System

Abstract

To provide a system that effectively match between users with specialized knowledge and users with dreams or goals.SOLUTION: A system according to the embodiment comprises a first reception unit, a second reception unit, and a matching unit. The first reception unit receives registration of information about a plurality of first users each having specialized knowledge. The second reception unit receives goals or dreams that a plurality of second users each wants to achieve. The matching unit matches the first users and the second users based on the information accepted by the first reception unit and the information accepted by the second reception unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not effectively match users with specialized knowledge with users with dreams and goals, and there is room for improvement.

[0005] The system according to the embodiment aims to perform effective matching between users with specialized knowledge and users with dreams and goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a first reception unit, a second reception unit, and a matching unit. The first reception unit receives registration of information of a plurality of first users each having specialized knowledge. The second reception unit receives a goal or dream that each of the plurality of second users wants to achieve. The matching unit performs matching between the first user and the second user based on the information received by the first reception unit and the information received by the second reception unit. Effect of the Invention

[0007] The system according to the embodiment can perform effective matching between users with specialized knowledge and users with dreams and goals. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

[0017] 1, a 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. The database 24 and the communication I / F 26 are also 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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The system according to the embodiment of the present invention is a system that matches people's "desire to teach" with "realizing their dreams." This system builds a platform where users with specialized knowledge can register their own experiences and knowledge, and is based on a huge amount of information. Users describe their dreams and goals that they want to achieve, and AI selects the optimal means and precedents from the huge amount of information and receives suggestions. If AI determines that it is desirable for a registrant and a user to contact each other, it also acts as an intermediary. For example, multiple first users with specialized knowledge register their experiences and knowledge on the platform. This information is accepted by the first reception unit. For example, if a first user has knowledge of programming, he or she registers that knowledge and experience on the platform. Next, multiple second users describe the goals and dreams that they want to achieve on the platform. This information is accepted by the second reception unit. For example, if a second user has a goal of "I want to learn programming," he or she describes that goal on the platform. Based on the information accepted by the first reception unit and the second reception unit, the matching unit performs matching between the first user and the second user. For example, a first user with knowledge of programming is matched with a second user who wants to learn programming. Furthermore, the matching unit uses AI to select and present the optimal means and precedents. For example, AI analyzes past success cases and suggests the optimal means for learning programming to the second user. It also has a communication unit that supports communication between the matched first and second users. For example, it provides chat and video calling functions to enable users to communicate smoothly with each other. This system efficiently matches users with specialized knowledge and users with dreams and goals, allowing them to help each other. For example, a second user who wants to learn programming can receive direct guidance from a first user who has programming knowledge. In addition, AI suggests the optimal means and precedents, allowing users to efficiently achieve their goals. As a result, the system efficiently matches users with specialized knowledge and users with goals, allowing them to help each other.

[0029] The matching system according to the embodiment includes a first reception unit, a second reception unit, and a matching unit. The first reception unit receives registration of information of a plurality of first users each having specialized knowledge. The first reception unit, for example, enables a user with specialized knowledge to register his / her own experience and knowledge on the platform. For example, if a first user has knowledge of programming, the knowledge and experience can be registered on the platform. The second reception unit receives goals or dreams that each of a plurality of second users wants to achieve. The second reception unit, for example, enables a user to describe the dreams and goals that the user wants to achieve on the platform. For example, if a second user has a goal of "wanting to learn programming," the goal can be described on the platform. The matching unit performs matching between the first user and the second user based on the information received by the first reception unit and the information received by the second reception unit. The matching unit, for example, matches a first user who has knowledge of programming with a second user who wants to learn programming. The matching unit uses AI to select and present optimal means and precedents. For example, the AI ​​analyzes past success cases and suggests optimal ways for the second user to learn programming. This allows the matching system according to the embodiment to efficiently match users with specialized knowledge and users with goals.

[0030] The first reception unit accepts registration of information of a plurality of first users each having specialized knowledge. The first reception unit, for example, enables users with specialized knowledge to register their own experience and knowledge on the platform. Specifically, the users can enter detailed information such as their field of expertise, years of experience, past projects, and acquired qualifications through a dedicated registration form. For example, a user with programming knowledge can describe in detail the programming languages ​​they can use, the development environment, and the contents of projects they have worked on in the past. In addition, the users can upload self-introductions and profile photos, making it easier for other users to understand the person's background and expertise. Furthermore, the first reception unit has a function of automatically classifying the registered information and storing it in a database. This allows the information to be searched and referenced quickly and accurately in the subsequent matching process. For example, when a user with programming knowledge is registered, the information is classified into the "programming" category and is also tagged with other related categories (e.g., web development, mobile app development, etc.) as necessary. This allows the first reception unit to efficiently manage information of users with specialized knowledge and provide a foundation for the matching unit to perform optimal matching.

[0031] The second reception unit receives the goals or dreams that each of the multiple second users wants to achieve. The second reception unit, for example, enables the user to describe the dreams or goals that the user wants to achieve on the platform. Specifically, the user can describe in detail his or her goals or dreams, the deadline to achieve them, the support content required, etc. through a dedicated input form. For example, a user who has the goal of "I want to learn programming" can input the programming language he or she wants to learn, the desired learning style (e.g., online courses, individual instruction, etc.), past learning experience, etc. In addition, the user can also set specific steps and intermediate goals for achieving the goal, which makes it easier to visualize the progress of goal achievement. In addition, the second reception unit has a function of automatically classifying the registered goals and dreams and storing them in a database. This allows the information to be searched and referenced quickly and accurately in the subsequent matching process. For example, if the goal of "I want to learn programming" is registered, the information is classified into the "programming learning" category and is also tagged with other related categories (e.g., web development learning, mobile app development learning, etc.) as necessary. This enables the second reception unit to efficiently manage information on users who have goals, and provides a foundation for the matching unit to perform optimal matching.

[0032] The matching unit performs matching between the first user and the second user based on the information received by the first reception unit and the information received by the second reception unit. For example, the matching unit matches a first user who has programming knowledge with a second user who wants to learn programming. Specifically, the matching unit uses AI to analyze the information of both users and achieve optimal matching. The AI ​​builds and executes an optimal matching algorithm based on past success cases and user feedback. For example, the AI ​​considers the depth of the first user's expertise and years of experience, the second user's learning style and deadline for achieving goals, and proposes the most suitable combination. The AI ​​can also analyze the communication style and personality traits of users and match users who are compatible with each other. This allows the matching unit to achieve high-precision matching that takes into account not only the matching of knowledge and goals, but also the compatibility and smoothness of communication between users. In addition, the matching unit also has a follow-up function after matching, and supports the user's process until they achieve their goal. For example, the progress can be checked periodically and additional support and resources can be provided as necessary. This enables the matching unit to provide comprehensive support for users to achieve their goals and maximize the effectiveness of the entire system.

[0033] The matching system includes a communication unit that supports communication between a first user and a second user matched by the matching unit. The communication unit supports communication between the first user and the second user matched by the matching unit. The communication unit provides, for example, a chat function or a video call function, enabling users to communicate smoothly with each other. For example, the communication unit enables users to exchange messages in real time using the chat function. In addition, the communication unit enables users to communicate while seeing each other's faces using the video call function. This allows the matching system to smoothly communicate between matched users.

[0034] The matching system includes a presentation unit in which the matching unit uses AI to select and present appropriate means and precedents. The presentation unit selects and presents appropriate means and precedents using AI. For example, the presentation unit uses AI to analyze past success cases and propose optimal means for learning programming to the second user. For example, the presentation unit selects and presents optimal means for learning programming based on past success cases. Also, the presentation unit selects and presents optimal precedents for learning programming based on past success cases. In this way, the matching system can present optimal means and precedents to the user by using AI.

[0035] The first reception unit analyzes the user's past registration history when registering the specialized knowledge, and selects an appropriate registration method. The first reception unit analyzes the user's past registration history when registering the specialized knowledge, and selects an appropriate registration method. For example, the first reception unit proposes registration in a similar format based on the format of the specialized knowledge registered by the user in the past. The first reception unit can also propose registration with a similar level of detail based on the level of detail of the content registered by the user in the past. The first reception unit can also propose registration in a similar time period based on the time period registered by the user in the past. In this way, the first reception unit can propose an optimal registration method by analyzing the past registration history. A part or all of the above-mentioned processing in the first reception unit may be performed, for example, using AI, or may be performed without using AI.

[0036] The first reception unit performs filtering based on the user's current occupation and field of interest when registering the specialized knowledge. The first reception unit performs filtering based on the user's current occupation and field of interest when registering the specialized knowledge. For example, the first reception unit preferentially registers specialized knowledge related to the user's occupation. The first reception unit can also preferentially register specialized knowledge related to the user's field of interest. The first reception unit can also preferentially register specialized knowledge related to both the user's occupation and field of interest. Thereby, the first reception unit can preferentially register highly relevant knowledge by filtering the specialized knowledge based on the user's occupation and field of interest. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, AI, or may be performed without using AI.

[0037] The first reception unit preferentially registers highly relevant knowledge in consideration of the geographical location information of the user when registering the specialized knowledge. The first reception unit preferentially registers highly relevant knowledge in consideration of the geographical location information of the user when registering the specialized knowledge. For example, the first reception unit preferentially registers specialized knowledge related to the user's current location. The first reception unit can also register highly relevant specialized knowledge based on the user's past movement history. The first reception unit can also register highly relevant specialized knowledge based on the user's future movement schedule. Thereby, the first reception unit can preferentially register highly relevant specialized knowledge by considering the geographical location information of the user. A part or all of the above-mentioned processing in the first reception unit may be performed, for example, using AI or may be performed without using AI.

[0038] The first reception unit analyzes the social media activity of the user at the time of registering the expertise, and registers the related knowledge. The first reception unit analyzes the social media activity of the user at the time of registering the expertise, and registers the related knowledge. For example, the first reception unit registers the related expertise based on the content shared by the user on social media. The first reception unit can also register the related expertise based on the accounts the user follows on social media. The first reception unit can also register the related expertise based on the groups the user participates in on social media. In this way, the first reception unit can register the related expertise by analyzing the social media activity. A part or all of the above-mentioned processing in the first reception unit may be performed, for example, using AI, or may be performed without using AI.

[0039] The second reception unit analyzes the user's past writing history when describing a goal or a dream, and selects the optimal writing method. The second reception unit analyzes the user's past writing history when describing a goal or a dream, and selects the optimal writing method. For example, the second reception unit proposes writing in a similar format based on the format of the goal or dream written by the user in the past. The second reception unit can also propose writing with a similar level of detail based on the level of detail of the content written by the user in the past. The second reception unit can also propose writing in a similar time period based on the time period written by the user in the past. As a result, the second reception unit can propose the optimal writing method by analyzing the past writing history. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The second reception unit filters the goals and dreams based on the user's current living situation and field of interest when describing the goals and dreams. The second reception unit filters the goals and dreams based on the user's current living situation and field of interest when describing the goals and dreams. For example, the second reception unit preferentially describes goals and dreams related to the user's current occupation. The second reception unit can also preferentially describe goals and dreams related to the user's field of interest. The second reception unit can also preferentially describe goals and dreams related to both the user's living situation and field of interest. As a result, the second reception unit can preferentially describe highly relevant goals and dreams by filtering the goals and dreams based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the second reception unit may be performed using AI, for example, or may be performed without using AI.

[0041] The second reception unit, when describing a goal or a dream, preferentially describes a highly relevant goal or dream in consideration of the geographical location information of the user. When describing a goal or a dream, the second reception unit, when describing a goal or a dream, preferentially describes a highly relevant goal or dream in consideration of the geographical location information of the user. For example, the second reception unit preferentially describes a goal or a dream related to the user's current location. The second reception unit can also describe a highly relevant goal or dream based on the user's past movement history. The second reception unit can also describe a highly relevant goal or dream based on the user's future movement schedule. Thereby, the second reception unit can preferentially describe a highly relevant goal or dream by considering the geographical location information of the user. A part or all of the above-mentioned processing in the second reception unit may be performed, for example, using AI, or may be performed without using AI.

[0042] The second reception unit analyzes the user's social media activity when describing the goal or dream, and describes the related goal or dream. The second reception unit analyzes the user's social media activity when describing the goal or dream, and describes the related goal or dream. For example, the second reception unit describes the related goal or dream based on the content shared by the user on social media. The second reception unit can also describe the related goal or dream based on the account the user follows on social media. The second reception unit can also describe the related goal or dream based on the group the user participates in on social media. In this way, the second reception unit can describe the related goal or dream by analyzing the social media activity. A part or all of the above-mentioned processing in the second reception unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The matching unit improves the accuracy of matching by considering the interrelationship between the specialized knowledge and the goal or dream during matching. The matching unit improves the accuracy of matching by considering the interrelationship between the specialized knowledge and the goal or dream during matching. For example, the matching unit preferentially matches when the contents of the specialized knowledge and the goal or dream match. The matching unit can also preferentially match when the specialized knowledge and the goal or dream are highly related. The matching unit can also analyze the interrelationship between the specialized knowledge and the goal or dream and perform optimal matching. Thereby, the matching unit can improve the accuracy of matching by considering the interrelationship between the specialized knowledge and the goal or dream. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI.

[0044] The matching unit performs matching in consideration of the attribute information of the user at the time of matching. The matching unit performs matching in consideration of the attribute information of the user at the time of matching. For example, the matching unit performs matching based on the age and sex of the user. The matching unit can also perform matching based on the occupation and field of interest of the user. The matching unit can also perform matching based on the geographical location information of the user. Thereby, the matching unit can perform more appropriate matching by considering the attribute information of the user. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI.

[0045] The matching unit performs matching taking into consideration the geographical distribution of the specialized knowledge and the goals or dreams. The matching unit performs matching taking into consideration the geographical distribution of the specialized knowledge and the goals or dreams. For example, the matching unit performs matching preferentially when the geographical distribution of the specialized knowledge and the goals or dreams coincides. The matching unit can also perform matching preferentially when the geographical distribution of the specialized knowledge and the goals or dreams is close. The matching unit can also analyze the geographical distribution of the specialized knowledge and the goals or dreams and perform optimal matching. This allows the matching unit to perform more appropriate matching by taking into consideration the geographical distribution of the specialized knowledge and the goals or dreams. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI.

[0046] The matching unit improves the accuracy of matching by referring to related literature during matching. The matching unit improves the accuracy of matching by referring to related literature during matching. For example, the matching unit evaluates the relevance between specialized knowledge and goals or dreams based on related literature. The matching unit can also set optimal matching criteria based on related literature. The matching unit can also improve the accuracy of matching based on related literature. Thereby, the matching unit can improve the accuracy of matching by referring to related literature. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The communication unit selects the optimal method by referring to the user's past communication history when communicating. The communication unit selects the optimal method by referring to the user's past communication history when communicating. For example, the communication unit preferentially suggests communication means (chat, video call, etc.) that the user has used in the past. The communication unit can also suggest a similar method based on a communication method that the user has used successfully in the past. The communication unit can also suggest communication at the optimal timing based on the user's past communication history. In this way, the communication unit can suggest the optimal communication method by referring to the past communication history. A part or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The communication unit selects the optimal method in consideration of the device information of the user when communicating. The communication unit selects the optimal method in consideration of the device information of the user when communicating. For example, when the user is using a smartphone, the communication unit suggests chat or video calling. In addition, when the user is using a tablet, the communication unit can also suggest video calling that makes use of the large screen. In addition, when the user is using a desktop, the communication unit can also suggest screen sharing that allows detailed information to be shared. In this way, the communication unit can suggest the optimal communication method by considering the device information of the user. A part or all of the above-mentioned processing in the communication unit may be performed, for example, using AI, or may be performed without using AI.

[0049] The presentation unit improves the accuracy of presentation by considering the interrelationship between the specialized knowledge and the goal or dream at the time of presentation. The presentation unit improves the accuracy of presentation by considering the interrelationship between the specialized knowledge and the goal or dream at the time of presentation. For example, the presentation unit preferentially presents the specialized knowledge when the contents of the goal or dream match. In addition, the presentation unit can also preferentially present the specialized knowledge when the specialized knowledge is highly related to the goal or dream. In addition, the presentation unit can analyze the interrelationship between the specialized knowledge and the goal or dream and perform optimal presentation. Thereby, the presentation unit can improve the accuracy of presentation by considering the interrelationship between the specialized knowledge and the goal or dream. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0050] The presentation unit presents the information taking into consideration the attribute information of the user when presenting the information. The presentation unit presents the information taking into consideration the attribute information of the user when presenting the information. For example, the presentation unit presents the information based on the age and sex of the user. The presentation unit can also present the information based on the occupation and field of interest of the user. The presentation unit can also present the information based on the geographical location information of the user. This allows the presentation unit to present the information more appropriately by taking into consideration the attribute information of the user. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI.

[0051] The presentation unit presents the expert knowledge and the geographical distribution of the goal or dream in consideration of the expert knowledge. The presentation unit presents the expert knowledge and the geographical distribution of the goal or dream in consideration of the expert knowledge. For example, the presentation unit presents the expert knowledge with priority when the geographical distribution of the goal or dream matches. The presentation unit can also present the expert knowledge with priority when the geographical distribution of the goal or dream is close. The presentation unit can also analyze the geographical distribution of the expert knowledge and the goal or dream and present the optimal expert knowledge. This allows the presentation unit to present the expert knowledge and the goal or dream in a more appropriate manner by considering the geographical distribution of the expert knowledge and the goal or dream. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0052] The presentation unit improves the accuracy of presentation by referring to related literature at the time of presentation. The presentation unit improves the accuracy of presentation by referring to related literature at the time of presentation. For example, the presentation unit evaluates the relevance of specialized knowledge to goals or dreams based on related literature. The presentation unit can also set optimal presentation criteria based on related literature. The presentation unit can also improve the accuracy of presentation based on related literature. Thereby, the presentation unit can improve the accuracy of presentation by referring to related literature. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The matching system can also analyze the user's past matching history and select the optimal matching method. For example, based on past successful matching patterns, matching with a similar pattern can be suggested. Also, by avoiding matching patterns that have failed in the past, matching can be performed with a higher success rate. Furthermore, if the user's past matching history shows that a certain time period or day of the week has a high success rate, matching can be performed with that time period or day of the week. In this way, the matching system can utilize past data to provide more accurate matching.

[0055] The communication unit can also analyze the user's communication style and suggest the optimal communication method. For example, if the user prefers text-based communication, the chat function can be provided preferentially. Also, if the user prefers video calls, the video call function can be provided preferentially. Furthermore, if the user prefers voice calls, the voice call function can be provided. In this way, the communication unit can provide the optimal communication method according to the user's preferences.

[0056] The presentation unit can also analyze the user's learning style and suggest the most suitable learning method. For example, if the user prefers visual learning, a learning method using videos and illustrations can be provided. If the user prefers auditory learning, a learning method using audio guides and podcasts can be provided. Furthermore, if the user prefers practical learning, a learning method through actual projects and exercises can be provided. In this way, the presentation unit can provide the most suitable learning method according to the user's learning style.

[0057] The first reception unit can also adjust the registration contents in consideration of the user's current skill level when registering specialized knowledge. For example, a novice user is prompted to register basic specialized knowledge, and an advanced user is prompted to register advanced specialized knowledge. In addition, the level of detail of the registration contents can be adjusted according to the user's skill level. This allows the first reception unit to provide optimal registration contents according to the user's skill level.

[0058] When registering expertise, the first reception unit can also adjust the registration content taking into account the user's past feedback. For example, based on a format of expertise that has been highly rated in the past, the first reception unit can suggest registration in a similar format. Also, by avoiding a format of expertise that has been poorly rated in the past, it is possible to provide registration content that will receive a higher rating. Furthermore, it is possible to analyze the user's past feedback and suggest improvements to the registration content. In this way, the first reception unit can encourage the registration of higher quality expertise by utilizing past feedback.

[0059] The first reception unit can also adjust the registration contents in consideration of the user's future goals when registering the specialized knowledge. For example, if the user has a future career goal, the first reception unit can encourage the user to register specialized knowledge related to the goal. Also, if the user is aiming at a specific project, the first reception unit can encourage the user to register specialized knowledge related to the project. Furthermore, the first reception unit can determine the priority of the registration contents based on the user's future goal. This allows the first reception unit to provide the optimal registration contents according to the user's future goal.

[0060] The first reception unit can also adjust the registration contents in consideration of the user's social network when registering expertise. For example, if the user belongs to a specific expert group, the first reception unit can encourage the user to register expertise related to the group. Also, if the user participates in a specific community, the first reception unit can encourage the user to register expertise related to the community. Furthermore, the first reception unit can analyze the user's social network and suggest the registration of highly relevant expertise. This allows the first reception unit to provide optimal registration contents according to the user's social network.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The first reception unit receives registration of information of a plurality of first users each having specialized knowledge. For example, users with specialized knowledge can register their own experience and knowledge on the platform. If a first user has knowledge of programming, that knowledge and experience can be registered on the platform. Step 2: The second reception unit receives the goals or dreams that each of the multiple second users wants to achieve. For example, the users can describe the dreams or goals that they want to achieve on the platform. If a second user has a goal of "learning programming," the user can describe the goal on the platform. Step 3: The matching unit matches the first user with the second user based on the information received by the first reception unit and the information received by the second reception unit. For example, it matches a first user who has programming knowledge with a second user who wants to learn programming. The matching unit uses AI to select and present optimal methods and precedents. For example, the AI ​​analyzes past success stories and suggests optimal methods for learning programming to the second user.

[0063] (Example 2) The system according to the embodiment of the present invention is a system that matches people's "desire to teach" with "realizing their dreams." This system builds a platform where users with specialized knowledge can register their own experiences and knowledge, and is based on a huge amount of information. Users describe their dreams and goals that they want to achieve, and AI selects the optimal means and precedents from the huge amount of information and receives suggestions. If AI determines that it is desirable for a registrant and a user to contact each other, it also acts as an intermediary. For example, multiple first users with specialized knowledge register their experiences and knowledge on the platform. This information is accepted by the first reception unit. For example, if a first user has knowledge of programming, he or she registers that knowledge and experience on the platform. Next, multiple second users describe the goals and dreams that they want to achieve on the platform. This information is accepted by the second reception unit. For example, if a second user has a goal of "I want to learn programming," he or she describes that goal on the platform. Based on the information accepted by the first reception unit and the second reception unit, the matching unit performs matching between the first user and the second user. For example, a first user with knowledge of programming is matched with a second user who wants to learn programming. Furthermore, the matching unit uses AI to select and present the optimal means and precedents. For example, AI analyzes past success cases and suggests the optimal means for learning programming to the second user. It also has a communication unit that supports communication between the matched first and second users. For example, it provides chat and video calling functions to enable users to communicate smoothly with each other. This system efficiently matches users with specialized knowledge and users with dreams and goals, allowing them to help each other. For example, a second user who wants to learn programming can receive direct guidance from a first user who has programming knowledge. In addition, AI suggests the optimal means and precedents, allowing users to efficiently achieve their goals. As a result, the system efficiently matches users with specialized knowledge and users with goals, allowing them to help each other.

[0064] The matching system according to the embodiment includes a first reception unit, a second reception unit, and a matching unit. The first reception unit receives registration of information of a plurality of first users each having specialized knowledge. The first reception unit, for example, enables a user with specialized knowledge to register his / her own experience and knowledge on the platform. For example, if a first user has knowledge of programming, the knowledge and experience can be registered on the platform. The second reception unit receives goals or dreams that each of a plurality of second users wants to achieve. The second reception unit, for example, enables a user to describe the dreams and goals that the user wants to achieve on the platform. For example, if a second user has a goal of "wanting to learn programming," the goal can be described on the platform. The matching unit performs matching between the first user and the second user based on the information received by the first reception unit and the information received by the second reception unit. The matching unit, for example, matches a first user who has knowledge of programming with a second user who wants to learn programming. The matching unit uses AI to select and present optimal means and precedents. For example, the AI ​​analyzes past success cases and suggests optimal ways for the second user to learn programming. This allows the matching system according to the embodiment to efficiently match users with specialized knowledge and users with goals.

[0065] The first reception unit accepts registration of information of a plurality of first users each having specialized knowledge. The first reception unit, for example, enables users with specialized knowledge to register their own experience and knowledge on the platform. Specifically, the users can enter detailed information such as their field of expertise, years of experience, past projects, and acquired qualifications through a dedicated registration form. For example, a user with programming knowledge can describe in detail the programming languages ​​they can use, the development environment, and the contents of projects they have worked on in the past. In addition, the users can upload self-introductions and profile photos, making it easier for other users to understand the person's background and expertise. Furthermore, the first reception unit has a function of automatically classifying the registered information and storing it in a database. This allows the information to be searched and referenced quickly and accurately in the subsequent matching process. For example, when a user with programming knowledge is registered, the information is classified into the "programming" category and is also tagged with other related categories (e.g., web development, mobile app development, etc.) as necessary. This allows the first reception unit to efficiently manage information of users with specialized knowledge and provide a foundation for the matching unit to perform optimal matching.

[0066] The second reception unit receives the goals or dreams that each of the multiple second users wants to achieve. The second reception unit, for example, enables the user to describe the dreams or goals that the user wants to achieve on the platform. Specifically, the user can describe in detail his or her goals or dreams, the deadline to achieve them, the support content required, etc. through a dedicated input form. For example, a user who has the goal of "I want to learn programming" can input the programming language he or she wants to learn, the desired learning style (e.g., online courses, individual instruction, etc.), past learning experience, etc. In addition, the user can also set specific steps and intermediate goals for achieving the goal, which makes it easier to visualize the progress of goal achievement. In addition, the second reception unit has a function of automatically classifying the registered goals and dreams and storing them in a database. This allows the information to be searched and referenced quickly and accurately in the subsequent matching process. For example, if the goal of "I want to learn programming" is registered, the information is classified into the "programming learning" category and is also tagged with other related categories (e.g., web development learning, mobile app development learning, etc.) as necessary. This enables the second reception unit to efficiently manage information on users who have goals, and provides a foundation for the matching unit to perform optimal matching.

[0067] The matching unit performs matching between the first user and the second user based on the information received by the first reception unit and the information received by the second reception unit. For example, the matching unit matches a first user who has programming knowledge with a second user who wants to learn programming. Specifically, the matching unit uses AI to analyze the information of both users and achieve optimal matching. The AI ​​builds and executes an optimal matching algorithm based on past success cases and user feedback. For example, the AI ​​considers the depth of the first user's expertise and years of experience, the second user's learning style and deadline for achieving goals, and proposes the most suitable combination. The AI ​​can also analyze the communication style and personality traits of users and match users who are compatible with each other. This allows the matching unit to achieve high-precision matching that takes into account not only the matching of knowledge and goals, but also the compatibility and smoothness of communication between users. In addition, the matching unit also has a follow-up function after matching, and supports the user's process until they achieve their goal. For example, the progress can be checked periodically and additional support and resources can be provided as necessary. This enables the matching unit to provide comprehensive support for users to achieve their goals and maximize the effectiveness of the entire system.

[0068] The matching system includes a communication unit that supports communication between a first user and a second user matched by the matching unit. The communication unit supports communication between the first user and the second user matched by the matching unit. The communication unit provides, for example, a chat function or a video call function, enabling users to communicate smoothly with each other. For example, the communication unit enables users to exchange messages in real time using the chat function. In addition, the communication unit enables users to communicate while seeing each other's faces using the video call function. This allows the matching system to smoothly communicate between matched users.

[0069] The matching system includes a presentation unit in which the matching unit uses AI to select and present appropriate means and precedents. The presentation unit selects and presents appropriate means and precedents using AI. For example, the presentation unit uses AI to analyze past success cases and propose optimal means for learning programming to the second user. For example, the presentation unit selects and presents optimal means for learning programming based on past success cases. Also, the presentation unit selects and presents optimal precedents for learning programming based on past success cases. In this way, the matching system can present optimal means and precedents to the user by using AI.

[0070] The first reception unit estimates the user's emotion and adjusts the timing of registering the specialized knowledge based on the estimated user's emotion. The first reception unit estimates the user's emotion and adjusts the timing of registering the specialized knowledge based on the estimated user's emotion. For example, when the user is stressed, the first reception unit prompts the user to register the specialized knowledge during a time when the user can relax. In addition, when the user is excited, the first reception unit can also prompt the user to register the specialized knowledge on the spot. In addition, when the user is tired, the first reception unit can also prompt the user to register the specialized knowledge after resting. In this way, the first reception unit can adjust the timing of registering the specialized knowledge according to the user's emotion, thereby prompting the user to register the specialized knowledge at a more appropriate timing. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0071] The first reception unit analyzes the user's past registration history when registering the specialized knowledge, and selects an appropriate registration method. The first reception unit analyzes the user's past registration history when registering the specialized knowledge, and selects an appropriate registration method. For example, the first reception unit proposes registration in a similar format based on the format of the specialized knowledge registered by the user in the past. The first reception unit can also propose registration with a similar level of detail based on the level of detail of the content registered by the user in the past. The first reception unit can also propose registration in a similar time period based on the time period registered by the user in the past. In this way, the first reception unit can propose an optimal registration method by analyzing the past registration history. A part or all of the above-mentioned processing in the first reception unit may be performed, for example, using AI, or may be performed without using AI.

[0072] The first reception unit performs filtering based on the user's current occupation and field of interest when registering the specialized knowledge. The first reception unit performs filtering based on the user's current occupation and field of interest when registering the specialized knowledge. For example, the first reception unit preferentially registers specialized knowledge related to the user's occupation. The first reception unit can also preferentially register specialized knowledge related to the user's field of interest. The first reception unit can also preferentially register specialized knowledge related to both the user's occupation and field of interest. Thereby, the first reception unit can preferentially register highly relevant knowledge by filtering the specialized knowledge based on the user's occupation and field of interest. A part or all of the above-mentioned processing in the first reception unit may be performed using, for example, AI, or may be performed without using AI.

[0073] The first reception unit estimates the user's emotion and determines the priority order of the expertise to be registered based on the estimated user's emotion. The first reception unit estimates the user's emotion and determines the priority order of the expertise to be registered based on the estimated user's emotion. For example, when the user is excited, the first reception unit preferentially registers the latest expertise. In addition, when the user is relaxed, the first reception unit can also preferentially register detailed expertise. In addition, when the user is stressed, the first reception unit can also preferentially register simple expertise. In this way, the first reception unit can register in a more appropriate order by determining the priority order of the expertise according to the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0074] The first reception unit preferentially registers highly relevant knowledge in consideration of the geographical location information of the user when registering the specialized knowledge. The first reception unit preferentially registers highly relevant knowledge in consideration of the geographical location information of the user when registering the specialized knowledge. For example, the first reception unit preferentially registers specialized knowledge related to the user's current location. The first reception unit can also register highly relevant specialized knowledge based on the user's past movement history. The first reception unit can also register highly relevant specialized knowledge based on the user's future movement schedule. Thereby, the first reception unit can preferentially register highly relevant specialized knowledge by considering the geographical location information of the user. A part or all of the above-mentioned processing in the first reception unit may be performed, for example, using AI or may be performed without using AI.

[0075] The first reception unit analyzes the social media activity of the user at the time of registering the expertise, and registers the related knowledge. The first reception unit analyzes the social media activity of the user at the time of registering the expertise, and registers the related knowledge. For example, the first reception unit registers the related expertise based on the content shared by the user on social media. The first reception unit can also register the related expertise based on the accounts the user follows on social media. The first reception unit can also register the related expertise based on the groups the user participates in on social media. In this way, the first reception unit can register the related expertise by analyzing the social media activity. A part or all of the above-mentioned processing in the first reception unit may be performed, for example, using AI, or may be performed without using AI.

[0076] The second reception unit estimates the user's emotion and adjusts the timing of writing the goal or dream based on the estimated user's emotion. The second reception unit estimates the user's emotion and adjusts the timing of writing the goal or dream based on the estimated user's emotion. For example, the second reception unit urges the user to write the goal or dream when the user is relaxed. In addition, the second reception unit can also urge the user to write the goal or dream on the spot when the user is excited. In addition, the second reception unit can also urge the user to write the goal or dream after resting when the user is tired. In this way, the second reception unit can urge the user to write at a more appropriate timing by adjusting the timing of writing the goal or dream according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0077] The second reception unit analyzes the user's past writing history when describing a goal or a dream, and selects the optimal writing method. The second reception unit analyzes the user's past writing history when describing a goal or a dream, and selects the optimal writing method. For example, the second reception unit proposes writing in a similar format based on the format of the goal or dream written by the user in the past. The second reception unit can also propose writing with a similar level of detail based on the level of detail of the content written by the user in the past. The second reception unit can also propose writing in a similar time period based on the time period written by the user in the past. As a result, the second reception unit can propose the optimal writing method by analyzing the past writing history. A part or all of the above-mentioned processing in the second reception unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The second reception unit filters the goals and dreams based on the user's current living situation and field of interest when describing the goals and dreams. The second reception unit filters the goals and dreams based on the user's current living situation and field of interest when describing the goals and dreams. For example, the second reception unit preferentially describes goals and dreams related to the user's current occupation. The second reception unit can also preferentially describe goals and dreams related to the user's field of interest. The second reception unit can also preferentially describe goals and dreams related to both the user's living situation and field of interest. As a result, the second reception unit can preferentially describe highly relevant goals and dreams by filtering the goals and dreams based on the user's living situation and field of interest. A part or all of the above-mentioned processing in the second reception unit may be performed using AI, for example, or may be performed without using AI.

[0079] The second reception unit estimates the user's emotion and determines the priority order of the goals and dreams to be described based on the estimated user's emotion. The second reception unit estimates the user's emotion and determines the priority order of the goals and dreams to be described based on the estimated user's emotion. For example, when the user is excited, the second reception unit preferentially describes challenging goals and dreams. In addition, when the user is relaxed, the second reception unit can also preferentially describe detailed goals and dreams. In addition, when the user is stressed, the second reception unit can also preferentially describe simple goals and dreams. In this way, the second reception unit can determine the priority order of goals and dreams according to the user's emotion, thereby making it possible to perform description in a more appropriate order. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0080] The second reception unit, when describing a goal or a dream, preferentially describes a highly relevant goal or dream in consideration of the geographical location information of the user. When describing a goal or a dream, the second reception unit, when describing a goal or a dream, preferentially describes a highly relevant goal or dream in consideration of the geographical location information of the user. For example, the second reception unit preferentially describes a goal or a dream related to the user's current location. The second reception unit can also describe a highly relevant goal or dream based on the user's past movement history. The second reception unit can also describe a highly relevant goal or dream based on the user's future movement schedule. Thereby, the second reception unit can preferentially describe a highly relevant goal or dream by considering the geographical location information of the user. A part or all of the above-mentioned processing in the second reception unit may be performed, for example, using AI, or may be performed without using AI.

[0081] The second reception unit analyzes the user's social media activity when describing the goal or dream, and describes the related goal or dream. The second reception unit analyzes the user's social media activity when describing the goal or dream, and describes the related goal or dream. For example, the second reception unit describes the related goal or dream based on the content shared by the user on social media. The second reception unit can also describe the related goal or dream based on the account the user follows on social media. The second reception unit can also describe the related goal or dream based on the group the user participates in on social media. In this way, the second reception unit can describe the related goal or dream by analyzing the social media activity. A part or all of the above-mentioned processing in the second reception unit may be performed, for example, using AI, or may be performed without using AI.

[0082] The matching unit estimates the user's emotion and adjusts the matching criteria based on the estimated user's emotion. The matching unit estimates the user's emotion and adjusts the matching criteria based on the estimated user's emotion. For example, the matching unit uses detailed matching criteria when the user is relaxed. The matching unit can also use quick matching criteria when the user is excited. The matching unit can also use simple matching criteria when the user is stressed. This allows the matching unit to adjust the matching criteria according to the user's emotion, thereby performing more appropriate matching. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0083] The matching unit improves the accuracy of matching by considering the interrelationship between the specialized knowledge and the goal or dream during matching. The matching unit improves the accuracy of matching by considering the interrelationship between the specialized knowledge and the goal or dream during matching. For example, the matching unit preferentially matches when the contents of the specialized knowledge and the goal or dream match. The matching unit can also preferentially match when the specialized knowledge and the goal or dream are highly related. The matching unit can also analyze the interrelationship between the specialized knowledge and the goal or dream and perform optimal matching. Thereby, the matching unit can improve the accuracy of matching by considering the interrelationship between the specialized knowledge and the goal or dream. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI.

[0084] The matching unit performs matching in consideration of the attribute information of the user at the time of matching. The matching unit performs matching in consideration of the attribute information of the user at the time of matching. For example, the matching unit performs matching based on the age and sex of the user. The matching unit can also perform matching based on the occupation and field of interest of the user. The matching unit can also perform matching based on the geographical location information of the user. Thereby, the matching unit can perform more appropriate matching by considering the attribute information of the user. A part or all of the above-mentioned processing in the matching unit may be performed using, for example, AI, or may be performed without using AI.

[0085] The matching unit estimates the user's emotion and adjusts the order of displaying the matching results based on the estimated user's emotion. The matching unit estimates the user's emotion and adjusts the order of displaying the matching results based on the estimated user's emotion. For example, the matching unit displays detailed matching results when the user is relaxed. The matching unit can also display quick matching results when the user is excited. The matching unit can also display simple matching results when the user is stressed. This allows the matching unit to provide more appropriate results by adjusting the display order of the matching results according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0086] The matching unit performs matching taking into consideration the geographical distribution of the specialized knowledge and the goals or dreams. The matching unit performs matching taking into consideration the geographical distribution of the specialized knowledge and the goals or dreams. For example, the matching unit performs matching preferentially when the geographical distribution of the specialized knowledge and the goals or dreams coincides. The matching unit can also perform matching preferentially when the geographical distribution of the specialized knowledge and the goals or dreams is close. The matching unit can also analyze the geographical distribution of the specialized knowledge and the goals or dreams and perform optimal matching. This allows the matching unit to perform more appropriate matching by taking into consideration the geographical distribution of the specialized knowledge and the goals or dreams. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI.

[0087] The matching unit improves the accuracy of matching by referring to related literature during matching. The matching unit improves the accuracy of matching by referring to related literature during matching. For example, the matching unit evaluates the relevance between specialized knowledge and goals or dreams based on related literature. The matching unit can also set optimal matching criteria based on related literature. The matching unit can also improve the accuracy of matching based on related literature. Thereby, the matching unit can improve the accuracy of matching by referring to related literature. A part or all of the above-mentioned processing in the matching unit may be performed, for example, using AI, or may be performed without using AI.

[0088] The communication unit estimates the user's emotion and adjusts the communication method based on the estimated user's emotion. The communication unit estimates the user's emotion and adjusts the communication method based on the estimated user's emotion. For example, the communication unit encourages communication in a calm tone when the user is nervous. The communication unit can also encourage communication in a casual tone when the user is relaxed. The communication unit can also encourage communication in an energetic tone when the user is excited. This allows the communication unit to adjust the communication method according to the user's emotion, thereby enabling more appropriate communication. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0089] The communication unit selects the optimal method by referring to the user's past communication history when communicating. The communication unit selects the optimal method by referring to the user's past communication history when communicating. For example, the communication unit preferentially suggests communication means (chat, video call, etc.) that the user has used in the past. The communication unit can also suggest a similar method based on a communication method that the user has used successfully in the past. The communication unit can also suggest communication at the optimal timing based on the user's past communication history. In this way, the communication unit can suggest the optimal communication method by referring to the past communication history. A part or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The communication unit estimates the user's emotion and determines the priority of communication based on the estimated user's emotion. The communication unit estimates the user's emotion and determines the priority of communication based on the estimated user's emotion. For example, when the user is nervous, the communication unit prioritizes important communication. Also, when the user is relaxed, the communication unit can prioritize casual communication. Also, when the user is excited, the communication unit can prioritize quick communication. In this way, the communication unit can determine the priority of communication according to the user's emotion, thereby enabling communication to be performed in a more appropriate order. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0091] The communication unit selects the optimal method in consideration of the device information of the user when communicating. The communication unit selects the optimal method in consideration of the device information of the user when communicating. For example, when the user is using a smartphone, the communication unit suggests chat or video calling. In addition, when the user is using a tablet, the communication unit can also suggest video calling that makes use of the large screen. In addition, when the user is using a desktop, the communication unit can also suggest screen sharing that allows detailed information to be shared. In this way, the communication unit can suggest the optimal communication method by considering the device information of the user. A part or all of the above-mentioned processing in the communication unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The presentation unit estimates the user's emotion and determines the priority order of the means and precedents to be presented based on the estimated user's emotion. The presentation unit estimates the user's emotion and determines the priority order of the means and precedents to be presented based on the estimated user's emotion. For example, the presentation unit preferentially presents detailed precedents when the user is relaxed. In addition, the presentation unit can also preferentially present quick means when the user is excited. In addition, the presentation unit can also preferentially present simple means when the user is stressed. In this way, the presentation unit can present in a more appropriate order by determining the priority order of the means and precedents to be presented according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0093] The presentation unit improves the accuracy of presentation by considering the interrelationship between the specialized knowledge and the goal or dream at the time of presentation. The presentation unit improves the accuracy of presentation by considering the interrelationship between the specialized knowledge and the goal or dream at the time of presentation. For example, the presentation unit preferentially presents the specialized knowledge when the contents of the goal or dream match. In addition, the presentation unit can also preferentially present the specialized knowledge when the specialized knowledge is highly related to the goal or dream. In addition, the presentation unit can analyze the interrelationship between the specialized knowledge and the goal or dream and perform optimal presentation. Thereby, the presentation unit can improve the accuracy of presentation by considering the interrelationship between the specialized knowledge and the goal or dream. Part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0094] The presentation unit presents the information taking into consideration the attribute information of the user when presenting the information. The presentation unit presents the information taking into consideration the attribute information of the user when presenting the information. For example, the presentation unit presents the information based on the age and sex of the user. The presentation unit can also present the information based on the occupation and field of interest of the user. The presentation unit can also present the information based on the geographical location information of the user. This allows the presentation unit to present the information more appropriately by taking into consideration the attribute information of the user. A part or all of the above-mentioned processing in the presentation unit may be performed using, for example, AI, or may be performed without using AI.

[0095] The presentation unit estimates the user's emotion and adjusts the means to be presented and the display method of the precedent based on the estimated user's emotion. The presentation unit estimates the user's emotion and adjusts the means to be presented and the display method of the precedent based on the estimated user's emotion. For example, the presentation unit provides a display method including detailed information when the user is relaxed. The presentation unit can also provide a quick display method when the user is excited. The presentation unit can also provide a simple display method when the user is stressed. Thereby, the presentation unit can adjust the means to be presented and the display method of the precedent according to the user's emotion, thereby performing a more appropriate display. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples.

[0096] The presentation unit presents the expert knowledge and the geographical distribution of the goal or dream in consideration of the expert knowledge. The presentation unit presents the expert knowledge and the geographical distribution of the goal or dream in consideration of the expert knowledge. For example, the presentation unit presents the expert knowledge with priority when the geographical distribution of the goal or dream matches. The presentation unit can also present the expert knowledge with priority when the geographical distribution of the goal or dream is close. The presentation unit can also analyze the geographical distribution of the expert knowledge and the goal or dream and present the optimal expert knowledge. This allows the presentation unit to present the expert knowledge and the goal or dream in a more appropriate manner by considering the geographical distribution of the expert knowledge and the goal or dream. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0097] The presentation unit improves the accuracy of presentation by referring to related literature at the time of presentation. The presentation unit improves the accuracy of presentation by referring to related literature at the time of presentation. For example, the presentation unit evaluates the relevance of specialized knowledge to goals or dreams based on related literature. The presentation unit can also set optimal presentation criteria based on related literature. The presentation unit can also improve the accuracy of presentation based on related literature. Thereby, the presentation unit can improve the accuracy of presentation by referring to related literature. A part or all of the above-mentioned processing in the presentation unit may be performed, for example, using AI, or may be performed without using AI.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The matching system can also analyze the user's past matching history and select the optimal matching method. For example, based on past successful matching patterns, matching with a similar pattern can be suggested. Also, by avoiding matching patterns that have failed in the past, matching can be performed with a higher success rate. Furthermore, if the user's past matching history shows that a certain time period or day of the week has a high success rate, matching can be performed with that time period or day of the week. In this way, the matching system can utilize past data to provide more accurate matching.

[0100] The communication unit can also analyze the user's communication style and suggest the optimal communication method. For example, if the user prefers text-based communication, the chat function can be provided preferentially. Also, if the user prefers video calls, the video call function can be provided preferentially. Furthermore, if the user prefers voice calls, the voice call function can be provided. In this way, the communication unit can provide the optimal communication method according to the user's preferences.

[0101] The presentation unit can also analyze the user's learning style and suggest the most suitable learning method. For example, if the user prefers visual learning, a learning method using videos and illustrations can be provided. If the user prefers auditory learning, a learning method using audio guides and podcasts can be provided. Furthermore, if the user prefers practical learning, a learning method through actual projects and exercises can be provided. In this way, the presentation unit can provide the most suitable learning method according to the user's learning style.

[0102] The first reception unit can also estimate the user's emotions and adjust the registered content of the expert knowledge based on the estimated user's emotions. For example, if the user is relaxed, the first reception unit can encourage the user to register detailed expert knowledge. Also, if the user is excited, the first reception unit can encourage the user to register concise expert knowledge. Furthermore, if the user is stressed, the first reception unit can simplify the registered content. In this way, the first reception unit can encourage the user to register more appropriate expertise by adjusting the registered content of the expert knowledge according to the user's emotions.

[0103] The first reception unit can also adjust the registration contents in consideration of the user's current skill level when registering specialized knowledge. For example, a novice user is prompted to register basic specialized knowledge, and an advanced user is prompted to register advanced specialized knowledge. In addition, the level of detail of the registration contents can be adjusted according to the user's skill level. This allows the first reception unit to provide optimal registration contents according to the user's skill level.

[0104] When registering expertise, the first reception unit can also adjust the registration content taking into account the user's past feedback. For example, based on a format of expertise that has been highly rated in the past, the first reception unit can suggest registration in a similar format. Also, by avoiding a format of expertise that has been poorly rated in the past, it is possible to provide registration content that will receive a higher rating. Furthermore, it is possible to analyze the user's past feedback and suggest improvements to the registration content. In this way, the first reception unit can encourage the registration of higher quality expertise by utilizing past feedback.

[0105] The first reception unit can also estimate the user's emotions and suggest a method of registering expertise based on the estimated user's emotions. For example, if the user is relaxed, a detailed guide can be provided to encourage registration. Also, if the user is excited, a concise guide can be provided to encourage quick registration. Furthermore, if the user is stressed, a step-by-step guide can be provided to support registration. In this way, the first reception unit can provide an optimal registration method according to the user's emotions.

[0106] The first reception unit can also adjust the registration contents in consideration of the user's future goals when registering the specialized knowledge. For example, if the user has a future career goal, the first reception unit can encourage the user to register specialized knowledge related to the goal. Also, if the user is aiming at a specific project, the first reception unit can encourage the user to register specialized knowledge related to the project. Furthermore, the first reception unit can determine the priority of the registration contents based on the user's future goal. This allows the first reception unit to provide the optimal registration contents according to the user's future goal.

[0107] The first reception unit can also adjust the registration contents in consideration of the user's social network when registering expertise. For example, if the user belongs to a specific expert group, the first reception unit can encourage the user to register expertise related to the group. Also, if the user participates in a specific community, the first reception unit can encourage the user to register expertise related to the community. Furthermore, the first reception unit can analyze the user's social network and suggest the registration of highly relevant expertise. This allows the first reception unit to provide optimal registration contents according to the user's social network.

[0108] The second reception unit can also estimate the user's emotions and adjust the description of the goals and dreams based on the estimated user's emotions. For example, if the user is relaxed, the second reception unit can encourage the user to describe the goals and dreams in detail. Also, if the user is excited, the second reception unit can encourage the user to describe the goals and dreams in concise terms. Furthermore, if the user is stressed, the second reception unit can simplify the description. In this way, the second reception unit can encourage the user to describe the goals and dreams more appropriately by adjusting the description of the goals and dreams according to the user's emotions.

[0109] The process flow of the second embodiment will be briefly described below.

[0110] Step 1: The first reception unit receives registration of information of a plurality of first users each having specialized knowledge. For example, users with specialized knowledge can register their own experience and knowledge on the platform. If a first user has knowledge of programming, that knowledge and experience can be registered on the platform. Step 2: The second reception unit receives the goals or dreams that each of the multiple second users wants to achieve. For example, the users can describe the dreams or goals that they want to achieve on the platform. If a second user has a goal of "learning programming," the user can describe the goal on the platform. Step 3: The matching unit matches the first user with the second user based on the information received by the first reception unit and the information received by the second reception unit. For example, it matches a first user who has programming knowledge with a second user who wants to learn programming. The matching unit uses AI to select and present optimal methods and precedents. For example, the AI ​​analyzes past success stories and suggests optimal methods for learning programming to the second user.

[0111] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the 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 voice data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0113] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0114] For example, the first reception unit and the second reception unit are realized by either the smart device 14 or the data processing device 12. For example, the control unit 46A of the smart device 14 has a function of receiving the user's specialized knowledge and goals, and the specific processing unit 290 of the data processing device 12 performs matching based on the received information. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects and presents optimal means and precedents using AI. The communication unit is realized, for example, by the control unit 46A of the smart device 14, and provides a chat function and a video call function. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0115] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0116] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, 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. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0122] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0125] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0126] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0127] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0128] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0129] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0130] For example, the first reception unit and the second reception unit are realized by either the smart glasses 214 or the data processing device 12. For example, the control unit 46A of the smart glasses 214 has a function of receiving the user's specialized knowledge and goals, and the specific processing unit 290 of the data processing device 12 performs matching based on the received information. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects and presents optimal means and precedents using AI. The communication unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides a chat function and a video call function. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0133] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0134] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. 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. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0135] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0137] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0138] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0141] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0145] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0146] For example, the first reception unit and the second reception unit are realized by either the headset type terminal 314 or the data processing device 12. For example, the control unit 46A of the headset type terminal 314 has a function of receiving the user's specialized knowledge and goals, and the specific processing unit 290 of the data processing device 12 performs matching based on the received information. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects and presents optimal means and precedents using AI. The communication unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides a chat function and a video call function. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0147] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0148] 7, a 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.

[0149] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. 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. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0151] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0153] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0154] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0155] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0156] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0157] The storage 32 stores a data generation model 58 and an 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 emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0158] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0159] Note that a device other than the data processing device 12 may 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 a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0160] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0161] The data generation model 58 is a so-called generative AI. An example of the 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0162] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0163] For example, the first reception unit and the second reception unit are realized by either the robot 414 or the data processing device 12. For example, the control unit 46A of the robot 414 has a function of receiving the user's specialized knowledge and goals, and the specific processing unit 290 of the data processing device 12 performs matching based on the received information. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and selects and presents optimal means and precedents using AI. The communication unit is realized, for example, by the control unit 46A of the robot 414, and provides a chat function and a video call function. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0164] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0165] FIG. 9 is a diagram showing an emotion map 400 on 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 emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0166] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0167] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0168] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0169] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0172] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0173] In addition, 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 upon request from the data processing device 12.

[0174] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0175] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0176] The hardware resource that executes the specific process may be one of these various processors, or may be 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). Also, the hardware resource that executes the specific process may be a single processor.

[0177] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0178] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0179] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0180] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0181] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0182] (Appendix 1) a first reception unit that receives registration of information of a plurality of first users each having specialized knowledge; a second reception unit that receives a goal or a dream that each of the second users wants to achieve; a matching unit that performs matching between the first user and the second user based on the information accepted by the first accepting unit and the information accepted by the second accepting unit. A system characterized by: (Appendix 2) a communication unit that supports communication between the first user and the second user matched by the matching unit; 2. The system of claim 1, (Appendix 3) The matching unit is Equipped with a presentation section that uses AI to select and present appropriate measures and precedents 2. The system of claim 1, (Appendix 4) The first reception unit is To estimate a user's emotion and adjust the timing of registering expertise based on the estimated user's emotion. 2. The system of claim 1, (Appendix 5) The first reception unit is When registering expertise, analyze the user's past registration history and select the appropriate registration method. 2. The system of claim 1, (Appendix 6) The first reception unit is Filtering expertise registration based on a user's current occupation or area of ​​interest 2. The system of claim 1, (Appendix 7) The first reception unit is A user's emotion is estimated, and a priority order of expertise to be registered is determined based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The first reception unit is When registering expertise, the system prioritizes registering knowledge that is highly relevant by taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 9) The first reception unit is When registering expertise, analyze users' social media activity and register related knowledge 2. The system of claim 1, (Appendix 10) The second reception unit is Estimate the user's emotions and adjust the timing of describing goals and dreams based on the estimated user emotions 2. The system of claim 1, (Appendix 11) The second reception unit is When describing goals and dreams, the system analyzes the user's past writing history and selects the optimal writing method. 2. The system of claim 1, (Appendix 12) The second reception unit is Filtering goals and dreams based on the user's current life situation and interests 2. The system of claim 1, (Appendix 13) The second reception unit is Estimate the user's emotions and determine the priority of the goals and dreams to be described based on the estimated user's emotions. 2. The system of claim 1, (Appendix 14) The second reception unit is When describing goals and dreams, the system takes into account the user's geographic location information and prioritizes describing goals and dreams that are more relevant to them. 2. The system of claim 1, (Appendix 15) The second reception unit is When describing goals and dreams, analyze the user's social media activity and describe related goals and dreams. 2. The system of claim 1, (Appendix 16) The matching unit is Estimate the user's emotions and adjust the matching criteria based on the estimated user's emotions. 2. The system of claim 1, (Appendix 17) The matching unit is When matching, improve the accuracy of the match by considering the correlation between expertise and goals / dreams. 2. The system of claim 1, (Appendix 18) The matching unit is When matching, the user's attribute information is taken into consideration. 2. The system of claim 1, (Appendix 19) The matching unit is Inferring user emotions and adjusting the order in which matching results are displayed based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The matching unit is When matching, the matching is done taking into account the geographical distribution of expertise and goals and dreams. 2. The system of claim 1, (Appendix 21) The matching unit is When matching, refer to related literature to improve matching accuracy. 2. The system of claim 1, (Appendix 22) The communication unit includes: Estimate user emotions and adjust communication methods based on the estimated user emotions 3. The system of claim 2, (Appendix 23) The communication unit includes: When communicating, select the best method by referring to the user's past communication history 3. The system of claim 2, (Appendix 24) The communication unit includes: Estimate user emotions and prioritize communications based on the estimated user emotions 3. The system of claim 2, (Appendix 25) The communication unit includes: When communicating, select the optimal method by taking into account the user's device information 3. The system of claim 2, (Appendix 26) The presentation unit is Estimate the user's emotions and determine the priority of the measures and precedents to be presented based on the estimated user's emotions. 4. The system of claim 3. (Appendix 27) The presentation unit is When presenting, improve the accuracy of the presentation by considering the correlation between expertise and goals and dreams 4. The system of claim 3. (Appendix 28) The presentation unit is When presenting information, the user's attribute information is taken into consideration. 4. The system of claim 3. (Appendix 29) The presentation unit is Infer the user's emotions and adjust the means of presentation and the method of displaying precedents based on the estimated user emotions. 4. The system of claim 3. (Appendix 30) The presentation unit is When making a presentation, make it with consideration given to the geographical distribution of expertise and goals and dreams. 4. The system of claim 3. (Appendix 31) The presentation unit is When presenting, refer to related literature to improve the accuracy of the presentation 4. The system of claim 3. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a first reception unit that receives registration of information of a plurality of first users each having specialized knowledge; a second reception unit that receives a goal or a dream that each of the second users wants to achieve; a matching unit that performs matching between the first user and the second user based on the information accepted by the first accepting unit and the information accepted by the second accepting unit. A system characterized by:

2. a communication unit that supports communication between the first user and the second user matched by the matching unit; 2. The system of claim 1 .

3. The matching unit is Equipped with a presentation section that uses AI to select and present appropriate measures and precedents 2. The system of claim 1 .

4. The first reception unit is To estimate a user's emotion and adjust the timing of registering expertise based on the estimated user's emotion.

2. The system of claim 1 .

5. The first reception unit is When registering expertise, analyze the user's past registration history and select the appropriate registration method.

2. The system of claim 1 .

6. The first reception unit is Filtering expertise registration based on a user's current occupation or area of ​​interest 2. The system of claim 1 .

7. The first reception unit is A user's emotion is estimated, and a priority order of expertise to be registered is determined based on the estimated user's emotion.

2. The system of claim 1 .

8. The first reception unit is When registering expertise, the system takes into account the user's geographic location information to prioritize the registration of knowledge that is highly relevant 2. The system of claim 1 .

9. The first reception unit is When registering expertise, analyze users' social media activity and register related knowledge 2. The system of claim 1 .

10. The second reception unit is Estimate the user's emotions and adjust the timing of describing goals and dreams based on the estimated user emotions 2. The system of claim 1 .

Images