System

Abstract

An object of a system according to an embodiment is to analyze profile information of a user and propose an appropriate matching candidate.SOLUTION: A system includes a reception unit, an analysis unit, and a provision unit. The reception unit receives profile information of a user. The analysis unit analyzes the information received by the reception unit and proposes a matching candidate. The providing unit provides a template of the message based on the matching candidate proposed by the analysis 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 an instruction sentence related to a description of 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately propose match candidates based on user profile information, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze a user's profile information and propose suitable match candidates. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives profile information of a user. The analysis unit analyzes the information received by the reception unit and proposes matching candidates. The provision unit provides message templates based on the matching candidates proposed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the profile information of the user and suggest suitable match candidates. [Brief explanation 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. [Figure 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. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 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. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 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 INVENTION

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

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

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single 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 coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, 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 symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and 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. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "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, RAM 30, and 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 (for example, 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., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with 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 CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 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 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. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A matching system according to an embodiment of the present invention efficiently analyzes a user's profile information, proposes optimal match candidates, and provides message templates. In the matching system, a user inputs profile information, and a generation AI analyzes the information to propose optimal match candidates. Furthermore, message templates are provided to the user based on the match candidates proposed by the generation AI. For example, in the matching system, a user inputs detailed information such as their name, age, hobbies, and interests. This information is input to the generation AI. The generation AI then analyzes the input information and proposes optimal match candidates. For example, for a user whose hobby is watching movies, other users with the same hobby can be proposed. Furthermore, message templates are provided to the user based on the match candidates proposed by the generation AI. For example, a template such as "Hello, I love movies too. What movies have you seen recently?" can be provided as a first message. This allows users to efficiently match. This allows the matching system to analyze a user's profile information, propose optimal match candidates, and provide message templates. For example, users with common hobbies can be matched and smoothly start communicating, making it easier for romantic relationships to develop.

[0029] A matching system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives profile information from a user. The user's profile information may include, but is not limited to, information such as a name, age, hobbies, and interests. The reception unit stores the information entered by the user in a database. The reception unit may also transmit the information entered by the user to a generation AI. The analysis unit uses the generation AI to analyze the information received by the reception unit and propose optimal match candidates. The analysis unit may propose other users with common hobbies based on the user's hobbies and interests. The analysis unit may also perform analysis using past match data. For example, the analysis unit may extract successful match patterns based on the past match data and propose optimal match candidates based on the extracted patterns. The provision unit provides a message template to the user based on the match candidates proposed by the generation AI. For example, the provision unit may provide a template such as "Hello, I love movies too. What movies have you seen recently?" as a first message. The provision unit may also generate message templates based on the user's profile information. For example, the providing unit generates an appropriate message template based on the user's hobbies and interests, thereby enabling the matching system according to the embodiment to efficiently analyze the user's profile information, propose optimal match candidates, and provide message templates.

[0030] The reception unit can receive detailed information such as the user's name, age, hobbies, and interests. The detailed information includes, but is not limited to, the name, age, hobbies, and interests. For example, the reception unit stores the name entered by the user in a database. The reception unit can also store the age entered by the user in a database. The reception unit can also store the hobbies and interests entered by the user in a database. For example, if the user enters watching movies as a hobby, the reception unit stores that information in the database. This makes it possible to receive detailed profile information of the user.

[0031] The analysis unit can suggest matching candidates based on the user's profile information. For example, the analysis unit suggests other users who share the same hobbies based on the user's hobbies and interests. For example, the analysis unit suggests other users who share the same hobbies to a user whose hobby is watching movies. The analysis unit can also suggest appropriate matching candidates based on the user's age and gender. For example, the analysis unit suggests users who are similar in age. Furthermore, the analysis unit can perform analysis by referring to past matching data. For example, the analysis unit extracts successful matching patterns based on past matching data and suggests optimal matching candidates based on those patterns. This makes it possible to suggest optimal matching candidates based on the user's profile information.

[0032] The providing unit can provide a message template based on the proposed matching candidates. For example, the providing unit can provide a template such as "Hello, I love movies too. What movies have you seen recently?" as a first message. The providing unit can also generate a message template based on the user's profile information. For example, the providing unit generates an appropriate message template based on the user's hobbies and interests. Furthermore, the providing unit can provide a message template to the user based on the matching candidates proposed by the generation AI. For example, the providing unit provides an appropriate message template for the matching candidates proposed by the generation AI. This makes it possible to provide a message template based on the matching candidates proposed by the generation AI.

[0033] The analysis unit can perform analysis based on past matching data. For example, the analysis unit extracts successful matching patterns based on the past matching data and proposes optimal matching candidates based on the extracted patterns. The analysis unit can also eliminate unsuccessful matching patterns based on the past matching data, thereby improving the accuracy of the proposals. Furthermore, the analysis unit can analyze user profile information by referring to the past matching data. For example, the analysis unit proposes optimal matching candidates based on the user's hobbies and interests based on the past matching data. In this way, the accuracy of the proposals is improved by performing analysis by referring to the past matching data.

[0034] The providing unit can generate a message template based on the user's profile information. The providing unit generates an appropriate message template based on, for example, the user's hobbies and interests. For example, the providing unit provides a movie-related message template to a user whose hobby is watching movies. The providing unit can also generate an appropriate message template based on the user's age and gender. For example, the providing unit provides an appropriate message template to users who are similar in age. Furthermore, the providing unit can also provide a message template to a user based on matching candidates proposed by the generation AI. For example, the providing unit provides an appropriate message template for a matching candidate proposed by the generation AI. In this way, by generating a message template based on the user's profile information, more appropriate messages can be provided.

[0035] The reception unit can analyze the user's past profile information and suggest an input format. For example, the reception unit automatically suggests the optimal input format based on the profile information the user has input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input format to be used in a specific time period based on the user's past input history. For example, the reception unit automatically suggests the optimal input format based on the profile information the user has input in the past. In this way, the optimal input format can be suggested by analyzing the user's past profile information.

[0036] The reception unit can customize input items based on the user's current living situation and areas of interest when entering profile information. For example, the reception unit automatically displays related input items when the user enters their current living situation. The reception unit can also customize input items based on the user's areas of interest and prioritize the input of related information. Furthermore, the reception unit can analyze the user's current living situation and areas of interest and suggest optimal input items. For example, the reception unit automatically displays related input items when the user enters their current living situation. This allows the user to enter more appropriate information by customizing input items based on the user's current living situation and areas of interest.

[0037] When inputting profile information, the reception unit can select an input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the profile information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface optimized for text input. Furthermore, if the user selects image input, the reception unit can also input the profile information using image recognition technology. For example, if the user selects voice input, the reception unit inputs the profile information using voice recognition technology. This allows for more efficient input by selecting the optimal input means according to the user's input method.

[0038] When entering profile information, the reception unit can prioritize input of highly relevant information based on the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize input of information related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize input of related information based on the user's current location. Furthermore, the reception unit can analyze the user's geographical location information and suggest optimal input items. For example, if the user lives in a specific area, the reception unit can prioritize input of information related to that area. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration.

[0039] When entering profile information, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit automatically inputs profile information based on information that the user has made public on social media. The reception unit can also analyze the user's social media activity and input related information. Furthermore, the reception unit can also input related information by taking into account the user's friendships on social media. For example, the reception unit automatically inputs profile information based on information that the user has made public on social media. In this way, related information can be input by analyzing the user's social media activity.

[0040] The reception unit can adjust the input method by reflecting the user's past feedback when entering profile information. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and optimize the input procedure. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0041] When proposing matching candidates, the analysis unit can adjust the level of detail of the proposal based on the user's profile information. For example, if the user's hobbies and interests are important, the analysis unit can propose detailed matching candidates based on that. In addition, if the user's age and gender are important, the analysis unit can also propose detailed matching candidates based on that. Furthermore, the analysis unit can propose matching candidates with an optimal level of detail based on the user's past matching history. For example, if the user's hobbies and interests are important, the analysis unit can propose detailed matching candidates based on that. This allows for more appropriate proposals by adjusting the level of detail of the proposal based on the importance of the user's profile information.

[0042] When proposing matching candidates, the analysis unit can apply a proposal algorithm according to the user's hobbies and interests. For example, if the user's hobby is watching movies, the analysis unit can apply an algorithm that preferentially proposes other users who have the same hobby. Also, if the user's interest is sports, the analysis unit can apply an algorithm that preferentially proposes other users who have the same interest. Furthermore, the analysis unit can analyze the user's hobbies and interests and apply an optimal proposal algorithm. For example, if the user's hobby is watching movies, the analysis unit can apply an algorithm that preferentially proposes other users who have the same hobby. In this way, by applying different proposal algorithms according to the user's hobbies and interests, more appropriate matching candidates can be proposed.

[0043] When proposing matching candidates, the analysis unit can improve the accuracy of the suggestions based on the user's past matching results. For example, the analysis unit analyzes the user's past matching results and improves the accuracy of the suggestions based on successful matching patterns. The analysis unit can also eliminate unsuccessful matching patterns from the user's past matching results and improve the accuracy of the suggestions. Furthermore, the analysis unit can also suggest optimal matching candidates by referring to the user's past matching results. For example, the analysis unit analyzes the user's past matching results and improves the accuracy of the suggestions based on successful matching patterns. In this way, the accuracy of the suggestions is improved by referring to the user's past matching results.

[0044] When proposing match candidates, the analysis unit can prioritize proposing highly relevant candidates based on the user's geographical location information. For example, if the user lives in a specific area, the analysis unit can prioritize proposing match candidates related to that area. Also, if the user is traveling, the analysis unit can prioritize proposing related match candidates based on the user's current location. Furthermore, the analysis unit can analyze the user's geographical location information and propose optimal match candidates. For example, if the user lives in a specific area, the analysis unit can prioritize proposing match candidates related to that area. In this way, by taking the user's geographical location information into consideration, highly relevant match candidates can be prioritized.

[0045] When proposing match candidates, the analysis unit can analyze the user's social media activity and suggest related candidates. The analysis unit can, for example, suggest related match candidates based on information that the user has made public on social media. The analysis unit can also analyze the user's social media activity and suggest related match candidates. Furthermore, the analysis unit can also suggest related match candidates based on the user's friendships on social media. For example, the analysis unit can suggest related match candidates based on information that the user has made public on social media. In this way, related match candidates can be suggested by analyzing the user's social media activity.

[0046] When proposing matching candidates, the analysis unit can adjust the proposal method by reflecting the user's past feedback. The analysis unit can propose an optimal proposal method based on, for example, feedback provided by the user in the past. The analysis unit can also customize the proposal algorithm based on the user's past feedback. Furthermore, the analysis unit can analyze the user's past feedback and optimize the proposal procedure. For example, the analysis unit can propose an optimal proposal method based on feedback provided by the user in the past. In this way, the proposal method can be optimized by reflecting the user's past feedback.

[0047] When providing a message template, the providing unit can adjust the content of the template based on the profile information of the matching candidate. The providing unit provides a detailed message template based on, for example, the hobbies and interests of the matching candidate. The providing unit can also provide a detailed message template based on the age and gender of the matching candidate. Furthermore, the providing unit can provide a message template with an optimal level of detail based on the profile information of the matching candidate. For example, the providing unit provides a detailed message template based on the hobbies and interests of the matching candidate. This makes it possible to provide a more appropriate message by adjusting the level of detail of the template based on the profile information of the matching candidate.

[0048] When providing message templates, the providing unit can provide multiple templates according to the user's hobbies and interests. For example, if the user's hobby is watching movies, the providing unit can provide a message template related to movies. Also, if the user's interest is sports, the providing unit can provide a message template related to sports. Furthermore, the providing unit can analyze the user's hobbies and interests and provide an optimal message template. For example, if the user's hobby is watching movies, the providing unit can provide a message template related to movies. In this way, by providing different templates according to the user's hobbies and interests, more appropriate messages can be provided.

[0049] When providing a message template, the providing unit can improve the accuracy of the template based on the user's past message history. For example, the providing unit analyzes the user's past message history and improves the accuracy of the template based on patterns of successful messages. The providing unit can also improve the accuracy of the template by eliminating unsuccessful message patterns from the user's past message history. Furthermore, the providing unit can provide an optimal message template by referring to the user's past message history. For example, the providing unit analyzes the user's past message history and improves the accuracy of the template based on patterns of successful messages. In this way, the accuracy of the template is improved by referring to the user's past message history.

[0050] When providing message templates, the providing unit can determine the order of templates based on the submission date of matching candidates. For example, if a matching candidate has been submitted recently, the providing unit can provide a message template related to that candidate with priority. Also, if a matching candidate has been submitted in the past, the providing unit can provide a message template related to that candidate. Furthermore, the providing unit can analyze the submission date of the matching candidate and provide an optimal message template. For example, if a matching candidate has been submitted recently, the providing unit can provide a message template related to that candidate with priority. In this way, by determining the priority of templates based on the submission date of the matching candidate, a more appropriate message can be provided.

[0051] When providing message templates, the providing unit can determine the order of the templates based on the relevance of the matching candidates. For example, if the relevance of a matching candidate is high, the providing unit can preferentially provide a message template related to that candidate. Also, if the relevance of a matching candidate is low, the providing unit can postpone the message template related to that candidate. Furthermore, the providing unit can analyze the relevance of the matching candidates and provide the message templates in an optimal order. For example, if the relevance of a matching candidate is high, the providing unit preferentially provides a message template related to that candidate. In this way, by adjusting the order of templates based on the relevance of the matching candidates, a more appropriate message can be provided.

[0052] When providing a message template, the providing unit can determine the use of technical terms in the template depending on the user's level of expertise. For example, if the user's level of expertise is high, the providing unit can provide a message template that uses a lot of technical terms. In addition, if the user's level of expertise is low, the providing unit can provide a simple message template that avoids technical terms. Furthermore, the providing unit can analyze the user's level of expertise and provide a message template in which the use of technical terms is adjusted to be optimal. For example, if the user's level of expertise is high, the providing unit can provide a message template that uses a lot of technical terms. In this way, by adjusting the use of technical terms in the template depending on the user's level of expertise, a more appropriate message can be provided.

[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 reception unit can also provide real-time feedback based on the user's input. For example, if the user inputs "watching movies" as a hobby, the reception unit can present an additional question such as "What movie have you seen recently?". The reception unit can also suggest related topics and questions based on the information entered by the user. Furthermore, the reception unit can provide appropriate advice and suggestions in real time for the information entered by the user. This allows the user to enter more detailed and comprehensive profile information.

[0055] The analysis unit can also estimate the user's lifestyle and values ​​based on the user's profile information and suggest matching candidates based on that. For example, if the user likes outdoor activities, other users who also enjoy outdoor activities can be suggested. Also, if the user is interested in environmental protection, users who share the same values ​​can be suggested. Furthermore, the analysis unit can prioritize suggesting users who share common hobbies and interests based on the user's lifestyle and values. This makes it possible to suggest more appropriate matching candidates based on the user's lifestyle and values.

[0056] The providing unit can also analyze the user's past message history and improve the accuracy of the template based on patterns of successful messages. For example, it analyzes messages that the user has sent in the past that received a good response from the recipient and provides a template that incorporates that pattern. It can also improve the accuracy of the template by eliminating messages that the user has sent in the past that received a poor response from the recipient. Furthermore, it can also provide an optimal message template by referring to the user's past message history. In this way, the accuracy of the template can be improved by referring to the user's past message history.

[0057] The analysis unit can also predict the user's future behavior and interests based on the user's profile information and suggest matching candidates based on that. For example, if the user recently started a new hobby, other users related to that hobby can be suggested. Also, if the user is planning a trip in the future, users related to that travel destination can be suggested. Furthermore, the analysis unit can predict the user's future behavior and interests and suggest optimal matching candidates based on that. This makes it possible to suggest more appropriate matching candidates based on the user's future behavior and interests.

[0058] The analysis unit can also estimate the user's health condition and fitness level based on the user's profile information and suggest matching candidates based on that. For example, if the user has an active lifestyle, other users with a similar active lifestyle can be suggested. Also, if the user is interested in health, users who are also interested in health can be suggested. Furthermore, based on the user's health condition and fitness level, users who share common health goals can be preferentially suggested. This allows more appropriate matching candidates to be suggested based on the user's health condition and fitness level.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives the user's profile information. The user's profile information may include, but is not limited to, for example, name, age, hobbies, and interests. The reception unit, for example, stores the information entered by the user in a database. The reception unit may also send the information entered by the user to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and propose optimal match candidates. For example, the analysis unit proposes other users with common hobbies based on the user's hobbies and interests. The analysis unit can also perform analysis by referring to past matching data. For example, the analysis unit extracts successful matching patterns based on past matching data and proposes optimal match candidates based on those patterns. Step 3: The provider provides a message template to the user based on the matching candidates proposed by the generation AI. For example, the provider may provide a template such as "Hello, I love movies too. What movies have you seen recently?" as a first message. The provider can also generate message templates based on the user's profile information. For example, the provider may generate an appropriate message template based on the user's hobbies and interests.

[0061] (Example 2) A matching system according to an embodiment of the present invention efficiently analyzes a user's profile information, proposes optimal match candidates, and provides message templates. In the matching system, a user inputs profile information, and a generation AI analyzes the information to propose optimal match candidates. Furthermore, message templates are provided to the user based on the match candidates proposed by the generation AI. For example, in the matching system, a user inputs detailed information such as their name, age, hobbies, and interests. This information is input to the generation AI. The generation AI then analyzes the input information and proposes optimal match candidates. For example, for a user whose hobby is watching movies, other users with the same hobby can be proposed. Furthermore, message templates are provided to the user based on the match candidates proposed by the generation AI. For example, a template such as "Hello, I love movies too. What movies have you seen recently?" can be provided as a first message. This allows users to efficiently match. This allows the matching system to analyze a user's profile information, propose optimal match candidates, and provide message templates. For example, users with common hobbies can be matched and smoothly start communicating, making it easier for romantic relationships to develop.

[0062] A matching system according to an embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives profile information from a user. The user's profile information may include, but is not limited to, information such as a name, age, hobbies, and interests. The reception unit stores the information entered by the user in a database. The reception unit may also transmit the information entered by the user to a generation AI. The analysis unit uses the generation AI to analyze the information received by the reception unit and propose optimal match candidates. The analysis unit may propose other users with common hobbies based on the user's hobbies and interests. The analysis unit may also perform analysis using past match data. For example, the analysis unit may extract successful match patterns based on the past match data and propose optimal match candidates based on the extracted patterns. The provision unit provides a message template to the user based on the match candidates proposed by the generation AI. For example, the provision unit may provide a template such as "Hello, I love movies too. What movies have you seen recently?" as a first message. The provision unit may also generate message templates based on the user's profile information. For example, the providing unit generates an appropriate message template based on the user's hobbies and interests, thereby enabling the matching system according to the embodiment to efficiently analyze the user's profile information, propose optimal match candidates, and provide message templates.

[0063] The reception unit can receive detailed information such as the user's name, age, hobbies, and interests. The detailed information includes, but is not limited to, the name, age, hobbies, and interests. For example, the reception unit stores the name entered by the user in a database. The reception unit can also store the age entered by the user in a database. The reception unit can also store the hobbies and interests entered by the user in a database. For example, if the user enters watching movies as a hobby, the reception unit stores that information in the database. This makes it possible to receive detailed profile information of the user.

[0064] The analysis unit can suggest matching candidates based on the user's profile information. For example, the analysis unit suggests other users who share the same hobbies based on the user's hobbies and interests. For example, the analysis unit suggests other users who share the same hobbies to a user whose hobby is watching movies. The analysis unit can also suggest appropriate matching candidates based on the user's age and gender. For example, the analysis unit suggests users who are similar in age. Furthermore, the analysis unit can perform analysis by referring to past matching data. For example, the analysis unit extracts successful matching patterns based on past matching data and suggests optimal matching candidates based on those patterns. This makes it possible to suggest optimal matching candidates based on the user's profile information.

[0065] The providing unit can provide a message template based on the proposed matching candidates. For example, the providing unit can provide a template such as "Hello, I love movies too. What movies have you seen recently?" as a first message. The providing unit can also generate a message template based on the user's profile information. For example, the providing unit generates an appropriate message template based on the user's hobbies and interests. Furthermore, the providing unit can provide a message template to the user based on the matching candidates proposed by the generation AI. For example, the providing unit provides an appropriate message template for the matching candidates proposed by the generation AI. This makes it possible to provide a message template based on the matching candidates proposed by the generation AI.

[0066] The analysis unit can perform analysis based on past matching data. For example, the analysis unit extracts successful matching patterns based on the past matching data and proposes optimal matching candidates based on the extracted patterns. The analysis unit can also eliminate unsuccessful matching patterns based on the past matching data, thereby improving the accuracy of the proposals. Furthermore, the analysis unit can analyze user profile information by referring to the past matching data. For example, the analysis unit proposes optimal matching candidates based on the user's hobbies and interests based on the past matching data. In this way, the accuracy of the proposals is improved by performing analysis by referring to the past matching data.

[0067] The providing unit can generate a message template based on the user's profile information. The providing unit generates an appropriate message template based on, for example, the user's hobbies and interests. For example, the providing unit provides a movie-related message template to a user whose hobby is watching movies. The providing unit can also generate an appropriate message template based on the user's age and gender. For example, the providing unit provides an appropriate message template to users who are similar in age. Furthermore, the providing unit can also provide a message template to a user based on matching candidates proposed by the generation AI. For example, the providing unit provides an appropriate message template for a matching candidate proposed by the generation AI. In this way, by generating a message template based on the user's profile information, more appropriate messages can be provided.

[0068] The reception unit can analyze the user's emotions and adjust the profile information input method based on the analyzed user's emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick profile information input. For example, if the user is nervous, the reception unit can provide a simple interface and minimize input steps. This allows for more appropriate input by adjusting the profile information input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The reception unit can analyze the user's past profile information and suggest an input format. For example, the reception unit automatically suggests the optimal input format based on the profile information the user has input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest an input format to be used in a specific time period based on the user's past input history. For example, the reception unit automatically suggests the optimal input format based on the profile information the user has input in the past. In this way, the optimal input format can be suggested by analyzing the user's past profile information.

[0070] The reception unit can customize input items based on the user's current living situation and areas of interest when entering profile information. For example, the reception unit automatically displays related input items when the user enters their current living situation. The reception unit can also customize input items based on the user's areas of interest and prioritize the input of related information. Furthermore, the reception unit can analyze the user's current living situation and areas of interest and suggest optimal input items. For example, the reception unit automatically displays related input items when the user enters their current living situation. This allows the user to enter more appropriate information by customizing input items based on the user's current living situation and areas of interest.

[0071] When inputting profile information, the reception unit can select an input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the profile information using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also provide an interface optimized for text input. Furthermore, if the user selects image input, the reception unit can also input the profile information using image recognition technology. For example, if the user selects voice input, the reception unit inputs the profile information using voice recognition technology. This allows for more efficient input by selecting the optimal input means according to the user's input method.

[0072] The reception unit can analyze the user's emotions and determine the priority of the profile information to be input based on the analyzed user's emotions. For example, if the user is nervous, the reception unit can prioritize input of basic information (such as name and age). Furthermore, if the user is relaxed, the reception unit can prioritize input of detailed information (such as hobbies and interests). Furthermore, if the user is in a hurry, the reception unit can prioritize input of the most important information. For example, if the user is nervous, the reception unit can prioritize input of basic information. Thus, by determining the priority of the profile information according to the user's emotions, more appropriate information can be prioritized and input. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When entering profile information, the reception unit can prioritize input of highly relevant information based on the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize input of information related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize input of related information based on the user's current location. Furthermore, the reception unit can analyze the user's geographical location information and suggest optimal input items. For example, if the user lives in a specific area, the reception unit can prioritize input of information related to that area. In this way, highly relevant information can be prioritized by taking the user's geographical location information into consideration.

[0074] When entering profile information, the reception unit can analyze the user's social media activity and input related information. For example, the reception unit automatically inputs profile information based on information that the user has made public on social media. The reception unit can also analyze the user's social media activity and input related information. Furthermore, the reception unit can also input related information by taking into account the user's friendships on social media. For example, the reception unit automatically inputs profile information based on information that the user has made public on social media. In this way, related information can be input by analyzing the user's social media activity.

[0075] The reception unit can adjust the input method by reflecting the user's past feedback when entering profile information. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also customize the input interface based on the user's past feedback. Furthermore, the reception unit can analyze the user's past feedback and optimize the input procedure. For example, the reception unit can suggest an optimal input method based on feedback provided by the user in the past. In this way, the optimal input method can be provided by reflecting the user's past feedback.

[0076] The analysis unit can analyze the user's emotions and adjust the method of suggesting match candidates based on the analyzed user's emotions. For example, if the user is relaxed, the analysis unit can suggest detailed match candidates. Furthermore, if the user is nervous, the analysis unit can suggest simple and intuitive match candidates. Furthermore, if the user is in a hurry, the analysis unit can prioritize suggesting the most important match candidates. For example, if the user is relaxed, the analysis unit can suggest detailed match candidates. This allows for more appropriate suggestions by adjusting the method of suggesting match candidates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] When proposing matching candidates, the analysis unit can adjust the level of detail of the proposal based on the user's profile information. For example, if the user's hobbies and interests are important, the analysis unit can propose detailed matching candidates based on that. In addition, if the user's age and gender are important, the analysis unit can also propose detailed matching candidates based on that. Furthermore, the analysis unit can propose matching candidates with an optimal level of detail based on the user's past matching history. For example, if the user's hobbies and interests are important, the analysis unit can propose detailed matching candidates based on that. This allows for more appropriate proposals by adjusting the level of detail of the proposal based on the importance of the user's profile information.

[0078] When proposing matching candidates, the analysis unit can apply a proposal algorithm according to the user's hobbies and interests. For example, if the user's hobby is watching movies, the analysis unit can apply an algorithm that preferentially proposes other users who have the same hobby. Also, if the user's interest is sports, the analysis unit can apply an algorithm that preferentially proposes other users who have the same interest. Furthermore, the analysis unit can analyze the user's hobbies and interests and apply an optimal proposal algorithm. For example, if the user's hobby is watching movies, the analysis unit can apply an algorithm that preferentially proposes other users who have the same hobby. In this way, by applying different proposal algorithms according to the user's hobbies and interests, more appropriate matching candidates can be proposed.

[0079] When proposing matching candidates, the analysis unit can improve the accuracy of the suggestions based on the user's past matching results. For example, the analysis unit analyzes the user's past matching results and improves the accuracy of the suggestions based on successful matching patterns. The analysis unit can also eliminate unsuccessful matching patterns from the user's past matching results and improve the accuracy of the suggestions. Furthermore, the analysis unit can also suggest optimal matching candidates by referring to the user's past matching results. For example, the analysis unit analyzes the user's past matching results and improves the accuracy of the suggestions based on successful matching patterns. In this way, the accuracy of the suggestions is improved by referring to the user's past matching results.

[0080] The analysis unit can analyze the user's emotions and determine the priority of proposed match candidates based on the analyzed user's emotions. For example, if the user is relaxed, the analysis unit can prioritize proposing detailed match candidates. Furthermore, if the user is nervous, the analysis unit can prioritize proposing simple and intuitive match candidates. Furthermore, if the user is in a hurry, the analysis unit can prioritize proposing the most important match candidates. For example, if the user is relaxed, the analysis unit prioritizes proposing detailed match candidates. This allows for more appropriate proposals by prioritizing match candidates according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] When proposing match candidates, the analysis unit can prioritize proposing highly relevant candidates based on the user's geographical location information. For example, if the user lives in a specific area, the analysis unit can prioritize proposing match candidates related to that area. Also, if the user is traveling, the analysis unit can prioritize proposing related match candidates based on the user's current location. Furthermore, the analysis unit can analyze the user's geographical location information and propose optimal match candidates. For example, if the user lives in a specific area, the analysis unit can prioritize proposing match candidates related to that area. In this way, by taking the user's geographical location information into consideration, highly relevant match candidates can be prioritized.

[0082] When proposing match candidates, the analysis unit can analyze the user's social media activity and suggest related candidates. The analysis unit can, for example, suggest related match candidates based on information that the user has made public on social media. The analysis unit can also analyze the user's social media activity and suggest related match candidates. Furthermore, the analysis unit can also suggest related match candidates based on the user's friendships on social media. For example, the analysis unit can suggest related match candidates based on information that the user has made public on social media. In this way, related match candidates can be suggested by analyzing the user's social media activity.

[0083] When proposing matching candidates, the analysis unit can adjust the proposal method by reflecting the user's past feedback. The analysis unit can propose an optimal proposal method based on, for example, feedback provided by the user in the past. The analysis unit can also customize the proposal algorithm based on the user's past feedback. Furthermore, the analysis unit can analyze the user's past feedback and optimize the proposal procedure. For example, the analysis unit can propose an optimal proposal method based on feedback provided by the user in the past. In this way, the proposal method can be optimized by reflecting the user's past feedback.

[0084] The providing unit can analyze the user's emotions and adjust the expression method of the message template based on the analyzed user's emotions. For example, if the user is nervous, the providing unit can provide a simple and intuitive message template. Furthermore, if the user is relaxed, the providing unit can also provide a detailed message template. Furthermore, if the user is in a hurry, the providing unit can provide a short and to-the-point message template. For example, if the user is nervous, the providing unit can provide a simple and intuitive message template. This allows for adjusting the expression method of the message template according to the user's emotions, thereby providing a more appropriate message. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] When providing a message template, the providing unit can adjust the content of the template based on the profile information of the matching candidate. The providing unit provides a detailed message template based on, for example, the hobbies and interests of the matching candidate. The providing unit can also provide a detailed message template based on the age and gender of the matching candidate. Furthermore, the providing unit can provide a message template with an optimal level of detail based on the profile information of the matching candidate. For example, the providing unit provides a detailed message template based on the hobbies and interests of the matching candidate. This makes it possible to provide a more appropriate message by adjusting the level of detail of the template based on the profile information of the matching candidate.

[0086] When providing message templates, the providing unit can provide multiple templates according to the user's hobbies and interests. For example, if the user's hobby is watching movies, the providing unit can provide a message template related to movies. Also, if the user's interest is sports, the providing unit can provide a message template related to sports. Furthermore, the providing unit can analyze the user's hobbies and interests and provide an optimal message template. For example, if the user's hobby is watching movies, the providing unit can provide a message template related to movies. In this way, by providing different templates according to the user's hobbies and interests, more appropriate messages can be provided.

[0087] When providing a message template, the providing unit can improve the accuracy of the template based on the user's past message history. For example, the providing unit analyzes the user's past message history and improves the accuracy of the template based on patterns of successful messages. The providing unit can also improve the accuracy of the template by eliminating unsuccessful message patterns from the user's past message history. Furthermore, the providing unit can provide an optimal message template by referring to the user's past message history. For example, the providing unit analyzes the user's past message history and improves the accuracy of the template based on patterns of successful messages. In this way, the accuracy of the template is improved by referring to the user's past message history.

[0088] The providing unit can analyze the user's emotions and adjust the length of the message template based on the analyzed user's emotions. For example, if the user is nervous, the providing unit can provide a short and to-the-point message template. Furthermore, if the user is relaxed, the providing unit can also provide a longer message template including detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide a short message template that can be sent quickly. For example, if the user is nervous, the providing unit can provide a short and to-the-point message template. This allows for adjusting the length of the message template according to the user's emotions, thereby providing a more appropriate message. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] When providing message templates, the providing unit can determine the order of templates based on the submission date of matching candidates. For example, if a matching candidate has been submitted recently, the providing unit can provide a message template related to that candidate with priority. Also, if a matching candidate has been submitted in the past, the providing unit can provide a message template related to that candidate. Furthermore, the providing unit can analyze the submission date of the matching candidate and provide an optimal message template. For example, if a matching candidate has been submitted recently, the providing unit can provide a message template related to that candidate with priority. In this way, by determining the priority of templates based on the submission date of the matching candidate, a more appropriate message can be provided.

[0090] When providing message templates, the providing unit can determine the order of the templates based on the relevance of the matching candidates. For example, if the relevance of a matching candidate is high, the providing unit can preferentially provide a message template related to that candidate. Also, if the relevance of a matching candidate is low, the providing unit can postpone the message template related to that candidate. Furthermore, the providing unit can analyze the relevance of the matching candidates and provide the message templates in an optimal order. For example, if the relevance of a matching candidate is high, the providing unit preferentially provides a message template related to that candidate. In this way, by adjusting the order of templates based on the relevance of the matching candidates, a more appropriate message can be provided.

[0091] When providing a message template, the providing unit can determine the use of technical terms in the template depending on the user's level of expertise. For example, if the user's level of expertise is high, the providing unit can provide a message template that uses a lot of technical terms. In addition, if the user's level of expertise is low, the providing unit can provide a simple message template that avoids technical terms. Furthermore, the providing unit can analyze the user's level of expertise and provide a message template in which the use of technical terms is adjusted to be optimal. For example, if the user's level of expertise is high, the providing unit can provide a message template that uses a lot of technical terms. In this way, by adjusting the use of technical terms in the template depending on the user's level of expertise, a more appropriate message can be provided. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit may be realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit may be realized by the reception device 38 of the smart device 14 and receive profile information entered by the user. For example, the analysis unit may be realized by the specific processing unit 290 of the data processing device 12 and analyze the user's profile information using a generation AI to propose optimal matching candidates. For example, the provision unit may be realized by the output device 40 of the smart device 14 and provide the user with a message template based on the matching candidates proposed by the generation AI. For example, the reception unit, analysis unit, and provision unit may be realized by at least one of the data processing device 12 and the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, and provision unit may be realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit may be realized by the microphone 238 of the smart glasses 214 and receive profile information entered by the user. For example, the analysis unit may be realized by the specific processing unit 290 of the data processing device 12 and analyze the user's profile information using a generation AI to propose optimal match candidates. For example, the provision unit may be realized by the speaker 240 of the smart glasses 214 and provide the user with a message template based on the match candidates proposed by the generation AI. For example, the reception unit, analysis unit, and provision unit may be realized by at least one of the data processing device 12 and the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives profile information input by the user. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's profile information using a generation AI and proposes optimal matching candidates. For example, the provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides the user with a message template based on the matching candidates proposed by the generation AI. For example, the reception unit, analysis unit, and provision unit may be realized by at least one of the data processing device 12 and the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and provision unit may be realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit may be realized by the microphone 238 of the robot 414 and receive profile information input by the user. For example, the analysis unit may be realized by the specific processing unit 290 of the data processing device 12 and analyze the user's profile information using a generation AI to propose optimal match candidates. For example, the provision unit may be realized by the speaker 240 of the robot 414 and provide the user with a message template based on the match candidates proposed by the generation AI. For example, the reception unit, analysis unit, and provision unit may be realized by at least one of the data processing device 12 and the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can also provide real-time feedback based on the user's input. For example, if the user inputs "watching movies" as a hobby, the reception unit can present an additional question such as "What movie have you seen recently?". The reception unit can also suggest related topics and questions based on the information entered by the user. Furthermore, the reception unit can provide appropriate advice and suggestions in real time for the information entered by the user. This allows the user to enter more detailed and comprehensive profile information.

[0094] The analysis unit can also estimate the user's lifestyle and values ​​based on the user's profile information and suggest matching candidates based on that. For example, if the user likes outdoor activities, other users who also enjoy outdoor activities can be suggested. Also, if the user is interested in environmental protection, users who share the same values ​​can be suggested. Furthermore, the analysis unit can prioritize suggesting users who share common hobbies and interests based on the user's lifestyle and values. This makes it possible to suggest more appropriate matching candidates based on the user's lifestyle and values.

[0095] The providing unit can also analyze the user's emotions and adjust the tone of the message template based on the analyzed user's emotions. For example, if the user is nervous, a message template with a gentle tone can be provided. If the user is relaxed, a message template with a casual tone can be provided. Furthermore, if the user is excited, a message template with an energetic tone can be provided. In this way, by adjusting the tone of the message according to the user's emotions, more appropriate communication can be supported.

[0096] The providing unit can also analyze the user's past message history and improve the accuracy of the template based on patterns of successful messages. For example, it analyzes messages that the user has sent in the past that received a good response from the recipient and provides a template that incorporates that pattern. It can also improve the accuracy of the template by eliminating messages that the user has sent in the past that received a poor response from the recipient. Furthermore, it can also provide an optimal message template by referring to the user's past message history. In this way, the accuracy of the template can be improved by referring to the user's past message history.

[0097] The analysis unit can also analyze the user's emotions and adjust the method of suggesting match candidates based on the analyzed user's emotions. For example, if the user is relaxed, detailed match candidates can be suggested. If the user is nervous, simple and intuitive match candidates can be suggested. Furthermore, if the user is in a hurry, the most important match candidates can be suggested preferentially. This allows for more appropriate suggestions by adjusting the method of suggesting match candidates according to the user's emotions.

[0098] The providing unit can also analyze the user's emotions and adjust the length of the message template based on the analyzed user's emotions. For example, if the user is nervous, a short and to-the-point message template can be provided. Alternatively, if the user is relaxed, a longer message template including detailed explanations can be provided. Furthermore, if the user is in a hurry, a short message template that can be sent quickly can be provided. In this way, by adjusting the length of the message template according to the user's emotions, more appropriate messages can be provided.

[0099] The analysis unit can also predict the user's future behavior and interests based on the user's profile information and suggest matching candidates based on that. For example, if the user recently started a new hobby, other users related to that hobby can be suggested. Also, if the user is planning a trip in the future, users related to that travel destination can be suggested. Furthermore, the analysis unit can predict the user's future behavior and interests and suggest optimal matching candidates based on that. This makes it possible to suggest more appropriate matching candidates based on the user's future behavior and interests.

[0100] The providing unit can also analyze the user's emotions and adjust the expression style of the message template based on the analyzed user's emotions. For example, if the user is nervous, a simple and intuitive message template can be provided. If the user is relaxed, a detailed message template can be provided. Furthermore, if the user is in a hurry, a short and to-the-point message template can be provided. In this way, by adjusting the expression style of the message template according to the user's emotions, a more appropriate message can be provided.

[0101] The analysis unit can also estimate the user's health condition and fitness level based on the user's profile information and suggest matching candidates based on that. For example, if the user has an active lifestyle, other users with a similar active lifestyle can be suggested. Also, if the user is interested in health, users who are also interested in health can be suggested. Furthermore, based on the user's health condition and fitness level, users who share common health goals can be preferentially suggested. This allows more appropriate matching candidates to be suggested based on the user's health condition and fitness level.

[0102] The providing unit can also analyze the user's emotions and adjust the content of the message template based on the analyzed user's emotions. For example, if the user is nervous, a message template with content that will relax the user can be provided. Also, if the user is relaxed, a message template with casual content can be provided. Furthermore, if the user is excited, a message template with energetic content can be provided. In this way, by adjusting the content of the message according to the user's emotions, more appropriate communication can be supported.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives the user's profile information. The user's profile information may include, but is not limited to, for example, name, age, hobbies, and interests. The reception unit, for example, stores the information entered by the user in a database. The reception unit may also send the information entered by the user to the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information received by the reception unit and propose optimal match candidates. For example, the analysis unit proposes other users with common hobbies based on the user's hobbies and interests. The analysis unit can also perform analysis by referring to past matching data. For example, the analysis unit extracts successful matching patterns based on past matching data and proposes optimal match candidates based on those patterns. Step 3: The provider provides a message template to the user based on the matching candidates proposed by the generation AI. For example, the provider may provide a template such as "Hello, I love movies too. What movies have you seen recently?" as a first message. The provider can also generate message templates based on the user's profile information. For example, the provider may generate an appropriate message template based on the user's hobbies and interests.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0106] 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 engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above 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 necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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, RAM 30, and 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.

[0112] 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.

[0113] 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 audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] 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 carried out in a secure state.

[0116] Fig. 4 shows an example of the 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.

[0117] 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.

[0118] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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. The smart glasses 214 also 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 perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0122] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, 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, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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, RAM 30, and 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.

[0128] 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.

[0129] 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 audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and 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 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] 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 carried out in a secure state.

[0132] 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.

[0133] 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.

[0134] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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.

[0136] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] 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 audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio 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 audio data.

[0138] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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 also 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. Furthermore, 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, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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, RAM 30, and 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.

[0144] 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.

[0145] 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 audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] 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 carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0149] 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.

[0150] 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.

[0151] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0153] 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] 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 voice indicating a user input regarding 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.

[0155] 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. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing 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. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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 also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates 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. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically 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.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of 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, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] 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.

[0166] 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 USB (Universal Serial Bus) 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.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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.

[0171] As an example of a system configured with a single 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 system that uses a processor that realizes the functions of an 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.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation 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, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects 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 replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 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 reception unit that receives user profile information; an analysis unit that analyzes the information received by the reception unit and proposes matching candidates; a providing unit that provides a message template based on the matching candidates proposed by the analyzing unit. A system characterized by:

2. The reception unit Accepts detailed information about the user's name, age, hobbies, and interests 2. The system of claim 1.

3. The analysis unit Suggesting potential matches based on user profile information 2. The system of claim 1.

4. The providing unit Providing message templates based on suggested matches 2. The system of claim 1.

5. The analysis unit Conduct analysis based on past matching data 2. The system of claim 1.

6. The providing unit Generate message templates based on user profile information 2. The system of claim 1.

7. The reception unit Analyzing user emotions and adjusting how profile information is entered based on the analyzed user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past profile information and suggests input formats 2. The system of claim 1.

9. The reception unit As you fill out your profile information, it will adjust based on your current life situation and interests.

2. The system of claim 1.

10. The reception unit When entering profile information, select an input method according to the user's input method.

2. The system of claim 1.

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