals and vision. The system according to feature 1.

5. The aforementioned proposal section is, Learn from user reactions and feedback, and incorporate them into future suggestions. The system according to feature 1.

6. The aforementioned registration unit is It estimates the user's emotions and adjusts how profile information is entered based on those estimated emotions. The system according to feature 1.

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

8. The aforementioned registration unit is When registering profile information, input fields are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned registration unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system according to feature 1.

10. The aforementioned registration unit is When registering profile information, the system prioritizes input of highly relevant information, taking into account the user's geographical location. The system according to feature 1.

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