system

Abstract

The system according to the embodiment aims to efficiently measure the growth of new employees and mid-career employees and provide appropriate guidance. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a training unit, a measurement unit, and a feedback unit. The collection unit collects data on the work content or progress of new employees or mid-career employees. The analysis unit analyzes the data collected by the collection unit. The training unit provides guidance or advice to elders based on the analysis results obtained by the analysis unit. The measurement unit measures the growth of new employees or mid-career employees based on the guidance or advice provided by the training unit. The feedback unit feeds back the growth results measured by the measurement unit to elders.

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 techniques have had the problem of making it difficult to efficiently measure the growth of new employees and mid-career employees and provide them with appropriate guidance.

[0005] The system according to the embodiment aims to efficiently measure the growth of new employees and mid-career employees and provide appropriate guidance. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a training unit, a measurement unit, and a feedback unit. The collection unit collects data on the work content or progress of new employees or mid-career employees. The analysis unit analyzes the data collected by the collection unit. The training unit provides guidance or advice to the elder based on the analysis results obtained by the analysis unit. The measurement unit measures the growth of the new employees or mid-career employees based on the guidance or advice provided by the training unit. The feedback unit feeds back the growth results measured by the measurement unit to the elder. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently measure the growth of new employees and mid-career employees and provide appropriate guidance. [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) An elder support system according to an embodiment of the present invention provides work support and growth measurement for new and mid-career employees. This elder support system collects and analyzes the work content and progress of new and mid-career employees, provides appropriate guidance and advice to elders, measures their growth, and provides feedback on the results. For example, the elder support system collects data from new and mid-career employees' daily work reports and task management tools, and uses AI to analyze them. Next, the AI ​​provides appropriate guidance and advice to elders based on the analysis results. For example, if a new employee is struggling with a particular task, the AI ​​identifies the cause and suggests specific guidance methods to the elder. Furthermore, the AI ​​measures the growth of new and mid-career employees and provides feedback on the results to elders. For example, it measures the level of proficiency and skill improvement in tasks and reports them to elders. This allows elders to efficiently support the growth of new and mid-career employees. By working with AI support, new and mid-career employees can smoothly become accustomed to their work. Additionally, Elders can provide appropriate guidance while receiving feedback from AI, effectively supporting the growth of new and mid-career employees. This allows the Elder Support System to efficiently provide work assistance and growth measurement for new and mid-career employees.

[0029] An elder support system according to an embodiment includes a collection unit, an analysis unit, a training unit, a measurement unit, and a feedback unit. The collection unit collects data on the work content or progress of new employees or mid-career employees. The collection unit can collect, for example, data from daily work reports and task management tools. For example, the collection unit can automatically collect daily work reports and acquire data from task management tools. The collection unit can also use AI to collect work content and progress in real time. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data using AI to understand the work progress of new employees and mid-career employees. For example, the analysis unit can analyze the content of daily work reports and evaluate the degree of task completion and the progress of projects. The training unit provides guidance and advice to elders based on the analysis results obtained by the analysis unit. For example, the training unit can use AI to suggest specific training methods to elders based on the analysis results. For example, if a new employee is struggling with a particular task, the training department can identify the cause and propose specific training methods to the elder. The measurement department measures the growth of the new employee or mid-career employee based on the guidance and advice provided by the training department. The measurement department can, for example, use AI to measure the new employee's or mid-career employee's level of proficiency in the task and the degree of skill improvement. For example, the measurement department can evaluate the level of proficiency in the task and measure the degree of skill improvement. The feedback department feeds back the growth results measured by the measurement department to the elder. The feedback department can, for example, use AI to report the measurement results to the elder and provide appropriate feedback. For example, the feedback department can provide specific feedback to the elder based on the measurement results. As a result, the elder assistance system according to the embodiment can efficiently provide work assistance and growth measurement for new employees and mid-career employees.

[0030] The collection unit can collect data from daily work reports or task management tools. The collection unit can, for example, automatically collect daily work reports. For example, the collection unit can collect daily work reports in electronic format and convert them into a format that is easy for AI to analyze. The collection unit can also acquire data from task management tools. For example, the collection unit can collect task progress and completion levels using the task management tool's API. Furthermore, the collection unit can use AI to collect work content and progress in real time. For example, the collection unit can use AI to analyze data from daily work reports and task management tools and grasp progress in real time. In this way, the progress of work can be grasped by collecting data from daily work reports and task management tools.

[0031] The analysis unit analyzes the collected data and can grasp the progress of work by new employees and mid-career employees. The analysis unit can analyze the collected data using, for example, AI. For example, the analysis unit can analyze the contents of daily work reports and evaluate the degree of completion of tasks and the progress of projects. The analysis unit can also analyze data from task management tools and grasp the progress of tasks. For example, the analysis unit can evaluate the degree of completion and progress of tasks and grasp the progress of projects. Furthermore, the analysis unit can analyze the collected data in real time using AI. For example, the analysis unit can use AI to analyze daily work reports and data from task management tools and grasp the progress in real time. This makes it possible to accurately grasp the progress of work by analyzing the collected data.

[0032] The training department can propose specific training methods to elders based on the analysis results. For example, using AI, the training department can propose specific training methods to elders based on the analysis results. For example, if a new employee is struggling with a specific task, the training department can identify the cause and propose specific training methods to the elder. The training department can also use AI to provide appropriate advice to elders based on the analysis results. For example, the training department can propose specific methods for new employees to improve specific skills. Furthermore, the training department can use AI to suggest the timing and frequency of training to elders based on the analysis results. For example, if a new employee is struggling with a specific task, the training department can suggest that the elder provide training at that time. In this way, by proposing specific training methods based on the analysis results, the elder's training becomes more effective.

[0033] The measurement unit can measure the degree of work proficiency and skill improvement of new employees and mid-career employees. The measurement unit can, for example, use AI to measure the degree of work proficiency and skill improvement of new employees and mid-career employees. For example, the measurement unit can evaluate work proficiency and measure the degree of skill improvement. The measurement unit can also use AI to measure work proficiency and skill improvement in real time. For example, the measurement unit can use AI to analyze data from daily work reports and task management tools and measure the degree of proficiency and skill improvement in real time. Furthermore, the measurement unit can use AI to quantitatively evaluate the degree of work proficiency and skill improvement. For example, the measurement unit can use AI to quantify the degree of work proficiency and skill improvement and report it to elders. This allows for accurate understanding of growth by measuring the degree of work proficiency and skill improvement.

[0034] The feedback unit can report the measurement results to the elder and provide appropriate feedback. The feedback unit can, for example, use AI to report the measurement results to the elder. For example, the feedback unit can provide specific feedback to the elder based on the measurement results. The feedback unit can also use AI to report the measurement results to the elder in real time. For example, the feedback unit can have AI analyze the measurement results and report them to the elder in real time. Furthermore, the feedback unit can also use AI to visually display the measurement results. For example, the feedback unit can display the measurement results as graphs or charts and provide them to the elder in a format that is visually easy to understand. In this way, appropriate feedback can be provided by reporting the measurement results to the elder.

[0035] The collection unit can analyze past business data and select the optimal data collection method. The collection unit can analyze past business data, for example, using AI. For example, the collection unit can discover from past business data that data collection is effective during a specific time period and collect data during that time period. The collection unit can also analyze from past data which collection method is most efficient for a specific task and adopt that method. Furthermore, the collection unit can compare past examples of successful and unsuccessful data collection and select the optimal collection method. For example, the collection unit can select the optimal collection method based on past examples of successful data collection and collect data efficiently. In this way, the optimal data collection method can be selected by analyzing past data.

[0036] The collection unit can filter business data based on specific projects or tasks when collecting the data. For example, by using AI, the collection unit can filter business data based on specific projects or tasks when collecting the data. For example, the collection unit can collect only data related to a specific project and exclude other data. The collection unit can also prioritize the collection of data related to a specific task, thereby enabling efficient data management. Furthermore, the collection unit can filter and collect necessary data according to the progress of the project. For example, the collection unit can prioritize the collection of highly relevant data based on the progress of the project, thereby enabling efficient data management. This enables efficient data management by filtering data based on specific projects or tasks.

[0037] The collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration when collecting business data. The collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration when collecting business data, for example, by using AI. For example, the collection unit can prioritize collecting business data from a specific office or site. The collection unit can also prioritize collecting data from geographically close locations, allowing for efficient data management. Furthermore, the collection unit can also prioritize collecting data from projects related to a specific region. For example, the collection unit can prioritize collecting data from projects related to a specific region, allowing for efficient data management. In this way, highly relevant data can be efficiently collected by taking geographical location information into consideration.

[0038] The collection unit can analyze social media activities when collecting business data and collect related data. The collection unit can, for example, use AI to analyze social media activities when collecting business data and collect related data. For example, the collection unit can analyze the social media activities of new employees and mid-career employees and collect data related to work. The collection unit can also collect work-related posts on social media and use them for analysis. For example, the collection unit can collect work-related posts on social media and use them for analysis. Furthermore, the collection unit can collect feedback on social media and use them as business data. For example, the collection unit can collect feedback on social media and use them as business data. In this way, business-related data can be efficiently collected by analyzing social media activities.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the business when analyzing data. The analysis unit can adjust the level of detail of the analysis based on the importance of the business when analyzing data, for example, by using AI. For example, the analysis unit can perform a detailed analysis on a highly important business to obtain highly accurate results. The analysis unit can also perform a simple analysis on a less important business to process data efficiently. Furthermore, the analysis unit can appropriately allocate analysis resources according to the importance of the business. For example, the analysis unit can perform a detailed analysis on a highly important business and a simple analysis on a less important business to process data efficiently. As a result, adjusting the level of detail of the analysis based on the importance of the business enables efficient data processing.

[0040] The analysis unit can apply different analysis algorithms depending on the business category when analyzing data. The analysis unit can apply different analysis algorithms depending on the business category when analyzing data, for example, by using AI. For example, the analysis unit can apply an analysis algorithm dedicated to project management to data related to project management. The analysis unit can also apply an analysis algorithm dedicated to task management to data related to task management. Furthermore, the analysis unit can select and apply an optimal analysis algorithm depending on the business category. For example, the analysis unit can obtain highly accurate analysis results by selecting and applying an optimal analysis algorithm depending on the business category. In this way, highly accurate analysis results can be obtained by applying an optimal analysis algorithm depending on the business category.

[0041] The analysis unit can determine the priority of analysis based on the submission date of tasks when analyzing data. The analysis unit can determine the priority of analysis based on the submission date of tasks when analyzing data, for example, using AI. For example, the analysis unit can prioritize analysis for tasks with an approaching deadline. The analysis unit can also postpone analysis for tasks with a distant submission date. Furthermore, the analysis unit can appropriately allocate analysis resources according to the submission date of tasks. For example, the analysis unit can prioritize analysis for tasks with an approaching deadline and postpone analysis for tasks with a distant submission date, thereby enabling efficient data processing. In this way, efficient data processing is possible by determining the priority of analysis based on the submission date.

[0042] The analysis unit can adjust the order of analysis based on the relevance of tasks when analyzing data. The analysis unit can adjust the order of analysis based on the relevance of tasks when analyzing data, for example, by using AI. For example, the analysis unit can prioritize analysis of highly relevant tasks. The analysis unit can also postpone analysis of less relevant tasks. Furthermore, the analysis unit can appropriately allocate analysis resources according to the relevance of tasks. For example, the analysis unit can prioritize analysis of highly relevant tasks and postpone analysis of less relevant tasks, thereby enabling efficient data processing. As a result, adjusting the order of analysis based on relevance enables efficient data processing.

[0043] The guidance department can adjust the level of detail of guidance based on the importance of the work when providing guidance. The guidance department can adjust the level of detail of guidance based on the importance of the work when providing guidance, for example, by using AI. For example, the guidance department can provide detailed guidance for work of high importance. The guidance department can also provide simple guidance for work of low importance. Furthermore, the guidance department can appropriately allocate guidance resources according to the importance of the work. For example, the guidance department can provide detailed guidance for work of high importance and simple guidance for work of low importance, thereby enabling efficient guidance. As a result, efficient guidance is possible by adjusting the level of detail of guidance based on the importance of the work.

[0044] The coaching department can apply different coaching algorithms depending on the task category when providing guidance. The coaching department can apply different coaching algorithms depending on the task category when providing guidance, for example, by using AI. For example, the coaching department can apply a coaching algorithm dedicated to project management to coaching on project management. Furthermore, the coaching department can apply a coaching algorithm dedicated to task management to coaching on task management. Furthermore, the coaching department can select and apply an optimal coaching algorithm depending on the task category. For example, the coaching department can select and apply an optimal coaching algorithm depending on the task category, thereby enabling effective guidance. As a result, applying an optimal coaching algorithm depending on the task category enables effective guidance.

[0045] The guidance department can determine the priority of guidance based on the submission date of work when providing guidance. The guidance department can determine the priority of guidance based on the submission date of work when providing guidance, for example, by using AI. For example, the guidance department can prioritize guidance for work with an approaching deadline. The guidance department can also postpone guidance for work with a distant submission date. Furthermore, the guidance department can appropriately allocate guidance resources according to the submission date of work. For example, the guidance department can prioritize guidance for work with an approaching deadline and postpone guidance for work with a distant submission date, thereby enabling efficient guidance. In this way, efficient guidance is possible by determining the priority of guidance based on the submission date.

[0046] The training department can adjust the order of training based on the relevance of tasks when providing training. The training department can adjust the order of training based on the relevance of tasks when providing training, for example, by using AI. For example, the training department can prioritize training for highly relevant tasks. The training department can also postpone training for less relevant tasks. Furthermore, the training department can appropriately allocate training resources according to the relevance of tasks. For example, the training department can prioritize training for highly relevant tasks and postpone training for less relevant tasks, thereby enabling efficient training. As a result, efficient training is possible by adjusting the order of training based on relevance.

[0047] The measurement unit can optimize the measurement algorithm by referring to past business data when measuring growth. The measurement unit can optimize the measurement algorithm by referring to past business data when measuring growth, for example, using AI. For example, the measurement unit can select the optimal measurement algorithm based on past business data. The measurement unit can also analyze which measurement method is most effective for a specific task from past data and adopt that method. Furthermore, the measurement unit can compare past measurement results and select the optimal measurement algorithm. For example, the measurement unit can select the optimal measurement algorithm based on successful examples of past data collection and perform efficient growth measurement. In this way, the optimal measurement algorithm can be selected by referring to past data.

[0048] The measurement unit can apply different measurement methods depending on the task category when measuring growth. The measurement unit can apply different measurement methods depending on the task category when measuring growth, for example, by using AI. For example, the measurement unit can apply a measurement method dedicated to project management to measure growth related to project management. Furthermore, the measurement unit can apply a measurement method dedicated to task management to measure growth related to task management. Furthermore, the measurement unit can select and apply an optimal measurement method depending on the task category. For example, the measurement unit can obtain highly accurate measurement results by selecting and applying an optimal measurement method depending on the task category. In this way, highly accurate measurement results can be obtained by applying an optimal measurement method depending on the task category.

[0049] The measurement unit can weight the measurement data based on the submission time of the task when measuring growth. The measurement unit can weight the measurement data based on the submission time of the task when measuring growth, for example, using AI. For example, the measurement unit can increase the weighting of the measurement data for tasks with an approaching deadline. The measurement unit can also decrease the weighting of the measurement data for tasks with a more distant submission date. Furthermore, the measurement unit can appropriately adjust the weighting of the measurement data depending on the submission time of the task. For example, the measurement unit can increase the weighting of the measurement data for tasks with an approaching deadline and decrease the weighting of the measurement data for tasks with a more distant submission date, thereby enabling efficient growth measurement. As a result, weighting the measurement data based on the submission time enables efficient measurement.

[0050] The measurement unit can adjust the order of measurements based on the relevance of tasks when measuring growth. The measurement unit can adjust the order of measurements based on the relevance of tasks when measuring growth, for example, by using AI. For example, the measurement unit can prioritize growth measurement for highly relevant tasks. The measurement unit can also postpone growth measurement for less relevant tasks. Furthermore, the measurement unit can appropriately allocate resources for growth measurement according to the relevance of tasks. For example, the measurement unit can prioritize growth measurement for highly relevant tasks and postpone growth measurement for less relevant tasks, thereby enabling efficient growth measurement. As a result, adjusting the order of measurements based on relevance enables efficient measurement.

[0051] The feedback unit can adjust the level of detail of the feedback based on the importance of the task when providing feedback. The feedback unit can adjust the level of detail of the feedback based on the importance of the task when providing feedback, for example, by using AI. For example, the feedback unit can provide detailed feedback for tasks with high importance. The feedback unit can also provide simple feedback for tasks with low importance. Furthermore, the feedback unit can appropriately allocate feedback resources according to the importance of the task. For example, the feedback unit can provide detailed feedback for tasks with high importance and simple feedback for tasks with low importance, thereby enabling efficient feedback. As a result, adjusting the level of detail of the feedback based on the importance of the task enables efficient feedback.

[0052] The feedback unit can apply different feedback algorithms depending on the task category when providing feedback. The feedback unit can apply different feedback algorithms depending on the task category when providing feedback, for example, by using AI. For example, the feedback unit can apply a feedback algorithm dedicated to project management to feedback regarding project management. Furthermore, the feedback unit can apply a feedback algorithm dedicated to task management to feedback regarding task management. Furthermore, the feedback unit can select and apply an optimal feedback algorithm depending on the task category. For example, the feedback unit can select and apply an optimal feedback algorithm depending on the task category, thereby enabling effective feedback. As a result, applying an optimal feedback algorithm depending on the task category enables effective feedback.

[0053] The feedback unit can determine the priority of feedback based on the submission date of the work when providing feedback. The feedback unit can determine the priority of feedback based on the submission date of the work when providing feedback, for example, by using AI. For example, the feedback unit can provide feedback preferentially to work whose deadline is approaching. The feedback unit can also postpone providing feedback to work whose submission date is further away. Furthermore, the feedback unit can appropriately allocate feedback resources according to the submission date of the work. For example, the feedback unit can provide feedback preferentially to work whose deadline is approaching and postpone providing feedback to work whose submission date is further away, thereby enabling efficient feedback. In this way, efficient feedback can be provided by determining the priority of feedback based on the submission date.

[0054] The feedback unit can adjust the order of feedback based on the relevance of tasks when providing feedback. The feedback unit can adjust the order of feedback based on the relevance of tasks when providing feedback, for example, by using AI. For example, the feedback unit can prioritize feedback for highly relevant tasks. The feedback unit can also postpone feedback for less relevant tasks. Furthermore, the feedback unit can appropriately allocate feedback resources according to the relevance of tasks. For example, the feedback unit can prioritize feedback for highly relevant tasks and postpone feedback for less relevant tasks, thereby enabling efficient feedback. As a result, efficient feedback is possible by adjusting the order of feedback based on relevance.

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

[0056] The elder support system can further include a compensation department. The compensation department can provide appropriate compensation and incentives to new employees and mid-career employees based on their work performance and level of growth. For example, the compensation department can evaluate the level of work proficiency and skill improvement and provide bonuses to new employees who achieve excellent results. The compensation department can also evaluate the level of contribution to a specific project and provide special rewards to mid-career employees who contribute to the project's success. Furthermore, the compensation department can provide opportunities for promotion and salary increases based on periodic evaluations. Thus, having a compensation department can increase the motivation of new employees and mid-career employees and improve work efficiency.

[0057] The collection unit can also monitor the health status of new employees and mid-career employees and collect the data along with their work data. For example, the collection unit can use a wearable device to monitor their heart rate and stress level to understand their health status. The collection unit can also collect the results of regular health checks and integrate them with the work data for analysis. Furthermore, the collection unit can adjust work loads based on their health status and take measures to prevent overwork. In this way, by monitoring their health status, it is possible to maintain the health of new employees and mid-career employees and improve work efficiency.

[0058] The collection department can further analyze the social media activities of new employees and mid-career employees and integrate and collect this data with business data. For example, the collection department can collect work-related posts on social media and integrate and analyze them with business data. The collection department can also collect feedback on social media and use it as business data. Furthermore, the collection department can analyze social media activity to understand the progress and challenges of work. This makes it possible to efficiently collect data related to work by analyzing social media activity.

[0059] The analysis department can also refer to the past work data of new employees and mid-career employees to predict their work performance. For example, the analysis department can predict performance for specific tasks based on past work data and provide the predicted results to elders. The analysis department can also identify success factors for specific projects from past data and use them in future projects. Furthermore, the analysis department can analyze past data and suggest improvements to improve work efficiency. This makes it possible to predict work performance by referring to past data, enabling efficient business operations.

[0060] The training department can further analyze the learning styles of new employees and mid-career employees and provide optimal training methods. For example, the training department can understand the learning styles of new employees and mid-career employees through questionnaires and interviews and adjust the training methods based on that. The training department can also analyze past training results, identify effective training methods, and apply them to new employees and mid-career employees. Furthermore, the training department can create individual training plans based on learning styles and provide effective training. This allows for more effective training by adjusting the training methods based on learning styles.

[0061] The measurement department can also evaluate the extracurricular activities of new employees and mid-career employees and reflect them in growth measurement. For example, the measurement department can evaluate participation in volunteer activities and company events and reflect them in growth measurement. The measurement department can also evaluate extracurricular skill-building activities (for example, taking online courses or obtaining qualifications) and reflect them in growth measurement. Furthermore, the measurement department can evaluate extracurricular team-building activities and reflect them in growth measurement. In this way, by evaluating extracurricular activities, the overall growth of new employees and mid-career employees can be measured.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection department collects the work content and progress of new or mid-career employees. For example, data from daily work reports and task management tools can be collected, and AI can be used to collect work content and progress in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, AI can be used to analyze the contents of daily work reports to evaluate the degree of task completion and the progress of projects. Step 3: The guidance department provides guidance and advice to the elder based on the analysis results obtained by the analysis department. For example, AI can be used to suggest specific guidance methods to the elder based on the analysis results. Step 4: The measurement department measures the growth of new and mid-career employees based on the guidance and advice provided by the leadership department. For example, AI can be used to measure job proficiency and skill improvement. Step 5: The feedback unit feeds back the growth results measured by the measurement unit to the elder. For example, AI can be used to report the measurement results to the elder and provide appropriate feedback.

[0064] (Example 2) An elder support system according to an embodiment of the present invention provides work support and growth measurement for new and mid-career employees. This elder support system collects and analyzes the work content and progress of new and mid-career employees, provides appropriate guidance and advice to elders, measures their growth, and provides feedback on the results. For example, the elder support system collects data from new and mid-career employees' daily work reports and task management tools, and uses AI to analyze them. Next, the AI ​​provides appropriate guidance and advice to elders based on the analysis results. For example, if a new employee is struggling with a particular task, the AI ​​identifies the cause and suggests specific guidance methods to the elder. Furthermore, the AI ​​measures the growth of new and mid-career employees and provides feedback on the results to elders. For example, it measures the level of proficiency and skill improvement in tasks and reports them to elders. This allows elders to efficiently support the growth of new and mid-career employees. By working with AI support, new and mid-career employees can smoothly become accustomed to their work. Additionally, Elders can provide appropriate guidance while receiving feedback from AI, effectively supporting the growth of new and mid-career employees. This allows the Elder Support System to efficiently provide work assistance and growth measurement for new and mid-career employees.

[0065] An elder support system according to an embodiment includes a collection unit, an analysis unit, a training unit, a measurement unit, and a feedback unit. The collection unit collects data on the work content or progress of new employees or mid-career employees. The collection unit can collect, for example, data from daily work reports and task management tools. For example, the collection unit can automatically collect daily work reports and acquire data from task management tools. The collection unit can also use AI to collect work content and progress in real time. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit can analyze the collected data using AI to understand the work progress of new employees and mid-career employees. For example, the analysis unit can analyze the content of daily work reports and evaluate the degree of task completion and the progress of projects. The training unit provides guidance and advice to elders based on the analysis results obtained by the analysis unit. For example, the training unit can use AI to suggest specific training methods to elders based on the analysis results. For example, if a new employee is struggling with a particular task, the training department can identify the cause and propose specific training methods to the elder. The measurement department measures the growth of the new employee or mid-career employee based on the guidance and advice provided by the training department. The measurement department can, for example, use AI to measure the new employee's or mid-career employee's level of proficiency in the task and the degree of skill improvement. For example, the measurement department can evaluate the level of proficiency in the task and measure the degree of skill improvement. The feedback department feeds back the growth results measured by the measurement department to the elder. The feedback department can, for example, use AI to report the measurement results to the elder and provide appropriate feedback. For example, the feedback department can provide specific feedback to the elder based on the measurement results. As a result, the elder assistance system according to the embodiment can efficiently provide work assistance and growth measurement for new employees and mid-career employees.

[0066] The collection unit can collect data from daily work reports or task management tools. The collection unit can, for example, automatically collect daily work reports. For example, the collection unit can collect daily work reports in electronic format and convert them into a format that is easy for AI to analyze. The collection unit can also acquire data from task management tools. For example, the collection unit can collect task progress and completion levels using the task management tool's API. Furthermore, the collection unit can use AI to collect work content and progress in real time. For example, the collection unit can use AI to analyze data from daily work reports and task management tools and grasp progress in real time. In this way, the progress of work can be grasped by collecting data from daily work reports and task management tools.

[0067] The analysis unit analyzes the collected data and can grasp the progress of work by new employees and mid-career employees. The analysis unit can analyze the collected data using, for example, AI. For example, the analysis unit can analyze the contents of daily work reports and evaluate the degree of completion of tasks and the progress of projects. The analysis unit can also analyze data from task management tools and grasp the progress of tasks. For example, the analysis unit can evaluate the degree of completion and progress of tasks and grasp the progress of projects. Furthermore, the analysis unit can analyze the collected data in real time using AI. For example, the analysis unit can use AI to analyze daily work reports and data from task management tools and grasp the progress in real time. This makes it possible to accurately grasp the progress of work by analyzing the collected data.

[0068] The training department can propose specific training methods to elders based on the analysis results. For example, using AI, the training department can propose specific training methods to elders based on the analysis results. For example, if a new employee is struggling with a specific task, the training department can identify the cause and propose specific training methods to the elder. The training department can also use AI to provide appropriate advice to elders based on the analysis results. For example, the training department can propose specific methods for new employees to improve specific skills. Furthermore, the training department can use AI to suggest the timing and frequency of training to elders based on the analysis results. For example, if a new employee is struggling with a specific task, the training department can suggest that the elder provide training at that time. In this way, by proposing specific training methods based on the analysis results, the elder's training becomes more effective.

[0069] The measurement unit can measure the degree of work proficiency and skill improvement of new employees and mid-career employees. The measurement unit can, for example, use AI to measure the degree of work proficiency and skill improvement of new employees and mid-career employees. For example, the measurement unit can evaluate work proficiency and measure the degree of skill improvement. The measurement unit can also use AI to measure work proficiency and skill improvement in real time. For example, the measurement unit can use AI to analyze data from daily work reports and task management tools and measure the degree of proficiency and skill improvement in real time. Furthermore, the measurement unit can use AI to quantitatively evaluate the degree of work proficiency and skill improvement. For example, the measurement unit can use AI to quantify the degree of work proficiency and skill improvement and report it to elders. This allows for accurate understanding of growth by measuring the degree of work proficiency and skill improvement.

[0070] The feedback unit can report the measurement results to the elder and provide appropriate feedback. The feedback unit can, for example, use AI to report the measurement results to the elder. For example, the feedback unit can provide specific feedback to the elder based on the measurement results. The feedback unit can also use AI to report the measurement results to the elder in real time. For example, the feedback unit can have AI analyze the measurement results and report them to the elder in real time. Furthermore, the feedback unit can also use AI to visually display the measurement results. For example, the feedback unit can display the measurement results as graphs or charts and provide them to the elder in a format that is visually easy to understand. In this way, appropriate feedback can be provided by reporting the measurement results to the elder.

[0071] The collection unit can estimate the emotions of new employees and mid-career employees and adjust the timing of collecting business data based on the estimated emotions. The collection unit can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the collection unit can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The collection unit can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the collection unit can analyze the tone and speed of speech to estimate emotions. The collection unit can also estimate the emotions of new employees and mid-career employees using survey results. For example, the collection unit can analyze survey responses to estimate emotions. The collection unit can adjust the timing of collecting business data based on the estimated emotions. For example, if a new employee is feeling stressed, the collection unit can temporarily delay collection of business data and collect it when they are relaxed. Furthermore, if a large number of mid-career employees are working, the collection unit can collect business data at that time, allowing for efficient data acquisition. Furthermore, if a new employee is tired, the data collection department can collect work data after their break to obtain more accurate data. This allows the data collection timing to be adjusted based on their emotions, resulting in more accurate data.

[0072] The collection unit can analyze past business data and select the optimal data collection method. The collection unit can analyze past business data, for example, using AI. For example, the collection unit can discover from past business data that data collection is effective during a specific time period and collect data during that time period. The collection unit can also analyze from past data which collection method is most efficient for a specific task and adopt that method. Furthermore, the collection unit can compare past examples of successful and unsuccessful data collection and select the optimal collection method. For example, the collection unit can select the optimal collection method based on past examples of successful data collection and collect data efficiently. In this way, the optimal data collection method can be selected by analyzing past data.

[0073] The collection unit can filter business data based on specific projects or tasks when collecting the data. For example, by using AI, the collection unit can filter business data based on specific projects or tasks when collecting the data. For example, the collection unit can collect only data related to a specific project and exclude other data. The collection unit can also prioritize the collection of data related to a specific task, thereby enabling efficient data management. Furthermore, the collection unit can filter and collect necessary data according to the progress of the project. For example, the collection unit can prioritize the collection of highly relevant data based on the progress of the project, thereby enabling efficient data management. This enables efficient data management by filtering data based on specific projects or tasks.

[0074] The collection unit can estimate the emotions of new employees and mid-career employees and determine the priority of data to be collected based on the estimated emotions. The collection unit can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the collection unit can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The collection unit can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the collection unit can analyze the tone and speed of voice to estimate emotions. The collection unit can also estimate the emotions of new employees and mid-career employees using survey results. For example, the collection unit can analyze survey responses to estimate emotions. The collection unit can determine the priority of data to be collected based on the estimated emotions. For example, if the new employee is feeling stressed, the collection unit can postpone collection of less important data. Also, if the mid-career employee is relaxed, the collection unit can prioritize collection of more important data. Furthermore, when there is a large number of new employees, the data collection department can prioritize the collection of data directly related to their work. This allows important data to be collected preferentially by determining the priority of data based on emotions.

[0075] The collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration when collecting business data. The collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration when collecting business data, for example, by using AI. For example, the collection unit can prioritize collecting business data from a specific office or site. The collection unit can also prioritize collecting data from geographically close locations, allowing for efficient data management. Furthermore, the collection unit can also prioritize collecting data from projects related to a specific region. For example, the collection unit can prioritize collecting data from projects related to a specific region, allowing for efficient data management. In this way, highly relevant data can be efficiently collected by taking geographical location information into consideration.

[0076] The collection unit can analyze social media activities when collecting business data and collect related data. The collection unit can, for example, use AI to analyze social media activities when collecting business data and collect related data. For example, the collection unit can analyze the social media activities of new employees and mid-career employees and collect data related to work. The collection unit can also collect work-related posts on social media and use them for analysis. For example, the collection unit can collect work-related posts on social media and use them for analysis. Furthermore, the collection unit can collect feedback on social media and use them as business data. For example, the collection unit can collect feedback on social media and use them as business data. In this way, business-related data can be efficiently collected by analyzing social media activities.

[0077] The analysis unit can estimate the emotions of new employees and mid-career employees and adjust the data analysis method based on the estimated emotions. The analysis unit can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the analysis unit can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The analysis unit can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the analysis unit can analyze the tone and speed of speech to estimate emotions. The analysis unit can also estimate the emotions of new employees and mid-career employees using survey results. For example, the analysis unit can analyze survey responses to estimate emotions. The analysis unit can adjust the data analysis method based on the estimated emotions. For example, if the new employee is feeling stressed, the analysis unit can adopt a simple analysis method to reduce the burden on the employee. If the mid-career employee is relaxed, the analysis unit can perform a detailed analysis to gain deeper insights. If the new employee is concentrating, the analysis unit can adopt a complex analysis method to obtain highly accurate results. This allows you to adjust your analysis method based on emotions, resulting in more appropriate analysis results.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of the business when analyzing data. The analysis unit can adjust the level of detail of the analysis based on the importance of the business when analyzing data, for example, by using AI. For example, the analysis unit can perform a detailed analysis on a highly important business to obtain highly accurate results. The analysis unit can also perform a simple analysis on a less important business to process data efficiently. Furthermore, the analysis unit can appropriately allocate analysis resources according to the importance of the business. For example, the analysis unit can perform a detailed analysis on a highly important business and a simple analysis on a less important business to process data efficiently. As a result, adjusting the level of detail of the analysis based on the importance of the business enables efficient data processing.

[0079] The analysis unit can apply different analysis algorithms depending on the business category when analyzing data. The analysis unit can apply different analysis algorithms depending on the business category when analyzing data, for example, by using AI. For example, the analysis unit can apply an analysis algorithm dedicated to project management to data related to project management. The analysis unit can also apply an analysis algorithm dedicated to task management to data related to task management. Furthermore, the analysis unit can select and apply an optimal analysis algorithm depending on the business category. For example, the analysis unit can obtain highly accurate analysis results by selecting and applying an optimal analysis algorithm depending on the business category. In this way, highly accurate analysis results can be obtained by applying an optimal analysis algorithm depending on the business category.

[0080] The analysis unit can estimate the emotions of new employees and mid-career employees and adjust the display method of the analysis results based on the estimated emotions. The analysis unit can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the analysis unit can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The analysis unit can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the analysis unit can analyze the tone and speed of speech to estimate emotions. The analysis unit can also estimate the emotions of new employees and mid-career employees using survey results. For example, the analysis unit can analyze survey responses to estimate emotions. The analysis unit can adjust the display method of the analysis results based on the estimated emotions. For example, if a new employee is feeling stressed, the analysis unit can provide a simple, highly visible display method. If a mid-career employee is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, if a new employee is concentrating, the analysis unit can provide a display method that focuses on the main points. This allows for highly visible display by adjusting the display method based on the emotion.

[0081] The analysis unit can determine the priority of analysis based on the submission date of tasks when analyzing data. The analysis unit can determine the priority of analysis based on the submission date of tasks when analyzing data, for example, using AI. For example, the analysis unit can prioritize analysis for tasks with an approaching deadline. The analysis unit can also postpone analysis for tasks with a distant submission date. Furthermore, the analysis unit can appropriately allocate analysis resources according to the submission date of tasks. For example, the analysis unit can prioritize analysis for tasks with an approaching deadline and postpone analysis for tasks with a distant submission date, thereby enabling efficient data processing. In this way, efficient data processing is possible by determining the priority of analysis based on the submission date.

[0082] The analysis unit can adjust the order of analysis based on the relevance of tasks when analyzing data. The analysis unit can adjust the order of analysis based on the relevance of tasks when analyzing data, for example, by using AI. For example, the analysis unit can prioritize analysis of highly relevant tasks. The analysis unit can also postpone analysis of less relevant tasks. Furthermore, the analysis unit can appropriately allocate analysis resources according to the relevance of tasks. For example, the analysis unit can prioritize analysis of highly relevant tasks and postpone analysis of less relevant tasks, thereby enabling efficient data processing. As a result, adjusting the order of analysis based on relevance enables efficient data processing.

[0083] The coaching department can estimate the emotions of new employees and mid-career employees and adjust the way they are coached based on the estimated emotions. The coaching department can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the coaching department can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The coaching department can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the coaching department can analyze the tone and speed of speech to estimate emotions. Furthermore, the coaching department can estimate the emotions of new employees and mid-career employees using survey results. For example, the coaching department can analyze survey responses to estimate emotions. The coaching department can adjust the way they are coached based on the estimated emotions. For example, if a new employee is feeling stressed, the coaching department can provide gentle guidance. If a mid-career employee is relaxed, the coaching department can provide detailed guidance. Furthermore, if a new employee is concentrating, the coaching department can provide specific guidance. This allows for more effective instruction by adjusting the way instruction is presented based on emotions.

[0084] The guidance department can adjust the level of detail of guidance based on the importance of the work when providing guidance. The guidance department can adjust the level of detail of guidance based on the importance of the work when providing guidance, for example, by using AI. For example, the guidance department can provide detailed guidance for work of high importance. The guidance department can also provide simple guidance for work of low importance. Furthermore, the guidance department can appropriately allocate guidance resources according to the importance of the work. For example, the guidance department can provide detailed guidance for work of high importance and simple guidance for work of low importance, thereby enabling efficient guidance. As a result, efficient guidance is possible by adjusting the level of detail of guidance based on the importance of the work.

[0085] The coaching department can apply different coaching algorithms depending on the task category when providing guidance. The coaching department can apply different coaching algorithms depending on the task category when providing guidance, for example, by using AI. For example, the coaching department can apply a coaching algorithm dedicated to project management to coaching on project management. Furthermore, the coaching department can apply a coaching algorithm dedicated to task management to coaching on task management. Furthermore, the coaching department can select and apply an optimal coaching algorithm depending on the task category. For example, the coaching department can select and apply an optimal coaching algorithm depending on the task category, thereby enabling effective guidance. As a result, applying an optimal coaching algorithm depending on the task category enables effective guidance.

[0086] The training department can estimate the emotions of new employees and mid-career employees and adjust the length of the training based on the estimated emotions. The training department can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the training department can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The training department can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the training department can analyze the tone and speed of speech to estimate emotions. Furthermore, the training department can estimate the emotions of new employees and mid-career employees using survey results. For example, the training department can analyze the survey responses to estimate emotions. The training department can adjust the length of the training based on the estimated emotions. For example, if the new employee is feeling stressed, the training department can provide short training. If the mid-career employee is relaxed, the training department can provide detailed training. Furthermore, if the new employee is concentrating, the training department can provide specific training. This allows more appropriate training to be provided by adjusting the length of the training based on emotions.

[0087] The guidance department can determine the priority of guidance based on the submission date of work when providing guidance. The guidance department can determine the priority of guidance based on the submission date of work when providing guidance, for example, by using AI. For example, the guidance department can prioritize guidance for work with an approaching deadline. The guidance department can also postpone guidance for work with a distant submission date. Furthermore, the guidance department can appropriately allocate guidance resources according to the submission date of work. For example, the guidance department can prioritize guidance for work with an approaching deadline and postpone guidance for work with a distant submission date, thereby enabling efficient guidance. In this way, efficient guidance is possible by determining the priority of guidance based on the submission date.

[0088] The training department can adjust the order of training based on the relevance of tasks when providing training. The training department can adjust the order of training based on the relevance of tasks when providing training, for example, by using AI. For example, the training department can prioritize training for highly relevant tasks. The training department can also postpone training for less relevant tasks. Furthermore, the training department can appropriately allocate training resources according to the relevance of tasks. For example, the training department can prioritize training for highly relevant tasks and postpone training for less relevant tasks, thereby enabling efficient training. As a result, efficient training is possible by adjusting the order of training based on relevance.

[0089] The measurement unit can estimate the emotions of new employees and mid-career employees and adjust the growth measurement method based on the estimated emotions. The measurement unit can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the measurement unit can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The measurement unit can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the measurement unit can analyze the tone and speed of voice to estimate emotions. The measurement unit can also estimate the emotions of new employees and mid-career employees using questionnaire results. For example, the measurement unit can analyze questionnaire responses to estimate emotions. The measurement unit can adjust the growth measurement method based on the estimated emotions. For example, if the new employee is feeling stressed, the measurement unit can adopt a simple measurement method to reduce the burden. Also, if the mid-career employee is relaxed, the measurement unit can perform a detailed measurement to gain deeper insights. Furthermore, when new employees are concentrated, the measurement department can adopt complex measurement methods to obtain highly accurate results, which allows for more appropriate measurement by adjusting the growth measurement method based on emotions.

[0090] The measurement unit can optimize the measurement algorithm by referring to past business data when measuring growth. The measurement unit can optimize the measurement algorithm by referring to past business data when measuring growth, for example, using AI. For example, the measurement unit can select the optimal measurement algorithm based on past business data. The measurement unit can also analyze which measurement method is most effective for a specific task from past data and adopt that method. Furthermore, the measurement unit can compare past measurement results and select the optimal measurement algorithm. For example, the measurement unit can select the optimal measurement algorithm based on successful examples of past data collection and perform efficient growth measurement. In this way, the optimal measurement algorithm can be selected by referring to past data.

[0091] The measurement unit can apply different measurement methods depending on the task category when measuring growth. The measurement unit can apply different measurement methods depending on the task category when measuring growth, for example, by using AI. For example, the measurement unit can apply a measurement method dedicated to project management to measure growth related to project management. Furthermore, the measurement unit can apply a measurement method dedicated to task management to measure growth related to task management. Furthermore, the measurement unit can select and apply an optimal measurement method depending on the task category. For example, the measurement unit can obtain highly accurate measurement results by selecting and applying an optimal measurement method depending on the task category. In this way, highly accurate measurement results can be obtained by applying an optimal measurement method depending on the task category.

[0092] The measurement unit can estimate the emotions of new employees and mid-career employees and determine the priority of growth measurement based on the estimated emotions. The measurement unit can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the measurement unit can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The measurement unit can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the measurement unit can analyze the tone and speed of voice to estimate emotions. The measurement unit can also estimate the emotions of new employees and mid-career employees using questionnaire results. For example, the measurement unit can analyze questionnaire responses to estimate emotions. The measurement unit can determine the priority of growth measurement based on the estimated emotions. For example, if the new employee is feeling stressed, the measurement unit can postpone growth measurement of less importance. Furthermore, if the mid-career employee is relaxed, the measurement unit can prioritize growth measurement of more importance. Furthermore, when there is a large number of new employees, the measurement department can prioritize growth measurements that are directly related to work. This allows important measurements to be prioritized by determining the priority of growth measurements based on emotions.

[0093] The measurement unit can weight the measurement data based on the submission time of the task when measuring growth. The measurement unit can weight the measurement data based on the submission time of the task when measuring growth, for example, using AI. For example, the measurement unit can increase the weighting of the measurement data for tasks with an approaching deadline. The measurement unit can also decrease the weighting of the measurement data for tasks with a more distant submission date. Furthermore, the measurement unit can appropriately adjust the weighting of the measurement data depending on the submission time of the task. For example, the measurement unit can increase the weighting of the measurement data for tasks with an approaching deadline and decrease the weighting of the measurement data for tasks with a more distant submission date, thereby enabling efficient growth measurement. As a result, weighting the measurement data based on the submission time enables efficient measurement.

[0094] The measurement unit can adjust the order of measurements based on the relevance of tasks when measuring growth. The measurement unit can adjust the order of measurements based on the relevance of tasks when measuring growth, for example, by using AI. For example, the measurement unit can prioritize growth measurement for highly relevant tasks. The measurement unit can also postpone growth measurement for less relevant tasks. Furthermore, the measurement unit can appropriately allocate resources for growth measurement according to the relevance of tasks. For example, the measurement unit can prioritize growth measurement for highly relevant tasks and postpone growth measurement for less relevant tasks, thereby enabling efficient growth measurement. As a result, adjusting the order of measurements based on relevance enables efficient measurement.

[0095] The feedback unit can estimate the emotions of new employees and mid-career employees and adjust the way in which feedback is expressed based on the estimated emotions. The feedback unit can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the feedback unit can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The feedback unit can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the feedback unit can analyze the tone and speed of speech to estimate emotions. The feedback unit can also estimate the emotions of new employees and mid-career employees using survey results. For example, the feedback unit can analyze survey responses to estimate emotions. The feedback unit can adjust the way in which feedback is expressed based on the estimated emotions. For example, if the new employee is feeling stressed, the feedback unit can provide gentle feedback. If the mid-career employee is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the new employee is concentrating, the feedback unit can provide specific feedback. This allows for more effective feedback by adjusting how feedback is expressed based on emotions.

[0096] The feedback unit can adjust the level of detail of the feedback based on the importance of the task when providing feedback. The feedback unit can adjust the level of detail of the feedback based on the importance of the task when providing feedback, for example, by using AI. For example, the feedback unit can provide detailed feedback for tasks with high importance. The feedback unit can also provide simple feedback for tasks with low importance. Furthermore, the feedback unit can appropriately allocate feedback resources according to the importance of the task. For example, the feedback unit can provide detailed feedback for tasks with high importance and simple feedback for tasks with low importance, thereby enabling efficient feedback. As a result, adjusting the level of detail of the feedback based on the importance of the task enables efficient feedback.

[0097] The feedback unit can apply different feedback algorithms depending on the task category when providing feedback. The feedback unit can apply different feedback algorithms depending on the task category when providing feedback, for example, by using AI. For example, the feedback unit can apply a feedback algorithm dedicated to project management to feedback regarding project management. Furthermore, the feedback unit can apply a feedback algorithm dedicated to task management to feedback regarding task management. Furthermore, the feedback unit can select and apply an optimal feedback algorithm depending on the task category. For example, the feedback unit can select and apply an optimal feedback algorithm depending on the task category, thereby enabling effective feedback. As a result, applying an optimal feedback algorithm depending on the task category enables effective feedback.

[0098] The feedback unit can estimate the emotions of new employees and mid-career employees and adjust the length of the feedback based on the estimated emotions. The feedback unit can estimate the emotions of new employees and mid-career employees using, for example, AI. For example, the feedback unit can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The feedback unit can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the feedback unit can analyze the tone and speed of speech to estimate emotions. The feedback unit can also estimate the emotions of new employees and mid-career employees using survey results. For example, the feedback unit can analyze survey responses to estimate emotions. The feedback unit can adjust the length of the feedback based on the estimated emotions. For example, the feedback unit can provide short feedback if the new employee is feeling stressed. The feedback unit can provide detailed feedback if the mid-career employee is relaxed. The feedback unit can provide specific feedback if the new employee is concentrating. This enables more appropriate feedback to be provided by adjusting the length of feedback based on emotions.

[0099] The feedback unit can determine the priority of feedback based on the submission date of the work when providing feedback. The feedback unit can determine the priority of feedback based on the submission date of the work when providing feedback, for example, by using AI. For example, the feedback unit can provide feedback preferentially to work whose deadline is approaching. The feedback unit can also postpone providing feedback to work whose submission date is further away. Furthermore, the feedback unit can appropriately allocate feedback resources according to the submission date of the work. For example, the feedback unit can provide feedback preferentially to work whose deadline is approaching and postpone providing feedback to work whose submission date is further away, thereby enabling efficient feedback. In this way, efficient feedback can be provided by determining the priority of feedback based on the submission date.

[0100] The feedback unit can adjust the order of feedback based on the relevance of tasks when providing feedback. The feedback unit can adjust the order of feedback based on the relevance of tasks when providing feedback, for example, by using AI. For example, the feedback unit can prioritize feedback for highly relevant tasks. The feedback unit can also postpone feedback for less relevant tasks. Furthermore, the feedback unit can appropriately allocate feedback resources according to the relevance of tasks. For example, the feedback unit can prioritize feedback for highly relevant tasks and postpone feedback for less relevant tasks, thereby enabling efficient feedback. As a result, efficient feedback is possible by adjusting the order of feedback based on relevance. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, guidance unit, measurement unit, and feedback 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 data from daily work reports and task management tools using the control unit 46A of the smart device 14, and collects work content and progress in real time using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12 to understand the progress of work. For example, the guidance unit proposes specific guidance methods to the elder based on the analysis results using the specific processing unit 290 of the data processing device 12. For example, the measurement unit measures the growth of new employees and mid-career employees using the specific processing unit 290 of the data processing device 12. For example, the feedback unit feeds back the measurement results to the elder using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, guidance unit, measurement unit, and feedback 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 data from daily work reports and task management tools using the control unit 46A of the smart glasses 214, and collects work content and progress in real time using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12 to grasp the progress of work. For example, the guidance unit proposes specific guidance methods to the elder based on the analysis results using the specific processing unit 290 of the data processing device 12. For example, the measurement unit measures the growth of new employees and mid-career employees using the specific processing unit 290 of the data processing device 12. For example, the feedback unit feeds back the measurement results to the elder using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, guidance unit, measurement unit, and feedback unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects daily work reports and data from task management tools using the control unit 46A of the headset terminal 314, and collects work content and progress in real time using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12 to understand the progress of work. For example, the guidance unit proposes specific guidance methods to the elder based on the analysis results using the specific processing unit 290 of the data processing device 12. For example, the measurement unit measures the growth of new employees and mid-career employees using the specific processing unit 290 of the data processing device 12. For example, the feedback unit feeds back the measurement results to the elder using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, guidance unit, measurement unit, and feedback 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 daily work reports and data from task management tools using the control unit 46A of the robot 414, and collects work content and progress in real time using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the collected data using the specific processing unit 290 of the data processing device 12 to understand the progress of work. For example, the guidance unit proposes specific guidance methods to the elder based on the analysis results using the specific processing unit 290 of the data processing device 12. For example, the measurement unit measures the growth of new employees and mid-career employees using the specific processing unit 290 of the data processing device 12. For example, the feedback unit feeds back the measurement results to the elder using the specific processing unit 290 of the data processing device 12.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The elder support system can further include a compensation department. The compensation department can provide appropriate compensation and incentives to new employees and mid-career employees based on their work performance and level of growth. For example, the compensation department can evaluate the level of work proficiency and skill improvement and provide bonuses to new employees who achieve excellent results. The compensation department can also evaluate the level of contribution to a specific project and provide special rewards to mid-career employees who contribute to the project's success. Furthermore, the compensation department can provide opportunities for promotion and salary increases based on periodic evaluations. Thus, having a compensation department can increase the motivation of new employees and mid-career employees and improve work efficiency.

[0103] The collection unit can also monitor the health status of new employees and mid-career employees and collect the data along with their work data. For example, the collection unit can use a wearable device to monitor their heart rate and stress level to understand their health status. The collection unit can also collect the results of regular health checks and integrate them with the work data for analysis. Furthermore, the collection unit can adjust work loads based on their health status and take measures to prevent overwork. In this way, by monitoring their health status, it is possible to maintain the health of new employees and mid-career employees and improve work efficiency.

[0104] The analysis unit can further estimate the emotions of new employees and mid-career employees and adjust the data analysis method based on the estimated emotions. For example, the analysis unit can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The analysis unit can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the analysis unit can analyze the tone and speed of speech to estimate emotions. The analysis unit can also estimate the emotions of new employees and mid-career employees using survey results. For example, the analysis unit can analyze survey responses to estimate emotions. The analysis unit can adjust the data analysis method based on the estimated emotions. For example, if the new employee is feeling stressed, the analysis unit can adopt a simple analysis method to reduce the burden on the employee. If the mid-career employee is relaxed, the analysis unit can perform a detailed analysis to gain deeper insights. If the new employee is concentrating, the analysis unit can adopt a complex analysis method to obtain highly accurate results. As a result, more appropriate analysis results can be obtained by adjusting the analysis method based on emotions.

[0105] The training department can further estimate the emotions of new employees and mid-career employees and adjust the way in which they are coached based on the estimated emotions. For example, the training department can estimate the emotions of new employees and mid-career employees using facial expression recognition technology. The training department can also estimate the emotions of new employees and mid-career employees using voice analysis technology. For example, the training department can analyze the tone and speed of speech to estimate emotions. The training department can also estimate the emotions of new employees and mid-career employees using survey results. For example, the training department can analyze the survey responses to estimate emotions. The training department can adjust the way in which they are coached based on the estimated emotions. For example, if the new employee is feeling stressed, the training department can provide gentle guidance. If the mid-career employee is relaxed, the training department can provide detailed guidance. If the new employee is concentrating, the training department can provide specific guidance. This makes it possible to adjust the way in which guidance is coached based on emotions.

[0106] The measurement unit can further estimate the emotions of the new employee or mid-career employee and adjust the growth measurement method based on the estimated emotions. For example, the measurement unit can estimate the emotions of the new employee or mid-career employee using facial expression recognition technology. The measurement unit can also estimate the emotions of the new employee or mid-career employee using voice analysis technology. For example, the measurement unit can analyze the tone and speed of the voice to estimate the emotions. The measurement unit can also estimate the emotions of the new employee or mid-career employee using questionnaire results. For example, the measurement unit can analyze the questionnaire responses to estimate the emotions. The measurement unit can adjust the growth measurement method based on the estimated emotions. For example, if the new employee is feeling stressed, the measurement unit can adopt a simple measurement method to reduce the burden. If the mid-career employee is relaxed, the measurement unit can perform a detailed measurement to obtain deep insights. If the new employee is concentrating, the measurement unit can adopt a complex measurement method to obtain highly accurate results. This enables more appropriate measurement by adjusting the growth measurement method based on emotions.

[0107] The feedback unit can further estimate the emotion of the new employee or mid-career employee and adjust the manner in which the feedback is expressed based on the estimated emotion. For example, the feedback unit can estimate the emotion of the new employee or mid-career employee using facial expression recognition technology. The feedback unit can also estimate the emotion of the new employee or mid-career employee using voice analysis technology. For example, the feedback unit can analyze the tone and speed of the voice to estimate the emotion. The feedback unit can also estimate the emotion of the new employee or mid-career employee using survey results. For example, the feedback unit can analyze the survey responses to estimate the emotion. The feedback unit can adjust the manner in which the feedback is expressed based on the estimated emotion. For example, if the new employee is feeling stressed, the feedback unit can provide gentle feedback. If the mid-career employee is relaxed, the feedback unit can provide detailed feedback. If the new employee is concentrating, the feedback unit can provide specific feedback. This makes it possible to provide more effective feedback by adjusting the manner in which the feedback is expressed based on the emotion.

[0108] The collection department can further analyze the social media activities of new employees and mid-career employees and integrate and collect this data with business data. For example, the collection department can collect work-related posts on social media and integrate and analyze them with business data. The collection department can also collect feedback on social media and use it as business data. Furthermore, the collection department can analyze social media activity to understand the progress and challenges of work. This makes it possible to efficiently collect data related to work by analyzing social media activity.

[0109] The analysis department can also refer to the past work data of new employees and mid-career employees to predict their work performance. For example, the analysis department can predict performance for specific tasks based on past work data and provide the predicted results to elders. The analysis department can also identify success factors for specific projects from past data and use them in future projects. Furthermore, the analysis department can analyze past data and suggest improvements to improve work efficiency. This makes it possible to predict work performance by referring to past data, enabling efficient business operations.

[0110] The training department can further analyze the learning styles of new employees and mid-career employees and provide optimal training methods. For example, the training department can understand the learning styles of new employees and mid-career employees through questionnaires and interviews and adjust the training methods based on that. The training department can also analyze past training results, identify effective training methods, and apply them to new employees and mid-career employees. Furthermore, the training department can create individual training plans based on learning styles and provide effective training. This allows for more effective training by adjusting the training methods based on learning styles.

[0111] The measurement department can also evaluate the extracurricular activities of new employees and mid-career employees and reflect them in growth measurement. For example, the measurement department can evaluate participation in volunteer activities and company events and reflect them in growth measurement. The measurement department can also evaluate extracurricular skill-building activities (for example, taking online courses or obtaining qualifications) and reflect them in growth measurement. Furthermore, the measurement department can evaluate extracurricular team-building activities and reflect them in growth measurement. In this way, by evaluating extracurricular activities, the overall growth of new employees and mid-career employees can be measured.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The collection department collects the work content and progress of new or mid-career employees. For example, data from daily work reports and task management tools can be collected, and AI can be used to collect work content and progress in real time. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, AI can be used to analyze the contents of daily work reports to evaluate the degree of task completion and the progress of projects. Step 3: The guidance department provides guidance and advice to the elder based on the analysis results obtained by the analysis department. For example, AI can be used to suggest specific guidance methods to the elder based on the analysis results. Step 4: The measurement department measures the growth of new and mid-career employees based on the guidance and advice provided by the leadership department. For example, AI can be used to measure job proficiency and skill improvement. Step 5: The feedback unit feeds back the growth results measured by the measurement unit to the elder. For example, AI can be used to report the measurement results to the elder and provide appropriate feedback.

[0114] 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.

[0115] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] 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.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] 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.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] 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.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] [Explanation of symbols]

[0186] 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 information on the work content or progress of new employees or mid-career employees; an analysis unit that analyzes the data collected by the collection unit; a guidance unit that provides guidance or advice to the elder based on the analysis results obtained by the analysis unit; a measuring unit that measures the growth of new employees or mid-career employees based on the guidance or advice provided by the guidance unit; A feedback unit that feeds back the growth results measured by the measurement unit to the elder. A system characterized by:

2. The collecting unit Collect data from work reports or task management tools 2. The system of claim 1.

3. The analysis unit Analyze the collected data to understand the progress of new and mid-career employees.

2. The system of claim 1.

4. The leadership team: Based on the analysis results, we propose specific teaching methods to elders.

2. The system of claim 1.

5. The measurement unit Measure the degree of work proficiency and skill improvement of new and mid-career employees 2. The system of claim 1.

6. The feedback unit Report the results to the elder and provide appropriate feedback 2. The system of claim 1.

7. The collecting unit Estimate the emotions of new and mid-career employees and adjust the timing of work data collection based on the estimated emotions.

2. The system of claim 1.

8. The collecting unit Analyze past business data and select the optimal data collection method 2. The system of claim 1.

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