System

Abstract

The object and advantages of the invention will be realized and attained by means of the elements and combinations particularly pointed out in the claims.SOLUTION: A system includes a data collection unit, a data analysis unit, a matching unit, and a notification unit. The data collection unit collects data such as hobbies, concerns, and job specialties of employees. The data analysis part analyzes the employee data collected by the data collection part. The matching unit matches employees having a common point or mutual interest based on the employee data analyzed by the data analysis unit. The notification unit notifies the employees matched by the matching unit of an opportunity for interaction.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not providing sufficient effective means to promote interaction between employees and improve internal engagement.

[0005] The system according to the embodiment aims to promote interaction between employees and improve engagement within the company. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, a matching unit, and a notification unit. The data collection unit collects data such as employees' hobbies, interests, and job expertise. The data analysis unit analyzes the employee data collected by the data collection unit. The matching unit matches employees who share commonalities or mutual interests based on the employee data analyzed by the data analysis unit. The notification unit notifies employees matched by the matching unit of opportunities to interact. [Effects of the Invention]

[0007] The system according to the embodiment can promote interaction between employees and improve engagement within the company. [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 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) An AI service according to an embodiment of the present invention is a system that promotes interaction between employees and improves internal engagement. This system uses AI to analyze data such as employees' hobbies, interests, and job expertise, and matches employees with commonalities and mutual interests. It also provides various opportunities for employees to easily participate in interactions, such as lunch meetings, after-work activities, and team-building events, creating an environment that fosters friendships within the workplace. In this way, the AI ​​service can promote interaction between employees and improve internal engagement.

[0029] The AI ​​service according to the embodiment includes a data collection unit, a data analysis unit, a matching unit, and a notification unit. The data collection unit collects data such as employees' hobbies, interests, and job expertise. For example, the data collection unit collects data based on information such as employees' registered hobbies, interests, and past project experience. The data collection unit can also collect profile information and survey results provided by employees. The data analysis unit analyzes the employee data collected by the data collection unit. For example, AI analyzes data such as employees' hobbies, interests, and job expertise to identify employees with commonalities or mutual interests. The data analysis unit can also analyze data based on past project experience and employee feedback. The matching unit matches employees with commonalities or mutual interests based on the employee data analyzed by the data analysis unit. For example, it pairs employees with the same hobbies or employees interested in the same project. The matching unit also notifies employees of the matching results to provide opportunities for employees to naturally deepen their interactions with each other. The notification unit notifies employees matched by the matching unit of interaction opportunities. For example, the AI ​​can send a notification such as "Would you like to join this week's lunch meeting?", and if an employee wishes to participate, it can arrange the date, time, and location. The notification unit can also suggest more effective opportunities for interaction based on past participation history and feedback. This allows the AI ​​service according to the embodiment to promote interaction between employees and improve internal engagement. For example, deepening interactions between employees through common hobbies can improve the atmosphere in the workplace and increase work efficiency. Furthermore, team-building events can strengthen trust between employees.

[0030] The data collection unit analyzes employees' social media activities to detect changes in their hobbies and interests. For example, to analyze employees' social media activities, the data collection unit collects publicly posted content and uses text mining technology to extract hobbies and interests. For example, it detects new hobbies and interests from the content posted by employees. The data collection unit can also analyze employees' "like" and comment history on social media to detect changes in their hobbies and interests. For example, it can analyze increases and decreases in interest in specific topics. The data collection unit can also analyze changes in employees' followers and followed accounts on social media to detect changes in their hobbies and interests. For example, it can identify changes in hobbies based on the content of newly followed accounts. This allows for understanding changes in employees' hobbies and interests and enables more appropriate matching.

[0031] The data collection unit collects employees' health data and suggests opportunities for interaction based on their health status. For example, the data collection unit collects health data such as heart rate and number of steps from employees' fitness trackers and suggests opportunities for interaction based on their health status. For example, it suggests walking meetings. The data collection unit can also collect employees' sleep data and suggest opportunities for interaction based on their health status. For example, it can suggest relaxation sessions. The data collection unit can also collect employees' dietary data and suggest opportunities for interaction based on their health status. For example, it can suggest healthy lunch meetings. This makes it possible to provide appropriate opportunities for interaction based on employees' health status.

[0032] The data collection department collects data on employees' family structures and lifestyles and suggests family-oriented events. For example, the data collection department collects employees' family structure data and suggests family-oriented events. For example, it may plan a picnic or barbecue event for the whole family. The data collection department can also collect employees' lifestyle data and suggest family-oriented events. For example, it may plan a family-oriented sporting event. The data collection department can also suggest family-oriented events based on the hobbies and interests of employees' families. For example, it may plan an art workshop for the whole family. This makes it possible to provide appropriate events based on employees' family structures and lifestyles.

[0033] The matching unit analyzes the employee's past interaction history to identify and reuse successful matching patterns. The matching unit, for example, analyzes the employee's past interaction history to identify successful matching patterns. For example, commonalities between past successful pairings are extracted and reused. The matching unit can also analyze the employee's past participation history in social events to identify successful matching patterns. For example, a matching pattern is identified based on the success rate at a specific event. The matching unit can also analyze the employee's past feedback to identify successful matching patterns. For example, a matching pattern with a large amount of positive feedback is reused. This allows for more effective matching by reusing past successful matching patterns.

[0034] The matching department analyzes employee job performance data and performs matching to improve work efficiency. For example, the matching department analyzes employee job performance data and performs matching to improve work efficiency. For example, it pairs employees with high performance. The matching department can also match employees with complementary skills based on employee job performance data. For example, it pairs employees with programming skills and employees with design skills. The matching department can also match employees suitable for a specific project based on employee job performance data. For example, it pairs employees with skills that match the project requirements. In this way, work efficiency can be improved by matching based on job performance data.

[0035] The matching unit uses the geographical location information of employees to match nearby employees with each other. The matching unit, for example, uses the geographical location information of employees to match nearby employees with each other. For example, it pairs employees who are in the same office or floor. The matching unit can also match employees who live in the same area based on the geographical location information of employees. For example, it pairs employees who live in the same city or nearby areas. The matching unit can also match employees at business trip destinations or event venues based on the geographical location information of employees. For example, it pairs employees who are on the same business trip destination. In this way, matching using geographical location information can promote interaction between employees.

[0036] The matching department analyzes employees' skill sets and performs matching based on the complementary relationships of the skills. For example, the matching department analyzes employees' skill sets and performs matching based on the complementary relationships of the skills. For example, it pairs employees with programming skills with employees with design skills. The matching department can also match employees with skills that match the requirements of a project based on the employees' skill sets. For example, it pairs employees with marketing skills with employees with sales skills. The matching department can also perform matching for learning and training based on the employees' skill sets. For example, it pairs employees with specific skills to provide guidance to other employees. This makes it possible to promote cooperation between employees by matching based on the complementary relationships of skills.

[0037] The notification department analyzes employee schedules in real time and suggests opportunities for interaction at the optimal time. For example, the notification department analyzes employee schedules in real time and suggests opportunities for interaction at the optimal time. For example, it finds free time and suggests a lunch meeting. The notification department can also suggest opportunities for interaction before or after important events based on employee schedules. For example, it can suggest an interaction event for relaxing before or after a meeting. The notification department can also suggest opportunities for interaction based on the progress of a project based on employee schedules. For example, it can suggest a team building event at a milestone in a project. This makes it possible to suggest opportunities for interaction at the optimal time based on employee schedules.

[0038] The notification unit analyzes employees' past participation history and sends notifications preferentially to employees who are highly motivated to participate. The notification unit, for example, analyzes employees' past participation history and sends notifications preferentially to employees who are highly motivated to participate. For example, it sends notifications of new events to employees who have participated in many events in the past. The notification unit can also send notifications to employees who are highly motivated to participate in specific events based on employees' past participation history. For example, it sends notifications of related events to employees who are interested in a specific topic. The notification unit can also send appropriate notifications to employees who are less motivated to participate based on employees' past participation history. For example, it sends notifications offering special incentives to increase motivation to participate. This allows notifications to be sent preferentially to employees who are highly motivated to participate, maximizing opportunities for interaction.

[0039] The notification department proposes a social event using virtual reality (VR) to enable interaction that transcends physical distance. The notification department, for example, proposes a social event using virtual reality (VR) to enable interaction that transcends physical distance. For example, it holds a VR conference or a VR team building event. The notification department can also propose online events that employees can participate in using VR. For example, it proposes virtual tours or workshops using VR. The notification department can also propose social events that employees can participate in remotely using VR. For example, it proposes a remote lunch meeting using VR. In this way, social events using VR can enable interaction between employees that transcends physical distance.

[0040] The notification department forms online communities based on employees' hobbies and interests to promote daily interaction. The notification department forms online communities based on employees' hobbies and interests to promote daily interaction. For example, it creates online groups such as a book club or a sports club. The notification department can also form online forums on specific themes based on employees' hobbies and interests. For example, it can create a discussion forum on technology topics. The notification department can also suggest regular online meetings based on employees' hobbies and interests. For example, it can suggest a weekly online reading group. In this way, online communities based on hobbies and interests can promote daily interaction.

[0041] The data analysis unit analyzes past event data, identifies factors that contributed to success and failure, and reflects these in the next event. The data analysis unit, for example, analyzes past event data and identifies factors that contributed to success and failure. For example, it evaluates events based on the number of participants and feedback. The data analysis unit can also analyze the emotional responses of participants in past events and identify factors that contributed to success and failure. For example, it identifies factors that contributed to events with a high number of positive emotional responses. The data analysis unit can also analyze the progress of past events and identify factors that contributed to success and failure. For example, it evaluates based on the event schedule and content. In this way, by analyzing past event data and identifying factors that contributed to success and failure, it is possible to increase the success rate of the next event.

[0042] The data analysis department collects employee feedback in real time and makes improvements as the event progresses. For example, the data analysis department builds a system that collects employee feedback in real time and makes improvements as the event progresses. For example, it uses online surveys and comment functions. The data analysis department can also take immediate action based on employee feedback as the event progresses. For example, it can adjust the content of the event based on the feedback. The data analysis department can also analyze employee feedback in real time and identify areas for improvement as the event progresses. For example, it can optimize the progress of the event based on the feedback. In this way, the quality of the event can be improved by collecting feedback in real time and making improvements as the event progresses.

[0043] The data analysis department introduces new event formats by referring to success stories from different industries and companies. For example, the data analysis department introduces new event formats by referring to success stories from different industries and companies. For example, it could adopt a successful team building event from another company. The data analysis department can also introduce new event formats based on industry best practices. For example, it could plan an event that reflects industry trends. The data analysis department can also introduce new event formats by referring to success stories from different companies. For example, it could hold a workshop based on success stories from other companies. In this way, by referring to success stories from different industries and companies, it is possible to introduce new event formats and increase the diversity of the event.

[0044] The data analysis department plans events that employees' families and friends can also participate in, promoting interaction both inside and outside the company. The data analysis department plans events that employees' families and friends can also participate in, promoting interaction both inside and outside the company. For example, it holds family days and events for friends. The data analysis department can also plan events based on the hobbies and interests of employees' families and friends. For example, it can plan sporting events for families and art workshops for friends. The data analysis department can also plan events based on the participation history of employees' families and friends. For example, it can plan new events based on feedback from events that employees have attended in the past. In this way, by planning events that employees' families and friends can also participate in, it is possible to promote interaction both inside and outside the company.

[0045] The data analysis department monitors employee engagement scores in real time and immediately proposes countermeasures if the score drops. The data analysis department, for example, builds a system that monitors employee engagement scores in real time and immediately proposes countermeasures if the score drops. For example, it provides individual support to employees with low engagement scores. The data analysis department can also propose countermeasures if the score drops based on the employee's engagement score. For example, it can propose a special training program for employees whose engagement score has dropped. The data analysis department can also analyze employee engagement scores in real time and immediately propose countermeasures if the score drops. For example, it can propose a refreshing vacation to employees whose engagement score has dropped. In this way, engagement can be maintained by monitoring engagement scores in real time and immediately proposing countermeasures if the score drops.

[0046] The data analysis department proposes individual measures to improve engagement based on employees' career paths and goal settings. The data analysis department, for example, builds a system that proposes individual measures to improve engagement based on employees' career paths and goal settings. For example, it proposes training programs according to career goals. The data analysis department can also propose individual measures based on employees' career paths and goal settings. For example, it proposes a mentoring program aimed at specific career goals. The data analysis department can also propose individual measures to improve engagement based on employees' career paths and goal settings. For example, it proposes feedback sessions based on short-term goals. In this way, it is possible to improve employee engagement by proposing individual measures based on career paths and goal settings.

[0047] The data analysis department may hold workshops inviting external experts and consultants to improve employee engagement. The data analysis department may, for example, hold workshops inviting external experts and consultants to improve employee engagement. For example, they may hold leadership training or team building sessions. The data analysis department may also hold workshops inviting experts in specific fields to improve employee engagement. For example, they may hold training sessions inviting marketing experts. The data analysis department may also hold workshops inviting consultants to improve employee engagement. For example, they may hold strategy sessions inviting business consultants. In this way, by holding workshops inviting external experts and consultants, employee engagement can be improved.

[0048] The data analysis department introduces a program that incorporates gamification elements to improve employee engagement. For example, the data analysis department introduces a program that incorporates gamification elements to improve employee engagement. For example, a point system or badges may be used to increase employee motivation. The data analysis department can also introduce a program that uses a leaderboard to improve employee engagement. For example, employees are ranked based on their performance. The data analysis department can also introduce a game-style training program to improve employee engagement. For example, a quiz-style training session may be conducted. In this way, employee engagement can be improved by introducing a program that incorporates gamification elements.

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

[0050] The data collection unit collects employees' health data and suggests opportunities for interaction based on their health status. For example, it collects health data such as heart rate and number of steps from employees' fitness trackers and suggests opportunities for interaction based on their health status. For example, it suggests walking meetings. The data collection unit can also collect employees' sleep data and suggest opportunities for interaction based on their health status. For example, it can suggest relaxation sessions. The data collection unit can also collect employees' dietary data and suggest opportunities for interaction based on their health status. For example, it can suggest healthy lunch meetings. This makes it possible to provide appropriate opportunities for interaction based on employees' health status.

[0051] The data collection department collects data on employees' family structures and lifestyles and suggests family-oriented events. For example, it collects employee family structure data and suggests family-oriented events. For example, it may plan a family picnic or barbecue event. The data collection department can also collect employee lifestyle data and suggest family-oriented events. For example, it may plan a family-oriented sporting event. The data collection department can also suggest family-oriented events based on the hobbies and interests of employees' families. For example, it may plan an art workshop for families. This makes it possible to provide appropriate events based on employees' family structures and lifestyles.

[0052] The matching unit analyzes the employee's past interaction history to identify and reuse successful matching patterns. For example, the matching unit analyzes the employee's past interaction history to identify successful matching patterns. For example, commonalities between past successful pairings are extracted and reused. The matching unit can also analyze the employee's past participation history in social events to identify successful matching patterns. For example, a matching pattern is identified based on the success rate at a specific event. The matching unit can also analyze the employee's past feedback to identify successful matching patterns. For example, a matching pattern with a large amount of positive feedback is reused. This allows for more effective matching by reusing past successful matching patterns.

[0053] The matching department analyzes employee job performance data and performs matching to improve work efficiency. For example, it analyzes employee job performance data and performs matching to improve work efficiency. For example, it pairs high-performing employees with each other. The matching department can also match employees with complementary skills based on employee job performance data. For example, it can pair employees with programming skills with employees with design skills. The matching department can also match employees suitable for specific projects based on employee job performance data. For example, it can pair employees with skills that match the project requirements. In this way, work efficiency can be improved by matching based on job performance data.

[0054] The matching unit uses the geographical location information of employees to match nearby employees with each other. For example, it uses the geographical location information of employees to match nearby employees with each other. For example, it may pair employees who are in the same office or floor. The matching unit can also match employees who live in the same area based on the geographical location information of employees. For example, it may pair employees who live in the same city or nearby areas. The matching unit can also match employees who are on business trips or at event venues based on the geographical location information of employees. For example, it may pair employees who are on the same business trip. This makes it possible to promote interaction between employees by matching using geographical location information.

[0055] The matching department analyzes employees' skill sets and matches them based on the complementary relationships between skills. For example, it analyzes employees' skill sets and matches them based on the complementary relationships between skills. For example, it may pair employees with programming skills with employees with design skills. The matching department can also match employees with skills that match the requirements of a project based on their skill sets. For example, it may pair employees with marketing skills with employees with sales skills. The matching department can also match employees for learning and training based on their skill sets. For example, it may pair employees with specific skills to provide training to other employees. This makes it possible to promote cooperation between employees by matching employees based on the complementary relationships between skills.

[0056] The notification department analyzes employee schedules in real time and suggests opportunities for interaction at the optimal time. For example, it analyzes employee schedules in real time and suggests opportunities for interaction at the optimal time. For example, it finds free time and suggests a lunch meeting. The notification department can also suggest opportunities for interaction before or after important events based on employee schedules. For example, it can suggest an interaction event for relaxing before or after a meeting. The notification department can also suggest opportunities for interaction based on the progress of a project, based on employee schedules. For example, it can suggest a team building event at a milestone in a project. This makes it possible to suggest opportunities for interaction at the optimal time based on employee schedules.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The data collection department collects data such as employees' hobbies, interests, and job expertise. For example, data is collected based on information registered by employees about their hobbies, interests, and past project experience. The data collection department can also collect profile information and survey results provided by employees. Step 2: The data analysis department analyzes the employee data collected by the data collection department. For example, AI can analyze data such as employees' hobbies, interests, and job specialties to identify employees with commonalities and mutual interests. The data analysis department can also analyze data based on past project experience and employee feedback. Step 3: The Matching Department matches employees with commonalities and mutual interests based on the employee data analyzed by the Data Analysis Department. For example, it pairs employees with the same hobbies or interests in the same projects. The Matching Department also notifies employees of the matching results to provide opportunities for employees to deepen their interactions naturally. Step 4: The notification department notifies employees matched by the matching department of opportunities for interaction. For example, the AI ​​can send a notification such as "Would you like to join our lunch meeting this week?", and if the employee wishes to participate, it will arrange the date, time, and location. The notification department can also suggest more effective opportunities for interaction based on past participation history and feedback.

[0059] (Example 2) An AI service according to an embodiment of the present invention is a system that promotes interaction between employees and improves internal engagement. This system uses AI to analyze data such as employees' hobbies, interests, and job expertise, and matches employees with commonalities and mutual interests. It also provides various opportunities for employees to easily participate in interactions, such as lunch meetings, after-work activities, and team-building events, creating an environment that fosters friendships within the workplace. In this way, the AI ​​service can promote interaction between employees and improve internal engagement.

[0060] The AI ​​service according to the embodiment includes a data collection unit, a data analysis unit, a matching unit, and a notification unit. The data collection unit collects data such as employees' hobbies, interests, and job expertise. For example, the data collection unit collects data based on information such as employees' registered hobbies, interests, and past project experience. The data collection unit can also collect profile information and survey results provided by employees. The data analysis unit analyzes the employee data collected by the data collection unit. For example, AI analyzes data such as employees' hobbies, interests, and job expertise to identify employees with commonalities or mutual interests. The data analysis unit can also analyze data based on past project experience and employee feedback. The matching unit matches employees with commonalities or mutual interests based on the employee data analyzed by the data analysis unit. For example, it pairs employees with the same hobbies or employees interested in the same project. The matching unit also notifies employees of the matching results to provide opportunities for employees to naturally deepen their interactions with each other. The notification unit notifies employees matched by the matching unit of interaction opportunities. For example, the AI ​​can send a notification such as "Would you like to join this week's lunch meeting?", and if an employee wishes to participate, it can arrange the date, time, and location. The notification unit can also suggest more effective opportunities for interaction based on past participation history and feedback. This allows the AI ​​service according to the embodiment to promote interaction between employees and improve internal engagement. For example, deepening interactions between employees through common hobbies can improve the atmosphere in the workplace and increase work efficiency. Furthermore, team-building events can strengthen trust between employees.

[0061] The data collection unit monitors the emotional state of employees in real time and updates the data based on emotional fluctuations. The data collection unit, for example, uses a wearable device to monitor the emotional state of employees in real time. For example, it measures heart rate and electrodermal activity to detect emotional fluctuations. The data collection unit can also capture the facial expressions of employees with a camera and analyze their emotions using an emotion estimation algorithm. For example, it calculates an emotion score based on changes in facial expressions. The data collection unit can also record the voice of employees and estimate their emotions using voice analysis technology. For example, it can analyze the tone and speed of voice to calculate an emotion score. This makes it possible to grasp the emotional state of employees in real time and improve the accuracy of the data.

[0062] The data collection unit analyzes employees' social media activities to detect changes in their hobbies and interests. For example, to analyze employees' social media activities, the data collection unit collects publicly posted content and uses text mining technology to extract hobbies and interests. For example, it detects new hobbies and interests from the content posted by employees. The data collection unit can also analyze employees' "like" and comment history on social media to detect changes in their hobbies and interests. For example, it can analyze increases and decreases in interest in specific topics. The data collection unit can also analyze changes in employees' followers and followed accounts on social media to detect changes in their hobbies and interests. For example, it can identify changes in hobbies based on the content of newly followed accounts. This allows for understanding changes in employees' hobbies and interests and enables more appropriate matching.

[0063] The data analysis unit uses the emotion estimation function to analyze the emotional nuances of data entered by employees and generate more accurate matching data. The data analysis unit, for example, uses the emotion estimation function to analyze the emotional nuances of text data entered by employees. For example, it detects positive and negative expressions and reflects them in the matching data. The data analysis unit can also analyze the emotional nuances of voice data entered by employees. For example, it analyzes the tone and speed of the voice and calculates an emotion score. The data analysis unit can also analyze the emotional nuances of facial expression data entered by employees. For example, it calculates an emotion score based on changes in facial expressions. This enables highly accurate matching that takes emotional nuances into account.

[0064] The data collection unit collects employees' health data and suggests opportunities for interaction based on their health status. For example, the data collection unit collects health data such as heart rate and number of steps from employees' fitness trackers and suggests opportunities for interaction based on their health status. For example, it suggests walking meetings. The data collection unit can also collect employees' sleep data and suggest opportunities for interaction based on their health status. For example, it can suggest relaxation sessions. The data collection unit can also collect employees' dietary data and suggest opportunities for interaction based on their health status. For example, it can suggest healthy lunch meetings. This makes it possible to provide appropriate opportunities for interaction based on employees' health status.

[0065] The data collection department collects data on employees' family structures and lifestyles and suggests family-oriented events. For example, the data collection department collects employees' family structure data and suggests family-oriented events. For example, it may plan a picnic or barbecue event for the whole family. The data collection department can also collect employees' lifestyle data and suggest family-oriented events. For example, it may plan a family-oriented sporting event. The data collection department can also suggest family-oriented events based on the hobbies and interests of employees' families. For example, it may plan an art workshop for the whole family. This makes it possible to provide appropriate events based on employees' family structures and lifestyles.

[0066] The data collection unit uses an emotion estimation function to estimate the emotions of employees when they enter data in real time, and provides an interface for eliciting positive emotions. For example, the data collection unit uses the emotion estimation function to analyze the emotions of employees when they enter data in real time, and provides an interface for eliciting positive emotions. For example, an encouraging message is displayed. The data collection unit can also capture the facial expressions of employees when they enter data with a camera, analyze their emotions using an emotion estimation algorithm, and provide an interface for eliciting positive emotions. For example, a message encouraging them to smile is displayed. The data collection unit can also record the voices of employees when they enter data, estimate their emotions using voice analysis technology, and provide an interface for eliciting positive emotions. For example, relaxing music is played. This can elicit positive emotions when employees enter data, improving the quality of the data.

[0067] The matching unit analyzes the employee's past interaction history to identify and reuse successful matching patterns. The matching unit, for example, analyzes the employee's past interaction history to identify successful matching patterns. For example, commonalities between past successful pairings are extracted and reused. The matching unit can also analyze the employee's past participation history in social events to identify successful matching patterns. For example, a matching pattern is identified based on the success rate at a specific event. The matching unit can also analyze the employee's past feedback to identify successful matching patterns. For example, a matching pattern with a large amount of positive feedback is reused. This allows for more effective matching by reusing past successful matching patterns.

[0068] The matching department analyzes employee job performance data and performs matching to improve work efficiency. For example, the matching department analyzes employee job performance data and performs matching to improve work efficiency. For example, it pairs employees with high performance. The matching department can also match employees with complementary skills based on employee job performance data. For example, it pairs employees with programming skills and employees with design skills. The matching department can also match employees suitable for a specific project based on employee job performance data. For example, it pairs employees with skills that match the project requirements. In this way, work efficiency can be improved by matching based on job performance data.

[0069] The matching unit uses the emotion estimation function to evaluate the emotional compatibility between employees and prioritize emotionally positive matching. The matching unit, for example, uses the emotion estimation function to evaluate the emotional compatibility between employees and prioritize positive matching. For example, it pairs employees with high emotion scores. The matching unit can also match employees with high empathy levels based on the emotional compatibility between employees. For example, it pairs employees who show common emotional reactions. The matching unit can also use the emotion estimation function to evaluate the emotional compatibility between employees and perform matching that avoids negative emotions. For example, it avoids matching employees with low emotion scores. This prioritizes emotionally positive matching, thereby promoting interaction between employees.

[0070] The matching unit uses the geographical location information of employees to match nearby employees with each other. The matching unit, for example, uses the geographical location information of employees to match nearby employees with each other. For example, it pairs employees who are in the same office or floor. The matching unit can also match employees who live in the same area based on the geographical location information of employees. For example, it pairs employees who live in the same city or nearby areas. The matching unit can also match employees at business trip destinations or event venues based on the geographical location information of employees. For example, it pairs employees who are on the same business trip destination. In this way, matching using geographical location information can promote interaction between employees.

[0071] The matching department analyzes employees' skill sets and performs matching based on the complementary relationships of the skills. For example, the matching department analyzes employees' skill sets and performs matching based on the complementary relationships of the skills. For example, it pairs employees with programming skills with employees with design skills. The matching department can also match employees with skills that match the requirements of a project based on the employees' skill sets. For example, it pairs employees with marketing skills with employees with sales skills. The matching department can also perform matching for learning and training based on the employees' skill sets. For example, it pairs employees with specific skills to provide guidance to other employees. This makes it possible to promote cooperation between employees by matching based on the complementary relationships of skills.

[0072] The matching unit uses the emotion estimation function to collect employees' emotional reactions to the matching results and continuously improves the matching algorithm. For example, the matching unit uses the emotion estimation function to collect employees' emotional reactions to the matching results and improves the algorithm. For example, it prioritizes matching conditions that have a high number of positive reactions. The matching unit can also improve the matching algorithm based on employee feedback. For example, it analyzes the feedback and adjusts the matching conditions. The matching unit can also use the emotion estimation function to improve the algorithm to avoid negative reactions to the matching results. For example, it eliminates matching conditions that have a high number of negative reactions. This makes it possible to improve the accuracy of matching by improving the algorithm based on emotional reactions.

[0073] The notification department analyzes employee schedules in real time and suggests opportunities for interaction at the optimal time. For example, the notification department analyzes employee schedules in real time and suggests opportunities for interaction at the optimal time. For example, it finds free time and suggests a lunch meeting. The notification department can also suggest opportunities for interaction before or after important events based on employee schedules. For example, it can suggest an interaction event for relaxing before or after a meeting. The notification department can also suggest opportunities for interaction based on the progress of a project based on employee schedules. For example, it can suggest a team building event at a milestone in a project. This makes it possible to suggest opportunities for interaction at the optimal time based on employee schedules.

[0074] The notification unit analyzes employees' past participation history and sends notifications preferentially to employees who are highly motivated to participate. The notification unit, for example, analyzes employees' past participation history and sends notifications preferentially to employees who are highly motivated to participate. For example, it sends notifications of new events to employees who have participated in many events in the past. The notification unit can also send notifications to employees who are highly motivated to participate in specific events based on employees' past participation history. For example, it sends notifications of related events to employees who are interested in a specific topic. The notification unit can also send appropriate notifications to employees who are less motivated to participate based on employees' past participation history. For example, it sends notifications offering special incentives to increase motivation to participate. This allows notifications to be sent preferentially to employees who are highly motivated to participate, maximizing opportunities for interaction.

[0075] The notification unit uses the emotion estimation function to predict the emotions that employees will have toward the social opportunity and makes suggestions that will elicit positive emotions. The notification unit, for example, uses the emotion estimation function to predict the emotions that employees will have toward the social opportunity and makes suggestions that will elicit positive emotions. For example, it suggests events that employees are likely to be interested in. The notification unit can also make suggestions to elicit positive emotions based on the employee's past emotional reactions. For example, it re-suggests events that employees have previously responded positively to. The notification unit can also use the emotion estimation function to make suggestions to prevent employees from having negative emotions toward the social opportunity. For example, it adjusts the content of the event to avoid negative emotions. In this way, suggestions are made to help employees feel positive emotions toward the social opportunity, thereby increasing their motivation to participate.

[0076] The notification department proposes a social event using virtual reality (VR) to enable interaction that transcends physical distance. The notification department, for example, proposes a social event using virtual reality (VR) to enable interaction that transcends physical distance. For example, it holds a VR conference or a VR team building event. The notification department can also propose online events that employees can participate in using VR. For example, it proposes virtual tours or workshops using VR. The notification department can also propose social events that employees can participate in remotely using VR. For example, it proposes a remote lunch meeting using VR. In this way, social events using VR can enable interaction between employees that transcends physical distance.

[0077] The notification department forms online communities based on employees' hobbies and interests to promote daily interaction. The notification department forms online communities based on employees' hobbies and interests to promote daily interaction. For example, it creates online groups such as a book club or a sports club. The notification department can also form online forums on specific themes based on employees' hobbies and interests. For example, it can create a discussion forum on technology topics. The notification department can also suggest regular online meetings based on employees' hobbies and interests. For example, it can suggest a weekly online reading group. In this way, online communities based on hobbies and interests can promote daily interaction.

[0078] The notification unit uses the emotion estimation function to monitor employees' emotional reactions to interaction opportunities in real time and perform follow-up at the optimal timing. The notification unit, for example, uses the emotion estimation function to monitor employees' emotional reactions to interaction opportunities in real time and perform follow-up at the optimal timing. For example, sending positive feedback after an event. The notification unit can also adjust the content of follow-up based on employees' emotional reactions. For example, if there is a negative reaction, suggesting areas for improvement. The notification unit can also use the emotion estimation function to monitor employees' emotional reactions in real time and adjust the timing of follow-up. For example, follow-up is performed when the emotion score is high. In this way, the effectiveness of interaction can be maximized by monitoring emotional reactions in real time and performing follow-up at the optimal timing.

[0079] The data analysis unit analyzes past event data, identifies factors that contributed to success and failure, and reflects these in the next event. The data analysis unit, for example, analyzes past event data and identifies factors that contributed to success and failure. For example, it evaluates events based on the number of participants and feedback. The data analysis unit can also analyze the emotional responses of participants in past events and identify factors that contributed to success and failure. For example, it identifies factors that contributed to events with a high number of positive emotional responses. The data analysis unit can also analyze the progress of past events and identify factors that contributed to success and failure. For example, it evaluates based on the event schedule and content. In this way, by analyzing past event data and identifying factors that contributed to success and failure, it is possible to increase the success rate of the next event.

[0080] The data analysis department collects employee feedback in real time and makes improvements as the event progresses. For example, the data analysis department builds a system that collects employee feedback in real time and makes improvements as the event progresses. For example, it uses online surveys and comment functions. The data analysis department can also take immediate action based on employee feedback as the event progresses. For example, it can adjust the content of the event based on the feedback. The data analysis department can also analyze employee feedback in real time and identify areas for improvement as the event progresses. For example, it can optimize the progress of the event based on the feedback. In this way, the quality of the event can be improved by collecting feedback in real time and making improvements as the event progresses.

[0081] The data analysis unit uses the emotion estimation function to monitor the emotional state of employees during the event and make adjustments to elicit positive emotions. The data analysis unit, for example, uses the emotion estimation function to monitor the emotional state of employees during the event and make adjustments to elicit positive emotions. For example, if the emotion score is low, the content of the event is changed. The data analysis unit can also adjust the progress of the event based on the emotional state of employees. For example, if the emotion score is high, the event is extended. The data analysis unit can also use the emotion estimation function to analyze the emotional state of employees during the event in real time and provide feedback to elicit positive emotions. For example, if the emotion score is low, an encouraging message is displayed. In this way, the effectiveness of the event can be maximized by monitoring the emotional state during the event and making adjustments to elicit positive emotions.

[0082] The data analysis department introduces new event formats by referring to success stories from different industries and companies. For example, the data analysis department introduces new event formats by referring to success stories from different industries and companies. For example, it could adopt a successful team building event from another company. The data analysis department can also introduce new event formats based on industry best practices. For example, it could plan an event that reflects industry trends. The data analysis department can also introduce new event formats by referring to success stories from different companies. For example, it could hold a workshop based on success stories from other companies. In this way, by referring to success stories from different industries and companies, it is possible to introduce new event formats and increase the diversity of the event.

[0083] The data analysis department plans events that employees' families and friends can also participate in, promoting interaction both inside and outside the company. The data analysis department plans events that employees' families and friends can also participate in, promoting interaction both inside and outside the company. For example, it holds family days and events for friends. The data analysis department can also plan events based on the hobbies and interests of employees' families and friends. For example, it can plan sporting events for families and art workshops for friends. The data analysis department can also plan events based on the participation history of employees' families and friends. For example, it can plan new events based on feedback from events that employees have attended in the past. In this way, by planning events that employees' families and friends can also participate in, it is possible to promote interaction both inside and outside the company.

[0084] The data analysis unit uses the emotion estimation function to predict employees' emotional reactions from the event planning stage and propose optimal event content. The data analysis unit, for example, uses the emotion estimation function to predict employees' emotional reactions from the event planning stage and propose optimal event content. For example, it plans an event that elicits positive emotions. The data analysis unit can also propose optimal event content based on employees' past emotional reactions. For example, it re-proposes event content that has previously elicited positive reactions. The data analysis unit can also use the emotion estimation function to predict employees' emotional reactions from the event planning stage and propose event content that avoids negative emotions. For example, it adjusts event content to avoid negative emotions. In this way, it is possible to propose optimal event content by predicting emotional reactions from the event planning stage.

[0085] The data analysis department monitors employee engagement scores in real time and immediately proposes countermeasures if the score drops. The data analysis department, for example, builds a system that monitors employee engagement scores in real time and immediately proposes countermeasures if the score drops. For example, it provides individual support to employees with low engagement scores. The data analysis department can also propose countermeasures if the score drops based on the employee's engagement score. For example, it can propose a special training program for employees whose engagement score has dropped. The data analysis department can also analyze employee engagement scores in real time and immediately propose countermeasures if the score drops. For example, it can propose a refreshing vacation to employees whose engagement score has dropped. In this way, engagement can be maintained by monitoring engagement scores in real time and immediately proposing countermeasures if the score drops.

[0086] The data analysis department proposes individual measures to improve engagement based on employees' career paths and goal settings. The data analysis department, for example, builds a system that proposes individual measures to improve engagement based on employees' career paths and goal settings. For example, it proposes training programs according to career goals. The data analysis department can also propose individual measures based on employees' career paths and goal settings. For example, it proposes a mentoring program aimed at specific career goals. The data analysis department can also propose individual measures to improve engagement based on employees' career paths and goal settings. For example, it proposes feedback sessions based on short-term goals. In this way, it is possible to improve employee engagement by proposing individual measures based on career paths and goal settings.

[0087] The data analysis unit uses the emotion estimation function to continuously monitor the emotional state of employees and propose measures to maintain positive emotions. For example, the data analysis unit uses the emotion estimation function to continuously monitor the emotional state of employees and propose measures to maintain positive emotions. For example, if the emotion score drops, it proposes a refreshing vacation. The data analysis unit can also propose measures to maintain positive emotions based on the emotional state of employees. For example, it can provide a special incentive if the emotion score is high. The data analysis unit can also use the emotion estimation function to continuously analyze the emotional state of employees and provide feedback to maintain positive emotions. For example, it can display an encouraging message if the emotion score drops. In this way, by continuously monitoring the emotional state and proposing measures to maintain positive emotions, it is possible to improve employee engagement.

[0088] The data analysis department may hold workshops inviting external experts and consultants to improve employee engagement. The data analysis department may, for example, hold workshops inviting external experts and consultants to improve employee engagement. For example, they may hold leadership training or team building sessions. The data analysis department may also hold workshops inviting experts in specific fields to improve employee engagement. For example, they may hold training sessions inviting marketing experts. The data analysis department may also hold workshops inviting consultants to improve employee engagement. For example, they may hold strategy sessions inviting business consultants. In this way, by holding workshops inviting external experts and consultants, employee engagement can be improved.

[0089] The data analysis department introduces a program that incorporates gamification elements to improve employee engagement. For example, the data analysis department introduces a program that incorporates gamification elements to improve employee engagement. For example, a point system or badges may be used to increase employee motivation. The data analysis department can also introduce a program that uses a leaderboard to improve employee engagement. For example, employees are ranked based on their performance. The data analysis department can also introduce a game-style training program to improve employee engagement. For example, a quiz-style training session may be conducted. In this way, employee engagement can be improved by introducing a program that incorporates gamification elements.

[0090] The data analysis unit uses the emotion estimation function to monitor employees' emotional reactions to engagement improvement measures in real time and continuously evaluate the effectiveness of the measures. The data analysis unit, for example, uses the emotion estimation function to monitor employees' emotional reactions to engagement improvement measures in real time and continuously evaluate the effectiveness of the measures. For example, it measures the effectiveness of the measures based on emotion scores. The data analysis unit can also evaluate the effectiveness of the engagement improvement measures based on employee feedback. For example, it analyzes the feedback and identifies areas for improvement in the measures. The data analysis unit can also use the emotion estimation function to analyze employees' emotional reactions to engagement improvement measures in real time and continuously evaluate the effectiveness of the measures. For example, it evaluates the effectiveness of the measures based on fluctuations in emotion scores. In this way, by monitoring emotional reactions in real time and continuously evaluating the effectiveness of the measures, the effectiveness of the engagement improvement measures can be maximized.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The data collection unit collects employees' health data and suggests opportunities for interaction based on their health status. For example, it collects health data such as heart rate and number of steps from employees' fitness trackers and suggests opportunities for interaction based on their health status. For example, it suggests walking meetings. The data collection unit can also collect employees' sleep data and suggest opportunities for interaction based on their health status. For example, it can suggest relaxation sessions. The data collection unit can also collect employees' dietary data and suggest opportunities for interaction based on their health status. For example, it can suggest healthy lunch meetings. This makes it possible to provide appropriate opportunities for interaction based on employees' health status.

[0093] The data collection department collects data on employees' family structures and lifestyles and suggests family-oriented events. For example, it collects employee family structure data and suggests family-oriented events. For example, it may plan a family picnic or barbecue event. The data collection department can also collect employee lifestyle data and suggest family-oriented events. For example, it may plan a family-oriented sporting event. The data collection department can also suggest family-oriented events based on the hobbies and interests of employees' families. For example, it may plan an art workshop for families. This makes it possible to provide appropriate events based on employees' family structures and lifestyles.

[0094] The data collection unit uses an emotion estimation function to estimate the emotions of employees when they enter data in real time, and provides an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze the emotions of employees when they enter data in real time, and provide an interface for eliciting positive emotions. For example, an encouraging message can be displayed. The data collection unit can also capture the facial expressions of employees when they enter data with a camera, analyze their emotions using an emotion estimation algorithm, and provide an interface for eliciting positive emotions. For example, a message encouraging them to smile can be displayed. The data collection unit can also record the voices of employees when they enter data, estimate their emotions using voice analysis technology, and provide an interface for eliciting positive emotions. For example, relaxing music can be played. This can elicit positive emotions when employees enter data, improving the quality of the data.

[0095] The matching unit analyzes the employee's past interaction history to identify and reuse successful matching patterns. For example, the matching unit analyzes the employee's past interaction history to identify successful matching patterns. For example, commonalities between past successful pairings are extracted and reused. The matching unit can also analyze the employee's past participation history in social events to identify successful matching patterns. For example, a matching pattern is identified based on the success rate at a specific event. The matching unit can also analyze the employee's past feedback to identify successful matching patterns. For example, a matching pattern with a large amount of positive feedback is reused. This allows for more effective matching by reusing past successful matching patterns.

[0096] The matching department analyzes employee job performance data and performs matching to improve work efficiency. For example, it analyzes employee job performance data and performs matching to improve work efficiency. For example, it pairs high-performing employees with each other. The matching department can also match employees with complementary skills based on employee job performance data. For example, it can pair employees with programming skills with employees with design skills. The matching department can also match employees suitable for specific projects based on employee job performance data. For example, it can pair employees with skills that match the project requirements. In this way, work efficiency can be improved by matching based on job performance data.

[0097] The matching unit uses the emotion estimation function to evaluate the emotional compatibility between employees and prioritize emotionally positive matching. For example, the emotion estimation function is used to evaluate the emotional compatibility between employees and prioritize positive matching. For example, employees with high emotion scores are paired together. The matching unit can also match employees with high empathy levels based on the emotional compatibility between employees. For example, employees who show common emotional reactions are paired together. The matching unit can also use the emotion estimation function to evaluate the emotional compatibility between employees and perform matching that avoids negative emotions. For example, matching employees with low emotion scores is avoided. This makes it possible to promote interaction between employees by prioritizing emotionally positive matching.

[0098] The matching unit uses the geographical location information of employees to match nearby employees with each other. For example, it uses the geographical location information of employees to match nearby employees with each other. For example, it may pair employees who are in the same office or floor. The matching unit can also match employees who live in the same area based on the geographical location information of employees. For example, it may pair employees who live in the same city or nearby areas. The matching unit can also match employees who are on business trips or at event venues based on the geographical location information of employees. For example, it may pair employees who are on the same business trip. This makes it possible to promote interaction between employees by matching using geographical location information.

[0099] The matching department analyzes employees' skill sets and matches them based on the complementary relationships between skills. For example, it analyzes employees' skill sets and matches them based on the complementary relationships between skills. For example, it may pair employees with programming skills with employees with design skills. The matching department can also match employees with skills that match the requirements of a project based on their skill sets. For example, it may pair employees with marketing skills with employees with sales skills. The matching department can also match employees for learning and training based on their skill sets. For example, it may pair employees with specific skills to provide training to other employees. This makes it possible to promote cooperation between employees by matching employees based on the complementary relationships between skills.

[0100] The matching unit uses the emotion estimation function to collect employees' emotional reactions to the matching results and continuously improves the matching algorithm. For example, the emotion estimation function is used to collect employees' emotional reactions to the matching results and improve the algorithm. For example, matching conditions that have a high number of positive reactions are prioritized. The matching unit can also improve the matching algorithm based on employee feedback. For example, the feedback is analyzed and the matching conditions are adjusted. The matching unit can also use the emotion estimation function to improve the algorithm to avoid negative reactions to the matching results. For example, matching conditions that have a high number of negative reactions are eliminated. This makes it possible to improve the accuracy of matching by improving the algorithm based on emotional reactions.

[0101] The notification department analyzes employee schedules in real time and suggests opportunities for interaction at the optimal time. For example, it analyzes employee schedules in real time and suggests opportunities for interaction at the optimal time. For example, it finds free time and suggests a lunch meeting. The notification department can also suggest opportunities for interaction before or after important events based on employee schedules. For example, it can suggest an interaction event for relaxing before or after a meeting. The notification department can also suggest opportunities for interaction based on the progress of a project, based on employee schedules. For example, it can suggest a team building event at a milestone in a project. This makes it possible to suggest opportunities for interaction at the optimal time based on employee schedules.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The data collection department collects data such as employees' hobbies, interests, and job expertise. For example, data is collected based on information registered by employees about their hobbies, interests, and past project experience. The data collection department can also collect profile information and survey results provided by employees. Step 2: The data analysis department analyzes the employee data collected by the data collection department. For example, AI can analyze data such as employees' hobbies, interests, and job specialties to identify employees with commonalities and mutual interests. The data analysis department can also analyze data based on past project experience and employee feedback. Step 3: The Matching Department matches employees with commonalities and mutual interests based on the employee data analyzed by the Data Analysis Department. For example, it pairs employees with the same hobbies or interests in the same projects. The Matching Department also notifies employees of the matching results to provide opportunities for employees to deepen their interactions naturally. Step 4: The notification department notifies employees matched by the matching department of opportunities for interaction. For example, the AI ​​can send a notification such as "Would you like to join our lunch meeting this week?", and if the employee wishes to participate, it will arrange the date, time, and location. The notification department can also suggest more effective opportunities for interaction based on past participation history and feedback.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects data on employees' hobbies, interests, job specialties, etc. a data analysis unit that analyzes the employee data collected by the data collection unit; a matching unit that matches employees who have commonalities or mutual interests based on the employee data analyzed by the data analysis unit; a notification unit that notifies employees matched by the matching unit of opportunities for interaction. A system characterized by:

2. The data collection unit Monitoring the employee's emotional state in real time and updating the data based on emotional fluctuations 2. The system of claim 1.

3. The data collection unit Collecting health data of said employees and suggesting said interaction opportunities based on their health status 2. The system of claim 1.

4. The matching unit Analyze the employee's past interactions to identify and reuse successful matching patterns 2. The system of claim 1.

5. The notification unit Analyze the employee's schedule in real time and suggest the most appropriate opportunity for interaction.

2. The system of claim 1.

6. The data analysis unit Using emotion estimation, monitor the employee's emotional state during the event and make adjustments to elicit positive emotions.

2. The system of claim 1.

7. The data analysis unit Using emotion estimation functionality, the company continuously monitors the employee's emotional state and suggests measures to maintain positive emotions.

2. The system of claim 1.

8. The data analysis unit Using emotion estimation functionality, the emotional nuances of the data entered by the employee are analyzed to generate more accurate matching data.

2. The system of claim 1.

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