System

Abstract

An object of a system according to an embodiment is to analyze a work flow of employees, extract work that can be handled in response to an AI, and appropriately assign an reduced working hours holder.SOLUTION: A system according to an embodiment includes a workflow analysis unit, a work extraction unit, an assignment unit, and a cooperation unit. The workflow analysis unit analyzes a workflow of an employee. The business extraction unit extracts a business whose AI can be coped with from the business analyzed by the business flow analysis unit. The assignment unit assigns a reduced working hours holder to a roler having a large task weight extracted by the task extraction unit. The cooperation unit performs cooperation between the business hours of the reduced working hours holder and the AI response.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately analyze employees' work flows efficiently, extract tasks that can be handled by AI, and assign them appropriately to part-time workers, so there is room for improvement in improving work efficiency and employee satisfaction.

[0005] The system of the embodiment aims to analyze employees' work flow, extract tasks that can be handled by AI, and appropriately assign them to employees working reduced hours. [Means for solving the problem]

[0006] The system according to the embodiment comprises a workflow analysis unit, a task extraction unit, an assignment unit, and a collaboration unit. The workflow analysis unit analyzes the workflow of employees. The task extraction unit extracts tasks that can be handled by AI from the tasks analyzed by the workflow analysis unit. The assignment unit assigns reduced-hours workers to roles with a high workload extracted by the task extraction unit. The collaboration unit coordinates the working hours of reduced-hours workers with AI support. [Effects of the Invention]

[0007] The system of the embodiment can analyze employees' work flow, extract tasks that can be handled by AI, and appropriately assign them to employees working reduced hours. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's 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 read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The reduced-hours system according to an embodiment of the present invention uses AI to analyze employee workflows, extract tasks that can be handled by AI, and effectively allocate reduced-hours workers. This allows the reduced-hours system to improve employee productivity.

[0029] The reduced-hours system according to the embodiment includes a workflow analysis unit, a task extraction unit, an assignment unit, and a collaboration unit. The workflow analysis unit analyzes an employee's workflow. For example, the workflow analysis unit collects data on employees' daily tasks and project work, and analyzes the data using AI. The workflow analysis unit can also analyze the type, frequency, and duration of tasks to evaluate the efficiency of the tasks. The task extraction unit extracts tasks that can be handled by AI from the tasks analyzed by the workflow analysis unit. For example, the task extraction unit extracts routine work such as data entry and standard report creation. The task extraction unit can also prioritize the extraction of tasks that can be handled by AI. The assignment unit assigns reduced-hours employees to roles with a high workload extracted by the task extraction unit. For example, the assignment unit assigns reduced-hours employees to employees with a high workload. The assignment unit can also effectively assign reduced-hours employees to distribute the work load. The collaboration unit coordinates the working hours of reduced-hours employees with AI support. For example, the collaboration department allows AI to take over the work previously performed by part-time workers, and handle it at night or on holidays. The collaboration department can also check the status of the work that part-time workers handled by AI on the next business day and follow up as necessary. This allows the part-time work system according to the embodiment to improve employee productivity. For example, even if part-time workers find it difficult to work full-time due to family circumstances, they can still work more easily by choosing part-time work. Furthermore, by analyzing the workflow, work can be prevented from becoming too dependent on individual employees, and work can be leveled out.

[0030] The workflow analysis unit can use employees' past performance data to evaluate the efficiency of work and propose optimal workflows. For example, the workflow analysis unit collects employees' past performance data and uses AI to analyze that data to evaluate work efficiency. For example, the evaluation is based on the time it takes to complete a task or the error rate. The workflow analysis unit also proposes optimal workflows. For example, it can propose procedures and methods for improving work efficiency. This makes it possible to utilize employees' past performance data to improve work efficiency.

[0031] The business flow analysis unit can take into account the interdependencies between tasks and make proposals to optimize the linkage of tasks. The business flow analysis unit, for example, performs analysis taking into account the interdependencies between tasks. For example, it analyzes the data flow between tasks and the dependencies between tasks. The business flow analysis unit also makes proposals to optimize the linkage of tasks. For example, it can propose procedures and methods for smoothing the linkage of tasks. This makes it possible to improve the linkage of tasks by taking into account the interdependencies between tasks.

[0032] The workflow analysis unit can apply the results of the analyzed workflow to different industries and departments, and develop it as a general-purpose business improvement tool. For example, the workflow analysis unit develops a general-purpose business improvement tool to apply the results of the workflow analyzed by AI to different industries and departments. For example, it provides tools that correspond to different industries such as manufacturing and services. The workflow analysis unit also applies the results of the workflow to other departments. For example, it can achieve similar business improvements in different departments such as the sales department and the human resources department. This allows the results of the workflow analysis to be developed as a general-purpose business improvement tool and applied to different industries and departments.

[0033] The workflow analysis unit can use the results of the analyzed workflow to match employee skills and achieve optimal personnel placement. For example, the workflow analysis unit develops a system that matches employee skills based on the results of workflow analyzed by AI. For example, it compares the work content with employee skill sets and proposes optimal personnel placement. The workflow analysis unit also optimizes employee placement based on the results of skill matching. For example, it can prioritize the placement of employees with specific skills. This makes it possible to use the results of workflow analysis to match employee skills and achieve optimal personnel placement.

[0034] The assignment department can consider employees' skill sets when analyzing work weights and assign the most suitable shortened-hours worker. For example, the assignment department develops a system that considers employees' skill sets when AI analyzes work weights and assigns the most suitable shortened-hours worker. For example, employees with specific skills are prioritized for assignment. The assignment department also effectively assigns shortened-hours workers based on their skill sets. For example, by assigning employees with skills appropriate to the work content, work efficiency can be improved. This makes it possible to improve work efficiency by considering employees' skill sets and assigning the most suitable shortened-hours worker.

[0035] When analyzing work weights, the assignment department can make flexible assignments by taking into account seasonal fluctuations in work and the progress of projects. For example, when AI analyzes work weights, the assignment department develops a system that makes flexible assignments by taking into account seasonal fluctuations in work. For example, by allocating employees working reduced hours depending on busy and slow seasons. The assignment department also makes assignments by taking into account the progress of projects. For example, it can appropriately allocate necessary resources depending on the progress of a project. This makes it possible to make flexible assignments by taking into account seasonal fluctuations in work and the progress of a project, thereby improving work efficiency.

[0036] The Assignment Department can apply the results of the task weight analysis to other departments and projects to optimize resources across the company. For example, the Assignment Department can develop a system to apply to other departments and projects based on the results of AI's task weight analysis. For example, the Assignment Department can share task weight data to optimize resources across the company. The Assignment Department can also achieve resource optimization in other departments and projects. For example, it can efficiently allocate resources between different departments. This allows the results of the task weight analysis to be applied to other departments and projects to optimize resources across the company, thereby improving business efficiency.

[0037] When taking over the work of a part-time worker, the Collaboration Department can monitor the progress of the work in real time and issue alerts as necessary. For example, the Collaboration Department will develop a system that monitors the progress of work in real time when AI takes over the work of a part-time worker. For example, it will issue an alert if the progress of the work is behind schedule. The Collaboration Department will also provide necessary support depending on the progress. For example, it can allocate additional resources to work that is behind schedule. In this way, by monitoring the progress of work in real time and issuing alerts as necessary, it is possible to prevent work delays and support efficient work execution.

[0038] When taking over the work of a part-time worker, the Collaboration Department can evaluate the quality of the work and provide feedback on areas for improvement. For example, the Collaboration Department could develop a system that evaluates the quality of work when AI takes over the work of a part-time worker. For example, the quality could be evaluated based on the degree of completion of the work and the error rate. The Collaboration Department could also provide feedback on areas for improvement based on the results of the quality evaluation. For example, it could make specific suggestions for improving work procedures and methods. In this way, by evaluating the quality of work and providing feedback on areas for improvement, the quality of work can be improved.

[0039] The Collaboration Department can apply the collaboration between reduced-hours workers and AI to other business processes and departments, thereby improving business efficiency across the company. For example, the Collaboration Department can develop a system that applies the collaboration between reduced-hours workers and AI to other business processes and departments. For example, it can realize similar collaboration in different departments, such as the sales department and the human resources department. The Collaboration Department can also apply the collaboration between reduced-hours workers and AI to other business processes. For example, it can improve efficiency in different business processes, such as project management and customer support. This allows the collaboration between reduced-hours workers and AI to be applied to other business processes and departments, improving business efficiency across the company, thereby improving productivity across the company.

[0040] The Collaboration Department can support flexible working styles by combining collaboration between reduced-hours workers and AI with remote work and flextime systems. For example, the Collaboration Department will develop a system that combines collaboration between reduced-hours workers and AI with remote work and flextime systems. For example, AI will support work during remote work. The Collaboration Department will also apply collaboration between reduced-hours workers and AI to flextime systems. For example, AI can handle work outside of core hours, making it possible to achieve flexible working styles. In this way, collaboration between reduced-hours workers and AI can be combined with remote work and flextime systems to support flexible working styles, improving the ease of working for employees.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The workflow analysis unit analyzes employee health data, identifies tasks that pose a high health risk, and prioritizes these as tasks that can be handled by AI. For example, employee health data can be collected and AI can analyze the data to identify tasks that pose a high health risk. For example, tasks that may have a negative impact on health, such as long hours of desk work or heavy labor, can be extracted. The workflow analysis unit can also protect employee health by having AI handle tasks that pose a high health risk. This reduces employee health risks and improves work efficiency.

[0043] The workflow analysis unit can use employees' past performance data to evaluate work efficiency and propose optimal workflows. For example, employees' past performance data can be collected and AI can analyze that data to evaluate work efficiency. For example, the evaluation can be based on the time it takes to complete a task or the error rate. The workflow analysis unit can also propose optimal workflows. For example, it can propose procedures and methods for improving work efficiency. This makes it possible to utilize employees' past performance data to improve work efficiency.

[0044] The workflow analysis unit can apply the results of the analyzed workflow to different industries and departments, and develop it as a general-purpose business improvement tool. For example, a general-purpose business improvement tool can be developed to apply the results of the workflow analyzed by AI to different industries and departments. For example, tools corresponding to different industries such as manufacturing and service can be provided. The workflow analysis unit also applies the results of the workflow to other departments. For example, similar business improvements can be achieved in different departments such as the sales department and the human resources department. This allows the results of workflow analysis to be developed as a general-purpose business improvement tool and applied to different industries and departments.

[0045] The assignment department can assign the most suitable short-hours worker by taking into account the skill set of the employee. For example, when AI analyzes work weights, a system can be developed that takes into account the skill set of the employee and assigns the most suitable short-hours worker. For example, employees with specific skills can be prioritized for assignment. The assignment department can also effectively assign short-hours workers based on their skill set. For example, by assigning employees with skills appropriate to the work content, work efficiency can be improved. This allows work efficiency to be improved by taking into account the skill set of the employee and assigning the most suitable short-hours worker.

[0046] When analyzing work loads, the assignment department can make flexible assignments by taking into account seasonal fluctuations in work and the progress of projects. For example, a system can be developed in which, when AI analyzes work loads, flexible assignments are made by taking into account seasonal fluctuations in work. For example, employees working reduced hours can be assigned depending on busy and slow periods. The assignment department can also make assignments by taking into account the progress of projects. For example, the necessary resources can be appropriately assigned depending on the progress of the project. This allows for flexible assignments that take into account seasonal fluctuations in work and the progress of projects, thereby improving work efficiency.

[0047] When taking over the work of a part-time worker, the Collaboration Department can monitor the progress of the work in real time and issue alerts as necessary. For example, a system will be developed in which AI monitors the progress of work in real time when taking over the work of a part-time worker. For example, an alert will be issued if the progress of the work is behind schedule. The Collaboration Department will also provide necessary support depending on the progress. For example, additional resources can be allocated to work that is behind schedule. This will prevent work delays and support efficient work execution by monitoring the progress of work in real time and issuing alerts as necessary.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The workflow analysis unit analyzes employees' workflow. For example, the workflow analysis unit collects data on employees' daily work and project work, and the AI ​​analyzes that data. The workflow analysis unit can also analyze the type, frequency, and duration of work to evaluate the efficiency of the work. Step 2: The task extraction unit extracts tasks that can be handled by AI from the tasks analyzed by the workflow analysis unit. For example, the task extraction unit extracts routine work such as data entry and standard report creation. The task extraction unit can also prioritize the extraction of tasks that can be handled by AI. Step 3: The assignment department assigns reduced-hours workers to the roles with high workloads extracted by the task extraction department. For example, the assignment department assigns reduced-hours workers to employees with a heavy workload. The assignment department can also effectively assign reduced-hours workers to distribute the workload. Step 4: The coordination department coordinates the working hours of employees working reduced hours with the AI ​​response. For example, the coordination department allows the AI ​​to take over the work previously performed by employees working reduced hours, and handles it at night or on holidays. The coordination department can also check the status of the work that the AI ​​handled for employees working reduced hours on the next business day, and follow up as necessary.

[0050] (Example 2) The reduced-hours system according to an embodiment of the present invention uses AI to analyze employee workflows, extract tasks that can be handled by AI, and effectively allocate reduced-hours workers. This allows the reduced-hours system to improve employee productivity.

[0051] The reduced-hours system according to the embodiment includes a workflow analysis unit, a task extraction unit, an assignment unit, and a collaboration unit. The workflow analysis unit analyzes an employee's workflow. For example, the workflow analysis unit collects data on employees' daily tasks and project work, and analyzes the data using AI. The workflow analysis unit can also analyze the type, frequency, and duration of tasks to evaluate the efficiency of the tasks. The task extraction unit extracts tasks that can be handled by AI from the tasks analyzed by the workflow analysis unit. For example, the task extraction unit extracts routine work such as data entry and standard report creation. The task extraction unit can also prioritize the extraction of tasks that can be handled by AI. The assignment unit assigns reduced-hours employees to roles with a high workload extracted by the task extraction unit. For example, the assignment unit assigns reduced-hours employees to employees with a high workload. The assignment unit can also effectively assign reduced-hours employees to distribute the work load. The collaboration unit coordinates the working hours of reduced-hours employees with AI support. For example, the collaboration department allows AI to take over the work previously performed by part-time workers, and handle it at night or on holidays. The collaboration department can also check the status of the work that part-time workers handled by AI on the next business day and follow up as necessary. This allows the part-time work system according to the embodiment to improve employee productivity. For example, even if part-time workers find it difficult to work full-time due to family circumstances, they can still work more easily by choosing part-time work. Furthermore, by analyzing the workflow, work can be prevented from becoming too dependent on individual employees, and work can be leveled out.

[0052] The workflow analysis unit can identify stressful tasks based on employee emotional data and prioritize them as tasks that can be handled by AI. The workflow analysis unit, for example, collects employee emotional data and identifies stressful tasks. For example, regular questionnaires or biometric sensors can be used to measure employee stress levels. The workflow analysis unit also prioritizes stressful tasks as tasks that can be handled by AI. For example, by having AI handle tasks that have a high stress level, employee stress can be reduced. This reduces employee stress and improves work efficiency.

[0053] The workflow analysis unit can use employees' past performance data to evaluate the efficiency of work and propose optimal workflows. For example, the workflow analysis unit collects employees' past performance data and uses AI to analyze that data to evaluate work efficiency. For example, the evaluation is based on the time it takes to complete a task or the error rate. The workflow analysis unit also proposes optimal workflows. For example, it can propose procedures and methods for improving work efficiency. This makes it possible to utilize employees' past performance data to improve work efficiency.

[0054] The business flow analysis unit can take into account the interdependencies between tasks and make proposals to optimize the linkage of tasks. The business flow analysis unit, for example, performs analysis taking into account the interdependencies between tasks. For example, it analyzes the data flow between tasks and the dependencies between tasks. The business flow analysis unit also makes proposals to optimize the linkage of tasks. For example, it can propose procedures and methods for smoothing the linkage of tasks. This makes it possible to improve the linkage of tasks by taking into account the interdependencies between tasks.

[0055] The workflow analysis unit can apply the results of the analyzed workflow to different industries and departments, and develop it as a general-purpose business improvement tool. For example, the workflow analysis unit develops a general-purpose business improvement tool to apply the results of the workflow analyzed by AI to different industries and departments. For example, it provides tools that correspond to different industries such as manufacturing and services. The workflow analysis unit also applies the results of the workflow to other departments. For example, it can achieve similar business improvements in different departments such as the sales department and the human resources department. This allows the results of the workflow analysis to be developed as a general-purpose business improvement tool and applied to different industries and departments.

[0056] The workflow analysis unit can use the results of the analyzed workflow to match employee skills and achieve optimal personnel placement. For example, the workflow analysis unit develops a system that matches employee skills based on the results of workflow analyzed by AI. For example, it compares the work content with employee skill sets and proposes optimal personnel placement. The workflow analysis unit also optimizes employee placement based on the results of skill matching. For example, it can prioritize the placement of employees with specific skills. This makes it possible to use the results of workflow analysis to match employee skills and achieve optimal personnel placement.

[0057] The workflow analysis unit can use the emotion estimation function to provide feedback to improve employee motivation based on the results of the workflow analysis. The workflow analysis unit, for example, uses the emotion estimation function to develop a system that provides feedback to improve employee motivation based on the results of the workflow analysis. For example, it provides positive feedback or encouraging messages. The workflow analysis unit also suggests specific actions to improve motivation based on the employee's emotional data. For example, it can provide support according to the employee's emotional state. This makes it possible to provide feedback to improve employee motivation and improve work efficiency.

[0058] The assignment department can identify employees with heavy workloads based on employee emotional data and assign them preferentially to reduced-hours workers. For example, the assignment department uses AI to analyze employee emotional data and identify employees with heavy workloads. For example, it measures stress levels and fatigue levels and lists employees with heavy workloads. The assignment department also preferentially assigns reduced-hours workers to employees with heavy workloads. For example, by assigning reduced-hours workers to employees with heavy workloads, the workload can be reduced. In this way, by identifying employees with heavy workloads and preferentially assigning reduced-hours workers, employee stress can be reduced and work efficiency can be improved.

[0059] The assignment department can consider employees' skill sets when analyzing work weights and assign the most suitable shortened-hours worker. For example, the assignment department develops a system that considers employees' skill sets when AI analyzes work weights and assigns the most suitable shortened-hours worker. For example, employees with specific skills are prioritized for assignment. The assignment department also effectively assigns shortened-hours workers based on their skill sets. For example, by assigning employees with skills appropriate to the work content, work efficiency can be improved. This makes it possible to improve work efficiency by considering employees' skill sets and assigning the most suitable shortened-hours worker.

[0060] When analyzing work weights, the assignment department can make flexible assignments by taking into account seasonal fluctuations in work and the progress of projects. For example, when AI analyzes work weights, the assignment department develops a system that makes flexible assignments by taking into account seasonal fluctuations in work. For example, by allocating employees working reduced hours depending on busy and slow seasons. The assignment department also makes assignments by taking into account the progress of projects. For example, it can appropriately allocate necessary resources depending on the progress of a project. This makes it possible to make flexible assignments by taking into account seasonal fluctuations in work and the progress of a project, thereby improving work efficiency.

[0061] The Assignment Department can apply the results of the task weight analysis to other departments and projects to optimize resources across the company. For example, the Assignment Department can develop a system to apply to other departments and projects based on the results of AI's task weight analysis. For example, the Assignment Department can share task weight data to optimize resources across the company. The Assignment Department can also achieve resource optimization in other departments and projects. For example, it can efficiently allocate resources between different departments. This allows the results of the task weight analysis to be applied to other departments and projects to optimize resources across the company, thereby improving business efficiency.

[0062] The assignment unit can use the emotion estimation function to make assignments to reduce employee stress based on the results of the work weight analysis. The assignment unit, for example, uses the emotion estimation function to develop a system that makes assignments to reduce employee stress based on the results of the work weight analysis. For example, the assignment unit assigns employees with high stress levels to reduced-hours work to reduce their workload. The assignment unit also proposes specific actions to reduce stress based on employee emotion data. For example, it can provide programs to reduce workloads and provide relaxation programs. As a result, by using the emotion estimation function to make assignments to reduce employee stress, it is possible to improve employee health and work efficiency.

[0063] When taking over the work of a part-time worker, the collaboration department can optimize the priority of work based on the employee's emotional data. For example, when AI takes over the work of a part-time worker, the collaboration department collects employee emotional data and optimizes the priority of work. For example, it determines the priority of work based on the employee's stress level and fatigue level. The collaboration department also proposes specific actions to optimize the priority of work. For example, it can reallocate work according to importance and urgency. In this way, work efficiency can be improved by optimizing the priority of work based on the employee's emotional data.

[0064] When taking over the work of a part-time worker, the Collaboration Department can monitor the progress of the work in real time and issue alerts as necessary. For example, the Collaboration Department will develop a system that monitors the progress of work in real time when AI takes over the work of a part-time worker. For example, it will issue an alert if the progress of the work is behind schedule. The Collaboration Department will also provide necessary support depending on the progress. For example, it can allocate additional resources to work that is behind schedule. In this way, by monitoring the progress of work in real time and issuing alerts as necessary, it is possible to prevent work delays and support efficient work execution.

[0065] When taking over the work of a part-time worker, the Collaboration Department can evaluate the quality of the work and provide feedback on areas for improvement. For example, the Collaboration Department could develop a system that evaluates the quality of work when AI takes over the work of a part-time worker. For example, the quality could be evaluated based on the degree of completion of the work and the error rate. The Collaboration Department could also provide feedback on areas for improvement based on the results of the quality evaluation. For example, it could make specific suggestions for improving work procedures and methods. In this way, by evaluating the quality of work and providing feedback on areas for improvement, the quality of work can be improved.

[0066] The Collaboration Department can apply the collaboration between reduced-hours workers and AI to other business processes and departments, thereby improving business efficiency across the company. For example, the Collaboration Department can develop a system that applies the collaboration between reduced-hours workers and AI to other business processes and departments. For example, it can realize similar collaboration in different departments, such as the sales department and the human resources department. The Collaboration Department can also apply the collaboration between reduced-hours workers and AI to other business processes. For example, it can improve efficiency in different business processes, such as project management and customer support. This allows the collaboration between reduced-hours workers and AI to be applied to other business processes and departments, improving business efficiency across the company, thereby improving productivity across the company.

[0067] The Collaboration Department can support flexible working styles by combining collaboration between reduced-hours workers and AI with remote work and flextime systems. For example, the Collaboration Department will develop a system that combines collaboration between reduced-hours workers and AI with remote work and flextime systems. For example, AI will support work during remote work. The Collaboration Department will also apply collaboration between reduced-hours workers and AI to flextime systems. For example, AI can handle work outside of core hours, making it possible to achieve flexible working styles. In this way, collaboration between reduced-hours workers and AI can be combined with remote work and flextime systems to support flexible working styles, improving the ease of working for employees.

[0068] The collaboration department can use the emotion estimation function to monitor the emotional state of employees in collaboration with reduced-hours workers and AI, and provide appropriate support. For example, the collaboration department can use the emotion estimation function to develop a system that monitors the emotional state of employees in collaboration with reduced-hours workers and AI. For example, it can analyze employees' stress levels and fatigue levels in real time. The collaboration department also provides appropriate support according to their emotional state. For example, it can provide counseling or technical support to employees with high stress levels. In this way, by using the emotion estimation function to monitor employees' emotional state and provide appropriate support, it is possible to reduce employee stress and improve work efficiency.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The workflow analysis unit analyzes employee health data, identifies tasks that pose a high health risk, and prioritizes these as tasks that can be handled by AI. For example, employee health data can be collected and AI can analyze the data to identify tasks that pose a high health risk. For example, tasks that may have a negative impact on health, such as long hours of desk work or heavy labor, can be extracted. The workflow analysis unit can also protect employee health by having AI handle tasks that pose a high health risk. This reduces employee health risks and improves work efficiency.

[0071] The workflow analysis unit can identify tasks that are low in motivation based on employee emotional data and prioritize them as tasks that can be handled by AI. For example, employee emotional data can be collected and AI can analyze the data to identify tasks that are low in motivation. For example, tasks that have the potential to lower motivation, such as monotonous or repetitive tasks, can be extracted. The workflow analysis unit can also improve employee motivation by having AI handle tasks that are low in motivation. This can improve employee motivation and improve work efficiency.

[0072] The workflow analysis unit can use employees' past performance data to evaluate work efficiency and propose optimal workflows. For example, employees' past performance data can be collected and AI can analyze that data to evaluate work efficiency. For example, the evaluation can be based on the time it takes to complete a task or the error rate. The workflow analysis unit can also propose optimal workflows. For example, it can propose procedures and methods for improving work efficiency. This makes it possible to utilize employees' past performance data to improve work efficiency.

[0073] The workflow analysis unit can optimize work priorities based on employee emotional data. For example, employee emotional data can be collected and AI can analyze the data to determine work priorities. For example, work priorities can be optimized based on stress levels and fatigue levels. The workflow analysis unit also suggests specific actions to optimize work priorities. For example, it can reassign tasks according to their importance and urgency. This makes it possible to improve work efficiency by optimizing work priorities based on employee emotional data.

[0074] The workflow analysis unit can apply the results of the analyzed workflow to different industries and departments, and develop it as a general-purpose business improvement tool. For example, a general-purpose business improvement tool can be developed to apply the results of the workflow analyzed by AI to different industries and departments. For example, tools corresponding to different industries such as manufacturing and service can be provided. The workflow analysis unit also applies the results of the workflow to other departments. For example, similar business improvements can be achieved in different departments such as the sales department and the human resources department. This allows the results of workflow analysis to be developed as a general-purpose business improvement tool and applied to different industries and departments.

[0075] The workflow analysis unit can make suggestions for optimizing the coordination of tasks based on employee emotional data. For example, it collects employee emotional data and uses AI to analyze it to evaluate the coordination of tasks. For example, it analyzes the data flow between tasks and the dependencies between tasks. The workflow analysis unit also suggests specific actions to optimize the coordination of tasks. For example, it can suggest procedures and methods for smoother task coordination. This makes it possible to improve work efficiency by optimizing the coordination of tasks based on employee emotional data.

[0076] The assignment department can assign the most suitable short-hours worker by taking into account the skill set of the employee. For example, when AI analyzes work weights, a system can be developed that takes into account the skill set of the employee and assigns the most suitable short-hours worker. For example, employees with specific skills can be prioritized for assignment. The assignment department can also effectively assign short-hours workers based on their skill set. For example, by assigning employees with skills appropriate to the work content, work efficiency can be improved. This allows work efficiency to be improved by taking into account the skill set of the employee and assigning the most suitable short-hours worker.

[0077] The assignment department can identify employees with heavy workloads based on employee emotional data and assign them preferentially to reduced-hours workers. For example, AI can analyze employees' emotional data and identify employees with heavy workloads. For example, it can measure stress levels and fatigue levels and create a list of employees with heavy workloads. The assignment department can also preferentially assign reduced-hours workers to employees with heavy workloads. For example, by assigning reduced-hours workers to employees with heavy workloads, the workload can be reduced. In this way, by identifying employees with heavy workloads and preferentially assigning reduced-hours workers, it is possible to reduce employee stress and improve work efficiency.

[0078] When analyzing work loads, the assignment department can make flexible assignments by taking into account seasonal fluctuations in work and the progress of projects. For example, a system can be developed in which, when AI analyzes work loads, flexible assignments are made by taking into account seasonal fluctuations in work. For example, employees working reduced hours can be assigned depending on busy and slow periods. The assignment department can also make assignments by taking into account the progress of projects. For example, the necessary resources can be appropriately assigned depending on the progress of the project. This allows for flexible assignments that take into account seasonal fluctuations in work and the progress of projects, thereby improving work efficiency.

[0079] When taking over the work of a part-time worker, the Collaboration Department can monitor the progress of the work in real time and issue alerts as necessary. For example, a system will be developed in which AI monitors the progress of work in real time when taking over the work of a part-time worker. For example, an alert will be issued if the progress of the work is behind schedule. The Collaboration Department will also provide necessary support depending on the progress. For example, additional resources can be allocated to work that is behind schedule. This will prevent work delays and support efficient work execution by monitoring the progress of work in real time and issuing alerts as necessary.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The workflow analysis unit analyzes employees' workflow. For example, the workflow analysis unit collects data on employees' daily work and project work, and the AI ​​analyzes that data. The workflow analysis unit can also analyze the type, frequency, and duration of work to evaluate the efficiency of the work. Step 2: The task extraction unit extracts tasks that can be handled by AI from the tasks analyzed by the workflow analysis unit. For example, the task extraction unit extracts routine work such as data entry and standard report creation. The task extraction unit can also prioritize the extraction of tasks that can be handled by AI. Step 3: The assignment department assigns reduced-hours workers to the roles with high workloads extracted by the task extraction department. For example, the assignment department assigns reduced-hours workers to employees with a heavy workload. The assignment department can also effectively assign reduced-hours workers to distribute the workload. Step 4: The coordination department coordinates the working hours of employees working reduced hours with the AI ​​response. For example, the coordination department allows the AI ​​to take over the work previously performed by employees working reduced hours, and handles it at night or on holidays. The coordination department can also check the status of the work that the AI ​​handled for employees working reduced hours on the next business day, and follow up as necessary.

[0082] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0088] 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.

[0089] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0092] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0093] 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.

[0094] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0095] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0096] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] 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.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0108] 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.

[0109] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 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.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] 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.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0123] 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.

[0124] 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.

[0125] 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 user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0126] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0127] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0128] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0129] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0131] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0132] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0133] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0134] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0135] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0136] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0137] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0138] 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.

[0139] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0140] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0141] 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.

[0142] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0143] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0144] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0145] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0146] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0147] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0148] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0149] 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 workflow analysis unit that analyzes employee workflows; a task extraction unit that extracts tasks that can be handled by AI from the tasks analyzed by the task flow analysis unit; an assignment unit that assigns reduced-hours workers to the role-playing personnel with a high workload extracted by the task extraction unit; It also has a coordination department that coordinates the working hours of employees working reduced hours with AI support. A system characterized by:

2. The business flow analysis unit Identify stressful tasks based on employee emotional data and prioritize them as tasks that can be handled by AI.

2. The system of claim 1.

3. The business flow analysis unit The results of the analyzed business flow will be applied to different industries and departments, and developed as a general-purpose business improvement tool.

2. The system of claim 1.

4. The assignment unit Based on employee emotional data, employees with heavy workloads are identified and assigned to those with reduced working hours as a priority.

2. The system of claim 1.

5. The linking unit is When taking over the work of an employee working reduced hours, the priority of said work is optimized based on employee emotional data.

2. The system of claim 1.

6. The business flow analysis unit Using emotion estimation functionality, feedback is provided to improve employee motivation based on the results of workflow analysis.

2. The system of claim 1.

7. The assignment unit Using emotion estimation function, assign tasks to reduce employee stress based on the results of work weight analysis.

2. The system of claim 1.

8. The linking unit is Using the emotion estimation function, the AI ​​will work with the shortened working hours worker to monitor the employee's emotional state and provide appropriate support.

2. The system of claim 1.

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