System

Abstract

An object of a system according to an embodiment is to automatically match a user with a compatible partner in consideration of various desired conditions and skills of the user.SOLUTION: A system includes an information inputting part, a generation AI part, and a matching part. The information input unit receives information such as a desired condition, a way of working, a hobby, and a skill of the user. The generation and AI unit analyzes the information received by the information receiving unit. The matching unit automatically performs matching with a compatible partner on the basis of the information analyzed by the generation and AI unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, matching services offered as corporate employee benefits had the problem of making it difficult to find a compatible partner that took into account the user's diverse desired conditions and skills.

[0005] The system according to the embodiment aims to automatically match users with compatible partners by taking into consideration the user's various desired conditions and skills. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, a generation AI unit, and a matching unit. The information input unit receives information such as a user's desired conditions, working style, hobbies, and skills. The generation AI unit analyzes the information received by the information input unit. The matching unit automatically matches compatible partners based on the information analyzed by the generation AI unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically match users with compatible partners by taking into consideration the user's various desired conditions and skills. [Brief explanation of the drawings]

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

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A matching app according to an embodiment of the present invention is a system provided as a corporate employee benefit to promote employee retention and solve problems in the matchmaking market. This system uses a generation AI to automatically match compatible partners based on desired conditions as well as information such as work style, hobbies, and skills. This is expected to increase employees' motivation for work by allowing them to have a stable family life, and also allows users to be matched with stable partners.

[0029] A matching app according to an embodiment includes an information input unit, a generation AI unit, and a matching unit. The information input unit accepts information such as a user's desired conditions, working style, hobbies, and skills. For example, the user inputs desired conditions such as age, gender, and place of residence. The information input unit can also accept input of the user's working style (e.g., remote work, flextime, etc.), hobbies (e.g., reading, outdoors, etc.), and skills (e.g., programming, design, etc.). The generation AI unit analyzes the information accepted by the information input unit. For example, the generation AI unit uses a text generation AI (e.g., LLM) to analyze information such as the user's desired conditions, working style, hobbies, and skills. The generation AI unit can also analyze user information using a multimodal generation AI. The generation AI unit analyzes user information using a machine learning algorithm to find a compatible partner. The matching unit automatically matches compatible partners based on the information analyzed by the generation AI unit. For example, the matching unit calculates the similarity between the user's information and the other party's information using cosine similarity to find a compatible partner. The matching unit can also use the Jaccard coefficient to calculate the degree of similarity between the user's information and the other party's information. As a result, the matching app according to the embodiment promotes employee continuity as a corporate employee benefit, thereby resolving issues in the matchmaking market. For example, if an employee has a stable family, they will feel more responsible and secure in their work, which will promote long-term employment. Furthermore, by using a matching app that is limited to corporate employee benefits, users can be matched with a relatively stable partner. For example, partners who work at the same company are more likely to share common topics and values ​​and are therefore more trustworthy.

[0030] The information input unit can input employee profile information. The information input unit inputs profile information such as the employee's name, age, and job position. The information input unit can also incorporate employee health data (e.g., data from a fitness tracker) and perform matching that takes into account the impact of health status on compatibility. For example, the information input unit can analyze the employee's heart rate and sleep data obtained from the fitness tracker and prioritize matching employees with good health status. It can also match employees with low stress levels based on the health data. This improves the accuracy of matching based on the employee's profile information.

[0031] The generation AI unit can match compatible partners based on desired conditions. For example, the generation AI unit matches compatible partners based on the user's desired conditions (e.g., age, gender, place of residence, etc.). For example, if a user inputs conditions such as "I'm looking for a partner in their 30s, living in Tokyo, and whose hobby is reading," the generation AI unit will find a compatible partner based on those conditions. The generation AI unit can also evaluate the degree of future career compatibility based on the user's career path and work history, and perform matching that takes long-term compatibility into consideration. For example, it can analyze employee career path data and match employees with the same future career goals. This improves the accuracy of matching based on desired conditions.

[0032] The generation AI unit can match compatible partners based on information such as work style, hobbies, and skills. For example, the generation AI unit matches compatible partners based on the user's work style (e.g., remote work, flextime, etc.), hobbies (e.g., outdoors, reading, etc.), and skills (e.g., programming, design, etc.). For example, when a user inputs information such as "someone who can work remotely," "someone whose hobbies are outdoors," or "someone with programming skills," the generation AI unit finds compatible partners based on that information. The generation AI unit can also use its emotion estimation function to analyze employees' stress levels and happiness levels and match emotionally stable partners. For example, it can use the emotion estimation function to analyze employees' stress levels and match employees with low stress levels. This improves the accuracy of matching based on information such as work style, hobbies, and skills.

[0033] The matching unit can incorporate employee health data and perform matching that takes into account the impact of health status on compatibility. For example, the matching unit analyzes employees' heart rate and sleep data obtained from fitness trackers, and prioritizes matching employees with good health status. For example, it can match employees who exercise regularly. It can also match employees with low stress levels based on health data. For example, matching employees who are good at stress management can build healthy relationships. It can also use health data to match employees with similar lifestyles. For example, it can match employees who have the habit of going to bed early and getting up early. This makes it possible to match employees with health status in mind.

[0034] The matching department can evaluate the degree of future career compatibility based on employees' career paths and work history, and perform matching that takes long-term compatibility into consideration. For example, the matching department analyzes employees' career path data and matches employees with the same future career goals. For example, it can match employees who are aiming to become managers. It can also match employees with the same work experience based on their work history. For example, it can match employees with project management experience. It can also evaluate the degree of career compatibility and match employees who are likely to work in the same department or project in the future. For example, it can match employees with careers in the same technical field. This makes it possible to perform matching that takes into consideration the degree of future career compatibility.

[0035] The system can integrate mental healthcare and counseling services. For example, the system can add a mental healthcare function to a matching app, allowing employees to receive counseling when they feel stressed. For example, it can provide a function to book online counseling sessions. It can also match employees who are good at managing stress based on mental healthcare data. For example, it can match employees who undergo regular mental health checks. It can also integrate counseling services so that matching occurs when employees are emotionally stable. For example, matching occurs after employees have received counseling. In this way, integrating mental healthcare and counseling services supports the mental stability of employees.

[0036] The system can enable matching between companies. For example, the system can add a function that allows employees of different companies to be matched with each other, promoting interaction between companies. For example, it can match employees of different companies in the same industry with each other. It can also hold matching events between companies to provide a place where employees of different companies can interact directly. For example, it can hold a joint matching party. It can also integrate employee benefit data from different companies to match employees who share common employee benefits. For example, it can match employees who belong to the same sports club with each other. This makes it possible to promote interaction between employees of different companies.

[0037] The generation AI unit learns the user's past selection history and can perform more accurate matching. The generation AI unit, for example, learns the user's past selection history and improves the accuracy of matching based on desired conditions. For example, matching is performed based on the characteristics of partners selected in the past. It can also analyze past selection history to understand the user's preferences and tendencies. For example, matching is performed based on the hobbies and skills of partners selected in the past. It can also learn past selection history and prioritize matching with partners that the user prefers. For example, matching is performed based on the characteristics of partners that the user has given high ratings to in the past. In this way, by learning past selection history, the accuracy of matching is improved.

[0038] The generation AI unit can analyze the user's lifestyle and daily behavioral patterns to suggest compatible partners. The generation AI unit, for example, analyzes the user's lifestyle data to improve the accuracy of matching based on desired conditions. For example, it prioritizes matching with partners who have the same lifestyle. It can also analyze daily behavioral patterns to suggest compatible partners. For example, it can match with partners who are active at the same time. It can also suggest partners that the user prefers based on their lifestyle and behavioral patterns. For example, it can match with partners who have the same hobbies and habits. This makes it possible to suggest compatible partners by analyzing lifestyles and behavioral patterns.

[0039] The generation AI unit can analyze a user's social media data and create a more detailed profile. The generation AI unit, for example, analyzes a user's social media data and creates a detailed profile. For example, a profile is created based on the user's posts and the accounts they follow. It can also understand a user's hobbies and interests in detail based on social media data. For example, a profile is created based on the content the user frequently posts. It can also analyze social media data and understand a user's lifestyle and values ​​in detail. For example, a profile is created based on the user's posts and comments. This makes it possible to create a more detailed profile by analyzing social media data.

[0040] The generation AI unit can incorporate the opinions of the user's friends and family and perform matching from a third-party perspective. The generation AI unit can incorporate the opinions of the user's friends and family and perform matching from a third-party perspective. For example, it can prioritize matching with partners recommended by friends and family. It can also suggest partners that the user prefers based on the opinions of friends and family. For example, it can prioritize matching with partners recommended by friends and family. It can also incorporate a third-party perspective and suggest partners that the user prefers. For example, it can prioritize matching with partners recommended by friends and family. In this way, by incorporating a third-party perspective, more appropriate matching is possible.

[0041] The generation AI unit can evaluate the user's motivation to grow and learn based on information about hobbies and skills, and suggest partners with whom they can grow together. The generation AI unit can evaluate the user's motivation to grow and learn based on information about hobbies and skills, for example, and suggest partners with whom they can grow together. For example, it can prioritize matching with partners who have the same hobbies. It can also suggest partners that the user prefers based on their motivation to grow and learn. For example, it can prioritize matching with partners with the same skills. It can also analyze information about hobbies and skills and suggest partners that the user prefers. For example, it can prioritize matching with partners with the same hobbies and skills. This makes it possible to suggest partners with whom you can grow together by evaluating your motivation to grow and learn.

[0042] The generation AI unit can predict the user's future life plan based on information about work style, hobbies, and skills, and perform matching that takes long-term compatibility into consideration. The generation AI unit can predict the user's future life plan based on information about work style, hobbies, and skills, and perform matching that takes long-term compatibility into consideration. For example, it can prioritize matching with partners who have the same future life plan. It can also suggest partners that the user prefers based on the future life plan. For example, it can prioritize matching with partners who have the same life plan. It can also analyze information about work style, hobbies, and skills, predict the user's future life plan, and perform matching that takes long-term compatibility into consideration. For example, it can prioritize matching with partners who have the same future life plan. This makes it possible to predict future life plans and perform matching that takes long-term compatibility into consideration.

[0043] The generation AI unit can analyze the user's participation history in local communities and events based on information about work style, hobbies, and skills, and suggest partners who belong to common communities. The generation AI unit can, for example, analyze the user's participation history in local communities and events based on information about work style, hobbies, and skills, and suggest partners who belong to common communities. For example, it can prioritize matching with partners who belong to the same local community. It can also suggest partners that the user prefers based on the participation history in local communities and events. For example, it can prioritize matching with partners who have participated in the same event. It can also analyze information about work style, hobbies, and skills, and suggest partners who belong to common communities based on the user's participation history in local communities and events. For example, it can prioritize matching with partners who belong to the same local community. This makes it possible to suggest partners who belong to common communities by analyzing the participation history in local communities and events.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] Matching apps can learn from a user's past selection history to perform more accurate matching. For example, matching is performed based on the characteristics of partners selected in the past. Past selection history can also be analyzed to understand the user's preferences and tendencies. For example, matching is performed based on the hobbies and skills of partners selected in the past. Past selection history can also be learned to prioritize matching with partners that the user prefers. For example, matching is performed based on the characteristics of partners that the user has given high ratings in the past. In this way, by learning past selection history, the accuracy of matching can be improved.

[0046] Matching apps can analyze a user's lifestyle and daily behavioral patterns to suggest compatible partners. For example, they can prioritize matching with people who have the same lifestyle. They can also analyze daily behavioral patterns to suggest compatible partners. For example, they can match with people who are active at the same time. They can also suggest partners that the user prefers based on their lifestyle and behavioral patterns. For example, they can match with people who have the same hobbies and habits. This makes it possible to suggest compatible partners by analyzing lifestyles and behavioral patterns.

[0047] Matching apps can analyze a user's social media data to create a more detailed profile. For example, a profile can be created based on the user's posts and the accounts they follow. Social media data can also be used to understand a user's hobbies and interests in detail. For example, a profile can be created based on the content the user frequently posts. Social media data can also be analyzed to understand a user's lifestyle and values ​​in detail. For example, a profile can be created based on the content the user posts and comments. In this way, analyzing social media data makes it possible to create a more detailed profile.

[0048] Matching apps can incorporate the opinions of the user's friends and family to perform matching from a third-party perspective. For example, they can prioritize matching with people recommended by friends and family. They can also suggest people the user likes based on the opinions of friends and family. For example, they can prioritize matching with people recommended by friends and family. They can also incorporate a third-party perspective to suggest people the user likes. For example, they can prioritize matching with people recommended by friends and family. In this way, incorporating a third-party perspective makes it possible to perform more appropriate matching.

[0049] A matching app can evaluate a user's motivation to grow and learn based on information about hobbies and skills, and suggest partners with whom they can grow together. For example, it can prioritize matching with people who share the same hobbies. It can also suggest partners that the user prefers based on motivation to grow and learn. For example, it can prioritize matching with people who have the same skills. It can also analyze information about hobbies and skills and suggest partners that the user prefers. For example, it can prioritize matching with people who have the same hobbies and skills. This makes it possible to suggest partners with whom you can grow together by evaluating motivation to grow and learn.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information input unit accepts information such as the user's desired conditions, working style, hobbies, and skills. For example, the user inputs desired conditions such as age, gender, and place of residence. The information input unit can also input the user's working style (e.g., remote work, flextime, etc.), hobbies (e.g., reading, outdoors, etc.), and skills (e.g., programming, design, etc.). Step 2: The generation AI unit analyzes the information received by the information input unit. For example, the generation AI unit uses a text generation AI (e.g., LLM) to analyze information such as the user's desired conditions, work style, hobbies, and skills. The generation AI unit can also use a multimodal generation AI to analyze the user's information. The generation AI unit uses a machine learning algorithm to analyze the user's information and find a compatible partner. Step 3: The matching unit automatically matches compatible partners based on the information analyzed by the generation AI unit. For example, the matching unit uses cosine similarity to calculate the similarity between the user's information and the other party's information and finds compatible partners. The matching unit can also use the Jaccard coefficient to calculate the similarity between the user's information and the other party's information.

[0052] (Example 2) A matching app according to an embodiment of the present invention is a system provided as a corporate employee benefit to promote employee retention and solve problems in the matchmaking market. This system uses a generation AI to automatically match compatible partners based on desired conditions as well as information such as work style, hobbies, and skills. This is expected to increase employees' motivation for work by allowing them to have a stable family life, and also allows users to be matched with stable partners.

[0053] A matching app according to an embodiment includes an information input unit, a generation AI unit, and a matching unit. The information input unit accepts information such as a user's desired conditions, working style, hobbies, and skills. For example, the user inputs desired conditions such as age, gender, and place of residence. The information input unit can also accept input of the user's working style (e.g., remote work, flextime, etc.), hobbies (e.g., reading, outdoors, etc.), and skills (e.g., programming, design, etc.). The generation AI unit analyzes the information accepted by the information input unit. For example, the generation AI unit uses a text generation AI (e.g., LLM) to analyze information such as the user's desired conditions, working style, hobbies, and skills. The generation AI unit can also analyze user information using a multimodal generation AI. The generation AI unit analyzes user information using a machine learning algorithm to find a compatible partner. The matching unit automatically matches compatible partners based on the information analyzed by the generation AI unit. For example, the matching unit calculates the similarity between the user's information and the other party's information using cosine similarity to find a compatible partner. The matching unit can also use the Jaccard coefficient to calculate the degree of similarity between the user's information and the other party's information. As a result, the matching app according to the embodiment promotes employee continuity as a corporate employee benefit, thereby resolving issues in the matchmaking market. For example, if an employee has a stable family, they will feel more responsible and secure in their work, which will promote long-term employment. Furthermore, by using a matching app that is limited to corporate employee benefits, users can be matched with a relatively stable partner. For example, partners who work at the same company are more likely to share common topics and values ​​and are therefore more trustworthy.

[0054] The information input unit can input employee profile information. The information input unit inputs profile information such as the employee's name, age, and job position. The information input unit can also incorporate employee health data (e.g., data from a fitness tracker) and perform matching that takes into account the impact of health status on compatibility. For example, the information input unit can analyze the employee's heart rate and sleep data obtained from the fitness tracker and prioritize matching employees with good health status. It can also match employees with low stress levels based on the health data. This improves the accuracy of matching based on the employee's profile information.

[0055] The generation AI unit can match compatible partners based on desired conditions. For example, the generation AI unit matches compatible partners based on the user's desired conditions (e.g., age, gender, place of residence, etc.). For example, if a user inputs conditions such as "I'm looking for a partner in their 30s, living in Tokyo, and whose hobby is reading," the generation AI unit will find a compatible partner based on those conditions. The generation AI unit can also evaluate the degree of future career compatibility based on the user's career path and work history, and perform matching that takes long-term compatibility into consideration. For example, it can analyze employee career path data and match employees with the same future career goals. This improves the accuracy of matching based on desired conditions.

[0056] The generation AI unit can match compatible partners based on information such as work style, hobbies, and skills. For example, the generation AI unit matches compatible partners based on the user's work style (e.g., remote work, flextime, etc.), hobbies (e.g., outdoors, reading, etc.), and skills (e.g., programming, design, etc.). For example, when a user inputs information such as "someone who can work remotely," "someone whose hobbies are outdoors," or "someone with programming skills," the generation AI unit finds compatible partners based on that information. The generation AI unit can also use its emotion estimation function to analyze employees' stress levels and happiness levels and match emotionally stable partners. For example, it can use the emotion estimation function to analyze employees' stress levels and match employees with low stress levels. This improves the accuracy of matching based on information such as work style, hobbies, and skills.

[0057] The matching unit can incorporate employee health data and perform matching that takes into account the impact of health status on compatibility. For example, the matching unit analyzes employees' heart rate and sleep data obtained from fitness trackers, and prioritizes matching employees with good health status. For example, it can match employees who exercise regularly. It can also match employees with low stress levels based on health data. For example, matching employees who are good at stress management can build healthy relationships. It can also use health data to match employees with similar lifestyles. For example, it can match employees who have the habit of going to bed early and getting up early. This makes it possible to match employees with health status in mind.

[0058] The matching department can evaluate the degree of future career compatibility based on employees' career paths and work history, and perform matching that takes long-term compatibility into consideration. For example, the matching department analyzes employees' career path data and matches employees with the same future career goals. For example, it can match employees who are aiming to become managers. It can also match employees with the same work experience based on their work history. For example, it can match employees with project management experience. It can also evaluate the degree of career compatibility and match employees who are likely to work in the same department or project in the future. For example, it can match employees with careers in the same technical field. This makes it possible to perform matching that takes into consideration the degree of future career compatibility.

[0059] The matching unit can use the emotion estimation function to analyze the stress levels and happiness levels of employees and match them with emotionally stable partners. For example, the matching unit can use the emotion estimation function to analyze the stress levels of employees and match employees with low stress levels. For example, it can match relaxed employees with relaxed employees. It can also analyze happiness levels and match emotionally stable employees with emotionally stable partners. For example, it can match employees with many positive emotions with each other. It can also match partners with whom an emotionally stable relationship can be built based on the emotion estimation data. For example, it can match employees with few emotional fluctuations with each other. This makes it possible to match emotionally stable partners.

[0060] The system can integrate mental healthcare and counseling services. For example, the system can add a mental healthcare function to a matching app, allowing employees to receive counseling when they feel stressed. For example, it can provide a function to book online counseling sessions. It can also match employees who are good at managing stress based on mental healthcare data. For example, it can match employees who undergo regular mental health checks. It can also integrate counseling services so that matching occurs when employees are emotionally stable. For example, matching occurs after employees have received counseling. In this way, integrating mental healthcare and counseling services supports the mental stability of employees.

[0061] The system can enable matching between companies. For example, the system can add a function that allows employees of different companies to be matched with each other, promoting interaction between companies. For example, it can match employees of different companies in the same industry with each other. It can also hold matching events between companies to provide a place where employees of different companies can interact directly. For example, it can hold a joint matching party. It can also integrate employee benefit data from different companies to match employees who share common employee benefits. For example, it can match employees who belong to the same sports club with each other. This makes it possible to promote interaction between employees of different companies.

[0062] The system can use the emotion estimation function to monitor emotions when employees use an app in real time and provide an interface that elicits positive emotions. For example, the system can use the emotion estimation function to monitor emotions when employees use an app in real time and provide an interface that elicits positive emotions. For example, positive messages or images can be displayed. The system can also provide an interface for eliciting positive emotions when employees use the app based on the emotion estimation data. For example, an encouraging message can be displayed if the emotion score is low. The system can also monitor emotions in real time and provide an interface for eliciting positive emotions. For example, a compliment can be displayed if the emotion score is high. In this way, the system can provide an interface that elicits positive emotions, thereby improving the employee's app usage experience.

[0063] The generation AI unit learns the user's past selection history and can perform more accurate matching. The generation AI unit, for example, learns the user's past selection history and improves the accuracy of matching based on desired conditions. For example, matching is performed based on the characteristics of partners selected in the past. It can also analyze past selection history to understand the user's preferences and tendencies. For example, matching is performed based on the hobbies and skills of partners selected in the past. It can also learn past selection history and prioritize matching with partners that the user prefers. For example, matching is performed based on the characteristics of partners that the user has given high ratings to in the past. In this way, by learning past selection history, the accuracy of matching is improved.

[0064] The generation AI unit can analyze the user's lifestyle and daily behavioral patterns to suggest compatible partners. The generation AI unit, for example, analyzes the user's lifestyle data to improve the accuracy of matching based on desired conditions. For example, it prioritizes matching with partners who have the same lifestyle. It can also analyze daily behavioral patterns to suggest compatible partners. For example, it can match with partners who are active at the same time. It can also suggest partners that the user prefers based on their lifestyle and behavioral patterns. For example, it can match with partners who have the same hobbies and habits. This makes it possible to suggest compatible partners by analyzing lifestyles and behavioral patterns.

[0065] The generation AI unit can use the emotion estimation function to analyze the emotion a user has when entering desired conditions, and prioritize matching emotionally positive conditions. The generation AI unit can, for example, use the emotion estimation function to analyze the emotion a user has when entering desired conditions, and prioritize matching conditions with positive emotions. For example, prioritizing conditions that evoke a strong emotion of joy at the time of entry. The generation AI unit can also analyze the user's emotion and prioritize matching emotionally positive conditions. For example, prioritizing conditions that evoke a strong positive emotion at the time of entry. The generation AI unit can also analyze the emotion a user has when entering desired conditions, based on the emotion estimation data, and prioritize matching positive conditions. For example, prioritizing conditions that evoke a strong positive emotion at the time of entry. This prioritizes matching emotionally positive conditions, thereby improving user satisfaction.

[0066] The generation AI unit can analyze a user's social media data and create a more detailed profile. The generation AI unit, for example, analyzes a user's social media data and creates a detailed profile. For example, a profile is created based on the user's posts and the accounts they follow. It can also understand a user's hobbies and interests in detail based on social media data. For example, a profile is created based on the content the user frequently posts. It can also analyze social media data and understand a user's lifestyle and values ​​in detail. For example, a profile is created based on the user's posts and comments. This makes it possible to create a more detailed profile by analyzing social media data.

[0067] The generation AI unit can incorporate the opinions of the user's friends and family and perform matching from a third-party perspective. The generation AI unit can incorporate the opinions of the user's friends and family and perform matching from a third-party perspective. For example, it can prioritize matching with partners recommended by friends and family. It can also suggest partners that the user prefers based on the opinions of friends and family. For example, it can prioritize matching with partners recommended by friends and family. It can also incorporate a third-party perspective and suggest partners that the user prefers. For example, it can prioritize matching with partners recommended by friends and family. In this way, by incorporating a third-party perspective, more appropriate matching is possible.

[0068] The generation AI unit can use the emotion estimation function to provide real-time feedback on the emotions the user feels when entering their desired conditions and propose optimal conditions. The generation AI unit can, for example, use the emotion estimation function to provide real-time feedback on the emotions the user feels when entering their desired conditions and propose optimal conditions. For example, it can prioritize conditions that evoke strong positive emotions at the time of entry. It can also analyze the user's emotions in real time and propose optimal conditions. For example, it can prioritize conditions that evoke strong positive emotions at the time of entry. It can also provide real-time feedback on the emotions the user feels when entering their desired conditions and propose optimal conditions based on the emotion estimation data. For example, it can prioritize conditions that evoke strong positive emotions at the time of entry. This makes it possible to provide real-time feedback and propose optimal conditions.

[0069] The generation AI unit can evaluate the user's motivation to grow and learn based on information about hobbies and skills, and suggest partners with whom they can grow together. The generation AI unit can evaluate the user's motivation to grow and learn based on information about hobbies and skills, for example, and suggest partners with whom they can grow together. For example, it can prioritize matching with partners who have the same hobbies. It can also suggest partners that the user prefers based on their motivation to grow and learn. For example, it can prioritize matching with partners with the same skills. It can also analyze information about hobbies and skills and suggest partners that the user prefers. For example, it can prioritize matching with partners with the same hobbies and skills. This makes it possible to suggest partners with whom you can grow together by evaluating your motivation to grow and learn.

[0070] The generation AI unit can use the emotion estimation function to analyze the user's passion and satisfaction for their hobbies and skills, and match them with partners who are emotionally fulfilling. The generation AI unit can, for example, use the emotion estimation function to analyze the user's passion and satisfaction for their hobbies and skills, and match them with partners who are emotionally fulfilling. For example, it can prioritize matching with partners who have a strong passion for their hobbies. It can also suggest partners that the user prefers based on their passion and satisfaction for their hobbies and skills. For example, it can prioritize matching with partners who have a high level of satisfaction with their skills. It can also analyze the user's passion and satisfaction for their hobbies and skills based on the emotion estimation data, and match them with partners who are emotionally fulfilling. For example, it can prioritize matching with partners who have a strong passion for their hobbies. This makes it possible to match partners who are emotionally fulfilling by analyzing their passion and satisfaction for their hobbies and skills.

[0071] The generation AI unit can predict the user's future life plan based on information about work style, hobbies, and skills, and perform matching that takes long-term compatibility into consideration. The generation AI unit can predict the user's future life plan based on information about work style, hobbies, and skills, and perform matching that takes long-term compatibility into consideration. For example, it can prioritize matching with partners who have the same future life plan. It can also suggest partners that the user prefers based on the future life plan. For example, it can prioritize matching with partners who have the same life plan. It can also analyze information about work style, hobbies, and skills, predict the user's future life plan, and perform matching that takes long-term compatibility into consideration. For example, it can prioritize matching with partners who have the same future life plan. This makes it possible to predict future life plans and perform matching that takes long-term compatibility into consideration.

[0072] The generation AI unit can analyze the user's participation history in local communities and events based on information about work style, hobbies, and skills, and suggest partners who belong to common communities. The generation AI unit can, for example, analyze the user's participation history in local communities and events based on information about work style, hobbies, and skills, and suggest partners who belong to common communities. For example, it can prioritize matching with partners who belong to the same local community. It can also suggest partners that the user prefers based on the participation history in local communities and events. For example, it can prioritize matching with partners who have participated in the same event. It can also analyze information about work style, hobbies, and skills, and suggest partners who belong to common communities based on the user's participation history in local communities and events. For example, it can prioritize matching with partners who belong to the same local community. This makes it possible to suggest partners who belong to common communities by analyzing the participation history in local communities and events.

[0073] The generation AI unit can use the emotion estimation function to analyze the emotions of a user when entering information about their working style, hobbies, and skills in real time, and suggest the most suitable information. For example, the generation AI unit can use the emotion estimation function to analyze the emotions of a user when entering information about their working style, hobbies, and skills in real time, and suggest the most suitable information. For example, it can prioritize information that has a strong positive emotion at the time of entry. It can also analyze the user's emotions in real time and suggest the most suitable information. For example, it can prioritize information that has a strong positive emotion at the time of entry. It can also analyze the emotions of a user when entering information about their working style, hobbies, and skills in real time, and suggest the most suitable information, based on the emotion estimation data. For example, it can prioritize information that has a strong positive emotion at the time of entry. This makes it possible to analyze emotions in real time and suggest the most suitable information.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] Matching apps can learn from a user's past selection history to perform more accurate matching. For example, matching is performed based on the characteristics of partners selected in the past. Past selection history can also be analyzed to understand the user's preferences and tendencies. For example, matching is performed based on the hobbies and skills of partners selected in the past. Past selection history can also be learned to prioritize matching with partners that the user prefers. For example, matching is performed based on the characteristics of partners that the user has given high ratings in the past. In this way, by learning past selection history, the accuracy of matching can be improved.

[0076] Matching apps can analyze a user's lifestyle and daily behavioral patterns to suggest compatible partners. For example, they can prioritize matching with people who have the same lifestyle. They can also analyze daily behavioral patterns to suggest compatible partners. For example, they can match with people who are active at the same time. They can also suggest partners that the user prefers based on their lifestyle and behavioral patterns. For example, they can match with people who have the same hobbies and habits. This makes it possible to suggest compatible partners by analyzing lifestyles and behavioral patterns.

[0077] Matching apps can analyze a user's social media data to create a more detailed profile. For example, a profile can be created based on the user's posts and the accounts they follow. Social media data can also be used to understand a user's hobbies and interests in detail. For example, a profile can be created based on the content the user frequently posts. Social media data can also be analyzed to understand a user's lifestyle and values ​​in detail. For example, a profile can be created based on the content the user posts and comments. In this way, analyzing social media data makes it possible to create a more detailed profile.

[0078] Matching apps can incorporate the opinions of the user's friends and family to perform matching from a third-party perspective. For example, they can prioritize matching with people recommended by friends and family. They can also suggest people the user likes based on the opinions of friends and family. For example, they can prioritize matching with people recommended by friends and family. They can also incorporate a third-party perspective to suggest people the user likes. For example, they can prioritize matching with people recommended by friends and family. In this way, incorporating a third-party perspective makes it possible to perform more appropriate matching.

[0079] A matching app can evaluate a user's motivation to grow and learn based on information about hobbies and skills, and suggest partners with whom they can grow together. For example, it can prioritize matching with people who share the same hobbies. It can also suggest partners that the user prefers based on motivation to grow and learn. For example, it can prioritize matching with people who have the same skills. It can also analyze information about hobbies and skills and suggest partners that the user prefers. For example, it can prioritize matching with people who have the same hobbies and skills. This makes it possible to suggest partners with whom you can grow together by evaluating motivation to grow and learn.

[0080] The matching app can use the emotion estimation function to analyze the emotions of the user when entering desired conditions, and prioritize matching emotionally positive conditions. For example, conditions that evoke a strong emotion of joy at the time of entry can be prioritized. The matching app can also analyze the user's emotions and prioritize matching emotionally positive conditions. For example, conditions that evoke a strong positive emotion at the time of entry can be prioritized. The matching app can also analyze the emotions of the user when entering desired conditions based on the emotion estimation data, and prioritize matching positive conditions. For example, conditions that evoke a strong positive emotion at the time of entry can be prioritized. In this way, by prioritizing matching emotionally positive conditions, user satisfaction can be improved.

[0081] A dating app can use the emotion estimation function to monitor users' emotions in real time when using the app and provide an interface that elicits positive emotions. For example, it can display positive messages or images. It can also provide an interface that elicits positive emotions when employees use the app based on the emotion estimation data. For example, it can display an encouraging message if the emotion score is low. It can also monitor emotions in real time and provide an interface that elicits positive emotions. For example, it can display compliments if the emotion score is high. In this way, it can provide an interface that elicits positive emotions, improving the employee's app usage experience.

[0082] A matching app can use the emotion estimation function to analyze a user's passion and satisfaction for their hobbies and skills, and match them with partners who are emotionally fulfilling. For example, it can prioritize matching with partners who are passionate about their hobbies. It can also suggest partners that the user prefers based on their passion and satisfaction for their hobbies and skills. For example, it can prioritize matching with partners who are highly satisfied with their skills. It can also analyze a user's passion and satisfaction for their hobbies and skills based on emotion estimation data, and match them with partners who are emotionally fulfilling. For example, it can prioritize matching with partners who are passionate about their hobbies. This makes it possible to match emotionally fulfilling partners by analyzing their passion and satisfaction for their hobbies and skills.

[0083] The matching app can use the emotion estimation function to analyze the emotions of users in real time when they enter information about their working style, hobbies, and skills, and suggest the most suitable information. For example, information with a strong positive emotion at the time of entry can be given priority. The matching app can also analyze the emotions of users in real time and suggest the most suitable information. For example, information with a strong positive emotion at the time of entry can be given priority. The matching app can also analyze the emotions of users in real time when they enter information about their working style, hobbies, and skills, and suggest the most suitable information, based on the emotion estimation data. For example, information with a strong positive emotion at the time of entry can be given priority. This makes it possible to analyze emotions in real time and suggest the most suitable information.

[0084] Using the emotion estimation function, a matching app can provide real-time feedback on the emotions of users when they enter their desired conditions and suggest optimal conditions. For example, conditions that evoke strong positive emotions at the time of entry can be prioritized. It can also analyze the user's emotions in real time and suggest optimal conditions. For example, conditions that evoke strong positive emotions at the time of entry can be prioritized. It can also provide real-time feedback on the emotions of users when they enter their desired conditions based on the emotion estimation data and suggest optimal conditions. For example, conditions that evoke strong positive emotions at the time of entry can be prioritized. This makes it possible to provide real-time feedback and suggest optimal conditions.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The information input unit accepts information such as the user's desired conditions, working style, hobbies, and skills. For example, the user inputs desired conditions such as age, gender, and place of residence. The information input unit can also input the user's working style (e.g., remote work, flextime, etc.), hobbies (e.g., reading, outdoors, etc.), and skills (e.g., programming, design, etc.). Step 2: The generation AI unit analyzes the information received by the information input unit. For example, the generation AI unit uses a text generation AI (e.g., LLM) to analyze information such as the user's desired conditions, work style, hobbies, and skills. The generation AI unit can also use a multimodal generation AI to analyze the user's information. The generation AI unit uses a machine learning algorithm to analyze the user's information and find a compatible partner. Step 3: The matching unit automatically matches compatible partners based on the information analyzed by the generation AI unit. For example, the matching unit uses cosine similarity to calculate the similarity between the user's information and the other party's information and finds compatible partners. The matching unit can also use the Jaccard coefficient to calculate the similarity between the user's information and the other party's information.

[0087] 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.

[0088] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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. [Explanation of symbols]

[0154] 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. It is a matching app provided as a company employee benefit. an information input unit that accepts information such as the user's desired conditions, working style, hobbies, and skills; a generation AI unit that analyzes the information received by the information input unit; A matching unit that automatically matches compatible partners based on the information analyzed by the generation AI unit. A system characterized by:

2. The information input unit Enter employee profile information 2. The system of claim 1.

3. The generation AI unit Matching compatible partners based on the desired conditions 2. The system of claim 1.

4. The generation AI unit Matching compatible partners based on information such as work style, hobbies, and skills 2. The system of claim 1.

5. The matching unit Incorporating employee health data to match employees by taking into account the impact of their health status on compatibility 2. The system of claim 1.

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