system

Abstract

The system of the embodiment aims to stimulate communication between employees and maximize the benefits of free address systems. [Solution] A system according to an embodiment includes a collection unit, a proposal unit, and an operation unit. The collection unit collects open information within a company. The proposal unit analyzes the information collected by the collection unit and proposes communication partners. The operation unit runs a shuffle lunch with the partners proposed by the proposal unit.

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 communication between employees as more people worked from home, and the benefits of free address systems could not be fully utilized.

[0005] The system of the embodiment aims to stimulate communication between employees and maximize the benefits of free address systems. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a proposal unit, and an operation unit. The collection unit collects open information within the company. The proposal unit analyzes the information collected by the collection unit and proposes communication partners. The operation unit runs a shuffle lunch with the partners proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can stimulate communication between employees and maximize the benefits of free address systems. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication activation system according to an embodiment of the present invention utilizes a generation AI to encourage employees who frequently work from home to come to the office and to stimulate communication among employees. This system collects open information within the company, and the generation AI suggests the optimal communication partner for each occasion. Next, a shuffle lunch is held with the suggested partner. This shuffle lunch is subsidized by the company. Employees who come to the office have lunch with the partner suggested by the generation AI, stimulating spontaneous communication and enabling them to work well while stimulating each other. For example, open information within the company is collected, including employee project information, skill sets, and past work history. This information is input into the generation AI. Next, the generation AI analyzes the collected information and suggests the optimal communication partner for each occasion. For example, employees involved in different projects or employees with different skill sets are often suggested. This increases opportunities for employees with different perspectives and knowledge to interact with each other. A shuffle lunch is held with the suggested partner. This shuffle lunch is subsidized by the company, and employees can enjoy the lunch for free. For example, by having lunch with someone suggested by the generation AI, employees who normally have no contact with each other can interact and share new ideas and knowledge. This system can encourage employees who often work from home to come to the office and stimulate communication between employees. For example, coming to the office increases direct communication and strengthens team cohesion. In addition, interaction between employees with different perspectives and knowledge increases the likelihood of new ideas and innovations being born. Furthermore, shuffled lunches stimulate spontaneous communication. For example, casual conversations over lunch can sometimes lead to the birth of new projects. In this way, employees can inspire each other and do good work. In this way, the communication activation system can encourage employees who often work from home to come to the office and stimulate communication between employees.

[0029] A communication activation system according to an embodiment includes a collection unit, a proposal unit, and an operation unit. The collection unit collects open information within the company. The open information within the company includes, for example, employee project information, skill sets, and past work history, but is not limited to these examples. The collection unit, for example, collects employee project information. The project information includes the project's objectives, progress, participating members, and the like. The collection unit, for example, collects employee skill sets. The skill sets include programming languages, management skills, specialized knowledge, and the like. The collection unit, for example, collects employee past work history. The work history includes past projects, assigned tasks, results, and the like. The proposal unit analyzes the information collected by the collection unit and proposes optimal communication partners. The proposal unit, for example, analyzes the collected information and proposes pairs of employees involved in different projects. The proposal unit, for example, analyzes the collected information and proposes pairs of employees with different skill sets. The proposal unit, for example, analyzes the collected information and proposes pairs of employees with different perspectives and knowledge. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input collected information into a generation AI and make a suggestion using a generation AI model that suggests optimal communication partners. The management unit manages a shuffle lunch with the partners suggested by the suggestion unit. For example, the management unit holds a shuffle lunch with the suggested partners. The shuffle lunch is subsidized by the company, so employees can enjoy the lunch for free. For example, the management unit manages the specific management method of the shuffle lunch. For example, the management unit manages the schedule of the shuffle lunch. For example, the management unit selects participants for the shuffle lunch. For example, the management unit determines the location of the shuffle lunch. In this way, the communication activation system according to the embodiment can activate communication between employees. Some or all of the above-described processing in the management unit may be performed using a generation AI, or may be performed without using a generation AI.For example, the operations department can input the shuffle lunch schedule into a generative AI and use the generative AI model to suggest the optimal schedule.

[0030] The collection unit can collect information on employees' project information or skill sets, and past work history. The collection unit, for example, collects employees' project information. The project information includes the project's objectives, progress, participating members, etc. The collection unit, for example, collects employees' skill sets. The skill sets include programming languages, management skills, specialized knowledge, etc. The collection unit, for example, collects employees' past work history. The work history includes past projects, assigned tasks, results, etc. By collecting information such as employees' project information, skill sets, and past work history, more appropriate communication partners can be suggested. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input employees' project information into the generation AI and have the generation AI analyze the project information.

[0031] The suggestion unit can analyze the collected information and suggest pairs of employees involved in different projects or pairs of employees with different skill sets. The suggestion unit, for example, analyzes the collected information and suggests pairs of employees involved in different projects. The suggestion unit, for example, analyzes the collected information and suggests pairs of employees with different skill sets. The suggestion unit, for example, analyzes the collected information and suggests pairs of employees with different perspectives and knowledge. In this way, by suggesting pairs of employees involved in different projects or skill sets, opportunities for employees with different perspectives and knowledge to interact with each other increase. Some or all of the above-described processing in the suggestion unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the suggestion unit can input the collected information into a generative AI and make suggestions using a generative AI model that suggests optimal communication partners.

[0032] The operations department can host a shuffle lunch with the proposed partners and manage company subsidies. For example, the operations department hosts a shuffle lunch with the proposed partners. The shuffle lunch is subsidized by the company, allowing employees to enjoy the lunch for free. For example, the operations department manages the specific operation methods of the shuffle lunch. For example, the operations department manages the schedule of the shuffle lunch. For example, the operations department selects participants for the shuffle lunch. For example, the operations department determines the location of the shuffle lunch. In this way, by hosting the shuffle lunch and managing company subsidies, spontaneous communication between employees is stimulated. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the shuffle lunch schedule into a generation AI and operate the event using a generation AI model that proposes an optimal schedule.

[0033] The suggestion unit can set the frequency of suggestions or selection criteria. The suggestion unit, for example, sets the frequency of suggestions. The frequency of suggestions includes once a week, once a month, etc. The suggestion unit, for example, sets the selection criteria. The selection criteria include project relevance, complementarity of skill sets, etc. By setting the frequency of suggestions and the selection criteria, more effective suggestions of communication partners can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the frequency of suggestions and selection criteria into the generation AI and make suggestions using a generation AI model that makes optimal suggestions.

[0034] The operations department can manage the specific operation methods of the shuffle lunch. The operations department, for example, manages the schedule of the shuffle lunch. The operations department, for example, selects participants for the shuffle lunch. The operations department, for example, determines the location of the shuffle lunch. By managing the specific operation methods of the shuffle lunch, the lunch can be held smoothly. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the schedule of the shuffle lunch into a generation AI and operate the event using a generation AI model that proposes an optimal schedule.

[0035] The collection unit can analyze the employee's past communication history and select an information collection method. For example, the collection unit collects information by prioritizing communication tools that the employee has used favorably in the past. For example, the collection unit collects information from people with whom the employee has frequently interacted in the past. For example, the collection unit prioritizes collection of related information based on the history of projects in which the employee has participated in the past. This enables more effective information collection by analyzing the past communication history. Some or all of the above-mentioned processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect information by inputting the employee's past communication history into a generation AI and using a generation AI model that selects the optimal information collection method.

[0036] When collecting information, the collection unit can filter the information based on the employee's current project progress or areas of interest. For example, the collection unit prioritizes collecting information related to projects currently underway by the employee. For example, the collection unit filters and collects related information based on the employee's areas of interest. For example, the collection unit collects information related to tasks currently being undertaken by the employee. By filtering information based on the current project progress or areas of interest, more relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect information by inputting the employee's project progress or areas of interest into a generation AI and using a generation AI model that filters out optimal information.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the employee's geographical location information. For example, when the employee is in the office, the collection unit prioritizes collecting information around the office. For example, when the employee is on a business trip, the collection unit prioritizes collecting information about the business trip destination. For example, when the employee is working from home, the collection unit prioritizes collecting information about the area around the employee's home. In this way, by taking geographical location information into consideration, more relevant information can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the employee's geographical location information into a generation AI and collect information using a generation AI model that collects optimal information.

[0038] When collecting information, the collection unit can analyze the social media activities of employees and collect relevant information. The collection unit, for example, collects relevant information based on information shared by employees on social media. The collection unit, for example, collects relevant information based on information about accounts followed by employees. The collection unit, for example, collects relevant information based on information about online communities in which employees participate. This allows for the collection of more relevant information by analyzing social media activities. Some or all of the above-described processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect information using a generation AI model that inputs employees' social media activities into a generation AI and collects optimal information.

[0039] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the employee. For example, the proposal unit makes detailed proposals to employees involved in important projects. For example, the proposal unit makes concise proposals to employees involved in general work. For example, the proposal unit makes proposals including basic information to new employees. In this way, by adjusting the level of detail of the proposal based on the importance of the employee, more appropriate proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can make proposals using a generation AI model that inputs the importance of employees into the generation AI and adjusts the level of detail of the proposal.

[0040] When making a proposal, the proposal unit can apply different proposal algorithms depending on the employee category. For example, the proposal unit applies a technical proposal algorithm to employees in the technical department. For example, the proposal unit applies a proposal algorithm specialized for sales to employees in the sales department. For example, the proposal unit applies a proposal algorithm related to administrative work to employees in the administrative department. This enables more effective proposals by applying different proposal algorithms depending on the employee category. Some or all of the above-mentioned processing in the proposal unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the proposal unit can input employee categories into a generative AI and make proposals using a generative AI model that applies the optimal proposal algorithm.

[0041] When making a proposal, the proposal department can determine the priority of the proposal based on the employee's submission time. For example, the proposal department prioritizes proposals with an approaching deadline. For example, the proposal department postpones proposals related to long-term projects. For example, the proposal department gives top priority to proposals with high urgency. In this way, by determining the priority of proposals based on the employee's submission time, more effective proposals can be made. Some or all of the above-mentioned processing in the proposal department may be performed using a generation AI, or may be performed without using a generation AI. For example, the proposal department can input the employee's submission time into a generation AI and make proposals using a generation AI model that determines the priority of proposals.

[0042] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of employees. For example, the suggestion unit makes proposals first to directly related employees. For example, the suggestion unit makes proposals later to indirectly related employees. For example, the suggestion unit adjusts the order of proposals by taking into account the overall impact. In this way, adjusting the order of proposals based on the relevance of employees enables more effective proposals. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the suggestion unit can make proposals using a generative AI model that inputs the relevance of employees into the generative AI and adjusts the order of proposals.

[0043] When operating Shuffle Lunch, the operations department can analyze employees' past participation history to select the optimal operation method. For example, the operations department improves the operation method based on feedback from employees who have participated in the past. For example, the operations department prioritizes popular operation methods based on past participation history. For example, the operations department analyzes past participation history and proposes new operation methods. In this way, by analyzing past participation history, more effective operation of Shuffle Lunch becomes possible. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input employees' past participation history into a generation AI and operate the system using a generation AI model that selects the optimal operation method.

[0044] When operating shuffle lunch, the operations department can customize the operation method based on the employee's current living situation. For example, the operations department may suggest an online shuffle lunch to an employee who is working from home. For example, the operations department may suggest a shuffle lunch at the business trip destination to an employee who is on a business trip. For example, the operations department may suggest an in-office shuffle lunch to an employee who is working in the office. In this way, by customizing the operation method based on the current living situation, more appropriate shuffle lunch operation becomes possible. Some or all of the above-mentioned processing in the operations department may be performed using a generative AI, or may be performed without using a generative AI. For example, the operations department may input the employee's living situation into the generative AI and operate using a generative AI model that suggests the optimal operation method.

[0045] When operating Shuffle Lunch, the operations department can select the optimal operation method by taking into account the geographic location information of employees. For example, if an employee is in the office, the operations department will suggest restaurants near the office. For example, if an employee is on a business trip, the operations department will suggest restaurants at the business trip destination. For example, if an employee is working from home, the operations department will suggest restaurants near the employee's home. In this way, by taking geographic location information into consideration, more appropriate operation of Shuffle Lunch becomes possible. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the geographic location information of employees into a generation AI and operate using a generation AI model that selects the optimal operation method.

[0046] When operating Shuffle Lunch, the operations department can analyze employees' social media activity and propose operation methods. For example, the operations department proposes related lunches based on information shared by employees on social media. For example, the operations department proposes related lunches based on information about accounts followed by employees. For example, the operations department proposes related lunches based on information about online communities in which employees participate. This makes it possible to propose more appropriate Shuffle Lunches by analyzing social media activity. Some or all of the above-mentioned processing in the operations department may be performed using a generative AI, or may be performed without using a generative AI. For example, the operations department can input employees' social media activity into a generative AI and operate the system using a generative AI model that proposes optimal operation methods.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The collection unit can analyze an employee's past communication history and determine the priority of information collection. For example, it can prioritize collecting information from people with whom the employee has frequently interacted in the past. It can prioritize collecting related information based on the history of projects the employee has participated in in the past. It can prioritize collecting information from communication tools that the employee has preferred to use in the past. In this way, analyzing past communication history enables more effective information collection. Some or all of the above-mentioned processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI.

[0049] The collection unit can determine the priority of information collection by taking into account the geographical location information of employees. For example, if an employee is in the office, priority is given to collecting information around the office. If an employee is on a business trip, priority is given to collecting information about the business trip destination. If an employee is working from home, priority is given to collecting information around the employee's home. In this way, more relevant information can be collected by taking into account the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using or without using the generation AI.

[0050] The proposal unit can apply different proposal algorithms depending on the employee category. For example, a technical proposal algorithm is applied to employees in the technical department. A proposal algorithm specialized for sales is applied to employees in the sales department. A proposal algorithm related to administrative work is applied to employees in the administrative department. By applying different proposal algorithms depending on the employee category, more effective proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using a generation AI, or may be performed without using a generation AI.

[0051] When operating Shuffle Lunch, the operations department can analyze employees' past participation history and select the optimal operation method. For example, the operation method can be improved based on feedback from employees who have participated in the past. Popular operation methods can be prioritized based on past participation history. New operation methods can be proposed by analyzing past participation history. In this way, by analyzing past participation history, Shuffle Lunch can be operated more effectively. Some or all of the above-mentioned processing in the operations department can be performed using or without using generation AI.

[0052] When operating shuffle lunch, the operations department can customize the operation methods based on the current living situation of the employee. For example, for an employee working from home, an online shuffle lunch is suggested. For an employee on a business trip, a shuffle lunch at the business trip destination is suggested. For an employee working in the office, a shuffle lunch in the office is suggested. In this way, by customizing the operation methods based on the current living situation, it becomes possible to operate a more appropriate shuffle lunch. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI.

[0053] When operating Shuffle Lunch, the operations department can analyze employees' social media activity and suggest operation methods. For example, related lunches can be suggested based on information shared by employees on social media. Related lunches can be suggested based on information about accounts employees follow. Related lunches can be suggested based on information about online communities employees participate in. In this way, analyzing social media activity makes it possible to suggest more appropriate Shuffle Lunches. Some or all of the above-mentioned processing in the operations department may be performed using or without using generation AI.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The collection department collects open information within the company. Open information within the company includes, for example, employee project information, skill sets, past work history, etc. The collection department, for example, collects employee project information. Project information includes project objectives, progress, participating members, etc. The collection department, for example, collects employee skill sets. Skill sets include programming languages, management skills, specialized knowledge, etc. The collection department, for example, collects employee past work history. Work history includes past projects, responsibilities, results, etc. Step 2: The suggestion unit analyzes the information collected by the collection unit and suggests optimal communication partners. For example, the suggestion unit analyzes the collected information and suggests pairs of employees involved in different projects. For example, the suggestion unit analyzes the collected information and suggests pairs of employees with different skill sets. For example, the suggestion unit analyzes the collected information and suggests pairs of employees with different perspectives or knowledge. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the collected information into a generation AI and make suggestions using a generation AI model that suggests optimal communication partners. Step 3: The operations department runs a shuffle lunch with the people proposed by the proposal department. For example, the operations department holds a shuffle lunch with the people proposed. The shuffle lunch is subsidized by the company, so employees can enjoy the lunch for free. For example, the operations department manages the specific operations method of the shuffle lunch. For example, the operations department manages the schedule of the shuffle lunch. For example, the operations department selects participants for the shuffle lunch. For example, the operations department decides the location where the shuffle lunch will be held. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the shuffle lunch schedule into a generation AI and operate the event using a generation AI model that proposes an optimal schedule.

[0056] (Example 2) A communication activation system according to an embodiment of the present invention utilizes a generation AI to encourage employees who frequently work from home to come to the office and to stimulate communication among employees. This system collects open information within the company, and the generation AI suggests the optimal communication partner for each occasion. Next, a shuffle lunch is held with the suggested partner. This shuffle lunch is subsidized by the company. Employees who come to the office have lunch with the partner suggested by the generation AI, stimulating spontaneous communication and enabling them to work well while stimulating each other. For example, open information within the company is collected, including employee project information, skill sets, and past work history. This information is input into the generation AI. Next, the generation AI analyzes the collected information and suggests the optimal communication partner for each occasion. For example, employees involved in different projects or employees with different skill sets are often suggested. This increases opportunities for employees with different perspectives and knowledge to interact with each other. A shuffle lunch is held with the suggested partner. This shuffle lunch is subsidized by the company, and employees can enjoy the lunch for free. For example, by having lunch with someone suggested by the generation AI, employees who normally have no contact with each other can interact and share new ideas and knowledge. This system can encourage employees who often work from home to come to the office and stimulate communication between employees. For example, coming to the office increases direct communication and strengthens team cohesion. In addition, interaction between employees with different perspectives and knowledge increases the likelihood of new ideas and innovations being born. Furthermore, shuffled lunches stimulate spontaneous communication. For example, casual conversations over lunch can sometimes lead to the birth of new projects. In this way, employees can inspire each other and do good work. In this way, the communication activation system can encourage employees who often work from home to come to the office and stimulate communication between employees.

[0057] A communication activation system according to an embodiment includes a collection unit, a proposal unit, and an operation unit. The collection unit collects open information within the company. The open information within the company includes, for example, employee project information, skill sets, and past work history, but is not limited to these examples. The collection unit, for example, collects employee project information. The project information includes the project's objectives, progress, participating members, and the like. The collection unit, for example, collects employee skill sets. The skill sets include programming languages, management skills, specialized knowledge, and the like. The collection unit, for example, collects employee past work history. The work history includes past projects, assigned tasks, results, and the like. The proposal unit analyzes the information collected by the collection unit and proposes optimal communication partners. The proposal unit, for example, analyzes the collected information and proposes pairs of employees involved in different projects. The proposal unit, for example, analyzes the collected information and proposes pairs of employees with different skill sets. The proposal unit, for example, analyzes the collected information and proposes pairs of employees with different perspectives and knowledge. Some or all of the above-described processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit may input collected information into a generation AI and make a suggestion using a generation AI model that suggests optimal communication partners. The management unit manages a shuffle lunch with the partners suggested by the suggestion unit. For example, the management unit holds a shuffle lunch with the suggested partners. The shuffle lunch is subsidized by the company, so employees can enjoy the lunch for free. For example, the management unit manages the specific management method of the shuffle lunch. For example, the management unit manages the schedule of the shuffle lunch. For example, the management unit selects participants for the shuffle lunch. For example, the management unit determines the location of the shuffle lunch. In this way, the communication activation system according to the embodiment can activate communication between employees. Some or all of the above-described processing in the management unit may be performed using a generation AI, or may be performed without using a generation AI.For example, the operations department can input the shuffle lunch schedule into a generative AI and use the generative AI model to suggest the optimal schedule.

[0058] The collection unit can collect information on employees' project information or skill sets, and past work history. The collection unit, for example, collects employees' project information. The project information includes the project's objectives, progress, participating members, etc. The collection unit, for example, collects employees' skill sets. The skill sets include programming languages, management skills, specialized knowledge, etc. The collection unit, for example, collects employees' past work history. The work history includes past projects, assigned tasks, results, etc. By collecting information such as employees' project information, skill sets, and past work history, more appropriate communication partners can be suggested. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input employees' project information into the generation AI and have the generation AI analyze the project information.

[0059] The suggestion unit can analyze the collected information and suggest pairs of employees involved in different projects or pairs of employees with different skill sets. The suggestion unit, for example, analyzes the collected information and suggests pairs of employees involved in different projects. The suggestion unit, for example, analyzes the collected information and suggests pairs of employees with different skill sets. The suggestion unit, for example, analyzes the collected information and suggests pairs of employees with different perspectives and knowledge. In this way, by suggesting pairs of employees involved in different projects or skill sets, opportunities for employees with different perspectives and knowledge to interact with each other increase. Some or all of the above-described processing in the suggestion unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the suggestion unit can input the collected information into a generative AI and make suggestions using a generative AI model that suggests optimal communication partners.

[0060] The operations department can host a shuffle lunch with the proposed partners and manage company subsidies. For example, the operations department hosts a shuffle lunch with the proposed partners. The shuffle lunch is subsidized by the company, allowing employees to enjoy the lunch for free. For example, the operations department manages the specific operation methods of the shuffle lunch. For example, the operations department manages the schedule of the shuffle lunch. For example, the operations department selects participants for the shuffle lunch. For example, the operations department determines the location of the shuffle lunch. In this way, by hosting the shuffle lunch and managing company subsidies, spontaneous communication between employees is stimulated. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the shuffle lunch schedule into a generation AI and operate the event using a generation AI model that proposes an optimal schedule.

[0061] The suggestion unit can set the frequency of suggestions or selection criteria. The suggestion unit, for example, sets the frequency of suggestions. The frequency of suggestions includes once a week, once a month, etc. The suggestion unit, for example, sets the selection criteria. The selection criteria include project relevance, complementarity of skill sets, etc. By setting the frequency of suggestions and the selection criteria, more effective suggestions of communication partners can be made. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the frequency of suggestions and selection criteria into the generation AI and make suggestions using a generation AI model that makes optimal suggestions.

[0062] The operations department can manage the specific operation methods of the shuffle lunch. The operations department, for example, manages the schedule of the shuffle lunch. The operations department, for example, selects participants for the shuffle lunch. The operations department, for example, determines the location of the shuffle lunch. By managing the specific operation methods of the shuffle lunch, the lunch can be held smoothly. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the schedule of the shuffle lunch into a generation AI and operate the event using a generation AI model that proposes an optimal schedule.

[0063] The collection unit can estimate the employee's emotions and adjust the timing of information collection based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit collects information during a relaxed time. For example, if the employee is concentrating, the collection unit postpones information collection so as not to disturb the employee's concentration. For example, if the employee is relaxed, the collection unit collects information at that time to obtain more accurate information. This allows for adjusting the timing of information collection based on the employee's emotions to obtain more accurate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generative AI. For example, the collection unit can collect information using a generative AI model that inputs employee emotion data into the generative AI and adjusts the timing of information collection.

[0064] The collection unit can analyze the employee's past communication history and select an information collection method. For example, the collection unit collects information by prioritizing communication tools that the employee has used favorably in the past. For example, the collection unit collects information from people with whom the employee has frequently interacted in the past. For example, the collection unit prioritizes collection of related information based on the history of projects in which the employee has participated in the past. This enables more effective information collection by analyzing the past communication history. Some or all of the above-mentioned processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect information by inputting the employee's past communication history into a generation AI and using a generation AI model that selects the optimal information collection method.

[0065] When collecting information, the collection unit can filter the information based on the employee's current project progress or areas of interest. For example, the collection unit prioritizes collecting information related to projects currently underway by the employee. For example, the collection unit filters and collects related information based on the employee's areas of interest. For example, the collection unit collects information related to tasks currently being undertaken by the employee. By filtering information based on the current project progress or areas of interest, more relevant information can be collected. Some or all of the above-described processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect information by inputting the employee's project progress or areas of interest into a generation AI and using a generation AI model that filters out optimal information.

[0066] The collection unit can estimate the employee's emotions and determine the priority of information to be collected based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit prioritizes collecting relaxing information. For example, if the employee is excited, the collection unit prioritizes collecting stimulating information. For example, if the employee is concentrating, the collection unit prioritizes collecting work-related information. This allows for more appropriate information to be collected by prioritizing information based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generative AI, or may be performed without using the generative AI. For example, the collection unit can input employee emotion data into a generative AI and collect information using a generative AI model that determines the priority of information.

[0067] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the employee's geographical location information. For example, when the employee is in the office, the collection unit prioritizes collecting information around the office. For example, when the employee is on a business trip, the collection unit prioritizes collecting information about the business trip destination. For example, when the employee is working from home, the collection unit prioritizes collecting information about the area around the employee's home. In this way, by taking geographical location information into consideration, more relevant information can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can input the employee's geographical location information into a generation AI and collect information using a generation AI model that collects optimal information.

[0068] When collecting information, the collection unit can analyze the social media activities of employees and collect relevant information. The collection unit, for example, collects relevant information based on information shared by employees on social media. The collection unit, for example, collects relevant information based on information about accounts followed by employees. The collection unit, for example, collects relevant information based on information about online communities in which employees participate. This allows for the collection of more relevant information by analyzing social media activities. Some or all of the above-described processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the collection unit can collect information using a generation AI model that inputs employees' social media activities into a generation AI and collects optimal information.

[0069] The suggestion unit can estimate the employee's emotions and adjust the way the suggestion is expressed based on the estimated employee's emotions. For example, if the employee is relaxed, the suggestion unit makes the suggestion using casual expressions. For example, if the employee is nervous, the suggestion unit makes the suggestion using formal expressions. For example, if the employee is excited, the suggestion unit makes the suggestion using energetic expressions. This enables more effective suggestions by adjusting the way the suggestion is expressed based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using or without the generative AI. For example, the suggestion unit can make suggestions using a generative AI model that inputs employee emotion data into the generative AI and adjusts the way the suggestion is expressed.

[0070] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the employee. For example, the proposal unit makes detailed proposals to employees involved in important projects. For example, the proposal unit makes concise proposals to employees involved in general work. For example, the proposal unit makes proposals including basic information to new employees. In this way, by adjusting the level of detail of the proposal based on the importance of the employee, more appropriate proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the proposal unit can make proposals using a generation AI model that inputs the importance of employees into the generation AI and adjusts the level of detail of the proposal.

[0071] When making a proposal, the proposal unit can apply different proposal algorithms depending on the employee category. For example, the proposal unit applies a technical proposal algorithm to employees in the technical department. For example, the proposal unit applies a proposal algorithm specialized for sales to employees in the sales department. For example, the proposal unit applies a proposal algorithm related to administrative work to employees in the administrative department. This enables more effective proposals by applying different proposal algorithms depending on the employee category. Some or all of the above-mentioned processing in the proposal unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the proposal unit can input employee categories into a generative AI and make proposals using a generative AI model that applies the optimal proposal algorithm.

[0072] The suggestion unit can estimate the employee's emotions and adjust the length of the suggestion based on the estimated employee's emotions. For example, if the employee is in a hurry, the suggestion unit can make a short, to-the-point suggestion. For example, if the employee is relaxed, the suggestion unit can make a longer suggestion with detailed explanations. For example, if the employee is excited, the suggestion unit can make a suggestion with visually stimulating effects. This allows for more appropriate suggestions by adjusting the length of the suggestion based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using the generative AI, or can be performed without the generative AI. For example, the suggestion unit can make suggestions using a generative AI model that inputs employee emotion data into the generative AI and adjusts the length of the suggestion.

[0073] When making a proposal, the proposal department can determine the priority of the proposal based on the employee's submission time. For example, the proposal department prioritizes proposals with an approaching deadline. For example, the proposal department postpones proposals related to long-term projects. For example, the proposal department gives top priority to proposals with high urgency. In this way, by determining the priority of proposals based on the employee's submission time, more effective proposals can be made. Some or all of the above-mentioned processing in the proposal department may be performed using a generation AI, or may be performed without using a generation AI. For example, the proposal department can input the employee's submission time into a generation AI and make proposals using a generation AI model that determines the priority of proposals.

[0074] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of employees. For example, the suggestion unit makes proposals first to directly related employees. For example, the suggestion unit makes proposals later to indirectly related employees. For example, the suggestion unit adjusts the order of proposals by taking into account the overall impact. In this way, adjusting the order of proposals based on the relevance of employees enables more effective proposals. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generative AI, or may be performed without using a generative AI. For example, the suggestion unit can make proposals using a generative AI model that inputs the relevance of employees into the generative AI and adjusts the order of proposals.

[0075] The operations department can estimate employee emotions and adjust the Shuffle Lunch operation method based on the estimated employee emotions. For example, if an employee is relaxed, the operations department can suggest a lunch with a casual atmosphere. For example, if an employee is nervous, the operations department can suggest a lunch with a formal atmosphere. For example, if an employee is excited, the operations department can suggest a lunch with an active atmosphere. This allows for more appropriate lunch operation by adjusting the Shuffle Lunch operation method based on employee emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-mentioned processing in the operations department may be performed using or without the generative AI. For example, the operations department can input employee emotion data into the generative AI and use a generative AI model to adjust the Shuffle Lunch operation method.

[0076] When operating Shuffle Lunch, the operations department can analyze employees' past participation history to select the optimal operation method. For example, the operations department improves the operation method based on feedback from employees who have participated in the past. For example, the operations department prioritizes popular operation methods based on past participation history. For example, the operations department analyzes past participation history and proposes new operation methods. In this way, by analyzing past participation history, more effective operation of Shuffle Lunch becomes possible. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input employees' past participation history into a generation AI and operate the system using a generation AI model that selects the optimal operation method.

[0077] When operating shuffle lunch, the operations department can customize the operation method based on the employee's current living situation. For example, the operations department may suggest an online shuffle lunch to an employee who is working from home. For example, the operations department may suggest a shuffle lunch at the business trip destination to an employee who is on a business trip. For example, the operations department may suggest an in-office shuffle lunch to an employee who is working in the office. In this way, by customizing the operation method based on the current living situation, more appropriate shuffle lunch operation becomes possible. Some or all of the above-mentioned processing in the operations department may be performed using a generative AI, or may be performed without using a generative AI. For example, the operations department may input the employee's living situation into the generative AI and operate using a generative AI model that suggests the optimal operation method.

[0078] The operations department can estimate employee emotions and prioritize shuffled lunches based on the estimated employee emotions. For example, if an employee is feeling stressed, the operations department prioritizes relaxing lunches. For example, if an employee is excited, the operations department prioritizes stimulating lunches. For example, if an employee is concentrating, the operations department prioritizes work-related lunches. This enables more appropriate lunch recommendations by prioritizing shuffled lunches based on employee emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the operations department may be performed using or without the generative AI. For example, the operations department can input employee emotion data into a generative AI and use a generative AI model to prioritize shuffled lunches.

[0079] When operating Shuffle Lunch, the operations department can select the optimal operation method by taking into account the geographic location information of employees. For example, if an employee is in the office, the operations department will suggest restaurants near the office. For example, if an employee is on a business trip, the operations department will suggest restaurants at the business trip destination. For example, if an employee is working from home, the operations department will suggest restaurants near the employee's home. In this way, by taking geographic location information into consideration, more appropriate operation of Shuffle Lunch becomes possible. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the geographic location information of employees into a generation AI and operate using a generation AI model that selects the optimal operation method.

[0080] When operating Shuffle Lunch, the operations department can analyze employees' social media activity and propose operation methods. For example, the operations department proposes related lunches based on information shared by employees on social media. For example, the operations department proposes related lunches based on information about accounts followed by employees. For example, the operations department proposes related lunches based on information about online communities in which employees participate. This makes it possible to propose more appropriate Shuffle Lunches by analyzing social media activity. Some or all of the above-mentioned processing in the operations department may be performed using a generative AI, or may be performed without using a generative AI. For example, the operations department can input employees' social media activity into a generative AI and operate the system using a generative AI model that proposes optimal operation methods. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, suggestion unit, and management unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects employee project information and skill sets using the camera 42 and microphone 38B of the smart device 14, and transmits the information to the data processing device 12 via the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected information, and suggests optimal communication partners. The management unit is realized, for example, by the control unit 46A of the smart device 14, and manages the schedule and participants of the shuffle lunch. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, suggestion unit, and management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects employee project information and skill sets using the camera 42 and microphone 238 of the smart glasses 214, and transmits the information to the data processing device 12 via the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and suggests optimal communication partners. The management unit is realized, for example, by the control unit 46A of the smart glasses 214, and manages the schedule and participants of the shuffle lunch. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, suggestion unit, and management unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects employee project information and skill sets using the camera 42 and microphone 238 of the headset terminal 314, and transmits the information to the data processing device 12 via the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and suggests optimal communication partners. The management unit is realized, for example, by the control unit 46A of the headset terminal 314, and manages the shuffle lunch schedule and participants. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, suggestion unit, and management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects employee project information and skill sets using the camera 42 and microphone 238 of the robot 414, and transmits the information to the data processing device 12 via the control unit 46A. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information and suggests optimal communication partners. The management unit is realized, for example, by the control unit 46A of the robot 414, and manages the schedule and participants of the shuffle lunch.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The suggestion unit can estimate the employee's emotions and adjust the timing of the suggestion based on the estimated emotions. For example, if the employee is feeling stressed, the suggestion can be made during a relaxed time. If the employee is concentrating, the suggestion can be postponed so as not to disturb the employee's concentration. If the employee is relaxed, the suggestion can be made at that time, promoting more effective communication. This allows for more appropriate suggestions by adjusting the timing of the suggestion based on the employee's emotions. The estimation of emotions is achieved using an emotion engine or a generation AI. The generation AI can be a text generation AI or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI.

[0083] The collection unit can analyze an employee's past communication history and determine the priority of information collection. For example, it can prioritize collecting information from people with whom the employee has frequently interacted in the past. It can prioritize collecting related information based on the history of projects the employee has participated in in the past. It can prioritize collecting information from communication tools that the employee has preferred to use in the past. In this way, analyzing past communication history enables more effective information collection. Some or all of the above-mentioned processing in the collection unit may be performed using a generation AI, or may be performed without using a generation AI.

[0084] The suggestion unit can estimate the employee's emotions and customize the content of the suggestion based on the estimated emotions. For example, if the employee is relaxed, a casual suggestion is made. If the employee is nervous, a formal suggestion is made. If the employee is excited, an energetic suggestion is made. This allows for more effective suggestions by customizing the content of the suggestion based on the employee's emotions. The estimation of emotions is achieved using an emotion engine or a generation AI. The generation AI may be a text generation AI or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI.

[0085] The operations department can estimate the emotions of employees and determine the theme of the shuffle lunch based on the estimated emotions. For example, if an employee is relaxed, a lunch with a relaxing theme is suggested. If an employee is tense, a lunch with a tension-relieving theme is suggested. If an employee is excited, a lunch with an energetic theme is suggested. This allows for more appropriate lunch management by determining the theme of the shuffle lunch based on the emotions of employees. Emotion estimation is achieved using an emotion engine or a generative AI. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above-mentioned processing in the operations department may be performed using a generative AI, or may be performed without using a generative AI.

[0086] The suggestion unit can estimate the employee's emotions and adjust the frequency of suggestions based on the estimated emotions. For example, if the employee is feeling stressed, the frequency of suggestions is reduced. If the employee is relaxed, the frequency of suggestions is increased. If the employee is concentrating, the frequency of suggestions is adjusted so as not to disturb the employee's concentration. In this way, adjusting the frequency of suggestions based on the employee's emotions enables more appropriate suggestions. The estimation of emotions is achieved using an emotion engine or a generation AI. The generation AI may be a text generation AI or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI.

[0087] The collection unit can determine the priority of information collection by taking into account the geographical location information of employees. For example, if an employee is in the office, priority is given to collecting information around the office. If an employee is on a business trip, priority is given to collecting information about the business trip destination. If an employee is working from home, priority is given to collecting information around the employee's home. In this way, more relevant information can be collected by taking into account the geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using or without using the generation AI.

[0088] The proposal unit can apply different proposal algorithms depending on the employee category. For example, a technical proposal algorithm is applied to employees in the technical department. A proposal algorithm specialized for sales is applied to employees in the sales department. A proposal algorithm related to administrative work is applied to employees in the administrative department. By applying different proposal algorithms depending on the employee category, more effective proposals can be made. Some or all of the above-mentioned processing in the proposal unit may be performed using a generation AI, or may be performed without using a generation AI.

[0089] When operating Shuffle Lunch, the operations department can analyze employees' past participation history and select the optimal operation method. For example, the operation method can be improved based on feedback from employees who have participated in the past. Popular operation methods can be prioritized based on past participation history. New operation methods can be proposed by analyzing past participation history. In this way, by analyzing past participation history, Shuffle Lunch can be operated more effectively. Some or all of the above-mentioned processing in the operations department can be performed using or without using generation AI.

[0090] When operating shuffle lunch, the operations department can customize the operation methods based on the current living situation of the employee. For example, for an employee working from home, an online shuffle lunch is suggested. For an employee on a business trip, a shuffle lunch at the business trip destination is suggested. For an employee working in the office, a shuffle lunch in the office is suggested. In this way, by customizing the operation methods based on the current living situation, it becomes possible to operate a more appropriate shuffle lunch. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI.

[0091] When operating Shuffle Lunch, the operations department can analyze employees' social media activity and suggest operation methods. For example, related lunches can be suggested based on information shared by employees on social media. Related lunches can be suggested based on information about accounts employees follow. Related lunches can be suggested based on information about online communities employees participate in. In this way, analyzing social media activity makes it possible to suggest more appropriate Shuffle Lunches. Some or all of the above-mentioned processing in the operations department may be performed using or without using generation AI.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection department collects open information within the company. Open information within the company includes, for example, employee project information, skill sets, past work history, etc. The collection department, for example, collects employee project information. Project information includes project objectives, progress, participating members, etc. The collection department, for example, collects employee skill sets. Skill sets include programming languages, management skills, specialized knowledge, etc. The collection department, for example, collects employee past work history. Work history includes past projects, responsibilities, results, etc. Step 2: The suggestion unit analyzes the information collected by the collection unit and suggests optimal communication partners. For example, the suggestion unit analyzes the collected information and suggests pairs of employees involved in different projects. For example, the suggestion unit analyzes the collected information and suggests pairs of employees with different skill sets. For example, the suggestion unit analyzes the collected information and suggests pairs of employees with different perspectives or knowledge. Some or all of the above-mentioned processing in the suggestion unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the collected information into a generation AI and make suggestions using a generation AI model that suggests optimal communication partners. Step 3: The operations department runs a shuffle lunch with the people proposed by the proposal department. For example, the operations department holds a shuffle lunch with the people proposed. The shuffle lunch is subsidized by the company, so employees can enjoy the lunch for free. For example, the operations department manages the specific operations method of the shuffle lunch. For example, the operations department manages the schedule of the shuffle lunch. For example, the operations department selects participants for the shuffle lunch. For example, the operations department decides the location where the shuffle lunch will be held. Some or all of the above-mentioned processing in the operations department may be performed using a generation AI, or may be performed without using a generation AI. For example, the operations department can input the shuffle lunch schedule into a generation AI and operate the event using a generation AI model that proposes an optimal schedule.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0142] 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.

[0143] 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.

[0144] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects open information within the company, a suggestion unit that analyzes the information collected by the collection unit and suggests communication partners; and a management unit that manages the shuffle lunch with the partner proposed by the proposal unit. A system characterized by:

2. The collecting unit Collect information about employees' projects, skill sets, and past work history 2. The system of claim 1.

3. The proposal unit Analyze the collected information and suggest matching employees who are involved in different projects or who have different skill sets.

2. The system of claim 1.

4. The management department: Host a shuffle lunch with proposed partners and manage company grants 2. The system of claim 1.

5. The proposal unit Set the frequency or selection criteria for proposals 2. The system of claim 1.

6. The management department: Manage the specific operations of Shuffle Lunch 2. The system of claim 1.

7. The collecting unit Estimate employee emotions and adjust the timing of information gathering based on the estimated employee emotions 2. The system of claim 1.

8. The collecting unit Analyze employees' past communication history and select information collection methods 2. The system of claim 1.

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