System

Abstract

An object of the system according to the embodiment is to propose an optimal arrangement based on personal information and work contents of employees and provide a specific communication policy to a superior.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection part collects personal information and business contents of employees. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an appropriate arrangement based on the analysis result obtained by the analysis unit. The providing unit provides the communication policy to the superior based on the arrangement proposed by the proposing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately propose optimal placement based on employees' personal information and work content, and do not provide managers with specific communication policies, so there is room for improvement.

[0005] The system according to the embodiment aims to propose optimal placement based on the personal information and work content of employees, and to provide specific communication guidelines to supervisors. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects personal information and work details of employees. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an appropriate placement based on the analysis results obtained by the analysis unit. The provision unit provides a communication policy to a supervisor based on the placement proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can propose optimal placement based on the personal information and work content of employees and provide specific communication guidelines to supervisors. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention collects personal information and work details of employees, analyzes them using a generation AI, proposes optimal placements, and provides communication guidelines to supervisors. This system collects personal information, such as employee pulse survey results, aptitude test results, and StrengthsFinder results, as well as organizational work details. The generation AI analyzes these data to propose optimal placements and provide supervisors with specific communication guidelines with subordinates. For example, the system can understand employee satisfaction and stress levels from pulse survey results and identify strengths and weaknesses from aptitude test results. The generation AI then proposes optimal placements for each employee based on the collected data. For example, assigning employees with the most appropriate skills for specific tasks can improve work efficiency. The generation AI also allows supervisors to receive advice on communication guidelines with their subordinates. For example, it can suggest effective communication methods based on the subordinates' personality traits. This system enables the system to assign employees to the right positions and facilitate smooth communication between supervisors and subordinates. For example, assigning employees with the most suitable skills to a specific project increases the project's success rate. Also, when a manager communicates appropriately with their subordinates based on their personality traits, the subordinates' motivation increases and work efficiency is improved. Furthermore, generative AI can optimize the work content of the entire organization based on the collected data. For example, assigning employees with the most suitable skills to a specific task increases work efficiency. Also, allowing managers to receive advice on communication policies with their subordinates smooths communication throughout the organization. This improves productivity throughout the organization and increases work efficiency.

[0029] The placement proposal system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects personal information and job details of employees. Examples of personal information include, but are not limited to, pulse survey results, aptitude test results, and StrengthsFinder results. The collection unit, for example, identifies employee satisfaction and stress levels from pulse survey results. The collection unit can also identify employee strengths and weaknesses from aptitude test results. The collection unit can also collect StrengthsFinder data to identify employee personality traits. The analysis unit uses generative AI to analyze the information collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these. For example, the analysis unit can identify employee strengths and weaknesses using data mining technology. The analysis unit can also evaluate employee satisfaction and stress levels using statistical analysis. The analysis unit can also analyze employee personality traits using machine learning algorithms. The proposal unit proposes optimal placements based on the analysis results obtained by the analysis unit. The suggestions are made based on criteria such as, for example, skill matching and workload balance, but are not limited to such examples. For example, the suggestion unit assigns employees with the most suitable skills for specific tasks. The suggestion unit can also propose employee assignments taking workload balance into consideration. Furthermore, the suggestion unit can propose optimal assignments based on the personality traits of employees. The provision unit provides a communication policy to the supervisor based on the assignments proposed by the suggestion unit. The provision is made based on criteria such as, for example, feedback frequency and communication means, but is not limited to such examples. For example, the provision unit suggests to the supervisor what type of communication would be effective based on the personality traits of the subordinate. The provision unit can also adjust the feedback frequency and suggest that the supervisor provide feedback at an appropriate time. Furthermore, the provision unit can select a communication means and suggest that the supervisor communicate using an appropriate means.As a result, the placement proposal system according to the embodiment can collect and analyze personal information and work details of employees, propose optimal placements, and provide communication guidelines to supervisors.

[0030] The collection unit can collect pulse survey results, aptitude test details, and StrengthsFinder data. The collection unit, for example, collects pulse survey results. Pulse survey results include, but are not limited to, employee satisfaction and stress levels. For example, the collection unit can periodically conduct pulse surveys and collect the results. The collection unit can also collect aptitude test details. The aptitude test details include, but are not limited to, employee strengths and weaknesses. For example, the collection unit can conduct aptitude tests and collect the results. The collection unit can also collect StrengthsFinder data. StrengthsFinder data includes, but is not limited to, employee personality traits. For example, the collection unit can conduct StrengthsFinder tests and collect the results. By collecting data such as pulse survey results, aptitude test details, and StrengthsFinder data, detailed information about employees can be obtained. Some or all of the above-described processing by the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input pulse survey results to the generation AI and cause the generation AI to collect data.

[0031] The analysis unit can identify employee strengths and weaknesses based on the collected data. The analysis unit, for example, identifies employee strengths based on the collected data. For example, the analysis unit can identify employee strengths using data mining technology. The analysis unit can also identify employee weaknesses based on the collected data. For example, the analysis unit can identify employee weaknesses using statistical analysis. The analysis unit can also comprehensively identify employee strengths and weaknesses based on the collected data. For example, the analysis unit can identify employee strengths and weaknesses using a machine learning algorithm. This makes it possible to identify employee strengths and weaknesses based on the collected data and propose optimal placements. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI identify employee strengths and weaknesses.

[0032] The proposal unit can assign employees with appropriate skills for specific tasks. For example, the proposal unit assigns employees with the most appropriate skills for specific tasks. For example, the proposal unit uses skill matching technology to identify employees with the most appropriate skills for specific tasks and assigns the employees. The proposal unit can also propose employee assignments taking into account workload balance. For example, the proposal unit evaluates workload balance and proposes appropriate assignments. Furthermore, the proposal unit can propose optimal assignments based on employee personality traits. For example, the proposal unit proposes appropriate assignments taking into account employee personality traits. This improves work efficiency by assigning employees with the most appropriate skills for specific tasks. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can use a generation AI to identify employees with the most appropriate skills for specific tasks and have the generation AI execute a proposal to assign the employees.

[0033] The providing unit can provide a communication policy to a superior based on the personality traits of the subordinate. The providing unit provides the communication policy to the superior based on, for example, the personality traits of the subordinate. For example, the providing unit analyzes the personality traits of the subordinate and suggests to the superior what type of communication would be effective. The providing unit can also adjust the frequency of feedback and suggest that the superior provide feedback at an appropriate time. For example, the providing unit adjusts the frequency of feedback based on the personality traits of the subordinate. Furthermore, the providing unit can select a means of communication and suggest that the superior communicate using an appropriate means. For example, the providing unit selects an appropriate means of communication based on the personality traits of the subordinate. As a result, by providing the superior with a communication policy based on the personality traits of the subordinate, communication between the superior and the subordinate becomes smoother. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the personality traits of the subordinate into a generating AI and cause the generating AI to execute the proposed communication policy.

[0034] The provision unit can collect the effectiveness of the provided communication policy and provide feedback. The provision unit, for example, collects the effectiveness of the provided communication policy. For example, the provision unit collects feedback from a superior and evaluates the effectiveness of the communication policy. The provision unit can also collect feedback from subordinates and evaluate the effectiveness of the communication policy. Furthermore, the provision unit can improve the communication policy based on the collected feedback. For example, the provision unit identifies improvements to the communication policy based on the feedback and proposes them to the superior. In this way, the communication policy is improved by collecting the effects of the provided communication policy and providing feedback. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the collected feedback to a generation AI, which can identify improvements to the communication policy and propose them to the superior.

[0035] The collection unit can analyze each employee's past work history and select the optimal data collection method. The collection unit, for example, analyzes each employee's past work history. For example, the collection unit analyzes past project history and evaluates the employee's work performance. The collection unit can also analyze past work content and identify the employee's skill set. Furthermore, the collection unit can identify the employee's work tendency based on the past work history. Next, the collection unit selects the optimal data collection method based on the analysis results. For example, the collection unit selects a text-based data collection method for an employee who has used a lot of text input in the past. The collection unit can also select a voice-based data collection method for an employee who has used a lot of voice input in the past. Furthermore, the collection unit can select an image-based data collection method for an employee who has used a lot of image data in the past. In this way, the optimal data collection method can be selected by analyzing each employee's past work history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past work history data into a generation AI and have the generation AI select the optimal data collection method.

[0036] The collection unit can filter data based on the employee's current project or area of ​​interest when collecting data. For example, the collection unit takes into account the employee's current project when collecting data. For example, the collection unit prioritizes collecting only data related to the current project. The collection unit can also filter data by taking into account the employee's area of ​​interest. For example, the collection unit prioritizes collecting data related to the employee's area of ​​interest. Furthermore, the collection unit can dynamically filter required data according to the progress of the project. For example, the collection unit adjusts the priority of data to be collected based on the progress of the project. This allows highly relevant data to be collected by filtering data based on the employee's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the employee's current project or area of ​​interest into the generation AI and have the generation AI filter the data.

[0037] The collection unit can select an appropriate collection method depending on the employee's input method when collecting data. The collection unit, for example, takes into account the employee's input method when collecting data. For example, the collection unit can select a collection method that prioritizes voice input for an employee who is good at voice input. The collection unit can also select a collection method that prioritizes text input for an employee who is good at text input. Furthermore, the collection unit can select a collection method that prioritizes image input for an employee who processes a lot of image data. This enables efficient data collection by selecting the optimal collection method depending on the employee's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the employee's input method into a generation AI, causing the generation AI to select the optimal collection method.

[0038] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of employees when collecting data. The collection unit, for example, considers the geographical location information of employees when collecting data. For example, if an employee is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. Furthermore, if an employee is working remotely, the collection unit can also prioritize collecting data related to their home. Furthermore, if an employee is in the office, the collection unit can prioritize collecting data related to the office. In this way, by taking into account the geographical location information of employees, highly relevant data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the geographical location information of employees to the generation AI, causing the generation AI to prioritize the collection of highly relevant data.

[0039] The collection unit can analyze employees' social media activities and collect related data when collecting data. The collection unit, for example, analyzes employees' social media activities when collecting data. For example, the collection unit collects related data based on information shared by employees on social media. The collection unit can also analyze employees' social media activities and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the employees' friends on social media. In this way, related data can be collected by analyzing employees' social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on employees' social media activities into a generation AI, causing the generation AI to collect related data.

[0040] The collection unit can customize the collection method by reflecting employees' past feedback when collecting data. The collection unit, for example, collects employees' past feedback. For example, the collection unit may collect employee feedback by conducting a questionnaire. The collection unit may also collect employee feedback through interviews. The collection unit may also collect employee feedback through performance reviews. Next, the collection unit customizes the data collection method based on the collected feedback. For example, the collection unit may improve the collection method based on the past feedback to enable efficient data collection. The collection unit may also customize the collection means by reflecting employees' past feedback. Furthermore, the collection unit may adjust the timing and frequency of collection based on the feedback. In this way, the collection method can be customized by reflecting employees' past feedback, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit may input employees' past feedback data into a generation AI, which may then customize the collection method.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, evaluates the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit, for example, identifies the data category. For example, the analysis unit applies different analysis algorithms depending on the category, such as text data, image data, or audio data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can also apply an audio analysis algorithm to audio data. This enables highly accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI, causing the generation AI to apply an appropriate analysis algorithm.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. The analysis unit, for example, collects the employee's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results to improve accuracy. The analysis unit can also adjust the level of detail of the analysis by referring to the employee's past analysis results. Furthermore, the analysis unit can determine the priority of the analysis based on the past analysis results. For example, the analysis unit prioritizes analysis of important data based on the past analysis results. In this way, the accuracy of the analysis is improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, which can then improve the accuracy of the analysis.

[0044] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, identifies the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from the time when the data was collected to the generation AI, and have the generation AI determine the analysis priority.

[0045] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the employee's level of expertise. The analysis unit, for example, evaluates the employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terms to employees with high levels of expertise. The analysis unit can also provide analysis results that are explained in simpler terms to employees with low levels of expertise. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the employee's level of expertise. For example, the analysis unit can adjust the use of technical terms in the analysis based on the employee's level of expertise. This makes it possible to provide analysis results that are easy to understand by adjusting the use of technical terms in the analysis according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the employee's level of expertise into a generation AI, causing the generation AI to adjust the use of technical terms in the analysis.

[0047] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the employee's skill set. The suggestion unit, for example, evaluates the employee's skill set. For example, the suggestion unit provides a detailed proposal to an employee with a high skill set. The suggestion unit can also provide a simplified proposal to an employee with a low skill set. Furthermore, the suggestion unit can dynamically adjust the level of detail of the proposal according to the employee's skill set. For example, the suggestion unit adjusts the level of detail of the proposal based on the employee's skill set. In this way, by adjusting the level of detail of the proposal based on the employee's skill set, it is possible to provide an appropriate level of proposal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data on the employee's skill set to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0048] When making a proposal, the proposal unit can apply different proposal algorithms depending on the business category. The proposal unit, for example, identifies the business category. For example, the proposal unit applies different proposal algorithms depending on categories such as project management, marketing, and human resources management. For example, the proposal unit applies a proposal algorithm dedicated to project management to project management. The proposal unit can also apply a proposal algorithm dedicated to marketing to marketing. Furthermore, the proposal unit can apply a proposal algorithm dedicated to human resources management to human resources management. In this way, by applying different proposal algorithms depending on the business category, it is possible to provide optimal proposals for the business. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the business category into a generation AI and cause the generation AI to apply an appropriate proposal algorithm.

[0049] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the employee's past proposal results. The suggestion unit, for example, collects the employee's past proposal results. For example, the suggestion unit adjusts the proposal algorithm based on the past proposal results to improve accuracy. The suggestion unit can also adjust the level of detail of the proposal by referring to the employee's past proposal results. Furthermore, the suggestion unit can determine the priority of the proposal based on the past proposal results. For example, the suggestion unit prioritizes important proposals based on the past proposal results. This improves the accuracy of the proposal by referring to the employee's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal result data into a generation AI, causing the generation AI to improve the accuracy of the proposal.

[0050] The proposal unit can determine the priority of proposals based on the submission time of the work when making a proposal. The proposal unit, for example, identifies the submission time of the work. For example, the proposal unit prioritizes proposals for work whose deadline is approaching. The proposal unit can also postpone proposals for work whose submission time is further away. Furthermore, the proposal unit can dynamically adjust the priority of proposals according to the submission time of the work. For example, the proposal unit determines the priority of proposals based on the submission time of the work. In this way, by determining the priority of proposals based on the submission time of the work, it is possible to preferentially propose work whose deadline is approaching. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission time of the work into the generation AI and cause the generation AI to determine the priority of proposals.

[0051] The suggestion unit can adjust the order of proposals based on the relevance of tasks when making a proposal. The suggestion unit, for example, evaluates the relevance of tasks. For example, the suggestion unit prioritizes proposals for highly relevant tasks. The suggestion unit can also postpone proposals for less relevant tasks. Furthermore, the suggestion unit can dynamically adjust the order of proposals according to the relevance of tasks. For example, the suggestion unit adjusts the order of proposals based on the relevance of tasks. In this way, by adjusting the order of proposals based on the relevance of tasks, highly relevant tasks can be prioritized for proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the relevance of tasks to a generation AI and cause the generation AI to adjust the order of proposals.

[0052] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the employee's level of expertise. The suggestion unit, for example, evaluates the employee's level of expertise. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to an employee with high technical expertise. The suggestion unit can also provide a proposal explained in simpler terms to an employee with low technical expertise. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the employee's level of expertise. For example, the suggestion unit adjusts the use of technical terminology in the proposal based on the employee's level of expertise. This makes it possible to provide an easy-to-understand proposal by adjusting the use of technical terminology in the proposal according to the employee's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the employee's level of expertise into a generation AI, causing the generation AI to adjust the use of technical terminology in the proposal.

[0053] When providing a communication policy, the providing unit can select an optimal policy based on the subordinate's personality traits. The providing unit, for example, evaluates the subordinate's personality traits. For example, if the subordinate is introverted, the providing unit can select an individual speaking policy. Also, if the subordinate is extroverted, the providing unit can select a group speaking policy. Furthermore, the providing unit can dynamically adjust the communication method according to the subordinate's personality traits. For example, the providing unit selects an optimal communication policy based on the subordinate's personality traits. As a result, effective communication is possible by selecting an optimal policy based on the subordinate's personality traits. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the subordinate's personality traits to a generating AI, causing the generating AI to select an optimal communication policy.

[0054] When providing a communication policy, the providing unit can customize the policy by referring to the supervisor's past communication history. The providing unit, for example, collects the supervisor's past communication history. For example, the providing unit collects email history and evaluates the supervisor's past communication methods. The providing unit can also collect meeting records and evaluate the supervisor's past communication methods. Furthermore, the providing unit can customize the communication policy based on the supervisor's past communication history. For example, the providing unit customizes the policy by referring to communication methods that the supervisor has used successfully in the past. The providing unit can also customize the policy to avoid communication methods that the supervisor has used unsuccessfully in the past. In this way, an effective communication policy can be provided by referring to the supervisor's past communication history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the supervisor's past communication history into a generation AI and cause the generation AI to customize the communication policy.

[0055] When providing a communication policy, the provision unit can improve the policy by reflecting feedback from subordinates. The provision unit, for example, collects feedback from subordinates. For example, the provision unit may collect feedback from subordinates by conducting a survey. The provision unit may also collect feedback from subordinates through interviews. Furthermore, the provision unit may collect feedback from subordinates through performance reviews. Next, the provision unit improves the communication policy based on the collected feedback. For example, the provision unit may identify areas for improvement in the communication policy based on the feedback and propose them to a supervisor. The provision unit may also adjust the details of the policy by reflecting the feedback from subordinates. Furthermore, the provision unit may determine priorities for the communication policy based on the feedback. In this way, the communication policy is improved by reflecting the feedback from subordinates. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit may input feedback data from subordinates into a generation AI and cause the generation AI to improve the communication policy.

[0056] When providing a communication policy, the providing unit can select an optimal policy by taking into account the subordinate's geographical location information. The providing unit, for example, collects the subordinate's geographical location information. For example, the providing unit collects GPS data to identify the subordinate's current location. The providing unit can also use a location information service to identify the subordinate's current location. Next, the providing unit selects an optimal communication policy based on the subordinate's geographical location information. For example, if the subordinate is on a business trip, the providing unit can provide a policy related to the business trip destination. Also, if the subordinate is working remotely, the providing unit can provide a policy related to their home. Furthermore, if the subordinate is in the office, the providing unit can provide a policy related to the office. In this way, by taking the subordinate's geographical location information into account, a highly relevant communication policy can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the subordinate's geographical location information data to a generation AI, causing the generation AI to select an optimal communication policy.

[0057] When providing a communication policy, the provision unit can analyze the social media activity of the subordinate and propose the policy. The provision unit, for example, analyzes the social media activity of the subordinate. For example, the provision unit proposes a relevant policy based on information shared by the subordinate on social media. The provision unit can also analyze the content of the subordinate's social media activity and propose a relevant policy. Furthermore, the provision unit can propose a relevant policy by referring to the activity of the subordinate's friends on social media. In this way, by analyzing the subordinate's social media activity, a highly relevant communication policy can be proposed. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data on the subordinate's social media activity into a generation AI and cause the generation AI to propose a relevant policy.

[0058] When providing a communication policy, the provision unit can customize the policy by reflecting past feedback from subordinates. The provision unit, for example, collects past feedback from subordinates. For example, the provision unit may collect feedback from subordinates by conducting a survey. The provision unit may also collect feedback from subordinates through interviews. The provision unit may also collect feedback from subordinates through performance reviews. Next, the provision unit customizes the communication policy based on the collected feedback. For example, the provision unit may identify improvements to the communication policy based on the feedback and propose them to a supervisor. The provision unit may also adjust the details of the policy by reflecting the feedback from subordinates. Furthermore, the provision unit may determine priorities for the communication policy based on the feedback. In this way, an effective communication policy can be provided by reflecting the past feedback from subordinates. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit may input past feedback data from subordinates into a generation AI and cause the generation AI to customize the communication policy.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The analysis department can analyze the success rate of employees' past projects and identify the factors that led to success. For example, the analysis department can collect past project data and identify commonalities between successful projects. The analysis department can also analyze data from failed projects and identify the factors that led to failure. Furthermore, the analysis department can compare the factors that led to success and failure and provide guidelines for risk management in future projects. In this way, by identifying the factors that led to success and failure based on past project data, the success rate of future projects can be improved.

[0061] The collection department can collect employee health data and analyze its correlation with work performance. For example, the collection department can collect heart rate and sleep data from employees' fitness trackers and wearable devices. The collection department can also collect the results of regular health checkups to understand employees' health status. Furthermore, the collection department can integrate health data with work performance data and analyze the impact of health status on work performance. This makes it possible to provide health management guidelines that contribute to improving work performance based on employee health data.

[0062] The proposal department can make placement proposals taking into account employees' career paths. For example, the proposal department can analyze employees' past career histories and propose placements that match their future career goals. The proposal department can also propose training and education programs to improve employees' skills. Furthermore, the proposal department can also suggest the timing of transfers and promotions based on employees' career paths. In this way, by making placement proposals that take into account employees' career paths, it is possible to improve employee motivation and work efficiency.

[0063] The provision department can provide a communication policy based on the supervisor's leadership style. For example, the provision department can evaluate the supervisor's leadership style and propose a communication policy according to the style, such as charismatic, democratic, or directive. The provision department can also adjust the frequency and method of communication with subordinates based on the supervisor's leadership style. Furthermore, the provision department can also propose a feedback method according to the leadership style. In this way, by providing a communication policy based on the supervisor's leadership style, effective leadership can be demonstrated.

[0064] The collection department can collect information about employees' hobbies and interests and analyze the relevance to their work. For example, the collection department can conduct a survey about employees' hobbies and interests and collect the data. The collection department can also collect social media data about employees' hobbies and interests. Furthermore, the collection department can analyze the relevance between hobbies and interests and work content and propose work assignments that contribute to improving employee motivation. This makes it possible to improve work efficiency and employee satisfaction by assigning work that takes employees' hobbies and interests into consideration.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection department collects employees' personal information and job details. Personal information includes pulse survey results, aptitude test results, StrengthsFinder data, etc. The collection department uses the pulse survey results to understand employees' satisfaction and stress levels, identifies employees' strengths and weaknesses from the aptitude test results, and collects StrengthsFinder data to understand employees' personality traits. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques can be used to identify employees' strengths and weaknesses, statistical analysis can be used to evaluate employee satisfaction and stress levels, and machine learning algorithms can be used to analyze employees' personality traits. Step 3: The proposal unit proposes optimal placement based on the analysis results obtained by the analysis unit. The proposal is made according to criteria such as skill matching and workload balance. For example, it assigns employees with the most suitable skills for specific tasks, proposes employee placement taking workload balance into consideration, and proposes optimal placement based on employee personality traits. Step 4: The providing department provides the superior with a communication policy based on the allocation proposed by the proposing department. The provision is made according to criteria such as feedback frequency and communication means. For example, the providing department may suggest to the superior what type of communication would be effective based on the subordinate's personality traits, suggest adjusting the feedback frequency and providing feedback at an appropriate time, and select a communication means and suggest communicating through an appropriate means.

[0067] (Example 2) A system according to an embodiment of the present invention collects personal information and work details of employees, analyzes them using a generation AI, proposes optimal placements, and provides communication guidelines to supervisors. This system collects personal information, such as employee pulse survey results, aptitude test results, and StrengthsFinder results, as well as organizational work details. The generation AI analyzes these data to propose optimal placements and provide supervisors with specific communication guidelines with subordinates. For example, the system can understand employee satisfaction and stress levels from pulse survey results and identify strengths and weaknesses from aptitude test results. The generation AI then proposes optimal placements for each employee based on the collected data. For example, assigning employees with the most appropriate skills for specific tasks can improve work efficiency. The generation AI also allows supervisors to receive advice on communication guidelines with their subordinates. For example, it can suggest effective communication methods based on the subordinates' personality traits. This system enables the system to assign employees to the right positions and facilitate smooth communication between supervisors and subordinates. For example, assigning employees with the most suitable skills to a specific project increases the project's success rate. Also, when a manager communicates appropriately with their subordinates based on their personality traits, the subordinates' motivation increases and work efficiency is improved. Furthermore, generative AI can optimize the work content of the entire organization based on the collected data. For example, assigning employees with the most suitable skills to a specific task increases work efficiency. Also, allowing managers to receive advice on communication policies with their subordinates smooths communication throughout the organization. This improves productivity throughout the organization and increases work efficiency.

[0068] The placement proposal system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and a provision unit. The collection unit collects personal information and job details of employees. Examples of personal information include, but are not limited to, pulse survey results, aptitude test results, and StrengthsFinder results. The collection unit, for example, identifies employee satisfaction and stress levels from pulse survey results. The collection unit can also identify employee strengths and weaknesses from aptitude test results. The collection unit can also collect StrengthsFinder data to identify employee personality traits. The analysis unit uses generative AI to analyze the information collected by the collection unit. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods, but is not limited to these. For example, the analysis unit can identify employee strengths and weaknesses using data mining technology. The analysis unit can also evaluate employee satisfaction and stress levels using statistical analysis. The analysis unit can also analyze employee personality traits using machine learning algorithms. The proposal unit proposes optimal placements based on the analysis results obtained by the analysis unit. The suggestions are made based on criteria such as, for example, skill matching and workload balance, but are not limited to such examples. For example, the suggestion unit assigns employees with the most suitable skills for specific tasks. The suggestion unit can also propose employee assignments taking workload balance into consideration. Furthermore, the suggestion unit can propose optimal assignments based on the personality traits of employees. The provision unit provides a communication policy to the supervisor based on the assignments proposed by the suggestion unit. The provision is made based on criteria such as, for example, feedback frequency and communication means, but is not limited to such examples. For example, the provision unit suggests to the supervisor what type of communication would be effective based on the personality traits of the subordinate. The provision unit can also adjust the feedback frequency and suggest that the supervisor provide feedback at an appropriate time. Furthermore, the provision unit can select a communication means and suggest that the supervisor communicate using an appropriate means.As a result, the placement proposal system according to the embodiment can collect and analyze personal information and work details of employees, propose optimal placements, and provide communication guidelines to supervisors.

[0069] The collection unit can collect pulse survey results, aptitude test details, and StrengthsFinder data. The collection unit, for example, collects pulse survey results. Pulse survey results include, but are not limited to, employee satisfaction and stress levels. For example, the collection unit can periodically conduct pulse surveys and collect the results. The collection unit can also collect aptitude test details. The aptitude test details include, but are not limited to, employee strengths and weaknesses. For example, the collection unit can conduct aptitude tests and collect the results. The collection unit can also collect StrengthsFinder data. StrengthsFinder data includes, but is not limited to, employee personality traits. For example, the collection unit can conduct StrengthsFinder tests and collect the results. By collecting data such as pulse survey results, aptitude test details, and StrengthsFinder data, detailed information about employees can be obtained. Some or all of the above-described processing by the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input pulse survey results to the generation AI and cause the generation AI to collect data.

[0070] The analysis unit can identify employee strengths and weaknesses based on the collected data. The analysis unit, for example, identifies employee strengths based on the collected data. For example, the analysis unit can identify employee strengths using data mining technology. The analysis unit can also identify employee weaknesses based on the collected data. For example, the analysis unit can identify employee weaknesses using statistical analysis. The analysis unit can also comprehensively identify employee strengths and weaknesses based on the collected data. For example, the analysis unit can identify employee strengths and weaknesses using a machine learning algorithm. This makes it possible to identify employee strengths and weaknesses based on the collected data and propose optimal placements. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI identify employee strengths and weaknesses.

[0071] The proposal unit can assign employees with appropriate skills for specific tasks. For example, the proposal unit assigns employees with the most appropriate skills for specific tasks. For example, the proposal unit uses skill matching technology to identify employees with the most appropriate skills for specific tasks and assigns the employees. The proposal unit can also propose employee assignments taking into account workload balance. For example, the proposal unit evaluates workload balance and proposes appropriate assignments. Furthermore, the proposal unit can propose optimal assignments based on employee personality traits. For example, the proposal unit proposes appropriate assignments taking into account employee personality traits. This improves work efficiency by assigning employees with the most appropriate skills for specific tasks. Some or all of the above-described processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can use a generation AI to identify employees with the most appropriate skills for specific tasks and have the generation AI execute a proposal to assign the employees.

[0072] The providing unit can provide a communication policy to a superior based on the personality traits of the subordinate. The providing unit provides the communication policy to the superior based on, for example, the personality traits of the subordinate. For example, the providing unit analyzes the personality traits of the subordinate and suggests to the superior what type of communication would be effective. The providing unit can also adjust the frequency of feedback and suggest that the superior provide feedback at an appropriate time. For example, the providing unit adjusts the frequency of feedback based on the personality traits of the subordinate. Furthermore, the providing unit can select a means of communication and suggest that the superior communicate using an appropriate means. For example, the providing unit selects an appropriate means of communication based on the personality traits of the subordinate. As a result, by providing the superior with a communication policy based on the personality traits of the subordinate, communication between the superior and the subordinate becomes smoother. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the personality traits of the subordinate into a generating AI and cause the generating AI to execute the proposed communication policy.

[0073] The provision unit can collect the effectiveness of the provided communication policy and provide feedback. The provision unit, for example, collects the effectiveness of the provided communication policy. For example, the provision unit collects feedback from a superior and evaluates the effectiveness of the communication policy. The provision unit can also collect feedback from subordinates and evaluate the effectiveness of the communication policy. Furthermore, the provision unit can improve the communication policy based on the collected feedback. For example, the provision unit identifies improvements to the communication policy based on the feedback and proposes them to the superior. In this way, the communication policy is improved by collecting the effects of the provided communication policy and providing feedback. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input the collected feedback to a generation AI, which can identify improvements to the communication policy and propose them to the superior.

[0074] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can estimate the user's emotions using biometric data. For example, the collection unit can estimate the user's emotions based on heart rate and electrodermal activity. Next, the collection unit adjusts the timing of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the user is relaxed. The collection unit can also collect detailed data when the user is concentrating. Furthermore, if the user is tired, the collection unit can temporarily stop data collection and resume it after a break. This allows data to be collected at a more appropriate time by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI, causing the generation AI to adjust the timing of data collection.

[0075] The collection unit can analyze each employee's past work history and select the optimal data collection method. The collection unit, for example, analyzes each employee's past work history. For example, the collection unit analyzes past project history and evaluates the employee's work performance. The collection unit can also analyze past work content and identify the employee's skill set. Furthermore, the collection unit can identify the employee's work tendency based on the past work history. Next, the collection unit selects the optimal data collection method based on the analysis results. For example, the collection unit selects a text-based data collection method for an employee who has used a lot of text input in the past. The collection unit can also select a voice-based data collection method for an employee who has used a lot of voice input in the past. Furthermore, the collection unit can select an image-based data collection method for an employee who has used a lot of image data in the past. In this way, the optimal data collection method can be selected by analyzing each employee's past work history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input past work history data into a generation AI and have the generation AI select the optimal data collection method.

[0076] The collection unit can filter data based on the employee's current project or area of ​​interest when collecting data. For example, the collection unit takes into account the employee's current project when collecting data. For example, the collection unit prioritizes collecting only data related to the current project. The collection unit can also filter data by taking into account the employee's area of ​​interest. For example, the collection unit prioritizes collecting data related to the employee's area of ​​interest. Furthermore, the collection unit can dynamically filter required data according to the progress of the project. For example, the collection unit adjusts the priority of data to be collected based on the progress of the project. This allows highly relevant data to be collected by filtering data based on the employee's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the employee's current project or area of ​​interest into the generation AI and have the generation AI filter the data.

[0077] The collection unit can select an appropriate collection method depending on the employee's input method when collecting data. The collection unit, for example, takes into account the employee's input method when collecting data. For example, the collection unit can select a collection method that prioritizes voice input for an employee who is good at voice input. The collection unit can also select a collection method that prioritizes text input for an employee who is good at text input. Furthermore, the collection unit can select a collection method that prioritizes image input for an employee who processes a lot of image data. This enables efficient data collection by selecting the optimal collection method depending on the employee's input method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input data on the employee's input method into a generation AI, causing the generation AI to select the optimal collection method.

[0078] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit, for example, estimates the user's emotions. For example, the collection unit can estimate the user's emotions using facial expression recognition technology. The collection unit can also estimate the user's emotions using voice analysis technology. Furthermore, the collection unit can estimate the user's emotions using biometric data. For example, the collection unit can estimate the user's emotions based on heart rate or electrodermal activity. Next, the collection unit determines the priority of data to be collected based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can postpone collecting less important data. Also, if the user is relaxed, the collection unit can prioritize collecting more important data. Furthermore, if the user is concentrating, the collection unit can prioritize collecting detailed data. In this way, by determining the priority of data to be collected based on the user's emotions, important data can be collected preferentially. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, or without, an AI. For example, the collection unit may input user emotion data into the generation AI and cause the generation AI to determine the priority of the data to be collected.

[0079] The collection unit can prioritize collecting highly relevant data by taking into account the geographical location information of employees when collecting data. The collection unit, for example, considers the geographical location information of employees when collecting data. For example, if an employee is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. Furthermore, if an employee is working remotely, the collection unit can also prioritize collecting data related to their home. Furthermore, if an employee is in the office, the collection unit can prioritize collecting data related to the office. In this way, by taking into account the geographical location information of employees, highly relevant data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the geographical location information of employees to the generation AI, causing the generation AI to prioritize the collection of highly relevant data.

[0080] The collection unit can analyze employees' social media activities and collect related data when collecting data. The collection unit, for example, analyzes employees' social media activities when collecting data. For example, the collection unit collects related data based on information shared by employees on social media. The collection unit can also analyze employees' social media activities and collect related data. Furthermore, the collection unit can collect related data by referring to the activities of the employees' friends on social media. In this way, related data can be collected by analyzing employees' social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on employees' social media activities into a generation AI, causing the generation AI to collect related data.

[0081] The collection unit can customize the collection method by reflecting employees' past feedback when collecting data. The collection unit, for example, collects employees' past feedback. For example, the collection unit may collect employee feedback by conducting a questionnaire. The collection unit may also collect employee feedback through interviews. The collection unit may also collect employee feedback through performance reviews. Next, the collection unit customizes the data collection method based on the collected feedback. For example, the collection unit may improve the collection method based on the past feedback to enable efficient data collection. The collection unit may also customize the collection means by reflecting employees' past feedback. Furthermore, the collection unit may adjust the timing and frequency of collection based on the feedback. In this way, the collection method can be customized by reflecting employees' past feedback, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit may input employees' past feedback data into a generation AI, which may then customize the collection method.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the analysis unit can estimate the user's emotions using biometric data. For example, the analysis unit can estimate the user's emotions based on heart rate and electrodermal activity. Next, the analysis unit adjusts the presentation method of the analysis based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. By adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI, causing the generation AI to adjust the method of expression of the analysis.

[0083] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, evaluates the importance of the data. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the data. For example, the analysis unit prioritizes analysis of data with high importance. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0084] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. The analysis unit, for example, identifies the data category. For example, the analysis unit applies different analysis algorithms depending on the category, such as text data, image data, or audio data. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image analysis algorithm to image data. Furthermore, the analysis unit can also apply an audio analysis algorithm to audio data. This enables highly accurate analysis by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the data category to the generation AI, causing the generation AI to apply an appropriate analysis algorithm.

[0085] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. The analysis unit, for example, collects the employee's past analysis results. For example, the analysis unit adjusts the analysis algorithm based on the past analysis results to improve accuracy. The analysis unit can also adjust the level of detail of the analysis by referring to the employee's past analysis results. Furthermore, the analysis unit can determine the priority of the analysis based on the past analysis results. For example, the analysis unit prioritizes analysis of important data based on the past analysis results. In this way, the accuracy of the analysis is improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, which can then improve the accuracy of the analysis.

[0086] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. The analysis unit, for example, estimates the user's emotions. For example, the analysis unit can estimate the user's emotions using facial expression recognition technology. The analysis unit can also estimate the user's emotions using voice analysis technology. Furthermore, the analysis unit can estimate the user's emotions using biometric data. For example, the analysis unit can estimate the user's emotions based on heart rate and electrodermal activity. Next, the analysis unit adjusts the length of the analysis based on the estimated user's emotions. For example, the analysis unit can provide a short and concise analysis result if the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. Furthermore, the analysis unit can provide an analysis result with visually stimulating effects if the user is excited. In this way, by adjusting the length of the analysis based on the user's emotions, it is possible to provide an analysis result of the optimal length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without using AI. For example, the analysis unit may input user emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0087] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. The analysis unit, for example, identifies the time when the data was collected. For example, the analysis unit prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. Furthermore, the analysis unit can dynamically adjust the analysis priority according to the time when the data was collected. For example, the analysis unit determines the analysis priority based on the time when the data was collected. In this way, by determining the analysis priority based on the time when the data was collected, the most recent data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data from the time when the data was collected to the generation AI, and have the generation AI determine the analysis priority.

[0088] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, evaluates the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit adjusts the order of analysis based on the relevance of the data. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the relevance of the data to the generation AI and have the generation AI adjust the order of analysis.

[0089] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the employee's level of expertise. The analysis unit, for example, evaluates the employee's level of expertise. For example, the analysis unit can provide analysis results that use a lot of technical terms to employees with high levels of expertise. The analysis unit can also provide analysis results that are explained in simpler terms to employees with low levels of expertise. Furthermore, the analysis unit can adjust the way the analysis results are presented according to the employee's level of expertise. For example, the analysis unit can adjust the use of technical terms in the analysis based on the employee's level of expertise. This makes it possible to provide analysis results that are easy to understand by adjusting the use of technical terms in the analysis according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data on the employee's level of expertise into a generation AI, causing the generation AI to adjust the use of technical terms in the analysis.

[0090] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is presented based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. Furthermore, the suggestion unit can estimate the user's emotion using biometric data. For example, the suggestion unit can estimate the user's emotion based on heart rate or electrodermal activity. Next, the suggestion unit adjusts the way the suggestion is presented based on the estimated user's emotion. For example, if the user is nervous, the suggestion unit can provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that focus on the main points. In this way, by adjusting the way the suggestion is presented based on the user's emotion, the system can provide optimal suggestions for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data into the generation AI, causing the generation AI to adjust the proposed expression method.

[0091] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the employee's skill set. The suggestion unit, for example, evaluates the employee's skill set. For example, the suggestion unit provides a detailed proposal to an employee with a high skill set. The suggestion unit can also provide a simplified proposal to an employee with a low skill set. Furthermore, the suggestion unit can dynamically adjust the level of detail of the proposal according to the employee's skill set. For example, the suggestion unit adjusts the level of detail of the proposal based on the employee's skill set. In this way, by adjusting the level of detail of the proposal based on the employee's skill set, it is possible to provide an appropriate level of proposal. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input data on the employee's skill set to a generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0092] When making a proposal, the proposal unit can apply different proposal algorithms depending on the business category. The proposal unit, for example, identifies the business category. For example, the proposal unit applies different proposal algorithms depending on categories such as project management, marketing, and human resources management. For example, the proposal unit applies a proposal algorithm dedicated to project management to project management. The proposal unit can also apply a proposal algorithm dedicated to marketing to marketing. Furthermore, the proposal unit can apply a proposal algorithm dedicated to human resources management to human resources management. In this way, by applying different proposal algorithms depending on the business category, it is possible to provide optimal proposals for the business. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the business category into a generation AI and cause the generation AI to apply an appropriate proposal algorithm.

[0093] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the employee's past proposal results. The suggestion unit, for example, collects the employee's past proposal results. For example, the suggestion unit adjusts the proposal algorithm based on the past proposal results to improve accuracy. The suggestion unit can also adjust the level of detail of the proposal by referring to the employee's past proposal results. Furthermore, the suggestion unit can determine the priority of the proposal based on the past proposal results. For example, the suggestion unit prioritizes important proposals based on the past proposal results. This improves the accuracy of the proposal by referring to the employee's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input past proposal result data into a generation AI, causing the generation AI to improve the accuracy of the proposal.

[0094] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit, for example, estimates the user's emotion. For example, the suggestion unit can estimate the user's emotion using facial expression recognition technology. The suggestion unit can also estimate the user's emotion using voice analysis technology. Furthermore, the suggestion unit can estimate the user's emotion using biometric data. For example, the suggestion unit can estimate the user's emotion based on heart rate or electrodermal activity. Next, the suggestion unit adjusts the length of the suggestion based on the estimated user's emotion. For example, the suggestion unit can provide a short and to-the-point suggestion if the user is in a hurry. The suggestion unit can also provide a detailed suggestion if the user is relaxed. Furthermore, the suggestion unit can provide a suggestion with a visually stimulating effect if the user is excited. In this way, by adjusting the length of the suggestion based on the user's emotion, it is possible to provide a suggestion of an optimal length for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without using AI. For example, the suggestion unit may input user emotion data to the generation AI, causing the generation AI to adjust the length of the suggestion.

[0095] The proposal unit can determine the priority of proposals based on the submission time of the work when making a proposal. The proposal unit, for example, identifies the submission time of the work. For example, the proposal unit prioritizes proposals for work whose deadline is approaching. The proposal unit can also postpone proposals for work whose submission time is further away. Furthermore, the proposal unit can dynamically adjust the priority of proposals according to the submission time of the work. For example, the proposal unit determines the priority of proposals based on the submission time of the work. In this way, by determining the priority of proposals based on the submission time of the work, it is possible to preferentially propose work whose deadline is approaching. Some or all of the above-mentioned processing in the proposal unit may be performed using, for example, AI, or may be performed without using AI. For example, the proposal unit can input data on the submission time of the work into the generation AI and cause the generation AI to determine the priority of proposals.

[0096] The suggestion unit can adjust the order of proposals based on the relevance of tasks when making a proposal. The suggestion unit, for example, evaluates the relevance of tasks. For example, the suggestion unit prioritizes proposals for highly relevant tasks. The suggestion unit can also postpone proposals for less relevant tasks. Furthermore, the suggestion unit can dynamically adjust the order of proposals according to the relevance of tasks. For example, the suggestion unit adjusts the order of proposals based on the relevance of tasks. In this way, by adjusting the order of proposals based on the relevance of tasks, highly relevant tasks can be prioritized for proposal. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input data on the relevance of tasks to a generation AI and cause the generation AI to adjust the order of proposals.

[0097] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the employee's level of expertise. The suggestion unit, for example, evaluates the employee's level of expertise. For example, the suggestion unit provides a proposal that uses a lot of technical terminology to an employee with high technical expertise. The suggestion unit can also provide a proposal explained in simpler terms to an employee with low technical expertise. Furthermore, the suggestion unit can adjust the way the proposal is expressed according to the employee's level of expertise. For example, the suggestion unit adjusts the use of technical terminology in the proposal based on the employee's level of expertise. This makes it possible to provide an easy-to-understand proposal by adjusting the use of technical terminology in the proposal according to the employee's level of expertise. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the employee's level of expertise into a generation AI, causing the generation AI to adjust the use of technical terminology in the proposal.

[0098] The providing unit can estimate the user's emotions and adjust the manner in which the communication policy is expressed based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. Furthermore, the providing unit can estimate the user's emotions using biometric data. For example, the providing unit can estimate the user's emotions based on heart rate or electrodermal activity. Next, the providing unit adjusts the manner in which the communication policy is expressed based on the estimated user's emotions. For example, if the user is nervous, the providing unit can provide a simple, highly visible communication policy. Furthermore, if the user is relaxed, the providing unit can provide a detailed communication policy. Furthermore, if the user is in a hurry, the providing unit can provide a communication policy that focuses on the main points. In this way, by adjusting the manner in which the communication policy is expressed based on the user's emotions, the optimal communication policy for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI, causing the generation AI to adjust the expression method of the communication policy.

[0099] When providing a communication policy, the providing unit can select an optimal policy based on the subordinate's personality traits. The providing unit, for example, evaluates the subordinate's personality traits. For example, if the subordinate is introverted, the providing unit can select an individual speaking policy. Also, if the subordinate is extroverted, the providing unit can select a group speaking policy. Furthermore, the providing unit can dynamically adjust the communication method according to the subordinate's personality traits. For example, the providing unit selects an optimal communication policy based on the subordinate's personality traits. As a result, effective communication is possible by selecting an optimal policy based on the subordinate's personality traits. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the subordinate's personality traits to a generating AI, causing the generating AI to select an optimal communication policy.

[0100] When providing a communication policy, the providing unit can customize the policy by referring to the supervisor's past communication history. The providing unit, for example, collects the supervisor's past communication history. For example, the providing unit collects email history and evaluates the supervisor's past communication methods. The providing unit can also collect meeting records and evaluate the supervisor's past communication methods. Furthermore, the providing unit can customize the communication policy based on the supervisor's past communication history. For example, the providing unit customizes the policy by referring to communication methods that the supervisor has used successfully in the past. The providing unit can also customize the policy to avoid communication methods that the supervisor has used unsuccessfully in the past. In this way, an effective communication policy can be provided by referring to the supervisor's past communication history. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data of the supervisor's past communication history into a generation AI and cause the generation AI to customize the communication policy.

[0101] When providing a communication policy, the provision unit can improve the policy by reflecting feedback from subordinates. The provision unit, for example, collects feedback from subordinates. For example, the provision unit may collect feedback from subordinates by conducting a survey. The provision unit may also collect feedback from subordinates through interviews. Furthermore, the provision unit may collect feedback from subordinates through performance reviews. Next, the provision unit improves the communication policy based on the collected feedback. For example, the provision unit may identify areas for improvement in the communication policy based on the feedback and propose them to a supervisor. The provision unit may also adjust the details of the policy by reflecting the feedback from subordinates. Furthermore, the provision unit may determine priorities for the communication policy based on the feedback. In this way, the communication policy is improved by reflecting the feedback from subordinates. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit may input feedback data from subordinates into a generation AI and cause the generation AI to improve the communication policy.

[0102] The providing unit can estimate the user's emotions and determine the priority of communication policies based on the estimated user's emotions. The providing unit, for example, estimates the user's emotions. For example, the providing unit can estimate the user's emotions using facial expression recognition technology. The providing unit can also estimate the user's emotions using voice analysis technology. Furthermore, the providing unit can estimate the user's emotions using biometric data. For example, the providing unit can estimate the user's emotions based on heart rate or electrodermal activity. Next, the providing unit determines the priority of communication policies based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can postpone policies with lower importance. Furthermore, if the user is relaxed, the providing unit can prioritize policies with higher importance. Furthermore, if the user is concentrating, the providing unit can prioritize detailed policies. In this way, by determining the priority of communication policies based on the user's emotions, important policies can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using AI, or may be performed without using AI. For example, the providing unit may input user emotion data into the generation AI, causing the generation AI to determine the priority of communication policies.

[0103] When providing a communication policy, the providing unit can select an optimal policy by taking into account the subordinate's geographical location information. The providing unit, for example, collects the subordinate's geographical location information. For example, the providing unit collects GPS data to identify the subordinate's current location. The providing unit can also use a location information service to identify the subordinate's current location. Next, the providing unit selects an optimal communication policy based on the subordinate's geographical location information. For example, if the subordinate is on a business trip, the providing unit can provide a policy related to the business trip destination. Also, if the subordinate is working remotely, the providing unit can provide a policy related to their home. Furthermore, if the subordinate is in the office, the providing unit can provide a policy related to the office. In this way, by taking the subordinate's geographical location information into account, a highly relevant communication policy can be provided. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the subordinate's geographical location information data to a generation AI, causing the generation AI to select an optimal communication policy.

[0104] When providing a communication policy, the provision unit can analyze the social media activity of the subordinate and propose the policy. The provision unit, for example, analyzes the social media activity of the subordinate. For example, the provision unit proposes a relevant policy based on information shared by the subordinate on social media. The provision unit can also analyze the content of the subordinate's social media activity and propose a relevant policy. Furthermore, the provision unit can propose a relevant policy by referring to the activity of the subordinate's friends on social media. In this way, by analyzing the subordinate's social media activity, a highly relevant communication policy can be proposed. Some or all of the above-mentioned processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data on the subordinate's social media activity into a generation AI and cause the generation AI to propose a relevant policy.

[0105] When providing a communication policy, the provision unit can customize the policy by reflecting past feedback from subordinates. The provision unit, for example, collects past feedback from subordinates. For example, the provision unit may collect feedback from subordinates by conducting a survey. The provision unit may also collect feedback from subordinates through interviews. The provision unit may also collect feedback from subordinates through performance reviews. Next, the provision unit customizes the communication policy based on the collected feedback. For example, the provision unit may identify improvements to the communication policy based on the feedback and propose them to a supervisor. The provision unit may also adjust the details of the policy by reflecting the feedback from subordinates. Furthermore, the provision unit may determine priorities for the communication policy based on the feedback. In this way, an effective communication policy can be provided by reflecting the past feedback from subordinates. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit may input past feedback data from subordinates into a generation AI and cause the generation AI to customize the communication policy. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects personal information and work details of employees using the camera 42 and microphone 38B of the smart device 14, and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal placement based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart device 14, and provides a communication policy to the supervisor. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects personal information and work details of employees using the camera 42 and microphone 238 of the smart glasses 214, and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generative AI. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal placement based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214, and provides a communication policy to the supervisor. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects personal information and work details of employees using the camera 42 and microphone 238 of the headset type terminal 314, and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal placement based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314, and provides a communication policy to the supervisor. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects personal information and work details of employees using the camera 42 and microphone 238 of the robot 414, and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generative AI. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes an optimal placement based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414, and provides a communication policy to the supervisor.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis department can analyze the success rate of employees' past projects and identify the factors that led to success. For example, the analysis department can collect past project data and identify commonalities between successful projects. The analysis department can also analyze data from failed projects and identify the factors that led to failure. Furthermore, the analysis department can compare the factors that led to success and failure and provide guidelines for risk management in future projects. In this way, by identifying the factors that led to success and failure based on past project data, the success rate of future projects can be improved.

[0108] The collection department can collect employee health data and analyze its correlation with work performance. For example, the collection department can collect heart rate and sleep data from employees' fitness trackers and wearable devices. The collection department can also collect the results of regular health checkups to understand employees' health status. Furthermore, the collection department can integrate health data with work performance data and analyze the impact of health status on work performance. This makes it possible to provide health management guidelines that contribute to improving work performance based on employee health data.

[0109] The proposal department can make placement proposals taking into account employees' career paths. For example, the proposal department can analyze employees' past career histories and propose placements that match their future career goals. The proposal department can also propose training and education programs to improve employees' skills. Furthermore, the proposal department can also suggest the timing of transfers and promotions based on employees' career paths. In this way, by making placement proposals that take into account employees' career paths, it is possible to improve employee motivation and work efficiency.

[0110] The provision department can provide a communication policy based on the supervisor's leadership style. For example, the provision department can evaluate the supervisor's leadership style and propose a communication policy according to the style, such as charismatic, democratic, or directive. The provision department can also adjust the frequency and method of communication with subordinates based on the supervisor's leadership style. Furthermore, the provision department can also propose a feedback method according to the leadership style. In this way, by providing a communication policy based on the supervisor's leadership style, effective leadership can be demonstrated.

[0111] The collection unit can estimate the employee's emotions and adjust the frequency of data collection based on the estimated emotions. For example, if the employee is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the employee is relaxed. The collection unit can also collect detailed data when the employee is concentrating. Furthermore, if the employee is tired, the collection unit can temporarily stop data collection and resume it after the employee has rested. In this way, by adjusting the frequency of data collection based on the employee's emotions, data can be collected at more appropriate times.

[0112] The analysis unit can estimate the employee's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the employee is nervous, the analysis unit can provide simple, highly visible analysis results. If the employee is relaxed, the analysis unit can also provide detailed analysis results. Furthermore, if the employee is in a hurry, the analysis unit can also provide analysis results that focus on the main points. In this way, by adjusting the way the analysis results are presented based on the employee's emotions, more appropriate analysis results can be provided.

[0113] The proposal department can estimate the employee's emotions and adjust the content of the proposal based on the estimated emotions. For example, if an employee is feeling stressed, the proposal department can suggest less burdensome work. Also, if an employee is relaxed, the proposal department can suggest more challenging work. Furthermore, if an employee is focused, the proposal department can suggest an important project. In this way, by adjusting the content of the proposal based on the employee's emotions, it is possible to make proposals that are optimal for the employee's state.

[0114] The information providing department can estimate the employee's emotions and adjust the timing of communication based on the estimated emotions. For example, if an employee is feeling stressed, the information providing department can reduce the frequency of communication and communicate when the employee is relaxed. The information providing department can also convey important information at the right time when the employee is concentrating. Furthermore, if an employee is tired, the information providing department can temporarily refrain from communication and resume communication after the employee has rested. In this way, effective communication can be achieved by adjusting the timing of communication based on the employee's emotions.

[0115] The feedback department can estimate the employee's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the employee is feeling stressed, the feedback department can prioritize positive feedback. Also, if the employee is relaxed, the feedback department can provide constructive feedback. Furthermore, if the employee is focused, the feedback department can provide feedback that includes specific points for improvement. In this way, by adjusting the content of the feedback based on the employee's emotions, it is possible to provide effective feedback.

[0116] The collection department can collect information about employees' hobbies and interests and analyze the relevance to their work. For example, the collection department can conduct a survey about employees' hobbies and interests and collect the data. The collection department can also collect social media data about employees' hobbies and interests. Furthermore, the collection department can analyze the relevance between hobbies and interests and work content and propose work assignments that contribute to improving employee motivation. This makes it possible to improve work efficiency and employee satisfaction by assigning work that takes employees' hobbies and interests into consideration.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection department collects employees' personal information and job details. Personal information includes pulse survey results, aptitude test results, StrengthsFinder data, etc. The collection department uses the pulse survey results to understand employees' satisfaction and stress levels, identifies employees' strengths and weaknesses from the aptitude test results, and collects StrengthsFinder data to understand employees' personality traits. Step 2: The analysis unit uses the generation AI to analyze the information collected by the collection unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, data mining techniques can be used to identify employees' strengths and weaknesses, statistical analysis can be used to evaluate employee satisfaction and stress levels, and machine learning algorithms can be used to analyze employees' personality traits. Step 3: The proposal unit proposes optimal placement based on the analysis results obtained by the analysis unit. The proposal is made according to criteria such as skill matching and workload balance. For example, it assigns employees with the most suitable skills for specific tasks, proposes employee placement taking workload balance into consideration, and proposes optimal placement based on employee personality traits. Step 4: The providing department provides the superior with a communication policy based on the allocation proposed by the proposing department. The provision is made according to criteria such as feedback frequency and communication means. For example, the providing department may suggest to the superior what type of communication would be effective based on the subordinate's personality traits, suggest adjusting the feedback frequency and providing feedback at an appropriate time, and select a communication means and suggest communicating through an appropriate means.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects employees' personal information and work details; an analysis unit that analyzes the information collected by the collection unit; a proposal unit that proposes an appropriate placement based on the analysis result obtained by the analysis unit; a providing unit that provides a communication policy to a superior based on the placement proposed by the proposing unit; Equipped with A system characterized by:

2. The collecting unit Collect pulse survey results, aptitude test results, and StrengthsFinder data 2. The system of claim 1.

3. The analysis unit Identify employee strengths and weaknesses based on collected data 2. The system of claim 1.

4. The proposal unit Allocating employees with the right skills for specific tasks 2. The system of claim 1.

5. The providing unit Provide communication strategies to managers based on subordinates' personality traits 2. The system of claim 1.

6. The providing unit Gathering and providing feedback on the effectiveness of the communication policies provided 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Analyze each employee's past work history and select the most appropriate data collection method 2. The system of claim 1.

Images