System

Abstract

An object of a system according to an embodiment is to provide a place for matching and natural interaction in consideration of compatibility between employees.SOLUTION: A system includes a profile analysis part, a matching part, and an event planning part. A profile analysis part analyzes the profile, working method and sense of values of the employee. The matching unit matches co-workers who are compatible in character based on the data analyzed by the profile analysis unit. The event planning unit plans casual coffee break, lunch, and after-work events in order to provide a place where the employees matched by the matching unit are naturally connected to each other.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately match employees based on their compatibility with each other or provide a forum for natural interaction, so there is room for improvement.

[0005] The system according to the embodiment aims to provide a place for natural interaction and matching that takes into account the compatibility between employees. [Means for solving the problem]

[0006] The system according to the embodiment includes a profile analysis unit, a matching unit, and an event planning unit. The profile analysis unit analyzes employee profiles, work styles, and values. The matching unit matches employees with compatible personalities based on the data analyzed by the profile analysis unit. The event planning unit plans casual coffee breaks, lunches, and after-work events to provide a place for employees matched by the matching unit to connect naturally. [Effects of the Invention]

[0007] The system according to the embodiment can provide a place for natural interaction and matching that takes into account the compatibility between employees. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The AI ​​matching service according to an embodiment of the present invention is a system that uses AI to analyze employee profiles, working styles, and values, match colleagues with compatible personalities, and provide opportunities for employees to connect naturally, such as casual coffee breaks, lunches, and after-work events. This allows the AI ​​matching service to promote natural interactions between employees and create opportunities for office romance.

[0029] An AI matching service according to an embodiment includes a profile analysis unit, a matching unit, and an event planning unit. The profile analysis unit analyzes employee profiles, work styles, and values. For example, the profile analysis unit collects information such as employees' ages, genders, hobbies, and special skills, and analyzes their work styles and values. The matching unit matches colleagues with compatible personalities based on the data analyzed by the profile analysis unit. For example, the matching unit matches employee A and employee B if they share the same values ​​and hobbies. The event planning unit plans casual coffee break, lunch, and after-work events to provide a place for employees matched by the matching unit to connect naturally. For example, the event planning unit proposes an event to hold a coffee break at a cafe every Friday afternoon. This allows the AI ​​matching service to promote natural interactions between employees and create opportunities for office romances.

[0030] The profile analysis department can analyze employees' past project history and performance data to evaluate their work style and performance tendencies. For example, the profile analysis department collects employees' past project history and analyzes their roles and results in each project. For example, an employee with a lot of experience as a project manager may be evaluated as having strong leadership skills. The profile analysis department also analyzes performance data to evaluate employees' performance tendencies. For example, employee performance is evaluated based on sales results and goals achieved. This allows for more appropriate matching by evaluating employees' work style and performance tendencies.

[0031] The profile analysis unit analyzes employees' social media activities and comments in online communities to understand their individual interests and concerns in more detail. For example, the profile analysis unit analyzes employees' social media accounts and extracts their interests and concerns from the content of their posts and the accounts they follow. For example, it identifies employees who post frequently about specific hobbies or areas of interest. The profile analysis unit also analyzes comments in online communities to understand employees' interests and concerns. For example, it identifies employees' interests based on the forums they participate in and the frequency of their posts. This allows for a more detailed understanding of employees' interests and concerns, enabling more appropriate matching.

[0032] The profile analysis unit can analyze employees' health data and perform matching based on their health condition and lifestyle. The profile analysis unit, for example, analyzes data from a fitness tracker to evaluate an employee's health condition and lifestyle. For example, the health condition is evaluated based on exercise habits and sleep patterns. The profile analysis unit also analyzes health checkup results to evaluate an employee's health condition. For example, the health condition is evaluated based on weight and blood pressure. This allows for matching based on health condition and lifestyle, improving compatibility between employees.

[0033] The profile analysis unit can perform matching that takes into consideration the employee's family structure, whether or not they have pets, and private information. For example, the profile analysis unit takes into consideration the employee's family structure and matches employees with similar family environments. For example, it matches employees with children. The profile analysis unit also takes into consideration the presence or absence of pets and matches employees who own pets. For example, it matches employees who own dogs. In this way, matching that takes private information into consideration improves the compatibility between employees.

[0034] The matching unit can analyze an employee's communication style and identify colleagues with whom they have good chemistry. The matching unit, for example, analyzes the writing style of an employee's emails and evaluates their communication style. For example, it evaluates compatibility based on whether the writing style is polite or casual. The matching unit also analyzes what is said in meetings and evaluates their communication style. For example, it evaluates compatibility based on the frequency of comments and the depth of their content. In this way, by analyzing communication styles, it is possible to identify colleagues with whom they have good chemistry.

[0035] The matching unit can analyze employees' past feedback and evaluation data and perform matching based on mutual evaluations with other employees. For example, the matching unit analyzes employees' past feedback data and evaluates compatibility based on mutual evaluations with other employees. For example, it matches employees who have a lot of positive feedback. The matching unit also analyzes evaluation data and evaluates employee performance. For example, it evaluates compatibility based on 360-degree evaluations and self-evaluations. In this way, by performing matching based on past feedback and evaluation data, it is possible to identify colleagues who are compatible with each other.

[0036] The matching unit can analyze employees' music and movie preferences and match them with colleagues who share common interests. For example, the matching unit analyzes employees' music preferences and matches them with colleagues who share common interests. For example, it matches employees who like the same artists or genres. The matching unit also analyzes movie preferences and matches them with colleagues who share common interests. For example, it matches employees who like the same directors or movies. In this way, by matching based on common interests, it is possible to identify colleagues who get along well with each other.

[0037] The matching unit can analyze data on employees' travel history and places visited, and match colleagues with similar travel destination preferences. The matching unit, for example, analyzes employees' travel history and matches colleagues with similar travel destination preferences. For example, it matches employees who have visited the same country or city. The matching unit also analyzes data on places visited and matches colleagues with similar travel destination preferences. For example, it matches employees who like beach resorts and mountainous areas. In this way, by matching based on travel destination preferences, it is possible to identify colleagues who are compatible with each other.

[0038] The event planning department can analyze employees' past event participation history and plan new events based on the event format with the highest participation rate. For example, the event planning department analyzes employees' past event participation history and identifies the event format with the highest participation rate. For example, the event planning department plans a new event based on a casual lunch event or a sporting event. The event planning department also plans a new event that will attract employees' interest based on the event format with the highest participation rate. For example, the event planning department plans an event that incorporates popular themes or activities. In this way, by planning a new event based on past event participation history, it is possible to increase employees' motivation to participate.

[0039] The event planning department can analyze employees' food preferences and allergy information and plan events that offer optimal lunch menus. For example, the event planning department collects employees' food preferences and allergy information and plans events that offer optimal lunch menus. For example, vegetarian and gluten-free menus are provided. The event planning department also takes employees' allergy information into consideration and plans events that offer safe meals. For example, a nut-free menu is provided for employees with nut allergies. In this way, by providing lunch menus that take employees' food preferences and allergy information into consideration, participant satisfaction can be increased.

[0040] The event planning department can plan workshops and club activities that make use of employees' hobbies and special skills, promoting natural interactions between employees. For example, the event planning department can collect information about employees' hobbies and special skills and plan workshops that make use of them. For example, they can hold cooking classes or art workshops. The event planning department can also plan club activities that make use of employees' special skills. For example, they can establish music clubs or sports clubs. This can promote natural interactions between employees through workshops and club activities that make use of their hobbies and special skills.

[0041] The event planning department plans events that employees' families and pets can participate in, deepening private connections. The event planning department plans events that employees' families and pets can participate in, deepening private connections. For example, they hold family days and picnics where pets are allowed. The event planning department also plans activities that families and pets can participate in. For example, they provide games for families and activities for pets. In this way, employees can deepen private connections with each other through events that families and pets can participate in.

[0042] The matching unit can analyze an employee's past romantic experiences and preferences to identify the most compatible partner. For example, the matching unit collects an employee's past romantic experiences and analyzes preferences and patterns. For example, it identifies factors that led to success and failure in past relationships. The matching unit also identifies the optimal partner based on the employee's preferences. For example, it performs matching based on the characteristics and hobbies of an ideal partner. In this way, the success rate of office romances can be increased by identifying the optimal partner based on past romantic experiences and preferences.

[0043] The matching unit can analyze employees' communication history and provide natural conversation starters. The matching unit, for example, analyzes employees' communication history and provides natural conversation starters. For example, it can suggest common topics based on the content of past emails and chats. The matching unit can also analyze meeting minutes and provide common topics. For example, it can provide conversation starters based on project progress and common goals. In this way, by providing natural conversation starters based on communication history, it is possible to promote interaction between employees.

[0044] The matching department can propose date plans that make use of employees' hobbies and interests, and promote natural interactions between employees. For example, the matching department collects information about employees' hobbies and interests and proposes date plans that make use of them. For example, it provides date plans that are suitable for employees who share a common hobby. The matching department also proposes date plans based on employees' interests. For example, it proposes activities that are suitable for employees who share a common interest. In this way, by proposing date plans that make use of hobbies and interests, it is possible to promote natural interactions between employees.

[0045] The matching unit can analyze the history of successful dates of employees and propose new date plans that incorporate those elements. For example, the matching unit collects the history of successful dates of employees in the past and analyzes those elements. For example, it identifies the locations and activities of successful dates. The matching unit also proposes new date plans that incorporate elements of successful dates. For example, it proposes plans that incorporate popular date spots and activities. In this way, by proposing new date plans that incorporate elements of successful dates in the past, it is possible to promote interaction between employees.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The matching unit can analyze employees' preferences for music and movies and match them with colleagues who share common interests. For example, it can match employees who like the same artists or genres. The matching unit can also analyze movie preferences and match colleagues who share common interests. For example, it can match employees who like the same director or movies. In this way, by matching based on common interests, it is possible to identify colleagues who get along well with each other.

[0048] The event planning department can analyze employees' past event participation history and plan new events based on the event format with the highest participation rate. For example, the event planning department can analyze employees' past event participation history and identify the event format with the highest participation rate. For example, it can plan a new event based on a casual lunch event or a sporting event. The event planning department can also plan new events that will attract employees' interest based on event formats with high participation rates. For example, it can plan events that incorporate popular themes or activities. In this way, by planning new events based on past event participation history, it is possible to increase employees' motivation to participate.

[0049] The matching unit can analyze data on employees' travel history and places visited, and match them with colleagues who share the same travel destination preferences. For example, it can analyze an employee's travel history and match colleagues who share the same travel destination preferences. For example, it can match employees who have visited the same country or city. The matching unit can also analyze data on places visited and match colleagues who share the same travel destination preferences. For example, it can match employees who like beach resorts and mountainous areas. In this way, by matching based on travel destination preferences, it is possible to identify colleagues who are compatible with each other.

[0050] The event planning department can analyze employees' food preferences and allergy information and plan events that offer optimal lunch menus. For example, they can collect employees' food preferences and allergy information and plan events that offer optimal lunch menus. For example, they can provide vegetarian and gluten-free menus. The event planning department can also plan events that take employees' allergy information into consideration and offer safe meals. For example, they can provide nut-free menus for employees with nut allergies. In this way, they can provide lunch menus that take employees' food preferences and allergy information into consideration, thereby increasing participant satisfaction.

[0051] The event planning department can plan workshops and club activities that make use of employees' hobbies and special skills, promoting natural interactions between employees. For example, they can collect information about employees' hobbies and special skills and plan workshops that make use of them. For example, they can hold cooking classes or art workshops. The event planning department can also plan club activities that make use of employees' special skills. For example, they can establish music clubs or sports clubs. This can promote natural interactions between employees through workshops and club activities that make use of hobbies and special skills.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The Profile Analysis Department analyzes employee profiles, work styles, and values. For example, they collect information such as employees' age, gender, hobbies, and special skills, and analyze their work styles and values. Step 2: The matching department matches colleagues with compatible personalities based on the data analyzed by the profile analysis department. For example, if employee A and employee B have similar values ​​and hobbies, the two are matched. Step 3: The Event Planning Department plans casual coffee breaks, lunches, and after-work events to provide a place for employees matched by the Matching Department to naturally connect with each other. For example, they propose an event where a coffee break is held at a cafe every Friday afternoon.

[0054] (Example 2) The AI ​​matching service according to an embodiment of the present invention is a system that uses AI to analyze employee profiles, working styles, and values, match colleagues with compatible personalities, and provide opportunities for employees to connect naturally, such as casual coffee breaks, lunches, and after-work events. This allows the AI ​​matching service to promote natural interactions between employees and create opportunities for office romance.

[0055] An AI matching service according to an embodiment includes a profile analysis unit, a matching unit, and an event planning unit. The profile analysis unit analyzes employee profiles, work styles, and values. For example, the profile analysis unit collects information such as employees' ages, genders, hobbies, and special skills, and analyzes their work styles and values. The matching unit matches colleagues with compatible personalities based on the data analyzed by the profile analysis unit. For example, the matching unit matches employee A and employee B if they share the same values ​​and hobbies. The event planning unit plans casual coffee break, lunch, and after-work events to provide a place for employees matched by the matching unit to connect naturally. For example, the event planning unit proposes an event to hold a coffee break at a cafe every Friday afternoon. This allows the AI ​​matching service to promote natural interactions between employees and create opportunities for office romances.

[0056] The profile analysis department can analyze employees' past project history and performance data to evaluate their work style and performance tendencies. For example, the profile analysis department collects employees' past project history and analyzes their roles and results in each project. For example, an employee with a lot of experience as a project manager may be evaluated as having strong leadership skills. The profile analysis department also analyzes performance data to evaluate employees' performance tendencies. For example, employee performance is evaluated based on sales results and goals achieved. This allows for more appropriate matching by evaluating employees' work style and performance tendencies.

[0057] The profile analysis unit analyzes employees' social media activities and comments in online communities to understand their individual interests and concerns in more detail. For example, the profile analysis unit analyzes employees' social media accounts and extracts their interests and concerns from the content of their posts and the accounts they follow. For example, it identifies employees who post frequently about specific hobbies or areas of interest. The profile analysis unit also analyzes comments in online communities to understand employees' interests and concerns. For example, it identifies employees' interests based on the forums they participate in and the frequency of their posts. This allows for a more detailed understanding of employees' interests and concerns, enabling more appropriate matching.

[0058] The profile analysis unit uses the emotion estimation function to analyze the emotional responses of employees to events and projects they have participated in in the past, and can match employees based on positive experiences. For example, the profile analysis unit analyzes the emotional responses of employees to events they have participated in in the past and identifies positive experiences. For example, it analyzes facial expressions and comments made during events to extract data indicating positive emotions. The profile analysis unit also analyzes emotional responses to projects and matches employees based on positive experiences. For example, it matches employees based on successful experiences and projects with high satisfaction. In this way, matching based on positive experiences improves compatibility between employees.

[0059] The profile analysis unit can analyze employees' health data and perform matching based on their health condition and lifestyle. The profile analysis unit, for example, analyzes data from a fitness tracker to evaluate an employee's health condition and lifestyle. For example, the health condition is evaluated based on exercise habits and sleep patterns. The profile analysis unit also analyzes health checkup results to evaluate an employee's health condition. For example, the health condition is evaluated based on weight and blood pressure. This allows for matching based on health condition and lifestyle, improving compatibility between employees.

[0060] The profile analysis unit can perform matching that takes into consideration the employee's family structure, whether or not they have pets, and private information. For example, the profile analysis unit takes into consideration the employee's family structure and matches employees with similar family environments. For example, it matches employees with children. The profile analysis unit also takes into consideration the presence or absence of pets and matches employees who own pets. For example, it matches employees who own dogs. In this way, matching that takes private information into consideration improves the compatibility between employees.

[0061] The profile analysis unit uses the emotion estimation function to analyze the stress levels that employees experience on a daily basis, and can perform matching that leads to stress reduction. The profile analysis unit, for example, uses the emotion estimation function to analyze the employee's daily stress level. For example, it analyzes facial expressions and speech content while working to evaluate the stress level. The profile analysis unit also analyzes biometric data to evaluate the employee's stress level. For example, it evaluates stress levels based on heart rate and electrodermal activity. This allows for matching that leads to stress reduction, thereby reducing employee stress and enabling better matching.

[0062] The matching unit can analyze an employee's communication style and identify colleagues with whom they have good chemistry. The matching unit, for example, analyzes the writing style of an employee's emails and evaluates their communication style. For example, it evaluates compatibility based on whether the writing style is polite or casual. The matching unit also analyzes what is said in meetings and evaluates their communication style. For example, it evaluates compatibility based on the frequency of comments and the depth of their content. In this way, by analyzing communication styles, it is possible to identify colleagues with whom they have good chemistry.

[0063] The matching unit can analyze employees' past feedback and evaluation data and perform matching based on mutual evaluations with other employees. For example, the matching unit analyzes employees' past feedback data and evaluates compatibility based on mutual evaluations with other employees. For example, it matches employees who have a lot of positive feedback. The matching unit also analyzes evaluation data and evaluates employee performance. For example, it evaluates compatibility based on 360-degree evaluations and self-evaluations. In this way, by performing matching based on past feedback and evaluation data, it is possible to identify colleagues who are compatible with each other.

[0064] The matching unit can use the emotion estimation function to perform matching based on the relationship with the colleague about whom the employee has felt the most positive emotions in the past. The matching unit, for example, uses the emotion estimation function to identify the colleague about whom the employee has felt the most positive emotions in the past. For example, the matching unit analyzes emotional reactions in a specific project to identify colleagues who show positive emotions. The matching unit also matches employees based on positive emotions. For example, it matches employees based on successful experiences or projects that have given them high satisfaction. In this way, by performing matching based on positive emotions, it is possible to identify colleagues who are compatible with each other.

[0065] The matching unit can analyze employees' music and movie preferences and match them with colleagues who share common interests. For example, the matching unit analyzes employees' music preferences and matches them with colleagues who share common interests. For example, it matches employees who like the same artists or genres. The matching unit also analyzes movie preferences and matches them with colleagues who share common interests. For example, it matches employees who like the same directors or movies. In this way, by matching based on common interests, it is possible to identify colleagues who get along well with each other.

[0066] The matching unit can analyze data on employees' travel history and places visited, and match colleagues with similar travel destination preferences. The matching unit, for example, analyzes employees' travel history and matches colleagues with similar travel destination preferences. For example, it matches employees who have visited the same country or city. The matching unit also analyzes data on places visited and matches colleagues with similar travel destination preferences. For example, it matches employees who like beach resorts and mountainous areas. In this way, by matching based on travel destination preferences, it is possible to identify colleagues who are compatible with each other.

[0067] The matching unit uses the emotion estimation function to analyze the emotions felt by employees during specific time periods or days of the week, and can match them with colleagues who are most compatible with them during those time periods. The matching unit, for example, uses the emotion estimation function to analyze the emotions felt by employees during specific time periods or days of the week. For example, data showing positive emotions on weekends or specific time periods is extracted. The matching unit also matches employees based on the emotions felt during specific time periods or days of the week. For example, employees who show positive emotions during the same time period are matched with each other. In this way, by matching based on the emotions felt during specific time periods or days of the week, it is possible to identify colleagues who are compatible with each other.

[0068] The event planning department can analyze employees' past event participation history and plan new events based on the event format with the highest participation rate. For example, the event planning department analyzes employees' past event participation history and identifies the event format with the highest participation rate. For example, the event planning department plans a new event based on a casual lunch event or a sporting event. The event planning department also plans a new event that will attract employees' interest based on the event format with the highest participation rate. For example, the event planning department plans an event that incorporates popular themes or activities. In this way, by planning a new event based on past event participation history, it is possible to increase employees' motivation to participate.

[0069] The event planning department can analyze employees' food preferences and allergy information and plan events that offer optimal lunch menus. For example, the event planning department collects employees' food preferences and allergy information and plans events that offer optimal lunch menus. For example, vegetarian and gluten-free menus are provided. The event planning department also takes employees' allergy information into consideration and plans events that offer safe meals. For example, a nut-free menu is provided for employees with nut allergies. In this way, by providing lunch menus that take employees' food preferences and allergy information into consideration, participant satisfaction can be increased.

[0070] The event planning department can use the emotion estimation function to analyze the environment in which employees find most relaxing and plan an event that recreates that environment. The event planning department, for example, uses the emotion estimation function to analyze the environment in which employees find most relaxing. For example, the event planning department analyzes emotional responses in natural environments or quiet places to identify a relaxing environment. The event planning department also plans an event that recreates a relaxing environment. For example, the event planning department plans a relaxation event in a place surrounded by nature. In this way, by planning an event that recreates a relaxing environment, it is possible to increase the level of relaxation of employees.

[0071] The event planning department can plan workshops and club activities that make use of employees' hobbies and special skills, promoting natural interactions between employees. For example, the event planning department can collect information about employees' hobbies and special skills and plan workshops that make use of them. For example, they can hold cooking classes or art workshops. The event planning department can also plan club activities that make use of employees' special skills. For example, they can establish music clubs or sports clubs. This can promote natural interactions between employees through workshops and club activities that make use of their hobbies and special skills.

[0072] The event planning department plans events that employees' families and pets can participate in, deepening private connections. The event planning department plans events that employees' families and pets can participate in, deepening private connections. For example, they hold family days and picnics where pets are allowed. The event planning department also plans activities that families and pets can participate in. For example, they provide games for families and activities for pets. In this way, employees can deepen private connections with each other through events that families and pets can participate in.

[0073] The event planning department can use the emotion estimation function to analyze past events that employees enjoyed most and plan new events that incorporate those elements. The event planning department, for example, uses the emotion estimation function to analyze past events that employees enjoyed most. For example, it analyzes emotional reactions when participating in the event and identifies the elements that employees enjoyed. The event planning department also plans new events that incorporate elements of past events. For example, it plans events that incorporate popular activities or themes. In this way, by planning new events that incorporate elements of past events, it is possible to increase employees' motivation to participate.

[0074] The matching unit can analyze an employee's past romantic experiences and preferences to identify the most compatible partner. For example, the matching unit collects an employee's past romantic experiences and analyzes preferences and patterns. For example, it identifies factors that led to success and failure in past relationships. The matching unit also identifies the optimal partner based on the employee's preferences. For example, it performs matching based on the characteristics and hobbies of an ideal partner. In this way, the success rate of office romances can be increased by identifying the optimal partner based on past romantic experiences and preferences.

[0075] The matching unit can analyze employees' communication history and provide natural conversation starters. The matching unit, for example, analyzes employees' communication history and provides natural conversation starters. For example, it can suggest common topics based on the content of past emails and chats. The matching unit can also analyze meeting minutes and provide common topics. For example, it can provide conversation starters based on project progress and common goals. In this way, by providing natural conversation starters based on communication history, it is possible to promote interaction between employees.

[0076] The matching unit uses the emotion estimation function to recreate situations in which employees feel the most positive emotions, thereby promoting on-the-spot interactions. The matching unit, for example, uses the emotion estimation function to analyze situations in which employees feel the most positive emotions. For example, it analyzes emotional reactions in specific events or environments and identifies positive situations. The matching unit also recreates positive situations to promote on-the-spot interactions. For example, it recreates specific events or environments to promote interactions between employees. In this way, interactions between employees can be promoted by recreating situations in which positive emotions are felt.

[0077] The matching department can propose date plans that make use of employees' hobbies and interests, and promote natural interactions between employees. For example, the matching department collects information about employees' hobbies and interests and proposes date plans that make use of them. For example, it provides date plans that are suitable for employees who share a common hobby. The matching department also proposes date plans based on employees' interests. For example, it proposes activities that are suitable for employees who share a common interest. In this way, by proposing date plans that make use of hobbies and interests, it is possible to promote natural interactions between employees.

[0078] The matching unit can analyze the history of successful dates of employees and propose new date plans that incorporate those elements. For example, the matching unit collects the history of successful dates of employees in the past and analyzes those elements. For example, it identifies the locations and activities of successful dates. The matching unit also proposes new date plans that incorporate elements of successful dates. For example, it proposes plans that incorporate popular date spots and activities. In this way, by proposing new date plans that incorporate elements of successful dates in the past, it is possible to promote interaction between employees.

[0079] The matching unit uses the emotion estimation function to suggest the most relaxing date plan for employees, thereby promoting natural interactions. The matching unit, for example, uses the emotion estimation function to analyze the most relaxing date plan for employees. For example, it analyzes emotional reactions to specific date plans and identifies relaxing plans. The matching unit also suggests relaxing date plans. For example, it suggests a quiet cafe or a walk in nature. In this way, by suggesting relaxing date plans, it is possible to promote natural interactions between employees.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The matching unit can analyze employees' preferences for music and movies and match them with colleagues who share common interests. For example, it can match employees who like the same artists or genres. The matching unit can also analyze movie preferences and match colleagues who share common interests. For example, it can match employees who like the same director or movies. In this way, by matching based on common interests, it is possible to identify colleagues who get along well with each other.

[0082] The event planning department can analyze employees' past event participation history and plan new events based on the event format with the highest participation rate. For example, the event planning department can analyze employees' past event participation history and identify the event format with the highest participation rate. For example, it can plan a new event based on a casual lunch event or a sporting event. The event planning department can also plan new events that will attract employees' interest based on event formats with high participation rates. For example, it can plan events that incorporate popular themes or activities. In this way, by planning new events based on past event participation history, it is possible to increase employees' motivation to participate.

[0083] The matching unit can analyze data on employees' travel history and places visited, and match them with colleagues who share the same travel destination preferences. For example, it can analyze an employee's travel history and match colleagues who share the same travel destination preferences. For example, it can match employees who have visited the same country or city. The matching unit can also analyze data on places visited and match colleagues who share the same travel destination preferences. For example, it can match employees who like beach resorts and mountainous areas. In this way, by matching based on travel destination preferences, it is possible to identify colleagues who are compatible with each other.

[0084] The event planning department can analyze employees' food preferences and allergy information and plan events that offer optimal lunch menus. For example, they can collect employees' food preferences and allergy information and plan events that offer optimal lunch menus. For example, they can provide vegetarian and gluten-free menus. The event planning department can also plan events that take employees' allergy information into consideration and offer safe meals. For example, they can provide nut-free menus for employees with nut allergies. In this way, they can provide lunch menus that take employees' food preferences and allergy information into consideration, thereby increasing participant satisfaction.

[0085] The event planning department can plan workshops and club activities that make use of employees' hobbies and special skills, promoting natural interactions between employees. For example, they can collect information about employees' hobbies and special skills and plan workshops that make use of them. For example, they can hold cooking classes or art workshops. The event planning department can also plan club activities that make use of employees' special skills. For example, they can establish music clubs or sports clubs. This can promote natural interactions between employees through workshops and club activities that make use of hobbies and special skills.

[0086] The matching unit uses the emotion estimation function to analyze the emotions employees feel during specific times of the day or on specific days of the week, and can match them with colleagues who are most compatible with them during those times. For example, the emotion estimation function is used to analyze the emotions employees feel during specific times of the day or on specific days of the week. For example, data showing positive emotions on weekends or specific times of the day is extracted. The matching unit also matches employees based on the emotions they feel during specific times of the day or on specific days of the week. For example, it matches employees who show positive emotions during the same time of day. In this way, by matching based on the emotions felt during specific times of the day or on specific days of the week, it is possible to identify colleagues who are compatible with them.

[0087] The matching unit can use the emotion estimation function to perform matching based on the relationship with the colleague about whom the employee felt the most positive emotions in the past. For example, the emotion estimation function can be used to identify the colleague about whom the employee felt the most positive emotions in the past. For example, emotional reactions to a specific project can be analyzed to identify colleagues who show positive emotions. The matching unit also matches employees based on positive emotions. For example, it matches employees based on successful experiences or projects that give high satisfaction. In this way, by performing matching based on positive emotions, it is possible to identify colleagues who are compatible with each other.

[0088] The event planning department can use the emotion estimation function to analyze the environment in which employees find most relaxing and plan an event that recreates that environment. For example, the emotion estimation function can be used to analyze the environment in which employees find most relaxing. For example, the emotional responses in natural environments or quiet places can be analyzed to identify a relaxing environment. The event planning department can also plan an event that recreates a relaxing environment. For example, a relaxation event can be planned in a place surrounded by nature. In this way, by planning an event that recreates a relaxing environment, the level of relaxation of employees can be increased.

[0089] The matching unit uses the emotion estimation function to recreate situations in which employees feel the most positive emotions, thereby promoting on-the-spot interactions. For example, the emotion estimation function is used to analyze situations in which employees feel the most positive emotions. For example, emotional reactions in specific events or environments are analyzed to identify positive situations. The matching unit also recreates positive situations to promote on-the-spot interactions. For example, it recreates specific events or environments to promote interactions between employees. In this way, interactions between employees can be promoted by recreating situations in which positive emotions are felt.

[0090] The matching unit uses the emotion estimation function to suggest the most relaxing date plan for employees, thereby promoting natural interactions. For example, the emotion estimation function is used to analyze the most relaxing date plan for employees. For example, the emotional reactions to a specific date plan are analyzed to identify a relaxing plan. The matching unit also suggests a relaxing date plan. For example, it may suggest a quiet cafe or a walk in nature. In this way, by suggesting a relaxing date plan, natural interactions between employees can be promoted.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The Profile Analysis Department analyzes employee profiles, work styles, and values. For example, they collect information such as employees' age, gender, hobbies, and special skills, and analyze their work styles and values. Step 2: The matching department matches colleagues with compatible personalities based on the data analyzed by the profile analysis department. For example, if employee A and employee B have similar values ​​and hobbies, the two are matched. Step 3: The Event Planning Department plans casual coffee breaks, lunches, and after-work events to provide a place for employees matched by the Matching Department to naturally connect with each other. For example, they propose an event where a coffee break is held at a cafe every Friday afternoon.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Profile Analysis Department analyzes employee profiles, work styles, and values, and a matching unit that matches colleagues who have good personality compatibility based on the data analyzed by the profile analysis unit; An event planning department that plans casual coffee breaks, lunches, and after-work events to provide a place where employees matched by the matching department can naturally connect with each other. A system characterized by:

2. The profile analysis unit Using emotion estimation, the system analyzes employees' emotional responses to events and projects they have participated in in the past, and matches them based on positive experiences.

2. The system of claim 1.

3. The profile analysis unit Analyze employee health data and match employees based on their health status and lifestyle 2. The system of claim 1.

4. The matching unit Analyze employees' communication styles and identify compatible colleagues 2. The system of claim 1.

5. The event planning department Analyze employees' past event participation history and plan new events based on the event format with the highest participation rate.

2. The system of claim 1.

6. The profile analysis unit Using emotion estimation functionality, the company analyzes the stress levels employees experience on a daily basis and matches them with solutions that can help reduce stress.

2. The system of claim 1.

7. The matching unit Using emotion estimation, matching is performed based on the relationship with the colleague with whom the employee has had the most positive emotions in the past.

2. The system of claim 1.

8. The event planning department Using emotion estimation function, analyze the environment in which employees feel most relaxed and plan the event that recreates that environment.

2. The system of claim 1.

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