System

Abstract

An object of a system according to an embodiment is to promote natural interaction among employees and create opportunities for innovation.SOLUTION: A system includes an employee information collection part, a matching part, and an event proposal part. An employee information collection part collects a department of an employee, business contents, and an individual desire for communication. The matching unit provides optimal matching based on the information collected by the employee information collection unit. The event proposal unit proposes an event or an activity for promoting interaction on the basis of matching provided by the matching unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Previous technologies had the problem of not being able to efficiently match employees to promote natural interaction and create opportunities for innovation.

[0005] The system according to the embodiment aims to promote natural interactions between employees and create opportunities for innovation. [Means for solving the problem]

[0006] The system according to the embodiment includes an employee information collection unit, a matching unit, and an event suggestion unit. The employee information collection unit collects information about employees' departments, job duties, and personal interaction preferences. The matching unit provides optimal matching based on the information collected by the employee information collection unit. The event suggestion unit suggests events and activities to promote interaction based on the matching provided by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can promote natural interactions between employees and create opportunities for innovation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​matching system according to the embodiment of the present invention analyzes employees' departments, work contents, and personal interaction preferences, and provides efficient matching based on these analysis results. As a result, the AI ​​matching system can promote natural interactions between employees and create opportunities for innovation.

[0029] The AI ​​matching system according to the embodiment includes an employee information collection unit, a matching unit, and an event suggestion unit. The employee information collection unit collects information about an employee's department, job description, and personal interaction preferences. For example, the employee information collection unit collects the information when the employee enters their profile. The employee information collection unit can also collect an employee's past project history and performance data. For example, the employee information collection unit stores details, roles, and results of projects that the employee has previously worked on in a database. The matching unit provides optimal matching based on the information collected by the employee information collection unit. For example, if employees from different departments have common interests, the matching unit matches them based on that information. The matching unit can also match employees' skill sets with internal project needs to form optimal project teams. For example, the matching unit assigns employees with specific skills to project teams. The event suggestion unit suggests events and activities to promote interaction based on the matching provided by the matching unit. For example, the event suggestion unit suggests events and activities to promote interaction in a lounge. The event suggestion unit can also analyze the lounge usage history to identify the time periods when interactions are most active and concentrate events around those time periods. For example, the event suggestion unit can hold a technical exchange meeting during the time periods when interactions are most active based on past usage history. This allows the AI ​​matching system according to the embodiment to promote natural interactions between employees and create opportunities for innovation. For example, when employees share common interests, the likelihood of new projects being launched increases, improving the competitiveness of the entire company. In addition, active interactions in the lounge also improve employee motivation and satisfaction.

[0030] The employee information collection unit can analyze information when an employee enters their profile and determine what kind of interaction is desirable. For example, the employee information collection unit analyzes facial expressions and voice when an employee enters their profile to estimate emotions. For example, it uses a camera or microphone to analyze emotions in real time and provides feedback to elicit positive emotions. The employee information collection unit also uses an emotion estimation function to provide an interface to elicit positive emotions when an employee enters their profile. For example, it presents encouraging messages and success stories. The employee information collection unit also analyzes emotional data when the profile is entered and provides advice to strengthen positive emotions. For example, it displays appropriate encouragement or compliments based on the input content. This makes it possible to determine the optimal interaction based on the employee's profile information.

[0031] The matching department can match employees from different departments who share common interests based on that information, providing opportunities for interaction. For example, the matching department analyzes employees' internal social media accounts and external social media activity to identify trends in interests. For example, it analyzes the topics employees frequently post about and the groups they participate in, matching employees with shared interests. The matching department also uses natural language processing technology to analyze the content of social media posts and quantify employees' interests. For example, it identifies employees' areas of interest based on the frequency of use of specific keywords and hashtags. The matching department also monitors internal and external social media activity in real time to identify the latest trends. For example, it detects new topics of interest and matches employees related to those topics. This can promote interaction between employees from different departments.

[0032] The event suggestion unit can suggest events and activities to promote interaction in the lounge. For example, when an employee enters their profile, the event suggestion unit analyzes facial expressions and voice to estimate emotions. For example, it uses a camera or microphone to analyze emotions in real time and provides feedback to elicit positive emotions. The event suggestion unit also uses the emotion estimation function to provide an interface to elicit positive emotions when an employee enters their profile. For example, it presents encouraging messages and success stories. The event suggestion unit also analyzes emotional data when the profile is entered and provides advice to strengthen positive emotions. For example, it displays appropriate encouragement or compliments based on the input content. This makes it possible to suggest events and activities that promote interaction in the lounge.

[0033] The employee information collection department can analyze employees' past project history and performance data to predict the possibility of future interactions. For example, the employee information collection department stores employees' past project history in a database, and AI analyzes that data. For example, based on the member composition and skill sets of past successful projects, it identifies employees who would be ideal for future interactions on similar projects. The employee information collection department also analyzes performance data to promote interactions between employees who have achieved specific results. For example, it could select employees with high sales or customer satisfaction and create a forum for sharing their success stories. The employee information collection department also combines and analyzes project history and performance data to propose optimal team composition for future projects. For example, reuniting members of past successful projects increases the probability of success. This makes it possible to predict the possibility of future interactions based on past project history and performance data.

[0034] The employee information collection unit analyzes employees' social media activities both inside and outside the company to identify trends in their interests. For example, the employee information collection unit analyzes employees' internal social media activities and external social media activities to identify trends in their interests. For example, it analyzes the topics employees frequently post on and the groups they participate in, and matches employees with common interests. The employee information collection unit also analyzes the content of social media posts using natural language processing technology to quantify employees' interests. For example, it identifies employees' areas of interest based on the frequency of use of specific keywords and hashtags. The employee information collection unit also monitors internal and external social media activities in real time to identify the latest trends. For example, it detects topics that employees have recently become interested in, and matches employees related to those topics. This allows the unit to identify trends in interests based on social media activity.

[0035] The employee information collection unit can collect health data and fitness information of employees and suggest interactions based on their health status. The employee information collection unit, for example, collects health data and fitness information of employees and suggests interactions based on their health status. For example, it can match employees with the same fitness goals and provide opportunities for them to train together. The employee information collection unit also analyzes the health data and promotes interactions between employees with similar health status. For example, it can match employees with the same health challenges and encourage them to exchange information to improve their health. The employee information collection unit also suggests health events and activities based on the fitness information. For example, it can hold running clubs and yoga classes to promote interactions between employees who are interested in health. This makes it possible to suggest interactions based on health status based on the health data and fitness information.

[0036] The employee information collection unit can analyze employees' commuting routes and working hours, and perform matching to maximize opportunities for physical contact. The employee information collection unit, for example, analyzes employees' commuting routes and matches employees who use the same routes. For example, it suggests that employees with overlapping commuting times can commute together. The employee information collection unit also analyzes working hours and promotes interaction between employees who work the same time period. For example, it suggests that employees who work the same shift can interact during lunch time. The employee information collection unit also analyzes a combination of commuting routes and working hours, and performs matching to maximize opportunities for physical contact. For example, it suggests that employees who use the same route at the same time period can commute together. This makes it possible to perform matching that maximizes opportunities for physical contact based on commuting routes and working hours.

[0037] The matching department can match employee skill sets with internal project needs and form optimal project teams. For example, the matching department registers employee skill sets in a database and matches them with internal project needs. For example, it assigns employees with specific skills to project teams. The matching department also analyzes project needs and builds a system that suggests employees with the optimal skill sets. For example, it selects the optimal members based on the project requirements. The matching department also matches skill sets with project needs in real time and forms optimal project teams. For example, it instantly suggests the optimal members when a new project is launched. This makes it possible to form optimal project teams based on skill sets and project needs.

[0038] The matching department can analyze employees' career paths and suggest interactions that will contribute to future career growth. For example, the matching department registers employees' career paths in a database and suggests interactions that will contribute to future career growth. For example, it matches employees with similar career goals. The matching department also analyzes career paths and builds a system that provides opportunities for interactions that will be useful for future career growth. For example, it suggests mentors based on career goals. The matching department also analyzes a combination of career paths and interaction preferences and suggests interactions that will contribute to future career growth. For example, it provides a place where employees with similar career goals can exchange information. This makes it possible to suggest interactions that will contribute to future career growth based on career paths.

[0039] The matching unit can match employees who are in a common life stage with each other, taking into account the family structure and lifestyle of the employees. For example, the matching unit registers the family structure and lifestyle of employees in a database and matches employees who are in a common life stage with each other. For example, it matches employees who are raising children with each other. The matching unit also analyzes lifestyles and builds a system that promotes interaction between employees who are in a common life stage. For example, it provides a place where employees in the same life stage can exchange information. The matching unit also matches employees who are in a common life stage with each other, based on family structure and lifestyle. For example, it promotes interaction between employees in the same life stage to solve common problems. In this way, interaction between employees in a common life stage can be promoted by matching that takes family structure and lifestyle into consideration.

[0040] The matching department can suggest opportunities for interaction outside of work based on employees' hobbies and special skills. For example, the matching department registers employees' hobbies and special skills in a database and suggests opportunities for interaction outside of work. For example, it holds events where employees with the same hobbies can get together. The matching department also analyzes hobbies and special skills and builds a system that provides opportunities for interaction outside of work. For example, it suggests club activities related to hobbies and special skills. The matching department also suggests opportunities for interaction outside of work based on hobbies and special skills. For example, it provides a place where employees with the same hobbies can exchange information. This makes it possible to suggest opportunities for interaction outside of work based on hobbies and special skills.

[0041] The event suggestion unit analyzes the lounge usage history, identifies the time periods when interaction is most active, and can concentrate events around those time periods. For example, the event suggestion unit stores the lounge usage history in a database and uses AI to analyze the data. For example, it identifies the time periods when interaction is most active based on past usage history. The event suggestion unit also analyzes the usage history and builds a system that concentrates events around specific time periods. For example, it holds a technical exchange meeting around the time periods when interaction is most active. The event suggestion unit also monitors the lounge usage history in real time and identifies the time periods when interaction is most active. For example, it concentrates events around time periods when there are a large number of users. This makes it possible to concentrate events around time periods when interaction is most active based on the lounge usage history.

[0042] The event suggestion unit can use sensors in the lounge to grasp the congestion situation in real time and propose the optimal interaction space. The event suggestion unit, for example, uses sensors installed in the lounge to build a system that grasps the congestion situation in real time. For example, it analyzes data from the sensors and displays the congestion level. The event suggestion unit also develops an algorithm that proposes the optimal interaction space based on the congestion situation. For example, it prioritizes proposing spaces with low congestion levels. The event suggestion unit also builds a system that monitors the congestion situation in real time and proposes the optimal interaction space. For example, it holds an interaction event during a time period when congestion is low. This makes it possible to grasp the congestion situation in the lounge in real time and propose the optimal interaction space.

[0043] The event proposal department can periodically change the design and layout of the lounge to provide a fresh environment for interaction. For example, the event proposal department can build a system to periodically change the design and layout of the lounge. For example, they can introduce designs with different themes for each season. The event proposal department can also provide a fresh environment for interaction through layout changes. For example, they can create different interaction spaces by rearranging the furniture. The event proposal department can also periodically change the design and layout to prevent employees from getting bored. For example, they can regularly introduce new decorations and interior decor. In this way, the design and layout of the lounge can be periodically changed to provide a fresh environment for interaction.

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

[0045] The employee information collection department collects employee health and fitness data and can suggest interactions based on health status. For example, it can match employees with the same fitness goals and provide opportunities for them to train together. The employee information collection department also analyzes health data and promotes interactions between employees with similar health conditions. For example, it can match employees with the same health challenges and encourage them to exchange information to improve their health. The employee information collection department can also suggest health events and activities based on fitness information. For example, it can hold running clubs and yoga classes to promote interactions between employees who are interested in health. This makes it possible to suggest interactions based on health status based on health data and fitness information.

[0046] The matching department can analyze employees' career paths and suggest interactions that will contribute to future career growth. For example, it can match employees with similar career goals. The matching department can also analyze career paths and build a system that provides opportunities for interactions that will be useful for future career growth. For example, it can suggest mentors based on career goals. The matching department can also analyze a combination of career paths and interaction preferences to suggest interactions that will contribute to future career growth. For example, it can provide a place where employees with similar career goals can exchange information. This makes it possible to suggest interactions that will contribute to future career growth based on career paths.

[0047] The event suggestion unit can use sensors in the lounge to grasp the congestion situation in real time and propose the optimal interaction space. For example, a system is constructed that uses sensors installed in the lounge to grasp the congestion situation in real time. For example, data from the sensors is analyzed and the congestion level is displayed. The event suggestion unit also develops an algorithm that proposes the optimal interaction space based on the congestion situation. For example, spaces with low congestion levels are prioritized for proposal. The event suggestion unit also builds a system that monitors the congestion situation in real time and proposes the optimal interaction space. For example, a communication event is held during a time period when congestion is low. This makes it possible to grasp the congestion situation in the lounge in real time and propose the optimal interaction space.

[0048] The matching department can match employees who are in a common life stage with each other, taking into account their family structure and lifestyle. For example, it can register employees' family structures and lifestyles in a database and match employees who are in a common life stage with each other. For example, it can match employees who are raising children with each other. The matching department can also analyze lifestyles and build a system that promotes interaction between employees who are in a common life stage. For example, it can provide a place where employees in the same life stage can exchange information. The matching department can also match employees who are in a common life stage with each other, based on their family structure and lifestyle. For example, it can promote interaction between employees in the same life stage to solve common problems. This makes it possible to promote interaction between employees who are in a common life stage by matching employees who are in a common life stage with each other, taking into account their family structure and lifestyle.

[0049] The matching department can suggest opportunities for interaction outside of work based on employees' hobbies and special skills. For example, it can register employees' hobbies and special skills in a database and suggest opportunities for interaction outside of work. For example, it can hold events where employees with the same hobbies can get together. The matching department can also analyze hobbies and special skills and build a system that provides opportunities for interaction outside of work. For example, it can suggest club activities related to hobbies and special skills. The matching department can also suggest opportunities for interaction outside of work based on hobbies and special skills. For example, it can provide a place where employees with the same hobbies can exchange information. This makes it possible to suggest opportunities for interaction outside of work based on hobbies and special skills.

[0050] The employee information collection unit can analyze employees' commuting routes and working hours, and perform matching to maximize opportunities for physical contact. For example, it can analyze employees' commuting routes and match employees who use the same routes. For example, it can suggest that employees with overlapping commuting times can commute together. The employee information collection unit also analyzes working hours and promotes interaction between employees who work the same time period. For example, it can suggest that employees who work the same shift can interact during lunch time. The employee information collection unit also analyzes a combination of commuting routes and working hours, and performs matching to maximize opportunities for physical contact. For example, it can suggest that employees who take the same route at the same time period can commute together. This makes it possible to perform matching that maximizes opportunities for physical contact based on commuting routes and working hours.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The employee information collection department collects information about the employee's department, job description, and personal interaction preferences. For example, the employee information collection department collects this information when the employee enters their profile. The employee information collection department can also collect the employee's past project history and performance data. For example, the employee information collection department stores details of the employee's past projects, roles, and achievements in a database. Step 2: The Matching Department provides optimal matching based on the information collected by the Employee Information Collection Department. For example, if employees from different departments have common interests, the Matching Department can match them based on that information. The Matching Department can also match employees' skill sets with internal project needs to form optimal project teams. For example, the Matching Department can assign employees with specific skills to project teams. Step 3: The event suggestion unit suggests events and activities to promote interaction based on the matching provided by the matching unit. For example, the event suggestion unit suggests events and activities to promote interaction in the lounge. The event suggestion unit can also analyze the lounge usage history to identify the time periods when interaction is most active and concentrate events around those time periods. For example, the event suggestion unit may hold a technology exchange meeting during the time periods when interaction is most active based on past usage history.

[0053] (Example 2) The AI ​​matching system according to the embodiment of the present invention analyzes employees' departments, work contents, and personal interaction preferences, and provides efficient matching based on these analysis results. As a result, the AI ​​matching system can promote natural interactions between employees and create opportunities for innovation.

[0054] The AI ​​matching system according to the embodiment includes an employee information collection unit, a matching unit, and an event suggestion unit. The employee information collection unit collects information about an employee's department, job description, and personal interaction preferences. For example, the employee information collection unit collects the information when the employee enters their profile. The employee information collection unit can also collect an employee's past project history and performance data. For example, the employee information collection unit stores details, roles, and results of projects that the employee has previously worked on in a database. The matching unit provides optimal matching based on the information collected by the employee information collection unit. For example, if employees from different departments have common interests, the matching unit matches them based on that information. The matching unit can also match employees' skill sets with internal project needs to form optimal project teams. For example, the matching unit assigns employees with specific skills to project teams. The event suggestion unit suggests events and activities to promote interaction based on the matching provided by the matching unit. For example, the event suggestion unit suggests events and activities to promote interaction in a lounge. The event suggestion unit can also analyze the lounge usage history to identify the time periods when interactions are most active and concentrate events around those time periods. For example, the event suggestion unit can hold a technical exchange meeting during the time periods when interactions are most active based on past usage history. This allows the AI ​​matching system according to the embodiment to promote natural interactions between employees and create opportunities for innovation. For example, when employees share common interests, the likelihood of new projects being launched increases, improving the competitiveness of the entire company. In addition, active interactions in the lounge also improve employee motivation and satisfaction.

[0055] The employee information collection unit can analyze information when an employee enters their profile and determine what kind of interaction is desirable. For example, the employee information collection unit analyzes facial expressions and voice when an employee enters their profile to estimate emotions. For example, it uses a camera or microphone to analyze emotions in real time and provides feedback to elicit positive emotions. The employee information collection unit also uses an emotion estimation function to provide an interface to elicit positive emotions when an employee enters their profile. For example, it presents encouraging messages and success stories. The employee information collection unit also analyzes emotional data when the profile is entered and provides advice to strengthen positive emotions. For example, it displays appropriate encouragement or compliments based on the input content. This makes it possible to determine the optimal interaction based on the employee's profile information.

[0056] The matching department can match employees from different departments who share common interests based on that information, providing opportunities for interaction. For example, the matching department analyzes employees' internal social media accounts and external social media activity to identify trends in interests. For example, it analyzes the topics employees frequently post about and the groups they participate in, matching employees with shared interests. The matching department also uses natural language processing technology to analyze the content of social media posts and quantify employees' interests. For example, it identifies employees' areas of interest based on the frequency of use of specific keywords and hashtags. The matching department also monitors internal and external social media activity in real time to identify the latest trends. For example, it detects new topics of interest and matches employees related to those topics. This can promote interaction between employees from different departments.

[0057] The event suggestion unit can suggest events and activities to promote interaction in the lounge. For example, when an employee enters their profile, the event suggestion unit analyzes facial expressions and voice to estimate emotions. For example, it uses a camera or microphone to analyze emotions in real time and provides feedback to elicit positive emotions. The event suggestion unit also uses the emotion estimation function to provide an interface to elicit positive emotions when an employee enters their profile. For example, it presents encouraging messages and success stories. The event suggestion unit also analyzes emotional data when the profile is entered and provides advice to strengthen positive emotions. For example, it displays appropriate encouragement or compliments based on the input content. This makes it possible to suggest events and activities that promote interaction in the lounge.

[0058] The employee information collection department can analyze employees' past project history and performance data to predict the possibility of future interactions. For example, the employee information collection department stores employees' past project history in a database, and AI analyzes that data. For example, based on the member composition and skill sets of past successful projects, it identifies employees who would be ideal for future interactions on similar projects. The employee information collection department also analyzes performance data to promote interactions between employees who have achieved specific results. For example, it could select employees with high sales or customer satisfaction and create a forum for sharing their success stories. The employee information collection department also combines and analyzes project history and performance data to propose optimal team composition for future projects. For example, reuniting members of past successful projects increases the probability of success. This makes it possible to predict the possibility of future interactions based on past project history and performance data.

[0059] The employee information collection unit analyzes employees' social media activities both inside and outside the company to identify trends in their interests. For example, the employee information collection unit analyzes employees' internal social media activities and external social media activities to identify trends in their interests. For example, it analyzes the topics employees frequently post on and the groups they participate in, and matches employees with common interests. The employee information collection unit also analyzes the content of social media posts using natural language processing technology to quantify employees' interests. For example, it identifies employees' areas of interest based on the frequency of use of specific keywords and hashtags. The employee information collection unit also monitors internal and external social media activities in real time to identify the latest trends. For example, it detects topics that employees have recently become interested in, and matches employees related to those topics. This allows the unit to identify trends in interests based on social media activity.

[0060] The employee information collection unit can use the emotion estimation function to analyze the emotions of employees when they enter their profiles and provide feedback to elicit positive emotions. For example, the employee information collection unit analyzes facial expressions and voices when employees enter their profiles to estimate emotions. For example, it uses a camera or microphone to analyze emotions in real time and provides feedback to elicit positive emotions. The employee information collection unit also uses the emotion estimation function to provide an interface to elicit positive emotions when employees enter their profiles. For example, it presents encouraging messages and success stories. The employee information collection unit also analyzes emotional data when employees enter their profiles and provides advice to strengthen positive emotions. For example, it displays appropriate encouragement or compliments based on the input content. This makes it possible to provide feedback to elicit positive emotions when employees enter their profiles.

[0061] The employee information collection unit can collect health data and fitness information of employees and suggest interactions based on their health status. The employee information collection unit, for example, collects health data and fitness information of employees and suggests interactions based on their health status. For example, it can match employees with the same fitness goals and provide opportunities for them to train together. The employee information collection unit also analyzes the health data and promotes interactions between employees with similar health status. For example, it can match employees with the same health challenges and encourage them to exchange information to improve their health. The employee information collection unit also suggests health events and activities based on the fitness information. For example, it can hold running clubs and yoga classes to promote interactions between employees who are interested in health. This makes it possible to suggest interactions based on health status based on the health data and fitness information.

[0062] The employee information collection unit can analyze employees' commuting routes and working hours, and perform matching to maximize opportunities for physical contact. The employee information collection unit, for example, analyzes employees' commuting routes and matches employees who use the same routes. For example, it suggests that employees with overlapping commuting times can commute together. The employee information collection unit also analyzes working hours and promotes interaction between employees who work the same time period. For example, it suggests that employees who work the same shift can interact during lunch time. The employee information collection unit also analyzes a combination of commuting routes and working hours, and performs matching to maximize opportunities for physical contact. For example, it suggests that employees who use the same route at the same time period can commute together. This makes it possible to perform matching that maximizes opportunities for physical contact based on commuting routes and working hours.

[0063] The employee information collection unit can use the emotion estimation function to monitor the emotional state of employees in real time and suggest interactions at times when stress is low. The employee information collection unit, for example, uses the emotion estimation function to monitor the emotional state of employees in real time. For example, it analyzes the facial expressions and voice of employees using a camera or microphone to calculate an emotion score. The employee information collection unit also builds a system that suggests interactions at times when stress is low based on the emotional state. For example, it provides opportunities for interaction when the emotion score is stable. The employee information collection unit also monitors the emotional state in real time and promotes interactions at times when stress is low. For example, it suggests relaxing activities when the emotion score is low. This makes it possible to suggest interactions at times when stress is low based on the emotional state.

[0064] The matching department can match employee skill sets with internal project needs and form optimal project teams. For example, the matching department registers employee skill sets in a database and matches them with internal project needs. For example, it assigns employees with specific skills to project teams. The matching department also analyzes project needs and builds a system that suggests employees with the optimal skill sets. For example, it selects the optimal members based on the project requirements. The matching department also matches skill sets with project needs in real time and forms optimal project teams. For example, it instantly suggests the optimal members when a new project is launched. This makes it possible to form optimal project teams based on skill sets and project needs.

[0065] The matching department can analyze employees' career paths and suggest interactions that will contribute to future career growth. For example, the matching department registers employees' career paths in a database and suggests interactions that will contribute to future career growth. For example, it matches employees with similar career goals. The matching department also analyzes career paths and builds a system that provides opportunities for interactions that will be useful for future career growth. For example, it suggests mentors based on career goals. The matching department also analyzes a combination of career paths and interaction preferences and suggests interactions that will contribute to future career growth. For example, it provides a place where employees with similar career goals can exchange information. This makes it possible to suggest interactions that will contribute to future career growth based on career paths.

[0066] The matching unit uses the emotion estimation function to perform matching that takes into account the emotional state of employees, thereby promoting positive interactions. The matching unit, for example, uses the emotion estimation function to analyze the emotional state of employees in real time and perform matching that promotes positive interactions. For example, it matches employees with high emotional scores. The matching unit also develops a matching algorithm that takes into account the emotional state to promote positive interactions. For example, it prioritizes matching employees with stable emotional scores. The matching unit also builds a matching system that performs matching to promote positive interactions based on the emotion estimation data. For example, it provides opportunities for interaction when the emotional score is high. This makes it possible to promote positive interactions through matching that takes emotional states into account.

[0067] The matching unit can match employees who are in a common life stage with each other, taking into account the family structure and lifestyle of the employees. For example, the matching unit registers the family structure and lifestyle of employees in a database and matches employees who are in a common life stage with each other. For example, it matches employees who are raising children with each other. The matching unit also analyzes lifestyles and builds a system that promotes interaction between employees who are in a common life stage. For example, it provides a place where employees in the same life stage can exchange information. The matching unit also matches employees who are in a common life stage with each other, based on family structure and lifestyle. For example, it promotes interaction between employees in the same life stage to solve common problems. In this way, interaction between employees in a common life stage can be promoted by matching that takes family structure and lifestyle into consideration.

[0068] The matching department can suggest opportunities for interaction outside of work based on employees' hobbies and special skills. For example, the matching department registers employees' hobbies and special skills in a database and suggests opportunities for interaction outside of work. For example, it holds events where employees with the same hobbies can get together. The matching department also analyzes hobbies and special skills and builds a system that provides opportunities for interaction outside of work. For example, it suggests club activities related to hobbies and special skills. The matching department also suggests opportunities for interaction outside of work based on hobbies and special skills. For example, it provides a place where employees with the same hobbies can exchange information. This makes it possible to suggest opportunities for interaction outside of work based on hobbies and special skills.

[0069] The matching unit can use the emotion estimation function to analyze the emotional state of employees in real time and suggest interactions at the optimal time. The matching unit, for example, uses the emotion estimation function to analyze the emotional state of employees in real time and build a system that suggests interactions at the optimal time. For example, it provides opportunities for interaction when the emotion score is high. The matching unit also develops an algorithm that suggests interactions at the optimal time based on the emotional state. For example, it provides opportunities for interaction when the emotion score is stable. The matching unit also builds a system that suggests interactions at the optimal time based on the emotion estimation data. For example, it suggests relaxing activities when the emotion score is low. This makes it possible to analyze emotional states in real time and suggest interactions at the optimal time.

[0070] The event suggestion unit analyzes the lounge usage history, identifies the time periods when interaction is most active, and can concentrate events around those time periods. For example, the event suggestion unit stores the lounge usage history in a database and uses AI to analyze the data. For example, it identifies the time periods when interaction is most active based on past usage history. The event suggestion unit also analyzes the usage history and builds a system that concentrates events around specific time periods. For example, it holds a technical exchange meeting around the time periods when interaction is most active. The event suggestion unit also monitors the lounge usage history in real time and identifies the time periods when interaction is most active. For example, it concentrates events around time periods when there are a large number of users. This makes it possible to concentrate events around time periods when interaction is most active based on the lounge usage history.

[0071] The event suggestion unit can use sensors in the lounge to grasp the congestion situation in real time and propose the optimal interaction space. The event suggestion unit, for example, uses sensors installed in the lounge to build a system that grasps the congestion situation in real time. For example, it analyzes data from the sensors and displays the congestion level. The event suggestion unit also develops an algorithm that proposes the optimal interaction space based on the congestion situation. For example, it prioritizes proposing spaces with low congestion levels. The event suggestion unit also builds a system that monitors the congestion situation in real time and proposes the optimal interaction space. For example, it holds an interaction event during a time period when congestion is low. This makes it possible to grasp the congestion situation in the lounge in real time and propose the optimal interaction space.

[0072] The event suggestion unit can use the emotion estimation function to analyze the emotional state of event participants in a lounge and suggest activities to elicit positive emotions. The event suggestion unit, for example, uses the emotion estimation function to build a system that analyzes the emotional state of event participants in a lounge in real time. For example, it uses a camera and microphone to analyze the participants' facial expressions and voices and calculates an emotion score. The event suggestion unit also develops an algorithm that suggests activities to elicit positive emotions based on the emotional state. For example, it suggests relaxing activities when the emotion score is low. The event suggestion unit also builds a system that suggests activities to elicit positive emotions based on the emotion estimation data. For example, it provides opportunities for interaction when the emotion score is high. This makes it possible to analyze the emotional state and suggest activities to elicit positive emotions.

[0073] The event proposal department can periodically change the design and layout of the lounge to provide a fresh environment for interaction. For example, the event proposal department can build a system to periodically change the design and layout of the lounge. For example, they can introduce designs with different themes for each season. The event proposal department can also provide a fresh environment for interaction through layout changes. For example, they can create different interaction spaces by rearranging the furniture. The event proposal department can also periodically change the design and layout to prevent employees from getting bored. For example, they can regularly introduce new decorations and interior decor. In this way, the design and layout of the lounge can be periodically changed to provide a fresh environment for interaction.

[0074] The event suggestion unit uses the emotion estimation function to monitor the emotional state during interaction in the lounge in real time, and can provide an optimal interaction environment. The event suggestion unit, for example, uses the emotion estimation function to build a system that monitors the emotional state during interaction in the lounge in real time. For example, a camera and a microphone are used to analyze the facial expressions and voices of participants and calculate an emotion score. The event suggestion unit also develops an algorithm that provides an optimal interaction environment based on the emotional state. For example, when the emotion score is low, a relaxing environment is provided. The event suggestion unit also builds a system that provides an optimal interaction environment based on the emotion estimation data. For example, when the emotion score is high, an opportunity for interaction is provided. This makes it possible to monitor the emotional state in real time and provide an optimal interaction environment.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The employee information collection department collects employee health and fitness data and can suggest interactions based on health status. For example, it can match employees with the same fitness goals and provide opportunities for them to train together. The employee information collection department also analyzes health data and promotes interactions between employees with similar health conditions. For example, it can match employees with the same health challenges and encourage them to exchange information to improve their health. The employee information collection department can also suggest health events and activities based on fitness information. For example, it can hold running clubs and yoga classes to promote interactions between employees who are interested in health. This makes it possible to suggest interactions based on health status based on health data and fitness information.

[0077] The matching department can analyze employees' career paths and suggest interactions that will contribute to future career growth. For example, it can match employees with similar career goals. The matching department can also analyze career paths and build a system that provides opportunities for interactions that will be useful for future career growth. For example, it can suggest mentors based on career goals. The matching department can also analyze a combination of career paths and interaction preferences to suggest interactions that will contribute to future career growth. For example, it can provide a place where employees with similar career goals can exchange information. This makes it possible to suggest interactions that will contribute to future career growth based on career paths.

[0078] The event suggestion unit can use sensors in the lounge to grasp the congestion situation in real time and propose the optimal interaction space. For example, a system is constructed that uses sensors installed in the lounge to grasp the congestion situation in real time. For example, data from the sensors is analyzed and the congestion level is displayed. The event suggestion unit also develops an algorithm that proposes the optimal interaction space based on the congestion situation. For example, spaces with low congestion levels are prioritized for proposal. The event suggestion unit also builds a system that monitors the congestion situation in real time and proposes the optimal interaction space. For example, a communication event is held during a time period when congestion is low. This makes it possible to grasp the congestion situation in the lounge in real time and propose the optimal interaction space.

[0079] The matching department can match employees who are in a common life stage with each other, taking into account their family structure and lifestyle. For example, it can register employees' family structures and lifestyles in a database and match employees who are in a common life stage with each other. For example, it can match employees who are raising children with each other. The matching department can also analyze lifestyles and build a system that promotes interaction between employees who are in a common life stage. For example, it can provide a place where employees in the same life stage can exchange information. The matching department can also match employees who are in a common life stage with each other, based on their family structure and lifestyle. For example, it can promote interaction between employees in the same life stage to solve common problems. This makes it possible to promote interaction between employees who are in a common life stage by matching employees who are in a common life stage with each other, taking into account their family structure and lifestyle.

[0080] The matching department can suggest opportunities for interaction outside of work based on employees' hobbies and special skills. For example, it can register employees' hobbies and special skills in a database and suggest opportunities for interaction outside of work. For example, it can hold events where employees with the same hobbies can get together. The matching department can also analyze hobbies and special skills and build a system that provides opportunities for interaction outside of work. For example, it can suggest club activities related to hobbies and special skills. The matching department can also suggest opportunities for interaction outside of work based on hobbies and special skills. For example, it can provide a place where employees with the same hobbies can exchange information. This makes it possible to suggest opportunities for interaction outside of work based on hobbies and special skills.

[0081] The matching unit uses the emotion estimation function to perform matching that takes into account the emotional state of employees, thereby promoting positive interactions. For example, the emotion estimation function is used to analyze the emotional state of employees in real time, and perform matching that promotes positive interactions. For example, employees with high emotional scores are matched together. The matching unit also develops a matching algorithm that takes into account emotional states to promote positive interactions. For example, employees with stable emotional scores are matched preferentially. The matching unit also builds a matching system that performs matching to promote positive interactions based on the emotion estimation data. For example, opportunities for interaction are provided when the emotional score is high. This makes it possible to promote positive interactions through matching that takes emotional states into account.

[0082] The event suggestion unit can use the emotion estimation function to analyze the emotional state of event participants in a lounge and suggest activities to elicit positive emotions. For example, the emotion estimation function can be used to build a system that analyzes the emotional state of event participants in a lounge in real time. For example, a camera and microphone can be used to analyze the participants' facial expressions and voices and calculate an emotion score. The event suggestion unit can also develop an algorithm that suggests activities to elicit positive emotions based on the emotional state. For example, it can suggest relaxing activities when the emotion score is low. The event suggestion unit can also build a system that suggests activities to elicit positive emotions based on the emotion estimation data. For example, it can provide opportunities for interaction when the emotion score is high. This makes it possible to analyze the emotional state and suggest activities to elicit positive emotions.

[0083] The matching unit can use the emotion estimation function to analyze the emotional state of employees in real time and suggest interactions at the optimal time. For example, a system can be built using the emotion estimation function to analyze the emotional state of employees in real time and suggest interactions at the optimal time. For example, opportunities for interaction are provided when the emotion score is high. The matching unit can also develop an algorithm to suggest interactions at the optimal time based on the emotional state. For example, opportunities for interaction are provided when the emotion score is stable. The matching unit can also build a system to suggest interactions at the optimal time based on the emotion estimation data. For example, relaxing activities are suggested when the emotion score is low. This makes it possible to analyze emotional states in real time and suggest interactions at the optimal time.

[0084] The event suggestion unit can use the emotion estimation function to monitor the emotional state during interaction in the lounge in real time and provide an optimal interaction environment. For example, the emotion estimation function can be used to build a system that monitors the emotional state during interaction in the lounge in real time. For example, a camera and microphone can be used to analyze the facial expressions and voices of participants and calculate an emotion score. The event suggestion unit can also develop an algorithm that provides an optimal interaction environment based on the emotional state. For example, a relaxing environment can be provided when the emotion score is low. The event suggestion unit can also build a system that provides an optimal interaction environment based on the emotion estimation data. For example, an opportunity for interaction can be provided when the emotion score is high. This makes it possible to monitor the emotional state in real time and provide an optimal interaction environment.

[0085] The employee information collection unit can analyze employees' commuting routes and working hours, and perform matching to maximize opportunities for physical contact. For example, it can analyze employees' commuting routes and match employees who use the same routes. For example, it can suggest that employees with overlapping commuting times can commute together. The employee information collection unit also analyzes working hours and promotes interaction between employees who work the same time period. For example, it can suggest that employees who work the same shift can interact during lunch time. The employee information collection unit also analyzes a combination of commuting routes and working hours, and performs matching to maximize opportunities for physical contact. For example, it can suggest that employees who take the same route at the same time period can commute together. This makes it possible to perform matching that maximizes opportunities for physical contact based on commuting routes and working hours.

[0086] The processing flow of the second embodiment will be briefly explained below.

[0087] Step 1: The employee information collection department collects information about the employee's department, job description, and personal interaction preferences. For example, the employee information collection department collects this information when the employee enters their profile. The employee information collection department can also collect the employee's past project history and performance data. For example, the employee information collection department stores details of the employee's past projects, roles, and achievements in a database. Step 2: The Matching Department provides optimal matching based on the information collected by the Employee Information Collection Department. For example, if employees from different departments have common interests, the Matching Department can match them based on that information. The Matching Department can also match employees' skill sets with internal project needs to form optimal project teams. For example, the Matching Department can assign employees with specific skills to project teams. Step 3: The event suggestion unit suggests events and activities to promote interaction based on the matching provided by the matching unit. For example, the event suggestion unit suggests events and activities to promote interaction in the lounge. The event suggestion unit can also analyze the lounge usage history to identify the time periods when interaction is most active and concentrate events around those time periods. For example, the event suggestion unit may hold a technology exchange meeting during the time periods when interaction is most active based on past usage history.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0092] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0101] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0107] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0122] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

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

[0132] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 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.

[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 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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]

[0155] 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. An employee information collection department that collects information on employees' departments, work duties, and personal interaction preferences; a matching unit that provides optimal matching based on the information collected by the employee information collection unit; an event suggestion unit that suggests events and activities to promote interaction based on the matching provided by the matching unit; A system characterized by:

2. The employee information collection unit When employees fill out their profiles, we analyze that information to determine what interactions are best for them.

2. The system of claim 1.

3. The matching unit If employees from different departments have common interests, matching is carried out based on that information, and opportunities for interaction are provided.

2. The system of claim 1.

4. The event suggestion unit Suggesting events and activities to promote such interactions in the lounge 2. The system of claim 1.

5. The employee information collection unit Analyze the employee's past project history and performance data to predict the possibility of future interactions 2. The system of claim 1.

6. The employee information collection unit Analyze the employee's social media activity both inside and outside the company to understand trends in their interests 2. The system of claim 1.

7. The employee information collection unit Analyze the employee's emotions when filling out their profile and provide feedback to elicit positive emotions 2. The system of claim 1.

8. The employee information collection unit Collecting health and fitness data of said employees and suggesting said interactions based on their health status 2. The system of claim 1.

Images