system

Abstract

The system according to this embodiment aims to support workplace communication and employee mental health in real time. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a decision unit, and a proposal unit. The acquisition unit acquires images and voices of employees. The analysis unit analyzes the employee's facial expressions, tone of voice, and language use based on the information acquired by the acquisition unit. The decision unit determines an action in accordance with the analysis results obtained by the analysis unit. The proposal unit proposes the action determined by the decision unit to the employee.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, workplace communication and employees' mental health have not been sufficiently supported in real time, and there is room for improvement.

[0005] The system according to the embodiment aims to support workplace communication and employees' mental health in real time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a decision unit, and a proposal unit. The acquisition unit acquires images and voices of employees. The analysis unit analyzes the employee's facial expressions, tone of voice, and language use based on the information acquired by the acquisition unit. The decision unit determines an action based on the analysis results obtained by the analysis unit. The proposal unit proposes the action determined by the decision unit to the employee. [Effects of the Invention]

[0007] The system according to this embodiment can support workplace communication and employee mental health in real time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The Emotional Harmony Assistant System according to an embodiment of the present invention is an innovative system for supporting workplace communication and mental health. This system uses AI to analyze employees' facial expressions, tone of voice, and language use in real time and provide appropriate advice. For example, if the AI ​​detects that an employee giving a presentation during a meeting is nervous, it may suggest breathing exercises to help the employee relax or suggest that the meeting facilitator interject a lighthearted joke to ease the tension. This enhances the effectiveness of the presentation while simultaneously easing the tension of the participants. Furthermore, the AI ​​constantly monitors employees' stress levels and recommends that employees experiencing high stress levels take short breaks for meditation, stretching, or listening to relaxing music. For example, it may suggest a 5-minute desktop meditation to an employee fatigued from desk work, promoting mental and physical refreshment. In the case of collective stress felt by a team under tight deadlines, the AI ​​may advise the team leader to schedule a short break, helping to reduce stress for the entire team. By increasing employee well-being, this system improves the workplace atmosphere and contributes to increased productivity. A comfortable working environment for employees is essential for the sustainable growth of a company. This allows the emotional harmony assistant system to support workplace communication and mental health by analyzing employees' facial expressions, tone of voice, and language use in real time and providing appropriate advice.

[0029] The emotion harmony assistant system according to this embodiment comprises an acquisition unit, an analysis unit, a decision unit, and a suggestion unit. The acquisition unit acquires images and voices of employees. Images and voices of employees include, but are not limited to, still images, videos, and audio recordings. The acquisition unit can, for example, use a camera to capture the employee's facial expressions and a microphone to record the employee's voice. The acquisition unit can also acquire audio data directly from the employee's device. For example, the acquisition unit can acquire audio data from the employee's smartphone or personal computer. Furthermore, the acquisition unit can also acquire past image and audio data of employees. For example, the acquisition unit can acquire past meeting recordings and presentation videos of employees. The analysis unit analyzes the employee's facial expressions, voice tone, and language use based on the information acquired by the acquisition unit. The analysis unit can, for example, use facial recognition technology to analyze the employee's facial expressions. The analysis unit can also use voice analysis technology to analyze the employee's voice tone. Furthermore, the analysis unit can use natural language processing technology to analyze the employee's language use. For example, the analysis unit recognizes emotions such as smiles, anger, and sadness from the employee's facial expressions. Voice analysis technology analyzes the pitch, volume, and speed of an employee's voice to estimate their emotions. Natural language processing technology analyzes the employee's language use to determine whether they are using polite or casual language. The decision unit determines actions based on the analysis results obtained by the analysis unit. For example, if an employee is tense, the decision unit will determine actions to help them relax. The decision unit can also determine actions to reduce stress if the employee is feeling stressed. Furthermore, if the employee is concentrating, the decision unit can determine actions to help them maintain their concentration. For example, if an employee is tense, the decision unit will determine breathing techniques to help them relax. If an employee is feeling stressed, it will determine short periods of meditation. If an employee is concentrating, it will determine stretches to help them maintain their concentration. The suggestion unit proposes the actions determined by the decision unit to the employee. For example, the suggestion unit will suggest breathing techniques to help the employee relax. The suggestion unit can also suggest short periods of meditation. Furthermore, the suggestion unit can also suggest stretches.For example, the suggestion department might suggest deep breathing or diaphragmatic breathing to employees. It might also suggest a five-minute mindfulness meditation as a short meditation session, or shoulder and lower back stretches as stretches. In this way, the emotional harmony assistant system according to the embodiment can support workplace communication and mental health by analyzing employees' facial expressions, tone of voice, and language use in real time and providing appropriate advice.

[0030] The acquisition unit acquires images and audio of employees. These include, but are not limited to, still images, videos, and audio recordings. For example, the unit might use a camera to capture an employee's facial expressions and a microphone to record their voice. Specifically, the camera is high-resolution, capable of capturing subtle facial expressions. The microphone features noise cancellation, eliminating ambient noise for clear audio. Furthermore, the acquisition unit can acquire audio data directly from employees' devices. For example, it can acquire audio data from employees' smartphones or computers. This ensures reliable audio data acquisition even when employees are working remotely. Additionally, the acquisition unit can acquire past image and audio data of employees. For example, it can acquire past meeting recordings and presentation videos. This allows for an understanding of employees' past emotional states and communication styles. The acquisition unit centrally manages this data and can collaborate with other departments as needed. For example, the acquired data can be stored on a cloud server, making it accessible to the analysis and decision-making departments. Furthermore, by adjusting the frequency and accuracy of data acquisition, flexible responses to specific situations and conditions become possible. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes employees' facial expressions, voice tone, and language use based on information acquired by the data acquisition department. For example, the analysis department analyzes employees' facial expressions using facial recognition technology. Specifically, it uses an image recognition algorithm based on deep learning to extract facial feature points and estimate emotions. This allows for high-precision recognition of emotions such as smiles, anger, sadness, and surprise. The analysis department can also analyze employees' voice tone using voice analysis technology. Voice analysis includes the extraction of acoustic features and spectral analysis of voice signals, analyzing the pitch, volume, and speed of the voice in detail. This allows for the estimation of the employee's emotional state. Furthermore, the analysis department can analyze employees' language use using natural language processing technology. For example, it uses text mining technology to analyze the content of employee speech, identifying polite and casual language, positive and negative expressions. This allows for an understanding of the employee's communication style and emotional state. By combining these technologies, the analysis department can grasp the overall emotional state of employees in real time. Furthermore, the analysis department can utilize historical data and statistical information to analyze long-term emotional fluctuations and trends. This allows for continuous monitoring of employees' mental health and enables early detection of problems.

[0032] The decision-making unit determines actions based on the analysis results obtained by the analysis unit. For example, if an employee is feeling tense, the decision-making unit will determine actions to help them relax. Specifically, if an employee is feeling tense, it may suggest relaxation techniques such as deep breathing or diaphragmatic breathing. The decision-making unit can also determine actions to reduce stress if an employee is feeling stressed. For example, it may suggest short periods of meditation or light exercise. Furthermore, if an employee is concentrating, the decision-making unit can determine actions to help them maintain their concentration. For example, it may suggest stretching or short breaks to maintain concentration. The decision-making unit can customize these actions according to the individual employee's situation and suggest them at the optimal time. For example, if an employee has an important presentation coming up, it will provide specific advice to alleviate tension. Also, if an employee is participating in a long meeting, it will suggest taking a break at an appropriate time. The decision-making unit can also collect employee feedback and evaluate the effectiveness of the suggestions. This allows for continuous improvement of the suggestions and optimal support for employee mental health.

[0033] The suggestion department proposes actions to employees that have been decided by the decision department. For example, the suggestion department might suggest breathing exercises to employees to help them relax. Specifically, it might explain in detail how to do deep breathing and diaphragmatic breathing, and provide points to keep in mind when actually doing them. The suggestion department can also suggest short meditation sessions. For example, it might explain how to do a 5-minute mindfulness meditation and provide points to focus on during meditation. Furthermore, the suggestion department can suggest stretching exercises. For example, it might explain in detail how to do shoulder stretches and lower back stretches, and provide points to keep in mind when actually doing them. The suggestion department can notify employees of these suggestions on their devices. For example, it can send notifications to smartphones and computers so that employees can immediately take action on the suggestions. The suggestion department can also display the suggestions in a visually easy-to-understand way. For example, it can use videos and illustrations to illustrate the suggestions concretely. This allows employees to easily understand and take action on the suggestions. In addition, the suggestion department can collect employee feedback and evaluate the effectiveness of the suggestions. This allows for continuous improvement of the suggestions and optimal support for employee mental health. The proposal department can improve employee satisfaction by providing customized proposals tailored to the individual circumstances of each employee.

[0034] The suggestion unit can suggest breathing techniques for relaxation. For example, the suggestion unit may suggest deep breathing. For example, the suggestion unit may suggest diaphragmatic breathing. For example, the suggestion unit may suggest the 4-7-8 breathing technique. This allows for the reduction of tension by suggesting breathing techniques for relaxation to employees. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, if the suggestion unit detects tension in an employee, it can prompt the generating AI with "Please suggest breathing techniques for relaxation" and provide the employee with breathing techniques suggested by the generating AI.

[0035] The suggestion department can suggest a short meditation session. For example, it might suggest a 5-minute meditation session. The suggestion department could also suggest a mindfulness meditation session. For example, it could suggest a breathing meditation session. This would allow employees to refresh their minds and bodies by engaging in short meditation sessions. Some or all of the above-described processes in the suggestion department may be performed using AI, for example, or without AI. For example, if the suggestion department detects employee stress, it can prompt the generating AI with "Please suggest a short meditation session" and provide the employee with a meditation method suggested by the generating AI.

[0036] The suggestion unit can suggest stretches. For example, the suggestion unit can suggest shoulder stretches. For example, the suggestion unit can also suggest lower back stretches. For example, the suggestion unit can also suggest neck stretches. This allows employees to relieve physical tension by performing stretches. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, if the suggestion unit detects employee fatigue, it can prompt the generating AI with "Please suggest stretches" and provide the employee with stretches suggested by the generating AI.

[0037] The suggestion unit can suggest relaxing music. For example, the suggestion unit may suggest classical music. For example, the suggestion unit may suggest nature sounds. For example, the suggestion unit may suggest ambient music. This allows employees to reduce stress by listening to relaxing music. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, if the suggestion unit detects employee stress, it can prompt the generating AI with "Please suggest relaxing music," and provide the employee with music suggested by the generating AI.

[0038] The suggestion department can advise taking short breaks. For example, the suggestion department might suggest a 5-minute break. The suggestion department might also suggest a 10-minute break. The suggestion department might also suggest a 15-minute break. This allows employees to take short breaks, thereby reducing collective stress. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, if the suggestion department detects team stress, it can prompt a generating AI with "Please suggest a short break," and provide the team leader with the break time suggested by the generating AI.

[0039] The acquisition unit can analyze an employee's past behavioral history and select the optimal acquisition method. For example, the acquisition unit can analyze the situations in which an employee was relaxed in the past and acquire images and audio corresponding to those situations. For example, the acquisition unit can analyze the timing of times when an employee felt stressed in the past and acquire images and audio avoiding those times. For example, the acquisition unit can analyze the tasks during which an employee was focused in the past and acquire images and audio during those tasks. In this way, the optimal acquisition method can be selected by analyzing the employee's past behavioral history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the employee's past behavioral data into a generating AI and have the generating AI select the optimal acquisition method.

[0040] The acquisition unit can filter images and audio based on the employee's current work status and areas of interest when acquiring them. For example, if an employee is in a meeting, the acquisition unit will prioritize acquiring images and audio related to the meeting's content. For example, if an employee is giving a presentation, the acquisition unit will prioritize acquiring images and audio related to the presentation's content. For example, if an employee is relaxing, the acquisition unit will prioritize acquiring images and audio related to relaxation. By filtering based on the employee's work status and areas of interest, highly relevant information can be acquired. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the employee's work status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The acquisition unit can prioritize the acquisition of highly relevant information by considering the employee's geographical location when acquiring images and audio. For example, if the employee is in the office, the acquisition unit will prioritize the acquisition of information related to the situation inside the office. For example, if the employee is out of the office, the acquisition unit will prioritize the acquisition of information related to the situation at the location. For example, if the employee is working remotely from home, the acquisition unit will prioritize the acquisition of information related to the situation at home. In this way, by considering the employee's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the employee's geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant information.

[0042] The acquisition unit can analyze an employee's social media activity and obtain relevant information when acquiring images and audio. For example, the acquisition unit can acquire relevant images and audio based on information shared by the employee on social media. For example, the acquisition unit can acquire relevant images and audio based on information of accounts followed by the employee on social media. For example, the acquisition unit can acquire relevant images and audio based on information related to topics the employee has shown interest in on social media. In this way, relevant information can be obtained by analyzing the employee's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input employee social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0043] The analysis department can improve the accuracy of its analysis by referring to past employee behavior data. For example, the analysis department can refer to situations in which an employee was stressed in the past and perform an analysis based on those situations. For example, the analysis department can refer to situations in which an employee was relaxed in the past and perform an analysis based on those situations. For example, the analysis department can refer to situations in which an employee felt stressed in the past and perform an analysis based on those situations. In this way, the accuracy of the analysis is improved by referring to past employee behavior data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input past employee behavior data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0044] The analysis department can apply different analysis algorithms depending on the employee's work content during analysis. For example, if an employee is giving a presentation, the analysis department will apply an analysis algorithm specialized for presentations. For example, if an employee is attending a meeting, the analysis department will apply an analysis algorithm specialized for meetings. For example, if an employee is doing desk work, the analysis department will apply an analysis algorithm specialized for desk work. By applying different analysis algorithms according to the employee's work content, more appropriate analysis becomes possible. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input employee work content data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0045] The analysis department can perform analyses while considering the geographical distribution of employees. For example, if an employee is in the office, the analysis department will consider the situation inside the office. For example, if an employee is out of the office, the analysis department will consider the situation at their destination. For example, if an employee is working remotely, the analysis department will consider the situation at their home. This allows for more appropriate analysis by considering the geographical distribution of employees. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input employee geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0046] The analysis department can improve the accuracy of its analysis by referring to the employee's relevant literature during the analysis process. For example, the analysis department can refer to relevant literature that the employee has read in the past and perform analysis based on its content. For example, the analysis department can refer to relevant literature that the employee uses in their work and perform analysis based on its content. For example, the analysis department can refer to relevant literature that the employee is interested in and perform analysis based on its content. In this way, the accuracy of the analysis is improved by referring to the employee's relevant literature. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input employee relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0047] The decision-making unit can select an appropriate action by referring to the employee's past behavioral history when making a decision. For example, the decision-making unit can refer to what actions the employee has taken in the past to relax and select that action. For example, the decision-making unit can refer to what actions the employee has taken in the past to reduce stress and select that action. For example, the decision-making unit can refer to what actions the employee has taken in the past to maintain concentration and select that action. In this way, the optimal action can be selected by referring to the employee's past behavioral history. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the employee's past behavioral data into a generating AI and have the generating AI perform the selection of the optimal action.

[0048] The decision-making unit can determine the priority of actions based on the employee's work content when making action decisions. For example, if an employee is giving a presentation, the decision-making unit will prioritize actions related to the presentation. For example, if an employee is attending a meeting, the decision-making unit will prioritize actions related to the meeting. For example, if an employee is doing desk work, the decision-making unit will prioritize actions related to desk work. By prioritizing actions based on the employee's work content, a more appropriate action can be selected. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input employee work content data into a generating AI and have the generating AI perform the action priority determination.

[0049] The decision-making unit can select an appropriate action when making a decision, taking into account the employee's geographical location. For example, if the employee is in the office, the decision-making unit will select an action that can be performed within the office. If the employee is out of the office, the decision-making unit will select an action that can be performed outside the office. If the employee is working remotely, the decision-making unit will select an action that can be performed at home. In this way, the optimal action can be selected by taking into account the employee's geographical location. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the employee's geographical location into a generating AI and have the generating AI select the optimal action.

[0050] The decision-making unit can analyze an employee's social media activity to determine an action. For example, the decision-making unit can determine relevant actions based on information shared by the employee on social media. For example, the decision-making unit can determine relevant actions based on information from accounts followed by the employee on social media. For example, the decision-making unit can determine actions related to topics the employee has shown interest in on social media. In this way, relevant actions can be determined by analyzing the employee's social media activity. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input employee social media activity data into a generating AI and have the generating AI make the action decision.

[0051] The suggestion function can provide appropriate suggestions by referring to the employee's past behavioral data. For example, the suggestion function can make similar suggestions based on breathing techniques the employee has used to relax in the past. For example, the suggestion function can suggest meditation at a similar time based on the time the employee has meditated in the past. For example, the suggestion function can suggest similar stretches based on the stretching methods the employee has used in the past. In this way, the suggestion function can provide optimal suggestions by referring to the employee's past behavioral data. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the employee's past behavioral data into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0052] The suggestion function can apply different suggestion algorithms depending on the employee's work content when making suggestions. For example, if an employee is giving a presentation, the suggestion function might suggest breathing techniques to help them relax during the presentation. If an employee is attending a meeting, the suggestion function might suggest a short meditation session during the meeting. If an employee is doing desk work, the suggestion function might suggest stretches they can do while working at their desk. By applying different suggestion algorithms depending on the employee's work content, the suggestion function can provide more appropriate suggestions. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input employee work content data into a generating AI and have the generating AI apply an appropriate suggestion algorithm.

[0053] The suggestion function can provide appropriate suggestions by considering the employee's geographical location. For example, if the employee is in the office, the suggestion function can suggest relaxation methods that can be performed in the office. If the employee is out of the office, the suggestion function can suggest meditation methods that can be performed while out. If the employee is working remotely, the suggestion function can suggest stretching methods that can be performed at home. By considering the employee's geographical location, the function can provide optimal suggestions. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the employee's geographical location into a generating AI and have the generating AI provide appropriate suggestions.

[0054] The suggestion department can analyze employees' social media activity when making suggestions. For example, the suggestion department can suggest relevant relaxation methods based on information employees have shared on social media. For example, the suggestion department can suggest relevant meditation methods based on information about accounts employees follow on social media. For example, the suggestion department can suggest stretching methods related to topics employees have shown interest in on social media. In this way, relevant suggestions can be provided by analyzing employees' social media activity. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input employee social media activity data into a generating AI and have the generating AI perform the task of providing suggestions.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The emotional harmony assistant system can also be equipped with a learning unit. This learning unit learns to improve the accuracy of its suggestions based on the employee's past behavioral data and feedback data. For example, it can learn the effects of breathing techniques the employee has used to relax in the past and suggest the most effective breathing technique; learn the effects of meditation the employee has practiced in the past and suggest the optimal meditation method; or learn the effects of stretching the employee has practiced in the past and suggest the optimal stretching method. This allows the learning unit to provide suggestions tailored to the individual needs of each employee.

[0057] The emotional harmony assistant system can also be equipped with a notification unit. This unit notifies employees when it is time to perform suggested actions. For example, if an employee is feeling tense, the notification unit might notify them to practice relaxing breathing exercises. If an employee is feeling stressed, the notification unit might notify them to take a short meditation. If an employee is feeling fatigued, the notification unit might notify them to stretch. This allows the notification unit to support employees in performing suggested actions at the appropriate time.

[0058] The emotional harmony assistant system can also be equipped with a customization section. This customization section tailors its suggestions based on the individual employee's preferences and needs. For example, if an employee prefers a particular music genre, the customization section will suggest music of that genre. If an employee prefers a particular meditation method, the customization section will suggest that method. If an employee prefers a particular stretching method, the customization section will suggest that stretching method. This allows the customization section to provide suggestions tailored to the individual employee's preferences.

[0059] The emotional harmony assistant system can also include a rewards unit. The rewards unit provides rewards when employees perform suggested behaviors. For example, if an employee performs breathing exercises to relax, the rewards unit awards points. If an employee meditates for a short time, the rewards unit awards a badge. If an employee stretches, the rewards unit provides a reward. This allows the rewards unit to motivate employees to actively perform suggested behaviors.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The acquisition unit acquires images and audio of employees. These include, for example, still images, videos, and audio recordings. The acquisition unit uses a camera to capture employees' facial expressions and a microphone to record their voices. The acquisition unit can also acquire audio data from employees' smartphones or computers. Furthermore, the acquisition unit can acquire recordings of past meetings and presentation videos of employees. Step 2: The analysis unit analyzes the employee's facial expressions, voice tone, and language use based on the information acquired by the acquisition unit. The analysis unit analyzes the employee's facial expressions using facial recognition technology and the employee's voice tone using voice analysis technology. Furthermore, it analyzes the employee's language use using natural language processing technology. For example, it recognizes emotions such as smiles, anger, and sadness from the employee's facial expressions, and estimates emotions by analyzing the pitch, volume, and speed of the voice using voice analysis technology. It also analyzes the employee's language use using natural language processing technology to determine whether it is polite or casual. Step 3: The decision-making unit determines actions based on the analysis results obtained by the analysis unit. For example, if an employee is tense, it will determine actions to help them relax; if an employee is stressed, it will determine actions to reduce stress. Furthermore, if an employee is focused, it will determine actions to help them maintain their focus. Specifically, this might include breathing exercises to relax, short meditation sessions, or stretches to maintain concentration. Step 4: The proposal team proposes the actions decided by the decision team to the employees. For example, they might suggest breathing exercises to relax, short meditation sessions, or stretching. Specifically, they might suggest deep breathing, diaphragmatic breathing, 5-minute mindfulness meditation, or shoulder and lower back stretches.

[0062] (Example of form 2) The Emotional Harmony Assistant System according to an embodiment of the present invention is an innovative system for supporting workplace communication and mental health. This system uses AI to analyze employees' facial expressions, tone of voice, and language use in real time and provide appropriate advice. For example, if the AI ​​detects that an employee giving a presentation during a meeting is nervous, it may suggest breathing exercises to help the employee relax or suggest that the meeting facilitator interject a lighthearted joke to ease the tension. This enhances the effectiveness of the presentation while simultaneously easing the tension of the participants. Furthermore, the AI ​​constantly monitors employees' stress levels and recommends that employees experiencing high stress levels take short breaks for meditation, stretching, or listening to relaxing music. For example, it may suggest a 5-minute desktop meditation to an employee fatigued from desk work, promoting mental and physical refreshment. In the case of collective stress felt by a team under tight deadlines, the AI ​​may advise the team leader to schedule a short break, helping to reduce stress for the entire team. By increasing employee well-being, this system improves the workplace atmosphere and contributes to increased productivity. A comfortable working environment for employees is essential for the sustainable growth of a company. This allows the emotional harmony assistant system to support workplace communication and mental health by analyzing employees' facial expressions, tone of voice, and language use in real time and providing appropriate advice.

[0063] The emotion harmony assistant system according to this embodiment comprises an acquisition unit, an analysis unit, a decision unit, and a suggestion unit. The acquisition unit acquires images and voices of employees. Images and voices of employees include, but are not limited to, still images, videos, and audio recordings. The acquisition unit can, for example, use a camera to capture the employee's facial expressions and a microphone to record the employee's voice. The acquisition unit can also acquire audio data directly from the employee's device. For example, the acquisition unit can acquire audio data from the employee's smartphone or personal computer. Furthermore, the acquisition unit can also acquire past image and audio data of employees. For example, the acquisition unit can acquire past meeting recordings and presentation videos of employees. The analysis unit analyzes the employee's facial expressions, voice tone, and language use based on the information acquired by the acquisition unit. The analysis unit can, for example, use facial recognition technology to analyze the employee's facial expressions. The analysis unit can also use voice analysis technology to analyze the employee's voice tone. Furthermore, the analysis unit can use natural language processing technology to analyze the employee's language use. For example, the analysis unit recognizes emotions such as smiles, anger, and sadness from the employee's facial expressions. Voice analysis technology analyzes the pitch, volume, and speed of an employee's voice to estimate their emotions. Natural language processing technology analyzes the employee's language use to determine whether they are using polite or casual language. The decision unit determines actions based on the analysis results obtained by the analysis unit. For example, if an employee is tense, the decision unit will determine actions to help them relax. The decision unit can also determine actions to reduce stress if the employee is feeling stressed. Furthermore, if the employee is concentrating, the decision unit can determine actions to help them maintain their concentration. For example, if an employee is tense, the decision unit will determine breathing techniques to help them relax. If an employee is feeling stressed, it will determine short periods of meditation. If an employee is concentrating, it will determine stretches to help them maintain their concentration. The suggestion unit proposes the actions determined by the decision unit to the employee. For example, the suggestion unit will suggest breathing techniques to help the employee relax. The suggestion unit can also suggest short periods of meditation. Furthermore, the suggestion unit can also suggest stretches.For example, the suggestion department might suggest deep breathing or diaphragmatic breathing to employees. It might also suggest a five-minute mindfulness meditation as a short meditation session, or shoulder and lower back stretches as stretches. In this way, the emotional harmony assistant system according to the embodiment can support workplace communication and mental health by analyzing employees' facial expressions, tone of voice, and language use in real time and providing appropriate advice.

[0064] The acquisition unit acquires images and audio of employees. These include, but are not limited to, still images, videos, and audio recordings. For example, the unit might use a camera to capture an employee's facial expressions and a microphone to record their voice. Specifically, the camera is high-resolution, capable of capturing subtle facial expressions. The microphone features noise cancellation, eliminating ambient noise for clear audio. Furthermore, the acquisition unit can acquire audio data directly from employees' devices. For example, it can acquire audio data from employees' smartphones or computers. This ensures reliable audio data acquisition even when employees are working remotely. Additionally, the acquisition unit can acquire past image and audio data of employees. For example, it can acquire past meeting recordings and presentation videos. This allows for an understanding of employees' past emotional states and communication styles. The acquisition unit centrally manages this data and can collaborate with other departments as needed. For example, the acquired data can be stored on a cloud server, making it accessible to the analysis and decision-making departments. Furthermore, by adjusting the frequency and accuracy of data acquisition, flexible responses to specific situations and conditions become possible. This allows the acquisition unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis department analyzes employees' facial expressions, voice tone, and language use based on information acquired by the data acquisition department. For example, the analysis department analyzes employees' facial expressions using facial recognition technology. Specifically, it uses an image recognition algorithm based on deep learning to extract facial feature points and estimate emotions. This allows for high-precision recognition of emotions such as smiles, anger, sadness, and surprise. The analysis department can also analyze employees' voice tone using voice analysis technology. Voice analysis includes the extraction of acoustic features and spectral analysis of voice signals, analyzing the pitch, volume, and speed of the voice in detail. This allows for the estimation of the employee's emotional state. Furthermore, the analysis department can analyze employees' language use using natural language processing technology. For example, it uses text mining technology to analyze the content of employee speech, identifying polite and casual language, positive and negative expressions. This allows for an understanding of the employee's communication style and emotional state. By combining these technologies, the analysis department can grasp the overall emotional state of employees in real time. Furthermore, the analysis department can utilize historical data and statistical information to analyze long-term emotional fluctuations and trends. This allows for continuous monitoring of employees' mental health and enables early detection of problems.

[0066] The decision-making unit determines actions based on the analysis results obtained by the analysis unit. For example, if an employee is feeling tense, the decision-making unit will determine actions to help them relax. Specifically, if an employee is feeling tense, it may suggest relaxation techniques such as deep breathing or diaphragmatic breathing. The decision-making unit can also determine actions to reduce stress if an employee is feeling stressed. For example, it may suggest short periods of meditation or light exercise. Furthermore, if an employee is concentrating, the decision-making unit can determine actions to help them maintain their concentration. For example, it may suggest stretching or short breaks to maintain concentration. The decision-making unit can customize these actions according to the individual employee's situation and suggest them at the optimal time. For example, if an employee has an important presentation coming up, it will provide specific advice to alleviate tension. Also, if an employee is participating in a long meeting, it will suggest taking a break at an appropriate time. The decision-making unit can also collect employee feedback and evaluate the effectiveness of the suggestions. This allows for continuous improvement of the suggestions and optimal support for employee mental health.

[0067] The suggestion department proposes actions to employees that have been decided by the decision department. For example, the suggestion department might suggest breathing exercises to employees to help them relax. Specifically, it might explain in detail how to do deep breathing and diaphragmatic breathing, and provide points to keep in mind when actually doing them. The suggestion department can also suggest short meditation sessions. For example, it might explain how to do a 5-minute mindfulness meditation and provide points to focus on during meditation. Furthermore, the suggestion department can suggest stretching exercises. For example, it might explain in detail how to do shoulder stretches and lower back stretches, and provide points to keep in mind when actually doing them. The suggestion department can notify employees of these suggestions on their devices. For example, it can send notifications to smartphones and computers so that employees can immediately take action on the suggestions. The suggestion department can also display the suggestions in a visually easy-to-understand way. For example, it can use videos and illustrations to illustrate the suggestions concretely. This allows employees to easily understand and take action on the suggestions. In addition, the suggestion department can collect employee feedback and evaluate the effectiveness of the suggestions. This allows for continuous improvement of the suggestions and optimal support for employee mental health. The proposal department can improve employee satisfaction by providing customized proposals tailored to the individual circumstances of each employee.

[0068] The suggestion unit can suggest breathing techniques for relaxation. For example, the suggestion unit may suggest deep breathing. For example, the suggestion unit may suggest diaphragmatic breathing. For example, the suggestion unit may suggest the 4-7-8 breathing technique. This allows for the reduction of tension by suggesting breathing techniques for relaxation to employees. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, if the suggestion unit detects tension in an employee, it can prompt the generating AI with "Please suggest breathing techniques for relaxation" and provide the employee with breathing techniques suggested by the generating AI.

[0069] The suggestion department can suggest a short meditation session. For example, it might suggest a 5-minute meditation session. The suggestion department could also suggest a mindfulness meditation session. For example, it could suggest a breathing meditation session. This would allow employees to refresh their minds and bodies by engaging in short meditation sessions. Some or all of the above-described processes in the suggestion department may be performed using AI, for example, or without AI. For example, if the suggestion department detects employee stress, it can prompt the generating AI with "Please suggest a short meditation session" and provide the employee with a meditation method suggested by the generating AI.

[0070] The suggestion unit can suggest stretches. For example, the suggestion unit can suggest shoulder stretches. For example, the suggestion unit can also suggest lower back stretches. For example, the suggestion unit can also suggest neck stretches. This allows employees to relieve physical tension by performing stretches. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, if the suggestion unit detects employee fatigue, it can prompt the generating AI with "Please suggest stretches" and provide the employee with stretches suggested by the generating AI.

[0071] The suggestion unit can suggest relaxing music. For example, the suggestion unit may suggest classical music. For example, the suggestion unit may suggest nature sounds. For example, the suggestion unit may suggest ambient music. This allows employees to reduce stress by listening to relaxing music. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, if the suggestion unit detects employee stress, it can prompt the generating AI with "Please suggest relaxing music," and provide the employee with music suggested by the generating AI.

[0072] The suggestion department can advise taking short breaks. For example, the suggestion department might suggest a 5-minute break. The suggestion department might also suggest a 10-minute break. The suggestion department might also suggest a 15-minute break. This allows employees to take short breaks, thereby reducing collective stress. Some or all of the above processes in the suggestion department may be performed using AI, for example, or not using AI. For example, if the suggestion department detects team stress, it can prompt a generating AI with "Please suggest a short break," and provide the team leader with the break time suggested by the generating AI.

[0073] The acquisition unit can estimate an employee's emotions and adjust the timing of image and audio acquisition based on the estimated emotions. For example, if an employee is tense, the AI ​​can sense that emotion and acquire images and audio at the moment they relax. For example, if an employee is stressed, the AI ​​can sense that emotion and acquire images and audio when the stress has subsided. For example, if an employee is concentrating, the AI ​​can sense that emotion and adjust the timing of image and audio acquisition to avoid interrupting their concentration. By adjusting the timing of image and audio acquisition according to the employee's emotions, more appropriate information can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using AI or not using AI. For example, the acquisition unit can input employee emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The acquisition unit can analyze an employee's past behavioral history and select the optimal acquisition method. For example, the acquisition unit can analyze the situations in which an employee was relaxed in the past and acquire images and audio corresponding to those situations. For example, the acquisition unit can analyze the timing of times when an employee felt stressed in the past and acquire images and audio avoiding those times. For example, the acquisition unit can analyze the tasks during which an employee was focused in the past and acquire images and audio during those tasks. In this way, the optimal acquisition method can be selected by analyzing the employee's past behavioral history. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the employee's past behavioral data into a generating AI and have the generating AI select the optimal acquisition method.

[0075] The acquisition unit can filter images and audio based on the employee's current work status and areas of interest when acquiring them. For example, if an employee is in a meeting, the acquisition unit will prioritize acquiring images and audio related to the meeting's content. For example, if an employee is giving a presentation, the acquisition unit will prioritize acquiring images and audio related to the presentation's content. For example, if an employee is relaxing, the acquisition unit will prioritize acquiring images and audio related to relaxation. By filtering based on the employee's work status and areas of interest, highly relevant information can be acquired. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the employee's work status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0076] The data acquisition unit can estimate an employee's emotions and determine the priority of information to acquire based on the estimated emotions. For example, if an employee is tense, the data acquisition unit will prioritize acquiring information related to relaxation. For example, if an employee is stressed, the data acquisition unit will prioritize acquiring information related to stress reduction. For example, if an employee is focused, the data acquisition unit will prioritize acquiring information to maintain focus. This allows for the acquisition of more appropriate information by prioritizing information based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data acquisition unit may be performed using AI, for example, or not using AI. For example, the data acquisition unit can input employee emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0077] The acquisition unit can prioritize the acquisition of highly relevant information by considering the employee's geographical location when acquiring images and audio. For example, if the employee is in the office, the acquisition unit will prioritize the acquisition of information related to the situation inside the office. For example, if the employee is out of the office, the acquisition unit will prioritize the acquisition of information related to the situation at the location. For example, if the employee is working remotely from home, the acquisition unit will prioritize the acquisition of information related to the situation at home. In this way, by considering the employee's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the employee's geographical location information into a generating AI and have the generating AI perform the acquisition of highly relevant information.

[0078] The acquisition unit can analyze an employee's social media activity and obtain relevant information when acquiring images and audio. For example, the acquisition unit can acquire relevant images and audio based on information shared by the employee on social media. For example, the acquisition unit can acquire relevant images and audio based on information of accounts followed by the employee on social media. For example, the acquisition unit can acquire relevant images and audio based on information related to topics the employee has shown interest in on social media. In this way, relevant information can be obtained by analyzing the employee's social media activity. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input employee social media activity data into a generating AI and have the generating AI perform the acquisition of relevant information.

[0079] The analysis unit can estimate an employee's emotions and adjust the analysis methods for facial expressions, tone of voice, and speech based on the estimated emotions. For example, if an employee is tense, the analysis unit will focus on analyzing facial expressions and tone of voice that indicate tension. For example, if an employee is relaxed, the analysis unit will focus on analyzing facial expressions and tone of voice that indicate relaxation. For example, if an employee is stressed, the analysis unit will focus on analyzing speech and tone of voice that indicate stress. By adjusting the analysis method based on the employee's emotions, a more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the analysis method.

[0080] The analysis department can improve the accuracy of its analysis by referring to past employee behavior data. For example, the analysis department can refer to situations in which an employee was stressed in the past and perform an analysis based on those situations. For example, the analysis department can refer to situations in which an employee was relaxed in the past and perform an analysis based on those situations. For example, the analysis department can refer to situations in which an employee felt stressed in the past and perform an analysis based on those situations. In this way, the accuracy of the analysis is improved by referring to past employee behavior data. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input past employee behavior data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0081] The analysis department can apply different analysis algorithms depending on the employee's work content during analysis. For example, if an employee is giving a presentation, the analysis department will apply an analysis algorithm specialized for presentations. For example, if an employee is attending a meeting, the analysis department will apply an analysis algorithm specialized for meetings. For example, if an employee is doing desk work, the analysis department will apply an analysis algorithm specialized for desk work. By applying different analysis algorithms according to the employee's work content, more appropriate analysis becomes possible. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input employee work content data into a generating AI and have the generating AI execute the application of an appropriate analysis algorithm.

[0082] The analysis unit can estimate employees' emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if an employee is tense, the analysis unit provides a simple and highly visible display. For example, if an employee is relaxed, the analysis unit provides a display that includes detailed information. For example, if an employee is stressed, the analysis unit highlights information that helps reduce stress. This allows for the provision of more appropriate information by adjusting the display method based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input employee emotion data into a generative AI and have the generative AI adjust the display method.

[0083] The analysis department can perform analyses while considering the geographical distribution of employees. For example, if an employee is in the office, the analysis department will consider the situation inside the office. For example, if an employee is out of the office, the analysis department will consider the situation at their destination. For example, if an employee is working remotely, the analysis department will consider the situation at their home. This allows for more appropriate analysis by considering the geographical distribution of employees. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input employee geographical distribution data into a generating AI and have the generating AI perform the analysis.

[0084] The analysis department can improve the accuracy of its analysis by referring to the employee's relevant literature during the analysis process. For example, the analysis department can refer to relevant literature that the employee has read in the past and perform analysis based on its content. For example, the analysis department can refer to relevant literature that the employee uses in their work and perform analysis based on its content. For example, the analysis department can refer to relevant literature that the employee is interested in and perform analysis based on its content. In this way, the accuracy of the analysis is improved by referring to the employee's relevant literature. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input employee relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0085] The decision-making unit can estimate an employee's emotions and adjust its behavioral decision-making process based on those emotions. For example, if an employee is tense, the decision-making unit will prioritize actions to promote relaxation. If an employee is stressed, the decision-making unit will prioritize actions to reduce stress. If an employee is focused, the decision-making unit will prioritize actions to maintain focus. By adjusting the behavioral decision-making process based on the employee's emotions, more appropriate actions can be determined. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the decision-making unit may be performed using AI or not. For example, the decision-making unit can input employee emotion data into a generative AI and have the generative AI adjust its behavioral decision-making process.

[0086] The decision-making unit can select an appropriate action by referring to the employee's past behavioral history when making a decision. For example, the decision-making unit can refer to what actions the employee has taken in the past to relax and select that action. For example, the decision-making unit can refer to what actions the employee has taken in the past to reduce stress and select that action. For example, the decision-making unit can refer to what actions the employee has taken in the past to maintain concentration and select that action. In this way, the optimal action can be selected by referring to the employee's past behavioral history. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the employee's past behavioral data into a generating AI and have the generating AI perform the selection of the optimal action.

[0087] The decision-making unit can determine the priority of actions based on the employee's work content when making action decisions. For example, if an employee is giving a presentation, the decision-making unit will prioritize actions related to the presentation. For example, if an employee is attending a meeting, the decision-making unit will prioritize actions related to the meeting. For example, if an employee is doing desk work, the decision-making unit will prioritize actions related to desk work. By prioritizing actions based on the employee's work content, a more appropriate action can be selected. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input employee work content data into a generating AI and have the generating AI perform the action priority determination.

[0088] The decision unit can estimate an employee's emotions and adjust the display method of their actions based on the estimated emotions. For example, if an employee is tense, the decision unit provides a simple and highly visible display method. For example, if an employee is relaxed, the decision unit provides a display method that includes detailed information. For example, if an employee is stressed, the decision unit highlights and displays information that helps reduce stress. This allows for the provision of more appropriate information by adjusting the display method based on the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or not using AI. For example, the decision unit can input employee emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0089] The decision-making unit can select an appropriate action when making a decision, taking into account the employee's geographical location. For example, if the employee is in the office, the decision-making unit will select an action that can be performed within the office. If the employee is out of the office, the decision-making unit will select an action that can be performed outside the office. If the employee is working remotely, the decision-making unit will select an action that can be performed at home. In this way, the optimal action can be selected by taking into account the employee's geographical location. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the employee's geographical location into a generating AI and have the generating AI select the optimal action.

[0090] The decision-making unit can analyze an employee's social media activity to determine an action. For example, the decision-making unit can determine relevant actions based on information shared by the employee on social media. For example, the decision-making unit can determine relevant actions based on information from accounts followed by the employee on social media. For example, the decision-making unit can determine actions related to topics the employee has shown interest in on social media. In this way, relevant actions can be determined by analyzing the employee's social media activity. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input employee social media activity data into a generating AI and have the generating AI make the action decision.

[0091] The suggestion function can estimate an employee's emotions and adjust the way suggestions are presented based on those emotions. For example, if an employee is tense, the suggestion function might simply suggest breathing exercises to help them relax. If an employee is relaxed, the suggestion function might suggest detailed meditation techniques. If an employee is stressed, the suggestion function might visually present stretching exercises in an easy-to-understand way. By adjusting the way suggestions are presented based on the employee's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion function may be performed using AI or not. For example, the suggestion function can input employee emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0092] The suggestion function can provide appropriate suggestions by referring to the employee's past behavioral data. For example, the suggestion function can make similar suggestions based on breathing techniques the employee has used to relax in the past. For example, the suggestion function can suggest meditation at a similar time based on the time the employee has meditated in the past. For example, the suggestion function can suggest similar stretches based on the stretching methods the employee has used in the past. In this way, the suggestion function can provide optimal suggestions by referring to the employee's past behavioral data. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the employee's past behavioral data into a generating AI and have the generating AI perform the task of providing optimal suggestions.

[0093] The suggestion function can apply different suggestion algorithms depending on the employee's work content when making suggestions. For example, if an employee is giving a presentation, the suggestion function might suggest breathing techniques to help them relax during the presentation. If an employee is attending a meeting, the suggestion function might suggest a short meditation session during the meeting. If an employee is doing desk work, the suggestion function might suggest stretches they can do while working at their desk. By applying different suggestion algorithms depending on the employee's work content, the suggestion function can provide more appropriate suggestions. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input employee work content data into a generating AI and have the generating AI apply an appropriate suggestion algorithm.

[0094] The suggestion department can estimate an employee's emotions and prioritize suggestions based on those emotions. For example, if an employee is tense, the suggestion department will prioritize suggesting breathing exercises to help them relax. If an employee is stressed, the suggestion department will prioritize suggesting meditation to reduce stress. If an employee is focused, the suggestion department will prioritize suggesting stretches to maintain focus. By prioritizing suggestions based on the employee's emotions, the department can provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion department may be performed using AI or not. For example, the suggestion department can input employee emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0095] The suggestion function can provide appropriate suggestions by considering the employee's geographical location. For example, if the employee is in the office, the suggestion function can suggest relaxation methods that can be performed in the office. If the employee is out of the office, the suggestion function can suggest meditation methods that can be performed while out. If the employee is working remotely, the suggestion function can suggest stretching methods that can be performed at home. By considering the employee's geographical location, the function can provide optimal suggestions. Some or all of the above processing in the suggestion function may be performed using AI, for example, or without AI. For example, the suggestion function can input the employee's geographical location into a generating AI and have the generating AI provide appropriate suggestions.

[0096] The suggestion department can analyze employees' social media activity when making suggestions. For example, the suggestion department can suggest relevant relaxation methods based on information employees have shared on social media. For example, the suggestion department can suggest relevant meditation methods based on information about accounts employees follow on social media. For example, the suggestion department can suggest stretching methods related to topics employees have shown interest in on social media. In this way, relevant suggestions can be provided by analyzing employees' social media activity. Some or all of the above processing in the suggestion department may be performed using AI, for example, or not using AI. For example, the suggestion department can input employee social media activity data into a generating AI and have the generating AI perform the task of providing suggestions.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The emotional harmony assistant system can also include a feedback unit. The feedback unit monitors the employee's emotions and state after they perform a suggested action and feeds the results back to the analysis unit. For example, after an employee performs a breathing exercise to relax, the feedback unit re-records the employee's facial expression and tone of voice to evaluate whether it had a relaxing effect. After an employee performs a short meditation, the feedback unit re-measures the employee's stress level to confirm the stress reduction effect. After an employee performs stretches, the feedback unit re-evaluates the employee's physical tension to determine whether the tension has been relieved. This allows the feedback unit to evaluate the effectiveness of suggested actions in real time and incorporate the findings into future suggestions.

[0099] The emotional harmony assistant system can also be equipped with a learning unit. This learning unit learns to improve the accuracy of its suggestions based on the employee's past behavioral data and feedback data. For example, it can learn the effects of breathing techniques the employee has used to relax in the past and suggest the most effective breathing technique; learn the effects of meditation the employee has practiced in the past and suggest the optimal meditation method; or learn the effects of stretching the employee has practiced in the past and suggest the optimal stretching method. This allows the learning unit to provide suggestions tailored to the individual needs of each employee.

[0100] The emotional harmony assistant system can also be equipped with a notification unit. This unit notifies employees when it is time to perform suggested actions. For example, if an employee is feeling tense, the notification unit might notify them to practice relaxing breathing exercises. If an employee is feeling stressed, the notification unit might notify them to take a short meditation. If an employee is feeling fatigued, the notification unit might notify them to stretch. This allows the notification unit to support employees in performing suggested actions at the appropriate time.

[0101] The emotional harmony assistant system can also be equipped with a customization section. This customization section tailors its suggestions based on the individual employee's preferences and needs. For example, if an employee prefers a particular music genre, the customization section will suggest music of that genre. If an employee prefers a particular meditation method, the customization section will suggest that method. If an employee prefers a particular stretching method, the customization section will suggest that stretching method. This allows the customization section to provide suggestions tailored to the individual employee's preferences.

[0102] The emotional harmony assistant system can also include a rewards unit. The rewards unit provides rewards when employees perform suggested behaviors. For example, if an employee performs breathing exercises to relax, the rewards unit awards points. If an employee meditates for a short time, the rewards unit awards a badge. If an employee stretches, the rewards unit provides a reward. This allows the rewards unit to motivate employees to actively perform suggested behaviors.

[0103] The emotional harmony assistant system can also include an emotional sharing unit. This unit provides the functionality to share an employee's emotions with other employees. For example, if an employee is feeling tense, the emotional sharing unit notifies team members of their feelings and encourages support. If an employee is feeling stressed, the emotional sharing unit notifies their supervisor of their feelings and encourages appropriate action. If an employee is relaxed, the emotional sharing unit notifies colleagues of their feelings and shares a positive atmosphere. In this way, the emotional sharing unit can promote emotional harmony throughout the workplace.

[0104] The emotional harmony assistant system can also be equipped with an emotional prediction unit. This unit predicts future emotions based on the employee's past emotional data. For example, if an employee was tense in a particular situation in the past, the unit predicts the likelihood of them becoming tense again in a similar situation. If an employee felt stressed in a particular situation in the past, the unit predicts the likelihood of them feeling stressed again in a similar situation. If an employee was relaxed in a particular situation in the past, the unit predicts the likelihood of them feeling relaxed again in a similar situation. This allows the emotional prediction unit to predict the employee's future emotions and prepare appropriate responses in advance.

[0105] The emotional harmony assistant system can also be equipped with an emotional history unit. This unit records an employee's emotional history and provides the functionality to reference past emotional data. For example, it can record situations in which an employee has felt tense in the past and perform analysis based on that data. It can also record situations in which an employee has felt stressed in the past and perform analysis based on that data. Furthermore, it can record situations in which an employee has felt relaxed in the past and perform analysis based on that data. This allows the emotional history unit to provide more accurate analysis and suggestions by referencing the employee's emotional history.

[0106] The emotional harmony assistant system can also include an emotional training section. This section provides training for employees to control their emotions. For example, it could offer training in breathing techniques to relieve tension, meditation techniques to reduce stress, and stretching techniques to relax. This allows the emotional training section to improve employees' ability to control their own emotions.

[0107] The emotional harmony assistant system can also be equipped with an emotional analysis unit. This unit analyzes employee emotional data in detail and identifies emotional patterns. For example, if an employee tends to be tense during certain times of day, the emotional analysis unit identifies that pattern. If an employee tends to feel stressed during certain tasks, the emotional analysis unit identifies that pattern. If an employee tends to relax in certain situations, the emotional analysis unit identifies that pattern. This allows the emotional analysis unit to identify employee emotional patterns and make more effective suggestions.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The acquisition unit acquires images and audio of employees. These include, for example, still images, videos, and audio recordings. The acquisition unit uses a camera to capture employees' facial expressions and a microphone to record their voices. The acquisition unit can also acquire audio data from employees' smartphones or computers. Furthermore, the acquisition unit can acquire recordings of past meetings and presentation videos of employees. Step 2: The analysis unit analyzes the employee's facial expressions, voice tone, and language use based on the information acquired by the acquisition unit. The analysis unit analyzes the employee's facial expressions using facial recognition technology and the employee's voice tone using voice analysis technology. Furthermore, it analyzes the employee's language use using natural language processing technology. For example, it recognizes emotions such as smiles, anger, and sadness from the employee's facial expressions, and estimates emotions by analyzing the pitch, volume, and speed of the voice using voice analysis technology. It also analyzes the employee's language use using natural language processing technology to determine whether it is polite or casual. Step 3: The decision-making unit determines actions based on the analysis results obtained by the analysis unit. For example, if an employee is tense, it will determine actions to help them relax; if an employee is stressed, it will determine actions to reduce stress. Furthermore, if an employee is focused, it will determine actions to help them maintain their focus. Specifically, this might include breathing exercises to relax, short meditation sessions, or stretches to maintain concentration. Step 4: The proposal team proposes the actions decided by the decision team to the employees. For example, they might suggest breathing exercises to relax, short meditation sessions, or stretching. Specifically, they might suggest deep breathing, diaphragmatic breathing, 5-minute mindfulness meditation, or shoulder and lower back stretches.

[0110] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0111] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0112] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0113] For example, the acquisition unit can acquire the employee's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the employee's facial expressions, voice tone, and language use based on the acquired information. The decision unit is implemented by the specific processing unit 290 of the data processing device 12 and determines the appropriate action based on the analysis results. The suggestion unit is implemented by the control unit 46A of the smart device 14 and suggests the determined action to the employee. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] As shown in Figure 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.

[0116] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0120] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0121] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0122] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0123] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0124] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0126] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0128] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0129] For example, the acquisition unit can acquire the employee's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes the employee's facial expressions, voice tone, and language use based on the acquired information. The decision unit is implemented by the identification processing unit 290 of the data processing device 12 and determines the appropriate action based on the analysis results. The suggestion unit is implemented by the control unit 46A of the smart glasses 214 and suggests the determined action to the employee. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0133] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0134] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0136] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0137] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0138] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0139] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0140] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0141] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0142] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0143] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0144] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] For example, the acquisition unit can acquire the employee's facial expressions and voice using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the employee's facial expressions, voice tone, and language use based on the acquired information. The decision unit is implemented by the specific processing unit 290 of the data processing device 12 and determines the appropriate action based on the analysis results. The suggestion unit is implemented by the control unit 46A of the headset terminal 314 and suggests the determined action to the employee. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] As shown in Figure 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.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0154] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] For example, the acquisition unit can acquire the employee's facial expressions and voice using the camera 42 and microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the employee's facial expressions, voice tone, and language use based on the acquired information. The decision unit is implemented by the specific processing unit 290 of the data processing device 12 and determines the appropriate action based on the analysis results. The suggestion unit is implemented by the control unit 46A of the robot 414 and suggests the determined action to the employee. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0163] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0164] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0165] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0166] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0167] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0168] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0169] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0170] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0171] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0172] 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.

[0173] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0174] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0175] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0176] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0177] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0178] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0179] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0180] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0181] (Note 1) An acquisition unit that acquires images and audio of employees, Based on the information acquired by the acquisition department, the analysis department analyzes the employees' facial expressions, tone of voice, and language use. A decision unit that determines actions based on the analysis results obtained by the analysis unit, It comprises a proposal unit that proposes the actions decided by the decision unit to the employees. A system characterized by the following features. (Note 2) The aforementioned proposal section is, I suggest breathing techniques to help you relax. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, I suggest a short meditation session. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Suggesting stretches The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Suggesting relaxing music The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, I advise them to take short breaks. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition part is, The system estimates the employee's emotions and adjusts the timing of image and audio acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition part is, Analyze employees' past behavioral history and select the appropriate method for obtaining it. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When acquiring images and audio, filtering is performed based on the employee's current work status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition part is, Estimate employees' emotions and prioritize the information to be acquired based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring images and audio, the system prioritizes the acquisition of highly relevant information by considering the employee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring images and audio, we analyze employees' social media activity and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The analysis department, The system estimates employees' emotions and adjusts the analysis methods for facial expressions, tone of voice, and language use based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, we refer to past employee behavioral data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the employee's job duties. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The system estimates employee sentiment and adjusts how the analysis results are displayed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is When conducting the analysis, the geographical distribution of employees should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, we refer to relevant literature related to employees to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The decision-making body is, Estimate employees' emotions and adjust decision-making processes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The decision-making body is, When making decisions about actions, the appropriate course of action is selected by referring to the employee's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned determination unit, When making decisions about actions, prioritize those actions based on the employee's job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 22) The decision-making body is, The system estimates employees' emotions and adjusts how behaviors are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The decision-making body is, When making decisions, the appropriate course of action will be selected considering the geographical location of employees. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned determination unit, When making decisions, we analyze employees' social media activity to determine the appropriate course of action. The system described in Appendix 1, characterized by the features described herein. (Note 25) The proposal department, We estimate employees' emotions and adjust the way proposals are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The proposal department, When making suggestions, refer to the employee's past behavioral data to provide appropriate suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the employee's job responsibilities. The system described in Appendix 1, characterized by the features described herein. (Note 28) The proposal department, Estimate employees' emotions and prioritize proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The proposal department, When making proposals, we take into account the geographical location of employees to provide appropriate suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, we analyze employees' social media activity and make suggestions based on that analysis. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An acquisition unit that acquires images and audio of employees, Based on the information acquired by the aforementioned acquisition unit, an analysis unit analyzes the employee's facial expressions, tone of voice, and language use. A decision unit that determines an action in accordance with the analysis results obtained by the aforementioned analysis unit, The system comprises a proposal unit that proposes the action determined by the decision unit to the employee. A system characterized by the following features.

2. The aforementioned proposal section is, I suggest breathing techniques to help you relax. The system according to feature 1.

3. The aforementioned proposal section is, I suggest a short meditation session. The system according to feature 1.

4. The aforementioned proposal section is, Suggesting stretches The system according to feature 1.

5. The aforementioned proposal section is, Suggesting relaxing music The system according to feature 1.

6. The aforementioned proposal section is, I advise them to take short breaks. The system according to feature 1.

7. The acquisition unit is, The system estimates the employee's emotions and adjusts the timing of image and audio acquisition based on the estimated emotions. The system according to feature 1.

8. The acquisition unit is, Analyze employees' past behavioral history and select the appropriate method for obtaining it. The system according to feature 1.

Images