System

Abstract

To provide a system according to an embodiment that quickly and accurately arranges and analyzes a large amount of multifaceted data to present appropriate measures.SOLUTION: A system according to an embodiment comprises a collection unit, an arrangement unit, an analysis unit, a presentation unit, and a combination unit. The collection unit collects data. The arrangement unit arranges the data collected by the collection unit. The analysis unit analyzes the data arranged by the arrangement unit. The presentation unit presents measures on the basis of problems and tendencies specified by the analysis unit. The combination unit combines the measures presented by the presentation unit with information on a personnel manager.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, it is difficult to organize and analyze large amounts of multifaceted data, and there is a demand for rapid and accurate data creation, but there is room for improvement.

[0005] The system according to the embodiment aims to organize and analyze large amounts of multifaceted data quickly and accurately, and to suggest appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a sorting unit, an analysis unit, a presentation unit, and a combination unit. The collection unit collects data. The sorting unit organizes the data collected by the collection unit. The analysis unit analyzes the data organized by the sorting unit. The presentation unit presents solutions based on problems and trends identified by the analysis unit. The combination unit combines the solutions presented by the presentation unit with information from the human resources personnel. Effect of the Invention

[0007] The system according to the embodiment can quickly and accurately organize and analyze large amounts of multifaceted data, and suggest appropriate measures. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The data analysis system according to the embodiment of the present invention is a system that allows a generating AI to support the organization and analysis of large amounts of multifaceted data. In this data analysis system, the generating AI collects, organizes, and analyzes large amounts of data, identifies problems and trends, and presents specific measures. Furthermore, by combining information held by actual human resources personnel, highly accurate data can be created quickly. For example, the data analysis system collects and organizes data such as employees' working hours, performance evaluations, and skill sets. Next, the data analysis system analyzes the collected data and identifies problems and trends such as a decline in performance in a specific department or a shortage of a specific skill set. Furthermore, based on the identified problems and trends, the data analysis system presents specific measures such as the introduction of a training program for a specific skill set or the reallocation of resources to a specific department. Finally, the data analysis system can formulate an optimal human resources strategy by combining information held by actual human resources personnel. As a result, the data analysis system can efficiently organize and analyze large amounts of multifaceted data and quickly identify problems and trends. Furthermore, by combining the specific measures presented by the generating AI with information held by actual human resources personnel, highly accurate data can be created. This will improve the accuracy and efficiency of a company's human resources strategy.

[0029] The data analysis system according to the embodiment includes a collection unit, a sorting unit, an analysis unit, a presentation unit, and a combination unit. The collection unit collects data using a generation AI. The collection unit can collect data such as, for example, employee working hours, performance evaluation, and skill set. For example, the collection unit can acquire the employee's working hours from a digital time card system, the performance evaluation from a personnel evaluation system, and the skill set from the employee's self-reported data. The sorting unit organizes the data collected by the collection unit. The sorting unit automatically classifies and organizes the data using a generation AI. For example, the sorting unit can sort the employee's working hours data by date, sort the performance evaluation data by evaluation score, and classify the skill set data by skill category. The analysis unit analyzes the data sorted by the sorting unit. The analysis unit uses the generation AI to analyze patterns and trends of the data and identify problems and tendencies. For example, the analysis unit can identify a pattern of declining performance in a specific department and identify a shortage of a specific skill set. The presentation unit presents specific measures based on the problems and tendencies identified by the analysis unit. The presentation unit can use the generation AI to propose specific actions, such as introducing a training program for a specific skill set or reallocating resources to a specific department. The combination unit combines the actions presented by the presentation unit with the information of the human resources staff. The combination unit can use the generation AI to combine the presented actions with the experience and knowledge of the human resources staff to formulate an optimal human resources strategy. This allows the data analysis system according to the embodiment to efficiently organize and analyze large amounts of multifaceted data and quickly identify problems and trends. In addition, by combining the specific actions presented by the generation AI with information held by actual human resources staff, highly accurate data can be created. This improves the accuracy and efficiency of the company's human resources strategy.

[0030] The collection department collects data using the generation AI. Specifically, the collection department can collect data such as employees' working hours, performance evaluations, and skill sets from various sources. For example, employees' working hours data is automatically obtained from a digital time card system. The digital time card system records accurate working hours by employees punching in and out of work. This allows the collection department to accurately grasp each employee's working hours. Performance evaluation data is obtained from a personnel evaluation system. The personnel evaluation system stores the results of periodic evaluation interviews and performance reviews in a database, and the collection department automatically obtains these data. Skill set data is obtained from employees' self-reported data. Employees enter their skills, qualifications, and experience into a dedicated portal site, and these data are regularly updated by the collection department. In addition, the collection department also has the function of efficiently collecting these data using the generation AI and automatically detecting and correcting missing or inconsistent data. For example, if the working hours data contains an abnormal value, the generation AI detects the abnormality and makes appropriate corrections. In addition, if an input error or inconsistency occurs in performance evaluation data or skill set data, the generation AI will automatically correct it. This allows the collection department to collect accurate and reliable data and improve the data quality of the entire system.

[0031] The sorting unit sorts the data collected by the collection unit. Specifically, the sorting unit automatically classifies and sorts the data using the generation AI. For example, the employee's working hours data is sorted by date, and the monthly and weekly working hours for each employee are tallied. This makes it easy to grasp the working patterns and working hour trends of each employee. The performance evaluation data is sorted by evaluation score, and organized so that the evaluation results of each employee can be seen at a glance. In addition, detailed analysis is possible for each evaluation item, making it possible to clarify strengths and weaknesses in specific evaluation items. The skill set data is classified by skill category, and the skills and qualifications of each employee are listed. This makes it possible to quickly identify employees with specific skill sets and select personnel suitable for projects and tasks. The sorting unit also has the function of automatically detecting and correcting duplicate or missing data using the generation AI. For example, if there are multiple different skill set data for the same employee, the generation AI will integrate these data and organize them into consistent data. In addition, if there is missing data, the generation AI will fill in the missing parts based on other related data. This enables the organizer to ensure the consistency and integrity of the data and assist the analyzer in performing accurate data analysis.

[0032] The analysis department analyzes the data organized by the organization department. Specifically, the analysis department uses the generation AI to analyze patterns and trends in the data to identify problems and trends. For example, to identify patterns of declining performance in a specific department, past performance evaluation data is analyzed to clarify the period and factors of declining performance. The generation AI can use time-series data analysis and regression analysis to identify the cause of declining performance and propose specific actions for improvement. In addition, to identify shortages in specific skill sets, the generation AI can analyze employee skill set data and predict current skill distribution and future skill needs. The generation AI can use clustering and classification algorithms to identify gaps in skill sets and propose necessary training programs and recruitment plans. In addition, the analysis department can also detect unusual patterns and abnormal data using anomaly detection algorithms. For example, if a specific employee's working hours suddenly increase, the generation AI detects the anomaly and issues an alert to investigate the cause. This allows the analysis department to quickly and accurately analyze patterns and trends in the data to identify problems and trends. In addition, the analysis department can utilize past data and statistical information to perform long-term risk assessments and trend analysis. This will enable companies to predict future risks and opportunities and take appropriate measures.

[0033] The presentation unit presents specific measures based on the problems and trends identified by the analysis unit. Specifically, the presentation unit can use the generation AI to propose specific measures such as introducing a training program for a specific skill set or reallocating resources to a specific department. For example, if a specific skill set is lacking, the generation AI proposes an optimal training program based on the employee's skill set data. The training program is provided in various formats, such as online courses, workshops, and in-house training. In addition, if performance is declining in a specific department, the generation AI proposes reallocating resources from other departments and improving business processes. The presentation unit presents these proposals as specific action plans and provides them in an executable form. In addition, the presentation unit builds a feedback loop to evaluate the effectiveness of the proposed measures and continuously improve them. For example, to evaluate the effectiveness of the training program, the employee's skill set data is regularly updated and changes in skill levels before and after training are analyzed. In addition, to evaluate the effectiveness of resource reallocation, changes in performance after reallocation are monitored based on performance evaluation data. This allows the presentation unit to not only propose specific measures, but also provide a mechanism for evaluating their effectiveness and continuously improving them.

[0034] The combination unit combines the actions presented by the presentation unit with the information of the human resources personnel. Specifically, the combination unit can use the generation AI to combine the presented actions with the experience and knowledge of the human resources personnel to formulate an optimal human resources strategy. For example, the generation AI incorporates the opinions and experience of the human resources personnel to formulate an optimal implementation plan based on the training program and resource reallocation action plan proposed by the presentation unit. The human resources personnel can customize the actions proposed by the generation AI, taking into account the individual situations of employees and the characteristics of the organization. Furthermore, the combination unit uses the generation AI to analyze the effects of past human resources strategies and formulate a more effective strategy based on success and failure cases. For example, the effect of past training programs is analyzed to identify the characteristics and conditions of successful programs. In addition, the effect of resource reallocation is analyzed to clarify the patterns and factors of successful reallocation. As a result, the combination unit can combine the actions proposed by the presentation unit with the knowledge of the human resources personnel to formulate and execute an optimal human resources strategy. Furthermore, the combination unit can use the generation AI to continuously monitor the effect of the formulated human resources strategy and modify the strategy as necessary. This enables the Combination Department to improve the accuracy and effectiveness of a company's human resources strategy and optimize the performance of the entire organization.

[0035] The collection unit can collect data on the employee's working hours, performance evaluation, and skill set. The collection unit, for example, acquires the employee's working hours from a digital time card system. For example, the collection unit acquires the employee's arrival time, departure time, and break time from the digital time card system and stores them as working hours data. The collection unit can also acquire performance evaluations from a personnel evaluation system. For example, the collection unit acquires the employee's performance evaluation score, evaluation comments, and evaluation period from the personnel evaluation system and stores them as performance evaluation data. Furthermore, the collection unit can acquire the skill set from the employee's self-reported data. For example, the collection unit acquires the employee's self-reported technical skills, soft skills, and qualification information and stores them as skill set data. This allows detailed analysis to be performed by collecting various data on employees. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the working hours data acquired from the digital time card system into the generation AI, and have the generation AI organize and analyze the data.

[0036] The sorting unit can automatically classify and organize the collected data. The sorting unit automatically classifies and organizes the data using the generation AI. For example, the sorting unit sorts the employee's working hours data by date. For example, the sorting unit sorts the employee's arrival time, departure time, and break time by date and saves them as working hours data. The sorting unit can also sort the performance evaluation data by evaluation score. For example, the sorting unit sorts the employee's performance evaluation scores in descending order, and associates and saves the evaluation comments and evaluation period. Furthermore, the sorting unit can classify the skill set data by skill category. For example, the sorting unit classifies technical skills, soft skills, and qualification information by category and saves them as skill set data. This enables efficient data management through automatic classification and organization of data. Part or all of the above-mentioned processing in the sorting unit is performed using the generation AI. For example, the sorting unit can input the collected data to the generation AI and have the generation AI classify and organize the data.

[0037] The analysis unit can analyze data patterns and trends to identify problems and trends. The analysis unit uses the generation AI to analyze data patterns and trends to identify problems and trends. For example, the analysis unit identifies a pattern of declining performance in a specific department. For example, the analysis unit can analyze performance evaluation data of a specific department to identify a pattern of declining performance. The analysis unit can also identify a shortage of a specific skill set. For example, the analysis unit can analyze employee skill set data to identify a shortage of a specific skill set. Furthermore, the analysis unit can analyze data trends to predict future problems and trends. For example, the analysis unit can predict future declines in performance and shortages of skill sets based on past data. This allows problems and trends to be quickly identified by analyzing data patterns and trends. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input organized data into the generation AI and have the generation AI perform analysis of the data.

[0038] The presentation unit can present specific measures based on the identified problems and trends. The presentation unit presents specific measures based on the identified problems and trends using the generation AI. For example, the presentation unit proposes the introduction of a training program for a specific skill set. For example, the presentation unit can identify a shortage of a specific skill set and propose the introduction of a training program to compensate for the skill set. The presentation unit can also propose the reallocation of resources to a specific department. For example, the presentation unit can identify a decline in performance in a specific department and propose the reallocation of resources to the department. Furthermore, the presentation unit can also propose measures for future problems and trends based on data trends. For example, the presentation unit can predict a future decline in performance or a shortage of a skill set and propose measures for that. This makes it possible to respond quickly by presenting specific measures. Part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the identified problems and trends to the generation AI and cause the generation AI to execute a proposal for a specific measure.

[0039] The combination unit can combine the presented action with the information of the human resources staff. The combination unit uses the generation AI to combine the presented action with the information of the human resources staff. For example, the combination unit combines the presented training program introduction proposal with the experience and knowledge of the human resources staff. For example, the combination unit can combine the training program introduction proposal proposed by the generation AI with the experience and knowledge of the human resources staff to formulate an optimal training program. The combination unit can also combine the presented resource reallocation proposal with the information of the human resources staff. For example, the combination unit can combine the resource reallocation proposal proposed by the generation AI with the information of the human resources staff to formulate an optimal resource reallocation plan. Furthermore, the combination unit can combine actions for future problems and trends based on the data trend with the information of the human resources staff. For example, the combination unit can combine the actions for future problems and trends predicted by the generation AI with the information of the human resources staff to formulate an optimal human resources strategy. As a result, by combining with the information of the human resources staff, highly accurate data can be created. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input information about the proposed move and the human resources officer into the generation AI and have the generation AI execute the combination of information.

[0040] The collection unit can analyze the past data collection history and select the optimal collection method. The collection unit uses the generation AI to analyze the past data collection history and select the optimal collection method. For example, the collection unit identifies and applies the most efficient collection method from the past data collection history. For example, the collection unit can analyze the past data collection history, evaluate the collection frequency and the efficiency of the collection means, and select the optimal collection method. The collection unit can also identify and optimize improvements to the collection method based on the past data collection history. For example, the collection unit can analyze the past data collection history, identify improvements to the collection method, and optimize the collection means and collection frequency. Furthermore, the collection unit can analyze the past data collection history, find patterns in the collection method, and select the optimal method. For example, the collection unit can analyze the past data collection history, identify patterns in the collection method, and select the optimal collection method. As a result, efficient data collection is possible by analyzing the past data collection history. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0041] The collection unit can perform filtering based on the user's current work situation and field of interest when collecting data. The collection unit performs filtering based on the user's current work situation and field of interest when collecting data using the generation AI. For example, the collection unit takes into account the user's current work situation and collects only highly relevant data. For example, the collection unit can analyze the user's work situation and preferentially collect highly relevant data. The collection unit can also preferentially collect necessary data based on the user's field of interest. For example, the collection unit can analyze the user's field of interest and preferentially collect necessary data. Furthermore, the collection unit can adjust the range of data collection according to the user's work situation and field of interest. For example, the collection unit can analyze the user's work situation and field of interest and adjust the range of data collection. This makes it possible to collect data according to the user's work situation and field of interest. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's work situation and field of interest to the generation AI and cause the generation AI to perform filtering of data collection.

[0042] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. The collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting data using the generation AI. For example, the collection unit preferentially collects highly relevant data based on the user's current location. For example, the collection unit can acquire the user's current location from GPS data and preferentially collect data related to the area. The collection unit can also collect highly relevant data with reference to the user's past location information. For example, the collection unit can analyze the user's past location information and preferentially collect data related to the area. Furthermore, the collection unit can also select an optimal data collection method in consideration of the user's geographical location information. For example, the collection unit can analyze the user's geographical location information and select an optimal data collection method. This makes it possible to collect data based on the user's geographical location information. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the determination of the priority order of data collection.

[0043] The collection unit can analyze the user's social media activities and collect related data at the time of data collection. The collection unit uses the generation AI to analyze the user's social media activities and collect related data at the time of data collection. For example, the collection unit analyzes the user's social media activities and collects related data preferentially. For example, the collection unit can analyze the content of the user's social media posts and the number of likes, and collect related data preferentially. The collection unit can also collect necessary data based on the user's social media interests. For example, the collection unit can analyze the user's social media interests and collect necessary data preferentially. Furthermore, the collection unit can adjust the range of data collection with reference to the user's social media activities. For example, the collection unit can analyze the user's social media activities and adjust the range of data collection. This makes it possible to collect data based on the user's social media activities. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's social media activities to the generation AI and cause the generation AI to execute the determination of the priority of data collection.

[0044] The organizing unit can adjust the level of detail of the organizing based on the importance of the data when organizing the data. The organizing unit adjusts the level of detail of the organizing based on the importance of the data when organizing the data using the generation AI. For example, the organizing unit organizes important data in detail and simplifies data with low importance. For example, the organizing unit can evaluate the importance of the data, organize important data in detail, and simplify data with low importance. The organizing unit can also determine the priority of the organizing according to the importance of the data. For example, the organizing unit can evaluate the importance of the data and organize important data with priority. Furthermore, the organizing unit can organize data with high importance with priority, and efficiently manage data. For example, the organizing unit can evaluate the importance of the data and organize data with high importance with priority. This makes it possible to organize according to the importance of the data. A part or all of the above-mentioned processing in the organizing unit is performed using the generation AI. For example, the organizing unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the organizing.

[0045] The sorting unit can apply different sorting algorithms depending on the category of data when sorting data. The sorting unit applies different sorting algorithms depending on the category of data when sorting data using the generation AI. For example, the sorting unit selects an optimal sorting algorithm depending on the category of data. For example, the sorting unit can evaluate the category of data and select an optimal sorting algorithm. The sorting unit can also apply different sorting methods to each category to improve the accuracy of data. For example, the sorting unit can evaluate the category of data and apply different sorting methods to each category. Furthermore, the sorting unit can customize the sorting algorithm based on the category of data. For example, the sorting unit can evaluate the category of data and customize the sorting algorithm based on the category. This makes it possible to sort according to the category of data. A part or all of the above-mentioned processing in the sorting unit is performed using the generation AI. For example, the sorting unit can input the category of data to the generation AI and cause the generation AI to apply the sorting algorithm.

[0046] The sorting unit can determine the priority of sorting based on the submission date of the data when sorting the data. The sorting unit determines the priority of sorting based on the submission date of the data when sorting the data using the generation AI. For example, the sorting unit prioritizes sorting the latest data and responds quickly. For example, the sorting unit can evaluate the submission date of the data and prioritize sorting the latest data. The sorting unit can also determine the priority of sorting based on the submission date. For example, the sorting unit can evaluate the submission date of the data and determine the priority of sorting based on the submission date. Furthermore, the sorting unit can postpone data submitted earlier and prioritize sorting the latest data. For example, the sorting unit can evaluate the submission date of the data, postpone data submitted earlier and prioritize sorting the latest data. This makes it possible to sort based on the submission date of the data. Part or all of the above-mentioned processing in the sorting unit is performed using the generation AI. For example, the sorting unit can input the submission date of the data to the generation AI and cause the generation AI to execute the determination of the priority of sorting.

[0047] The organizing unit can adjust the order of organizing based on the relevance of data when organizing data. The organizing unit adjusts the order of organizing based on the relevance of data when organizing data using the generation AI. For example, the organizing unit prioritizes organizing highly relevant data and efficiently manages it. For example, the organizing unit can evaluate the relevance of data and prioritize organizing highly relevant data. The organizing unit can also determine the order of organizing based on the relevance of data. For example, the organizing unit can evaluate the relevance of data and determine the order of organizing based on the relevance. Furthermore, the organizing unit can postpone data with low relevance and prioritize organizing important data. For example, the organizing unit can evaluate the relevance of data, postpone data with low relevance and prioritize organizing important data. This makes it possible to organize based on the relevance of data. A part or all of the above-mentioned processing in the organizing unit is performed using the generation AI. For example, the organizing unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of organizing.

[0048] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of data when analyzing data. The analysis unit can improve the accuracy of the analysis by considering the interrelationships of data when analyzing data using the generation AI. For example, the analysis unit can perform a highly accurate analysis by considering the interrelationships of data. For example, the analysis unit can evaluate the interrelationships of data and perform a highly accurate analysis. The analysis unit can also improve the accuracy of the analysis based on the interrelationships of data. For example, the analysis unit can evaluate the interrelationships of data and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the interrelationships of data and select an optimal analysis method. For example, the analysis unit can evaluate the interrelationships of data and select an optimal analysis method. This enables a highly accurate analysis that takes the interrelationships of data into consideration. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the interrelationships of data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0049] The analysis unit can perform the analysis while considering the attribute information of the data submitter when analyzing the data. The analysis unit performs the analysis while considering the attribute information of the data submitter when analyzing the data using the generation AI. For example, the analysis unit performs a highly accurate analysis while considering the attribute information of the data submitter. For example, the analysis unit can evaluate the attribute information of the data submitter and perform a highly accurate analysis. The analysis unit can also improve the accuracy of the analysis based on the attribute information of the submitter. For example, the analysis unit can evaluate the attribute information of the data submitter and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the attribute information of the submitter and select the optimal analysis method. For example, the analysis unit can evaluate the attribute information of the data submitter and select the optimal analysis method. This enables a highly accurate analysis while considering the attribute information of the data submitter. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the attribute information of the data submitter to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0050] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the data when analyzing the data. The analysis unit performs the analysis while taking into consideration the geographical distribution of the data when analyzing the data using the generation AI. For example, the analysis unit performs a highly accurate analysis while taking into consideration the geographical distribution of the data. For example, the analysis unit can evaluate the geographical distribution of the data and perform a highly accurate analysis. The analysis unit can also improve the accuracy of the analysis based on the geographical distribution. For example, the analysis unit can evaluate the geographical distribution of the data and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the geographical distribution and select an optimal analysis method. For example, the analysis unit can evaluate the geographical distribution of the data and select an optimal analysis method. This enables a highly accurate analysis that takes into consideration the geographical distribution of the data. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the geographical distribution of the data to the generation AI and cause the generation AI to execute an improvement in the accuracy of the analysis.

[0051] The analysis unit can improve the accuracy of the analysis by referring to literature related to the data when analyzing the data. The analysis unit can improve the accuracy of the analysis by referring to literature related to the data when analyzing the data using the generation AI. For example, the analysis unit can perform a highly accurate analysis by referring to literature related to the data. For example, the analysis unit can evaluate literature related to the data and perform a highly accurate analysis. The analysis unit can also improve the accuracy of the analysis based on the related literature. For example, the analysis unit can evaluate literature related to the data and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the related literature and select an optimal analysis method. For example, the analysis unit can evaluate literature related to the data and select an optimal analysis method. This enables a highly accurate analysis by referring to literature related to the data. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input literature related to the data into the generation AI and cause the generation AI to perform an improvement in the accuracy of the analysis.

[0052] The presentation unit can adjust the level of detail of the presentation based on the importance of the problem when presenting a move. The presentation unit adjusts the level of detail of the presentation based on the importance of the problem when presenting a move using the generation AI. For example, the presentation unit presents detailed moves for important problems to encourage a quick response. For example, the presentation unit can evaluate the importance of a problem and present detailed moves for important problems. The presentation unit can also present simplified moves for problems of low importance to efficiently respond. For example, the presentation unit can evaluate the importance of a problem and present simplified moves for problems of low importance. Furthermore, the presentation unit can adjust the level of detail of the presentation according to the importance of the problem and provide an optimal move. For example, the presentation unit can evaluate the importance of a problem and adjust the level of detail of the presentation according to the importance. This makes it possible to present a move according to the importance of the problem. A part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the importance of the problem to the generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0053] The presentation unit can apply different presentation algorithms according to the category of the problem when presenting a move. The presentation unit applies different presentation algorithms according to the category of the problem when presenting a move using the generation AI. For example, the presentation unit selects an optimal presentation algorithm according to the category of the problem. For example, the presentation unit can evaluate the category of the problem and select an optimal presentation algorithm. The presentation unit can also apply different presentation methods for each category to improve the accuracy of the data. For example, the presentation unit can evaluate the category of the problem and apply different presentation methods for each category. Furthermore, the presentation unit can customize the presentation algorithm based on the category of the problem. For example, the presentation unit can evaluate the category of the problem and customize the presentation algorithm based on the category. This makes it possible to present an optimal move according to the category of the problem. A part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the category of the problem to the generation AI and cause the generation AI to execute the application of the presentation algorithm.

[0054] The presentation unit can determine the priority of presentation based on the submission time of the problem when presenting a move. The presentation unit uses the generation AI to determine the priority of presentation based on the submission time of the problem when presenting a move. For example, the presentation unit presents the latest problem with priority and responds quickly. For example, the presentation unit can evaluate the submission date of the problem and present the latest problem with priority. The presentation unit can also determine the priority of presentation based on the submission time. For example, the presentation unit can evaluate the submission date of the problem and determine the priority of presentation based on the submission time. Furthermore, the presentation unit can postpone the problem with an older submission time and present the latest problem with priority. For example, the presentation unit can evaluate the submission date of the problem, postpone the problem with an older submission time and present the latest problem with priority. This makes it possible to present a move based on the submission time of the problem. Part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the submission date of the problem to the generation AI and cause the generation AI to execute the determination of the priority of presentation.

[0055] The presentation unit can adjust the order of presentation based on the relevance of the problems when presenting moves. The presentation unit adjusts the order of presentation based on the relevance of the problems when presenting moves using the generation AI. For example, the presentation unit preferentially presents highly relevant problems and efficiently responds. For example, the presentation unit can evaluate the relevance of the problems and preferentially present highly relevant problems. The presentation unit can also determine the order of presentation based on the relevance of the problems. For example, the presentation unit can evaluate the relevance of the problems and determine the order of presentation based on the relevance. Furthermore, the presentation unit can postpone problems with low relevance and preferentially present important problems. For example, the presentation unit can evaluate the relevance of the problems, postpone problems with low relevance, and preferentially present important problems. This makes it possible to present moves based on the relevance of the problems. A part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the relevance of the problems to the generation AI and cause the generation AI to adjust the order of presentation.

[0056] The combination unit can select the optimal combination method by referring to the user's past information usage history when combining information. The combination unit selects the optimal combination method by referring to the user's past information usage history when combining information using the generation AI. For example, the combination unit selects the optimal combination method based on the user's past information usage history. For example, the combination unit can analyze the user's past information usage history and select the optimal combination method. The combination unit can also identify improvements to the combination method and optimize it by referring to the past usage history. For example, the combination unit can analyze the user's past information usage history and identify improvements to the combination method and optimize it. Furthermore, the combination unit can analyze the user's past information usage history and select the most efficient combination method. For example, the combination unit can analyze the user's past information usage history and select the most efficient combination method. This makes it possible to combine information optimally based on the user's past information usage history. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input the user's past information usage history into the generation AI and cause the generation AI to select the optimal combination method.

[0057] The combination unit can customize the combination means based on the user's current work situation when combining information. The combination unit customizes the combination means based on the user's current work situation when combining information using the generation AI. For example, the combination unit selects the optimal combination means in consideration of the user's current work situation. For example, the combination unit can evaluate the user's work situation and select the optimal combination means. The combination unit can also customize the combination means according to the work situation. For example, the combination unit can evaluate the user's work situation and customize the combination means according to the work situation. Furthermore, the combination unit can adjust the range of the combination means based on the user's work situation. For example, the combination unit can evaluate the user's work situation and adjust the range of the combination means based on the work situation. This makes it possible to combine information according to the user's current work situation. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input the user's work situation to the generation AI and cause the generation AI to execute customization of the combination means.

[0058] The combination unit can select the optimal combination method in consideration of the geographical location information of the user when combining information. The combination unit selects the optimal combination method in consideration of the geographical location information of the user when combining information using the generation AI. For example, the combination unit selects the optimal combination method based on the user's current location. For example, the combination unit can acquire the user's current location from GPS data and combine information related to the area with priority. The combination unit can also select the optimal combination method with reference to the user's past location information. For example, the combination unit can analyze the user's past location information and combine information related to the area with priority. Furthermore, the combination unit can also select the optimal combination means in consideration of the user's geographical location information. For example, the combination unit can analyze the user's geographical location information and select the optimal combination means. This makes it possible to combine the optimal information based on the user's geographical location information. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal combination method.

[0059] The combination unit can analyze the user's social media activity and propose a combination means when combining information. The combination unit uses the generation AI to analyze the user's social media activity and propose a combination means when combining information. For example, the combination unit analyzes the user's social media activity and proposes an optimal combination means. For example, the combination unit can analyze the user's social media posting content and the number of likes and propose an optimal combination means. The combination unit can also propose an optimal combination means based on the user's social media interests. For example, the combination unit can analyze the user's social media interests and propose an optimal combination means. Furthermore, the combination unit can adjust the range of combination means by referring to the user's social media activities. For example, the combination unit can analyze the user's social media activities and adjust the range of combination means. This makes it possible to combine information optimally based on the user's social media activities. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input the user's social media activities to the generation AI and cause the generation AI to execute the proposal of combination means.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The data analysis system may further include a prediction unit. The prediction unit may predict future problems and trends based on collected data and past trends. For example, the prediction unit may predict the possibility that a decline in performance in a particular department will continue and take measures early. The prediction unit may also predict the possibility that the demand for a particular skill set will increase and prepare the necessary training programs in advance. Furthermore, the prediction unit may predict the risk of employee turnover and take appropriate measures. This makes it possible to identify future problems and trends in advance and respond quickly.

[0062] The collection unit can further include a function for collecting user feedback. For example, the collection unit can collect employees' satisfaction and dissatisfaction with their daily work in the form of a questionnaire. The collection unit can also collect evaluations of training programs that employees have participated in and measure their effectiveness. Furthermore, the collection unit can collect improvements and new ideas proposed by employees and use them to improve the entire organization. This makes it possible to collect data that reflects the voices of employees, and to formulate a more practical personnel strategy.

[0063] The sorting unit may further include a function for detecting and deleting duplicate data. For example, the sorting unit may detect multiple data entries related to the same employee and delete the duplicates. The sorting unit may also check the consistency of data obtained from different systems to create a consistent data set. Furthermore, the sorting unit may include a function for complementing missing values ​​in the data to ensure the completeness of the data. This improves the quality of the data and enables accurate analysis.

[0064] The analysis department can further include a function for detecting anomalies in data. For example, if there is an abnormal pattern in the data on employees' working hours, the analysis department can detect it and issue an alert. In addition, if there is an unnatural fluctuation in the performance evaluation data, the analysis department can detect it and investigate the cause. Furthermore, if there is a sudden increase or decrease in a specific skill in the skill set data, the analysis department can detect it and take appropriate measures. This makes it possible to discover anomalies in the data early and respond quickly.

[0065] The presentation unit may further include a visualization function. For example, the presentation unit may visually display the results of the data analysis as graphs or charts. The presentation unit may also highlight and visually emphasize specific problems or trends. Furthermore, the presentation unit may display data trends using animation to enable an intuitive understanding of dynamically changing data. This makes it possible to present the results of the data analysis in a visually easy-to-understand manner and support rapid decision-making.

[0066] The process flow of the first embodiment will be briefly described below.

[0067] Step 1: The collection unit collects data using the generation AI. For example, data such as employees' working hours, performance evaluations, and skill sets are collected. Specifically, working hours are obtained from a digital time card system, performance evaluations are obtained from a personnel evaluation system, and skill sets are obtained from employees' self-reported data. Step 2: The sorting unit sorts the data collected by the collection unit. It uses generative AI to automatically classify and sort the data. For example, it sorts working hours data by date, sorts performance evaluation data by evaluation score, and classifies skill set data by skill category. Step 3: The analysis department analyzes the data organized by the organization department. Generative AI is used to analyze patterns and trends in the data to identify problems and trends. For example, it identifies patterns of declining performance in a particular department or a shortage of a particular skill set. Step 4: The proposal unit proposes specific actions based on the problems and trends identified by the analysis unit. Using generative AI, it suggests specific actions such as introducing a training program for a specific skill set or reallocating resources to a specific department. Step 5: The combination unit combines the actions presented by the presentation unit with the information of the HR personnel. Using a generation AI, the proposed actions are combined with the experience and knowledge of the HR personnel to formulate an optimal HR strategy.

[0068] (Example 2) The data analysis system according to the embodiment of the present invention is a system that allows a generating AI to support the organization and analysis of large amounts of multifaceted data. In this data analysis system, the generating AI collects, organizes, and analyzes large amounts of data, identifies problems and trends, and presents specific measures. Furthermore, by combining information held by actual human resources personnel, highly accurate data can be created quickly. For example, the data analysis system collects and organizes data such as employees' working hours, performance evaluations, and skill sets. Next, the data analysis system analyzes the collected data and identifies problems and trends such as a decline in performance in a specific department or a shortage of a specific skill set. Furthermore, based on the identified problems and trends, the data analysis system presents specific measures such as the introduction of a training program for a specific skill set or the reallocation of resources to a specific department. Finally, the data analysis system can formulate an optimal human resources strategy by combining information held by actual human resources personnel. As a result, the data analysis system can efficiently organize and analyze large amounts of multifaceted data and quickly identify problems and trends. Furthermore, by combining the specific measures presented by the generating AI with information held by actual human resources personnel, highly accurate data can be created. This will improve the accuracy and efficiency of a company's human resources strategy.

[0069] The data analysis system according to the embodiment includes a collection unit, a sorting unit, an analysis unit, a presentation unit, and a combination unit. The collection unit collects data using a generation AI. The collection unit can collect data such as, for example, employee working hours, performance evaluation, and skill set. For example, the collection unit can acquire the employee's working hours from a digital time card system, the performance evaluation from a personnel evaluation system, and the skill set from the employee's self-reported data. The sorting unit organizes the data collected by the collection unit. The sorting unit automatically classifies and organizes the data using a generation AI. For example, the sorting unit can sort the employee's working hours data by date, sort the performance evaluation data by evaluation score, and classify the skill set data by skill category. The analysis unit analyzes the data sorted by the sorting unit. The analysis unit uses the generation AI to analyze patterns and trends of the data and identify problems and tendencies. For example, the analysis unit can identify a pattern of declining performance in a specific department and identify a shortage of a specific skill set. The presentation unit presents specific measures based on the problems and tendencies identified by the analysis unit. The presentation unit can use the generation AI to propose specific actions, such as introducing a training program for a specific skill set or reallocating resources to a specific department. The combination unit combines the actions presented by the presentation unit with the information of the human resources staff. The combination unit can use the generation AI to combine the presented actions with the experience and knowledge of the human resources staff to formulate an optimal human resources strategy. This allows the data analysis system according to the embodiment to efficiently organize and analyze large amounts of multifaceted data and quickly identify problems and trends. In addition, by combining the specific actions presented by the generation AI with information held by actual human resources staff, highly accurate data can be created. This improves the accuracy and efficiency of the company's human resources strategy.

[0070] The collection department collects data using the generation AI. Specifically, the collection department can collect data such as employees' working hours, performance evaluations, and skill sets from various sources. For example, employees' working hours data is automatically obtained from a digital time card system. The digital time card system records accurate working hours by employees punching in and out of work. This allows the collection department to accurately grasp each employee's working hours. Performance evaluation data is obtained from a personnel evaluation system. The personnel evaluation system stores the results of periodic evaluation interviews and performance reviews in a database, and the collection department automatically obtains these data. Skill set data is obtained from employees' self-reported data. Employees enter their skills, qualifications, and experience into a dedicated portal site, and these data are regularly updated by the collection department. In addition, the collection department also has the function of efficiently collecting these data using the generation AI and automatically detecting and correcting missing or inconsistent data. For example, if the working hours data contains an abnormal value, the generation AI detects the abnormality and makes appropriate corrections. In addition, if an input error or inconsistency occurs in performance evaluation data or skill set data, the generation AI will automatically correct it. This allows the collection department to collect accurate and reliable data and improve the data quality of the entire system.

[0071] The sorting unit sorts the data collected by the collection unit. Specifically, the sorting unit automatically classifies and sorts the data using the generation AI. For example, the employee's working hours data is sorted by date, and the monthly and weekly working hours for each employee are tallied. This makes it easy to grasp the working patterns and working hour trends of each employee. The performance evaluation data is sorted by evaluation score, and organized so that the evaluation results of each employee can be seen at a glance. In addition, detailed analysis is possible for each evaluation item, making it possible to clarify strengths and weaknesses in specific evaluation items. The skill set data is classified by skill category, and the skills and qualifications of each employee are listed. This makes it possible to quickly identify employees with specific skill sets and select personnel suitable for projects and tasks. The sorting unit also has the function of automatically detecting and correcting duplicate or missing data using the generation AI. For example, if there are multiple different skill set data for the same employee, the generation AI will integrate these data and organize them into consistent data. In addition, if there is missing data, the generation AI will fill in the missing parts based on other related data. This enables the organizer to ensure the consistency and integrity of the data and assist the analyzer in performing accurate data analysis.

[0072] The analysis department analyzes the data organized by the organization department. Specifically, the analysis department uses the generation AI to analyze patterns and trends in the data to identify problems and trends. For example, to identify patterns of declining performance in a specific department, past performance evaluation data is analyzed to clarify the period and factors of declining performance. The generation AI can use time-series data analysis and regression analysis to identify the cause of declining performance and propose specific actions for improvement. In addition, to identify shortages in specific skill sets, the generation AI can analyze employee skill set data and predict current skill distribution and future skill needs. The generation AI can use clustering and classification algorithms to identify gaps in skill sets and propose necessary training programs and recruitment plans. In addition, the analysis department can also detect unusual patterns and abnormal data using anomaly detection algorithms. For example, if a specific employee's working hours suddenly increase, the generation AI detects the anomaly and issues an alert to investigate the cause. This allows the analysis department to quickly and accurately analyze patterns and trends in the data to identify problems and trends. In addition, the analysis department can utilize past data and statistical information to perform long-term risk assessments and trend analysis. This will enable companies to predict future risks and opportunities and take appropriate measures.

[0073] The presentation unit presents specific measures based on the problems and trends identified by the analysis unit. Specifically, the presentation unit can use the generation AI to propose specific measures such as introducing a training program for a specific skill set or reallocating resources to a specific department. For example, if a specific skill set is lacking, the generation AI proposes an optimal training program based on the employee's skill set data. The training program is provided in various formats, such as online courses, workshops, and in-house training. In addition, if performance is declining in a specific department, the generation AI proposes reallocating resources from other departments and improving business processes. The presentation unit presents these proposals as specific action plans and provides them in an executable form. In addition, the presentation unit builds a feedback loop to evaluate the effectiveness of the proposed measures and continuously improve them. For example, to evaluate the effectiveness of the training program, the employee's skill set data is regularly updated and changes in skill levels before and after training are analyzed. In addition, to evaluate the effectiveness of resource reallocation, changes in performance after reallocation are monitored based on performance evaluation data. This allows the presentation unit to not only propose specific measures, but also provide a mechanism for evaluating their effectiveness and continuously improving them.

[0074] The combination unit combines the actions presented by the presentation unit with the information of the human resources personnel. Specifically, the combination unit can use the generation AI to combine the presented actions with the experience and knowledge of the human resources personnel to formulate an optimal human resources strategy. For example, the generation AI incorporates the opinions and experience of the human resources personnel to formulate an optimal implementation plan based on the training program and resource reallocation action plan proposed by the presentation unit. The human resources personnel can customize the actions proposed by the generation AI, taking into account the individual situations of employees and the characteristics of the organization. Furthermore, the combination unit uses the generation AI to analyze the effects of past human resources strategies and formulate a more effective strategy based on success and failure cases. For example, the effect of past training programs is analyzed to identify the characteristics and conditions of successful programs. In addition, the effect of resource reallocation is analyzed to clarify the patterns and factors of successful reallocation. As a result, the combination unit can combine the actions proposed by the presentation unit with the knowledge of the human resources personnel to formulate and execute an optimal human resources strategy. Furthermore, the combination unit can use the generation AI to continuously monitor the effect of the formulated human resources strategy and modify the strategy as necessary. This enables the Combination Department to improve the accuracy and effectiveness of a company's human resources strategy and optimize the performance of the entire organization.

[0075] The collection unit can collect data on the employee's working hours, performance evaluation, and skill set. The collection unit, for example, acquires the employee's working hours from a digital time card system. For example, the collection unit acquires the employee's arrival time, departure time, and break time from the digital time card system and stores them as working hours data. The collection unit can also acquire performance evaluations from a personnel evaluation system. For example, the collection unit acquires the employee's performance evaluation score, evaluation comments, and evaluation period from the personnel evaluation system and stores them as performance evaluation data. Furthermore, the collection unit can acquire the skill set from the employee's self-reported data. For example, the collection unit acquires the employee's self-reported technical skills, soft skills, and qualification information and stores them as skill set data. This allows detailed analysis to be performed by collecting various data on employees. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the working hours data acquired from the digital time card system into the generation AI, and have the generation AI organize and analyze the data.

[0076] The sorting unit can automatically classify and organize the collected data. The sorting unit automatically classifies and organizes the data using the generation AI. For example, the sorting unit sorts the employee's working hours data by date. For example, the sorting unit sorts the employee's arrival time, departure time, and break time by date and saves them as working hours data. The sorting unit can also sort the performance evaluation data by evaluation score. For example, the sorting unit sorts the employee's performance evaluation scores in descending order, and associates and saves the evaluation comments and evaluation period. Furthermore, the sorting unit can classify the skill set data by skill category. For example, the sorting unit classifies technical skills, soft skills, and qualification information by category and saves them as skill set data. This enables efficient data management through automatic classification and organization of data. Part or all of the above-mentioned processing in the sorting unit is performed using the generation AI. For example, the sorting unit can input the collected data to the generation AI and have the generation AI classify and organize the data.

[0077] The analysis unit can analyze data patterns and trends to identify problems and trends. The analysis unit uses the generation AI to analyze data patterns and trends to identify problems and trends. For example, the analysis unit identifies a pattern of declining performance in a specific department. For example, the analysis unit can analyze performance evaluation data of a specific department to identify a pattern of declining performance. The analysis unit can also identify a shortage of a specific skill set. For example, the analysis unit can analyze employee skill set data to identify a shortage of a specific skill set. Furthermore, the analysis unit can analyze data trends to predict future problems and trends. For example, the analysis unit can predict future declines in performance and shortages of skill sets based on past data. This allows problems and trends to be quickly identified by analyzing data patterns and trends. Some or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input organized data into the generation AI and have the generation AI perform analysis of the data.

[0078] The presentation unit can present specific measures based on the identified problems and trends. The presentation unit presents specific measures based on the identified problems and trends using the generation AI. For example, the presentation unit proposes the introduction of a training program for a specific skill set. For example, the presentation unit can identify a shortage of a specific skill set and propose the introduction of a training program to compensate for the skill set. The presentation unit can also propose the reallocation of resources to a specific department. For example, the presentation unit can identify a decline in performance in a specific department and propose the reallocation of resources to the department. Furthermore, the presentation unit can also propose measures for future problems and trends based on data trends. For example, the presentation unit can predict a future decline in performance or a shortage of a skill set and propose measures for that. This makes it possible to respond quickly by presenting specific measures. Part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the identified problems and trends to the generation AI and cause the generation AI to execute a proposal for a specific measure.

[0079] The combination unit can combine the presented action with the information of the human resources staff. The combination unit uses the generation AI to combine the presented action with the information of the human resources staff. For example, the combination unit combines the presented training program introduction proposal with the experience and knowledge of the human resources staff. For example, the combination unit can combine the training program introduction proposal proposed by the generation AI with the experience and knowledge of the human resources staff to formulate an optimal training program. The combination unit can also combine the presented resource reallocation proposal with the information of the human resources staff. For example, the combination unit can combine the resource reallocation proposal proposed by the generation AI with the information of the human resources staff to formulate an optimal resource reallocation plan. Furthermore, the combination unit can combine actions for future problems and trends based on the data trend with the information of the human resources staff. For example, the combination unit can combine the actions for future problems and trends predicted by the generation AI with the information of the human resources staff to formulate an optimal human resources strategy. As a result, by combining with the information of the human resources staff, highly accurate data can be created. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input information about the proposed move and the human resources officer into the generation AI and have the generation AI execute the combination of information.

[0080] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The collection unit estimates the user's emotions using the generation AI and adjusts the timing of data collection based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. For example, the collection unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and adjust the frequency of data collection. In addition, when the user is relaxed, the collection unit can increase the frequency of data collection and collect detailed data. For example, the collection unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and adjust the frequency of data collection. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting only important data and quickly process it. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and adjust the priority of data collection. This makes it possible to reduce the burden on the user by adjusting the timing of data collection according to the user's emotions. The estimation of emotions is realized by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the timing of data collection.

[0081] The collection unit can analyze the past data collection history and select the optimal collection method. The collection unit uses the generation AI to analyze the past data collection history and select the optimal collection method. For example, the collection unit identifies and applies the most efficient collection method from the past data collection history. For example, the collection unit can analyze the past data collection history, evaluate the collection frequency and the efficiency of the collection means, and select the optimal collection method. The collection unit can also identify and optimize improvements to the collection method based on the past data collection history. For example, the collection unit can analyze the past data collection history, identify improvements to the collection method, and optimize the collection means and collection frequency. Furthermore, the collection unit can analyze the past data collection history, find patterns in the collection method, and select the optimal method. For example, the collection unit can analyze the past data collection history, identify patterns in the collection method, and select the optimal collection method. As a result, efficient data collection is possible by analyzing the past data collection history. Part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0082] The collection unit can perform filtering based on the user's current work situation and field of interest when collecting data. The collection unit performs filtering based on the user's current work situation and field of interest when collecting data using the generation AI. For example, the collection unit takes into account the user's current work situation and collects only highly relevant data. For example, the collection unit can analyze the user's work situation and preferentially collect highly relevant data. The collection unit can also preferentially collect necessary data based on the user's field of interest. For example, the collection unit can analyze the user's field of interest and preferentially collect necessary data. Furthermore, the collection unit can adjust the range of data collection according to the user's work situation and field of interest. For example, the collection unit can analyze the user's work situation and field of interest and adjust the range of data collection. This makes it possible to collect data according to the user's work situation and field of interest. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's work situation and field of interest to the generation AI and cause the generation AI to perform filtering of data collection.

[0083] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. The collection unit estimates the user's emotions using the generation AI and determines the priority of data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting only important data. For example, the collection unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and prioritize collecting important data. In addition, the collection unit can also prioritize collecting detailed data when the user is relaxed. For example, the collection unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and prioritize collecting detailed data. In addition, the collection unit can also prioritize collecting data that can be collected quickly when the user is in a hurry. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and prioritize collecting data that can be collected quickly. This enables efficient data collection by determining the priority of data according to the user's emotions. The estimation of emotions is realized by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit may input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of the data.

[0084] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user when collecting data. The collection unit preferentially collects highly relevant data in consideration of the geographical location information of the user when collecting data using the generation AI. For example, the collection unit preferentially collects highly relevant data based on the user's current location. For example, the collection unit can acquire the user's current location from GPS data and preferentially collect data related to the area. The collection unit can also collect highly relevant data with reference to the user's past location information. For example, the collection unit can analyze the user's past location information and preferentially collect data related to the area. Furthermore, the collection unit can also select an optimal data collection method in consideration of the user's geographical location information. For example, the collection unit can analyze the user's geographical location information and select an optimal data collection method. This makes it possible to collect data based on the user's geographical location information. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to execute the determination of the priority order of data collection.

[0085] The collection unit can analyze the user's social media activities and collect related data at the time of data collection. The collection unit uses the generation AI to analyze the user's social media activities and collect related data at the time of data collection. For example, the collection unit analyzes the user's social media activities and collects related data preferentially. For example, the collection unit can analyze the content of the user's social media posts and the number of likes, and collect related data preferentially. The collection unit can also collect necessary data based on the user's social media interests. For example, the collection unit can analyze the user's social media interests and collect necessary data preferentially. Furthermore, the collection unit can adjust the range of data collection with reference to the user's social media activities. For example, the collection unit can analyze the user's social media activities and adjust the range of data collection. This makes it possible to collect data based on the user's social media activities. A part or all of the above-mentioned processing in the collection unit is performed using the generation AI. For example, the collection unit can input the user's social media activities to the generation AI and cause the generation AI to execute the determination of the priority of data collection.

[0086] The organizing unit can estimate the user's emotion and adjust the data organizing method based on the estimated user's emotion. The organizing unit can estimate the user's emotion using the generation AI and adjust the data organizing method based on the estimated user's emotion. For example, when the user is feeling stressed, the organizing unit applies a simple organizing method to reduce the burden. For example, the organizing unit can take a picture of the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and apply a simple organizing method. In addition, when the user is relaxed, the organizing unit can apply a detailed organizing method to improve the accuracy of the data. For example, the organizing unit can record the user's voice, determine whether the user is relaxed using a voice analysis technology, and apply a detailed organizing method. In addition, the organizing unit can apply a method that can organize data quickly when the user is in a hurry. For example, the organizing unit can collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and apply a method that can organize data quickly. This makes it possible to organize data according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the organizing unit is performed using the generation AI. For example, the organizing unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the method of organizing the data.

[0087] The organizing unit can adjust the level of detail of the organizing based on the importance of the data when organizing the data. The organizing unit adjusts the level of detail of the organizing based on the importance of the data when organizing the data using the generation AI. For example, the organizing unit organizes important data in detail and simplifies data with low importance. For example, the organizing unit can evaluate the importance of the data, organize important data in detail, and simplify data with low importance. The organizing unit can also determine the priority of the organizing according to the importance of the data. For example, the organizing unit can evaluate the importance of the data and organize important data with priority. Furthermore, the organizing unit can organize data with high importance with priority, and efficiently manage data. For example, the organizing unit can evaluate the importance of the data and organize data with high importance with priority. This makes it possible to organize according to the importance of the data. A part or all of the above-mentioned processing in the organizing unit is performed using the generation AI. For example, the organizing unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the organizing.

[0088] The sorting unit can apply different sorting algorithms depending on the category of data when sorting data. The sorting unit applies different sorting algorithms depending on the category of data when sorting data using the generation AI. For example, the sorting unit selects an optimal sorting algorithm depending on the category of data. For example, the sorting unit can evaluate the category of data and select an optimal sorting algorithm. The sorting unit can also apply different sorting methods to each category to improve the accuracy of data. For example, the sorting unit can evaluate the category of data and apply different sorting methods to each category. Furthermore, the sorting unit can customize the sorting algorithm based on the category of data. For example, the sorting unit can evaluate the category of data and customize the sorting algorithm based on the category. This makes it possible to sort according to the category of data. A part or all of the above-mentioned processing in the sorting unit is performed using the generation AI. For example, the sorting unit can input the category of data to the generation AI and cause the generation AI to apply the sorting algorithm.

[0089] The organizing unit can estimate the user's emotion and adjust the data sorting order based on the estimated user's emotion. The organizing unit can estimate the user's emotion using the generation AI and adjust the data sorting order based on the estimated user's emotion. For example, when the user is feeling stressed, the organizing unit prioritizes organizing important data. For example, the organizing unit can take a picture of the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and prioritize organizing important data. In addition, the organizing unit can prioritize organizing detailed data when the user is relaxed. For example, the organizing unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and prioritize organizing detailed data. In addition, the organizing unit can prioritize organizing data that can be quickly organized when the user is in a hurry. For example, the organizing unit can collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and prioritize organizing data that can be quickly organized. This makes it possible to adjust the data sorting order according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the organizing unit is performed using the generation AI. For example, the organizing unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the order in which the data is organized.

[0090] The sorting unit can determine the priority of sorting based on the submission date of the data when sorting the data. The sorting unit determines the priority of sorting based on the submission date of the data when sorting the data using the generation AI. For example, the sorting unit prioritizes sorting the latest data and responds quickly. For example, the sorting unit can evaluate the submission date of the data and prioritize sorting the latest data. The sorting unit can also determine the priority of sorting based on the submission date. For example, the sorting unit can evaluate the submission date of the data and determine the priority of sorting based on the submission date. Furthermore, the sorting unit can postpone data submitted earlier and prioritize sorting the latest data. For example, the sorting unit can evaluate the submission date of the data, postpone data submitted earlier and prioritize sorting the latest data. This makes it possible to sort based on the submission date of the data. Part or all of the above-mentioned processing in the sorting unit is performed using the generation AI. For example, the sorting unit can input the submission date of the data to the generation AI and cause the generation AI to execute the determination of the priority of sorting.

[0091] The organizing unit can adjust the order of organizing based on the relevance of data when organizing data. The organizing unit adjusts the order of organizing based on the relevance of data when organizing data using the generation AI. For example, the organizing unit prioritizes organizing highly relevant data and efficiently manages it. For example, the organizing unit can evaluate the relevance of data and prioritize organizing highly relevant data. The organizing unit can also determine the order of organizing based on the relevance of data. For example, the organizing unit can evaluate the relevance of data and determine the order of organizing based on the relevance. Furthermore, the organizing unit can postpone data with low relevance and prioritize organizing important data. For example, the organizing unit can evaluate the relevance of data, postpone data with low relevance and prioritize organizing important data. This makes it possible to organize based on the relevance of data. A part or all of the above-mentioned processing in the organizing unit is performed using the generation AI. For example, the organizing unit can input the relevance of data to the generation AI and cause the generation AI to adjust the order of organizing.

[0092] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user's emotions. The analysis unit can estimate the user's emotions using the generation AI and adjust the analysis criteria based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit applies simple analysis criteria to reduce the burden. For example, the analysis unit can take a picture of the user's facial expression with a camera, use an emotion estimation algorithm to determine whether the user is feeling stressed, and apply simple analysis criteria. In addition, when the user is relaxed, the analysis unit can apply detailed analysis criteria to improve the accuracy of the data. For example, the analysis unit can record the user's voice, use voice analysis technology to determine whether the user is relaxed, and apply detailed analysis criteria. In addition, the analysis unit can apply criteria that can be analyzed quickly when the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor, use an emotion estimation algorithm to determine whether the user is in a hurry, and apply criteria that can be analyzed quickly. This makes it possible to adjust the analysis criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis criteria.

[0093] The analysis unit can improve the accuracy of the analysis by considering the interrelationships of data when analyzing data. The analysis unit can improve the accuracy of the analysis by considering the interrelationships of data when analyzing data using the generation AI. For example, the analysis unit can perform a highly accurate analysis by considering the interrelationships of data. For example, the analysis unit can evaluate the interrelationships of data and perform a highly accurate analysis. The analysis unit can also improve the accuracy of the analysis based on the interrelationships of data. For example, the analysis unit can evaluate the interrelationships of data and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the interrelationships of data and select an optimal analysis method. For example, the analysis unit can evaluate the interrelationships of data and select an optimal analysis method. This enables a highly accurate analysis that takes the interrelationships of data into consideration. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the interrelationships of data to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0094] The analysis unit can perform the analysis while considering the attribute information of the data submitter when analyzing the data. The analysis unit performs the analysis while considering the attribute information of the data submitter when analyzing the data using the generation AI. For example, the analysis unit performs a highly accurate analysis while considering the attribute information of the data submitter. For example, the analysis unit can evaluate the attribute information of the data submitter and perform a highly accurate analysis. The analysis unit can also improve the accuracy of the analysis based on the attribute information of the submitter. For example, the analysis unit can evaluate the attribute information of the data submitter and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the attribute information of the submitter and select the optimal analysis method. For example, the analysis unit can evaluate the attribute information of the data submitter and select the optimal analysis method. This enables a highly accurate analysis while considering the attribute information of the data submitter. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the attribute information of the data submitter to the generation AI and cause the generation AI to execute the improvement of the accuracy of the analysis.

[0095] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user's emotions. The analysis unit can estimate the user's emotions using a generation AI and adjust the display order of the analysis results based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can prioritize displaying important analysis results. For example, the analysis unit can take a picture of the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and prioritize displaying important analysis results. In addition, the analysis unit can also prioritize displaying detailed analysis results when the user is relaxed. For example, the analysis unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and prioritize displaying detailed analysis results. In addition, the analysis unit can also prioritize displaying analysis results that can be displayed quickly when the user is in a hurry. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and prioritize displaying analysis results that can be displayed quickly. This makes it possible to adjust the display order of analysis results according to the user's emotions. The estimation of emotions is realized by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the display order of the analysis results.

[0096] The analysis unit can perform the analysis while taking into consideration the geographical distribution of the data when analyzing the data. The analysis unit performs the analysis while taking into consideration the geographical distribution of the data when analyzing the data using the generation AI. For example, the analysis unit performs a highly accurate analysis while taking into consideration the geographical distribution of the data. For example, the analysis unit can evaluate the geographical distribution of the data and perform a highly accurate analysis. The analysis unit can also improve the accuracy of the analysis based on the geographical distribution. For example, the analysis unit can evaluate the geographical distribution of the data and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the geographical distribution and select an optimal analysis method. For example, the analysis unit can evaluate the geographical distribution of the data and select an optimal analysis method. This enables a highly accurate analysis that takes into consideration the geographical distribution of the data. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input the geographical distribution of the data to the generation AI and cause the generation AI to execute an improvement in the accuracy of the analysis.

[0097] The analysis unit can improve the accuracy of the analysis by referring to literature related to the data when analyzing the data. The analysis unit can improve the accuracy of the analysis by referring to literature related to the data when analyzing the data using the generation AI. For example, the analysis unit can perform a highly accurate analysis by referring to literature related to the data. For example, the analysis unit can evaluate literature related to the data and perform a highly accurate analysis. The analysis unit can also improve the accuracy of the analysis based on the related literature. For example, the analysis unit can evaluate literature related to the data and improve the accuracy of the analysis. Furthermore, the analysis unit can analyze the related literature and select an optimal analysis method. For example, the analysis unit can evaluate literature related to the data and select an optimal analysis method. This enables a highly accurate analysis by referring to literature related to the data. A part or all of the above-mentioned processing in the analysis unit is performed using the generation AI. For example, the analysis unit can input literature related to the data into the generation AI and cause the generation AI to perform an improvement in the accuracy of the analysis.

[0098] The presentation unit can estimate the user's emotions and adjust the presentation method of the move based on the estimated user's emotions. The presentation unit can estimate the user's emotions using a generation AI and adjust the presentation method of the move based on the estimated user's emotions. For example, when the user is feeling stressed, the presentation unit applies a simple presentation method to reduce the burden. For example, the presentation unit can take a picture of the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and apply a simple presentation method. In addition, when the user is relaxed, the presentation unit can apply a detailed presentation method to improve the accuracy of the data. For example, the presentation unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and apply a detailed presentation method. In addition, the presentation unit can apply a method that can quickly present the move when the user is in a hurry. For example, the presentation unit can collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and apply a method that can quickly present the move. This makes it possible to adjust the presentation method of the move according to the user's emotions. The estimation of emotions is realized by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the presentation method of the move.

[0099] The presentation unit can adjust the level of detail of the presentation based on the importance of the problem when presenting a move. The presentation unit adjusts the level of detail of the presentation based on the importance of the problem when presenting a move using the generation AI. For example, the presentation unit presents detailed moves for important problems to encourage a quick response. For example, the presentation unit can evaluate the importance of a problem and present detailed moves for important problems. The presentation unit can also present simplified moves for problems of low importance to efficiently respond. For example, the presentation unit can evaluate the importance of a problem and present simplified moves for problems of low importance. Furthermore, the presentation unit can adjust the level of detail of the presentation according to the importance of the problem and provide an optimal move. For example, the presentation unit can evaluate the importance of a problem and adjust the level of detail of the presentation according to the importance. This makes it possible to present a move according to the importance of the problem. A part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the importance of the problem to the generation AI and cause the generation AI to adjust the level of detail of the presentation.

[0100] The presentation unit can apply different presentation algorithms according to the category of the problem when presenting a move. The presentation unit applies different presentation algorithms according to the category of the problem when presenting a move using the generation AI. For example, the presentation unit selects an optimal presentation algorithm according to the category of the problem. For example, the presentation unit can evaluate the category of the problem and select an optimal presentation algorithm. The presentation unit can also apply different presentation methods for each category to improve the accuracy of the data. For example, the presentation unit can evaluate the category of the problem and apply different presentation methods for each category. Furthermore, the presentation unit can customize the presentation algorithm based on the category of the problem. For example, the presentation unit can evaluate the category of the problem and customize the presentation algorithm based on the category. This makes it possible to present an optimal move according to the category of the problem. A part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the category of the problem to the generation AI and cause the generation AI to execute the application of the presentation algorithm.

[0101] The presentation unit can estimate the user's emotions and determine the priority of moves based on the estimated user's emotions. The presentation unit can estimate the user's emotions using a generation AI and determine the priority of moves based on the estimated user's emotions. For example, if the user is feeling stressed, the presentation unit preferentially presents important moves. For example, the presentation unit can take a picture of the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and preferentially present important moves. In addition, the presentation unit can also preferentially present detailed moves when the user is relaxed. For example, the presentation unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and preferentially present detailed moves. Furthermore, the presentation unit can also preferentially present moves that can be presented quickly when the user is in a hurry. For example, the presentation unit can collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and preferentially present moves that can be presented quickly. This makes it possible to determine the priority of moves according to the user's emotions. The estimation of emotions is realized by using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the user's emotion data to the generation AI and cause the generation AI to determine the priority order of moves.

[0102] The presentation unit can determine the priority of presentation based on the submission time of the problem when presenting a move. The presentation unit uses the generation AI to determine the priority of presentation based on the submission time of the problem when presenting a move. For example, the presentation unit presents the latest problem with priority and responds quickly. For example, the presentation unit can evaluate the submission date of the problem and present the latest problem with priority. The presentation unit can also determine the priority of presentation based on the submission time. For example, the presentation unit can evaluate the submission date of the problem and determine the priority of presentation based on the submission time. Furthermore, the presentation unit can postpone the problem with an older submission time and present the latest problem with priority. For example, the presentation unit can evaluate the submission date of the problem, postpone the problem with an older submission time and present the latest problem with priority. This makes it possible to present a move based on the submission time of the problem. Part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the submission date of the problem to the generation AI and cause the generation AI to execute the determination of the priority of presentation.

[0103] The presentation unit can adjust the order of presentation based on the relevance of the problems when presenting moves. The presentation unit adjusts the order of presentation based on the relevance of the problems when presenting moves using the generation AI. For example, the presentation unit preferentially presents highly relevant problems and efficiently responds. For example, the presentation unit can evaluate the relevance of the problems and preferentially present highly relevant problems. The presentation unit can also determine the order of presentation based on the relevance of the problems. For example, the presentation unit can evaluate the relevance of the problems and determine the order of presentation based on the relevance. Furthermore, the presentation unit can postpone problems with low relevance and preferentially present important problems. For example, the presentation unit can evaluate the relevance of the problems, postpone problems with low relevance, and preferentially present important problems. This makes it possible to present moves based on the relevance of the problems. A part or all of the above-mentioned processing in the presentation unit is performed using the generation AI. For example, the presentation unit can input the relevance of the problems to the generation AI and cause the generation AI to adjust the order of presentation.

[0104] The combination unit can estimate the user's emotion and adjust the information combination method based on the estimated user's emotion. The combination unit can estimate the user's emotion using the generation AI and adjust the information combination method based on the estimated user's emotion. For example, when the user is feeling stressed, the combination unit applies a simple combination method to reduce the burden. For example, the combination unit can take a picture of the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and apply a simple combination method. In addition, when the user is relaxed, the combination unit can apply a detailed combination method to improve the accuracy of the data. For example, the combination unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and apply a detailed combination method. In addition, the combination unit can apply a method that can be quickly combined when the user is in a hurry. For example, the combination unit can collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and apply a method that can be quickly combined. This makes it possible to adjust the information combination method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processes in the combination unit are performed using the generation AI. For example, the combination unit may input the user's emotion data to the generation AI and cause the generation AI to adjust the method of combining information.

[0105] The combination unit can select the optimal combination method by referring to the user's past information usage history when combining information. The combination unit selects the optimal combination method by referring to the user's past information usage history when combining information using the generation AI. For example, the combination unit selects the optimal combination method based on the user's past information usage history. For example, the combination unit can analyze the user's past information usage history and select the optimal combination method. The combination unit can also identify improvements to the combination method and optimize it by referring to the past usage history. For example, the combination unit can analyze the user's past information usage history and identify improvements to the combination method and optimize it. Furthermore, the combination unit can analyze the user's past information usage history and select the most efficient combination method. For example, the combination unit can analyze the user's past information usage history and select the most efficient combination method. This makes it possible to combine information optimally based on the user's past information usage history. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input the user's past information usage history into the generation AI and cause the generation AI to select the optimal combination method.

[0106] The combination unit can customize the combination means based on the user's current work situation when combining information. The combination unit customizes the combination means based on the user's current work situation when combining information using the generation AI. For example, the combination unit selects the optimal combination means in consideration of the user's current work situation. For example, the combination unit can evaluate the user's work situation and select the optimal combination means. The combination unit can also customize the combination means according to the work situation. For example, the combination unit can evaluate the user's work situation and customize the combination means according to the work situation. Furthermore, the combination unit can adjust the range of the combination means based on the user's work situation. For example, the combination unit can evaluate the user's work situation and adjust the range of the combination means based on the work situation. This makes it possible to combine information according to the user's current work situation. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input the user's work situation to the generation AI and cause the generation AI to execute customization of the combination means.

[0107] The combination unit can estimate the user's emotion and determine the priority of information combination based on the estimated user's emotion. The combination unit can estimate the user's emotion using the generation AI and determine the priority of information combination based on the estimated user's emotion. For example, the combination unit preferentially combines important information when the user is feeling stressed. For example, the combination unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and preferentially combine important information. In addition, the combination unit can preferentially combine detailed information when the user is relaxed. For example, the combination unit can record the user's voice, determine whether the user is relaxed using voice analysis technology, and preferentially combine detailed information. In addition, the combination unit can preferentially combine information that can be quickly combined when the user is in a hurry. For example, the combination unit can collect the user's biometric data (heart rate and skin electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and preferentially combine information that can be quickly combined. This makes it possible to determine the priority of information combination according to the user's emotion. Emotion estimation is realized using an emotion estimation function using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. A part or all of the above-mentioned processes in the combination unit are performed using the generation AI. For example, the combination unit may input user emotion data to the generation AI and cause the generation AI to determine the priority order of information combinations.

[0108] The combination unit can select the optimal combination method in consideration of the geographical location information of the user when combining information. The combination unit selects the optimal combination method in consideration of the geographical location information of the user when combining information using the generation AI. For example, the combination unit selects the optimal combination method based on the user's current location. For example, the combination unit can acquire the user's current location from GPS data and combine information related to the area with priority. The combination unit can also select the optimal combination method with reference to the user's past location information. For example, the combination unit can analyze the user's past location information and combine information related to the area with priority. Furthermore, the combination unit can also select the optimal combination means in consideration of the user's geographical location information. For example, the combination unit can analyze the user's geographical location information and select the optimal combination means. This makes it possible to combine the optimal information based on the user's geographical location information. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input the user's geographical location information to the generation AI and cause the generation AI to select the optimal combination method.

[0109] The combination unit can analyze the user's social media activity and propose a combination means when combining information. The combination unit uses the generation AI to analyze the user's social media activity and propose a combination means when combining information. For example, the combination unit analyzes the user's social media activity and proposes an optimal combination means. For example, the combination unit can analyze the user's social media posting content and the number of likes and propose an optimal combination means. The combination unit can also propose an optimal combination means based on the user's social media interests. For example, the combination unit can analyze the user's social media interests and propose an optimal combination means. Furthermore, the combination unit can adjust the range of combination means by referring to the user's social media activities. For example, the combination unit can analyze the user's social media activities and adjust the range of combination means. This makes it possible to combine information optimally based on the user's social media activities. A part or all of the above-mentioned processing in the combination unit is performed using the generation AI. For example, the combination unit can input the user's social media activities to the generation AI and cause the generation AI to execute the proposal of combination means.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] The data analysis system may further include a prediction unit. The prediction unit may predict future problems and trends based on collected data and past trends. For example, the prediction unit may predict the possibility that a decline in performance in a particular department will continue and take measures early. The prediction unit may also predict the possibility that the demand for a particular skill set will increase and prepare the necessary training programs in advance. Furthermore, the prediction unit may predict the risk of employee turnover and take appropriate measures. This makes it possible to identify future problems and trends in advance and respond quickly.

[0112] The collection unit can further include a function for collecting user feedback. For example, the collection unit can collect employees' satisfaction and dissatisfaction with their daily work in the form of a questionnaire. The collection unit can also collect evaluations of training programs that employees have participated in and measure their effectiveness. Furthermore, the collection unit can collect improvements and new ideas proposed by employees and use them to improve the entire organization. This makes it possible to collect data that reflects the voices of employees, and to formulate a more practical personnel strategy.

[0113] The sorting unit may further include a function for detecting and deleting duplicate data. For example, the sorting unit may detect multiple data entries related to the same employee and delete the duplicates. The sorting unit may also check the consistency of data obtained from different systems to create a consistent data set. Furthermore, the sorting unit may include a function for complementing missing values ​​in the data to ensure the completeness of the data. This improves the quality of the data and enables accurate analysis.

[0114] The analysis department can further include a function for detecting anomalies in data. For example, if there is an abnormal pattern in the data on employees' working hours, the analysis department can detect it and issue an alert. In addition, if there is an unnatural fluctuation in the performance evaluation data, the analysis department can detect it and investigate the cause. Furthermore, if there is a sudden increase or decrease in a specific skill in the skill set data, the analysis department can detect it and take appropriate measures. This makes it possible to discover anomalies in the data early and respond quickly.

[0115] The presentation unit may further include a visualization function. For example, the presentation unit may visually display the results of the data analysis as graphs or charts. The presentation unit may also highlight and visually emphasize specific problems or trends. Furthermore, the presentation unit may display data trends using animation to enable an intuitive understanding of dynamically changing data. This makes it possible to present the results of the data analysis in a visually easy-to-understand manner and support rapid decision-making.

[0116] The combination unit can further estimate the user's emotion and adjust the information combination method based on the estimated user's emotion. For example, when the user is feeling stressed, the combination unit applies a simple combination method to reduce the burden. For example, the combination unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and apply the simple combination method. In addition, when the user is relaxed, the combination unit can apply a detailed combination method to improve the accuracy of the data. For example, the combination unit can record the user's voice, determine whether the user is relaxed using a voice analysis technology, and apply the detailed combination method. In addition, when the user is in a hurry, the combination unit can also apply a method that can quickly combine the information. For example, the combination unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and apply a method that can quickly combine the information. This makes it possible to adjust the information combination method according to the user's emotion.

[0117] The collection unit can further estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. For example, the collection unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and adjust the frequency of data collection. In addition, when the user is relaxed, the collection unit can increase the frequency of data collection and collect detailed data. For example, the collection unit can record the user's voice, determine whether the user is relaxed using a voice analysis technology, and adjust the frequency of data collection. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting only important data and quickly process it. For example, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and adjust the priority of data collection. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotion.

[0118] The organizing unit can further estimate the user's emotion and adjust the data organizing method based on the estimated user's emotion. For example, when the user is feeling stressed, the organizing unit applies a simple organizing method to reduce the burden on the user. For example, the organizing unit can take a picture of the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and apply the simple organizing method. In addition, when the user is relaxed, the organizing unit can apply a detailed organizing method to improve the accuracy of the data. For example, the organizing unit can record the user's voice, determine whether the user is relaxed using a voice analysis technology, and apply the detailed organizing method. In addition, when the user is in a hurry, the organizing unit can apply a method that can organize the data quickly. For example, the organizing unit can collect the user's biological data (heart rate and skin electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and apply a method that can organize the data quickly. This makes it possible to organize data according to the user's emotion.

[0119] The analysis unit can further estimate the user's emotion and adjust the analysis criteria based on the estimated user's emotion. For example, when the user is feeling stressed, the analysis unit applies simple analysis criteria to reduce the burden. For example, the analysis unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and apply simple analysis criteria. In addition, when the user is relaxed, the analysis unit can apply detailed analysis criteria to improve the accuracy of the data. For example, the analysis unit can record the user's voice, determine whether the user is relaxed using a voice analysis technology, and apply detailed analysis criteria. In addition, when the user is in a hurry, the analysis unit can apply criteria that can be analyzed quickly. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and apply criteria that can be analyzed quickly. This makes it possible to adjust the analysis criteria according to the user's emotion.

[0120] The presentation unit can further estimate the user's emotion and adjust the presentation method of the move based on the estimated user's emotion. For example, when the user is feeling stressed, the presentation unit applies a simple presentation method to reduce the burden. For example, the presentation unit can capture the user's facial expression with a camera, determine whether the user is feeling stressed using an emotion estimation algorithm, and apply a simple presentation method. In addition, when the user is relaxed, the presentation unit can apply a detailed presentation method to improve the accuracy of the data. For example, the presentation unit can record the user's voice, determine whether the user is relaxed using a voice analysis technology, and apply a detailed presentation method. In addition, the presentation unit can apply a method that can be presented quickly when the user is in a hurry. For example, the presentation unit can collect the user's biological data (heart rate and skin electrodermal activity) with a sensor, determine whether the user is in a hurry using an emotion estimation algorithm, and apply a method that can be presented quickly. This makes it possible to adjust the presentation method of the move according to the user's emotion.

[0121] The process flow of the second embodiment will be briefly described below.

[0122] Step 1: The collection unit collects data using the generation AI. For example, data such as employees' working hours, performance evaluations, and skill sets are collected. Specifically, working hours are obtained from a digital time card system, performance evaluations are obtained from a personnel evaluation system, and skill sets are obtained from employees' self-reported data. Step 2: The sorting unit sorts the data collected by the collection unit. It uses generative AI to automatically classify and sort the data. For example, it sorts working hours data by date, sorts performance evaluation data by evaluation score, and classifies skill set data by skill category. Step 3: The analysis department analyzes the data organized by the organization department. Generative AI is used to analyze patterns and trends in the data to identify problems and trends. For example, it identifies patterns of declining performance in a particular department or a shortage of a particular skill set. Step 4: The proposal unit proposes specific actions based on the problems and trends identified by the analysis unit. Using generative AI, it suggests specific actions such as introducing a training program for a specific skill set or reallocating resources to a specific department. Step 5: The combination unit combines the actions presented by the presentation unit with the information of the HR personnel. Using a generation AI, the proposed actions are combined with the experience and knowledge of the HR personnel to formulate an optimal HR strategy.

[0123] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0124] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0125] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements including the above-mentioned collection unit, sorting unit, analysis unit, presentation unit, and combination unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart device 14, and collects data such as employees' working hours, performance evaluations, and skill sets. The sorting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically classifies and organizes the collected data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes patterns and trends of data to identify problems and tendencies. The presentation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes specific measures. The combination unit is realized, for example, by the control unit 46A of the smart device 14, and combines the presented measures with information on the personnel personnel to formulate an optimal personnel strategy. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0130] 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.

[0131] 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 the voice according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0133] 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 performed in a secure state.

[0134] Fig. 4 shows an example of 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.

[0135] 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.

[0136] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0139] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0140] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0141] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements including the above-mentioned collection unit, sorting unit, analysis unit, presentation unit, and combination unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the smart glasses 214, and collects data such as employees' working hours, performance evaluations, and skill sets. The sorting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically classifies and organizes the collected data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes patterns and trends of data to identify problems and tendencies. The presentation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes specific measures. The combination unit is realized, for example, by the control unit 46A of the smart glasses 214, and combines the presented measures with information on the personnel personnel to formulate an optimal personnel strategy. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0146] 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.

[0147] 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 the voice according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0149] 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 performed in a secure state.

[0150] 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.

[0151] 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.

[0152] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0153] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0154] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0155] 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 voice indicating a user input for 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.

[0156] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0157] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements including the above-mentioned collection unit, sorting unit, analysis unit, presentation unit, and combination unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the headset type terminal 314, and collects data such as employees' working hours, performance evaluations, and skill sets. The sorting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically classifies and organizes the collected data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes patterns and trends of data to identify problems and tendencies. The presentation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes specific measures. The combination unit is realized, for example, by the control unit 46A of the headset type terminal 314, and combines the presented measures with information on the personnel personnel to formulate an optimal personnel strategy. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0162] 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.

[0163] 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 the voice according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0165] 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 performed in a secure state.

[0166] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0167] 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.

[0168] 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.

[0169] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0170] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0171] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0172] 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 a voice indicating a user input for 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.

[0173] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0174] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0175] Each of the multiple elements including the above-mentioned collection unit, sorting unit, analysis unit, presentation unit, and combination unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the computer 36 of the robot 414, and collects data such as employees' working hours, performance evaluations, and skill sets. The sorting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically classifies and organizes the collected data. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes patterns and trends of data to identify problems and tendencies. The presentation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and proposes specific measures. The combination unit is realized, for example, by the control unit 46A of the robot 414, and combines the presented measures with information on the personnel manager to formulate an optimal personnel strategy. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0176] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0177] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0178] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0179] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0180] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0181] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0183] 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.

[0184] 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 Universal Serial Bus (USB) 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.

[0185] In addition, 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 upon request from the data processing device 12.

[0186] 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.

[0187] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0188] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0189] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0190] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0191] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0192] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0193] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0194] (Appendix 1) A collection unit that collects data; A data organizer that organizes the data collected by the data collector; an analysis unit that analyzes the data organized by the organization unit; A suggestion unit that suggests a strategy based on the problem or tendency identified by the analysis unit; A combination unit is provided that combines the moves presented by the presentation unit with information on personnel personnel. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on employee hours, performance reviews, and skill sets 2. The system of claim 1, (Appendix 3) The sorting unit includes: Automatically classify and organize collected data 2. The system of claim 1, (Appendix 4) The analysis unit includes: Analyze data for patterns and trends to identify issues and trends 2. The system of claim 1, (Appendix 5) The presentation unit is Suggest specific actions based on identified issues and trends 2. The system of claim 1, (Appendix 6) The combination unit includes: Combine the proposed action with information from the HR manager 2. The system of claim 1, (Appendix 7) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze past data collection history and select the optimal collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: Filter data as it is collected based on the user's current work situation and interests 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 13) The sorting unit includes: Infer user sentiment and adjust how data is organized based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The sorting unit includes: When organizing data, adjust the level of organization based on the importance of the data 2. The system of claim 1, (Appendix 15) The sorting unit includes: When reducing data, apply different reduction algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The sorting unit includes: Infer user sentiment and adjust the data sorting order based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The sorting unit includes: When organizing data, prioritize organizing based on when the data was submitted. 2. The system of claim 1, (Appendix 18) The sorting unit includes: When organizing data, adjust the order of organization based on the relevance of the data 2. The system of claim 1, (Appendix 19) The analysis unit includes: Infer user sentiment and adjust analysis criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 20) The analysis unit includes: When analyzing data, take into account the correlation between data and improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 21) The analysis unit includes: When analyzing data, consider the attribute information of the data submitter. 2. The system of claim 1, (Appendix 22) The analysis unit includes: Infer user sentiment and adjust the display order of analysis results based on the inferred user sentiment 2. The system of claim 1, (Appendix 23) The analysis unit includes: When analyzing data, take into account the geographical distribution of the data. 2. The system of claim 1, (Appendix 24) The analysis unit includes: When analyzing data, refer to related literature to improve the accuracy of the analysis. 2. The system of claim 1, (Appendix 25) The presentation unit is Estimate the user's emotions and adjust the way the moves are presented based on the estimated user emotions 2. The system of claim 1, (Appendix 26) The presentation unit is When presenting a solution, adjust the level of detail based on the importance of the problem 2. The system of claim 1, (Appendix 27) The presentation unit is When suggesting moves, apply different suggestion algorithms depending on the problem category 2. The system of claim 1, (Appendix 28) The presentation unit is Estimate the user's emotions and prioritize moves based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The presentation unit is When presenting solutions, prioritize proposals based on when the issues were submitted 2. The system of claim 1, (Appendix 30) The presentation unit is When presenting moves, adjust the order of presentation based on the relevance of the problem 2. The system of claim 1, (Appendix 31) The combination unit includes: Inferring user emotions and adjusting the information combination method based on the estimated user emotions 2. The system of claim 1, (Appendix 32) The combination unit includes: When combining information, the optimal combination method is selected by referring to the user's past information usage history. 2. The system of claim 1, (Appendix 33) The combination unit includes: When combining information, customize the combination method based on the user's current work situation. 2. The system of claim 1, (Appendix 34) The combination unit includes: Estimate user's emotions and determine the priority of information combinations based on the estimated user's emotions 2. The system of claim 1, (Appendix 35) The combination unit includes: When combining information, the optimal combination method is selected taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 36) The combination unit includes: When combining information, the system analyzes users' social media activity to suggest combination methods. 2. The system of claim 1, [Explanation of symbols]

[0195] 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 unit that collects data; A data organizer that organizes the data collected by the data collector; an analysis unit that analyzes the data organized by the organization unit; A suggestion unit that suggests a strategy based on the problem or tendency identified by the analysis unit; A combination unit is provided that combines the moves presented by the presentation unit with information on personnel personnel. A system characterized by:

2. The collecting unit includes: Collect data on employee hours, performance reviews, and skill sets 2. The system of claim 1 .

3. The sorting unit includes: Automatically classify and organize collected data 2. The system of claim 1 .

4. The analysis unit includes: Analyze data for patterns and trends to identify issues and trends 2. The system of claim 1 .

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

6. The collecting unit includes: Analyze past data collection history and select the optimal collection method 2. The system of claim 1 .

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