System

Abstract

An object of a system according to an embodiment is to activate communication between employees and promote attendance.SOLUTION: A system includes an information collection part, an analysis part, a proposal part, and a lunch opening part. The information collection unit collects open information in a company. The analysis unit analyzes the information collected by the information collection unit. The proposal unit proposes an optimum communication partner on the basis of the information analyzed by the analysis unit. The lunch opening unit opens a shuffle lunch between the communication partners proposed by the proposal unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem of a lack of means to stimulate communication between employees as more and more people work from home.

[0005] The system according to the embodiment aims to stimulate communication between employees and encourage them to come to work. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, a proposal unit, and a lunch hosting unit. The information collection unit collects open information within the company. The analysis unit analyzes the information collected by the information collection unit. The proposal unit proposes optimal communication partners based on the information analyzed by the analysis unit. The lunch hosting unit holds a shuffle lunch between the communication partners proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can stimulate communication between employees and encourage them to come to work. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The communication activation system according to the embodiment of the present invention is a system that utilizes a generative AI to activate communication between employees and encourage them to come to work. As a result, the communication activation system can activate communication between employees and encourage them to come to work.

[0029] A communication activation system according to an embodiment includes an information collection unit, an analysis unit, a proposal unit, and a lunch hosting unit. The information collection unit collects open information within a company. For example, it collects posts on an internal bulletin board. The information collection unit can also collect project progress reports. The information collection unit can also collect employee profile information. For example, it collects employee skill sets and job title information. The analysis unit analyzes the information collected by the information collection unit. For example, it analyzes the information using text mining technology. The analysis unit can also analyze the information using data mining technology. The analysis unit can also analyze the information using emotion analysis technology. For example, it analyzes posts on an internal bulletin board using text mining technology. The data mining technology analyzes project progress reports to understand the progress of the project. The emotion analysis technology analyzes employee profile information to understand the employee's emotional state. The proposal unit proposes optimal communication partners based on the information analyzed by the analysis unit. For example, the proposal unit proposes communication partners taking into account skill complementarity. The proposal department can also suggest communication partners by taking into account the relevance of the project. The proposal department can also suggest communication partners by taking into account the interests and hobbies of employees. For example, the proposal department can suggest employees with programming skills and employees with design skills by taking into account the complementarity of their skills. The proposal department can suggest employees involved in the same project by taking into account the relevance of the project. The proposal department can suggest employees with the same hobbies by taking into account the interests and hobbies of employees. The lunch hosting department can host a shuffle lunch with the communication partners suggested by the proposal department. For example, the lunch hosting department can suggest a place to have lunch with the suggested employees. The lunch hosting department can also suggest a lunch menu. The lunch hosting department can also suggest a lunch time. For example, the lunch hosting department can suggest lunch in the cafeteria. For the lunch menu, it can suggest a menu that suits the employee's preferences. For the lunch time, it can suggest a time that suits the employee's schedule.As a result, the communication activation system according to the embodiment can activate communication between employees and encourage them to come to work. For example, when an employee comes to work and has lunch with someone suggested by the generation AI, spontaneous communication is activated. Employees can share new perspectives and ideas with each other. Coming to work increases direct communication and strengthens team cohesion.

[0030] The information collection unit can collect employee profile information or project progress. The information collection unit, for example, collects employee profile information. For example, it collects information such as the employee's name, job title, skill set, and hobbies. The information collection unit can also collect project progress. For example, it collects the completion status of project tasks and the project status. This allows detailed information about employees to be collected and the most suitable communication partners to be suggested.

[0031] The suggestion unit can analyze the employee's past lunch history or conversation content to suggest compatible partners. The suggestion unit, for example, stores the employee's past lunch history in a database and analyzes common lunch partners and frequency. For example, it prioritizes suggesting people with whom the employee has had lunch many times in the past. The suggestion unit also performs text analysis of the employee's past conversation content to suggest partners with common topics and interests. For example, it prioritizes suggesting people with common hobbies and interests. The suggestion unit also comprehensively analyzes the employee's past lunch history and conversation content to suggest compatible partners. For example, it identifies compatible partners from the content of conversations at past lunches and suggests them as the next lunch partner. This makes it possible to suggest compatible partners based on the employee's past lunch history and conversation content.

[0032] The suggestion unit can analyze the employee's health data and suggest the communication partner according to the health condition. The suggestion unit, for example, analyzes the employee's step count data and pairs employees who exercise less to promote a healthy lifestyle. For example, it suggests employees who walk less as lunch partners. The suggestion unit also analyzes the employee's sleep data and pairs employees who are sleep-deprived to promote relaxed communication. For example, it suggests employees who sleep less as lunch partners. The suggestion unit also comprehensively analyzes the employee's health data and suggests communication partners according to the employee's health condition. For example, it suggests employees who exercise more balanced with their sleep duration as lunch partners. This makes it possible to suggest optimal communication partners based on the employee's health data.

[0033] The suggestion unit can analyze an employee's hobbies or interests and suggest people who share the same hobbies. For example, the suggestion unit extracts hobbies and interests from the employee's profile information and suggests people who share the same hobbies. For example, it suggests employees who share the same sports or music interests as lunch partners. The suggestion unit also analyzes the content posted on the employee's internal SNS or communication tool to suggest people who share the same hobbies. For example, it suggests employees who share the same topics about movies or books as lunch partners. The suggestion unit also analyzes the employee's survey results and self-introductions to suggest people who share the same hobbies. For example, it suggests employees who share the same travel destinations or hobby activities as lunch partners. In this way, it is possible to suggest people who share the same hobbies based on the employee's hobbies and interests.

[0034] The suggestion unit can analyze employees' attendance histories and prioritize pairing employees who come to the office less frequently. The suggestion unit, for example, stores employees' attendance histories in a database and prioritizes pairing employees who come to the office less frequently. For example, it suggests employees who come to the office less often as lunch partners. The suggestion unit also analyzes the attendance histories and pairs employees who come to the office less frequently, thereby increasing their motivation to come to the office. For example, it suggests employees who come to the office less frequently as lunch partners, increasing the enjoyment of coming to the office. The suggestion unit also increases opportunities for employees to come to the office by pairing employees who come to the office less frequently based on their attendance histories. For example, it suggests employees who come to the office less frequently as lunch partners, helping them develop a habit of coming to the office. In this way, it increases the motivation to come to the office by preferentially pairing employees who come to the office less frequently.

[0035] The suggestion unit can analyze an employee's career goals and suggest people who can help them achieve those goals. For example, the suggestion unit registers the employee's career goals in a database and suggests people who can help them achieve their goals. For example, it suggests employees who have the same career goals as lunch partners. The suggestion unit also analyzes career goals and suggests people who have skills and experience that can help them achieve their goals. For example, it suggests employees who have project experience related to the career goals as lunch partners. The suggestion unit also suggests people who can help them achieve their goals based on the career goals. For example, it suggests employees who have skills related to the career goals as lunch partners. In this way, it is possible to suggest people who can help them achieve their goals based on the employee's career goals.

[0036] The proposal department can analyze the project progress status of employees in real time and propose the partner according to the needs of the project. For example, the proposal department analyzes the project progress status of employees in real time and proposes the partner according to the project needs. For example, if the project progress is behind schedule, it proposes employees who need support. The proposal department also analyzes the project progress status and proposes partners who have skills according to the project needs. For example, it proposes employees who are suitable for a project that requires specific technical skills. The proposal department also proposes partners according to the project needs based on the project progress status. For example, if the project progress is going well, it proposes employees who can provide new ideas. This makes it possible to propose the optimal partner based on the project progress status.

[0037] The proposal department can pair employees from different departments together to generate new perspectives and ideas. For example, the proposal department can pair employees from different departments together to generate new perspectives and ideas. For example, the proposal department can suggest that employees from the engineering department and the sales department be lunch partners. The proposal department can also pair employees from different departments together to promote communication between departments. For example, the proposal department can suggest that employees from the marketing department and the development department be lunch partners. The proposal department can also pair employees from different departments together to strengthen cooperation between departments. For example, the proposal department can suggest that employees from the human resources department and the finance department be lunch partners. In this way, by pairing employees from different departments together, new perspectives and ideas can be generated.

[0038] The suggestion unit can analyze employees' working hours and suggest people who work during the same hours. For example, the suggestion unit registers employees' working hours in a database and suggests people who work during the same hours. For example, it suggests employees who work the same shift as lunch partners. The suggestion unit also analyzes working hours and suggests people who work during the same hours, thereby increasing opportunities for communication. For example, it suggests employees who work during the same hours as lunch partners. The suggestion unit also promotes communication during working hours by suggesting people who work during the same hours based on the working hours. For example, it suggests employees who work during the same hours as lunch partners. This makes it possible to suggest the most suitable partners based on the employees' working hours.

[0039] The lunch hosting unit can suggest lunch menus or locations based on the employee's preferences. For example, the lunch hosting unit registers the employee's preferences in a database and suggests lunch menus and locations. For example, it suggests restaurants that offer vegetarian menus for vegetarian employees. The lunch hosting unit also analyzes the employee's past lunch history and suggests lunch menus and locations that match the employee's preferences. For example, it may suggest restaurants that have received high ratings in the past. The lunch hosting unit also suggests lunch menus and locations that match the employee's preferences based on the results of an employee survey. For example, it suggests restaurants that serve a particular dish for an employee who likes that dish. In this way, it is possible to suggest lunch menus and locations based on the employee's preferences.

[0040] The lunch scheduling unit can optimize lunch time slots to match the employee's schedule. For example, the lunch scheduling unit registers the employee's schedule in a database and suggests the optimal lunch time slot. For example, it adjusts lunch times taking into account the progress of meetings and projects. The lunch scheduling unit also analyzes the employee's calendar information and suggests lunch time slots that match the schedule. For example, it sets lunch time during a time slot with a lot of free time. The lunch scheduling unit also suggests the optimal lunch time slot based on the employee's working hours. For example, it sets lunch time during the most relaxing time slot during working hours. This makes it possible to optimize lunch time slots based on the employee's schedule.

[0041] The lunch organizing section can suggest an activity to be done after lunch, thereby further deepening the communication. For example, the lunch organizing section can suggest a short workshop as an activity to be done after lunch. For example, a team building workshop can be held. The lunch organizing section can also suggest a game as an activity to be done after lunch. For example, communication can be deepened through a simple quiz or board game. The lunch organizing section can also suggest a relaxing activity to be done after lunch. For example, a meditation or stretching session can be held. In this way, by suggesting an activity after lunch, communication can be further deepened.

[0042] The lunch organizing department can pair employees with different cultures or backgrounds to promote the intercultural exchange. The lunch organizing department, for example, promotes intercultural exchange by pairing employees with different cultures and backgrounds. For example, it may suggest employees involved in international projects as lunch partners. The lunch organizing department also deepens cultural understanding by pairing employees with different cultures and backgrounds. For example, it may suggest employees with different nationalities or languages ​​as lunch partners. The lunch organizing department also fosters a culture that respects diversity by pairing employees with different cultures and backgrounds. For example, it may suggest employees with different religions or values ​​as lunch partners. In this way, it is possible to promote intercultural exchange by pairing employees with different cultures and backgrounds.

[0043] The lunch hosting unit can set the lunch location to the cafe or restaurant outside the office to enhance the refreshing effect. The lunch hosting unit, for example, enhances the refreshing effect by setting the lunch location to a cafe or restaurant outside the office. For example, it can suggest popular cafes or restaurants in the neighborhood. Furthermore, by setting the lunch location outside the office, the lunch hosting unit provides an environment where the employee can relax away from their daily work. For example, it can suggest a restaurant in a park or along the beach. Furthermore, by setting the lunch location outside the office, the lunch hosting unit can promote communication in a new environment. For example, it can suggest a restaurant or cafe with a different atmosphere. In this way, the refreshing effect can be enhanced by setting the lunch location outside the office.

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

[0045] The proposal department can analyze employees' past project experience and suggest people with extensive project experience. For example, the proposal department can suggest employees with experience in successful projects as lunch partners. The proposal department can also suggest employees with project experience related to specific technologies or skills. For example, the proposal department can suggest employees with specific programming languages ​​or design skills as lunch partners. The proposal department can also generate new perspectives and ideas by pairing employees with different project experiences. For example, the proposal department can suggest employees with project experience in different industries or fields as lunch partners. This makes it possible to suggest optimal communication partners based on employees' project experience.

[0046] The suggestion unit can analyze an employee's educational background and specialized knowledge and suggest people who can share that specialized knowledge. For example, the suggestion unit can suggest employees who graduated from the same university or vocational school as lunch partners. The suggestion unit can also suggest employees who have specific specialized knowledge or qualifications. For example, it can suggest employees who have passed the same qualification exam as lunch partners. The suggestion unit can also promote the sharing of new knowledge by pairing employees with different specialized knowledge. For example, it can suggest employees with specialized knowledge in different fields as lunch partners. This makes it possible to suggest optimal communication partners based on an employee's educational background and specialized knowledge.

[0047] The suggestion unit can analyze the working styles of employees and suggest communication partners according to the working styles. For example, the suggestion unit suggests employees who primarily work remotely as lunch partners. The suggestion unit can also suggest employees who use a flextime system as lunch partners. For example, the suggestion unit suggests employees who work during the same hours as lunch partners. The suggestion unit can also generate ideas for new work styles by combining employees with different working styles. For example, the suggestion unit suggests employees who have a working style that combines remote work and office work as lunch partners. This makes it possible to suggest optimal communication partners based on employees' working styles.

[0048] The suggestion unit can analyze the career path of an employee and suggest the communication partner according to the career path. For example, the suggestion unit suggests employees who are on the same career path as lunch partners. The suggestion unit can also suggest employees who have different career paths as lunch partners. For example, it suggests employees in different departments or positions as lunch partners. The suggestion unit can also explore new career possibilities by combining employees with different career paths. For example, it suggests employees with different career paths as lunch partners. This makes it possible to suggest the optimal communication partner based on the employee's career path.

[0049] The suggestion unit can analyze the employee's lifestyle and suggest communication partners according to the lifestyle. For example, the suggestion unit suggests employees with the same lifestyle as lunch partners. The suggestion unit can also suggest employees with different lifestyles as lunch partners. For example, it suggests employees with the same hobbies or interests as lunch partners. The suggestion unit can also generate new lifestyle ideas by combining employees with different lifestyles. For example, it suggests employees with different lifestyles as lunch partners. This makes it possible to suggest optimal communication partners based on the employee's lifestyle.

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

[0051] Step 1: The information gathering department collects open information from within the company, such as posts on internal bulletin boards, project progress reports, and employee profile information (such as skill sets and job titles). Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it uses text mining, data mining, and sentiment analysis technologies to analyze posts on internal bulletin boards, project progress reports, and employee profile information. Step 3: The proposal unit proposes optimal communication partners based on the information analyzed by the analysis unit. For example, it proposes communication partners taking into consideration skill complementarity, project relevance, and employee interests and hobbies. Step 4: The Lunch Organizing Team organizes a shuffle lunch among the communication partners proposed by the Proposal Team. For example, they propose the location, menu, and time of the lunch.

[0052] (Example 2) The communication activation system according to the embodiment of the present invention is a system that utilizes a generative AI to activate communication between employees and encourage them to come to work. As a result, the communication activation system can activate communication between employees and encourage them to come to work.

[0053] A communication activation system according to an embodiment includes an information collection unit, an analysis unit, a proposal unit, and a lunch hosting unit. The information collection unit collects open information within a company. For example, it collects posts on an internal bulletin board. The information collection unit can also collect project progress reports. The information collection unit can also collect employee profile information. For example, it collects employee skill sets and job title information. The analysis unit analyzes the information collected by the information collection unit. For example, it analyzes the information using text mining technology. The analysis unit can also analyze the information using data mining technology. The analysis unit can also analyze the information using emotion analysis technology. For example, it analyzes posts on an internal bulletin board using text mining technology. The data mining technology analyzes project progress reports to understand the progress of the project. The emotion analysis technology analyzes employee profile information to understand the employee's emotional state. The proposal unit proposes optimal communication partners based on the information analyzed by the analysis unit. For example, the proposal unit proposes communication partners taking into account skill complementarity. The proposal department can also suggest communication partners by taking into account the relevance of the project. The proposal department can also suggest communication partners by taking into account the interests and hobbies of employees. For example, the proposal department can suggest employees with programming skills and employees with design skills by taking into account the complementarity of their skills. The proposal department can suggest employees involved in the same project by taking into account the relevance of the project. The proposal department can suggest employees with the same hobbies by taking into account the interests and hobbies of employees. The lunch hosting department can host a shuffle lunch with the communication partners suggested by the proposal department. For example, the lunch hosting department can suggest a place to have lunch with the suggested employees. The lunch hosting department can also suggest a lunch menu. The lunch hosting department can also suggest a lunch time. For example, the lunch hosting department can suggest lunch in the cafeteria. For the lunch menu, it can suggest a menu that suits the employee's preferences. For the lunch time, it can suggest a time that suits the employee's schedule.As a result, the communication activation system according to the embodiment can activate communication between employees and encourage them to come to work. For example, when an employee comes to work and has lunch with someone suggested by the generation AI, spontaneous communication is activated. Employees can share new perspectives and ideas with each other. Coming to work increases direct communication and strengthens team cohesion.

[0054] The information collection unit can collect employee profile information or project progress. The information collection unit, for example, collects employee profile information. For example, it collects information such as the employee's name, job title, skill set, and hobbies. The information collection unit can also collect project progress. For example, it collects the completion status of project tasks and the project status. This allows detailed information about employees to be collected and the most suitable communication partners to be suggested.

[0055] The suggestion unit can analyze the employee's emotional state in real time and suggest a communication partner at the optimal timing. For example, to analyze the employee's emotional state in real time, the suggestion unit uses facial recognition technology to analyze facial expressions and detect changes in emotions. For example, the suggestion unit monitors the employee's facial expressions through a camera to detect signs of stress or fatigue. In addition, to analyze the employee's emotional state, the suggestion unit uses voice recognition technology to analyze the tone and speed of conversation and detect changes in emotions. For example, audio data from telephone or video conference calls is analyzed to grasp changes in emotions in real time. In addition, to analyze the employee's emotional state, the suggestion unit uses a wearable device to monitor heart rate and electrodermal activity and detect changes in emotions. For example, heart rate fluctuations are analyzed through a smartwatch to evaluate stress levels. This makes it possible to suggest a communication partner at the optimal timing based on the employee's emotional state.

[0056] The suggestion unit can analyze the employee's past lunch history or conversation content to suggest compatible partners. The suggestion unit, for example, stores the employee's past lunch history in a database and analyzes common lunch partners and frequency. For example, it prioritizes suggesting people with whom the employee has had lunch many times in the past. The suggestion unit also performs text analysis of the employee's past conversation content to suggest partners with common topics and interests. For example, it prioritizes suggesting people with common hobbies and interests. The suggestion unit also comprehensively analyzes the employee's past lunch history and conversation content to suggest compatible partners. For example, it identifies compatible partners from the content of conversations at past lunches and suggests them as the next lunch partner. This makes it possible to suggest compatible partners based on the employee's past lunch history and conversation content.

[0057] The suggestion unit can analyze the employee's health data and suggest the communication partner according to the health condition. The suggestion unit, for example, analyzes the employee's step count data and pairs employees who exercise less to promote a healthy lifestyle. For example, it suggests employees who walk less as lunch partners. The suggestion unit also analyzes the employee's sleep data and pairs employees who are sleep-deprived to promote relaxed communication. For example, it suggests employees who sleep less as lunch partners. The suggestion unit also comprehensively analyzes the employee's health data and suggests communication partners according to the employee's health condition. For example, it suggests employees who exercise more balanced with their sleep duration as lunch partners. This makes it possible to suggest optimal communication partners based on the employee's health data.

[0058] The suggestion unit can analyze an employee's hobbies or interests and suggest people who share the same hobbies. For example, the suggestion unit extracts hobbies and interests from the employee's profile information and suggests people who share the same hobbies. For example, it suggests employees who share the same sports or music interests as lunch partners. The suggestion unit also analyzes the content posted on the employee's internal SNS or communication tool to suggest people who share the same hobbies. For example, it suggests employees who share the same topics about movies or books as lunch partners. The suggestion unit also analyzes the employee's survey results and self-introductions to suggest people who share the same hobbies. For example, it suggests employees who share the same travel destinations or hobby activities as lunch partners. In this way, it is possible to suggest people who share the same hobbies based on the employee's hobbies and interests.

[0059] The suggestion unit can analyze employees' attendance histories and prioritize pairing employees who come to the office less frequently. The suggestion unit, for example, stores employees' attendance histories in a database and prioritizes pairing employees who come to the office less frequently. For example, it suggests employees who come to the office less often as lunch partners. The suggestion unit also analyzes the attendance histories and pairs employees who come to the office less frequently, thereby increasing their motivation to come to the office. For example, it suggests employees who come to the office less frequently as lunch partners, increasing the enjoyment of coming to the office. The suggestion unit also increases opportunities for employees to come to the office by pairing employees who come to the office less frequently based on their attendance histories. For example, it suggests employees who come to the office less frequently as lunch partners, helping them develop a habit of coming to the office. In this way, it increases the motivation to come to the office by preferentially pairing employees who come to the office less frequently.

[0060] The suggestion unit can use the emotion estimation function to suggest the optimal communication partner based on the emotional state of the employee. For example, the suggestion unit uses the emotion estimation function to analyze the emotional state of the employee in real time and suggest the emotionally optimal communication partner. For example, the suggestion unit suggests employees who have positive emotions as lunch partners. The suggestion unit also uses the emotion estimation function to analyze the emotional state of the employee and suggest communication partners who are emotionally balanced. For example, the suggestion unit pairs an employee who is feeling stressed with an employee who is relaxed. The suggestion unit also uses the emotion estimation function to analyze the emotional state of the employee and match employees who need emotional support. For example, the suggestion unit suggests employees who have negative emotions as lunch partners so that they can support each other. In this way, the emotion estimation function can be used to suggest the optimal communication partner based on the emotional state of the employee.

[0061] The suggestion unit can analyze an employee's career goals and suggest people who can help them achieve those goals. For example, the suggestion unit registers the employee's career goals in a database and suggests people who can help them achieve their goals. For example, it suggests employees who have the same career goals as lunch partners. The suggestion unit also analyzes career goals and suggests people who have skills and experience that can help them achieve their goals. For example, it suggests employees who have project experience related to the career goals as lunch partners. The suggestion unit also suggests people who can help them achieve their goals based on the career goals. For example, it suggests employees who have skills related to the career goals as lunch partners. In this way, it is possible to suggest people who can help them achieve their goals based on the employee's career goals.

[0062] The proposal department can analyze the project progress status of employees in real time and propose the partner according to the needs of the project. For example, the proposal department analyzes the project progress status of employees in real time and proposes the partner according to the project needs. For example, if the project progress is behind schedule, it proposes employees who need support. The proposal department also analyzes the project progress status and proposes partners who have skills according to the project needs. For example, it proposes employees who are suitable for a project that requires specific technical skills. The proposal department also proposes partners according to the project needs based on the project progress status. For example, if the project progress is going well, it proposes employees who can provide new ideas. This makes it possible to propose the optimal partner based on the project progress status.

[0063] The proposal department can pair employees from different departments together to generate new perspectives and ideas. For example, the proposal department can pair employees from different departments together to generate new perspectives and ideas. For example, the proposal department can suggest that employees from the engineering department and the sales department be lunch partners. The proposal department can also pair employees from different departments together to promote communication between departments. For example, the proposal department can suggest that employees from the marketing department and the development department be lunch partners. The proposal department can also pair employees from different departments together to strengthen cooperation between departments. For example, the proposal department can suggest that employees from the human resources department and the finance department be lunch partners. In this way, by pairing employees from different departments together, new perspectives and ideas can be generated.

[0064] The suggestion unit can analyze employees' working hours and suggest people who work during the same hours. For example, the suggestion unit registers employees' working hours in a database and suggests people who work during the same hours. For example, it suggests employees who work the same shift as lunch partners. The suggestion unit also analyzes working hours and suggests people who work during the same hours, thereby increasing opportunities for communication. For example, it suggests employees who work during the same hours as lunch partners. The suggestion unit also promotes communication during working hours by suggesting people who work during the same hours based on the working hours. For example, it suggests employees who work during the same hours as lunch partners. This makes it possible to suggest the most suitable partners based on the employees' working hours.

[0065] The suggestion unit can use the emotion estimation function to suggest the optimal communication partner based on the emotional state of the employee. For example, the suggestion unit uses the emotion estimation function to analyze the emotional state of the employee in real time and suggest the emotionally optimal communication partner. For example, the suggestion unit suggests employees who have positive emotions as lunch partners. The suggestion unit also uses the emotion estimation function to analyze the emotional state of the employee and suggest communication partners who are emotionally balanced. For example, the suggestion unit pairs an employee who is feeling stressed with an employee who is relaxed. The suggestion unit also uses the emotion estimation function to analyze the emotional state of the employee and match employees who need emotional support. For example, the suggestion unit suggests employees who have negative emotions as lunch partners so that they can support each other. In this way, the emotion estimation function can be used to suggest the optimal communication partner based on the emotional state of the employee.

[0066] The lunch hosting unit can suggest lunch menus or locations based on the employee's preferences. For example, the lunch hosting unit registers the employee's preferences in a database and suggests lunch menus and locations. For example, it suggests restaurants that offer vegetarian menus for vegetarian employees. The lunch hosting unit also analyzes the employee's past lunch history and suggests lunch menus and locations that match the employee's preferences. For example, it may suggest restaurants that have received high ratings in the past. The lunch hosting unit also suggests lunch menus and locations that match the employee's preferences based on the results of an employee survey. For example, it suggests restaurants that serve a particular dish for an employee who likes that dish. In this way, it is possible to suggest lunch menus and locations based on the employee's preferences.

[0067] The lunch scheduling unit can optimize lunch time slots to match the employee's schedule. For example, the lunch scheduling unit registers the employee's schedule in a database and suggests the optimal lunch time slot. For example, it adjusts lunch times taking into account the progress of meetings and projects. The lunch scheduling unit also analyzes the employee's calendar information and suggests lunch time slots that match the schedule. For example, it sets lunch time during a time slot with a lot of free time. The lunch scheduling unit also suggests the optimal lunch time slot based on the employee's working hours. For example, it sets lunch time during the most relaxing time slot during working hours. This makes it possible to optimize lunch time slots based on the employee's schedule.

[0068] The lunch organizing section can suggest an activity to be done after lunch, thereby further deepening the communication. For example, the lunch organizing section can suggest a short workshop as an activity to be done after lunch. For example, a team building workshop can be held. The lunch organizing section can also suggest a game as an activity to be done after lunch. For example, communication can be deepened through a simple quiz or board game. The lunch organizing section can also suggest a relaxing activity to be done after lunch. For example, a meditation or stretching session can be held. In this way, by suggesting an activity after lunch, communication can be further deepened.

[0069] The lunch organizing department can pair employees with different cultures or backgrounds to promote the intercultural exchange. The lunch organizing department, for example, promotes intercultural exchange by pairing employees with different cultures and backgrounds. For example, it may suggest employees involved in international projects as lunch partners. The lunch organizing department also deepens cultural understanding by pairing employees with different cultures and backgrounds. For example, it may suggest employees with different nationalities or languages ​​as lunch partners. The lunch organizing department also fosters a culture that respects diversity by pairing employees with different cultures and backgrounds. For example, it may suggest employees with different religions or values ​​as lunch partners. In this way, it is possible to promote intercultural exchange by pairing employees with different cultures and backgrounds.

[0070] The lunch hosting unit can set the lunch location to the cafe or restaurant outside the office to enhance the refreshing effect. The lunch hosting unit, for example, enhances the refreshing effect by setting the lunch location to a cafe or restaurant outside the office. For example, it can suggest popular cafes or restaurants in the neighborhood. Furthermore, by setting the lunch location outside the office, the lunch hosting unit provides an environment where the employee can relax away from their daily work. For example, it can suggest a restaurant in a park or along the beach. Furthermore, by setting the lunch location outside the office, the lunch hosting unit can promote communication in a new environment. For example, it can suggest a restaurant or cafe with a different atmosphere. In this way, the refreshing effect can be enhanced by setting the lunch location outside the office.

[0071] The lunch hosting unit can use the emotion estimation function to suggest the optimal lunch partner and location based on the employee's emotional state. For example, the lunch hosting unit can use the emotion estimation function to analyze the employee's emotional state in real time and suggest the emotionally optimal lunch partner and location. For example, it can suggest a lunch in a relaxing environment. The lunch hosting unit can also use the emotion estimation function to analyze the employee's emotional state and suggest an emotionally balanced lunch partner and location. For example, it can suggest a relaxing location for an employee who is feeling stressed. The lunch hosting unit can also use the emotion estimation function to analyze the employee's emotional state and suggest a lunch partner and location for an employee who needs emotional support. For example, it can suggest a supportive partner and location for an employee who is feeling negative emotions. In this way, the emotion estimation function can be used to suggest the optimal lunch partner and location based on the employee's emotional state.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The proposal department can analyze employees' past project experience and suggest people with extensive project experience. For example, the proposal department can suggest employees with experience in successful projects as lunch partners. The proposal department can also suggest employees with project experience related to specific technologies or skills. For example, the proposal department can suggest employees with specific programming languages ​​or design skills as lunch partners. The proposal department can also generate new perspectives and ideas by pairing employees with different project experiences. For example, the proposal department can suggest employees with project experience in different industries or fields as lunch partners. This makes it possible to suggest optimal communication partners based on employees' project experience.

[0074] The suggestion unit can analyze an employee's educational background and specialized knowledge and suggest people who can share that specialized knowledge. For example, the suggestion unit can suggest employees who graduated from the same university or vocational school as lunch partners. The suggestion unit can also suggest employees who have specific specialized knowledge or qualifications. For example, it can suggest employees who have passed the same qualification exam as lunch partners. The suggestion unit can also promote the sharing of new knowledge by pairing employees with different specialized knowledge. For example, it can suggest employees with specialized knowledge in different fields as lunch partners. This makes it possible to suggest optimal communication partners based on an employee's educational background and specialized knowledge.

[0075] The suggestion unit can analyze the working styles of employees and suggest communication partners according to the working styles. For example, the suggestion unit suggests employees who primarily work remotely as lunch partners. The suggestion unit can also suggest employees who use a flextime system as lunch partners. For example, the suggestion unit suggests employees who work during the same hours as lunch partners. The suggestion unit can also generate ideas for new work styles by combining employees with different working styles. For example, the suggestion unit suggests employees who have a working style that combines remote work and office work as lunch partners. This makes it possible to suggest optimal communication partners based on employees' working styles.

[0076] The suggestion unit can analyze the career path of an employee and suggest the communication partner according to the career path. For example, the suggestion unit suggests employees who are on the same career path as lunch partners. The suggestion unit can also suggest employees who have different career paths as lunch partners. For example, it suggests employees in different departments or positions as lunch partners. The suggestion unit can also explore new career possibilities by combining employees with different career paths. For example, it suggests employees with different career paths as lunch partners. This makes it possible to suggest the optimal communication partner based on the employee's career path.

[0077] The suggestion unit can analyze the employee's lifestyle and suggest communication partners according to the lifestyle. For example, the suggestion unit suggests employees with the same lifestyle as lunch partners. The suggestion unit can also suggest employees with different lifestyles as lunch partners. For example, it suggests employees with the same hobbies or interests as lunch partners. The suggestion unit can also generate new lifestyle ideas by combining employees with different lifestyles. For example, it suggests employees with different lifestyles as lunch partners. This makes it possible to suggest optimal communication partners based on the employee's lifestyle.

[0078] The suggestion unit can analyze the emotional state of the employee and suggest the communication partner according to the emotional state. For example, the suggestion unit suggests employees who have positive emotions as lunch partners. The suggestion unit can also suggest employees who are feeling stressed as lunch partners. For example, the suggestion unit suggests employees who are feeling stressed as lunch partners. The suggestion unit can also provide emotional support by matching employees with different emotional states. For example, the suggestion unit suggests an employee who is relaxed and an employee who is feeling stressed as lunch partners. This makes it possible to suggest the optimal communication partner based on the emotional state of the employee.

[0079] The suggestion unit can analyze the emotional state of the employee and suggest the communication partner according to the emotional state. For example, the suggestion unit suggests employees who have positive emotions as lunch partners. The suggestion unit can also suggest employees who are feeling stressed as lunch partners. For example, the suggestion unit suggests employees who are feeling stressed as lunch partners. The suggestion unit can also provide emotional support by matching employees with different emotional states. For example, the suggestion unit suggests an employee who is relaxed and an employee who is feeling stressed as lunch partners. This makes it possible to suggest the optimal communication partner based on the emotional state of the employee.

[0080] The suggestion unit can analyze the emotional state of the employee and suggest the communication partner according to the emotional state. For example, the suggestion unit suggests employees who have positive emotions as lunch partners. The suggestion unit can also suggest employees who are feeling stressed as lunch partners. For example, the suggestion unit suggests employees who are feeling stressed as lunch partners. The suggestion unit can also provide emotional support by matching employees with different emotional states. For example, the suggestion unit suggests an employee who is relaxed and an employee who is feeling stressed as lunch partners. This makes it possible to suggest the optimal communication partner based on the emotional state of the employee.

[0081] The suggestion unit can analyze the emotional state of the employee and suggest the communication partner according to the emotional state. For example, the suggestion unit suggests employees who have positive emotions as lunch partners. The suggestion unit can also suggest employees who are feeling stressed as lunch partners. For example, the suggestion unit suggests employees who are feeling stressed as lunch partners. The suggestion unit can also provide emotional support by matching employees with different emotional states. For example, the suggestion unit suggests an employee who is relaxed and an employee who is feeling stressed as lunch partners. This makes it possible to suggest the optimal communication partner based on the emotional state of the employee.

[0082] The suggestion unit can analyze the emotional state of the employee and suggest the communication partner according to the emotional state. For example, the suggestion unit suggests employees who have positive emotions as lunch partners. The suggestion unit can also suggest employees who are feeling stressed as lunch partners. For example, the suggestion unit suggests employees who are feeling stressed as lunch partners. The suggestion unit can also provide emotional support by matching employees with different emotional states. For example, the suggestion unit suggests an employee who is relaxed and an employee who is feeling stressed as lunch partners. This makes it possible to suggest the optimal communication partner based on the emotional state of the employee.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The information gathering department collects open information from within the company, such as posts on internal bulletin boards, project progress reports, and employee profile information (such as skill sets and job titles). Step 2: The analysis unit analyzes the information collected by the information collection unit. For example, it uses text mining, data mining, and sentiment analysis technologies to analyze posts on internal bulletin boards, project progress reports, and employee profile information. Step 3: The proposal unit proposes optimal communication partners based on the information analyzed by the analysis unit. For example, it proposes communication partners taking into consideration skill complementarity, project relevance, and employee interests and hobbies. Step 4: The Lunch Organizing Team organizes a shuffle lunch among the communication partners proposed by the Proposal Team. For example, they propose the location, menu, and time of the lunch.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0091] 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.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] 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.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] 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.

[0125] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] 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.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] 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.

[0137] 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).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0139] 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."

[0140] 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.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] 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.

[0148] 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.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] 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.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Information Gathering Department collects open information from within the company, an analysis unit that analyzes the information collected by the information collection unit; a suggestion unit that suggests an optimal communication partner based on the information analyzed by the analysis unit; a lunch hosting unit that hosts a shuffle lunch among the communication partners proposed by the proposal unit. A system characterized by:

2. The information collecting unit Collect employee profile information or project status 2. The system of claim 1.

3. The proposal unit Analyze employees' emotional state in real time and suggest the right communication partner at the right time 2. The system of claim 1.

4. The proposal unit Analyze employees' past lunch history or conversations to suggest compatible partners 2. The system of claim 1.

5. The proposal unit Analyze employee health data and suggest communication partners based on their health status 2. The system of claim 1.

6. The proposal unit Analyze employees' hobbies or interests and suggest people with common interests 2. The system of claim 1.

7. The proposal unit Analyze employee attendance history and prioritize pairing employees who come to the office less frequently.

2. The system of claim 1.

8. The proposal unit Suggesting the most appropriate communication partner based on the employee's emotional state 2. The system of claim 1.

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