system

Abstract

The system according to this embodiment aims to incorporate evaluations from third parties and facilitate the easy expression of gratitude. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a matching unit. The reception unit receives evaluation buttons and comments. The analysis unit analyzes the data of the evaluation buttons and comments sent by the reception unit. The evaluation unit performs personnel evaluations based on the data analyzed by the analysis unit. The matching unit matches jobs based on the data analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is impossible to incorporate evaluations from third parties and it is difficult to easily convey a feeling of gratitude.

[0005] The system according to the embodiment aims to incorporate evaluations from third parties and easily convey a feeling of gratitude.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a matching unit. The reception unit receives evaluation buttons and comments. The analysis unit analyzes the data of the evaluation buttons and comments sent by the reception unit. The evaluation unit performs personnel evaluations based on the data analyzed by the analysis unit. The matching unit matches jobs based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment incorporates evaluations from third parties and allows users to easily express their gratitude. [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 labeled 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 5 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) An evaluation system according to an embodiment of the present invention provides a mechanism that allows for the incorporation of evaluations from third parties into an internal company evaluation system. This evaluation system introduces the "evaluation button" function of a social networking service into the company, enabling employees to express their gratitude to each other through "evaluation buttons" and comments. For example, an employee can send "evaluation buttons" and comments to other employees. This "evaluation button" and comment data is analyzed by AI and used for personnel evaluations and job matching. For example, if an employee receives many "evaluation buttons" and grateful comments from other employees, that employee's evaluation will improve. In addition, appropriate job matching is performed based on the data analyzed by the AI. This improves employee satisfaction and increases work efficiency. As a result, the evaluation system allows employees to easily express their gratitude to each other, improving employee satisfaction. Furthermore, AI-based data analysis enables optimization of personnel evaluations and job matching, leading to increased work efficiency. For example, it also enables early detection of excessive work, contributing to employee health management.

[0029] The evaluation system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a matching unit. The reception unit can send evaluation buttons and comments. For example, an employee can send an "evaluation button" or comments to another employee. The reception unit can, for example, easily convey feelings of gratitude or evaluation. The analysis unit analyzes the data of evaluation buttons and comments sent by the reception unit. For example, the AI ​​analyzes the number and content of evaluation buttons and comments received by each employee and calculates the employee's evaluation. The evaluation unit performs personnel evaluations based on the data analyzed by the analysis unit. For example, if an employee receives many evaluation buttons and comments of gratitude, that employee's evaluation will be higher. The matching unit matches employees with jobs based on the data analyzed by the analysis unit. For example, if an employee has specific skills or experience, a suitable job will be matched to that employee. This makes the most of employees' skills and experience and improves work efficiency.

[0030] The reception desk allows employees to send evaluations and comments. For example, employees can send evaluations and comments to other employees. Specifically, the reception desk provides the interface for the company's evaluation system, making it easy for employees to evaluate each other. The evaluation button allows for easy sending of positive feedback such as "Thank you," "Good job," and "Great work," while the comment section allows for free writing of specific words of gratitude and reasons for evaluation. This enables employees to express gratitude and evaluations to each other on a daily basis, improving the workplace atmosphere. The reception desk also records the history of evaluation button and comment submissions and stores it in a database for later use by the analytics department. Furthermore, the reception desk is designed to be accessible via smartphone apps and web browsers to facilitate the submission of evaluations. This allows employees to submit evaluations regardless of location or time. For example, they can send evaluations using their smartphones during meetings or while out of the office, ensuring that evaluation opportunities are not missed. In this way, the reception desk plays a role in promoting communication among employees and increasing overall workplace motivation.

[0031] The analysis department analyzes the evaluation button and comment data sent by the reception department. Specifically, it uses AI to analyze the number and content of evaluation buttons and comments received by each employee and calculates the employee's evaluation. The AI ​​uses natural language processing technology to analyze the content of comments and automatically classifies them into positive and negative evaluations. For example, a comment such as "That was a great presentation" is classified as a positive evaluation, while a comment such as "You needed a little more preparation" is recognized as an area for improvement. The number and types of evaluation buttons are also analyzed to calculate the employee's overall evaluation score. Furthermore, the analysis department can perform time-series analysis of the evaluation data to track changes in employee evaluations. This allows for the identification of the causes of increases or decreases in evaluations during specific periods, clarifying employee growth and challenges. For example, if an evaluation suddenly increases during a certain project period, it indicates that the employee's contribution to that project was highly evaluated. The analysis department can also aggregate evaluation data by department and team to understand evaluation trends across the entire organization. This allows for the analysis of success factors when a particular department or team receives high evaluations, and the findings can be shared with other departments and teams. The analysis department provides these analysis results to the evaluation and matching departments, which are then used to evaluate employees and match them with suitable jobs.

[0032] The evaluation department conducts performance evaluations based on data analyzed by the analytics department. Specifically, if an employee receives many positive feedback buttons and comments of appreciation, that employee's evaluation will be higher. The evaluation department comprehensively assesses employees' performance and contributions based on the evaluation scores and comments provided by the analytics department. For example, employees with high evaluation scores become eligible for promotion and salary increases. The evaluation department also analyzes employees' strengths and weaknesses based on evaluation data and provides individual feedback. This allows employees to receive specific advice on how to further develop their strengths and improve their weaknesses. Furthermore, the evaluation department designs employees' career paths based on evaluation data and provides appropriate training and development programs. For example, employees with strong leadership skills are provided with leadership training to develop them as future management candidates. The evaluation department also optimizes personnel allocation throughout the organization based on evaluation data, creating an environment where each employee can work most effectively. In this way, the evaluation department plays a role in increasing employee motivation and improving the overall performance of the organization.

[0033] The Matching Department matches employees with jobs based on data analyzed by the Analysis Department. Specifically, if an employee possesses certain skills or experience, they will be matched with a suitable job. Based on the employee's skills, experience, and evaluation data provided by the Analysis Department, the Matching Department assigns each employee to the most suitable project or task. For example, an employee with strong programming skills will be assigned to a software development project. The Matching Department also considers the employee's career path and growth goals, matching them with jobs that will benefit their future careers. This allows employees to build their careers while making the most of their skills and experience. Furthermore, the Matching Department monitors project progress and employee performance, and reallocates or adjusts jobs as needed. For example, if a project is short on resources, it will assign additional employees with the appropriate skills to ensure smooth project progress. The Matching Department also collects employee feedback to continuously improve the accuracy of its matching process. In this way, the Matching Department plays a role in maximizing the use of employees' skills and experience, improving work efficiency, and increasing employee satisfaction.

[0034] The data collection unit can collect data from rating buttons and comments. For example, the data collection unit can efficiently collect data from rating buttons and comments. Some or all of the processing described above in the data collection unit may be performed using AI or not.

[0035] The analysis unit can analyze the number and content of evaluation buttons and comments received by each employee to calculate their performance evaluation. For example, the analysis unit analyzes the number of clicks on evaluation buttons, the number of characters in comments, and the classification of their content as positive or negative. This allows for the accurate calculation of employee performance evaluations.

[0036] The matching department can match employees with suitable jobs based on their skills and experience. For example, the matching department uses factors such as employees' technical skills, project experience, and work history to make the most of their skills and experience.

[0037] The analysis unit can detect excessive work based on the analysis results. For example, the analysis unit can detect excessive work based on factors such as upper limits on working hours, insufficient rest time, and workload assessment. This allows for early detection of excessive work.

[0038] The analysis department can perform health management based on the analysis results. For example, the analysis department can perform health management based on health checkup results, stress level assessments, and fitness data analysis. This can contribute to the health management of employees.

[0039] The reception system can analyze a user's past rating history and select the optimal reception method. For example, it prioritizes displaying rating buttons and comment formats that the user has frequently used in the past. It also suggests methods for receiving rating buttons and comments during specific time periods based on the user's past rating history. Furthermore, the reception system customizes the methods for receiving rating buttons and comments based on the user's past rating history. This allows the system to provide the most suitable reception method based on the user's past rating history.

[0040] The reception system can filter submissions based on the user's current projects and areas of interest. For example, it prioritizes receiving rating buttons and comments related to projects the user is currently involved in. It also filters and displays relevant rating buttons and comments based on the user's areas of interest. Furthermore, it suggests appropriate rating buttons and comments based on the user's current project progress. This ensures that rating buttons and comments relevant to the user's current projects and areas of interest are prioritized.

[0041] The reception desk can prioritize receiving highly relevant rating buttons and comments by considering the user's geographical location. For example, if a user is in a specific office, the reception desk will prioritize rating buttons and comments related to that office. If a user is on a business trip, the reception desk will prioritize rating buttons and comments related to their destination. Furthermore, if a user is working remotely, the reception desk will prioritize rating buttons and comments related to remote work. This allows the reception desk to prioritize highly relevant rating buttons and comments based on the user's geographical location.

[0042] The reception desk can analyze a user's social media activity and accept relevant rating buttons and comments upon submission. For example, the reception desk prioritizes accepting rating buttons and comments related to projects the user has shared on social media. It also accepts rating buttons and comments related to topics of interest based on the user's social media activity. Furthermore, the reception desk suggests relevant rating buttons and comments based on the activity of the user's social media followers and friends. This allows the reception desk to accept relevant rating buttons and comments based on the user's social media activity.

[0043] The analysis unit can adjust the level of detail in its analysis based on the importance of the rating buttons and comments. For example, the analysis unit performs a detailed analysis on high-importance rating buttons and comments. It also performs a simplified analysis on low-importance rating buttons and comments. Furthermore, it performs a standard analysis on rating buttons and comments of medium importance. This allows the system to provide detailed analysis tailored to the importance of the rating buttons and comments.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the evaluation buttons and comments during analysis. For example, the analysis unit applies a technical analysis algorithm to technical evaluation buttons and comments. It also applies a management analysis algorithm to management-related evaluation buttons and comments. Furthermore, it applies a communication analysis algorithm to communication-related evaluation buttons and comments. This allows for the provision of appropriate analysis according to the category of the evaluation buttons and comments.

[0045] The analysis unit can determine the priority of analysis based on the submission timing of rating buttons and comments. For example, the analysis unit prioritizes analyzing recently submitted rating buttons and comments. It also prioritizes analyzing rating buttons and comments submitted within a specific period. Furthermore, the analysis unit dynamically adjusts the analysis priority based on the submission timing. This allows for analysis to be performed with appropriate priorities based on the submission timing.

[0046] The analysis unit can adjust the order of analysis based on the relevance of rating buttons and comments during the analysis process. For example, the analysis unit prioritizes analyzing rating buttons and comments with high relevance. Next, it analyzes rating buttons and comments with moderate relevance. Finally, it analyzes rating buttons and comments with low relevance. This allows the analysis to be performed in an appropriate order based on the relevance of rating buttons and comments.

[0047] The evaluation unit can analyze the user's past evaluation history during the evaluation process to select the optimal evaluation method. For example, the evaluation unit prioritizes suggesting evaluation methods that the user has used in the past. It also suggests specific evaluation methods based on the user's past evaluation history. Furthermore, the evaluation unit customizes evaluation methods based on the user's past evaluation history. This allows the evaluation unit to provide the most suitable evaluation method based on the user's past evaluation history.

[0048] The evaluation unit can customize the evaluation methods based on the user's current projects and areas of interest during the evaluation process. For example, the evaluation unit provides evaluation methods relevant to the project the user is currently involved in. It also suggests relevant evaluation methods based on the user's areas of interest. Furthermore, the evaluation unit provides appropriate evaluation methods according to the progress of the user's current project. This allows the evaluation unit to provide evaluation methods relevant to the user's current projects and areas of interest.

[0049] The evaluation unit can select the optimal evaluation method during the evaluation process, taking into account the user's geographical location. For example, if the user is in a specific office, the evaluation unit will provide an evaluation method related to that office. Furthermore, if the user is on a business trip, the evaluation unit will provide an evaluation method related to the business trip destination. Additionally, if the user is working remotely, the evaluation unit will provide an evaluation method related to remote work. This allows the evaluation unit to provide the optimal evaluation method based on the user's geographical location.

[0050] The evaluation unit can analyze the user's social media activity during the evaluation process and propose evaluation methods. For example, the evaluation unit can provide evaluation methods related to projects shared by the user on social media. It can also propose evaluation methods related to topics of interest based on the user's social media activity. Furthermore, the evaluation unit can propose relevant evaluation methods based on the activity of the user's social media followers and friends. This allows the system to provide relevant evaluation methods based on the user's social media activity.

[0051] The matching unit can analyze a user's past skills and experience during the matching process to select the optimal matching method. For example, the matching unit can provide the optimal matching method based on the user's skills and experience from past successful projects. It can also propose a matching method suitable for a specific project based on the user's past skills and experience. Furthermore, the matching unit analyzes the user's past skills and experience to provide the most efficient matching method. This allows the system to provide the optimal matching method based on the user's past skills and experience.

[0052] The matching unit can customize the matching method based on the user's current projects and areas of interest during the matching process. For example, the matching unit provides matching methods related to the projects the user is currently involved in. It also suggests relevant matching methods based on the user's areas of interest. Furthermore, the matching unit provides appropriate matching methods according to the progress of the user's current projects. This allows the system to provide matching methods relevant to the user's current projects and areas of interest.

[0053] The matching unit can select the optimal matching method by considering the user's geographical location information during the matching process. For example, if the user is in a specific office, the matching unit will provide a matching method related to that office. Furthermore, if the user is on a business trip, the matching unit will provide a matching method related to the business trip destination. Additionally, if the user is working remotely, the matching unit will provide a matching method related to remote work. This allows the system to provide the optimal matching method based on the user's geographical location information.

[0054] The matching unit can analyze a user's social media activity during the matching process and propose matching methods. For example, the matching unit can provide matching methods related to projects shared by the user on social media. It can also propose matching methods related to topics of interest based on the user's social media activity. Furthermore, the matching unit can propose relevant matching methods based on the activity of the user's social media followers and friends. This allows the system to provide relevant matching methods based on the user's social media activity.

[0055] The data collection unit can optimize its collection algorithm by referring to past collected data during the collection process. For example, the collection unit selects the optimal collection algorithm based on past collected data. It can also extract specific patterns from past collected data and adjust the collection algorithm accordingly. Furthermore, the collection unit analyzes past collected data to improve the accuracy of the collection algorithm. This allows the system to provide the optimal collection algorithm based on past collected data.

[0056] The data collection unit can weight the collected data based on when the rating buttons and comments were submitted. For example, the unit prioritizes collecting recently submitted rating buttons and comments and assigns them a higher weight. It can also prioritize collecting rating buttons and comments submitted within a specific period and adjust their weighting accordingly. Furthermore, the unit dynamically adjusts the weighting of the collected data based on the submission date. This allows for data collection with appropriate weighting based on the submission time.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The evaluation system can also include a feedback section. This feedback section can provide feedback on the evaluation buttons and comments received by employees. For example, it can suggest specific areas for improvement and strengths based on the evaluation buttons and comments received. Furthermore, the feedback section can automatically generate and send thank-you messages to employees for the evaluations they receive. In addition, the feedback section can offer career path suggestions and resources for skill development based on the evaluations received. This allows employees to improve themselves based on the evaluations they receive, contributing to the overall growth of the company.

[0059] The evaluation system can also include a notification function. This function can provide notifications regarding evaluation buttons and comments received by employees. For example, it can send real-time notifications when an employee receives a new evaluation button or comment. It can also periodically notify employees of the aggregated evaluation results, allowing them to understand their own evaluation status. Furthermore, the notification function can set alerts for specific evaluation buttons and comments to ensure important feedback is not missed. This increases employee awareness of evaluations and encourages them to actively utilize feedback.

[0060] The evaluation system can also include a training department. This department can propose appropriate training programs based on the evaluation buttons and comments received by employees. For example, it can suggest online courses or workshops for skill development based on the evaluations received. Furthermore, the training department can analyze employee evaluation data and create individualized training plans. In addition, based on the evaluations received, the training department can propose mentorship programs and facilitate interaction with experienced employees. This allows employees to pursue self-improvement based on their evaluations, thereby improving the overall skill level of the company.

[0061] The evaluation system can also include a compensation department. This department can provide rewards and incentives based on the evaluation buttons and comments employees receive. For example, it could offer bonuses or special leave to employees who receive a certain number of evaluation buttons. It could also recommend employees for promotion or salary increases based on the evaluations they receive. Furthermore, the compensation department could award internal recognition and certificates of appreciation based on the evaluations employees receive. This increases employee motivation for evaluation and improves overall company performance.

[0062] The evaluation system can also include a collaboration section. This section can facilitate collaboration among employees based on the evaluations and comments they receive. For example, the collaboration section can propose common projects and tasks based on the evaluations received. It can also analyze employee evaluation data and match employees with complementary skills. Furthermore, the collaboration section can propose team-building activities and workshops based on the evaluations received. This allows employees to build collaborative relationships with others based on their evaluations, improving teamwork across the entire company.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk can send rating buttons and comments. For example, employees can send "rating buttons" and comments to other employees. The reception desk can easily convey gratitude and appreciation. Step 2: The analysis unit analyzes the data of evaluation buttons and comments sent by the reception unit. For example, the AI ​​analyzes the number and content of evaluation buttons and comments received by each employee and calculates the employee's evaluation. Step 3: The evaluation department conducts performance evaluations based on the data analyzed by the analysis department. For example, if an employee receives many positive feedback buttons and thank-you comments, that employee's evaluation will be higher. Step 4: The matching unit matches jobs based on the data analyzed by the analysis unit. For example, if an employee has specific skills or experience, a suitable job will be matched to that employee. This allows for the maximum utilization of employees' skills and experience, leading to increased work efficiency.

[0065] (Example of form 2) An evaluation system according to an embodiment of the present invention provides a mechanism that allows for the incorporation of evaluations from third parties into an internal company evaluation system. This evaluation system introduces the "evaluation button" function of a social networking service into the company, enabling employees to express their gratitude to each other through "evaluation buttons" and comments. For example, an employee can send "evaluation buttons" and comments to other employees. This "evaluation button" and comment data is analyzed by AI and used for personnel evaluations and job matching. For example, if an employee receives many "evaluation buttons" and grateful comments from other employees, that employee's evaluation will improve. In addition, appropriate job matching is performed based on the data analyzed by the AI. This improves employee satisfaction and increases work efficiency. As a result, the evaluation system allows employees to easily express their gratitude to each other, improving employee satisfaction. Furthermore, AI-based data analysis enables optimization of personnel evaluations and job matching, leading to increased work efficiency. For example, it also enables early detection of excessive work, contributing to employee health management.

[0066] The evaluation system according to this embodiment comprises a reception unit, an analysis unit, an evaluation unit, and a matching unit. The reception unit can send evaluation buttons and comments. For example, an employee can send an "evaluation button" or comments to another employee. The reception unit can, for example, easily convey feelings of gratitude or evaluation. The analysis unit analyzes the data of evaluation buttons and comments sent by the reception unit. For example, the AI ​​analyzes the number and content of evaluation buttons and comments received by each employee and calculates the employee's evaluation. The evaluation unit performs personnel evaluations based on the data analyzed by the analysis unit. For example, if an employee receives many evaluation buttons and comments of gratitude, that employee's evaluation will be higher. The matching unit matches employees with jobs based on the data analyzed by the analysis unit. For example, if an employee has specific skills or experience, a suitable job will be matched to that employee. This makes the most of employees' skills and experience and improves work efficiency.

[0067] The reception desk allows employees to send evaluations and comments. For example, employees can send evaluations and comments to other employees. Specifically, the reception desk provides the interface for the company's evaluation system, making it easy for employees to evaluate each other. The evaluation button allows for easy sending of positive feedback such as "Thank you," "Good job," and "Great work," while the comment section allows for free writing of specific words of gratitude and reasons for evaluation. This enables employees to express gratitude and evaluations to each other on a daily basis, improving the workplace atmosphere. The reception desk also records the history of evaluation button and comment submissions and stores it in a database for later use by the analytics department. Furthermore, the reception desk is designed to be accessible via smartphone apps and web browsers to facilitate the submission of evaluations. This allows employees to submit evaluations regardless of location or time. For example, they can send evaluations using their smartphones during meetings or while out of the office, ensuring that evaluation opportunities are not missed. In this way, the reception desk plays a role in promoting communication among employees and increasing overall workplace motivation.

[0068] The analysis department analyzes the evaluation button and comment data sent by the reception department. Specifically, it uses AI to analyze the number and content of evaluation buttons and comments received by each employee and calculates the employee's evaluation. The AI ​​uses natural language processing technology to analyze the content of comments and automatically classifies them into positive and negative evaluations. For example, a comment such as "That was a great presentation" is classified as a positive evaluation, while a comment such as "You needed a little more preparation" is recognized as an area for improvement. The number and types of evaluation buttons are also analyzed to calculate the employee's overall evaluation score. Furthermore, the analysis department can perform time-series analysis of the evaluation data to track changes in employee evaluations. This allows for the identification of the causes of increases or decreases in evaluations during specific periods, clarifying employee growth and challenges. For example, if an evaluation suddenly increases during a certain project period, it indicates that the employee's contribution to that project was highly evaluated. The analysis department can also aggregate evaluation data by department and team to understand evaluation trends across the entire organization. This allows for the analysis of success factors when a particular department or team receives high evaluations, and the findings can be shared with other departments and teams. The analysis department provides these analysis results to the evaluation and matching departments, which are then used to evaluate employees and match them with suitable jobs.

[0069] The evaluation department conducts performance evaluations based on data analyzed by the analytics department. Specifically, if an employee receives many positive feedback buttons and comments of appreciation, that employee's evaluation will be higher. The evaluation department comprehensively assesses employees' performance and contributions based on the evaluation scores and comments provided by the analytics department. For example, employees with high evaluation scores become eligible for promotion and salary increases. The evaluation department also analyzes employees' strengths and weaknesses based on evaluation data and provides individual feedback. This allows employees to receive specific advice on how to further develop their strengths and improve their weaknesses. Furthermore, the evaluation department designs employees' career paths based on evaluation data and provides appropriate training and development programs. For example, employees with strong leadership skills are provided with leadership training to develop them as future management candidates. The evaluation department also optimizes personnel allocation throughout the organization based on evaluation data, creating an environment where each employee can work most effectively. In this way, the evaluation department plays a role in increasing employee motivation and improving the overall performance of the organization.

[0070] The Matching Department matches employees with jobs based on data analyzed by the Analysis Department. Specifically, if an employee possesses certain skills or experience, they will be matched with a suitable job. Based on the employee's skills, experience, and evaluation data provided by the Analysis Department, the Matching Department assigns each employee to the most suitable project or task. For example, an employee with strong programming skills will be assigned to a software development project. The Matching Department also considers the employee's career path and growth goals, matching them with jobs that will benefit their future careers. This allows employees to build their careers while making the most of their skills and experience. Furthermore, the Matching Department monitors project progress and employee performance, and reallocates or adjusts jobs as needed. For example, if a project is short on resources, it will assign additional employees with the appropriate skills to ensure smooth project progress. The Matching Department also collects employee feedback to continuously improve the accuracy of its matching process. In this way, the Matching Department plays a role in maximizing the use of employees' skills and experience, improving work efficiency, and increasing employee satisfaction.

[0071] The data collection unit can collect data from rating buttons and comments. For example, the data collection unit can efficiently collect data from rating buttons and comments. Some or all of the processing described above in the data collection unit may be performed using AI or not.

[0072] The analysis unit can analyze the number and content of evaluation buttons and comments received by each employee to calculate their performance evaluation. For example, the analysis unit analyzes the number of clicks on evaluation buttons, the number of characters in comments, and the classification of their content as positive or negative. This allows for the accurate calculation of employee performance evaluations.

[0073] The matching department can match employees with suitable jobs based on their skills and experience. For example, the matching department uses factors such as employees' technical skills, project experience, and work history to make the most of their skills and experience.

[0074] The analysis unit can detect excessive work based on the analysis results. For example, the analysis unit can detect excessive work based on factors such as upper limits on working hours, insufficient rest time, and workload assessment. This allows for early detection of excessive work.

[0075] The analysis department can perform health management based on the analysis results. For example, the analysis department can perform health management based on health checkup results, stress level assessments, and fitness data analysis. This can contribute to the health management of employees.

[0076] The reception system can estimate the user's emotions and adjust the timing of receiving ratings and comments based on the estimated emotions. For example, if the user is stressed, the reception system will delay receiving ratings and comments to encourage input when the user is relaxed. If the user is relaxed, the reception system will immediately accept ratings and comments to encourage input when emotions are heightened. Furthermore, if the user is in a hurry, the reception system will provide a simplified interface to quickly accept ratings and comments. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The reception system can analyze a user's past rating history and select the optimal reception method. For example, it prioritizes displaying rating buttons and comment formats that the user has frequently used in the past. It also suggests methods for receiving rating buttons and comments during specific time periods based on the user's past rating history. Furthermore, the reception system customizes the methods for receiving rating buttons and comments based on the user's past rating history. This allows the system to provide the most suitable reception method based on the user's past rating history.

[0078] The reception system can filter submissions based on the user's current projects and areas of interest. For example, it prioritizes receiving rating buttons and comments related to projects the user is currently involved in. It also filters and displays relevant rating buttons and comments based on the user's areas of interest. Furthermore, it suggests appropriate rating buttons and comments based on the user's current project progress. This ensures that rating buttons and comments relevant to the user's current projects and areas of interest are prioritized.

[0079] The reception system can estimate the user's emotions and determine the priority of the rating buttons and comments to accept based on those estimated emotions. For example, if a user feels strong gratitude, the reception system will set a high priority for rating buttons and comments based on that emotion. Conversely, if a user feels dissatisfied, the reception system will set a low priority for rating buttons and comments based on that emotion. Furthermore, if a user has neutral emotions, the reception system will accept rating buttons and comments with standard priority. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The reception desk can prioritize receiving highly relevant rating buttons and comments by considering the user's geographical location. For example, if a user is in a specific office, the reception desk will prioritize rating buttons and comments related to that office. If a user is on a business trip, the reception desk will prioritize rating buttons and comments related to their destination. Furthermore, if a user is working remotely, the reception desk will prioritize rating buttons and comments related to remote work. This allows the reception desk to prioritize highly relevant rating buttons and comments based on the user's geographical location.

[0081] The reception desk can analyze a user's social media activity and accept relevant rating buttons and comments upon submission. For example, the reception desk prioritizes accepting rating buttons and comments related to projects the user has shared on social media. It also accepts rating buttons and comments related to topics of interest based on the user's social media activity. Furthermore, the reception desk suggests relevant rating buttons and comments based on the activity of the user's social media followers and friends. This allows the reception desk to accept relevant rating buttons and comments based on the user's social media activity.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. If the user is in a hurry, the analysis unit provides concise analysis results. Furthermore, if the user is excited, the analysis unit provides visually appealing analysis results. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The analysis unit can adjust the level of detail in its analysis based on the importance of the rating buttons and comments. For example, the analysis unit performs a detailed analysis on high-importance rating buttons and comments. It also performs a simplified analysis on low-importance rating buttons and comments. Furthermore, it performs a standard analysis on rating buttons and comments of medium importance. This allows the system to provide detailed analysis tailored to the importance of the rating buttons and comments.

[0084] The analysis unit can apply different analysis algorithms depending on the category of the evaluation buttons and comments during analysis. For example, the analysis unit applies a technical analysis algorithm to technical evaluation buttons and comments. It also applies a management analysis algorithm to management-related evaluation buttons and comments. Furthermore, it applies a communication analysis algorithm to communication-related evaluation buttons and comments. This allows for the provision of appropriate analysis according to the category of the evaluation buttons and comments.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis. If the user is relaxed, the analysis unit provides a detailed analysis. Furthermore, if the user is excited, the analysis unit provides a visually appealing analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The analysis unit can determine the priority of analysis based on the submission timing of rating buttons and comments. For example, the analysis unit prioritizes analyzing recently submitted rating buttons and comments. It also prioritizes analyzing rating buttons and comments submitted within a specific period. Furthermore, the analysis unit dynamically adjusts the analysis priority based on the submission timing. This allows for analysis to be performed with appropriate priorities based on the submission timing.

[0087] The analysis unit can adjust the order of analysis based on the relevance of rating buttons and comments during the analysis process. For example, the analysis unit prioritizes analyzing rating buttons and comments with high relevance. Next, it analyzes rating buttons and comments with moderate relevance. Finally, it analyzes rating buttons and comments with low relevance. This allows the analysis to be performed in an appropriate order based on the relevance of rating buttons and comments.

[0088] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated emotions. For example, if the user is relaxed, the evaluation unit provides a detailed evaluation method. If the user is in a hurry, the evaluation unit provides a concise evaluation method. Furthermore, if the user is excited, the evaluation unit provides a visually appealing evaluation method. 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.

[0089] The evaluation unit can analyze the user's past evaluation history during the evaluation process to select the optimal evaluation method. For example, the evaluation unit prioritizes suggesting evaluation methods that the user has used in the past. It also suggests specific evaluation methods based on the user's past evaluation history. Furthermore, the evaluation unit customizes evaluation methods based on the user's past evaluation history. This allows the evaluation unit to provide the most suitable evaluation method based on the user's past evaluation history.

[0090] The evaluation unit can customize the evaluation methods based on the user's current projects and areas of interest during the evaluation process. For example, the evaluation unit provides evaluation methods relevant to the project the user is currently involved in. It also suggests relevant evaluation methods based on the user's areas of interest. Furthermore, the evaluation unit provides appropriate evaluation methods according to the progress of the user's current project. This allows the evaluation unit to provide evaluation methods relevant to the user's current projects and areas of interest.

[0091] The evaluation unit can estimate the user's emotions and determine the priority of the evaluation based on the estimated user emotions. For example, if the user feels a strong sense of gratitude, the evaluation unit will set a high priority for the evaluation based on that emotion. Conversely, if the user feels dissatisfied, the evaluation unit will set a low priority for the evaluation based on that emotion. Furthermore, if the user has neutral emotions, the evaluation unit will perform the evaluation with a standard priority. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The evaluation unit can select the optimal evaluation method during the evaluation process, taking into account the user's geographical location. For example, if the user is in a specific office, the evaluation unit will provide an evaluation method related to that office. Furthermore, if the user is on a business trip, the evaluation unit will provide an evaluation method related to the business trip destination. Additionally, if the user is working remotely, the evaluation unit will provide an evaluation method related to remote work. This allows the evaluation unit to provide the optimal evaluation method based on the user's geographical location.

[0093] The evaluation unit can analyze the user's social media activity during the evaluation process and propose evaluation methods. For example, the evaluation unit can provide evaluation methods related to projects shared by the user on social media. It can also propose evaluation methods related to topics of interest based on the user's social media activity. Furthermore, the evaluation unit can propose relevant evaluation methods based on the activity of the user's social media followers and friends. This allows the system to provide relevant evaluation methods based on the user's social media activity.

[0094] The matching unit can estimate the user's emotions and adjust the matching method based on the estimated emotions. For example, if the user is relaxed, the matching unit provides a detailed matching method. If the user is in a hurry, the matching unit provides a concise matching method. Furthermore, if the user is excited, the matching unit provides a visually appealing matching method. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The matching unit can analyze a user's past skills and experience during the matching process to select the optimal matching method. For example, the matching unit can provide the optimal matching method based on the user's skills and experience from past successful projects. It can also propose a matching method suitable for a specific project based on the user's past skills and experience. Furthermore, the matching unit analyzes the user's past skills and experience to provide the most efficient matching method. This allows the system to provide the optimal matching method based on the user's past skills and experience.

[0096] The matching unit can customize the matching method based on the user's current projects and areas of interest during the matching process. For example, the matching unit provides matching methods related to the projects the user is currently involved in. It also suggests relevant matching methods based on the user's areas of interest. Furthermore, the matching unit provides appropriate matching methods according to the progress of the user's current projects. This allows the system to provide matching methods relevant to the user's current projects and areas of interest.

[0097] The matching unit can estimate the user's emotions and determine matching priorities based on those estimated emotions. For example, if a user feels a strong sense of gratitude, the matching unit will set a high matching priority based on that emotion. Conversely, if a user feels dissatisfied, the matching unit will set a low matching priority based on that emotion. Furthermore, if a user has neutral emotions, the matching unit will perform matching with a standard priority. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The matching unit can select the optimal matching method by considering the user's geographical location information during the matching process. For example, if the user is in a specific office, the matching unit will provide a matching method related to that office. Furthermore, if the user is on a business trip, the matching unit will provide a matching method related to the business trip destination. Additionally, if the user is working remotely, the matching unit will provide a matching method related to remote work. This allows the system to provide the optimal matching method based on the user's geographical location information.

[0099] The matching unit can analyze a user's social media activity during the matching process and propose matching methods. For example, the matching unit can provide matching methods related to projects shared by the user on social media. It can also propose matching methods related to topics of interest based on the user's social media activity. Furthermore, the matching unit can propose relevant matching methods based on the activity of the user's social media followers and friends. This allows the system to provide relevant matching methods based on the user's social media activity.

[0100] The data collection unit can estimate the user's emotions and select the data to collect based on those estimated emotions. For example, if the user feels a strong sense of gratitude, the data collection unit will prioritize collecting positive evaluation data based on that emotion. If the user feels dissatisfied, the data collection unit will prioritize collecting negative evaluation data based on that emotion. Furthermore, if the user has neutral emotions, the data collection unit will collect balanced evaluation data. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The data collection unit can optimize its collection algorithm by referring to past collected data during the collection process. For example, the collection unit selects the optimal collection algorithm based on past collected data. It can also extract specific patterns from past collected data and adjust the collection algorithm accordingly. Furthermore, the collection unit analyzes past collected data to improve the accuracy of the collection algorithm. This allows the system to provide the optimal collection algorithm based on past collected data.

[0102] The data collection unit can estimate the user's emotions and adjust the collection frequency based on the estimated emotions. For example, if the user feels a strong sense of gratitude, the unit will set a higher collection frequency based on that emotion. Conversely, if the user feels dissatisfied, the unit will set a lower collection frequency based on that emotion. Furthermore, if the user has neutral emotions, the unit will collect data at a standard frequency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The data collection unit can weight the collected data based on when the rating buttons and comments were submitted. For example, the unit prioritizes collecting recently submitted rating buttons and comments and assigns them a higher weight. It can also prioritize collecting rating buttons and comments submitted within a specific period and adjust their weighting accordingly. Furthermore, the unit dynamically adjusts the weighting of the collected data based on the submission date. This allows for data collection with appropriate weighting based on the submission time.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The evaluation system can also include a feedback section. This feedback section can provide feedback on the evaluation buttons and comments received by employees. For example, it can suggest specific areas for improvement and strengths based on the evaluation buttons and comments received. Furthermore, the feedback section can automatically generate and send thank-you messages to employees for the evaluations they receive. In addition, the feedback section can offer career path suggestions and resources for skill development based on the evaluations received. This allows employees to improve themselves based on the evaluations they receive, contributing to the overall growth of the company.

[0106] The evaluation system can also include a notification function. This function can provide notifications regarding evaluation buttons and comments received by employees. For example, it can send real-time notifications when an employee receives a new evaluation button or comment. It can also periodically notify employees of the aggregated evaluation results, allowing them to understand their own evaluation status. Furthermore, the notification function can set alerts for specific evaluation buttons and comments to ensure important feedback is not missed. This increases employee awareness of evaluations and encourages them to actively utilize feedback.

[0107] The evaluation system can also include a training department. This department can propose appropriate training programs based on the evaluation buttons and comments received by employees. For example, it can suggest online courses or workshops for skill development based on the evaluations received. Furthermore, the training department can analyze employee evaluation data and create individualized training plans. In addition, based on the evaluations received, the training department can propose mentorship programs and facilitate interaction with experienced employees. This allows employees to pursue self-improvement based on their evaluations, thereby improving the overall skill level of the company.

[0108] The evaluation system can also include a compensation department. This department can provide rewards and incentives based on the evaluation buttons and comments employees receive. For example, it could offer bonuses or special leave to employees who receive a certain number of evaluation buttons. It could also recommend employees for promotion or salary increases based on the evaluations they receive. Furthermore, the compensation department could award internal recognition and certificates of appreciation based on the evaluations employees receive. This increases employee motivation for evaluation and improves overall company performance.

[0109] The evaluation system can also include a collaboration section. This section can facilitate collaboration among employees based on the evaluations and comments they receive. For example, the collaboration section can propose common projects and tasks based on the evaluations received. It can also analyze employee evaluation data and match employees with complementary skills. Furthermore, the collaboration section can propose team-building activities and workshops based on the evaluations received. This allows employees to build collaborative relationships with others based on their evaluations, improving teamwork across the entire company.

[0110] The evaluation system can also include an emotion analysis unit. This unit can analyze an employee's emotional state based on the evaluation buttons and comments they receive. For example, it can identify positive and negative emotions based on the evaluations they receive. Furthermore, it can monitor employees' emotional states in real time, enabling early detection of signs of stress and dissatisfaction. In addition, it can suggest appropriate support and counseling based on the employee's emotional state. This helps maintain employee mental health and improves the overall work environment within the company.

[0111] The evaluation system can also include an emotional feedback section. This section can provide emotionally responsive feedback based on the evaluation buttons and comments received by the employee. For example, if an employee receives a positive evaluation, the emotional feedback section can provide feedback to reinforce that positive feeling. Conversely, if an employee receives a negative evaluation, it can provide constructive feedback to alleviate that negative feeling. Furthermore, the emotional feedback section can adjust the timing and content of the feedback based on the employee's emotional state. This allows employees to appropriately process their emotions regarding evaluations and promotes positive behavior.

[0112] The evaluation system can also include an emotion monitoring unit. This unit can continuously monitor employees' emotional states based on the evaluation buttons and comments they receive. For example, it can analyze employees' emotional states from daily evaluation data and track long-term emotional changes. Furthermore, it can detect early signs of stress and burnout based on employees' emotional states. In addition, it can provide appropriate support and resources according to employees' emotional states. This helps maintain employee mental health and improves the overall work environment within the company.

[0113] The evaluation system can also include an emotional reporting section. This section can generate emotional status reports based on the evaluation buttons and comments received by employees. For example, the emotional reporting section can periodically report on employees' emotional states and provide them to managers. Furthermore, based on employee emotional states, the emotional reporting section can analyze the emotional trends of the entire team and assess the health of the organization. In addition, the emotional reporting section can propose appropriate action plans based on employees' emotional states. This allows managers to understand employees' emotional states and provide appropriate support.

[0114] The evaluation system can also include an emotional support unit. This unit can provide emotionally responsive support based on the evaluation buttons and comments received by employees. For example, if an employee receives a positive evaluation, the unit can provide support to help them maintain that positive feeling. If an employee receives a negative evaluation, it can provide counseling or mental health resources to alleviate that feeling. Furthermore, the emotional support unit can create appropriate support plans based on the employee's emotional state. This allows employees to process their emotions regarding evaluations appropriately and promotes positive behavior.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk can send rating buttons and comments. For example, employees can send "rating buttons" and comments to other employees. The reception desk can easily convey gratitude and appreciation. Step 2: The analysis unit analyzes the data of evaluation buttons and comments sent by the reception unit. For example, the AI ​​analyzes the number and content of evaluation buttons and comments received by each employee and calculates the employee's evaluation. Step 3: The evaluation department conducts performance evaluations based on the data analyzed by the analysis department. For example, if an employee receives many positive feedback buttons and thank-you comments, that employee's evaluation will be higher. Step 4: The matching unit matches jobs based on the data analyzed by the analysis unit. For example, if an employee has specific skills or experience, a suitable job will be matched to that employee. This allows for the maximum utilization of employees' skills and experience, leading to increased work efficiency.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, matching unit, and collection unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing employees to send "evaluation buttons" and comments to other employees. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the evaluation button and comment data. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which performs personnel evaluations based on the analyzed data. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which matches jobs based on the analyzed data. The collection unit is implemented by, for example, the control unit 46A of the smart device 14, which efficiently collects the evaluation button and comment data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, matching unit, and collection unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing employees to send "evaluation buttons" and comments to other employees. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the evaluation button and comment data. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which performs personnel evaluations based on the analyzed data. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which matches jobs based on the analyzed data. The collection unit is implemented by, for example, the control unit 46A of the smart glasses 214, which efficiently collects the evaluation button and comment data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, matching unit, and collection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing employees to send "evaluation buttons" and comments to other employees. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the evaluation button and comment data. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which performs personnel evaluations based on the analyzed data. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which matches jobs based on the analyzed data. The collection unit is implemented by, for example, the control unit 46A of the headset terminal 314, which efficiently collects the evaluation button and comment data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, evaluation unit, matching unit, and collection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing employees to send "evaluation buttons" and comments to other employees. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which uses AI to analyze the evaluation button and comment data. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which performs personnel evaluations based on the analyzed data. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which matches jobs based on the analyzed data. The collection unit is implemented by, for example, the control unit 46A of the robot 414, which efficiently collects the evaluation button and comment data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) The reception area for submitting ratings and comments, An analysis unit analyzes the evaluation button and comment data sent by the aforementioned reception unit, An evaluation unit that performs personnel evaluations based on the data analyzed by the aforementioned analysis unit, The system includes a matching unit that performs job matching based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) It includes a data collection unit that collects data from rating buttons and comments. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system analyzes the number and content of evaluation buttons and comments received by each employee to calculate their performance rating. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Matching employees with suitable jobs based on their skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, Detect excessive work based on analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Health management is performed based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of rating buttons and comment submissions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is At the time of registration, the system analyzes the user's past evaluation history and selects the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is During registration, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of rating buttons and comments to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During the submission process, the system prioritizes accepting highly relevant rating buttons and comments, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is Upon registration, the system analyzes the user's social media activity and accepts relevant rating buttons and comments. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail is adjusted based on the importance of rating buttons and comments. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the rating button and comment category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on the timing of rating button clicks and comment submissions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of rating buttons and comments. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, It estimates the user's emotions and adjusts the evaluation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, During the evaluation process, the system analyzes the user's past evaluation history to select the most suitable evaluation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation process, the evaluation method is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, It estimates the user's emotions and determines the priority of evaluations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, During evaluation, the optimal evaluation method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, During the evaluation process, we will analyze users' social media activity and propose evaluation methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The matching unit is It estimates the user's emotions and adjusts the matching method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The matching unit is During the matching process, the system analyzes the user's past skills and experience to select the most suitable matching method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The matching unit is During the matching process, the matching method is customized based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The matching unit is The system estimates the user's emotions and determines matching priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The matching unit is During the matching process, the system selects the optimal matching method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The matching unit is During the matching process, we analyze the user's social media activity and suggest matching methods. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned collection unit is The system estimates the user's emotions and selects the data to collect based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned collection unit is During data collection, the collection algorithm is optimized by referring to past collected data. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned collection unit is It estimates the user's sentiment and adjusts the frequency of data collection based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned collection unit is During data collection, the collected data is weighted based on the timing of rating button clicks and comment submissions. The system described in Appendix 2, characterized by the features described herein. [Explanation of Symbols]

[0189] 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. The reception area for submitting ratings and comments, An analysis unit analyzes the evaluation button and comment data sent by the aforementioned reception unit, An evaluation unit that performs personnel evaluations based on the data analyzed by the aforementioned analysis unit, The system includes a matching unit that performs job matching based on the data analyzed by the aforementioned analysis unit. A system characterized by the following features.

2. It includes a data collection unit that collects data from rating buttons and comments. The system according to feature 1.

3. The aforementioned analysis unit, The system analyzes the number and content of evaluation buttons and comments received by each employee to calculate their performance rating. The system according to feature 1.

4. The matching unit is Matching employees with suitable jobs based on their skills and experience. The system according to feature 1.

5. The aforementioned analysis unit, Detect excessive work based on analysis results. The system according to feature 1.

6. The aforementioned analysis unit, Health management is performed based on the analysis results. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of rating buttons and comment submissions based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is At the time of registration, the system analyzes the user's past evaluation history and selects the most suitable registration method. The system according to feature 1.

9. The aforementioned reception unit is During registration, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and determines the priority of rating buttons and comments to accept based on those estimated emotions. The system according to feature 1.

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