System

Abstract

An object of the system according to the embodiment is to simplify information and processes in a company and to quickly guide an appropriate department or person in charge.SOLUTION: A system according to an embodiment includes an information aggregation unit, an analysis unit, a guidance unit, and a flow integration unit. The information aggregation unit aggregates information in a company. The analysis unit analyzes the information aggregated by the information aggregation unit. The guidance unit guides an appropriate department or person in charge based on the information analyzed by the analysis unit. The flow integration unit integrates and presents the procedure flows on the basis of the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to quickly and efficiently grasp the complex information and processes within a company.

[0005] The system according to the embodiment aims to simplify information and processes within a company and quickly guide users to the appropriate department or person in charge. [Means for solving the problem]

[0006] The system according to the embodiment includes an information aggregation unit, an analysis unit, a guidance unit, and a flow integration unit. The information aggregation unit aggregates information within the company. The analysis unit analyzes the information aggregated by the information aggregation unit. The guidance unit guides the user to the appropriate department or person in charge based on the information analyzed by the analysis unit. The flow integration unit integrates and presents a procedure flow based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment simplifies information and processes within a company and can quickly guide users to the appropriate department or person in charge. [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 internal navigator AI system according to an embodiment of the present invention is a system that helps employees quickly obtain information. This system aggregates information such as internal company knowledge and application processes, and instantly guides employees on who to consult and what procedures they need to follow for specific issues or tasks. This allows the internal navigator AI system to help employees quickly obtain information, thereby improving work efficiency and convenience.

[0029] An internal navigator AI system according to an embodiment includes an information aggregation unit, an analysis unit, a guidance unit, and a flow integration unit. The information aggregation unit aggregates information within a company, for example, by centrally managing business data, employee feedback, project progress information, and the like. The analysis unit analyzes the aggregated information, for example, by using data mining, statistical analysis, and machine learning algorithms. The guidance unit guides the appropriate department or person in charge based on the analyzed information. For example, when an employee types "business trip application," the guidance unit provides contact information for the department and person in charge of the business trip application. The flow integration unit integrates and presents a procedural flow based on the analyzed information. For example, in the case of a business trip application, the flow integration unit presents steps such as filling out a business trip application form, obtaining approval from a supervisor, and submitting it to the accounting department as a single flow, clearly indicating the information and documents required at each step. This allows the internal navigator AI system to simplify internal information and processes and help employees quickly obtain information.

[0030] The analysis unit learns employees' past behavioral history and preferences, and can provide individually optimized information. For example, the analysis unit uses a generative AI to analyze employees' past behavioral history and learn preferences regarding specific tasks and projects. For example, an employee who has applied for many business trips in the past can be given priority in receiving information related to business trips. The analysis unit also provides individually optimized information based on the employee's preferences. For example, it can provide information related to specific projects or topics that the employee has shown interest in in the past. This makes it possible to provide optimal information based on the employee's past behavioral history and preferences.

[0031] The information aggregation unit can be equipped with an automatic update function that updates the aggregated information in real time and always provides the latest information. For example, the information aggregation unit uses a generation AI to collect information within a company in real time and always provides the latest information using the automatic update function. For example, when a new application process is added, that information is immediately reflected. The information aggregation unit also integrates the information into a database and collects data in real time to provide the latest information. This allows the latest information to always be provided.

[0032] The information aggregation unit can integrate and provide internal and external information, including external industry news and trend information. For example, the information aggregation unit uses a generation AI to collect external industry news and trend information, integrate it with internal information, and provide it. For example, it provides the latest technology trends and market trends along with internal project information. The information aggregation unit also uses RSS feeds and news APIs to collect external information in real time and integrate it with internal information. This allows internal and external information to be integrated and provided.

[0033] The guidance unit can provide more detailed information, including past response history and evaluations, in addition to the information about the department and person providing the guidance. For example, the guidance unit includes past response history in the information about the department and person providing the guidance using the generation AI. For example, it displays what kind of responses have been made in the past. The guidance unit also provides information including evaluations of the person in charge. For example, it displays evaluations such as customer satisfaction, response speed, and resolution rate. This makes it possible to provide detailed information, including past response history and evaluations.

[0034] When providing guidance, the guidance unit takes into account the schedules of the department and person in charge and can recommend the optimal timing for contact. For example, when the generation AI provides guidance, the guidance unit takes into account the schedules of the department and person in charge. For example, it can recommend a time when the person in charge is not in a meeting. The guidance unit also takes into account peak business hours and the person in charge's free time to recommend the optimal timing for contact. This makes it possible to recommend the optimal timing for contact.

[0035] The flow integration unit displays the required time and difficulty of each step in the procedure flow, making it easier for employees to plan procedures. For example, the flow integration unit displays the required time for each step in the procedure flow presented by the generation AI. For example, it displays the time it takes to fill out a business trip application form. The flow integration unit also displays the difficulty of each step. For example, it displays the complexity of the work and the skill level required. This makes it easier to plan procedures.

[0036] The flow integration unit can add recommended actions based on past procedural history to make procedures more efficient. For example, the flow integration unit uses a generative AI to analyze past procedural history and present recommended actions. For example, it refers to the steps of procedures that have been successful in the past. The flow integration unit also presents optimal procedures and recommended tools. For example, it suggests actions based on past success stories. This helps to make procedures more efficient.

[0037] The flow integration unit can provide reference information, including success stories and best practices from other employees. For example, the generation AI in the flow integration unit collects success stories from other employees and includes them in the procedure flow. For example, it presents specific examples of procedures that have been successful in the past. The flow integration unit also provides information including best practices. For example, it presents industry standards and optimal procedures. This allows success stories and best practices to be used as reference.

[0038] The flow integrator can provide the procedure flow via a mobile app or wearable device, allowing procedures to be carried out anytime, anywhere. For example, the flow integrator can provide the procedure flow presented by the generation AI via a mobile app, allowing employees to proceed with the procedure anytime, anywhere. For example, the procedure steps can be checked on a smartphone. The flow integrator can also provide the procedure flow via a wearable device. For example, the progress of the procedure can be checked on a smartwatch. This allows procedures to be carried out anytime, anywhere.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The internal navigator AI system also includes a voice recognition unit, which allows employees to input information by voice, making it easy to obtain information. For example, if an employee says, "Please tell me how to apply for a business trip," the voice recognition unit analyzes the content and provides appropriate information. The voice recognition unit can also proceed with procedures using voice commands. For example, it can recognize commands such as "proceed to the next step" and advance the procedural flow. This allows employees to obtain information and proceed with procedures without using their hands.

[0041] The analysis unit can monitor employees' health status and provide health-related information. For example, it can analyze an employee's step count and heart rate to understand their health condition. The analysis unit can also provide appropriate health information and advice based on the employee's health status. For example, for employees who are not getting enough exercise, it can explain the importance of exercise and provide simple exercise methods. It can also provide advice on relaxation methods and stress management for employees who are under a lot of stress. This can support the health of employees.

[0042] The information aggregation department can link with internal communication tools to promote information sharing among employees. For example, it can link with chat tools and email systems to automatically share important information. The information aggregation department can also link with project management tools to share project progress in real time. This allows for smooth information sharing among employees and improves work efficiency.

[0043] The information aggregation unit can provide information about entertainment and events within the company. For example, it can aggregate information about in-house movie screenings and sporting events and provide it to employees. The information aggregation unit can also provide entertainment information that is individually optimized based on the hobbies and interests of employees. For example, an employee who loves movies can be provided with the latest movie information and screening schedules. This helps employees to refresh themselves and promotes communication within the company.

[0044] The information department can guide employees to the most suitable departments and personnel based on their skills and experience. For example, an employee with a specific skill set can be guided to departments and projects where they can utilize those skills. The information department can also guide employees to the appropriate departments and personnel based on their career path. For example, an employee aiming to advance their career can be guided to departments and projects that offer growth opportunities. This allows employees to make the most of their skills and experience.

[0045] The Flow Integration Unit can add visual elements to the procedure flow to make it easier to understand visually. For example, each step can be displayed with an icon or diagram, making the procedure flow easier to understand visually. The Flow Integration Unit can also display the progress of the procedure in graphs and charts. This makes it easier for employees to grasp the progress of the procedure at a glance.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The Information Aggregation Department aggregates information within the company, such as business data, employee feedback, and project progress information, and manages it in a unified manner. Step 2: The analysis unit analyzes the aggregated information, for example, using data mining, statistical analysis, or machine learning algorithms. Step 3: The information section guides the employee to the appropriate department or person in charge based on the analyzed information. For example, if an employee enters "business trip application," the information section provides the contact information of the department and person in charge of business trip applications. Step 4: The flow integration unit integrates and presents a procedural flow based on the analyzed information. For example, in the case of a business trip application, the steps of filling out the application form, getting approval from a superior, and submitting it to the accounting department are presented as a single flow, clearly indicating the information and documents required at each step.

[0048] (Example 2) The internal navigator AI system according to an embodiment of the present invention is a system that helps employees quickly obtain information. This system aggregates information such as internal company knowledge and application processes, and instantly guides employees on who to consult and what procedures they need to follow for specific issues or tasks. This allows the internal navigator AI system to help employees quickly obtain information, thereby improving work efficiency and convenience.

[0049] An internal navigator AI system according to an embodiment includes an information aggregation unit, an analysis unit, a guidance unit, and a flow integration unit. The information aggregation unit aggregates information within a company, for example, by centrally managing business data, employee feedback, project progress information, and the like. The analysis unit analyzes the aggregated information, for example, by using data mining, statistical analysis, and machine learning algorithms. The guidance unit guides the appropriate department or person in charge based on the analyzed information. For example, when an employee types "business trip application," the guidance unit provides contact information for the department and person in charge of the business trip application. The flow integration unit integrates and presents a procedural flow based on the analyzed information. For example, in the case of a business trip application, the flow integration unit presents steps such as filling out a business trip application form, obtaining approval from a supervisor, and submitting it to the accounting department as a single flow, clearly indicating the information and documents required at each step. This allows the internal navigator AI system to simplify internal information and processes and help employees quickly obtain information.

[0050] The analysis unit learns employees' past behavioral history and preferences, and can provide individually optimized information. For example, the analysis unit uses a generative AI to analyze employees' past behavioral history and learn preferences regarding specific tasks and projects. For example, an employee who has applied for many business trips in the past can be given priority in receiving information related to business trips. The analysis unit also provides individually optimized information based on the employee's preferences. For example, it can provide information related to specific projects or topics that the employee has shown interest in in the past. This makes it possible to provide optimal information based on the employee's past behavioral history and preferences.

[0051] The information aggregation unit can be equipped with an automatic update function that updates the aggregated information in real time and always provides the latest information. For example, the information aggregation unit uses a generation AI to collect information within a company in real time and always provides the latest information using the automatic update function. For example, when a new application process is added, that information is immediately reflected. The information aggregation unit also integrates the information into a database and collects data in real time to provide the latest information. This allows the latest information to always be provided.

[0052] The analysis unit uses the emotion estimation function to analyze the emotion associated with the keywords entered by the employee, and can provide information according to the emotion. For example, the analysis unit uses the emotion estimation function to analyze the emotion associated with the keywords entered by the employee. For example, for keywords associated with positive emotions, related success stories and positive information are provided. For keywords associated with negative emotions, support information and solutions are provided. This makes it possible to provide information according to the employee's emotions.

[0053] The information aggregation unit can integrate and provide internal and external information, including external industry news and trend information. For example, the information aggregation unit uses a generation AI to collect external industry news and trend information, integrate it with internal information, and provide it. For example, it provides the latest technology trends and market trends along with internal project information. The information aggregation unit also uses RSS feeds and news APIs to collect external information in real time and integrate it with internal information. This allows internal and external information to be integrated and provided.

[0054] The guidance unit can provide more detailed information, including past response history and evaluations, in addition to the information about the department and person providing the guidance. For example, the guidance unit includes past response history in the information about the department and person providing the guidance using the generation AI. For example, it displays what kind of responses have been made in the past. The guidance unit also provides information including evaluations of the person in charge. For example, it displays evaluations such as customer satisfaction, response speed, and resolution rate. This makes it possible to provide detailed information, including past response history and evaluations.

[0055] When providing guidance, the guidance unit takes into account the schedules of the department and person in charge and can recommend the optimal timing for contact. For example, when the generation AI provides guidance, the guidance unit takes into account the schedules of the department and person in charge. For example, it can recommend a time when the person in charge is not in a meeting. The guidance unit also takes into account peak business hours and the person in charge's free time to recommend the optimal timing for contact. This makes it possible to recommend the optimal timing for contact.

[0056] The guidance unit uses the emotion estimation function to analyze the emotion associated with the keywords entered by the employee, and can guide the employee to the appropriate department or person in charge according to the emotion. For example, the guidance unit uses the emotion estimation function to analyze the emotion associated with the keywords entered by the employee. For example, an employee who is feeling stressed can be directed to the support department or person in charge. In addition, an employee who has positive emotions can be provided with related success stories and positive information. This makes it possible to guide the employee to the appropriate department or person in charge according to the employee's emotion.

[0057] The flow integration unit displays the required time and difficulty of each step in the procedure flow, making it easier for employees to plan procedures. For example, the flow integration unit displays the required time for each step in the procedure flow presented by the generation AI. For example, it displays the time it takes to fill out a business trip application form. The flow integration unit also displays the difficulty of each step. For example, it displays the complexity of the work and the skill level required. This makes it easier to plan procedures.

[0058] The flow integration unit can add recommended actions based on past procedural history to make procedures more efficient. For example, the flow integration unit uses a generative AI to analyze past procedural history and present recommended actions. For example, it refers to the steps of procedures that have been successful in the past. The flow integration unit also presents optimal procedures and recommended tools. For example, it suggests actions based on past success stories. This helps to make procedures more efficient.

[0059] The flow integration unit uses the emotion estimation function to analyze the emotions of employees as they go through procedures, and can provide support that matches their emotions. The flow integration unit, for example, uses the emotion estimation function to analyze the emotions of employees as they go through procedures. For example, an employee who is feeling stressed can be provided with relaxation techniques and support information. Also, an employee who has positive emotions can be provided with information that will increase motivation. This makes it possible to provide support that matches the emotions of employees.

[0060] The flow integration unit can provide reference information, including success stories and best practices from other employees. For example, the generation AI in the flow integration unit collects success stories from other employees and includes them in the procedure flow. For example, it presents specific examples of procedures that have been successful in the past. The flow integration unit also provides information including best practices. For example, it presents industry standards and optimal procedures. This allows success stories and best practices to be used as reference.

[0061] The flow integrator can provide the procedure flow via a mobile app or wearable device, allowing procedures to be carried out anytime, anywhere. For example, the flow integrator can provide the procedure flow presented by the generation AI via a mobile app, allowing employees to proceed with the procedure anytime, anywhere. For example, the procedure steps can be checked on a smartphone. The flow integrator can also provide the procedure flow via a wearable device. For example, the progress of the procedure can be checked on a smartwatch. This allows procedures to be carried out anytime, anywhere.

[0062] The flow integration unit uses the emotion estimation function to monitor in real time the emotional reactions of employees as they go through procedures, and can provide support to bring out positive emotions. The flow integration unit, for example, uses the emotion estimation function to monitor in real time the emotional reactions of employees as they go through procedures. For example, it can provide relaxation techniques and support information to employees who are feeling stressed. It can also provide support to bring out positive emotions. For example, it can provide information that increases motivation and success stories. This can provide support to bring out positive emotions.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The internal navigator AI system also includes a voice recognition unit, which allows employees to input information by voice, making it easy to obtain information. For example, if an employee says, "Please tell me how to apply for a business trip," the voice recognition unit analyzes the content and provides appropriate information. The voice recognition unit can also proceed with procedures using voice commands. For example, it can recognize commands such as "proceed to the next step" and advance the procedural flow. This allows employees to obtain information and proceed with procedures without using their hands.

[0065] The analysis unit can monitor employees' health status and provide health-related information. For example, it can analyze an employee's step count and heart rate to understand their health condition. The analysis unit can also provide appropriate health information and advice based on the employee's health status. For example, for employees who are not getting enough exercise, it can explain the importance of exercise and provide simple exercise methods. It can also provide advice on relaxation methods and stress management for employees who are under a lot of stress. This can support the health of employees.

[0066] The information aggregation department can link with internal communication tools to promote information sharing among employees. For example, it can link with chat tools and email systems to automatically share important information. The information aggregation department can also link with project management tools to share project progress in real time. This allows for smooth information sharing among employees and improves work efficiency.

[0067] The analysis unit can use the emotion estimation function to analyze employees' stress levels and provide information to reduce stress. For example, if an employee is feeling high stress, it can provide advice on relaxation methods and stress management. The analysis unit can also provide information on resources and support for stress reduction. For example, it can provide information on how to use counseling services or relaxation spaces. This helps reduce employee stress and provides a comfortable working environment.

[0068] The information aggregation unit can provide information about entertainment and events within the company. For example, it can aggregate information about in-house movie screenings and sporting events and provide it to employees. The information aggregation unit can also provide entertainment information that is individually optimized based on the hobbies and interests of employees. For example, an employee who loves movies can be provided with the latest movie information and screening schedules. This helps employees to refresh themselves and promotes communication within the company.

[0069] The information department can use the emotion estimation function to analyze employee motivation and provide information to improve motivation. For example, if an employee's motivation is declining, it can provide success stories and encouraging messages. The information department can also provide information on resources and support to increase employee motivation. For example, it can provide information on how to use training programs and career counseling. This can improve employee motivation and improve work efficiency.

[0070] The information department can guide employees to the most suitable departments and personnel based on their skills and experience. For example, an employee with a specific skill set can be guided to departments and projects where they can utilize those skills. The information department can also guide employees to the appropriate departments and personnel based on their career path. For example, an employee aiming to advance their career can be guided to departments and projects that offer growth opportunities. This allows employees to make the most of their skills and experience.

[0071] The information unit can use the emotion estimation function to provide feedback according to the employee's emotions. For example, if an employee has positive emotions, it can provide compliments or encouraging messages. If an employee has negative emotions, it can provide suggestions for improvement or support information. This makes it possible to provide feedback according to the employee's emotions, thereby improving employee motivation and satisfaction.

[0072] The Flow Integration Unit can add visual elements to the procedure flow to make it easier to understand visually. For example, each step can be displayed with an icon or diagram, making the procedure flow easier to understand visually. The Flow Integration Unit can also display the progress of the procedure in graphs and charts. This makes it easier for employees to grasp the progress of the procedure at a glance.

[0073] The flow integration unit can use its emotion estimation function to provide emotional support according to the progress of a procedure. For example, if a procedure is progressing smoothly, it can provide encouraging messages and advice on the next step. If the procedure is stalled, it can provide solutions and support information. This allows support to be provided according to the employee's emotions, improving the efficiency of procedures.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The Information Aggregation Department aggregates information within the company, such as business data, employee feedback, and project progress information, and manages it in a unified manner. Step 2: The analysis unit analyzes the aggregated information, for example, using data mining, statistical analysis, or machine learning algorithms. Step 3: The information section guides the employee to the appropriate department or person in charge based on the analyzed information. For example, if an employee enters "business trip application," the information section provides the contact information of the department and person in charge of business trip applications. Step 4: The flow integration unit integrates and presents a procedural flow based on the analyzed information. For example, in the case of a business trip application, the steps of filling out the application form, getting approval from a superior, and submitting it to the accounting department are presented as a single flow, clearly indicating the information and documents required at each step.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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, in order to avoid confusion and to 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.

[0142] 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]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an information aggregation department that aggregates information within the company; an analysis unit that analyzes the information aggregated by the information aggregation unit; a guidance unit that guides the user to an appropriate department or person in charge based on the information analyzed by the analysis unit; a flow integration unit that integrates and presents procedure flows based on the information analyzed by the analysis unit. A system characterized by:

2. The information aggregation unit The aggregated information is updated in real time, and an automatic update function is provided to ensure that the latest information is always provided.

2. The system of claim 1.

3. The guide unit is Provide more detailed information, including past response history and evaluations, on the information of the department and person in charge.

2. The system of claim 1.

4. The flow integration unit The procedure flow displays the time required and difficulty of each step, making it easier for employees to plan the procedure.

2. The system of claim 1.

5. The analysis unit Using the emotion estimation function, the emotions associated with keywords entered by employees are analyzed and information is provided according to those emotions.

2. The system of claim 1.

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