System

Abstract

A system is provided.SOLUTION: A system comprising: means for registering employee characteristic information; means for setting matching criteria between employees; and means for using a generated AI to suggest 1on1 meeting partners between employees based on the employee characteristic information and the matching criteria.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] The spread of telework has made it more difficult for employees to make new connections, and an increasing number of employees are struggling with interpersonal relationships. Furthermore, working within fixed teams means fewer opportunities for new discoveries and insights. This has led to problems such as a decline in employee motivation and information monopolization. Furthermore, it is difficult to find suitable people to hold one-on-one meetings with, making effective communication and the exchange of opinions difficult. Solutions to these issues are needed. [Means for solving the problem]

[0005] This invention solves the above problems by using a means for registering employee characteristic information, a means for setting matching criteria between employees, and a means for using a generation AI to suggest partners for 1-on-1 meetings between employees based on the employee characteristic information and matching criteria. Specifically, StrengthsFinder characteristics, areas of interest, hobbies, and regularly used services are registered as employee characteristic information, and the generation AI suggests optimal partners for 1-on-1 meetings based on this information. Furthermore, a function is provided to suggest optimal partners for specific purposes, such as career counseling, exchanging opinions to improve services, or making friends with similar hobbies, depending on the matching criteria.

[0006] An "employee" is an individual who belongs to a particular company or organization and performs work for that company or organization.

[0007] "Characteristic information" is information that indicates an employee's personal traits and characteristics, including StrengthsFinder traits, areas of interest, hobbies, and services that they regularly use.

[0008] StrengthsFinder is a tool or method for analyzing and classifying personal strengths and traits.

[0009] "Generative AI" refers to systems that use artificial intelligence techniques to generate information and perform analysis and predictions.

[0010] A "1-on-1 meeting" is a one-on-one interview or conference, usually held between employees for the purpose of exchanging opinions and sharing information.

[0011] "Matching criteria" refers to the rules and conditions for selecting appropriate partners for one-on-one meetings, and are based on purposes such as career counseling, service improvement, and making friends.

[0012] "Career counseling" refers to consultations that employees undertake to seek advice and information about their occupations and careers.

[0013] "Service improvement" refers to efforts and activities to improve the quality and efficiency of the services provided by companies and organizations.

[0014] "Making friends" refers to activities aimed at connecting employees with common hobbies and interests and building friendships. [Brief explanation of the drawings]

[0015] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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, a 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), and an APU (Accelerated Processing Unit).

[0019] 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.

[0020] 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.

[0021] 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), Bluetooth (registered trademark), etc.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0027] 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.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention provides a system that registers employee characteristic information and, based on that information, suggests the most suitable person to hold a one-on-one meeting with. A specific embodiment of this system will be described below.

[0037] First, the terminal defines the Employee class and OneOnOneMatcher class. The Employee class is a class for storing basic information about employees, such as their ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0038] Next, when a user wants the system to suggest partners for a one-on-one meeting, the user provides the system with their user ID and matching criteria, which can include career advice, exchanging opinions to improve the service, or making friends with similar hobbies. The server then uses this information to make appropriate matches.

[0039] Based on the given user ID, the server retrieves the user's characteristic information from the database. Then, the server filters the list of all employees (excluding the user) according to the matching criteria and selects the most suitable employee. Specifically, in the case of career counseling, it prioritizes employees with similar StrengthsFinder characteristics to the user; in the case of service improvement, it prioritizes employees with common interests; and in the case of making friends, it prioritizes employees with common hobbies.

[0040] For example, if a user wants to make friends, the server will list the employees with whom they have the most in common (hobbies and interests) and suggest the top five from that list. This process gives users the opportunity to have one-on-one meetings with people who are suitable for them.

[0041] A specific example is given below. For example, suppose the employee list contains the following data:

[0042] Employee A: Traits: "Strategy" and "Empathy", Interests: "AI" and "Machine Learning", Hobbies: "Reading" and "Cycling", Services usually used: "Chat Tool A" and "Video Conferencing B"

[0043] Employee B: Traits: "Communication skills" and "Adaptability", Interests: "Marketing" and "Sales", Hobbies: "Games" and "Guitar", Services usually used: "Chat tool C" and "Video conferencing D"

[0044] In this case, if the user is employee A and wants to make friends, the server may consider employee A's characteristics, interests, hobbies, and the services he or she regularly uses, and suggest employee B as the optimal partner for a one-on-one meeting. However, the final matching result will be based on the degree of similarity and criteria between the user's characteristics and those of other employees.

[0045] In this way, by building a system that effectively suggests people to meet with for one-on-one meetings, it is possible to promote interaction between employees and increase opportunities for information exchange.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The terminal defines an Employee class and creates an object with information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0049] Step 2:

[0050] The terminal defines a OneOnOneMatcher class and sets a constructor that receives a list of employees as an argument. This class includes a method for matching employees.

[0051] Step 3:

[0052] Users enter their user ID and matching criteria into the system, which can be selected from "career consultation," "exchanging opinions to improve the service," or "making friends."

[0053] Step 4:

[0054] The server retrieves specific user information from a database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and commonly used services.

[0055] Step 5:

[0056] The server creates a candidate list by excluding the designated user from the list of all employees.

[0057] Step 6:

[0058] The server sorts the list of candidates based on matching criteria: for career counseling, by the degree of match of StrengthsFinder characteristics; for service improvement discussions, by the number of common interests; for friendship meetings, by the number of common hobbies.

[0059] Step 7:

[0060] The server selects the top five employees from the sorted candidate list and suggests them to the user.

[0061] Step 8:

[0062] The user selects the desired partner for the one-on-one meeting from the displayed candidate list and sets up the meeting.

[0063] Example 1

[0064] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0065] Previous employee matching systems had difficulty automatically selecting the right person for a one-on-one meeting, making it impossible to maximize the opportunity for appropriate interaction and information exchange between employees. It was also difficult to match individuals based on their characteristics, hobbies, and interests, and finding the perfect match for each employee took a lot of time and effort, especially in large organizations.

[0066] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0067] In this invention, the server includes a means for the user to provide their own characteristic information and matching criteria, a means for the server to acquire the user's characteristic information from a database, and a means for the server to filter the employee list according to the matching criteria and select appropriate employees, thereby making it possible to efficiently and accurately promote opportunities for interaction and information exchange between employees.

[0068] "Employee characteristic information" refers to information about an employee, including the employee's personality traits, areas of interest, leisure activities, and frequently used tools.

[0069] "Matching criteria" are the elements that are used as criteria when proposing partners for one-on-one meetings between employees, and specifically include the purpose of career advice, exchanging opinions, or making friends with similar hobbies.

[0070] "Generative AI" refers to artificial intelligence technology that suggests the most suitable partner based on specific input (prompt text).

[0071] "User" refers to the employee who receives the proposal from the other party in the 1-on-1 meeting.

[0072] "Server" refers to a device or system that retrieves user characteristic information from a database, filters the employee list according to matching criteria, and selects suitable employees.

[0073] "Database" refers to a storage device that stores employee characteristic information.

[0074] The present invention is a system for effectively holding one-on-one meetings between employees. The purpose of this system is for users to provide their own characteristic information, and for the server to suggest the most suitable partners for one-on-one meetings based on that information. Specific embodiments of the present invention are described below.

[0075] First, to register the employee's characteristic information, the terminal creates an instance of the Employee class. The Employee class holds basic information such as the employee's ID, name, personality traits, interests, leisure activities, and frequently used tools. This information is stored in the database.

[0076] Next, if a user wants the system to suggest partners for a one-on-one meeting, they provide their user ID and matching criteria, which can be set to career advice, exchanging opinions, or making friends.

[0077] The server receives this information and retrieves the user's characteristics from a database. It then filters the employee list according to the matching criteria and selects suitable employees. Specifically, if the criterion is career advice, it prioritizes employees with similar personality traits to the user; if the criterion is opinion exchange, it prioritizes employees with common interests; and if the criterion is making friends, it prioritizes employees with common leisure activities.

[0078] For example, if the user is looking to make friends, the server might respond with the following prompt:

[0079] "User ID: 1

[0080] Matching criteria: Making friends

[0081] Based on this prompt, the server considers the characteristics of employee A (ID: 1) and suggests several people, including employee B, who has the most in common with him / her. This allows the user to find a suitable partner for a one-on-one meeting.

[0082] This system not only promotes interaction between employees and increases opportunities for information exchange, but also allows individual employees to more effectively improve their careers and work, which is expected to strengthen collaboration and improve knowledge sharing across the company.

[0083] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0084] Step 1:

[0085] The terminal registers employee characteristics information. Specifically, it creates an instance of the Employee class and saves information such as the employee's ID, name, personality traits, areas of interest, leisure activities, and frequently used tools in the database. This process requires basic employee information as input and generates an employee object as output.

[0086] Step 2:

[0087] Users provide their user ID and matching criteria to the system to have it suggest partners for one-on-one meetings. Matching criteria can include career advice, exchanging ideas, or making friends. This information is input into the generative AI model as a prompt. The system requires the user ID and matching criteria as input and generates a prompt as output.

[0088] Step 3:

[0089] The server retrieves user characteristic information from the database based on the user ID and prompt provided by the user. This process requires the user ID as input and retrieves the user characteristic information as output. The characteristic information includes the employee's personality traits, areas of interest, and leisure activities.

[0090] Step 4:

[0091] The server filters the employee list according to the acquired user characteristic information and matching criteria. The user characteristic information and matching criteria are required as input, and the filtered employee list is generated as output. For example, if the matching criteria is "making friends," employees with common hobbies are prioritized.

[0092] Step 5:

[0093] The server selects the most suitable employees from the filtered employee list and suggests the top five. This process requires the filtered employee list as input and generates a list of suggested employees as output. For example, if the criterion is making friends, the top five employees with common hobbies will be suggested to the user.

[0094] (Application example 1)

[0095] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0096] Conventional 1-on-1 meeting setting systems did not adequately optimize communication between employees, and the efficiency of information sharing within factories was a particular issue. Furthermore, manually setting up meetings and selecting participants was time-consuming and did not contribute to improving productivity.

[0097] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0098] In this invention, the server includes a means for registering employee characteristic information, a means for setting matching criteria between employees, a means for using a generation AI to suggest employees with whom to hold one-on-one meetings based on the employee characteristic information and the matching criteria, and a means for employees to instantly set up one-on-one meetings using smart glasses, thereby enabling factory workers to quickly and efficiently set up one-on-one meetings with optimal partners, share information, and solve problems.

[0099] "Employee characteristic information" is information that represents the characteristics of each employee, such as basic information, abilities, interests, and hobbies.

[0100] "Matching criteria" are criteria for efficiently setting up one-on-one meetings between employees, and are based on purposes such as career counseling, service improvement, and making friends.

[0101] "Generative AI" refers to artificial intelligence that suggests optimal results based on given data and criteria.

[0102] The "means for suggesting partners for 1-on-1 meetings" is a means that has the function of automatically suggesting the most suitable partner for a 1-on-1 meeting based on the employee's characteristic information and matching criteria.

[0103] "Smart glasses" are eyeglass-type devices that have the ability to visually display information, allowing users to check the information in real time.

[0104] "Instant setup methods" are methods that have the functionality to allow users to set up one-on-one meetings in a short amount of time.

[0105] The present invention provides a system that allows users to register employee characteristic information and instantly set up one-on-one meetings using smart glasses. Specific embodiments of the system are described below.

[0106] First, the system uses a terminal to register employee characteristics. The Employee class is defined on this terminal, which stores basic information such as employee ID, name, skills, interests, hobbies, and work experience. The OneOnOneMatcher class is also defined on the terminal, which is the class that performs the actual matching.

[0107] When a user wants to set up a one-on-one meeting, they provide their user ID and matching criteria to the system through the smart glasses, which can include career advice, exchanging opinions to improve services, or making friends.

[0108] The server receives the user's information and retrieves characteristic information from a database. Based on this information, it filters a list of all other employees (excluding the user) according to the specified matching criteria. Generative AI calculates a matching score and suggests the most suitable people for a one-on-one meeting. For example, when making friends, it focuses on common hobbies and interests and suggests a list of the top five people.

[0109] The hardware used for server processing includes smart glasses (e.g., Google Glass) and databases (e.g., SQLite). The software uses Python to retrieve data, perform calculations, filter, match, and display results. The generative AI takes characteristic information and matching criteria as input and predicts the best match.

[0110] For example, when employee A uses smart glasses to request a one-on-one meeting to make friends, employee B is suggested based on employee A's characteristics. This suggestion allows employee A to quickly set up a one-on-one meeting.

[0111] Example prompt sentence:

[0112] To optimize 1-on-1 meetings between employees, use smart glasses to suggest the best match based on their characteristics. Consider [career counseling | service improvement | making friends] as criteria for suggestions.

[0113] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0114] Step 1:

[0115] A user accesses the system using smart glasses and wishes to set up a one-on-one meeting. At that time, the user inputs his / her user ID and matching criteria (either career advice, service improvement, or making friends). The input data is the user ID and matching criteria. As output, this data is sent to the server.

[0116] Step 2:

[0117] The server retrieves the user's characteristic information from the database based on the submitted user ID. The characteristic information includes skills, interests, hobbies, work experience, etc. The input data is the user ID, and the characteristic information is obtained as output data. The characteristic information becomes the basic data for the matching process.

[0118] Step 3:

[0119] The server filters the list of all eligible employees based on the characteristics and matching criteria. The input data is the user's characteristics and matching criteria. The output data is a filtered list of employees. For example, if the criteria is to make friends, employees with common interests or hobbies will be listed.

[0120] Step 4:

[0121] The server uses the generative AI model to score the employee from the filtered employee list who is most compatible with the user. The input data is the filtered employee list, and a score is calculated based on the degree of match of characteristics. The output data is a list of the top five employees in descending order of score.

[0122] Step 5:

[0123] The server proposes a list of the top five employees to the user. The input data is the list of the top five employees, and the output data is the proposal. The user can check the proposal through the smart glasses and set up a one-on-one meeting.

[0124] Step 6:

[0125] The user selects a partner for a one-on-one meeting from the suggested employees and sets the meeting time. The input data is the user's selection and the meeting time. The output data is the set meeting information, which is sent to the server and notified to the other party.

[0126] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] This invention combines a system that suggests people to meet for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. The system aims to help employees form new connections and reduce stress in a telework environment.

[0128] First, the terminal defines an Employee class and registers basic employee information. This includes the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. It also defines a OneOnOneMatcher class and sets up a constructor that takes the employee list and emotion engine as arguments. This class contains methods for matching employees.

[0129] Next, users enter their user ID and matching criteria into the system. They can choose from career advice, exchanging opinions to improve services, or making friends. Furthermore, an emotion engine recognizes the user's emotional state and adjusts the matching criteria accordingly.

[0130] The server retrieves specific user information from the database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and regular services. In addition, the emotion engine evaluates the user's emotional state using voice, facial expression, or text analysis.

[0131] For example, if a user is assessed as being under stress, the server will consider the user's emotional state and suggest partners based on matching criteria aimed at reducing stress, such as employees who have relaxing topics to talk about or who share many common hobbies.

[0132] As a specific example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and the services he regularly uses are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and the services he regularly uses are "chat tool C" and "video conferencing D."

[0133] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0134] In this way, this system, combined with an emotion engine, can propose more effective and personalized one-on-one meetings between employees, thereby reducing employee stress and contributing to improved work efficiency and motivation.

[0135] The processing flow will be explained below.

[0136] Step 1:

[0137] The terminal defines an Employee class and creates an object with information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0138] Step 2:

[0139] The terminal defines a OneOnOneMatcher class and sets a constructor that receives the employee list and the emotion engine as arguments. This class includes a method for matching between employees.

[0140] Step 3:

[0141] Users input their user ID and matching criteria into the system, which can be selected from career advice, exchanging opinions to improve the service, or making friends.

[0142] Step 4:

[0143] The server retrieves specific user information from a database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and commonly used services.

[0144] Step 5:

[0145] The server analyzes the user's emotional state using an emotion engine that uses at least one of voice analysis, facial expression analysis, or text analysis to recognize the user's current emotion.

[0146] Step 6:

[0147] The server takes into account the user's emotional state and adjusts the matching criteria based on that emotional state, for example, if the user is feeling stressed, it will suggest people who have relaxing topics to talk about.

[0148] Step 7:

[0149] The server creates a candidate list by excluding the designated user from the list of all employees.

[0150] Step 8:

[0151] The server sorts the candidate list based on matching criteria: for career counseling, by the degree of match of StrengthsFinder characteristics; for service improvement, by the number of common interests; and for friend-making, by the number of common hobbies.

[0152] Step 9:

[0153] The server selects the top five employees from the sorted candidate list and suggests them to the user.

[0154] Step 10:

[0155] The user selects the desired partner for a one-on-one meeting from the displayed candidate list and sets up the meeting.

[0156] As a specific example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and the services he regularly uses are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and the services he regularly uses are "chat tool C" and "video conferencing D."

[0157] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0158] Example 2

[0159] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0160] Conventional 1-on-1 meeting suggestion systems suggest partners based on employee characteristics and matching criteria, but do not take the user's emotional state into account, which does not fully reduce stress or achieve optimal networking. Therefore, there is a need for more effective and personalized 1-on-1 meeting suggestion systems.

[0161] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0162] In this invention, the server includes means for registering employee characteristic information, means for setting matching criteria between employees, means for using a generation AI to suggest partners for 1-on-1 meetings between employees based on the employee characteristic information and the matching criteria, means for recognizing the user's emotional state using an emotion engine, and means for adjusting the matching criteria based on the recognized emotional state, thereby making it possible to suggest optimal partners for 1-on-1 meetings based on the user's emotional state.

[0163] "Employee characteristic information" is basic information about employees, such as their ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0164] "Matching criteria" are criteria for defining the purpose of one-on-one meetings, such as career counseling, exchanging opinions to improve services, or making friends.

[0165] "Generative AI" is an artificial intelligence technology that suggests suitable partners for one-on-one meetings based on employee characteristics and matching criteria.

[0166] An "emotion engine" is a system that recognizes a user's emotional state using technologies such as voice analysis, facial expression analysis, and text analysis.

[0167] The "means for suggesting partners for 1-on-1 meetings" is a method for suggesting partners for 1-on-1 meetings between employees based on characteristic information of the employees and matching criteria.

[0168] "Emotional state" refers to the user's current emotional state, and may include stress, relaxation, excitement, etc.

[0169] The "means for adjusting matching criteria based on the recognized emotional state" is a method for changing matching criteria in consideration of the user's emotional state and proposing more effective and personalized partners.

[0170] This invention combines a system that suggests people to meet for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. The system aims to help employees form new connections and reduce stress in a telework environment.

[0171] First, the terminal defines an Employee class and registers basic employee information, including the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. By using the Employee class, employee characteristic information can be managed centrally.

[0172] Next, the device defines the OneOnOneMatcher class and sets up a constructor that receives the employee list and emotion engine as arguments. This class includes a method for matching employees, and the matching criteria can be set to career advice, exchanging opinions to improve services, or making friends.

[0173] Users enter their user ID and matching criteria into the system. The emotion engine then recognizes the user's emotional state and adjusts the matching criteria based on that. The emotion engine evaluates the user's emotions using technologies such as voice analysis, facial expression analysis, and text analysis. Specific analysis technologies used include Google Cloud Speech-to-Text API, Microsoft Azure Face API, and OpenAI's GPT-3.

[0174] The server retrieves specific user information from the database based on the user ID. This information includes the user's StrengthsFinder traits, interests, hobbies, and commonly used services. Based on the retrieved information and the analysis results of the emotion engine, the server suggests the most suitable person for a one-on-one meeting.

[0175] For example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and his regularly used services are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and his regularly used services are "chat tool C" and "video conferencing D."

[0176] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0177] An example of a specific prompt is:

[0178] "Using basic information about Employee A and Employee B, please suggest the appropriate person for a 1-on-1 meeting for Employee A, who is feeling stressed by the emotion engine."

[0179] Possible reasons include:

[0180] In this way, this system, combined with an emotion engine, can propose more effective and personalized one-on-one meetings between employees, thereby reducing employee stress and contributing to improved work efficiency and motivation.

[0181] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0182] Step 1:

[0183] The terminal registers basic information about an employee. Specifically, the terminal defines an Employee class and creates an object that includes information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. The input for this operation is employee information (ID, name, characteristics, interests, hobbies, and services), and the output is an Employee object.

[0184] Step 2:

[0185] The terminal defines the OneOnOneMatcher class and sets a constructor that takes the employee list and emotion engine as arguments. The OneOnOneMatcher class contains a method for matching. The input of this operation is the employee list and emotion engine, and the output is a OneOnOneMatcher instance.

[0186] Step 3:

[0187] A user inputs their user ID and matching criteria into the system. The input criteria can include career advice, exchanging opinions to improve services, or making friends. The input of this operation is the user ID and matching criteria, and the output is the system's acceptance of the user information.

[0188] Step 4:

[0189] The server recognizes the user's emotional state using an emotion engine, which uses voice analysis, facial expression analysis, or text analysis to evaluate the user's emotional state. The input in this case is the user's real-time data (voice, image, text, etc.), and the output is the user's emotional state (stressed, relaxed, etc.).

[0190] Step 5:

[0191] The server retrieves specific user information from the database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and regular services. The input for this operation is the user ID, and the output is the user information (traits, interests, hobbies, and services).

[0192] Step 6:

[0193] The server proposes the best 1-on-1 meeting partner based on the acquired user information and the evaluation results of the emotion engine. To do this, the server uses the matching method of the OneOnOneMatcher instance. The input is user information and emotional state, and the output is information about the matched partner.

[0194] Step 7:

[0195] The server displays information about the proposed 1-on-1 meeting partners to the user. The input is the information about the matched partners, and the output is notification or display to the user.

[0196] As a specific example of operation, please refer to the following procedure.

[0197] 1. The terminal creates an Employee object.

[0198] 2. The terminal creates a OneOnOneMatcher instance.

[0199] 3. The user enters the user ID and matching criteria into the system.

[0200] 4. The server uses an emotion engine to recognize the user's emotional state.

[0201] 5. The server retrieves the user information from the database.

[0202] 6. The server uses a OneOnOneMatcher instance to perform the matching.

[0203] 7. The server displays the suggested partners' information to the user.

[0204] The above processing steps make it possible to propose one-on-one meetings between employees in a more effective and personalized manner.

[0205] (Application example 2)

[0206] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0207] Conventional 1-on-1 meeting suggestion systems were unable to consider the emotional state of employees when matching them, making it difficult to select an appropriate meeting partner. Employees who are feeling particularly stressed need to be provided with people and topics that will help them relax, but previous systems were unable to respond in this flexible manner. This resulted in issues such as being unable to fully reduce employee stress or improve work efficiency.

[0208] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering employee characteristic information, means for setting matching criteria between employees, means for using a generation AI to suggest partners for one-on-one meetings between employees based on the employee characteristic information and matching criteria, means for analyzing the emotional state of employees using an emotion recognition engine, and means for optimizing meeting partners based on the emotional state. This makes it possible to reduce employee stress and improve work efficiency by taking into account the employee's characteristic information and emotional state and suggesting more appropriate and relaxing meeting partners.

[0209] "Employee characteristic information" refers to information such as an employee's ID, name, characteristics, areas of interest, hobbies, and services they regularly use.

[0210] "Matching criteria" are criteria for selecting partners for one-on-one meetings between employees, and include criteria based on purposes such as career counseling, exchanging opinions to improve services, and making friends.

[0211] "Generative AI" refers to a system that uses artificial intelligence technology to suggest partners for one-on-one meetings based on employee characteristics and matching criteria.

[0212] "Emotion recognition engine" refers to a system that uses voice, facial expression, or text analysis to assess an employee's emotional state.

[0213] "Emotional state" refers to an employee's current psychological state and includes emotions such as stress, relaxation, and joy.

[0214] "Optimize" refers to making adjustments to get the best results based on specific conditions.

[0215] This invention combines a system that suggests people for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. This system aims to help create new communication among workers, particularly in factories, and reduce stress.

[0216] First, the terminal defines an Employee class and registers basic information about the worker. This includes the worker's ID, name, characteristics, areas of interest, hobbies, and services they regularly use. It also defines a OneOnOneMatcher class and sets up a constructor that receives a list of workers and an emotion recognition engine as arguments. This class includes methods for matching between workers.

[0217] Users can input their user ID and matching criteria into the system. Matching criteria can be selected from career advice, exchanging opinions to improve services, or making friends. In addition, an emotion recognition engine recognizes the user's emotional state and adjusts the matching criteria based on this.

[0218] The server retrieves specific user information from a database based on the user ID. This information includes the user's characteristics, interests, hobbies, and commonly used services. In addition, the emotion recognition engine evaluates the user's emotional state using voice analysis, facial expression analysis, or text analysis. Emotion data obtained from voice data or text input is analyzed using an API (e.g., Google Cloud Natural Language API).

[0219] For example, if the user is Worker A and is currently feeling stressed, the server will use the analysis results of the emotion recognition engine to suggest partners based on matching criteria aimed at relieving stress. For example, it will suggest workers who have relaxing topics to talk about or who share many common hobbies. This allows Worker A to exchange opinions or seek career advice in a relaxing environment.

[0220] Usage example:

[0221] Let's say Worker A has been busy with work recently and his stress level is high. The robot can sense this situation and suggest relaxing conversations with colleagues. In this way, this system, combined with an emotion recognition engine, can suggest one-on-one meetings between workers in a more effective and personalized manner. This reduces worker stress and contributes to improving work efficiency and motivation.

[0222] Example prompt for a generative AI model:

[0223] "If your recent emotional state has been stressful, suggest other workers who share your interests."

[0224] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0225] Step 1:

[0226] The terminal registers the characteristic information of the worker. As input, it receives information such as the worker's ID, name, characteristics (e.g., communication skills, adaptability), areas of interest (e.g., marketing, sales), hobbies (e.g., games, guitar), and services they regularly use (e.g., chat tool A, video conferencing B). This information is registered as an instance of the Employee class. As output, it generates and stores an Employee object with the registered characteristic information.

[0227] Step 2:

[0228] The user inputs their user ID and matching criteria into the system. As input, the system receives the user ID and matching criteria (e.g., career advice, exchanging opinions to improve services, making friends). The user also provides real-time emotional input data (e.g., voice data or text input) to the system. Based on this, the device receives the emotional data and transfers it to the emotion recognition engine. As output, the user ID and matching criteria are passed to the server.

[0229] Step 3:

[0230] The emotion recognition engine analyzes emotion input data. As input, it receives voice data, facial expression data, or text data. It analyzes this using an analysis API (e.g., Google Cloud Natural Language API) to determine the emotional state (e.g., stress, relaxation, joy). As output, it generates the analysis result as an emotional state and returns it to the server. For example, it may obtain a result such as "The user is feeling stressed."

[0231] Step 4:

[0232] The server retrieves user information from the database based on the analysis results of the emotion recognition engine and the user ID. As input, it receives the user ID and emotional state and sends a query to the database. The database returns the corresponding user's characteristic information (e.g., interests, hobbies, and services used). As output, it generates a dataset containing the user's characteristic information and passes it to OneOnOneMatcher.

[0233] Step 5:

[0234] The OneOnOneMatcher class suggests meeting partners based on a user's characteristics and emotional state. It receives characteristics, emotional state, and matching criteria as input. It runs a matching algorithm to create a list of partners that best fit the user's characteristics and emotional state. For example, if a user is feeling stressed, it will prioritize suggesting workers who share relaxing hobbies. As output, it generates a list of optimal meeting partners and returns it to the user.

[0235] Step 6:

[0236] The user receives a list of suggested meeting partners from the server. As input, the user receives a list of suggested meeting partners (e.g., names and IDs of multiple workers). Based on this, the user sets up and conducts 1-on-1 meetings with suitable meeting partners. As output, the actual meetings are set up and conducted.

[0237] This series of processes makes it possible to suggest optimal meeting partners that take into account the user's characteristic information and emotional state. This allows for the exchange of opinions and career advice in a relaxed environment, which is expected to reduce worker stress and improve work efficiency.

[0238] 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.

[0239] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0240] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0241] [Second embodiment]

[0242] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0243] 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.

[0244] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0245] 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.

[0246] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0247] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0248] 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.

[0249] 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.

[0250] 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 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.

[0251] 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.

[0252] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0253] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0254] The present invention provides a system that registers employee characteristic information and, based on that information, suggests the most suitable person to hold a one-on-one meeting with. A specific embodiment of this system will be described below.

[0255] First, the terminal defines the Employee class and OneOnOneMatcher class. The Employee class is a class for storing basic information about employees, such as their ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0256] Next, when a user wants the system to suggest partners for a one-on-one meeting, the user provides the system with their user ID and matching criteria, which can include career advice, exchanging opinions to improve the service, or making friends with similar hobbies. The server then uses this information to make appropriate matches.

[0257] Based on the given user ID, the server retrieves the user's characteristic information from the database. Then, the server filters the list of all employees (excluding the user) according to the matching criteria and selects the most suitable employee. Specifically, in the case of career counseling, it prioritizes employees with similar StrengthsFinder characteristics to the user; in the case of service improvement, it prioritizes employees with common interests; and in the case of making friends, it prioritizes employees with common hobbies.

[0258] For example, if a user wants to make friends, the server will list the employees with whom they have the most in common (hobbies and interests) and suggest the top five from that list. This process gives users the opportunity to have one-on-one meetings with people who are suitable for them.

[0259] A specific example is given below. For example, suppose the employee list contains the following data:

[0260] Employee A: Traits: "Strategy" and "Empathy", Interests: "AI" and "Machine Learning", Hobbies: "Reading" and "Cycling", Services usually used: "Chat Tool A" and "Video Conferencing B"

[0261] Employee B: Traits: "Communication skills" and "Adaptability", Interests: "Marketing" and "Sales", Hobbies: "Games" and "Guitar", Services usually used: "Chat tool C" and "Video conferencing D"

[0262] In this case, if the user is employee A and wants to make friends, the server may consider employee A's characteristics, interests, hobbies, and the services he or she regularly uses, and suggest employee B as the optimal partner for a one-on-one meeting. However, the final matching result will be based on the degree of similarity and criteria between the user's characteristics and those of other employees.

[0263] In this way, by building a system that effectively suggests people to meet with for one-on-one meetings, it is possible to promote interaction between employees and increase opportunities for information exchange.

[0264] The processing flow will be explained below.

[0265] Step 1:

[0266] The terminal defines an Employee class and creates an object with information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0267] Step 2:

[0268] The terminal defines a OneOnOneMatcher class and sets a constructor that receives a list of employees as an argument. This class includes a method for matching employees.

[0269] Step 3:

[0270] Users enter their user ID and matching criteria into the system, which can be selected from "career consultation," "exchanging opinions to improve the service," or "making friends."

[0271] Step 4:

[0272] The server retrieves specific user information from a database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and commonly used services.

[0273] Step 5:

[0274] The server creates a candidate list by excluding the designated user from the list of all employees.

[0275] Step 6:

[0276] The server sorts the list of candidates based on matching criteria: for career counseling, by the degree of match of StrengthsFinder characteristics; for service improvement discussions, by the number of common interests; for friendship meetings, by the number of common hobbies.

[0277] Step 7:

[0278] The server selects the top five employees from the sorted candidate list and suggests them to the user.

[0279] Step 8:

[0280] The user selects the desired partner for the one-on-one meeting from the displayed candidate list and sets up the meeting.

[0281] Example 1

[0282] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0283] Previous employee matching systems had difficulty automatically selecting the right person for a one-on-one meeting, making it impossible to maximize the opportunity for appropriate interaction and information exchange between employees. It was also difficult to match individuals based on their characteristics, hobbies, and interests, and finding the perfect match for each employee took a lot of time and effort, especially in large organizations.

[0284] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0285] In this invention, the server includes a means for the user to provide their own characteristic information and matching criteria, a means for the server to acquire the user's characteristic information from a database, and a means for the server to filter the employee list according to the matching criteria and select appropriate employees, thereby making it possible to efficiently and accurately promote opportunities for interaction and information exchange between employees.

[0286] "Employee characteristic information" refers to information about an employee, including the employee's personality traits, areas of interest, leisure activities, and frequently used tools.

[0287] "Matching criteria" are the elements that are used as criteria when proposing partners for one-on-one meetings between employees, and specifically include the purpose of career advice, exchanging opinions, or making friends with similar hobbies.

[0288] "Generative AI" refers to artificial intelligence technology that suggests the most suitable partner based on specific input (prompt text).

[0289] "User" refers to the employee who receives the proposal from the other party in the 1-on-1 meeting.

[0290] "Server" refers to a device or system that retrieves user characteristic information from a database, filters the employee list according to matching criteria, and selects suitable employees.

[0291] "Database" refers to a storage device that stores employee characteristic information.

[0292] The present invention is a system for effectively holding one-on-one meetings between employees. The purpose of this system is for users to provide their own characteristic information, and for the server to suggest the most suitable partners for one-on-one meetings based on that information. Specific embodiments of the present invention are described below.

[0293] First, to register the employee's characteristic information, the terminal creates an instance of the Employee class. The Employee class holds basic information such as the employee's ID, name, personality traits, interests, leisure activities, and frequently used tools. This information is stored in the database.

[0294] Next, if a user wants the system to suggest partners for a one-on-one meeting, they provide their user ID and matching criteria, which can be set to career advice, exchanging opinions, or making friends.

[0295] The server receives this information and retrieves the user's characteristics from a database. It then filters the employee list according to the matching criteria and selects suitable employees. Specifically, if the criterion is career advice, it prioritizes employees with similar personality traits to the user; if the criterion is opinion exchange, it prioritizes employees with common interests; and if the criterion is making friends, it prioritizes employees with common leisure activities.

[0296] For example, if the user is looking to make friends, the server might respond with the following prompt:

[0297] "User ID: 1

[0298] Matching criteria: Making friends

[0299] Based on this prompt, the server considers the characteristics of employee A (ID: 1) and suggests several people, including employee B, who has the most in common with him / her. This allows the user to find a suitable partner for a one-on-one meeting.

[0300] This system not only promotes interaction between employees and increases opportunities for information exchange, but also allows individual employees to more effectively improve their careers and work, which is expected to strengthen collaboration and improve knowledge sharing across the company.

[0301] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0302] Step 1:

[0303] The terminal registers employee characteristics information. Specifically, it creates an instance of the Employee class and saves information such as the employee's ID, name, personality traits, areas of interest, leisure activities, and frequently used tools in the database. This process requires basic employee information as input and generates an employee object as output.

[0304] Step 2:

[0305] Users provide their user ID and matching criteria to the system to have it suggest partners for one-on-one meetings. Matching criteria can include career advice, exchanging ideas, or making friends. This information is input into the generative AI model as a prompt. The system requires the user ID and matching criteria as input and generates a prompt as output.

[0306] Step 3:

[0307] The server retrieves user characteristic information from the database based on the user ID and prompt provided by the user. This process requires the user ID as input and retrieves the user characteristic information as output. The characteristic information includes the employee's personality traits, areas of interest, and leisure activities.

[0308] Step 4:

[0309] The server filters the employee list according to the acquired user characteristic information and matching criteria. The user characteristic information and matching criteria are required as input, and the filtered employee list is generated as output. For example, if the matching criteria is "making friends," employees with common hobbies are prioritized.

[0310] Step 5:

[0311] The server selects the most suitable employees from the filtered employee list and suggests the top five. This process requires the filtered employee list as input and generates a list of suggested employees as output. For example, if the criterion is making friends, the top five employees with common hobbies will be suggested to the user.

[0312] (Application example 1)

[0313] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0314] Conventional 1-on-1 meeting setting systems did not adequately optimize communication between employees, and the efficiency of information sharing within factories was a particular issue. Furthermore, manually setting up meetings and selecting participants was time-consuming and did not contribute to improving productivity.

[0315] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0316] In this invention, the server includes a means for registering employee characteristic information, a means for setting matching criteria between employees, a means for using a generation AI to suggest employees with whom to hold one-on-one meetings based on the employee characteristic information and the matching criteria, and a means for employees to instantly set up one-on-one meetings using smart glasses, thereby enabling factory workers to quickly and efficiently set up one-on-one meetings with optimal partners, share information, and solve problems.

[0317] "Employee characteristic information" is information that represents the characteristics of each employee, such as basic information, abilities, interests, and hobbies.

[0318] "Matching criteria" are criteria for efficiently setting up one-on-one meetings between employees, and are based on purposes such as career counseling, service improvement, and making friends.

[0319] "Generative AI" refers to artificial intelligence that suggests optimal results based on given data and criteria.

[0320] The "means for suggesting partners for 1-on-1 meetings" is a means that has the function of automatically suggesting the most suitable partner for a 1-on-1 meeting based on the employee's characteristic information and matching criteria.

[0321] "Smart glasses" are eyeglass-type devices that have the ability to visually display information, allowing users to check the information in real time.

[0322] "Instant setup methods" are methods that have the functionality to allow users to set up one-on-one meetings in a short amount of time.

[0323] The present invention provides a system that allows users to register employee characteristic information and instantly set up one-on-one meetings using smart glasses. Specific embodiments of the system are described below.

[0324] First, the system uses a terminal to register employee characteristics. The Employee class is defined on this terminal, which stores basic information such as employee ID, name, skills, interests, hobbies, and work experience. The OneOnOneMatcher class is also defined on the terminal, which is the class that performs the actual matching.

[0325] When a user wants to set up a one-on-one meeting, they provide their user ID and matching criteria to the system through the smart glasses, which can include career advice, exchanging opinions to improve services, or making friends.

[0326] The server receives the user's information and retrieves characteristic information from a database. Based on this information, it filters a list of all other employees (excluding the user) according to the specified matching criteria. Generative AI calculates a matching score and suggests the most suitable people for a one-on-one meeting. For example, when making friends, it focuses on common hobbies and interests and suggests a list of the top five people.

[0327] The hardware used for server processing includes smart glasses (e.g., Google Glass) and databases (e.g., SQLite). The software uses Python to retrieve data, perform calculations, filter, match, and display results. The generative AI takes characteristic information and matching criteria as input and predicts the best match.

[0328] For example, when employee A uses smart glasses to request a one-on-one meeting to make friends, employee B is suggested based on employee A's characteristics. This suggestion allows employee A to quickly set up a one-on-one meeting.

[0329] Example prompt sentence:

[0330] To optimize 1-on-1 meetings between employees, use smart glasses to suggest the best match based on their characteristics. Consider [career counseling | service improvement | making friends] as criteria for suggestions.

[0331] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0332] Step 1:

[0333] A user accesses the system using smart glasses and wishes to set up a one-on-one meeting. At that time, the user inputs his / her user ID and matching criteria (either career advice, service improvement, or making friends). The input data is the user ID and matching criteria. As output, this data is sent to the server.

[0334] Step 2:

[0335] The server retrieves the user's characteristic information from the database based on the submitted user ID. The characteristic information includes skills, interests, hobbies, work experience, etc. The input data is the user ID, and the characteristic information is obtained as output data. The characteristic information becomes the basic data for the matching process.

[0336] Step 3:

[0337] The server filters the list of all eligible employees based on the characteristics and matching criteria. The input data is the user's characteristics and matching criteria. The output data is a filtered list of employees. For example, if the criteria is to make friends, employees with common interests or hobbies will be listed.

[0338] Step 4:

[0339] The server uses the generative AI model to score the employee from the filtered employee list who is most compatible with the user. The input data is the filtered employee list, and a score is calculated based on the degree of match of characteristics. The output data is a list of the top five employees in descending order of score.

[0340] Step 5:

[0341] The server proposes a list of the top five employees to the user. The input data is the list of the top five employees, and the output data is the proposal. The user can check the proposal through the smart glasses and set up a one-on-one meeting.

[0342] Step 6:

[0343] The user selects a partner for a one-on-one meeting from the suggested employees and sets the meeting time. The input data is the user's selection and the meeting time. The output data is the set meeting information, which is sent to the server and notified to the other party.

[0344] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0345] This invention combines a system that suggests people to meet for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. The system aims to help employees form new connections and reduce stress in a telework environment.

[0346] First, the terminal defines an Employee class and registers basic employee information. This includes the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. It also defines a OneOnOneMatcher class and sets up a constructor that takes the employee list and emotion engine as arguments. This class contains methods for matching employees.

[0347] Next, users enter their user ID and matching criteria into the system. They can choose from career advice, exchanging opinions to improve services, or making friends. Furthermore, an emotion engine recognizes the user's emotional state and adjusts the matching criteria accordingly.

[0348] The server retrieves specific user information from the database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and regular services. In addition, the emotion engine evaluates the user's emotional state using voice, facial expression, or text analysis.

[0349] For example, if a user is assessed as being under stress, the server will consider the user's emotional state and suggest partners based on matching criteria aimed at reducing stress, such as employees who have relaxing topics to talk about or who share many common hobbies.

[0350] As a specific example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and the services he regularly uses are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and the services he regularly uses are "chat tool C" and "video conferencing D."

[0351] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0352] In this way, this system, combined with an emotion engine, can propose more effective and personalized one-on-one meetings between employees, thereby reducing employee stress and contributing to improved work efficiency and motivation.

[0353] The processing flow will be explained below.

[0354] Step 1:

[0355] The terminal defines an Employee class and creates an object with information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0356] Step 2:

[0357] The terminal defines a OneOnOneMatcher class and sets a constructor that receives the employee list and the emotion engine as arguments. This class includes a method for matching between employees.

[0358] Step 3:

[0359] Users input their user ID and matching criteria into the system, which can be selected from career advice, exchanging opinions to improve the service, or making friends.

[0360] Step 4:

[0361] The server retrieves specific user information from a database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and commonly used services.

[0362] Step 5:

[0363] The server analyzes the user's emotional state using an emotion engine that uses at least one of voice analysis, facial expression analysis, or text analysis to recognize the user's current emotion.

[0364] Step 6:

[0365] The server takes into account the user's emotional state and adjusts the matching criteria based on that emotional state, for example, if the user is feeling stressed, it will suggest people who have relaxing topics to talk about.

[0366] Step 7:

[0367] The server creates a candidate list by excluding the designated user from the list of all employees.

[0368] Step 8:

[0369] The server sorts the candidate list based on matching criteria: for career counseling, by the degree of match of StrengthsFinder characteristics; for service improvement, by the number of common interests; and for friend-making, by the number of common hobbies.

[0370] Step 9:

[0371] The server selects the top five employees from the sorted candidate list and suggests them to the user.

[0372] Step 10:

[0373] The user selects the desired partner for a one-on-one meeting from the displayed candidate list and sets up the meeting.

[0374] As a specific example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and the services he regularly uses are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and the services he regularly uses are "chat tool C" and "video conferencing D."

[0375] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0376] Example 2

[0377] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0378] Conventional 1-on-1 meeting suggestion systems suggest partners based on employee characteristics and matching criteria, but do not take the user's emotional state into account, which does not fully reduce stress or achieve optimal networking. Therefore, there is a need for more effective and personalized 1-on-1 meeting suggestion systems.

[0379] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0380] In this invention, the server includes means for registering employee characteristic information, means for setting matching criteria between employees, means for using a generation AI to suggest partners for 1-on-1 meetings between employees based on the employee characteristic information and the matching criteria, means for recognizing the user's emotional state using an emotion engine, and means for adjusting the matching criteria based on the recognized emotional state, thereby making it possible to suggest optimal partners for 1-on-1 meetings based on the user's emotional state.

[0381] "Employee characteristic information" is basic information about employees, such as their ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0382] "Matching criteria" are criteria for defining the purpose of one-on-one meetings, such as career counseling, exchanging opinions to improve services, or making friends.

[0383] "Generative AI" is an artificial intelligence technology that suggests suitable partners for one-on-one meetings based on employee characteristics and matching criteria.

[0384] An "emotion engine" is a system that recognizes a user's emotional state using technologies such as voice analysis, facial expression analysis, and text analysis.

[0385] The "means for suggesting partners for 1-on-1 meetings" is a method for suggesting partners for 1-on-1 meetings between employees based on characteristic information of the employees and matching criteria.

[0386] "Emotional state" refers to the user's current emotional state, and may include stress, relaxation, excitement, etc.

[0387] The "means for adjusting matching criteria based on the recognized emotional state" is a method for changing matching criteria in consideration of the user's emotional state and proposing more effective and personalized partners.

[0388] This invention combines a system that suggests people to meet for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. The system aims to help employees form new connections and reduce stress in a telework environment.

[0389] First, the terminal defines an Employee class and registers basic employee information, including the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. By using the Employee class, employee characteristic information can be managed centrally.

[0390] Next, the device defines the OneOnOneMatcher class and sets up a constructor that receives the employee list and emotion engine as arguments. This class includes a method for matching employees, and the matching criteria can be set to career advice, exchanging opinions to improve services, or making friends.

[0391] Users enter their user ID and matching criteria into the system. The emotion engine then recognizes the user's emotional state and adjusts the matching criteria based on that. The emotion engine evaluates the user's emotions using technologies such as voice analysis, facial expression analysis, and text analysis. Specific analysis technologies used include Google Cloud Speech-to-Text API, Microsoft Azure Face API, and OpenAI's GPT-3.

[0392] The server retrieves specific user information from the database based on the user ID. This information includes the user's StrengthsFinder traits, interests, hobbies, and commonly used services. Based on the retrieved information and the analysis results of the emotion engine, the server suggests the most suitable person for a one-on-one meeting.

[0393] For example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and his regularly used services are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and his regularly used services are "chat tool C" and "video conferencing D."

[0394] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0395] An example of a specific prompt is:

[0396] "Using basic information about Employee A and Employee B, please suggest the appropriate person for a 1-on-1 meeting for Employee A, who is feeling stressed by the emotion engine."

[0397] Possible reasons include:

[0398] In this way, this system, combined with an emotion engine, can propose more effective and personalized one-on-one meetings between employees, thereby reducing employee stress and contributing to improved work efficiency and motivation.

[0399] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0400] Step 1:

[0401] The terminal registers basic information about an employee. Specifically, the terminal defines an Employee class and creates an object that includes information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. The input for this operation is employee information (ID, name, characteristics, interests, hobbies, and services), and the output is an Employee object.

[0402] Step 2:

[0403] The terminal defines the OneOnOneMatcher class and sets a constructor that takes the employee list and emotion engine as arguments. The OneOnOneMatcher class contains a method for matching. The input of this operation is the employee list and emotion engine, and the output is a OneOnOneMatcher instance.

[0404] Step 3:

[0405] A user inputs their user ID and matching criteria into the system. The input criteria can include career advice, exchanging opinions to improve services, or making friends. The input of this operation is the user ID and matching criteria, and the output is the system's acceptance of the user information.

[0406] Step 4:

[0407] The server recognizes the user's emotional state using an emotion engine, which uses voice analysis, facial expression analysis, or text analysis to evaluate the user's emotional state. The input in this case is the user's real-time data (voice, image, text, etc.), and the output is the user's emotional state (stressed, relaxed, etc.).

[0408] Step 5:

[0409] The server retrieves specific user information from the database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and regular services. The input for this operation is the user ID, and the output is the user information (traits, interests, hobbies, and services).

[0410] Step 6:

[0411] The server proposes the best 1-on-1 meeting partner based on the acquired user information and the evaluation results of the emotion engine. To do this, the server uses the matching method of the OneOnOneMatcher instance. The input is user information and emotional state, and the output is information about the matched partner.

[0412] Step 7:

[0413] The server displays information about the proposed 1-on-1 meeting partners to the user. The input is the information about the matched partners, and the output is notification or display to the user.

[0414] As a specific example of operation, please refer to the following procedure.

[0415] 1. The terminal creates an Employee object.

[0416] 2. The terminal creates a OneOnOneMatcher instance.

[0417] 3. The user enters the user ID and matching criteria into the system.

[0418] 4. The server uses an emotion engine to recognize the user's emotional state.

[0419] 5. The server retrieves the user information from the database.

[0420] 6. The server uses a OneOnOneMatcher instance to perform the matching.

[0421] 7. The server displays the suggested partners' information to the user.

[0422] The above processing steps make it possible to propose one-on-one meetings between employees in a more effective and personalized manner.

[0423] (Application example 2)

[0424] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0425] Conventional 1-on-1 meeting suggestion systems were unable to consider the emotional state of employees when matching them, making it difficult to select an appropriate meeting partner. Employees who are feeling particularly stressed need to be provided with people and topics that will help them relax, but previous systems were unable to respond in this flexible manner. This resulted in issues such as being unable to fully reduce employee stress or improve work efficiency.

[0426] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering employee characteristic information, means for setting matching criteria between employees, means for using a generation AI to suggest partners for one-on-one meetings between employees based on the employee characteristic information and matching criteria, means for analyzing the emotional state of employees using an emotion recognition engine, and means for optimizing meeting partners based on the emotional state. This makes it possible to reduce employee stress and improve work efficiency by taking into account the employee's characteristic information and emotional state and suggesting more appropriate and relaxing meeting partners.

[0427] "Employee characteristic information" refers to information such as an employee's ID, name, characteristics, areas of interest, hobbies, and services they regularly use.

[0428] "Matching criteria" are criteria for selecting partners for one-on-one meetings between employees, and include criteria based on purposes such as career counseling, exchanging opinions to improve services, and making friends.

[0429] "Generative AI" refers to a system that uses artificial intelligence technology to suggest partners for one-on-one meetings based on employee characteristics and matching criteria.

[0430] "Emotion recognition engine" refers to a system that uses voice, facial expression, or text analysis to assess an employee's emotional state.

[0431] "Emotional state" refers to an employee's current psychological state and includes emotions such as stress, relaxation, and joy.

[0432] "Optimize" refers to making adjustments to get the best results based on specific conditions.

[0433] This invention combines a system that suggests people for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. This system aims to help create new communication among workers, particularly in factories, and reduce stress.

[0434] First, the terminal defines an Employee class and registers basic information about the worker. This includes the worker's ID, name, characteristics, areas of interest, hobbies, and services they regularly use. It also defines a OneOnOneMatcher class and sets up a constructor that receives a list of workers and an emotion recognition engine as arguments. This class includes methods for matching between workers.

[0435] Users can input their user ID and matching criteria into the system. Matching criteria can be selected from career advice, exchanging opinions to improve services, or making friends. In addition, an emotion recognition engine recognizes the user's emotional state and adjusts the matching criteria based on this.

[0436] The server retrieves specific user information from a database based on the user ID. This information includes the user's characteristics, interests, hobbies, and commonly used services. In addition, the emotion recognition engine evaluates the user's emotional state using voice analysis, facial expression analysis, or text analysis. Emotion data obtained from voice data or text input is analyzed using an API (e.g., Google Cloud Natural Language API).

[0437] For example, if the user is Worker A and is currently feeling stressed, the server will use the analysis results of the emotion recognition engine to suggest partners based on matching criteria aimed at relieving stress. For example, it will suggest workers who have relaxing topics to talk about or who share many common hobbies. This allows Worker A to exchange opinions or seek career advice in a relaxing environment.

[0438] Usage example:

[0439] Let's say Worker A has been busy with work recently and his stress level is high. The robot can sense this situation and suggest relaxing conversations with colleagues. In this way, this system, combined with an emotion recognition engine, can suggest one-on-one meetings between workers in a more effective and personalized manner. This reduces worker stress and contributes to improving work efficiency and motivation.

[0440] Example prompt for a generative AI model:

[0441] "If your recent emotional state has been stressful, suggest other workers who share your interests."

[0442] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0443] Step 1:

[0444] The terminal registers the characteristic information of the worker. As input, it receives information such as the worker's ID, name, characteristics (e.g., communication skills, adaptability), areas of interest (e.g., marketing, sales), hobbies (e.g., games, guitar), and services they regularly use (e.g., chat tool A, video conferencing B). This information is registered as an instance of the Employee class. As output, it generates and stores an Employee object with the registered characteristic information.

[0445] Step 2:

[0446] The user inputs their user ID and matching criteria into the system. As input, the system receives the user ID and matching criteria (e.g., career advice, exchanging opinions to improve services, making friends). The user also provides real-time emotional input data (e.g., voice data or text input) to the system. Based on this, the device receives the emotional data and transfers it to the emotion recognition engine. As output, the user ID and matching criteria are passed to the server.

[0447] Step 3:

[0448] The emotion recognition engine analyzes emotion input data. As input, it receives voice data, facial expression data, or text data. It analyzes this using an analysis API (e.g., Google Cloud Natural Language API) to determine the emotional state (e.g., stress, relaxation, joy). As output, it generates the analysis result as an emotional state and returns it to the server. For example, it may obtain a result such as "The user is feeling stressed."

[0449] Step 4:

[0450] The server retrieves user information from the database based on the analysis results of the emotion recognition engine and the user ID. As input, it receives the user ID and emotional state and sends a query to the database. The database returns the corresponding user's characteristic information (e.g., interests, hobbies, and services used). As output, it generates a dataset containing the user's characteristic information and passes it to OneOnOneMatcher.

[0451] Step 5:

[0452] The OneOnOneMatcher class suggests meeting partners based on a user's characteristics and emotional state. It receives characteristics, emotional state, and matching criteria as input. It runs a matching algorithm to create a list of partners that best fit the user's characteristics and emotional state. For example, if a user is feeling stressed, it will prioritize suggesting workers who share relaxing hobbies. As output, it generates a list of optimal meeting partners and returns it to the user.

[0453] Step 6:

[0454] The user receives a list of suggested meeting partners from the server. As input, the user receives a list of suggested meeting partners (e.g., names and IDs of multiple workers). Based on this, the user sets up and conducts 1-on-1 meetings with suitable meeting partners. As output, the actual meetings are set up and conducted.

[0455] This series of processes makes it possible to suggest optimal meeting partners that take into account the user's characteristic information and emotional state. This allows for the exchange of opinions and career advice in a relaxed environment, which is expected to reduce worker stress and improve work efficiency.

[0456] 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.

[0457] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0458] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0459] [Third embodiment]

[0460] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0461] 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.

[0462] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0463] 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.

[0464] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0465] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0466] 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.

[0467] 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.

[0468] 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 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.

[0469] 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.

[0470] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0471] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0472] The present invention provides a system that registers employee characteristic information and, based on that information, suggests the most suitable person to hold a one-on-one meeting with. A specific embodiment of this system will be described below.

[0473] First, the terminal defines the Employee class and OneOnOneMatcher class. The Employee class is a class for storing basic information about employees, such as their ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0474] Next, when a user wants the system to suggest partners for a one-on-one meeting, the user provides the system with their user ID and matching criteria, which can include career advice, exchanging opinions to improve the service, or making friends with similar hobbies. The server then uses this information to make appropriate matches.

[0475] Based on the given user ID, the server retrieves the user's characteristic information from the database. Then, the server filters the list of all employees (excluding the user) according to the matching criteria and selects the most suitable employee. Specifically, in the case of career counseling, it prioritizes employees with similar StrengthsFinder characteristics to the user; in the case of service improvement, it prioritizes employees with common interests; and in the case of making friends, it prioritizes employees with common hobbies.

[0476] For example, if a user wants to make friends, the server will list the employees with whom they have the most in common (hobbies and interests) and suggest the top five from that list. This process gives users the opportunity to have one-on-one meetings with people who are suitable for them.

[0477] A specific example is given below. For example, suppose the employee list contains the following data:

[0478] Employee A: Traits: "Strategy" and "Empathy", Interests: "AI" and "Machine Learning", Hobbies: "Reading" and "Cycling", Services usually used: "Chat Tool A" and "Video Conferencing B"

[0479] Employee B: Traits: "Communication skills" and "Adaptability", Interests: "Marketing" and "Sales", Hobbies: "Games" and "Guitar", Services usually used: "Chat tool C" and "Video conferencing D"

[0480] In this case, if the user is employee A and wants to make friends, the server may consider employee A's characteristics, interests, hobbies, and the services he or she regularly uses, and suggest employee B as the optimal partner for a one-on-one meeting. However, the final matching result will be based on the degree of similarity and criteria between the user's characteristics and those of other employees.

[0481] In this way, by building a system that effectively suggests people to meet with for one-on-one meetings, it is possible to promote interaction between employees and increase opportunities for information exchange.

[0482] The processing flow will be explained below.

[0483] Step 1:

[0484] The terminal defines an Employee class and creates an object with information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0485] Step 2:

[0486] The terminal defines a OneOnOneMatcher class and sets a constructor that receives a list of employees as an argument. This class includes a method for matching employees.

[0487] Step 3:

[0488] Users enter their user ID and matching criteria into the system, which can be selected from "career consultation," "exchanging opinions to improve the service," or "making friends."

[0489] Step 4:

[0490] The server retrieves specific user information from a database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and commonly used services.

[0491] Step 5:

[0492] The server creates a candidate list by excluding the designated user from the list of all employees.

[0493] Step 6:

[0494] The server sorts the list of candidates based on matching criteria: for career counseling, by the degree of match of StrengthsFinder characteristics; for service improvement discussions, by the number of common interests; for friendship meetings, by the number of common hobbies.

[0495] Step 7:

[0496] The server selects the top five employees from the sorted candidate list and suggests them to the user.

[0497] Step 8:

[0498] The user selects the desired partner for the one-on-one meeting from the displayed candidate list and sets up the meeting.

[0499] Example 1

[0500] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0501] Previous employee matching systems had difficulty automatically selecting the right person for a one-on-one meeting, making it impossible to maximize the opportunity for appropriate interaction and information exchange between employees. It was also difficult to match individuals based on their characteristics, hobbies, and interests, and finding the perfect match for each employee took a lot of time and effort, especially in large organizations.

[0502] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0503] In this invention, the server includes a means for the user to provide their own characteristic information and matching criteria, a means for the server to acquire the user's characteristic information from a database, and a means for the server to filter the employee list according to the matching criteria and select appropriate employees, thereby making it possible to efficiently and accurately promote opportunities for interaction and information exchange between employees.

[0504] "Employee characteristic information" refers to information about an employee, including the employee's personality traits, areas of interest, leisure activities, and frequently used tools.

[0505] "Matching criteria" are the elements that are used as criteria when proposing partners for one-on-one meetings between employees, and specifically include the purpose of career advice, exchanging opinions, or making friends with similar hobbies.

[0506] "Generative AI" refers to artificial intelligence technology that suggests the most suitable partner based on specific input (prompt text).

[0507] "User" refers to the employee who receives the proposal from the other party in the 1-on-1 meeting.

[0508] "Server" refers to a device or system that retrieves user characteristic information from a database, filters the employee list according to matching criteria, and selects suitable employees.

[0509] "Database" refers to a storage device that stores employee characteristic information.

[0510] The present invention is a system for effectively holding one-on-one meetings between employees. The purpose of this system is for users to provide their own characteristic information, and for the server to suggest the most suitable partners for one-on-one meetings based on that information. Specific embodiments of the present invention are described below.

[0511] First, to register the employee's characteristic information, the terminal creates an instance of the Employee class. The Employee class holds basic information such as the employee's ID, name, personality traits, interests, leisure activities, and frequently used tools. This information is stored in the database.

[0512] Next, if a user wants the system to suggest partners for a one-on-one meeting, they provide their user ID and matching criteria, which can be set to career advice, exchanging opinions, or making friends.

[0513] The server receives this information and retrieves the user's characteristics from a database. It then filters the employee list according to the matching criteria and selects suitable employees. Specifically, if the criterion is career advice, it prioritizes employees with similar personality traits to the user; if the criterion is opinion exchange, it prioritizes employees with common interests; and if the criterion is making friends, it prioritizes employees with common leisure activities.

[0514] For example, if the user is looking to make friends, the server might respond with the following prompt:

[0515] "User ID: 1

[0516] Matching criteria: Making friends

[0517] Based on this prompt, the server considers the characteristics of employee A (ID: 1) and suggests several people, including employee B, who has the most in common with him / her. This allows the user to find a suitable partner for a one-on-one meeting.

[0518] This system not only promotes interaction between employees and increases opportunities for information exchange, but also allows individual employees to more effectively improve their careers and work, which is expected to strengthen collaboration and improve knowledge sharing across the company.

[0519] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0520] Step 1:

[0521] The terminal registers employee characteristics information. Specifically, it creates an instance of the Employee class and saves information such as the employee's ID, name, personality traits, areas of interest, leisure activities, and frequently used tools in the database. This process requires basic employee information as input and generates an employee object as output.

[0522] Step 2:

[0523] Users provide their user ID and matching criteria to the system to have it suggest partners for one-on-one meetings. Matching criteria can include career advice, exchanging ideas, or making friends. This information is input into the generative AI model as a prompt. The system requires the user ID and matching criteria as input and generates a prompt as output.

[0524] Step 3:

[0525] The server retrieves user characteristic information from the database based on the user ID and prompt provided by the user. This process requires the user ID as input and retrieves the user characteristic information as output. The characteristic information includes the employee's personality traits, areas of interest, and leisure activities.

[0526] Step 4:

[0527] The server filters the employee list according to the acquired user characteristic information and matching criteria. The user characteristic information and matching criteria are required as input, and the filtered employee list is generated as output. For example, if the matching criteria is "making friends," employees with common hobbies are prioritized.

[0528] Step 5:

[0529] The server selects the most suitable employees from the filtered employee list and suggests the top five. This process requires the filtered employee list as input and generates a list of suggested employees as output. For example, if the criterion is making friends, the top five employees with common hobbies will be suggested to the user.

[0530] (Application example 1)

[0531] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0532] Conventional 1-on-1 meeting setting systems did not adequately optimize communication between employees, and the efficiency of information sharing within factories was a particular issue. Furthermore, manually setting up meetings and selecting participants was time-consuming and did not contribute to improving productivity.

[0533] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0534] In this invention, the server includes a means for registering employee characteristic information, a means for setting matching criteria between employees, a means for using a generation AI to suggest employees with whom to hold one-on-one meetings based on the employee characteristic information and the matching criteria, and a means for employees to instantly set up one-on-one meetings using smart glasses, thereby enabling factory workers to quickly and efficiently set up one-on-one meetings with optimal partners, share information, and solve problems.

[0535] "Employee characteristic information" is information that represents the characteristics of each employee, such as basic information, abilities, interests, and hobbies.

[0536] "Matching criteria" are criteria for efficiently setting up one-on-one meetings between employees, and are based on purposes such as career counseling, service improvement, and making friends.

[0537] "Generative AI" refers to artificial intelligence that suggests optimal results based on given data and criteria.

[0538] The "means for suggesting partners for 1-on-1 meetings" is a means that has the function of automatically suggesting the most suitable partner for a 1-on-1 meeting based on the employee's characteristic information and matching criteria.

[0539] "Smart glasses" are eyeglass-type devices that have the ability to visually display information, allowing users to check the information in real time.

[0540] "Instant setup methods" are methods that have the functionality to allow users to set up one-on-one meetings in a short amount of time.

[0541] The present invention provides a system that allows users to register employee characteristic information and instantly set up one-on-one meetings using smart glasses. Specific embodiments of the system are described below.

[0542] First, the system uses a terminal to register employee characteristics. The Employee class is defined on this terminal, which stores basic information such as employee ID, name, skills, interests, hobbies, and work experience. The OneOnOneMatcher class is also defined on the terminal, which is the class that performs the actual matching.

[0543] When a user wants to set up a one-on-one meeting, they provide their user ID and matching criteria to the system through the smart glasses, which can include career advice, exchanging opinions to improve services, or making friends.

[0544] The server receives the user's information and retrieves characteristic information from a database. Based on this information, it filters a list of all other employees (excluding the user) according to the specified matching criteria. Generative AI calculates a matching score and suggests the most suitable people for a one-on-one meeting. For example, when making friends, it focuses on common hobbies and interests and suggests a list of the top five people.

[0545] The hardware used for server processing includes smart glasses (e.g., Google Glass) and databases (e.g., SQLite). The software uses Python to retrieve data, perform calculations, filter, match, and display results. The generative AI takes characteristic information and matching criteria as input and predicts the best match.

[0546] For example, when employee A uses smart glasses to request a one-on-one meeting to make friends, employee B is suggested based on employee A's characteristics. This suggestion allows employee A to quickly set up a one-on-one meeting.

[0547] Example prompt sentence:

[0548] To optimize 1-on-1 meetings between employees, use smart glasses to suggest the best match based on their characteristics. Consider [career counseling | service improvement | making friends] as criteria for suggestions.

[0549] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0550] Step 1:

[0551] A user accesses the system using smart glasses and wishes to set up a one-on-one meeting. At that time, the user inputs his / her user ID and matching criteria (either career advice, service improvement, or making friends). The input data is the user ID and matching criteria. As output, this data is sent to the server.

[0552] Step 2:

[0553] The server retrieves the user's characteristic information from the database based on the submitted user ID. The characteristic information includes skills, interests, hobbies, work experience, etc. The input data is the user ID, and the characteristic information is obtained as output data. The characteristic information becomes the basic data for the matching process.

[0554] Step 3:

[0555] The server filters the list of all eligible employees based on the characteristics and matching criteria. The input data is the user's characteristics and matching criteria. The output data is a filtered list of employees. For example, if the criteria is to make friends, employees with common interests or hobbies will be listed.

[0556] Step 4:

[0557] The server uses the generative AI model to score the employee from the filtered employee list who is most compatible with the user. The input data is the filtered employee list, and a score is calculated based on the degree of match of characteristics. The output data is a list of the top five employees in descending order of score.

[0558] Step 5:

[0559] The server proposes a list of the top five employees to the user. The input data is the list of the top five employees, and the output data is the proposal. The user can check the proposal through the smart glasses and set up a one-on-one meeting.

[0560] Step 6:

[0561] The user selects a partner for a one-on-one meeting from the suggested employees and sets the meeting time. The input data is the user's selection and the meeting time. The output data is the set meeting information, which is sent to the server and notified to the other party.

[0562] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0563] This invention combines a system that suggests people to meet for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. The system aims to help employees form new connections and reduce stress in a telework environment.

[0564] First, the terminal defines an Employee class and registers basic employee information. This includes the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. It also defines a OneOnOneMatcher class and sets up a constructor that takes the employee list and emotion engine as arguments. This class contains methods for matching employees.

[0565] Next, users enter their user ID and matching criteria into the system. They can choose from career advice, exchanging opinions to improve services, or making friends. Furthermore, an emotion engine recognizes the user's emotional state and adjusts the matching criteria accordingly.

[0566] The server retrieves specific user information from the database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and regular services. In addition, the emotion engine evaluates the user's emotional state using voice, facial expression, or text analysis.

[0567] For example, if a user is assessed as being under stress, the server will consider the user's emotional state and suggest partners based on matching criteria aimed at reducing stress, such as employees who have relaxing topics to talk about or who share many common hobbies.

[0568] As a specific example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and the services he regularly uses are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and the services he regularly uses are "chat tool C" and "video conferencing D."

[0569] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0570] In this way, this system, combined with an emotion engine, can propose more effective and personalized one-on-one meetings between employees, thereby reducing employee stress and contributing to improved work efficiency and motivation.

[0571] The processing flow will be explained below.

[0572] Step 1:

[0573] The terminal defines an Employee class and creates an object with information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0574] Step 2:

[0575] The terminal defines a OneOnOneMatcher class and sets a constructor that receives the employee list and the emotion engine as arguments. This class includes a method for matching between employees.

[0576] Step 3:

[0577] Users input their user ID and matching criteria into the system, which can be selected from career advice, exchanging opinions to improve the service, or making friends.

[0578] Step 4:

[0579] The server retrieves specific user information from a database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and commonly used services.

[0580] Step 5:

[0581] The server analyzes the user's emotional state using an emotion engine that uses at least one of voice analysis, facial expression analysis, or text analysis to recognize the user's current emotion.

[0582] Step 6:

[0583] The server takes into account the user's emotional state and adjusts the matching criteria based on that emotional state, for example, if the user is feeling stressed, it will suggest people who have relaxing topics to talk about.

[0584] Step 7:

[0585] The server creates a candidate list by excluding the designated user from the list of all employees.

[0586] Step 8:

[0587] The server sorts the candidate list based on matching criteria: for career counseling, by the degree of match of StrengthsFinder characteristics; for service improvement, by the number of common interests; and for friend-making, by the number of common hobbies.

[0588] Step 9:

[0589] The server selects the top five employees from the sorted candidate list and suggests them to the user.

[0590] Step 10:

[0591] The user selects the desired partner for a one-on-one meeting from the displayed candidate list and sets up the meeting.

[0592] As a specific example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and the services he regularly uses are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and the services he regularly uses are "chat tool C" and "video conferencing D."

[0593] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0594] Example 2

[0595] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0596] Conventional 1-on-1 meeting suggestion systems suggest partners based on employee characteristics and matching criteria, but do not take the user's emotional state into account, which does not fully reduce stress or achieve optimal networking. Therefore, there is a need for more effective and personalized 1-on-1 meeting suggestion systems.

[0597] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0598] In this invention, the server includes means for registering employee characteristic information, means for setting matching criteria between employees, means for using a generation AI to suggest partners for 1-on-1 meetings between employees based on the employee characteristic information and the matching criteria, means for recognizing the user's emotional state using an emotion engine, and means for adjusting the matching criteria based on the recognized emotional state, thereby making it possible to suggest optimal partners for 1-on-1 meetings based on the user's emotional state.

[0599] "Employee characteristic information" is basic information about employees, such as their ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0600] "Matching criteria" are criteria for defining the purpose of one-on-one meetings, such as career counseling, exchanging opinions to improve services, or making friends.

[0601] "Generative AI" is an artificial intelligence technology that suggests suitable partners for one-on-one meetings based on employee characteristics and matching criteria.

[0602] An "emotion engine" is a system that recognizes a user's emotional state using technologies such as voice analysis, facial expression analysis, and text analysis.

[0603] The "means for suggesting partners for 1-on-1 meetings" is a method for suggesting partners for 1-on-1 meetings between employees based on characteristic information of the employees and matching criteria.

[0604] "Emotional state" refers to the user's current emotional state, and may include stress, relaxation, excitement, etc.

[0605] The "means for adjusting matching criteria based on the recognized emotional state" is a method for changing matching criteria in consideration of the user's emotional state and proposing more effective and personalized partners.

[0606] This invention combines a system that suggests people to meet for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. The system aims to help employees form new connections and reduce stress in a telework environment.

[0607] First, the terminal defines an Employee class and registers basic employee information, including the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. By using the Employee class, employee characteristic information can be managed centrally.

[0608] Next, the device defines the OneOnOneMatcher class and sets up a constructor that receives the employee list and emotion engine as arguments. This class includes a method for matching employees, and the matching criteria can be set to career advice, exchanging opinions to improve services, or making friends.

[0609] Users enter their user ID and matching criteria into the system. The emotion engine then recognizes the user's emotional state and adjusts the matching criteria based on that. The emotion engine evaluates the user's emotions using technologies such as voice analysis, facial expression analysis, and text analysis. Specific analysis technologies used include Google Cloud Speech-to-Text API, Microsoft Azure Face API, and OpenAI's GPT-3.

[0610] The server retrieves specific user information from the database based on the user ID. This information includes the user's StrengthsFinder traits, interests, hobbies, and commonly used services. Based on the retrieved information and the analysis results of the emotion engine, the server suggests the most suitable person for a one-on-one meeting.

[0611] For example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and his regularly used services are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and his regularly used services are "chat tool C" and "video conferencing D."

[0612] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0613] An example of a specific prompt is:

[0614] "Using basic information about Employee A and Employee B, please suggest the appropriate person for a 1-on-1 meeting for Employee A, who is feeling stressed by the emotion engine."

[0615] Possible reasons include:

[0616] In this way, this system, combined with an emotion engine, can propose more effective and personalized one-on-one meetings between employees, thereby reducing employee stress and contributing to improved work efficiency and motivation.

[0617] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0618] Step 1:

[0619] The terminal registers basic information about an employee. Specifically, the terminal defines an Employee class and creates an object that includes information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. The input for this operation is employee information (ID, name, characteristics, interests, hobbies, and services), and the output is an Employee object.

[0620] Step 2:

[0621] The terminal defines the OneOnOneMatcher class and sets a constructor that takes the employee list and emotion engine as arguments. The OneOnOneMatcher class contains a method for matching. The input of this operation is the employee list and emotion engine, and the output is a OneOnOneMatcher instance.

[0622] Step 3:

[0623] A user inputs their user ID and matching criteria into the system. The input criteria can include career advice, exchanging opinions to improve services, or making friends. The input of this operation is the user ID and matching criteria, and the output is the system's acceptance of the user information.

[0624] Step 4:

[0625] The server recognizes the user's emotional state using an emotion engine, which uses voice analysis, facial expression analysis, or text analysis to evaluate the user's emotional state. The input in this case is the user's real-time data (voice, image, text, etc.), and the output is the user's emotional state (stressed, relaxed, etc.).

[0626] Step 5:

[0627] The server retrieves specific user information from the database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and regular services. The input for this operation is the user ID, and the output is the user information (traits, interests, hobbies, and services).

[0628] Step 6:

[0629] The server proposes the best 1-on-1 meeting partner based on the acquired user information and the evaluation results of the emotion engine. To do this, the server uses the matching method of the OneOnOneMatcher instance. The input is user information and emotional state, and the output is information about the matched partner.

[0630] Step 7:

[0631] The server displays information about the proposed 1-on-1 meeting partners to the user. The input is the information about the matched partners, and the output is notification or display to the user.

[0632] As a specific example of operation, please refer to the following procedure.

[0633] 1. The terminal creates an Employee object.

[0634] 2. The terminal creates a OneOnOneMatcher instance.

[0635] 3. The user enters the user ID and matching criteria into the system.

[0636] 4. The server uses an emotion engine to recognize the user's emotional state.

[0637] 5. The server retrieves the user information from the database.

[0638] 6. The server uses a OneOnOneMatcher instance to perform the matching.

[0639] 7. The server displays the suggested partners' information to the user.

[0640] The above processing steps make it possible to propose one-on-one meetings between employees in a more effective and personalized manner.

[0641] (Application example 2)

[0642] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0643] Conventional 1-on-1 meeting suggestion systems were unable to consider the emotional state of employees when matching them, making it difficult to select an appropriate meeting partner. Employees who are feeling particularly stressed need to be provided with people and topics that will help them relax, but previous systems were unable to respond in this flexible manner. This resulted in issues such as being unable to fully reduce employee stress or improve work efficiency.

[0644] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering employee characteristic information, means for setting matching criteria between employees, means for using a generation AI to suggest partners for one-on-one meetings between employees based on the employee characteristic information and matching criteria, means for analyzing the emotional state of employees using an emotion recognition engine, and means for optimizing meeting partners based on the emotional state. This makes it possible to reduce employee stress and improve work efficiency by taking into account the employee's characteristic information and emotional state and suggesting more appropriate and relaxing meeting partners.

[0645] "Employee characteristic information" refers to information such as an employee's ID, name, characteristics, areas of interest, hobbies, and services they regularly use.

[0646] "Matching criteria" are criteria for selecting partners for one-on-one meetings between employees, and include criteria based on purposes such as career counseling, exchanging opinions to improve services, and making friends.

[0647] "Generative AI" refers to a system that uses artificial intelligence technology to suggest partners for one-on-one meetings based on employee characteristics and matching criteria.

[0648] "Emotion recognition engine" refers to a system that uses voice, facial expression, or text analysis to assess an employee's emotional state.

[0649] "Emotional state" refers to an employee's current psychological state and includes emotions such as stress, relaxation, and joy.

[0650] "Optimize" refers to making adjustments to get the best results based on specific conditions.

[0651] This invention combines a system that suggests people for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. This system aims to help create new communication among workers, particularly in factories, and reduce stress.

[0652] First, the terminal defines an Employee class and registers basic information about the worker. This includes the worker's ID, name, characteristics, areas of interest, hobbies, and services they regularly use. It also defines a OneOnOneMatcher class and sets up a constructor that receives a list of workers and an emotion recognition engine as arguments. This class includes methods for matching between workers.

[0653] Users can input their user ID and matching criteria into the system. Matching criteria can be selected from career advice, exchanging opinions to improve services, or making friends. In addition, an emotion recognition engine recognizes the user's emotional state and adjusts the matching criteria based on this.

[0654] The server retrieves specific user information from a database based on the user ID. This information includes the user's characteristics, interests, hobbies, and commonly used services. In addition, the emotion recognition engine evaluates the user's emotional state using voice analysis, facial expression analysis, or text analysis. Emotion data obtained from voice data or text input is analyzed using an API (e.g., Google Cloud Natural Language API).

[0655] For example, if the user is Worker A and is currently feeling stressed, the server will use the analysis results of the emotion recognition engine to suggest partners based on matching criteria aimed at relieving stress. For example, it will suggest workers who have relaxing topics to talk about or who share many common hobbies. This allows Worker A to exchange opinions or seek career advice in a relaxing environment.

[0656] Usage example:

[0657] Let's say Worker A has been busy with work recently and his stress level is high. The robot can sense this situation and suggest relaxing conversations with colleagues. In this way, this system, combined with an emotion recognition engine, can suggest one-on-one meetings between workers in a more effective and personalized manner. This reduces worker stress and contributes to improving work efficiency and motivation.

[0658] Example prompt for a generative AI model:

[0659] "If your recent emotional state has been stressful, suggest other workers who share your interests."

[0660] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0661] Step 1:

[0662] The terminal registers the characteristic information of the worker. As input, it receives information such as the worker's ID, name, characteristics (e.g., communication skills, adaptability), areas of interest (e.g., marketing, sales), hobbies (e.g., games, guitar), and services they regularly use (e.g., chat tool A, video conferencing B). This information is registered as an instance of the Employee class. As output, it generates and stores an Employee object with the registered characteristic information.

[0663] Step 2:

[0664] The user inputs their user ID and matching criteria into the system. As input, the system receives the user ID and matching criteria (e.g., career advice, exchanging opinions to improve services, making friends). The user also provides real-time emotional input data (e.g., voice data or text input) to the system. Based on this, the device receives the emotional data and transfers it to the emotion recognition engine. As output, the user ID and matching criteria are passed to the server.

[0665] Step 3:

[0666] The emotion recognition engine analyzes emotion input data. As input, it receives voice data, facial expression data, or text data. It analyzes this using an analysis API (e.g., Google Cloud Natural Language API) to determine the emotional state (e.g., stress, relaxation, joy). As output, it generates the analysis result as an emotional state and returns it to the server. For example, it may obtain a result such as "The user is feeling stressed."

[0667] Step 4:

[0668] The server retrieves user information from the database based on the analysis results of the emotion recognition engine and the user ID. As input, it receives the user ID and emotional state and sends a query to the database. The database returns the corresponding user's characteristic information (e.g., interests, hobbies, and services used). As output, it generates a dataset containing the user's characteristic information and passes it to OneOnOneMatcher.

[0669] Step 5:

[0670] The OneOnOneMatcher class suggests meeting partners based on a user's characteristics and emotional state. It receives characteristics, emotional state, and matching criteria as input. It runs a matching algorithm to create a list of partners that best fit the user's characteristics and emotional state. For example, if a user is feeling stressed, it will prioritize suggesting workers who share relaxing hobbies. As output, it generates a list of optimal meeting partners and returns it to the user.

[0671] Step 6:

[0672] The user receives a list of suggested meeting partners from the server. As input, the user receives a list of suggested meeting partners (e.g., names and IDs of multiple workers). Based on this, the user sets up and conducts 1-on-1 meetings with suitable meeting partners. As output, the actual meetings are set up and conducted.

[0673] This series of processes makes it possible to suggest optimal meeting partners that take into account the user's characteristic information and emotional state. This allows for the exchange of opinions and career advice in a relaxed environment, which is expected to reduce worker stress and improve work efficiency.

[0674] 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.

[0675] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0676] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0677] [Fourth embodiment]

[0678] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0679] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0680] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the 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).

[0681] 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.

[0682] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0683] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0684] 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.

[0685] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[0686] 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.

[0687] 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 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.

[0688] 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.

[0689] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0690] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0691] The present invention provides a system that registers employee characteristic information and, based on that information, suggests the most suitable person to hold a one-on-one meeting with. A specific embodiment of this system will be described below.

[0692] First, the terminal defines the Employee class and OneOnOneMatcher class. The Employee class is a class for storing basic information about employees, such as their ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0693] Next, when a user wants the system to suggest partners for a one-on-one meeting, the user provides the system with their user ID and matching criteria, which can include career advice, exchanging opinions to improve the service, or making friends with similar hobbies. The server then uses this information to make appropriate matches.

[0694] Based on the given user ID, the server retrieves the user's characteristic information from the database. Then, the server filters the list of all employees (excluding the user) according to the matching criteria and selects the most suitable employee. Specifically, in the case of career counseling, it prioritizes employees with similar StrengthsFinder characteristics to the user; in the case of service improvement, it prioritizes employees with common interests; and in the case of making friends, it prioritizes employees with common hobbies.

[0695] For example, if a user wants to make friends, the server will list the employees with whom they have the most in common (hobbies and interests) and suggest the top five from that list. This process gives users the opportunity to have one-on-one meetings with people who are suitable for them.

[0696] A specific example is given below. For example, suppose the employee list contains the following data:

[0697] Employee A: Traits: "Strategy" and "Empathy", Interests: "AI" and "Machine Learning", Hobbies: "Reading" and "Cycling", Services usually used: "Chat Tool A" and "Video Conferencing B"

[0698] Employee B: Traits: "Communication skills" and "Adaptability", Interests: "Marketing" and "Sales", Hobbies: "Games" and "Guitar", Services usually used: "Chat tool C" and "Video conferencing D"

[0699] In this case, if the user is employee A and wants to make friends, the server may consider employee A's characteristics, interests, hobbies, and the services he or she regularly uses, and suggest employee B as the optimal partner for a one-on-one meeting. However, the final matching result will be based on the degree of similarity and criteria between the user's characteristics and those of other employees.

[0700] In this way, by building a system that effectively suggests people to meet with for one-on-one meetings, it is possible to promote interaction between employees and increase opportunities for information exchange.

[0701] The processing flow will be explained below.

[0702] Step 1:

[0703] The terminal defines an Employee class and creates an object with information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0704] Step 2:

[0705] The terminal defines a OneOnOneMatcher class and sets a constructor that receives a list of employees as an argument. This class includes a method for matching employees.

[0706] Step 3:

[0707] Users enter their user ID and matching criteria into the system, which can be selected from "career consultation," "exchanging opinions to improve the service," or "making friends."

[0708] Step 4:

[0709] The server retrieves specific user information from a database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and commonly used services.

[0710] Step 5:

[0711] The server creates a candidate list by excluding the designated user from the list of all employees.

[0712] Step 6:

[0713] The server sorts the list of candidates based on matching criteria: for career counseling, by the degree of match of StrengthsFinder characteristics; for service improvement discussions, by the number of common interests; for friendship meetings, by the number of common hobbies.

[0714] Step 7:

[0715] The server selects the top five employees from the sorted candidate list and suggests them to the user.

[0716] Step 8:

[0717] The user selects the desired partner for the one-on-one meeting from the displayed candidate list and sets up the meeting.

[0718] Example 1

[0719] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0720] Previous employee matching systems had difficulty automatically selecting the right person for a one-on-one meeting, making it impossible to maximize the opportunity for appropriate interaction and information exchange between employees. It was also difficult to match individuals based on their characteristics, hobbies, and interests, and finding the perfect match for each employee took a lot of time and effort, especially in large organizations.

[0721] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0722] In this invention, the server includes a means for the user to provide their own characteristic information and matching criteria, a means for the server to acquire the user's characteristic information from a database, and a means for the server to filter the employee list according to the matching criteria and select appropriate employees, thereby making it possible to efficiently and accurately promote opportunities for interaction and information exchange between employees.

[0723] "Employee characteristic information" refers to information about an employee, including the employee's personality traits, areas of interest, leisure activities, and frequently used tools.

[0724] "Matching criteria" are the elements that are used as criteria when proposing partners for one-on-one meetings between employees, and specifically include the purpose of career advice, exchanging opinions, or making friends with similar hobbies.

[0725] "Generative AI" refers to artificial intelligence technology that suggests the most suitable partner based on specific input (prompt text).

[0726] "User" refers to the employee who receives the proposal from the other party in the 1-on-1 meeting.

[0727] "Server" refers to a device or system that retrieves user characteristic information from a database, filters the employee list according to matching criteria, and selects suitable employees.

[0728] "Database" refers to a storage device that stores employee characteristic information.

[0729] The present invention is a system for effectively holding one-on-one meetings between employees. The purpose of this system is for users to provide their own characteristic information, and for the server to suggest the most suitable partners for one-on-one meetings based on that information. Specific embodiments of the present invention are described below.

[0730] First, to register the employee's characteristic information, the terminal creates an instance of the Employee class. The Employee class holds basic information such as the employee's ID, name, personality traits, interests, leisure activities, and frequently used tools. This information is stored in the database.

[0731] Next, if a user wants the system to suggest partners for a one-on-one meeting, they provide their user ID and matching criteria, which can be set to career advice, exchanging opinions, or making friends.

[0732] The server receives this information and retrieves the user's characteristics from a database. It then filters the employee list according to the matching criteria and selects suitable employees. Specifically, if the criterion is career advice, it prioritizes employees with similar personality traits to the user; if the criterion is opinion exchange, it prioritizes employees with common interests; and if the criterion is making friends, it prioritizes employees with common leisure activities.

[0733] For example, if the user is looking to make friends, the server might respond with the following prompt:

[0734] "User ID: 1

[0735] Matching criteria: Making friends

[0736] Based on this prompt, the server considers the characteristics of employee A (ID: 1) and suggests several people, including employee B, who has the most in common with him / her. This allows the user to find a suitable partner for a one-on-one meeting.

[0737] This system not only promotes interaction between employees and increases opportunities for information exchange, but also allows individual employees to more effectively improve their careers and work, which is expected to strengthen collaboration and improve knowledge sharing across the company.

[0738] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0739] Step 1:

[0740] The terminal registers employee characteristics information. Specifically, it creates an instance of the Employee class and saves information such as the employee's ID, name, personality traits, areas of interest, leisure activities, and frequently used tools in the database. This process requires basic employee information as input and generates an employee object as output.

[0741] Step 2:

[0742] Users provide their user ID and matching criteria to the system to have it suggest partners for one-on-one meetings. Matching criteria can include career advice, exchanging ideas, or making friends. This information is input into the generative AI model as a prompt. The system requires the user ID and matching criteria as input and generates a prompt as output.

[0743] Step 3:

[0744] The server retrieves user characteristic information from the database based on the user ID and prompt provided by the user. This process requires the user ID as input and retrieves the user characteristic information as output. The characteristic information includes the employee's personality traits, areas of interest, and leisure activities.

[0745] Step 4:

[0746] The server filters the employee list according to the acquired user characteristic information and matching criteria. The user characteristic information and matching criteria are required as input, and the filtered employee list is generated as output. For example, if the matching criteria is "making friends," employees with common hobbies are prioritized.

[0747] Step 5:

[0748] The server selects the most suitable employees from the filtered employee list and suggests the top five. This process requires the filtered employee list as input and generates a list of suggested employees as output. For example, if the criterion is making friends, the top five employees with common hobbies will be suggested to the user.

[0749] (Application example 1)

[0750] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0751] Conventional 1-on-1 meeting setting systems did not adequately optimize communication between employees, and the efficiency of information sharing within factories was a particular issue. Furthermore, manually setting up meetings and selecting participants was time-consuming and did not contribute to improving productivity.

[0752] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0753] In this invention, the server includes a means for registering employee characteristic information, a means for setting matching criteria between employees, a means for using a generation AI to suggest employees with whom to hold one-on-one meetings based on the employee characteristic information and the matching criteria, and a means for employees to instantly set up one-on-one meetings using smart glasses, thereby enabling factory workers to quickly and efficiently set up one-on-one meetings with optimal partners, share information, and solve problems.

[0754] "Employee characteristic information" is information that represents the characteristics of each employee, such as basic information, abilities, interests, and hobbies.

[0755] "Matching criteria" are criteria for efficiently setting up one-on-one meetings between employees, and are based on purposes such as career counseling, service improvement, and making friends.

[0756] "Generative AI" refers to artificial intelligence that suggests optimal results based on given data and criteria.

[0757] The "means for suggesting partners for 1-on-1 meetings" is a means that has the function of automatically suggesting the most suitable partner for a 1-on-1 meeting based on the employee's characteristic information and matching criteria.

[0758] "Smart glasses" are eyeglass-type devices that have the ability to visually display information, allowing users to check the information in real time.

[0759] "Instant setup methods" are methods that have the functionality to allow users to set up one-on-one meetings in a short amount of time.

[0760] The present invention provides a system that allows users to register employee characteristic information and instantly set up one-on-one meetings using smart glasses. Specific embodiments of the system are described below.

[0761] First, the system uses a terminal to register employee characteristics. The Employee class is defined on this terminal, which stores basic information such as employee ID, name, skills, interests, hobbies, and work experience. The OneOnOneMatcher class is also defined on the terminal, which is the class that performs the actual matching.

[0762] When a user wants to set up a one-on-one meeting, they provide their user ID and matching criteria to the system through the smart glasses, which can include career advice, exchanging opinions to improve services, or making friends.

[0763] The server receives the user's information and retrieves characteristic information from a database. Based on this information, it filters a list of all other employees (excluding the user) according to the specified matching criteria. Generative AI calculates a matching score and suggests the most suitable people for a one-on-one meeting. For example, when making friends, it focuses on common hobbies and interests and suggests a list of the top five people.

[0764] The hardware used for server processing includes smart glasses (e.g., Google Glass) and databases (e.g., SQLite). The software uses Python to retrieve data, perform calculations, filter, match, and display results. The generative AI takes characteristic information and matching criteria as input and predicts the best match.

[0765] For example, when employee A uses smart glasses to request a one-on-one meeting to make friends, employee B is suggested based on employee A's characteristics. This suggestion allows employee A to quickly set up a one-on-one meeting.

[0766] Example prompt sentence:

[0767] To optimize 1-on-1 meetings between employees, use smart glasses to suggest the best match based on their characteristics. Consider [career counseling | service improvement | making friends] as criteria for suggestions.

[0768] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0769] Step 1:

[0770] A user accesses the system using smart glasses and wishes to set up a one-on-one meeting. At that time, the user inputs his / her user ID and matching criteria (either career advice, service improvement, or making friends). The input data is the user ID and matching criteria. As output, this data is sent to the server.

[0771] Step 2:

[0772] The server retrieves the user's characteristic information from the database based on the submitted user ID. The characteristic information includes skills, interests, hobbies, work experience, etc. The input data is the user ID, and the characteristic information is obtained as output data. The characteristic information becomes the basic data for the matching process.

[0773] Step 3:

[0774] The server filters the list of all eligible employees based on the characteristics and matching criteria. The input data is the user's characteristics and matching criteria. The output data is a filtered list of employees. For example, if the criteria is to make friends, employees with common interests or hobbies will be listed.

[0775] Step 4:

[0776] The server uses the generative AI model to score the employee from the filtered employee list who is most compatible with the user. The input data is the filtered employee list, and a score is calculated based on the degree of match of characteristics. The output data is a list of the top five employees in descending order of score.

[0777] Step 5:

[0778] The server proposes a list of the top five employees to the user. The input data is the list of the top five employees, and the output data is the proposal. The user can check the proposal through the smart glasses and set up a one-on-one meeting.

[0779] Step 6:

[0780] The user selects a partner for a one-on-one meeting from the suggested employees and sets the meeting time. The input data is the user's selection and the meeting time. The output data is the set meeting information, which is sent to the server and notified to the other party.

[0781] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0782] This invention combines a system that suggests people to meet for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. The system aims to help employees form new connections and reduce stress in a telework environment.

[0783] First, the terminal defines an Employee class and registers basic employee information. This includes the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. It also defines a OneOnOneMatcher class and sets up a constructor that takes the employee list and emotion engine as arguments. This class contains methods for matching employees.

[0784] Next, users enter their user ID and matching criteria into the system. They can choose from career advice, exchanging opinions to improve services, or making friends. Furthermore, an emotion engine recognizes the user's emotional state and adjusts the matching criteria accordingly.

[0785] The server retrieves specific user information from the database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and regular services. In addition, the emotion engine evaluates the user's emotional state using voice, facial expression, or text analysis.

[0786] For example, if a user is assessed as being under stress, the server will consider the user's emotional state and suggest partners based on matching criteria aimed at reducing stress, such as employees who have relaxing topics to talk about or who share many common hobbies.

[0787] As a specific example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and the services he regularly uses are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and the services he regularly uses are "chat tool C" and "video conferencing D."

[0788] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0789] In this way, this system, combined with an emotion engine, can propose more effective and personalized one-on-one meetings between employees, thereby reducing employee stress and contributing to improved work efficiency and motivation.

[0790] The processing flow will be explained below.

[0791] Step 1:

[0792] The terminal defines an Employee class and creates an object with information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0793] Step 2:

[0794] The terminal defines a OneOnOneMatcher class and sets a constructor that receives the employee list and the emotion engine as arguments. This class includes a method for matching between employees.

[0795] Step 3:

[0796] Users input their user ID and matching criteria into the system, which can be selected from career advice, exchanging opinions to improve the service, or making friends.

[0797] Step 4:

[0798] The server retrieves specific user information from a database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and commonly used services.

[0799] Step 5:

[0800] The server analyzes the user's emotional state using an emotion engine that uses at least one of voice analysis, facial expression analysis, or text analysis to recognize the user's current emotion.

[0801] Step 6:

[0802] The server takes into account the user's emotional state and adjusts the matching criteria based on that emotional state, for example, if the user is feeling stressed, it will suggest people who have relaxing topics to talk about.

[0803] Step 7:

[0804] The server creates a candidate list by excluding the designated user from the list of all employees.

[0805] Step 8:

[0806] The server sorts the candidate list based on matching criteria: for career counseling, by the degree of match of StrengthsFinder characteristics; for service improvement, by the number of common interests; and for friend-making, by the number of common hobbies.

[0807] Step 9:

[0808] The server selects the top five employees from the sorted candidate list and suggests them to the user.

[0809] Step 10:

[0810] The user selects the desired partner for a one-on-one meeting from the displayed candidate list and sets up the meeting.

[0811] As a specific example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and the services he regularly uses are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and the services he regularly uses are "chat tool C" and "video conferencing D."

[0812] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0813] Example 2

[0814] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0815] Conventional 1-on-1 meeting suggestion systems suggest partners based on employee characteristics and matching criteria, but do not take the user's emotional state into account, which does not fully reduce stress or achieve optimal networking. Therefore, there is a need for more effective and personalized 1-on-1 meeting suggestion systems.

[0816] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0817] In this invention, the server includes means for registering employee characteristic information, means for setting matching criteria between employees, means for using a generation AI to suggest partners for 1-on-1 meetings between employees based on the employee characteristic information and the matching criteria, means for recognizing the user's emotional state using an emotion engine, and means for adjusting the matching criteria based on the recognized emotional state, thereby making it possible to suggest optimal partners for 1-on-1 meetings based on the user's emotional state.

[0818] "Employee characteristic information" is basic information about employees, such as their ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use.

[0819] "Matching criteria" are criteria for defining the purpose of one-on-one meetings, such as career counseling, exchanging opinions to improve services, or making friends.

[0820] "Generative AI" is an artificial intelligence technology that suggests suitable partners for one-on-one meetings based on employee characteristics and matching criteria.

[0821] An "emotion engine" is a system that recognizes a user's emotional state using technologies such as voice analysis, facial expression analysis, and text analysis.

[0822] The "means for suggesting partners for 1-on-1 meetings" is a method for suggesting partners for 1-on-1 meetings between employees based on characteristic information of the employees and matching criteria.

[0823] "Emotional state" refers to the user's current emotional state, and may include stress, relaxation, excitement, etc.

[0824] The "means for adjusting matching criteria based on the recognized emotional state" is a method for changing matching criteria in consideration of the user's emotional state and proposing more effective and personalized partners.

[0825] This invention combines a system that suggests people to meet for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. The system aims to help employees form new connections and reduce stress in a telework environment.

[0826] First, the terminal defines an Employee class and registers basic employee information, including the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. By using the Employee class, employee characteristic information can be managed centrally.

[0827] Next, the device defines the OneOnOneMatcher class and sets up a constructor that receives the employee list and emotion engine as arguments. This class includes a method for matching employees, and the matching criteria can be set to career advice, exchanging opinions to improve services, or making friends.

[0828] Users enter their user ID and matching criteria into the system. The emotion engine then recognizes the user's emotional state and adjusts the matching criteria based on that. The emotion engine evaluates the user's emotions using technologies such as voice analysis, facial expression analysis, and text analysis. Specific analysis technologies used include Google Cloud Speech-to-Text API, Microsoft Azure Face API, and OpenAI's GPT-3.

[0829] The server retrieves specific user information from the database based on the user ID. This information includes the user's StrengthsFinder traits, interests, hobbies, and commonly used services. Based on the retrieved information and the analysis results of the emotion engine, the server suggests the most suitable person for a one-on-one meeting.

[0830] For example, suppose employee A has "communication skills" and "adaptability," his interests are "marketing" and "sales," his hobbies are "games" and "guitar," and his regularly used services are "chat tool A" and "video conferencing B." Employee B has "strategy" and "empathy," his interests are "AI" and "machine learning," his hobbies are "reading" and "cycling," and his regularly used services are "chat tool C" and "video conferencing D."

[0831] If the user is Employee A and their current emotional state is stressed, the server may suggest Employee B based on the analysis results of the emotion engine. This allows Employee A to exchange opinions or receive career advice in a relaxed environment.

[0832] An example of a specific prompt is:

[0833] "Using basic information about Employee A and Employee B, please suggest the appropriate person for a 1-on-1 meeting for Employee A, who is feeling stressed by the emotion engine."

[0834] Possible reasons include:

[0835] In this way, this system, combined with an emotion engine, can propose more effective and personalized one-on-one meetings between employees, thereby reducing employee stress and contributing to improved work efficiency and motivation.

[0836] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0837] Step 1:

[0838] The terminal registers basic information about an employee. Specifically, the terminal defines an Employee class and creates an object that includes information such as the employee's ID, name, StrengthsFinder characteristics, areas of interest, hobbies, and services they regularly use. The input for this operation is employee information (ID, name, characteristics, interests, hobbies, and services), and the output is an Employee object.

[0839] Step 2:

[0840] The terminal defines the OneOnOneMatcher class and sets a constructor that takes the employee list and emotion engine as arguments. The OneOnOneMatcher class contains a method for matching. The input of this operation is the employee list and emotion engine, and the output is a OneOnOneMatcher instance.

[0841] Step 3:

[0842] A user inputs their user ID and matching criteria into the system. The input criteria can include career advice, exchanging opinions to improve services, or making friends. The input of this operation is the user ID and matching criteria, and the output is the system's acceptance of the user information.

[0843] Step 4:

[0844] The server recognizes the user's emotional state using an emotion engine, which uses voice analysis, facial expression analysis, or text analysis to evaluate the user's emotional state. The input in this case is the user's real-time data (voice, image, text, etc.), and the output is the user's emotional state (stressed, relaxed, etc.).

[0845] Step 5:

[0846] The server retrieves specific user information from the database based on the user ID, including the user's StrengthsFinder traits, interests, hobbies, and regular services. The input for this operation is the user ID, and the output is the user information (traits, interests, hobbies, and services).

[0847] Step 6:

[0848] The server proposes the best 1-on-1 meeting partner based on the acquired user information and the evaluation results of the emotion engine. To do this, the server uses the matching method of the OneOnOneMatcher instance. The input is user information and emotional state, and the output is information about the matched partner.

[0849] Step 7:

[0850] The server displays information about the proposed 1-on-1 meeting partners to the user. The input is the information about the matched partners, and the output is notification or display to the user.

[0851] As a specific example of operation, please refer to the following procedure.

[0852] 1. The terminal creates an Employee object.

[0853] 2. The terminal creates a OneOnOneMatcher instance.

[0854] 3. The user enters the user ID and matching criteria into the system.

[0855] 4. The server uses an emotion engine to recognize the user's emotional state.

[0856] 5. The server retrieves the user information from the database.

[0857] 6. The server uses a OneOnOneMatcher instance to perform the matching.

[0858] 7. The server displays the suggested partners' information to the user.

[0859] The above processing steps make it possible to propose one-on-one meetings between employees in a more effective and personalized manner.

[0860] (Application example 2)

[0861] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0862] Conventional 1-on-1 meeting suggestion systems were unable to consider the emotional state of employees when matching them, making it difficult to select an appropriate meeting partner. Employees who are feeling particularly stressed need to be provided with people and topics that will help them relax, but previous systems were unable to respond in this flexible manner. This resulted in issues such as being unable to fully reduce employee stress or improve work efficiency.

[0863] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for registering employee characteristic information, means for setting matching criteria between employees, means for using a generation AI to suggest partners for one-on-one meetings between employees based on the employee characteristic information and matching criteria, means for analyzing the emotional state of employees using an emotion recognition engine, and means for optimizing meeting partners based on the emotional state. This makes it possible to reduce employee stress and improve work efficiency by taking into account the employee's characteristic information and emotional state and suggesting more appropriate and relaxing meeting partners.

[0864] "Employee characteristic information" refers to information such as an employee's ID, name, characteristics, areas of interest, hobbies, and services they regularly use.

[0865] "Matching criteria" are criteria for selecting partners for one-on-one meetings between employees, and include criteria based on purposes such as career counseling, exchanging opinions to improve services, and making friends.

[0866] "Generative AI" refers to a system that uses artificial intelligence technology to suggest partners for one-on-one meetings based on employee characteristics and matching criteria.

[0867] "Emotion recognition engine" refers to a system that uses voice, facial expression, or text analysis to assess an employee's emotional state.

[0868] "Emotional state" refers to an employee's current psychological state and includes emotions such as stress, relaxation, and joy.

[0869] "Optimize" refers to making adjustments to get the best results based on specific conditions.

[0870] This invention combines a system that suggests people for one-on-one meetings based on employee characteristics with an emotion engine that recognizes the user's emotions. This system aims to help create new communication among workers, particularly in factories, and reduce stress.

[0871] First, the terminal defines an Employee class and registers basic information about the worker. This includes the worker's ID, name, characteristics, areas of interest, hobbies, and services they regularly use. It also defines a OneOnOneMatcher class and sets up a constructor that receives a list of workers and an emotion recognition engine as arguments. This class includes methods for matching between workers.

[0872] Users can input their user ID and matching criteria into the system. Matching criteria can be selected from career advice, exchanging opinions to improve services, or making friends. In addition, an emotion recognition engine recognizes the user's emotional state and adjusts the matching criteria based on this.

[0873] The server retrieves specific user information from a database based on the user ID. This information includes the user's characteristics, interests, hobbies, and commonly used services. In addition, the emotion recognition engine evaluates the user's emotional state using voice analysis, facial expression analysis, or text analysis. Emotion data obtained from voice data or text input is analyzed using an API (e.g., Google Cloud Natural Language API).

[0874] For example, if the user is Worker A and is currently feeling stressed, the server will use the analysis results of the emotion recognition engine to suggest partners based on matching criteria aimed at relieving stress. For example, it will suggest workers who have relaxing topics to talk about or who share many common hobbies. This allows Worker A to exchange opinions or seek career advice in a relaxing environment.

[0875] Usage example:

[0876] Let's say Worker A has been busy with work recently and his stress level is high. The robot can sense this situation and suggest relaxing conversations with colleagues. In this way, this system, combined with an emotion recognition engine, can suggest one-on-one meetings between workers in a more effective and personalized manner. This reduces worker stress and contributes to improving work efficiency and motivation.

[0877] Example prompt for a generative AI model:

[0878] "If your recent emotional state has been stressful, suggest other workers who share your interests."

[0879] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0880] Step 1:

[0881] The terminal registers the characteristic information of the worker. As input, it receives information such as the worker's ID, name, characteristics (e.g., communication skills, adaptability), areas of interest (e.g., marketing, sales), hobbies (e.g., games, guitar), and services they regularly use (e.g., chat tool A, video conferencing B). This information is registered as an instance of the Employee class. As output, it generates and stores an Employee object with the registered characteristic information.

[0882] Step 2:

[0883] The user inputs their user ID and matching criteria into the system. As input, the system receives the user ID and matching criteria (e.g., career advice, exchanging opinions to improve services, making friends). The user also provides real-time emotional input data (e.g., voice data or text input) to the system. Based on this, the device receives the emotional data and transfers it to the emotion recognition engine. As output, the user ID and matching criteria are passed to the server.

[0884] Step 3:

[0885] The emotion recognition engine analyzes emotion input data. As input, it receives voice data, facial expression data, or text data. It analyzes this using an analysis API (e.g., Google Cloud Natural Language API) to determine the emotional state (e.g., stress, relaxation, joy). As output, it generates the analysis result as an emotional state and returns it to the server. For example, it may obtain a result such as "The user is feeling stressed."

[0886] Step 4:

[0887] The server retrieves user information from the database based on the analysis results of the emotion recognition engine and the user ID. As input, it receives the user ID and emotional state and sends a query to the database. The database returns the corresponding user's characteristic information (e.g., interests, hobbies, and services used). As output, it generates a dataset containing the user's characteristic information and passes it to OneOnOneMatcher.

[0888] Step 5:

[0889] The OneOnOneMatcher class suggests meeting partners based on a user's characteristics and emotional state. It receives characteristics, emotional state, and matching criteria as input. It runs a matching algorithm to create a list of partners that best fit the user's characteristics and emotional state. For example, if a user is feeling stressed, it will prioritize suggesting workers who share relaxing hobbies. As output, it generates a list of optimal meeting partners and returns it to the user.

[0890] Step 6:

[0891] The user receives a list of suggested meeting partners from the server. As input, the user receives a list of suggested meeting partners (e.g., names and IDs of multiple workers). Based on this, the user sets up and conducts 1-on-1 meetings with suitable meeting partners. As output, the actual meetings are set up and conducted.

[0892] This series of processes makes it possible to suggest optimal meeting partners that take into account the user's characteristic information and emotional state. This allows for the exchange of opinions and career advice in a relaxed environment, which is expected to reduce worker stress and improve work efficiency.

[0893] 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.

[0894] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0895] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[0896] 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.

[0897] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[0898] 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.

[0899] 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).

[0900] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0901] 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."

[0902] 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.

[0903] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[0904] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[0905] 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.

[0906] 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.

[0907] 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.

[0908] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[0909] The hardware resource that executes the specific processing 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 processing may be a single processor.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] 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.

[0914] The following is further disclosed regarding the above embodiment.

[0915] (Claim 1)

[0916] A means for registering employee characteristic information;

[0917] a means of setting matching criteria between employees;

[0918] A method for suggesting 1-on-1 meeting partners between employees based on employee characteristics and matching criteria using generative AI;

[0919] A system including:

[0920] (Claim 2)

[0921] 2. The system of claim 1, wherein the employee characteristic information includes at least StrengthsFinder characteristics, areas of interest, hobbies, and services used regularly.

[0922] (Claim 3)

[0923] The system according to claim 1, which has a means for suggesting partners for one-on-one meetings between employees based on matching criteria, suitable for one of the following purposes: career counseling, exchanging opinions to improve services, and making friends with the same hobbies.

[0924] "Example 1"

[0925] (Claim 1)

[0926] A means for registering employee characteristic information;

[0927] a means of setting matching criteria between employees;

[0928] A method for suggesting 1-on-1 meeting partners between employees based on employee characteristics and matching criteria using generative AI;

[0929] a means for users to provide their characteristic information and matching criteria;

[0930] A means for the server to acquire user characteristic information from a database;

[0931] a means for the server to filter the employee list according to matching criteria and select suitable employees;

[0932] A system including:

[0933] (Claim 2)

[0934] 2. The system according to claim 1, wherein the employee characteristic information includes at least personality traits, areas of interest, leisure activities, and frequently used tools.

[0935] (Claim 3)

[0936] The system according to claim 1, further comprising a means for suggesting partners for one-on-one meetings between employees based on matching criteria who are suitable for career advice, exchanging opinions, or making friends with similar hobbies.

[0937] "Application Example 1"

[0938] (Claim 1)

[0939] A means for registering employee characteristic information;

[0940] a means of setting matching criteria between employees;

[0941] A method for suggesting 1-on-1 meeting partners between employees based on employee characteristics and matching criteria using generative AI;

[0942] A way for employees to instantly set up one-on-one meetings using smart glasses,

[0943] A system including:

[0944] (Claim 2)

[0945] 2. The system of claim 1, wherein the employee characteristic information includes at least StrengthsFinder characteristics, areas of interest, hobbies, and services used regularly.

[0946] (Claim 3)

[0947] The system according to claim 1, which has a means for suggesting partners for one-on-one meetings between employees based on matching criteria, suitable for one of the following purposes: career counseling, exchanging opinions to improve services, and making friends with the same hobbies.

[0948] "Example 2: Combining Emotion Engines"

[0949] (Claim 1)

[0950] A means for registering employee characteristic information;

[0951] a means of setting matching criteria between employees;

[0952] A method for suggesting 1-on-1 meeting partners between employees based on employee characteristics and matching criteria using generative AI;

[0953] means for recognizing an emotional state of a user using an emotion engine;

[0954] means for adjusting matching criteria based on the perceived emotional state;

[0955] A system including:

[0956] (Claim 2)

[0957] 2. The system of claim 1, wherein the employee characteristic information includes at least StrengthsFinder characteristics, areas of interest, hobbies, and services used regularly.

[0958] (Claim 3)

[0959] The system according to claim 1, which has a means for suggesting partners for one-on-one meetings between employees based on matching criteria, suitable for one of the following purposes: career counseling, exchanging opinions to improve services, and making friends with the same hobbies.

[0960] "Application example 2 when combining emotion engines"

[0961] (Claim 1)

[0962] A means for registering employee characteristic information;

[0963] a means of setting matching criteria between employees;

[0964] A method for suggesting 1-on-1 meeting partners between employees based on employee characteristics and matching criteria using generative AI;

[0965] a means for analyzing the emotional state of employees using an emotion recognition engine;

[0966] A means for optimizing meeting partners based on emotional state;

[0967] A system including:

[0968] (Claim 2)

[0969] 2. The system of claim 1, wherein the employee characteristic information includes at least StrengthsFinder characteristics, areas of interest, hobbies, services regularly used, and emotional state.

[0970] (Claim 3)

[0971] The system according to claim 1, which has a means for suggesting partners for one-on-one meetings between employees based on matching criteria, to suggest partners suitable for any of the purposes of career counseling, exchanging opinions to improve services, and making friends with the same hobbies, and further has a means for optimizing the suggestions based on emotional states. [Explanation of symbols]

[0972] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for registering employee characteristic information; a means of setting matching criteria between employees; A method for suggesting 1-on-1 meeting partners between employees based on employee characteristics and matching criteria using generative AI; A system including:

2. 2. The system according to claim 1, wherein the employee characteristic information includes at least StrengthsFinder characteristics, areas of interest, hobbies, and services that are regularly used.

3. The system according to claim 1, which has a means for suggesting partners for one-on-one meetings between employees based on matching criteria, suitable for one of the following purposes: career counseling, exchanging opinions to improve services, and making friends with the same hobbies.

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