system

Abstract

The system according to the embodiment aims to understand the marriage conditions that a user really desires and to propose suitable partner candidates. [Solution] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a matching unit. The reception unit allows the user to input marriage conditions. The analysis unit analyzes the conditions received by the reception unit and identifies the marriage conditions the user truly desires. The proposal unit proposes suitable partner candidates based on the conditions analyzed by the analysis unit. The matching unit matches the user with the partner candidates proposed by the proposal unit.

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 have had the problem of making it difficult to accurately grasp the marriage conditions that users are truly seeking and to propose suitable partner candidates.

[0005] The system according to the embodiment aims to understand the marriage conditions that a user really desires and to propose suitable partner candidates. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a proposal unit, and a matching unit. The reception unit allows the user to input marriage conditions. The analysis unit analyzes the conditions received by the reception unit and identifies the marriage conditions the user truly desires. The proposal unit proposes suitable partner candidates based on the conditions analyzed by the analysis unit. The matching unit matches the user with the partner candidates proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the marriage conditions that the user is really looking for and propose suitable partner 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 marriage consultation system according to an embodiment of the present invention is a system in which a user inputs marriage conditions, a generation AI analyzes the conditions, identifies the marriage conditions the user truly desires, proposes optimal partner candidates, and performs matching. In the marriage consultation system, a user inputs marriage conditions, a generation AI analyzes the conditions, and identifies the marriage conditions the user truly desires. Furthermore, the marriage consultation system proposes optimal partner candidates based on the user's conditions and matches the user with the partner candidates. For example, a user inputs conditions such as "high annual income," "similar hobbies," and "family-oriented." The marriage consultation system then uses a generation AI to analyze the conditions entered by the user and propose the marriage conditions the user truly desires. For example, if a user inputs "high annual income," the generation AI discovers the true condition behind it, "desire for a stable life." Furthermore, the marriage consultation system uses a generation AI to propose optimal partner candidates based on the user's conditions. For example, if a user inputs "similar hobbies," the generation AI proposes partner candidates with the same hobbies. The marriage consultation system then uses a generation AI to analyze the compatibility between the user and the partner candidates and performs optimal matching. This allows users to speed up the process of getting married. By replacing the manpower portion with AI, the marriage consultation system can reduce labor costs and reduce the amount of money users have to pay. Also, by using AI to uncover what people really want in terms of marriage conditions, it can speed up the process of getting married.

[0029] A marriage consultation system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a matching unit. The reception unit receives input of marriage conditions from a user. The input marriage conditions include, but are not limited to, age, occupation, hobbies, and values. The reception unit provides an interface for the user to input the marriage conditions. The interface may be implemented, for example, as a web form or a mobile application. The analysis unit uses a generation AI to analyze the conditions received by the reception unit and identify the marriage conditions the user truly desires. The analysis may be performed, for example, using data mining technology or natural language processing technology. For example, the generation AI may identify the true underlying conditions based on the conditions entered by the user. The proposal unit uses the generation AI to propose optimal partner candidates based on the conditions analyzed by the analysis unit. The proposal may be performed, for example, by selecting optimal partner candidates from a database. For example, the generation AI may propose partner candidates with the same hobbies based on the user's conditions. The matching unit uses the generation AI to match the user with the partner candidates proposed by the proposal unit. Matching is performed, for example, by analyzing the compatibility between the user and partner candidates. For example, the generation AI analyzes the compatibility between the user and partner candidates and performs optimal matching. As a result, the marriage consultation system according to the embodiment can efficiently analyze the user's marriage requirements, propose optimal partner candidates, and perform matching.

[0030] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display as candidates marriage conditions that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest marriage conditions to be used in a specific time period based on the user's past input history. This improves the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0031] When entering marriage conditions, the reception unit can filter input items based on the user's current living situation and areas of interest. For example, when the user enters their current living situation, the reception unit preferentially displays related marriage conditions based on that information. The reception unit can also suggest related marriage conditions based on the user's areas of interest (hobbies, occupation, etc.). The reception unit can also analyze the user's living situation and areas of interest and automatically filter optimal input items. This allows the user to enter more appropriate marriage conditions by filtering the input items based on their living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's living situation data into a generation AI and have the generation AI filter optimal input items.

[0032] When inputting marriage conditions, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the marriage conditions using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input the marriage conditions using a keyboard or touch panel. Furthermore, if the user selects image input, the reception unit can also input the marriage conditions using image recognition technology. This improves input convenience by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0033] When inputting marriage conditions, the reception unit can prioritize inputting highly relevant conditions by taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize inputting marriage conditions related to that area. Furthermore, if the user is traveling, the reception unit can also suggest relevant marriage conditions based on the user's current location. Furthermore, the reception unit can analyze the user's geographical location information and prioritize inputting optimal marriage conditions. In this way, by taking the user's geographical location information into account, highly relevant marriage conditions can be prioritized. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to suggest highly relevant conditions.

[0034] When marriage conditions are input, the reception unit can analyze the user's social media activity and suggest related conditions. The reception unit can suggest related marriage conditions based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related marriage conditions. The reception unit can also suggest related marriage conditions by referring to the activity of the user's friends on social media. In this way, relevant marriage conditions can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related conditions.

[0035] The reception unit can provide an appropriate input method by reflecting the user's past feedback when entering marriage conditions. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also optimize the input procedure by referring to the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI suggest an optimal input method.

[0036] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the marriage conditions. For example, the analysis unit provides detailed analysis results for important marriage conditions. The analysis unit can also provide concise analysis results for less important marriage conditions. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the marriage conditions. This allows the provision of analysis results based on the importance of the marriage conditions, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of marriage conditions into the generation AI and have the generation AI adjust the accuracy of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of marriage conditions. For example, the analysis unit can apply an analysis algorithm based on economic data to economic conditions. The analysis unit can also apply an analysis algorithm based on social media data to hobbies and interests. The analysis unit can also apply an analysis algorithm based on psychological data to family relationships. In this way, by applying an analysis algorithm depending on the category of marriage conditions, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of marriage conditions into the generation AI and cause the generation AI to apply different analysis algorithms.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis result based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the order of analysis based on the submission date of the marriage conditions. For example, the analysis unit prioritizes analysis of recently submitted marriage conditions. The analysis unit can also postpone analysis of marriage conditions that were submitted earlier. The analysis unit can also dynamically adjust the analysis priority based on the submission date. This allows for providing analysis results based on the submission date of the marriage conditions, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the marriage conditions into the generation AI and have the generation AI determine the analysis order.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the marriage conditions. For example, the analysis unit prioritizes analysis of highly relevant marriage conditions. The analysis unit can also postpone analysis of less relevant marriage conditions. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the marriage conditions. This makes it possible to provide information that meets the user's needs by providing analysis results based on the relevance of the marriage conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of marriage conditions into the generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of analysis terminology according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit can provide analysis results using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the analysis unit can provide analysis results using concise, easy-to-understand language. The analysis unit can also dynamically adjust the use of technical terms according to the user's level of expertise. This provides analysis results according to the user's level of expertise, thereby deepening the user's understanding. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of terminology.

[0042] The suggestion unit can adjust the level of detail of the proposal based on the importance of the partner candidate when making a proposal. For example, the suggestion unit can provide detailed proposals for important partner candidates. The suggestion unit can also provide concise proposals for less important partner candidates. The suggestion unit can also dynamically adjust the level of detail of the proposal based on the importance of the partner candidate. This allows the provision of proposal results based on the importance of the partner candidate, thereby providing information that meets the user's needs. Some or all of the above-described processing by the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the partner candidate to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0043] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the partner candidate. For example, the proposal unit can apply a proposal algorithm based on economic data to economic conditions. The proposal unit can also apply a proposal algorithm based on social media data to hobbies and interests. The proposal unit can also apply a proposal algorithm based on psychological data to family relationships. In this way, by applying a proposal algorithm depending on the category of the partner candidate, more accurate proposal results can be provided. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input category data of the partner candidate into the generation AI and cause the generation AI to apply different proposal algorithms.

[0044] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, adjusts the current suggestion result based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0045] When making a proposal, the proposal unit can determine the order of proposals based on the submission dates of the partner candidates. For example, the proposal unit can prioritize the most recently submitted partner candidates. The proposal unit can also postpone the proposal of partner candidates that were submitted earlier. The proposal unit can also dynamically adjust the priority of proposals based on the submission dates. This makes it possible to provide information that meets the user's needs by providing proposal results based on the submission dates of the partner candidates. Some or all of the above-described processing by the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input submission date data of partner candidates into the generation AI and have the generation AI determine the order of proposals.

[0046] The suggestion unit can adjust the order of proposals based on the relevance of the partner candidates when making proposals. For example, the suggestion unit can prioritize proposing highly relevant partner candidates. The suggestion unit can also postpone proposing less relevant partner candidates. The suggestion unit can also dynamically adjust the order of proposals based on the relevance of the partner candidates. This allows for providing information that meets the user's needs by providing proposal results based on the relevance of the partner candidates. Some or all of the above-described processing by the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input relevance data of the partner candidates into a generation AI and cause the generation AI to adjust the order of proposals.

[0047] When making a proposal, the suggestion unit can adjust the use of proposed terminology according to the user's level of expertise. For example, if the user has specialized knowledge, the suggestion unit can provide a proposal using detailed technical terms. Furthermore, if the user does not have specialized knowledge, the suggestion unit can also provide a proposal using concise, easy-to-understand language. The suggestion unit can also dynamically adjust the use of proposed terminology according to the user's level of expertise. This allows the user to deepen their understanding by providing a proposal result according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terminology.

[0048] During matching, the matching unit can analyze the user's past matching history to select an appropriate matching method. The matching unit can, for example, propose an optimal matching method based on the user's past matching history. The matching unit can also analyze the user's past matching history and optimize the matching algorithm. The matching unit can also improve matching accuracy by referring to the user's past matching history. This improves matching accuracy by providing an optimal matching method based on the user's past matching history. Some or all of the above-described processing in the matching unit can be performed, for example, using AI or without AI. For example, the matching unit can input the user's past matching data into the generation AI and cause the generation AI to select the optimal matching method.

[0049] The matching unit can customize the matching means based on the user's current living situation during matching. The matching unit, for example, proposes the optimal matching means based on the user's current living situation. The matching unit can also customize the matching means according to the user's living situation. The matching unit can also analyze the user's living situation and provide the optimal matching means. This improves user satisfaction by providing a matching means according to the user's living situation. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's living situation data into the generation AI and cause the generation AI to propose the optimal matching means.

[0050] The matching unit can improve the matching means by reflecting user feedback during matching. For example, the matching unit improves the matching method based on user feedback. The matching unit can also analyze user feedback and optimize the matching algorithm. The matching unit can also improve the accuracy of matching by referring to user feedback. In this way, the accuracy of matching is improved by reflecting user feedback. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input user feedback data into the generation AI and cause the generation AI to improve the matching means.

[0051] The matching unit can select an appropriate matching method by taking into account the user's geographical location information when matching. The matching unit, for example, proposes an optimal matching method based on the user's geographical location information. The matching unit can also analyze the user's geographical location information and optimize the matching algorithm. The matching unit can also improve matching accuracy by referring to the user's geographical location information. This makes it possible to provide an optimal matching method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's geographical location data into the generation AI and cause the generation AI to select an optimal matching method.

[0052] During matching, the matching unit can analyze the user's social media activity and propose a matching method. The matching unit can propose an optimal matching method based on the user's social media activity, for example. The matching unit can also analyze the user's social media activity and optimize the matching algorithm. The matching unit can also improve the accuracy of matching by referring to the user's social media activity. In this way, the optimal matching method can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's social media data into a generation AI and have the generation AI propose an optimal matching method.

[0053] The matching unit can provide an appropriate matching method by reflecting the user's past feedback during matching. The matching unit can, for example, propose an optimal matching method based on the user's past feedback. The matching unit can also analyze the user's past feedback and optimize the matching algorithm. The matching unit can also improve matching accuracy by referring to the user's past feedback. In this way, the optimal matching method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input the user's feedback data into the generation AI and cause the generation AI to provide an appropriate matching method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can analyze the user's past purchasing history and analyze marriage conditions based on purchasing patterns. For example, if the user frequently purchases expensive products, it can be assumed that the user is looking for a financially stable partner. Also, if the user purchases a lot of health foods, it can be assumed that the user is looking for a health-conscious partner. Furthermore, if the user purchases a lot of travel-related products, it can be assumed that the user is looking for a partner who likes to travel. In this way, by analyzing marriage conditions based on the user's purchasing history, it is possible to provide more accurate analysis results.

[0056] The matching unit can analyze the user's past travel history and perform matching based on travel destination preferences. For example, if the user prefers beach resorts, partner candidates who also prefer beach resorts can be preferentially matched. Also, if the user prefers mountainous areas, partner candidates who also prefer mountainous areas can be preferentially matched. Furthermore, if the user prefers urban tourism, partner candidates who also prefer urban tourism can be preferentially matched. In this way, by performing matching based on the user's travel history, more appropriate partner candidates can be proposed.

[0057] The analysis unit can analyze the user's past fitness data and analyze marriage conditions based on fitness preferences. For example, if the user likes running, it can be assumed that the user is looking for a partner who also likes running. If the user likes yoga, it can be assumed that the user is looking for a partner who also likes yoga. Furthermore, if the user likes working out at the gym, it can be assumed that the user is looking for a partner who also likes working out at the gym. In this way, by analyzing marriage conditions based on the user's fitness data, it is possible to provide more accurate analysis results.

[0058] The matching unit can analyze the user's past event participation history and perform matching based on event preferences. For example, if the user likes music festivals, partner candidates who also like music festivals can be preferentially matched. If the user likes sporting events, partner candidates who also like sporting events can be preferentially matched. Furthermore, if the user likes art exhibitions, partner candidates who also like art exhibitions can be preferentially matched. In this way, by performing matching based on the user's event participation history, more appropriate partner candidates can be proposed.

[0059] The analysis unit can analyze the user's past reading history and analyze marriage conditions based on the user's reading preferences. For example, if the user likes mystery novels, it can be assumed that the user is looking for a partner who also likes mystery novels. If the user likes romance novels, it can be assumed that the user is looking for a partner who also likes romance novels. Furthermore, if the user likes non-fiction, it can be assumed that the user is looking for a partner who also likes non-fiction. In this way, by analyzing marriage conditions based on the user's reading history, it is possible to provide more accurate analysis results.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit receives the user's input of marriage conditions. The marriage conditions input by the user include, for example, age, occupation, hobbies, and values. The reception unit provides an interface for the user to input the marriage conditions, and the interface is realized as a web form, a mobile application, or the like. Step 2: The analysis unit uses the generation AI to analyze the conditions received by the reception unit and uncover the marriage conditions the user is truly seeking. The analysis is carried out using data mining and natural language processing technologies to discover the true conditions behind the conditions entered by the user. Step 3: The suggestion unit uses the generation AI to suggest optimal partner candidates based on the conditions analyzed by the analysis unit. The suggestion is made by selecting the most suitable partner candidates from the database, and suggests partner candidates with the same hobbies based on the user's conditions. Step 4: The matching unit uses the generation AI to match the user with the partner candidates proposed by the proposal unit. Matching is performed by analyzing the compatibility between the user and the partner candidates, achieving the optimal match.

[0062] (Example 2) A marriage consultation system according to an embodiment of the present invention is a system in which a user inputs marriage conditions, a generation AI analyzes the conditions, identifies the marriage conditions the user truly desires, proposes optimal partner candidates, and performs matching. In the marriage consultation system, a user inputs marriage conditions, a generation AI analyzes the conditions, and identifies the marriage conditions the user truly desires. Furthermore, the marriage consultation system proposes optimal partner candidates based on the user's conditions and matches the user with the partner candidates. For example, a user inputs conditions such as "high annual income," "similar hobbies," and "family-oriented." The marriage consultation system then uses a generation AI to analyze the conditions entered by the user and propose the marriage conditions the user truly desires. For example, if a user inputs "high annual income," the generation AI discovers the true condition behind it, "desire for a stable life." Furthermore, the marriage consultation system uses a generation AI to propose optimal partner candidates based on the user's conditions. For example, if a user inputs "similar hobbies," the generation AI proposes partner candidates with the same hobbies. The marriage consultation system then uses a generation AI to analyze the compatibility between the user and the partner candidates and performs optimal matching. This allows users to speed up the process of getting married. By replacing the manpower portion with AI, the marriage consultation system can reduce labor costs and reduce the amount of money users have to pay. Also, by using AI to uncover what people really want in terms of marriage conditions, it can speed up the process of getting married.

[0063] A marriage consultation system according to an embodiment includes a reception unit, an analysis unit, a proposal unit, and a matching unit. The reception unit receives input of marriage conditions from a user. The input marriage conditions include, but are not limited to, age, occupation, hobbies, and values. The reception unit provides an interface for the user to input the marriage conditions. The interface may be implemented, for example, as a web form or a mobile application. The analysis unit uses a generation AI to analyze the conditions received by the reception unit and identify the marriage conditions the user truly desires. The analysis may be performed, for example, using data mining technology or natural language processing technology. For example, the generation AI may identify the true underlying conditions based on the conditions entered by the user. The proposal unit uses the generation AI to propose optimal partner candidates based on the conditions analyzed by the analysis unit. The proposal may be performed, for example, by selecting optimal partner candidates from a database. For example, the generation AI may propose partner candidates with the same hobbies based on the user's conditions. The matching unit uses the generation AI to match the user with the partner candidates proposed by the proposal unit. Matching is performed, for example, by analyzing the compatibility between the user and partner candidates. For example, the generation AI analyzes the compatibility between the user and partner candidates and performs optimal matching. As a result, the marriage consultation system according to the embodiment can efficiently analyze the user's marriage requirements, propose optimal partner candidates, and perform matching.

[0064] The reception unit can estimate the user's emotions and customize the marriage condition input interface based on the estimated user emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive interface to reduce the effort required for input. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable interface. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick input of marriage conditions. This reduces the effort required for input by providing an interface tailored to the user's emotions and improves user satisfaction. 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. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0065] The reception unit can analyze the user's past input history and suggest an appropriate input method. For example, the reception unit can automatically display as candidates marriage conditions that the user has frequently input in the past. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest marriage conditions to be used in a specific time period based on the user's past input history. This improves the user's input efficiency by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0066] When entering marriage conditions, the reception unit can filter input items based on the user's current living situation and areas of interest. For example, when the user enters their current living situation, the reception unit preferentially displays related marriage conditions based on that information. The reception unit can also suggest related marriage conditions based on the user's areas of interest (hobbies, occupation, etc.). The reception unit can also analyze the user's living situation and areas of interest and automatically filter optimal input items. This allows the user to enter more appropriate marriage conditions by filtering the input items based on their living situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's living situation data into a generation AI and have the generation AI filter optimal input items.

[0067] When inputting marriage conditions, the reception unit can select an appropriate input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the marriage conditions using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input the marriage conditions using a keyboard or touch panel. Furthermore, if the user selects image input, the reception unit can also input the marriage conditions using image recognition technology. This improves input convenience by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's voice data to a generation AI and have the generation AI convert the voice data into text data.

[0068] The reception unit can estimate the user's emotions and determine the priority of the marriage conditions to be input based on the estimated user emotions. For example, when the user is stressed, the reception unit can cause the user to input important marriage conditions first. Furthermore, when the user is relaxed, the reception unit can cause the user to input detailed marriage conditions first. Furthermore, when the user is in a hurry, the reception unit can cause the user to input the most important marriage conditions first. Thus, by determining the priority of marriage conditions according to the user's emotions, important conditions can be input first. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0069] When inputting marriage conditions, the reception unit can prioritize inputting highly relevant conditions by taking into account the user's geographical location information. For example, if the user lives in a specific area, the reception unit can prioritize inputting marriage conditions related to that area. Furthermore, if the user is traveling, the reception unit can also suggest relevant marriage conditions based on the user's current location. Furthermore, the reception unit can analyze the user's geographical location information and prioritize inputting optimal marriage conditions. In this way, by taking the user's geographical location information into account, highly relevant marriage conditions can be prioritized. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to suggest highly relevant conditions.

[0070] When marriage conditions are input, the reception unit can analyze the user's social media activity and suggest related conditions. The reception unit can suggest related marriage conditions based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity and suggest related marriage conditions. The reception unit can also suggest related marriage conditions by referring to the activity of the user's friends on social media. In this way, relevant marriage conditions can be suggested by analyzing the user's social media activity. Some or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the user's social media data into a generation AI and have the generation AI suggest related conditions.

[0071] The reception unit can provide an appropriate input method by reflecting the user's past feedback when entering marriage conditions. The reception unit can, for example, suggest an optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also optimize the input procedure by referring to the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI suggest an optimal input method.

[0072] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that focus on the main points. This provides analysis results tailored to the user's emotions, thereby deepening the user's understanding. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] During analysis, the analysis unit can adjust the accuracy of the analysis based on the importance of the marriage conditions. For example, the analysis unit provides detailed analysis results for important marriage conditions. The analysis unit can also provide concise analysis results for less important marriage conditions. The analysis unit can also dynamically adjust the level of detail of the analysis based on the importance of the marriage conditions. This allows the provision of analysis results based on the importance of the marriage conditions, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of marriage conditions into the generation AI and have the generation AI adjust the accuracy of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of marriage conditions. For example, the analysis unit can apply an analysis algorithm based on economic data to economic conditions. The analysis unit can also apply an analysis algorithm based on social media data to hobbies and interests. The analysis unit can also apply an analysis algorithm based on psychological data to family relationships. In this way, by applying an analysis algorithm depending on the category of marriage conditions, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of marriage conditions into the generation AI and cause the generation AI to apply different analysis algorithms.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis result based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and optimize the analysis algorithm. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0076] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide a visually stimulating analysis result. This allows the user to deepen their understanding by providing the length of the analysis result according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can determine the order of analysis based on the submission date of the marriage conditions. For example, the analysis unit prioritizes analysis of recently submitted marriage conditions. The analysis unit can also postpone analysis of marriage conditions that were submitted earlier. The analysis unit can also dynamically adjust the analysis priority based on the submission date. This allows for providing analysis results based on the submission date of the marriage conditions, thereby providing information that meets the user's needs. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of the marriage conditions into the generation AI and have the generation AI determine the analysis order.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the marriage conditions. For example, the analysis unit prioritizes analysis of highly relevant marriage conditions. The analysis unit can also postpone analysis of less relevant marriage conditions. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the marriage conditions. This makes it possible to provide information that meets the user's needs by providing analysis results based on the relevance of the marriage conditions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of marriage conditions into the generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of analysis terminology according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit can provide analysis results using detailed technical terms. Alternatively, if the user does not have specialized knowledge, the analysis unit can provide analysis results using concise, easy-to-understand language. The analysis unit can also dynamically adjust the use of technical terms according to the user's level of expertise. This provides analysis results according to the user's level of expertise, thereby deepening the user's understanding. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of terminology.

[0080] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. Furthermore, if the user is relaxed, the suggestion unit can provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. This allows for deeper understanding of the user by providing suggestions based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The suggestion unit can adjust the level of detail of the proposal based on the importance of the partner candidate when making a proposal. For example, the suggestion unit can provide detailed proposals for important partner candidates. The suggestion unit can also provide concise proposals for less important partner candidates. The suggestion unit can also dynamically adjust the level of detail of the proposal based on the importance of the partner candidate. This allows the provision of proposal results based on the importance of the partner candidate, thereby providing information that meets the user's needs. Some or all of the above-described processing by the suggestion unit can be performed using, or without, AI, for example. For example, the suggestion unit can input importance data of the partner candidate to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0082] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the partner candidate. For example, the proposal unit can apply a proposal algorithm based on economic data to economic conditions. The proposal unit can also apply a proposal algorithm based on social media data to hobbies and interests. The proposal unit can also apply a proposal algorithm based on psychological data to family relationships. In this way, by applying a proposal algorithm depending on the category of the partner candidate, more accurate proposal results can be provided. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input category data of the partner candidate into the generation AI and cause the generation AI to apply different proposal algorithms.

[0083] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, adjusts the current suggestion result based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and optimize the suggestion algorithm. The suggestion unit can also improve the accuracy of the suggestion by referring to the user's past suggestion results. In this way, the accuracy of the suggestion is improved by referring to the user's past suggestion results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion data into the generation AI and cause the generation AI to improve the accuracy of the suggestion.

[0084] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. This allows for a deeper understanding of the user by providing the length of the suggestion results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] When making a proposal, the proposal unit can determine the order of proposals based on the submission dates of the partner candidates. For example, the proposal unit can prioritize the most recently submitted partner candidates. The proposal unit can also postpone the proposal of partner candidates that were submitted earlier. The proposal unit can also dynamically adjust the priority of proposals based on the submission dates. This makes it possible to provide information that meets the user's needs by providing proposal results based on the submission dates of the partner candidates. Some or all of the above-described processing by the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input submission date data of partner candidates into the generation AI and have the generation AI determine the order of proposals.

[0086] The suggestion unit can adjust the order of proposals based on the relevance of the partner candidates when making proposals. For example, the suggestion unit can prioritize proposing highly relevant partner candidates. The suggestion unit can also postpone proposing less relevant partner candidates. The suggestion unit can also dynamically adjust the order of proposals based on the relevance of the partner candidates. This allows for providing information that meets the user's needs by providing proposal results based on the relevance of the partner candidates. Some or all of the above-described processing by the suggestion unit can be performed using, or without, AI. For example, the suggestion unit can input relevance data of the partner candidates into a generation AI and cause the generation AI to adjust the order of proposals.

[0087] When making a proposal, the suggestion unit can adjust the use of proposed terminology according to the user's level of expertise. For example, if the user has specialized knowledge, the suggestion unit can provide a proposal using detailed technical terms. Furthermore, if the user does not have specialized knowledge, the suggestion unit can also provide a proposal using concise, easy-to-understand language. The suggestion unit can also dynamically adjust the use of proposed terminology according to the user's level of expertise. This allows the user to deepen their understanding by providing a proposal result according to the user's level of expertise. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of terminology.

[0088] The matching unit can estimate the user's emotions and adjust the matching method based on the estimated user emotions. For example, if the user is nervous, the matching unit can provide a simple, highly visible matching method. Furthermore, if the user is relaxed, the matching unit can provide a detailed matching method. Furthermore, if the user is in a hurry, the matching unit can provide a matching method that focuses on the key points. This improves user satisfaction by providing a matching method that suits the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or without AI. For example, the matching unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0089] During matching, the matching unit can analyze the user's past matching history to select an appropriate matching method. The matching unit can, for example, propose an optimal matching method based on the user's past matching history. The matching unit can also analyze the user's past matching history and optimize the matching algorithm. The matching unit can also improve matching accuracy by referring to the user's past matching history. This improves matching accuracy by providing an optimal matching method based on the user's past matching history. Some or all of the above-described processing in the matching unit can be performed, for example, using AI or without AI. For example, the matching unit can input the user's past matching data into the generation AI and cause the generation AI to select the optimal matching method.

[0090] The matching unit can customize the matching means based on the user's current living situation during matching. The matching unit, for example, proposes the optimal matching means based on the user's current living situation. The matching unit can also customize the matching means according to the user's living situation. The matching unit can also analyze the user's living situation and provide the optimal matching means. This improves user satisfaction by providing a matching means according to the user's living situation. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input the user's living situation data into the generation AI and cause the generation AI to propose the optimal matching means.

[0091] The matching unit can improve the matching means by reflecting user feedback during matching. For example, the matching unit improves the matching method based on user feedback. The matching unit can also analyze user feedback and optimize the matching algorithm. The matching unit can also improve the accuracy of matching by referring to user feedback. In this way, the accuracy of matching is improved by reflecting user feedback. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input user feedback data into the generation AI and cause the generation AI to improve the matching means.

[0092] The matching unit can estimate the user's emotions and determine matching priorities based on the estimated user emotions. For example, if the user is nervous, the matching unit can prioritize important matching. Furthermore, if the user is relaxed, the matching unit can also perform detailed matching. Furthermore, if the user is in a hurry, the matching unit can also prioritize the most important matching. This improves user satisfaction by providing matching priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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. Some or all of the above-described processing in the matching unit can be performed using, for example, an AI, or without an AI. For example, the matching unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0093] The matching unit can select an appropriate matching method by taking into account the user's geographical location information when matching. The matching unit, for example, proposes an optimal matching method based on the user's geographical location information. The matching unit can also analyze the user's geographical location information and optimize the matching algorithm. The matching unit can also improve matching accuracy by referring to the user's geographical location information. This makes it possible to provide an optimal matching method by taking the user's geographical location information into consideration. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's geographical location data into the generation AI and cause the generation AI to select an optimal matching method.

[0094] During matching, the matching unit can analyze the user's social media activity and propose a matching method. The matching unit can propose an optimal matching method based on the user's social media activity, for example. The matching unit can also analyze the user's social media activity and optimize the matching algorithm. The matching unit can also improve the accuracy of matching by referring to the user's social media activity. In this way, the optimal matching method can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit can input the user's social media data into a generation AI and have the generation AI propose an optimal matching method.

[0095] The matching unit can provide an appropriate matching method by reflecting the user's past feedback during matching. The matching unit can, for example, propose an optimal matching method based on the user's past feedback. The matching unit can also analyze the user's past feedback and optimize the matching algorithm. The matching unit can also improve matching accuracy by referring to the user's past feedback. In this way, the optimal matching method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the matching unit can be performed using, for example, AI, or can be performed without using AI. For example, the matching unit can input the user's feedback data into the generation AI and cause the generation AI to provide an appropriate matching method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, proposal unit, and matching unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and provides an interface for the user to input marriage conditions. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the conditions received by the reception unit using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal partner candidates based on the analyzed conditions. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the proposed partner candidates with the user. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, proposal unit, and matching unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for the user to input marriage conditions. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the conditions accepted by the reception unit using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal partner candidates based on the analyzed conditions. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the proposed partner candidates with the user. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, proposal unit, and matching 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 control unit 46A of the headset-type terminal 314 and provides an interface for the user to input marriage conditions. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the conditions received by the reception unit using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal partner candidates based on the analyzed conditions. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the proposed partner candidates with the user. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, proposal unit, and matching unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and provides an interface for the user to input marriage conditions. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the conditions received by the reception unit using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes optimal partner candidates based on the analyzed conditions. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and matches the proposed partner candidates with the user.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can monitor the user's health condition and adjust the marriage condition input interface based on the health condition. For example, if the user is tired, a simple and intuitive interface can be provided, reducing the effort required for input. Alternatively, if the user is healthy and energetic, detailed input options can be provided and a customizable interface can be suggested. Furthermore, if the user is ill or injured, voice input can be prioritized, allowing the user to quickly input marriage conditions. In this way, by providing an interface that corresponds to the user's health condition, the effort required for input can be reduced and user satisfaction can be improved.

[0098] The analysis unit can analyze the user's past purchasing history and analyze marriage conditions based on purchasing patterns. For example, if the user frequently purchases expensive products, it can be assumed that the user is looking for a financially stable partner. Also, if the user purchases a lot of health foods, it can be assumed that the user is looking for a health-conscious partner. Furthermore, if the user purchases a lot of travel-related products, it can be assumed that the user is looking for a partner who likes to travel. In this way, by analyzing marriage conditions based on the user's purchasing history, it is possible to provide more accurate analysis results.

[0099] The suggestion unit can estimate the user's emotions and customize the profile of the partner candidate to be suggested based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible profile can be provided. If the user is relaxed, a detailed profile can be provided. Furthermore, if the user is excited, a visually stimulating profile can be provided. In this way, by providing a profile according to the user's emotions, the system can deepen understanding of the user.

[0100] The matching unit can analyze the user's past travel history and perform matching based on travel destination preferences. For example, if the user prefers beach resorts, partner candidates who also prefer beach resorts can be preferentially matched. Also, if the user prefers mountainous areas, partner candidates who also prefer mountainous areas can be preferentially matched. Furthermore, if the user prefers urban tourism, partner candidates who also prefer urban tourism can be preferentially matched. In this way, by performing matching based on the user's travel history, more appropriate partner candidates can be proposed.

[0101] The reception unit can estimate the user's emotions and provide relaxing music when entering marriage conditions based on the estimated user emotions. For example, if the user is nervous, relaxing music can be played to reduce the effort required for input. Also, if the user is relaxed, music that helps the user concentrate can be provided. Furthermore, if the user is in a hurry, fast-paced music can be provided to enable the user to quickly enter marriage conditions. In this way, providing music that matches the user's emotions reduces the effort required for input and improves user satisfaction.

[0102] The analysis unit can analyze the user's past fitness data and analyze marriage conditions based on fitness preferences. For example, if the user likes running, it can be assumed that the user is looking for a partner who also likes running. If the user likes yoga, it can be assumed that the user is looking for a partner who also likes yoga. Furthermore, if the user likes working out at the gym, it can be assumed that the user is looking for a partner who also likes working out at the gym. In this way, by analyzing marriage conditions based on the user's fitness data, it is possible to provide more accurate analysis results.

[0103] The suggestion unit can estimate the user's emotions and adjust the introduction text of the partner candidate to be suggested based on the estimated user's emotions. For example, if the user is nervous, a simple and highly visible introduction text can be provided. If the user is relaxed, a detailed introduction text can be provided. Furthermore, if the user is excited, a visually stimulating introduction text can be provided. In this way, by providing an introduction text that matches the user's emotions, the system can deepen understanding of the user.

[0104] The matching unit can analyze the user's past event participation history and perform matching based on event preferences. For example, if the user likes music festivals, partner candidates who also like music festivals can be preferentially matched. If the user likes sporting events, partner candidates who also like sporting events can be preferentially matched. Furthermore, if the user likes art exhibitions, partner candidates who also like art exhibitions can be preferentially matched. In this way, by performing matching based on the user's event participation history, more appropriate partner candidates can be proposed.

[0105] The reception unit can estimate the user's emotions and provide a relaxing scent when entering marriage conditions based on the estimated user emotions. For example, if the user is nervous, a relaxing scent can be provided to reduce the effort required for input. Also, if the user is relaxed, a scent that helps the user concentrate can be provided. Furthermore, if the user is in a hurry, a refreshing scent can be provided to enable the user to quickly enter marriage conditions. In this way, providing a scent that matches the user's emotions reduces the effort required for input and improves user satisfaction.

[0106] The analysis unit can analyze the user's past reading history and analyze marriage conditions based on the user's reading preferences. For example, if the user likes mystery novels, it can be assumed that the user is looking for a partner who also likes mystery novels. If the user likes romance novels, it can be assumed that the user is looking for a partner who also likes romance novels. Furthermore, if the user likes non-fiction, it can be assumed that the user is looking for a partner who also likes non-fiction. In this way, by analyzing marriage conditions based on the user's reading history, it is possible to provide more accurate analysis results.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit receives the user's input of marriage conditions. The marriage conditions input by the user include, for example, age, occupation, hobbies, and values. The reception unit provides an interface for the user to input the marriage conditions, and the interface is realized as a web form, a mobile application, or the like. Step 2: The analysis unit uses the generation AI to analyze the conditions received by the reception unit and uncover the marriage conditions the user is truly seeking. The analysis is carried out using data mining and natural language processing technologies to discover the true conditions behind the conditions entered by the user. Step 3: The suggestion unit uses the generation AI to suggest optimal partner candidates based on the conditions analyzed by the analysis unit. The suggestion is made by selecting the most suitable partner candidates from the database, and suggests partner candidates with the same hobbies based on the user's conditions. Step 4: The matching unit uses the generation AI to match the user with the partner candidates proposed by the proposal unit. Matching is performed by analyzing the compatibility between the user and the partner candidates, achieving the optimal match.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 for allowing a user to input marriage conditions; an analysis unit that analyzes the conditions received by the reception unit and identifies the marriage conditions that the user really desires; a proposal unit that proposes suitable partner candidates based on the conditions analyzed by the analysis unit; a matching unit that matches users with partner candidates proposed by the proposal unit; A system characterized by:

2. The reception unit The user's emotions are estimated, and the marriage condition input interface is customized based on the estimated user's emotions.

2. The system of claim 1.

3. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

4. The reception unit When entering marriage criteria, filter the input items based on the user's current living situation and interests.

2. The system of claim 1.

5. The reception unit When entering marriage conditions, select the appropriate input method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's feelings and determine the priority of marriage conditions to be entered based on the estimated user's feelings.

2. The system of claim 1.

7. The reception unit When entering marriage conditions, the system takes into account the user's geographic location information and prioritizes the most relevant conditions.

2. The system of claim 1.

8. The reception unit When entering marriage conditions, the app analyzes the user's social media activity and suggests related conditions.

2. The system of claim 1.

9. The reception unit When entering marriage conditions, provide an appropriate input method based on the user's past feedback 2. The system of claim 1.

10. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

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