system

Abstract

The system according to this embodiment aims to analyze attendance reports and work reports to appropriately understand employee performance and mental health status. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a judgment unit. The reception unit receives attendance reports. The analysis unit analyzes the attendance reports and work reports received by the reception unit. The provision unit provides praise messages based on the results analyzed by the analysis unit. The judgment unit determines the mental health status based on the results analyzed by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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, the evaluation of employees and the grasp of their mental health status based on attendance reports and work reports have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze attendance reports and work reports and appropriately grasp the evaluation of employees and their mental health status.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a judgment unit. The reception unit receives attendance reports. The analysis unit analyzes the attendance reports and work reports received by the reception unit. The provision unit provides praise messages based on the results analyzed by the analysis unit. The judgment unit determines the mental health status based on the results analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze attendance reports and work reports to appropriately understand employee performance and mental health status. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The mental support system according to an embodiment of the present invention is a system that analyzes the personality and preferences of each employee and supports their daily work. This mental support system is based on the premise that employees submit a brief work report along with their attendance report every day. The system analyzes the submitted brief work report and provides effective praise based on the employee's personality and preferences. Furthermore, it utilizes the accumulated data of brief work reports to detect mental health issues early and improve the accuracy of performance reports through an automated creation function. This mechanism can alleviate employees' mental anxieties and improve their motivation, thereby improving the productivity of the company. For example, an employee submits a brief work report along with their attendance report every day. For example, the content might be, "Today I had a meeting for a new project." This report is entered into the system. Next, the system analyzes the submitted brief work report and provides effective praise based on the employee's personality and preferences. For example, it sends a praise message such as, "Thank you for participating in the meeting for the new project. Your opinion was very helpful." As a result, employees feel that their efforts are recognized and their motivation improves. Furthermore, the system utilizes the accumulated data of brief work reports to detect mental health issues early. For example, if an employee's reports increasingly contain negative language, the system may determine that the employee is experiencing mental health issues and provide appropriate support. The system also uses an automated generation function to improve the accuracy of performance reports. For instance, it automatically generates monthly performance reports based on employees' brief work reports. This reduces the effort required for employees to create reports, improving work efficiency. In this way, the system analyzes each employee's personality and preferences, providing effective praise and support to alleviate employee mental health anxieties and improve motivation. As a result, it can improve the company's productivity. Thus, a mental support system can alleviate employee mental health anxieties and improve motivation.

[0029] The mental support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a judgment unit. The reception unit receives attendance reports submitted daily by employees. Attendance reports include, but are not limited to, arrival time, departure time, and break times. The reception unit provides, for example, an interface for employees to input attendance reports. The interface is often provided through a web application or a mobile application. The analysis unit analyzes the attendance reports and brief work reports received by the reception unit. The analysis unit analyzes the report content using, for example, text analysis technology. Text analysis technology includes natural language processing technology and data mining technology. For example, the analysis unit extracts keywords from the report content and analyzes the employee's personality and preferences. The analysis unit can also perform sentiment analysis of the report content and evaluate the employee's mental state. The provision unit provides praise messages based on the results analyzed by the analysis unit. The provision unit generates effective praise messages based on, for example, the employee's personality and preferences. Praise messages are provided in the form of text messages, voice messages, etc. For example, the delivery unit can generate a message of praise such as, "Thank you for attending the meeting on the new project. Your feedback was very helpful." The judgment unit determines whether an employee is experiencing mental distress based on the results analyzed by the analysis unit. For example, if an employee's reports increasingly contain negative expressions, the judgment unit may determine that the employee is experiencing mental distress. The judgment unit can detect signs of mental distress early and provide appropriate support. As a result, the mental support system according to this embodiment can alleviate employees' mental anxieties and improve their motivation.

[0030] The service provider can generate praise messages based on employees' personalities and preferences. For example, it can generate praise messages based on the results of employee personality tests or surveys on hobbies and interests. For instance, it can determine whether an employee is introverted or extroverted based on the results of a personality test and generate a praise message accordingly. It can also generate praise messages related to topics that employees are interested in based on the results of a survey on hobbies and interests. For example, if an employee is interested in sports, the service provider can generate a praise message such as, "Your teamwork is excellent." By providing praise messages tailored to employees' personalities and preferences, motivation can be improved.

[0031] The assessment unit can utilize accumulated data from short work reports to detect mental health issues early. For example, the assessment unit analyzes the accumulated data from short work reports to evaluate the mental state of employees. For instance, if the assessment unit notices an increase in negative expressions in the reports, it may determine that the employee is experiencing mental health problems. The assessment unit can also perform emotional analysis of the reports to evaluate the mental state of employees. For example, the assessment unit evaluates the mental state of employees based on the emotional score of the reports. This allows for the early detection of mental health problems and the provision of appropriate support.

[0032] The analysis unit can automatically create business performance reports based on data from short business reports. For example, the analysis unit can analyze the data from short business reports and automatically create business performance reports. For example, the analysis unit can extract keywords from the report content and generate business performance reports based on those keywords. The analysis unit can also perform sentiment analysis on the report content and reflect it in the business performance reports. For example, the analysis unit can adjust the content of the business performance reports based on the sentiment score of the report content. As a result, business efficiency is improved through the automatic creation of performance reports.

[0033] The reception department can analyze a user's past attendance reporting history and select the appropriate reception method. For example, the reception department can store a user's past attendance reporting history in a database and perform analysis based on that data. For example, the reception department can analyze data such as past clock-in and clock-out times and leave history to select the optimal reception method. For example, the reception department can prioritize suggesting reporting methods that the user has frequently used in the past. The reception department can also suggest the most efficient reporting method based on the user's past reporting history. Furthermore, the reception department can analyze the user's past reporting history and optimize the timing of reports. In this way, the optimal reception method can be selected by analyzing past attendance reporting history.

[0034] The reception desk can filter attendance reports based on the user's current work status and areas of interest. For example, the reception desk can store the user's current work status in a database and use that information for filtering. For instance, the reception desk can prioritize reports related to projects the user is currently working on. It can also filter reports based on the user's areas of interest. Furthermore, the reception desk can adjust the priority of reports according to the user's work status. This allows for the reception of more relevant reports by filtering them based on the user's work status and areas of interest. For example, it can prioritize reports related to projects the user is currently working on. It can also filter reports based on the user's areas of interest. Furthermore, it can adjust the priority of reports according to the user's work status.

[0035] The reception desk can prioritize receiving attendance reports based on the user's geographical location information. For example, the reception desk can obtain the user's geographical location information from GPS data or IP addresses. For instance, if the user is in the office, the reception desk can prioritize receiving office-related reports. Similarly, if the user is on a business trip, it can prioritize reports related to their destination. Furthermore, if the user is working remotely, it can prioritize reports related to remote work. This allows for the prioritization of highly relevant reports by considering the user's geographical location information. For example, if the user is in the office, office-related reports are prioritized. Similarly, if the user is on a business trip, reports related to their destination are prioritized. Furthermore, if the user is working remotely, reports related to remote work are prioritized.

[0036] The reception department can analyze users' social media activity when receiving attendance reports and accept relevant reports. For example, the reception department can use social media APIs to analyze users' social media activity. For instance, it can accept relevant reports based on work-related information shared by users on social media. It can also extract work-related topics from users' social media activity and accept reports based on that information. Furthermore, the reception department can analyze users' social media posts and prioritize the acceptance of relevant reports. This allows the reception department to accept relevant reports by analyzing users' social media activity. For example, it can accept relevant reports based on work-related information shared by users on social media. It can also extract work-related topics from users' social media activity and accept reports based on that information. Furthermore, it can analyze users' social media posts and prioritize the acceptance of relevant reports.

[0037] The analysis unit can adjust the level of detail of the analysis based on the importance of the reported content. For example, to evaluate the importance of the reported content, the analysis unit evaluates it based on its impact on business operations and its urgency. For instance, the analysis unit can perform a detailed analysis on highly important reported content, while performing a concise analysis on less important content. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This allows for more efficient analysis by adjusting the level of detail based on the importance of the reported content. For example, a detailed analysis can be performed on highly important reported content, while a concise analysis can be performed on less important content. Furthermore, the depth of the analysis can be adjusted according to the importance.

[0038] The analysis unit can apply different analysis algorithms depending on the category of the report content during analysis. For example, the analysis unit can classify the report content into categories such as business categories or project categories. For instance, the analysis unit can apply a project management analysis algorithm to project reports. It can also apply a daily operations analysis algorithm to daily operations reports. Furthermore, it can select the optimal analysis algorithm for specific categories. This improves the accuracy of the analysis by applying the most suitable analysis algorithm for each category of report content. For example, a project management analysis algorithm can be applied to project reports. It can also apply a daily operations analysis algorithm to daily operations reports. Furthermore, it can select the optimal analysis algorithm for specific categories.

[0039] The analysis unit can determine the priority of analysis based on the submission date of the reports during the analysis process. For example, the analysis unit can store the submission dates of the reports in a database and determine the priority based on that data. For instance, the analysis unit can prioritize analyzing reports submitted earlier and postpone those submitted later. Furthermore, it can adjust the priority of analysis according to the submission date. This allows for more efficient analysis by determining the priority of analysis based on the submission date of the reports. For example, it can prioritize analyzing reports submitted earlier and postpone those submitted later. Furthermore, it can adjust the priority of analysis according to the submission date.

[0040] The analysis unit can adjust the order of analysis based on the relevance of the reported content during the analysis. For example, to evaluate the relevance of the reported content, the analysis unit evaluates based on the similarity of the content and related topics. For example, the analysis unit can prioritize the analysis of highly relevant reported content. It can also postpone the analysis of less relevant reported content. Furthermore, it can adjust the order of analysis according to the relevance. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the reported content. For example, it can prioritize the analysis of highly relevant reported content. It can also postpone the analysis of less relevant reported content. Furthermore, it can adjust the order of analysis according to the relevance.

[0041] The delivery department can adjust the level of detail in the praise message based on the importance of the report. For example, to evaluate the importance of the report, the delivery department can evaluate it based on its impact on business operations and its urgency. For example, the delivery department can provide a detailed praise message for highly important reports. Conversely, it can provide a concise praise message for less important reports. Furthermore, the delivery department can adjust the level of detail in the message according to its importance. This allows for the delivery of more appropriate praise messages by adjusting the level of detail in the message based on the importance of the report. For example, a detailed praise message can be provided for highly important reports. Conversely, a concise praise message can be provided for less important reports. Furthermore, the level of detail in the message can be adjusted according to its importance.

[0042] The service provider can apply different message generation algorithms depending on the category of the report content when providing praise messages. For example, the service provider can classify report content into categories such as business categories or project categories. For instance, the service provider can apply a project management praise message generation algorithm to project reports. It can also apply a daily work praise message generation algorithm to daily work reports. Furthermore, it can select the most appropriate praise message generation algorithm depending on a specific category. This allows for the provision of more appropriate praise messages by applying the optimal message generation algorithm according to the category of the report content. For example, a project management praise message generation algorithm can be applied to project reports. It can also apply a daily work praise message generation algorithm to daily work reports. Furthermore, it can select the most appropriate praise message generation algorithm depending on a specific category.

[0043] The delivery department can prioritize commendation messages based on the submission date of the report. For example, the delivery department can store the submission dates of reports in a database and use that information to determine priorities. For instance, it can prioritize commendation messages for reports submitted earlier, and postpone them for reports submitted later. Furthermore, it can adjust the message priority according to the submission date. This allows for the delivery of commendation messages in a more appropriate order by prioritizing them based on the submission date. For example, it can prioritize commendation messages for reports submitted earlier, and postpone them for reports submitted later. Furthermore, it can adjust the message priority according to the submission date.

[0044] The delivery department can adjust the order of commendation messages based on the relevance of the reported content when delivering them. For example, the delivery department can evaluate the relevance of the reported content based on similarity of content or related topics. For instance, the delivery department can prioritize delivering commendation messages to highly relevant reports. Conversely, it can postpone delivering commendation messages to less relevant reports. Furthermore, it can adjust the order of messages according to their relevance. This allows for the delivery of commendation messages in a more appropriate order by adjusting the order of messages based on the relevance of the reported content. For example, it can prioritize delivering commendation messages to highly relevant reports. Conversely, it can postpone delivering commendation messages to less relevant reports. Furthermore, it can adjust the order of messages according to their relevance.

[0045] The assessment unit can improve the accuracy of its assessment of mental health status based on the interrelationships of reported information. For example, to evaluate the interrelationships of reported information, the assessment unit performs evaluations based on the relevance and co-occurrence relationships of the content. For example, the assessment unit can analyze the interrelationships of multiple reported items and detect signs of mental health problems. The assessment unit can also improve the accuracy of its assessment of mental health problems by considering the interrelationships of reported items. Furthermore, the assessment unit can perform early detection of mental health problems based on the interrelationships of reported items. This improves the accuracy of the assessment of mental health problems by considering the interrelationships of reported items. For example, it can analyze the interrelationships of multiple reported items and detect signs of mental health problems. Furthermore, it can improve the accuracy of its assessment of mental health problems by considering the interrelationships of reported items. Furthermore, it can perform early detection of mental health problems based on the interrelationships of reported items.

[0046] The assessment unit can make a determination of mental health status based on the attribute information of the person submitting the report. For example, the assessment unit can store the attribute information of the person submitting the report in a database and make a determination based on that information. For example, the assessment unit can make a determination of mental health problems by considering attribute information such as the submitter's age and gender. It can also make a determination of mental health problems by considering attribute information such as the submitter's job duties and position. Furthermore, it can also make a determination of mental health problems by considering attribute information such as the submitter's work history and past mental health problems. This makes it possible to make a more appropriate determination of mental health problems by considering the submitter's attribute information. For example, the assessment unit can make a determination of mental health problems by considering attribute information such as the submitter's age and gender. It can also make a determination of mental health problems by considering attribute information such as the submitter's job duties and position. Furthermore, it can make a determination of mental health problems by considering attribute information such as the submitter's work history and past mental health problems.

[0047] The assessment unit can make a determination of mental health status based on the geographical distribution of reported data. For example, the assessment unit can store the geographical distribution of reported data in a database and make a determination based on that data. For instance, the assessment unit can analyze the geographical distribution of reported data and detect signs of mental health problems in specific regions. Furthermore, the assessment unit can improve the accuracy of mental health diagnosis by considering geographical distribution. In addition, the assessment unit can perform early detection of mental health problems based on geographical distribution. This allows for a more appropriate determination of mental health problems by considering the geographical distribution of reported data. For example, it can analyze the geographical distribution of reported data and detect signs of mental health problems in specific regions. Furthermore, it can improve the accuracy of mental health diagnosis by considering geographical distribution. Furthermore, it can perform early detection of mental health problems based on geographical distribution.

[0048] The assessment unit can improve the accuracy of its assessment of mental health status by referencing relevant literature related to the reported content. For example, the assessment unit can store academic papers and technical reports in a database to reference literature related to the reported content and perform assessments based on this information. For instance, the assessment unit can improve the accuracy of its assessment of mental health problems by referencing relevant literature. Furthermore, the assessment unit can detect signs of mental health problems based on relevant literature. In addition, the assessment unit can perform early detection of mental health problems by referencing relevant literature. Thus, by referencing relevant literature, the accuracy of the assessment of mental health problems is improved. For example, by referencing relevant literature, the accuracy of the assessment of mental health problems is improved. Furthermore, signs of mental health problems can be detected based on relevant literature. Furthermore, early detection of mental health problems can be performed by referencing relevant literature.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can predict business performance based on the user's past business report data. For example, the analysis unit can analyze past business report data to understand the user's business performance trends. Furthermore, the analysis unit can predict future business performance based on past data. In addition, the analysis unit can provide the user with advice on business improvement based on the prediction results. This can improve the user's business performance.

[0051] The evaluation unit can assess the quality of communication within a team based on the user's work reports. For example, it can analyze the user's work reports and evaluate the frequency and quality of communication within the team. Furthermore, if the evaluation unit determines that the quality of communication is declining, it can offer suggestions for improvement. It can also propose training or workshops to improve the quality of communication within the team, thereby enhancing team performance.

[0052] The analysis unit can automatically determine task priorities based on the user's work reports. For example, the analysis unit can analyze the user's work reports and evaluate the importance and urgency of tasks. Furthermore, the analysis unit can automatically determine task priorities based on the evaluation results. In addition, the analysis unit can notify the user of the progress of tasks in real time based on the priorities. This allows users to proceed with their work efficiently.

[0053] The reception desk can suggest the optimal reporting timing based on the user's past attendance reporting data. For example, the reception desk can analyze the user's past attendance reporting data and suggest the most efficient reporting time. Furthermore, the reception desk can adjust the reporting timing based on the user's work schedule. In addition, the reception desk can customize the reporting timing according to the user's individual needs. This allows users to submit attendance reports efficiently.

[0054] The assessment unit can evaluate business risks based on the user's business report. For example, the assessment unit can analyze the user's business report and identify business risk factors. Furthermore, the assessment unit can perform a risk assessment based on these risk factors. In addition, the assessment unit can make suggestions for risk mitigation based on the risk assessment results. This allows the user to understand business risks and take appropriate countermeasures.

[0055] The analysis unit can visualize the progress of tasks in real time based on the user's work reports. For example, the analysis unit can analyze the user's work reports and display the progress of tasks in graphs and charts. Furthermore, the analysis unit can identify delays and problems based on the progress. In addition, the analysis unit can update the progress in real time, providing users with the latest information. This allows users to understand the progress of their tasks and take appropriate measures.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives daily attendance reports from employees. Attendance reports include arrival time, departure time, break times, etc. The reception desk provides an interface for employees to enter their attendance reports, which is often provided through a web application or mobile application. Step 2: The analysis department analyzes the attendance reports and brief work reports received by the reception department. The analysis department uses text analysis technology to analyze the report content, extracts keywords, and analyzes the employee's personality and preferences. It also performs sentiment analysis to evaluate the employee's mental state. Step 3: The delivery department provides praise messages based on the results analyzed by the analysis department. The delivery department generates effective praise messages based on the employee's personality and preferences and delivers them in the form of text messages, voice messages, etc. Step 4: The assessment unit determines whether an employee is experiencing mental health issues based on the results analyzed by the analysis unit. If the assessment unit notices an increase in negative expressions in an employee's reports, it determines that the employee may be experiencing mental health issues and provides appropriate support to detect signs of mental health problems early.

[0058] (Example of form 2) The mental support system according to an embodiment of the present invention is a system that analyzes the personality and preferences of each employee and supports their daily work. This mental support system is based on the premise that employees submit a brief work report along with their attendance report every day. The system analyzes the submitted brief work report and provides effective praise based on the employee's personality and preferences. Furthermore, it utilizes the accumulated data of brief work reports to detect mental health issues early and improve the accuracy of performance reports through an automated creation function. This mechanism can alleviate employees' mental anxieties and improve their motivation, thereby improving the productivity of the company. For example, an employee submits a brief work report along with their attendance report every day. For example, the content might be, "Today I had a meeting for a new project." This report is entered into the system. Next, the system analyzes the submitted brief work report and provides effective praise based on the employee's personality and preferences. For example, it sends a praise message such as, "Thank you for participating in the meeting for the new project. Your opinion was very helpful." As a result, employees feel that their efforts are recognized and their motivation improves. Furthermore, the system utilizes the accumulated data of brief work reports to detect mental health issues early. For example, if an employee's reports increasingly contain negative language, the system may determine that the employee is experiencing mental health issues and provide appropriate support. The system also uses an automated generation function to improve the accuracy of performance reports. For instance, it automatically generates monthly performance reports based on employees' brief work reports. This reduces the effort required for employees to create reports, improving work efficiency. In this way, the system analyzes each employee's personality and preferences, providing effective praise and support to alleviate employee mental health anxieties and improve motivation. As a result, it can improve the company's productivity. Thus, a mental support system can alleviate employee mental health anxieties and improve motivation.

[0059] The mental support system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, and a judgment unit. The reception unit receives attendance reports submitted daily by employees. Attendance reports include, but are not limited to, arrival time, departure time, and break times. The reception unit provides, for example, an interface for employees to input attendance reports. The interface is often provided through a web application or a mobile application. The analysis unit analyzes the attendance reports and brief work reports received by the reception unit. The analysis unit analyzes the report content using, for example, text analysis technology. Text analysis technology includes natural language processing technology and data mining technology. For example, the analysis unit extracts keywords from the report content and analyzes the employee's personality and preferences. The analysis unit can also perform sentiment analysis of the report content and evaluate the employee's mental state. The provision unit provides praise messages based on the results analyzed by the analysis unit. The provision unit generates effective praise messages based on, for example, the employee's personality and preferences. Praise messages are provided in the form of text messages, voice messages, etc. For example, the delivery unit can generate a message of praise such as, "Thank you for attending the meeting on the new project. Your feedback was very helpful." The judgment unit determines whether an employee is experiencing mental distress based on the results analyzed by the analysis unit. For example, if an employee's reports increasingly contain negative expressions, the judgment unit may determine that the employee is experiencing mental distress. The judgment unit can detect signs of mental distress early and provide appropriate support. As a result, the mental support system according to this embodiment can alleviate employees' mental anxieties and improve their motivation.

[0060] The service provider can generate praise messages based on employees' personalities and preferences. For example, it can generate praise messages based on the results of employee personality tests or surveys on hobbies and interests. For instance, it can determine whether an employee is introverted or extroverted based on the results of a personality test and generate a praise message accordingly. It can also generate praise messages related to topics that employees are interested in based on the results of a survey on hobbies and interests. For example, if an employee is interested in sports, the service provider can generate a praise message such as, "Your teamwork is excellent." By providing praise messages tailored to employees' personalities and preferences, motivation can be improved.

[0061] The assessment unit can utilize accumulated data from short work reports to detect mental health issues early. For example, the assessment unit analyzes the accumulated data from short work reports to evaluate the mental state of employees. For instance, if the assessment unit notices an increase in negative expressions in the reports, it may determine that the employee is experiencing mental health problems. The assessment unit can also perform emotional analysis of the reports to evaluate the mental state of employees. For example, the assessment unit evaluates the mental state of employees based on the emotional score of the reports. This allows for the early detection of mental health problems and the provision of appropriate support.

[0062] The analysis unit can automatically create business performance reports based on data from short business reports. For example, the analysis unit can analyze the data from short business reports and automatically create business performance reports. For example, the analysis unit can extract keywords from the report content and generate business performance reports based on those keywords. The analysis unit can also perform sentiment analysis on the report content and reflect it in the business performance reports. For example, the analysis unit can adjust the content of the business performance reports based on the sentiment score of the report content. As a result, business efficiency is improved through the automatic creation of performance reports.

[0063] The reception desk can estimate the user's emotions and adjust the timing of attendance report submission based on the estimated emotions. For example, the reception desk can estimate emotions using facial expression analysis technology. For instance, it can analyze the user's facial expression data to determine whether the user is stressed or relaxed. The reception desk can also estimate emotions using voice analysis technology. For example, it can analyze the user's voice data to calculate an emotion score. Furthermore, the reception desk can also estimate emotions using text analysis technology. For example, it can analyze the user's text data to calculate an emotion score. This allows for more appropriate submission timing of attendance reports by adjusting the timing according to the user's emotions. For example, if the user is stressed, the system can delay the submission of the attendance report, allowing the user to submit it in a relaxed state. If the user is relaxed, the system can immediately accept the attendance report and process it quickly. Furthermore, if the user is in a hurry, the system can provide a simplified report form, allowing the user to complete the report quickly.

[0064] The reception department can analyze a user's past attendance reporting history and select the appropriate reception method. For example, the reception department can store a user's past attendance reporting history in a database and perform analysis based on that data. For example, the reception department can analyze data such as past clock-in and clock-out times and leave history to select the optimal reception method. For example, the reception department can prioritize suggesting reporting methods that the user has frequently used in the past. The reception department can also suggest the most efficient reporting method based on the user's past reporting history. Furthermore, the reception department can analyze the user's past reporting history and optimize the timing of reports. In this way, the optimal reception method can be selected by analyzing past attendance reporting history.

[0065] The reception desk can filter attendance reports based on the user's current work status and areas of interest. For example, the reception desk can store the user's current work status in a database and use that information for filtering. For instance, the reception desk can prioritize reports related to projects the user is currently working on. It can also filter reports based on the user's areas of interest. Furthermore, the reception desk can adjust the priority of reports according to the user's work status. This allows for the reception of more relevant reports by filtering them based on the user's work status and areas of interest. For example, it can prioritize reports related to projects the user is currently working on. It can also filter reports based on the user's areas of interest. Furthermore, it can adjust the priority of reports according to the user's work status.

[0066] The reception desk can estimate the user's emotions and determine the priority of attendance reports to be received based on those emotions. For example, the reception desk can estimate emotions using facial expression analysis technology. For instance, it can analyze the user's facial expression data to determine whether they are stressed or relaxed. The reception desk can also estimate emotions using voice analysis technology. For example, it can analyze the user's voice data to calculate an emotion score. Furthermore, the reception desk can also estimate emotions using text analysis technology. For example, it can analyze the user's text data to calculate an emotion score. This allows for the prioritization of attendance reports according to the user's emotions, enabling reports to be received in a more appropriate order. For example, if a user is stressed, the system will postpone less important reports. If a user is relaxed, the system can prioritize receiving more important reports. Furthermore, if a user is in a hurry, the system can prioritize receiving simplified reports.

[0067] The reception desk can prioritize receiving attendance reports based on the user's geographical location information. For example, the reception desk can obtain the user's geographical location information from GPS data or IP addresses. For instance, if the user is in the office, the reception desk can prioritize receiving office-related reports. Similarly, if the user is on a business trip, it can prioritize reports related to their destination. Furthermore, if the user is working remotely, it can prioritize reports related to remote work. This allows for the prioritization of highly relevant reports by considering the user's geographical location information. For example, if the user is in the office, office-related reports are prioritized. Similarly, if the user is on a business trip, reports related to their destination are prioritized. Furthermore, if the user is working remotely, reports related to remote work are prioritized.

[0068] The reception department can analyze users' social media activity when receiving attendance reports and accept relevant reports. For example, the reception department can use social media APIs to analyze users' social media activity. For instance, it can accept relevant reports based on work-related information shared by users on social media. It can also extract work-related topics from users' social media activity and accept reports based on that information. Furthermore, the reception department can analyze users' social media posts and prioritize the acceptance of relevant reports. This allows the reception department to accept relevant reports by analyzing users' social media activity. For example, it can accept relevant reports based on work-related information shared by users on social media. It can also extract work-related topics from users' social media activity and accept reports based on that information. Furthermore, it can analyze users' social media posts and prioritize the acceptance of relevant reports.

[0069] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, the analysis unit can estimate emotions using facial expression analysis technology. For instance, it can analyze the user's facial expression data to determine whether the user is stressed or relaxed. The analysis unit can also estimate emotions using voice analysis technology. For example, it can analyze the user's voice data and calculate an emotion score. Furthermore, the analysis unit can also estimate emotions using text analysis technology. For example, it can analyze the user's text data and calculate an emotion score. This allows the system to adjust the presentation of the analysis according to the user's emotions, thereby providing more appropriate analysis results. For example, if the user is relaxed, it can provide detailed analysis results. If the user is stressed, it can provide concise analysis results. Furthermore, if the user is in a hurry, it can provide analysis results that get straight to the point.

[0070] The analysis unit can adjust the level of detail of the analysis based on the importance of the reported content. For example, to evaluate the importance of the reported content, the analysis unit evaluates it based on its impact on business operations and its urgency. For instance, the analysis unit can perform a detailed analysis on highly important reported content, while performing a concise analysis on less important content. Furthermore, the analysis unit can adjust the depth of the analysis according to the importance. This allows for more efficient analysis by adjusting the level of detail based on the importance of the reported content. For example, a detailed analysis can be performed on highly important reported content, while a concise analysis can be performed on less important content. Furthermore, the depth of the analysis can be adjusted according to the importance.

[0071] The analysis unit can apply different analysis algorithms depending on the category of the report content during analysis. For example, the analysis unit can classify the report content into categories such as business categories or project categories. For instance, the analysis unit can apply a project management analysis algorithm to project reports. It can also apply a daily operations analysis algorithm to daily operations reports. Furthermore, it can select the optimal analysis algorithm for specific categories. This improves the accuracy of the analysis by applying the most suitable analysis algorithm for each category of report content. For example, a project management analysis algorithm can be applied to project reports. It can also apply a daily operations analysis algorithm to daily operations reports. Furthermore, it can select the optimal analysis algorithm for specific categories.

[0072] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit can estimate emotions using facial expression analysis technology. For instance, it can analyze the user's facial expression data to determine whether the user is stressed or relaxed. The analysis unit can also estimate emotions using voice analysis technology. For example, it can analyze the user's voice data and calculate an emotion score. Furthermore, the analysis unit can also estimate emotions using text analysis technology. For example, it can analyze the user's text data and calculate an emotion score. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. For example, if the user is relaxed, detailed analysis results can be provided. If the user is stressed, concise analysis results can be provided. Furthermore, if the user is in a hurry, a concise analysis result can be provided.

[0073] The analysis unit can determine the priority of analysis based on the submission date of the reports during the analysis process. For example, the analysis unit can store the submission dates of the reports in a database and determine the priority based on that data. For instance, the analysis unit can prioritize analyzing reports submitted earlier and postpone those submitted later. Furthermore, it can adjust the priority of analysis according to the submission date. This allows for more efficient analysis by determining the priority of analysis based on the submission date of the reports. For example, it can prioritize analyzing reports submitted earlier and postpone those submitted later. Furthermore, it can adjust the priority of analysis according to the submission date.

[0074] The analysis unit can adjust the order of analysis based on the relevance of the reported content during the analysis. For example, to evaluate the relevance of the reported content, the analysis unit evaluates based on the similarity of the content and related topics. For example, the analysis unit can prioritize the analysis of highly relevant reported content. It can also postpone the analysis of less relevant reported content. Furthermore, it can adjust the order of analysis according to the relevance. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the reported content. For example, it can prioritize the analysis of highly relevant reported content. It can also postpone the analysis of less relevant reported content. Furthermore, it can adjust the order of analysis according to the relevance.

[0075] The service provider can estimate the user's emotions and adjust the way praise messages are expressed based on those estimated emotions. For example, the service provider can estimate emotions using facial expression analysis technology. For instance, it can analyze the user's facial expression data to determine whether the user is stressed or relaxed. The service provider can also estimate emotions using voice analysis technology. For example, it can analyze the user's voice data to calculate an emotion score. Furthermore, the service provider can estimate emotions using text analysis technology. For example, it can analyze the user's text data to calculate an emotion score. This allows the service provider to provide more appropriate praise messages by adjusting the way praise messages are expressed according to the user's emotions. For example, if the user is relaxed, a detailed praise message can be provided. If the user is stressed, a concise praise message can be provided. Furthermore, if the user is in a hurry, a to-the-point praise message can be provided.

[0076] The delivery department can adjust the level of detail in the praise message based on the importance of the report. For example, to evaluate the importance of the report, the delivery department can evaluate it based on its impact on business operations and its urgency. For example, the delivery department can provide a detailed praise message for highly important reports. Conversely, it can provide a concise praise message for less important reports. Furthermore, the delivery department can adjust the level of detail in the message according to its importance. This allows for the delivery of more appropriate praise messages by adjusting the level of detail in the message based on the importance of the report. For example, a detailed praise message can be provided for highly important reports. Conversely, a concise praise message can be provided for less important reports. Furthermore, the level of detail in the message can be adjusted according to its importance.

[0077] The service provider can apply different message generation algorithms depending on the category of the report content when providing praise messages. For example, the service provider can classify report content into categories such as business categories or project categories. For instance, the service provider can apply a project management praise message generation algorithm to project reports. It can also apply a daily work praise message generation algorithm to daily work reports. Furthermore, it can select the most appropriate praise message generation algorithm depending on a specific category. This allows for the provision of more appropriate praise messages by applying the optimal message generation algorithm according to the category of the report content. For example, a project management praise message generation algorithm can be applied to project reports. It can also apply a daily work praise message generation algorithm to daily work reports. Furthermore, it can select the most appropriate praise message generation algorithm depending on a specific category.

[0078] The service provider can estimate the user's emotions and adjust the length of the praise message based on the estimated emotions. For example, the service provider can estimate emotions using facial expression analysis technology. For example, the service provider can analyze the user's facial expression data to determine whether the user is stressed or relaxed. The service provider can also estimate emotions using voice analysis technology. For example, the service provider can analyze the user's voice data and calculate an emotion score. Furthermore, the service provider can also estimate emotions using text analysis technology. For example, the service provider can analyze the user's text data and calculate an emotion score. This allows the service provider to provide more appropriate praise messages by adjusting the length of the praise message according to the user's emotions. For example, if the user is relaxed, a detailed praise message can be provided. If the user is stressed, a concise praise message can be provided. Furthermore, if the user is in a hurry, a praise message that gets straight to the point can be provided.

[0079] The delivery department can prioritize commendation messages based on the submission date of the report. For example, the delivery department can store the submission dates of reports in a database and use that information to determine priorities. For instance, it can prioritize commendation messages for reports submitted earlier, and postpone them for reports submitted later. Furthermore, it can adjust the message priority according to the submission date. This allows for the delivery of commendation messages in a more appropriate order by prioritizing them based on the submission date. For example, it can prioritize commendation messages for reports submitted earlier, and postpone them for reports submitted later. Furthermore, it can adjust the message priority according to the submission date.

[0080] The delivery department can adjust the order of commendation messages based on the relevance of the reported content when delivering them. For example, the delivery department can evaluate the relevance of the reported content based on similarity of content or related topics. For instance, the delivery department can prioritize delivering commendation messages to highly relevant reports. Conversely, it can postpone delivering commendation messages to less relevant reports. Furthermore, it can adjust the order of messages according to their relevance. This allows for the delivery of commendation messages in a more appropriate order by adjusting the order of messages based on the relevance of the reported content. For example, it can prioritize delivering commendation messages to highly relevant reports. Conversely, it can postpone delivering commendation messages to less relevant reports. Furthermore, it can adjust the order of messages according to their relevance.

[0081] The judgment unit can estimate the user's emotions and adjust the criteria for determining the user's mental health status based on the estimated emotions. For example, the judgment unit can estimate emotions using facial expression analysis technology. For example, the judgment unit can analyze the user's facial expression data to determine whether the user is stressed or relaxed. The judgment unit can also estimate emotions using voice analysis technology. For example, the judgment unit can analyze the user's voice data and calculate an emotion score. Furthermore, the judgment unit can also estimate emotions using text analysis technology. For example, the judgment unit can analyze the user's text data and calculate an emotion score. This allows for more accurate judgments by adjusting the criteria for determining mental health problems according to the user's emotions. For example, if the user is stressed, the criteria for determining mental health problems can be relaxed. Conversely, if the user is relaxed, the criteria for determining mental health problems can be made stricter. Furthermore, the criteria for determining mental health problems can be dynamically adjusted according to the user's emotions.

[0082] The assessment unit can improve the accuracy of its assessment of mental health status based on the interrelationships of reported information. For example, to evaluate the interrelationships of reported information, the assessment unit performs evaluations based on the relevance and co-occurrence relationships of the content. For example, the assessment unit can analyze the interrelationships of multiple reported items and detect signs of mental health problems. The assessment unit can also improve the accuracy of its assessment of mental health problems by considering the interrelationships of reported items. Furthermore, the assessment unit can perform early detection of mental health problems based on the interrelationships of reported items. This improves the accuracy of the assessment of mental health problems by considering the interrelationships of reported items. For example, it can analyze the interrelationships of multiple reported items and detect signs of mental health problems. Furthermore, it can improve the accuracy of its assessment of mental health problems by considering the interrelationships of reported items. Furthermore, it can perform early detection of mental health problems based on the interrelationships of reported items.

[0083] The assessment unit can make a determination of mental health status based on the attribute information of the person submitting the report. For example, the assessment unit can store the attribute information of the person submitting the report in a database and make a determination based on that information. For example, the assessment unit can make a determination of mental health problems by considering attribute information such as the submitter's age and gender. It can also make a determination of mental health problems by considering attribute information such as the submitter's job duties and position. Furthermore, it can also make a determination of mental health problems by considering attribute information such as the submitter's work history and past mental health problems. This makes it possible to make a more appropriate determination of mental health problems by considering the submitter's attribute information. For example, the assessment unit can make a determination of mental health problems by considering attribute information such as the submitter's age and gender. It can also make a determination of mental health problems by considering attribute information such as the submitter's job duties and position. Furthermore, it can make a determination of mental health problems by considering attribute information such as the submitter's work history and past mental health problems.

[0084] The judgment unit can estimate the user's emotions and adjust the order in which the results of the mental health assessment are displayed based on the estimated emotions. For example, the judgment unit can estimate emotions using facial expression analysis technology. For example, the judgment unit can analyze the user's facial expression data to determine whether the user is stressed or relaxed. The judgment unit can also estimate emotions using voice analysis technology. For example, the judgment unit can analyze the user's voice data and calculate an emotion score. Furthermore, the judgment unit can also estimate emotions using text analysis technology. For example, the judgment unit can analyze the user's text data and calculate an emotion score. This allows the judgment results to be displayed in a more appropriate order by adjusting the display order according to the user's emotions. For example, if the user is stressed, less important judgment results can be displayed later. Conversely, if the user is relaxed, more important judgment results can be displayed preferentially. Furthermore, the display order of the judgment results can be adjusted according to the user's emotions.

[0085] The assessment unit can make a determination of mental health status based on the geographical distribution of reported data. For example, the assessment unit can store the geographical distribution of reported data in a database and make a determination based on that data. For instance, the assessment unit can analyze the geographical distribution of reported data and detect signs of mental health problems in specific regions. Furthermore, the assessment unit can improve the accuracy of mental health diagnosis by considering geographical distribution. In addition, the assessment unit can perform early detection of mental health problems based on geographical distribution. This allows for a more appropriate determination of mental health problems by considering the geographical distribution of reported data. For example, it can analyze the geographical distribution of reported data and detect signs of mental health problems in specific regions. Furthermore, it can improve the accuracy of mental health diagnosis by considering geographical distribution. Furthermore, it can perform early detection of mental health problems based on geographical distribution.

[0086] The assessment unit can improve the accuracy of its assessment of mental health status by referencing relevant literature related to the reported content. For example, the assessment unit can store academic papers and technical reports in a database to reference literature related to the reported content and perform assessments based on this information. For instance, the assessment unit can improve the accuracy of its assessment of mental health problems by referencing relevant literature. Furthermore, the assessment unit can detect signs of mental health problems based on relevant literature. In addition, the assessment unit can perform early detection of mental health problems by referencing relevant literature. Thus, by referencing relevant literature, the accuracy of the assessment of mental health problems is improved. For example, by referencing relevant literature, the accuracy of the assessment of mental health problems is improved. Furthermore, signs of mental health problems can be detected based on relevant literature. Furthermore, early detection of mental health problems can be performed by referencing relevant literature. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and determination unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and provides an interface for employees to input attendance reports. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes attendance reports and short work reports. The provision unit is implemented, for example, by the output device 40 of the smart device 14 and generates and provides praise messages to employees. The determination unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines mental health issues. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and judgment unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for employees to input attendance reports. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes attendance reports and short work reports. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and generates and provides praise messages to employees. The judgment unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines mental health issues. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and judgment 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 microphone 238 of the headset terminal 314 and provides an interface for employees to input attendance reports. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes attendance reports and short work reports. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and generates and provides praise messages to employees. The judgment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines mental health issues. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, and judgment 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 microphone 238 of the robot 414 and provides an interface for employees to input attendance reports. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes attendance reports and short work reports. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and generates and provides praise messages to employees. The judgment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines mental health issues.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The analysis unit can predict business performance based on the user's past business report data. For example, the analysis unit can analyze past business report data to understand the user's business performance trends. Furthermore, the analysis unit can predict future business performance based on past data. In addition, the analysis unit can provide the user with advice on business improvement based on the prediction results. This can improve the user's business performance.

[0089] The service provider can suggest appropriate ways to refresh based on the user's work reports. For example, if the service provider analyzes the user's work reports and determines that the user is stressed, it can suggest ways to refresh. Furthermore, the service provider can customize refresh methods based on the user's hobbies and interests. In addition to suggesting refresh methods, the service provider can also provide resources and tools to actually perform these activities. This can reduce user stress and improve work efficiency.

[0090] The evaluation unit can assess the quality of communication within a team based on the user's work reports. For example, it can analyze the user's work reports and evaluate the frequency and quality of communication within the team. Furthermore, if the evaluation unit determines that the quality of communication is declining, it can offer suggestions for improvement. It can also propose training or workshops to improve the quality of communication within the team, thereby enhancing team performance.

[0091] The analysis unit can automatically determine task priorities based on the user's work reports. For example, the analysis unit can analyze the user's work reports and evaluate the importance and urgency of tasks. Furthermore, the analysis unit can automatically determine task priorities based on the evaluation results. In addition, the analysis unit can notify the user of the progress of tasks in real time based on the priorities. This allows users to proceed with their work efficiently.

[0092] The service provider can estimate the user's emotions and provide appropriate feedback based on those estimates. For example, it can analyze the user's emotions and, if they are feeling positive, provide messages of praise and encouragement. If they are feeling negative, it can provide messages of comfort and encouragement. Furthermore, the service provider can adjust the content and tone of the feedback based on the emotions. This allows users to receive appropriate feedback and maintain their motivation.

[0093] The reception desk can suggest the optimal reporting timing based on the user's past attendance reporting data. For example, the reception desk can analyze the user's past attendance reporting data and suggest the most efficient reporting time. Furthermore, the reception desk can adjust the reporting timing based on the user's work schedule. In addition, the reception desk can customize the reporting timing according to the user's individual needs. This allows users to submit attendance reports efficiently.

[0094] The service provider can estimate the user's emotions and, based on those estimates, suggest appropriate ways to refresh themselves. For example, the service provider can analyze the user's emotions and, if they are feeling stressed, suggest ways to refresh themselves. If they are relaxed, it can also suggest further ways to refresh themselves. Furthermore, the service provider can customize ways to refresh themselves based on the user's hobbies and interests. This allows the user to find appropriate ways to refresh themselves and reduce stress.

[0095] The assessment unit can evaluate business risks based on the user's business report. For example, the assessment unit can analyze the user's business report and identify business risk factors. Furthermore, the assessment unit can perform a risk assessment based on these risk factors. In addition, the assessment unit can make suggestions for risk mitigation based on the risk assessment results. This allows the user to understand business risks and take appropriate countermeasures.

[0096] The service provider can estimate the user's emotions and, based on those estimates, propose appropriate work improvements. For example, by analyzing the user's emotions, if they are feeling stressed, the service provider can propose ways to reduce their workload. If they are relaxed, it can also propose further improvements to streamline their work. Furthermore, the service provider can propose specific improvement measures based on the user's work content. This allows the user to improve their work efficiency.

[0097] The analysis unit can visualize the progress of tasks in real time based on the user's work reports. For example, the analysis unit can analyze the user's work reports and display the progress of tasks in graphs and charts. Furthermore, the analysis unit can identify delays and problems based on the progress. In addition, the analysis unit can update the progress in real time, providing users with the latest information. This allows users to understand the progress of their tasks and take appropriate measures.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The reception desk receives daily attendance reports from employees. Attendance reports include arrival time, departure time, break times, etc. The reception desk provides an interface for employees to enter their attendance reports, which is often provided through a web application or mobile application. Step 2: The analysis department analyzes the attendance reports and brief work reports received by the reception department. The analysis department uses text analysis technology to analyze the report content, extracts keywords, and analyzes the employee's personality and preferences. It also performs sentiment analysis to evaluate the employee's mental state. Step 3: The delivery department provides praise messages based on the results analyzed by the analysis department. The delivery department generates effective praise messages based on the employee's personality and preferences and delivers them in the form of text messages, voice messages, etc. Step 4: The assessment unit determines whether an employee is experiencing mental health issues based on the results analyzed by the analysis unit. If the assessment unit notices an increase in negative expressions in an employee's reports, it determines that the employee may be experiencing mental health issues and provides appropriate support to detect signs of mental health problems early.

[0100] 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.

[0101] 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 the following. 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 (for example, 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. 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 a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0102] 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.

[0103] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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. 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.

[0118] 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.

[0119] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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. 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.

[0134] 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.

[0135] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[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 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.

[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 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.

[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. 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 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.

[0152] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of Symbols]

[0172] 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 desk that accepts attendance reports, An analysis unit analyzes attendance reports and work reports received by the aforementioned reception unit, A providing unit that provides a message of praise based on the results of the analysis performed by the aforementioned analysis unit, The system includes a determination unit that determines the mental health status based on the results of the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned supply unit is, Generate compliment messages based on employees' personalities and preferences. The system according to feature 1.

3. The determination unit, Utilizing accumulated data from brief work reports to detect mental health issues early. The system according to feature 1.

4. The aforementioned analysis unit, Automatically generate business performance reports based on data from short business reports. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of attendance report submission based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past attendance reporting history and select the appropriate submission method. The system according to feature 1.

7. The aforementioned reception unit is When receiving attendance reports, filtering is performed based on the user's current work status and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of attendance reports to be received based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When receiving attendance reports, the system prioritizes receiving reports that are highly relevant based on the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When receiving attendance reports, the system analyzes the user's social media activity and accepts relevant reports. The system according to feature 1.

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