system

Abstract

The system according to the embodiment aims to maximize the work efficiency of part-time workers and prevent work from becoming dependent on individual employees. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an extraction unit, an assignment unit, and a response unit. The collection unit collects information indicating the work of each employee. The analysis unit analyzes the work flow of each employee based on the information collected by the collection unit. The extraction unit extracts tasks that can be handled by AI from the work flow analyzed by the analysis unit. The assignment unit assigns reduced-hours workers based on the AI-handleable tasks extracted by the extraction unit. The response unit uses AI to perform tasks outside of the hours during which the reduced-hours workers assigned by the assignment unit work.

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 has room for improvement in terms of maximizing the work efficiency of employees working reduced hours and preventing work from becoming dependent on individual skills.

[0005] The system according to the embodiment aims to maximize the work efficiency of part-time workers and prevent work from becoming dependent on individual employees. [Means for solving the problem]

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, an extraction unit, an assignment unit, and a response unit. The collection unit collects information indicating the work of each employee. The analysis unit analyzes the work flow of each employee based on the information collected by the collection unit. The extraction unit extracts tasks that can be handled by AI from the work flow analyzed by the analysis unit. The assignment unit assigns reduced-hours workers based on the AI-handleable tasks extracted by the extraction unit. The response unit uses AI to perform tasks outside of the time periods when the reduced-hours workers assigned by the assignment unit work. [Effects of the Invention]

[0007] The system according to the embodiment can maximize the work efficiency of part-time workers and prevent work from becoming dependent on individual employees. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a mechanism for improving employee productivity in a company's back office. This system uses AI to analyze each employee's workflow and identify tasks that can be handled by the AI. Next, reduced-hours employees are assigned to employees whose roles involve a large proportion of the extracted tasks. For example, reduced-hours employees perform the tasks from 10:00 to 17:00, while the AI ​​handles them after 17:00. The reduced-hours employees then check the status of the tasks handled by the AI ​​on the next business day. This system improves the satisfaction of reduced-hours employees and allows for the review of the roles of low-productivity employees. Furthermore, identifying workflows facilitates the leveling of tasks and prevents them from becoming overly personal. For example, the system analyzes each employee's workflow in detail and uses AI to analyze them. In this process, each step in the workflow is broken down into smaller steps to identify which parts can be handled by AI. For example, tasks that involve a large proportion of routine work, such as data entry and standardized report preparation, are often easily handled by AI. This allows for the identification of tasks that can be handled by AI. Next, reduced-hours employees are assigned to employees whose roles involve a large proportion of the extracted tasks. Specifically, for employees with high workloads, part-time workers are assigned to share some of the work. For example, part-time workers work from 10:00 to 17:00, and AI handles the work after 17:00. This reduces employee workload and improves work efficiency. Furthermore, part-time workers can check the status of the work handled by the AI ​​the next business day. This allows them to understand the results of the work handled by the AI ​​and make corrections or additional actions as necessary. For example, by reviewing reports created by the AI ​​and making necessary corrections, the quality of work can be maintained. This system improves the satisfaction of part-time employees. Part-time workers can have more flexibility in their work style, making it easier to balance work and personal life. Furthermore, reviewing the roles of low-productivity employees improves work efficiency. For example, assigning part-time workers to employees with high workloads improves the division of work and overall productivity. Furthermore, identifying work flows leads to leveling out work and preventing reliance on individuals. Detailed analysis of work flows and having AI analyze them further standardizes work.This allows work to be carried out efficiently without relying on specific employees. For example, by having AI handle routine tasks such as data entry and report creation, work can be prevented from becoming dependent on individuals and overall work efficiency can be improved. This allows the system to improve employee productivity and work efficiency.

[0029] A back-office business support system according to an embodiment includes a collection unit, an analysis unit, an extraction unit, an assignment unit, and a response unit. The collection unit collects information indicating each employee's work. The collection unit can collect, for example, information such as details of the tasks performed daily by employees, progress status, and the person in charge. The collection unit acquires data from, for example, a business management system or project management tool used by employees. The collection unit can also collect information manually entered by employees. For example, the collection unit collects the work details written by employees in daily and weekly reports. The analysis unit analyzes each employee's work flow based on the information collected by the collection unit. For example, the analysis unit analyzes each step of the work flow in detail to identify which parts can be AI-enabled. For example, the analysis unit identifies tasks that involve a lot of routine work, such as data entry and standard report creation. The analysis unit uses AI to perform analysis to improve the efficiency of the work flow. For example, the analysis unit identifies bottlenecks in the work flow and proposes improvements. The extraction unit extracts AI-enabled tasks from the work flow analyzed by the analysis unit. The extraction unit extracts tasks that can be handled by AI, such as data entry and standard report creation. The extraction unit uses AI to prioritize tasks that can be handled by AI based on the importance and frequency of the tasks. The assignment unit assigns employees to reduced-hours workers based on the tasks that can be handled by AI extracted by the extraction unit. The assignment unit assigns reduced-hours workers to employees with roles that require a high workload, for example. The assignment unit uses AI to select and assign the most suitable reduced-hours workers. The response unit uses AI to handle tasks outside of the hours during which the reduced-hours workers assigned by the assignment unit work. For example, the response unit automatically processes tasks outside of the hours during which the reduced-hours workers work. The response unit uses AI to monitor the progress of tasks in real time and make necessary adjustments. This allows the back-office business support system according to the embodiment to improve employee productivity and streamline business operations. For example, the collection unit collects information such as details of the tasks performed daily by employees, progress status, and the person in charge. The analysis unit analyzes the workflow of each employee based on the information collected by the collection unit.The extraction unit extracts tasks that can be handled by AI from the workflow analyzed by the analysis unit. The assignment unit assigns reduced-hours workers based on the AI-compatible tasks extracted by the extraction unit. The response unit has AI handle tasks outside of the hours when the reduced-hours workers assigned by the assignment unit work. As a result, the back-office business support system according to the embodiment can improve employee productivity and streamline business operations.

[0030] The collection unit can collect information indicating work. For example, the collection unit collects information such as details of tasks performed daily by employees, progress, and individuals in charge. The collection unit acquires data, for example, from a work management system or project management tool used by employees. The collection unit can also collect information manually entered by employees. For example, the collection unit collects the work content written by employees in daily or weekly reports. This allows the collection unit to efficiently collect information indicating work. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the work management system into AI, which analyzes the data and extracts information indicating work.

[0031] The analysis unit can analyze the workflow and extract tasks that can be adapted for AI. For example, the analysis unit can analyze each step of the workflow in detail and identify which parts can be adapted for AI. For example, the analysis unit can identify tasks that involve a lot of routine work, such as data entry and standard report creation. The analysis unit uses AI to perform analysis to improve the efficiency of the workflow. For example, the analysis unit can identify bottlenecks in the workflow and suggest areas for improvement. This allows the analysis unit to analyze the workflow in detail and identify tasks that can be adapted for AI. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input workflow data into AI, which analyzes the data and identifies tasks that can be adapted for AI.

[0032] The extraction unit can extract tasks that can be handled by AI. The extraction unit extracts tasks that can be handled by AI, such as data entry and routine report creation. The extraction unit uses AI to preferentially extract tasks that can be handled by AI based on the importance and frequency of the tasks. This allows the extraction unit to efficiently extract tasks that can be handled by AI. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input business flow data into AI, which analyzes the data and extracts tasks that can be handled by AI.

[0033] The assignment department can assign reduced-hours workers. For example, the assignment department assigns reduced-hours workers to employees with roles that involve a heavy workload. The assignment department uses AI to select and assign the most suitable reduced-hours workers. This allows the assignment department to efficiently assign reduced-hours workers. Some or all of the above-mentioned processing in the assignment department may be performed using AI, for example, or may be performed without using AI. For example, the assignment department can input business flow data into AI, which analyzes the data to select and assign the most suitable reduced-hours workers.

[0034] The response unit can execute the tasks handled by AI. For example, the response unit uses AI to automatically process tasks outside of the hours when part-time workers are working. The response unit uses AI to monitor the progress of tasks in real time and make necessary adjustments. This allows the response unit to efficiently execute the tasks handled by AI. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input business flow data into AI, which analyzes the data and executes the tasks.

[0035] Furthermore, the back-office business support system includes an adjustment unit that monitors the work progress of the part-time employees and the AI ​​in real time and makes necessary adjustments. The adjustment unit, for example, monitors the work progress of the part-time employees and the AI ​​in real time. The adjustment unit uses AI to monitor the work progress in real time and makes necessary adjustments. For example, the adjustment unit can monitor the work progress and make schedule changes or reallocate resources. The adjustment unit can monitor the work progress in real time and make necessary adjustments using AI. For example, the adjustment unit can monitor the work progress and make schedule changes or reallocate resources. This allows the adjustment unit to monitor the work progress of the part-time employees and the AI ​​in real time and make necessary adjustments. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the work progress into AI, which analyzes the data and makes necessary adjustments.

[0036] The back-office business support system further includes a provision unit that uses AI to identify improvements to the business process based on the work results of the part-time employees and the AI ​​and provides the identified improvements. The provision unit, for example, identifies improvements to the business process based on the work results of the part-time employees and the AI. The provision unit uses AI to identify improvements to the business process and provides the identified improvements. For example, the provision unit can identify bottlenecks in the business process and make suggestions for improving efficiency. The provision unit uses AI to identify improvements to the business process and provide the identified improvements. For example, the provision unit can identify bottlenecks in the business process and make suggestions for improving efficiency. As a result, the provision unit can identify improvements to the business process based on the work results of the part-time employees and the AI ​​and provide the identified improvements. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can input data on the work results into AI, which can analyze the data to identify improvements to the business process and provide the identified improvements.

[0037] The extraction unit can use AI to analyze each step of a business flow and identify which parts are AI-compatible. The extraction unit, for example, analyzes each step of a business flow in detail and identifies AI-compatible business. The extraction unit uses AI to analyze each step of a business flow in detail and identify which parts are AI-compatible. For example, the extraction unit analyzes data entry work in detail and identifies parts that are AI-compatible. The extraction unit uses AI to analyze each step of a business flow in detail and identify which parts are AI-compatible. For example, the extraction unit analyzes data entry work in detail and identifies parts that are AI-compatible. In this way, the extraction unit can analyze each step of a business flow in detail and identify AI-compatible business. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input business flow data into AI, and the AI ​​can analyze the data to identify AI-compatible business.

[0038] The assignment unit can assign reduced-hours workers to employees whose roles involve a high weighting of the extracted work. The assignment unit, for example, assigns the most suitable reduced-hours worker to employees whose roles involve a high weighting of work. The assignment unit uses AI to select and assign the most suitable reduced-hours worker to employees whose roles involve a high weighting of work. For example, the assignment unit assigns reduced-hours workers to employees whose roles involve a high weighting of work. The assignment unit uses AI to select and assign the most suitable reduced-hours worker to employees whose roles involve a high weighting of work. For example, the assignment unit assigns reduced-hours workers to employees whose roles involve a high weighting of work. This allows the assignment unit to assign the most suitable reduced-hours worker to employees whose roles involve a high weighting of work. Some or all of the above-described processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input business flow data into AI, which analyzes the data to select and assign the most suitable reduced-hours worker.

[0039] The response unit can record the results of the work handled by the AI. For example, the response unit records the results of the work handled by the AI ​​in detail and makes necessary corrections. The response unit uses AI to record the results of the work handled by the AI ​​in detail and makes necessary corrections. For example, the response unit records reports created by the AI ​​in detail and makes necessary corrections. The response unit uses AI to record the results of the work handled by the AI ​​in detail and makes necessary corrections. For example, the response unit records reports created by the AI ​​in detail and makes necessary corrections. In this way, the response unit can record the results of the work handled by the AI ​​in detail and make necessary corrections. Some or all of the above-mentioned processing in the response unit may be performed using AI, or may be performed without using AI, for example. For example, the response unit can input data on the work results into AI, which analyzes the data, records the work results, and makes necessary corrections.

[0040] The analysis unit can analyze the workflow in detail and promote standardization of the work. The analysis unit, for example, analyzes the workflow in detail and promotes standardization of the work. The analysis unit uses AI to analyze the workflow in detail and promote standardization of the work. For example, the analysis unit uses AI to analyze the workflow in detail and promote standardization of the work. For example, the analysis unit uses AI to analyze the workflow in detail and promote standardization of the work. This allows the analysis unit to analyze the workflow in detail and promote standardization of the work. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input workflow data into AI, which analyzes the data and promotes standardization of the work.

[0041] The collection unit can analyze each employee's past work history and select the optimal collection method. The collection unit, for example, analyzes each employee's past work history and selects the optimal collection method. The collection unit uses AI to analyze each employee's past work history and select the optimal collection method. For example, the collection unit analyzes each employee's past work history and prioritizes frequently used data collection methods. The collection unit uses AI to analyze each employee's past work history and select the optimal collection method. For example, the collection unit analyzes each employee's past work history and prioritizes frequently used data collection methods. This allows the collection unit to analyze each employee's past work history and select the optimal collection method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input work history data into AI, which analyzes the data and selects the optimal collection method.

[0042] The collection unit can filter the work information based on the employee's current project or area of ​​interest when collecting the work information. For example, the collection unit can filter the work information based on the employee's current project or area of ​​interest when collecting the work information. The collection unit can use AI to filter the work information based on the employee's current project or area of ​​interest when collecting the work information. For example, the collection unit can prioritize collecting information related to projects the employee is currently working on. The collection unit can use AI to filter the work information based on the employee's current project or area of ​​interest when collecting the work information. For example, the collection unit can prioritize collecting information related to projects the employee is currently working on. This allows the collection unit to filter the work information based on the employee's current project or area of ​​interest when collecting the work information. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or can be performed without using AI. For example, the collection unit can input work information data into AI, which can analyze the data and perform filtering.

[0043] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. The collection unit uses AI to prioritize collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. For example, if an employee is in a specific area, the collection unit prioritizes collecting information related to that area. The collection unit uses AI to prioritize collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. For example, if an employee is in a specific area, the collection unit prioritizes collecting information related to that area. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information data into AI, which analyzes the data and prioritizes collecting highly relevant information.

[0044] The collection unit can analyze employees' social media activities and collect related information when collecting business information. For example, the collection unit can analyze employees' social media activities and collect related information when collecting business information. The collection unit can use AI to analyze employees' social media activities and collect related information when collecting business information. For example, the collection unit can analyze employees' social media activities and collect related business information. The collection unit can use AI to analyze employees' social media activities and collect related information when collecting business information. For example, the collection unit can analyze employees' social media activities and collect related business information. In this way, the collection unit can analyze employees' social media activities and collect related information when collecting business information. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or can be performed without using AI. For example, the collection unit can input social media activity data into AI, which can analyze the data and collect related information.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the task when analyzing the business flow. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the task when analyzing the business flow. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the task when analyzing the business flow. For example, the analysis unit performs a detailed analysis for a task with high importance. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the task when analyzing the business flow. For example, the analysis unit performs a detailed analysis for a task with high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the task when analyzing the business flow. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input business flow data into AI, which analyzes the data and adjusts the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the category of the task when analyzing the workflow. For example, the analysis unit applies different analysis algorithms depending on the category of the task when analyzing the workflow. The analysis unit uses AI to apply different analysis algorithms depending on the category of the task when analyzing the workflow. For example, the analysis unit applies a specific algorithm to data entry tasks to perform analysis. The analysis unit uses AI to apply different analysis algorithms depending on the category of the task when analyzing the workflow. For example, the analysis unit applies a specific algorithm to data entry tasks to perform analysis. This allows the analysis unit to apply different analysis algorithms depending on the category of the task when analyzing the workflow. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input workflow data into AI, which analyzes the data and applies different analysis algorithms.

[0047] The analysis unit can determine the analysis priority based on the submission time of tasks when analyzing the workflow. The analysis unit, for example, determines the analysis priority based on the submission time of tasks when analyzing the workflow. The analysis unit uses AI to determine the analysis priority based on the submission time of tasks when analyzing the workflow. For example, the analysis unit prioritizes analyzing tasks with an upcoming submission deadline. The analysis unit uses AI to determine the analysis priority based on the submission time of tasks when analyzing the workflow. For example, the analysis unit prioritizes analyzing tasks with an upcoming submission deadline. This allows the analysis unit to determine the analysis priority based on the submission time of tasks when analyzing the workflow. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input workflow data into AI, which analyzes the data and determines the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of tasks when analyzing a business flow. The analysis unit, for example, adjusts the order of analysis based on the relevance of tasks when analyzing a business flow. The analysis unit uses AI to adjust the order of analysis based on the relevance of tasks when analyzing a business flow. For example, the analysis unit prioritizes analysis of highly related tasks. The analysis unit uses AI to adjust the order of analysis based on the relevance of tasks when analyzing a business flow. For example, the analysis unit prioritizes analysis of highly related tasks. This allows the analysis unit to adjust the order of analysis based on the relevance of tasks when analyzing a business flow. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input business flow data into AI, which analyzes the data and adjusts the order of analysis.

[0049] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between tasks during extraction. The extraction unit, for example, takes into account the interrelationships between tasks and extracts related tasks collectively. The extraction unit uses AI to take into account the interrelationships between tasks and extracts related tasks collectively. For example, the extraction unit analyzes the interrelationships between tasks and selects an optimal extraction method. The extraction unit uses AI to take into account the interrelationships between tasks and extracts related tasks collectively. For example, the extraction unit analyzes the interrelationships between tasks and selects an optimal extraction method. This allows the extraction unit to improve the accuracy of extraction by taking into account the interrelationships between tasks. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input data on the interrelationships between tasks into AI, which analyzes the data to improve the accuracy of extraction.

[0050] The extraction unit can perform extraction taking into account the geographical distribution of tasks. For example, the extraction unit takes into account the geographical distribution of tasks and prioritizes extracting related tasks. The extraction unit uses AI to take into account the geographical distribution of tasks and prioritizes extracting related tasks. For example, the extraction unit analyzes the geographical distribution of tasks and selects an optimal extraction method. The extraction unit uses AI to take into account the geographical distribution of tasks and prioritizes extracting related tasks. For example, the extraction unit analyzes the geographical distribution of tasks and selects an optimal extraction method. This allows the extraction unit to perform extraction taking into account the geographical distribution of tasks. Some or all of the above-described processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input geographical distribution data into AI, and the AI ​​can analyze the data and perform extraction.

[0051] The extraction unit can improve the accuracy of extraction by referring to literature related to the business during extraction. The extraction unit, for example, refers to literature related to the business and selects the optimal extraction method. The extraction unit uses AI to refer to literature related to the business and select the optimal extraction method. For example, the extraction unit improves the accuracy of extraction based on literature related to the business. The extraction unit uses AI to refer to literature related to the business and selects the optimal extraction method. For example, the extraction unit improves the accuracy of extraction based on literature related to the business. In this way, the extraction unit can improve the accuracy of extraction by referring to literature related to the business. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input data from related literature into AI, which analyzes the data to improve the accuracy of extraction.

[0052] When assigning work, the assignment unit can have employees with high work loads take on part of the work. For example, the assignment unit assigns part-time workers to employees with high work loads to have them take on part of the work. The assignment unit uses AI to assign part-time workers to employees with high work loads to have them take on part of the work. For example, the assignment unit assigns part-time workers to employees with high work loads to have them take on part of the work. The assignment unit uses AI to assign part-time workers to employees with high work loads to have them take on part of the work. In this way, the assignment unit can have employees with high work loads take on part of the work. Some or all of the above-mentioned processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input work load data into AI, which analyzes the data and adjusts the assignment method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The back-office business support system can further include a skill evaluation unit that evaluates employees' skill levels. The skill evaluation unit analyzes each employee's past work history and performance data to evaluate their skill level. For example, the skill evaluation unit can evaluate an employee's skill level based on the success rate of projects the employee has worked on in the past and the speed at which the employee completes tasks. The skill evaluation unit can also take into account the training the employee has received and the qualifications they have acquired. This allows the skill evaluation unit to accurately evaluate employees' skill levels and assign them appropriate tasks. Furthermore, the skill evaluation unit can propose training plans to improve employees' skills based on the evaluation results.

[0055] The collection unit can monitor employees' health conditions and collect health data. For example, the collection unit can obtain data from wearable devices that measure employees' heart rates and stress levels. The collection unit can also collect self-reported health data entered by employees. This allows the collection unit to understand employees' health conditions in real time and provide necessary support. For example, if an employee is experiencing high stress, the collection unit can make suggestions to reduce their workload.

[0056] The Assignment Department can assign tasks taking into consideration the employee's career path. For example, the Assignment Department can prioritize tasks that align with the employee's desired career path. The Assignment Department can also assign tasks that allow employees to acquire new skills. This allows the Assignment Department to improve work efficiency while supporting the employee's career growth. Furthermore, the Assignment Department can propose appropriate training plans based on the employee's career goals.

[0057] The response department can evaluate the results of tasks handled by AI and improve the AI's performance based on the evaluation results. For example, the response department can evaluate the quality of reports created by AI and identify areas for improvement. The response department can also evaluate the progress of tasks handled by AI and make suggestions for efficiency improvements. This allows the response department to continuously improve AI performance and increase the efficiency of tasks. Furthermore, the response department can update the AI's learning data based on the evaluation results to improve accuracy.

[0058] The data provision department can collect employee feedback and identify areas for improvement in business processes based on the feedback. For example, the data provision department can collect problems and areas for improvement that employees have identified with business processes. The data provision department can also identify bottlenecks in business processes based on employee feedback and make suggestions for improving efficiency. This allows the data provision department to continuously improve business processes by utilizing employee feedback. Furthermore, the data provision department can also propose measures to increase employee satisfaction based on the feedback results.

[0059] When extracting tasks, the extraction unit can improve the accuracy of the extraction by taking into account the employee's past performance data. For example, the extraction unit can preferentially extract tasks in which the employee has performed well in the past. The extraction unit can also extract tasks in a way that avoids tasks in which the employee has struggled in the past. In this way, the extraction unit can improve the accuracy of task extraction by utilizing the employee's past performance data. Furthermore, the extraction unit can optimize the method of extracting tasks based on the employee's performance data.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection unit collects information indicating the work of each employee. For example, the collection unit can collect information such as details of the tasks that employees perform daily, their progress, and the person in charge. The collection unit obtains data from the work management system or project management tool used by employees. It can also collect information manually entered by employees. For example, it collects the work details that employees write in their daily or weekly reports. Step 2: The analysis department analyzes each employee's workflow based on the information collected by the collection department. The analysis department analyzes each step of the workflow in detail and identifies which parts can be adapted for AI. For example, it identifies tasks that involve a lot of routine work, such as data entry and standard report creation. The analysis department uses AI to perform analysis to improve the efficiency of the workflow. For example, it identifies bottlenecks in the workflow and suggests areas for improvement. Step 3: The extraction unit extracts AI-compatible tasks from the workflow analyzed by the analysis unit. The extraction unit extracts tasks that can be handled by AI, such as data entry and standard report creation. The extraction unit uses AI to prioritize AI-compatible tasks based on the importance and frequency of the tasks. Step 4: The assignment department assigns reduced-hours workers based on the tasks that can be handled by AI extracted by the extraction department. The assignment department assigns reduced-hours workers to employees with roles that require a high workload. The assignment department uses AI to select and assign the most suitable reduced-hours workers. Step 5: The Response Department uses AI to handle work outside of the hours when the reduced-hours worker assigned by the Assignment Department works. The Response Department automatically handles work outside of the hours when the reduced-hours worker works. The Response Department uses AI to monitor the progress of work in real time and make any necessary adjustments.

[0062] (Example 2) A system according to an embodiment of the present invention is a mechanism for improving employee productivity in a company's back office. This system uses AI to analyze each employee's workflow and identify tasks that can be handled by the AI. Next, reduced-hours employees are assigned to employees whose roles involve a large proportion of the extracted tasks. For example, reduced-hours employees perform the tasks from 10:00 to 17:00, while the AI ​​handles them after 17:00. The reduced-hours employees then check the status of the tasks handled by the AI ​​on the next business day. This system improves the satisfaction of reduced-hours employees and allows for the review of the roles of low-productivity employees. Furthermore, identifying workflows facilitates the leveling of tasks and prevents them from becoming overly personal. For example, the system analyzes each employee's workflow in detail and uses AI to analyze them. In this process, each step in the workflow is broken down into smaller steps to identify which parts can be handled by AI. For example, tasks that involve a large proportion of routine work, such as data entry and standardized report preparation, are often easily handled by AI. This allows for the identification of tasks that can be handled by AI. Next, reduced-hours employees are assigned to employees whose roles involve a large proportion of the extracted tasks. Specifically, for employees with high workloads, part-time workers are assigned to share some of the work. For example, part-time workers work from 10:00 to 17:00, and AI handles the work after 17:00. This reduces employee workload and improves work efficiency. Furthermore, part-time workers can check the status of the work handled by the AI ​​the next business day. This allows them to understand the results of the work handled by the AI ​​and make corrections or additional actions as necessary. For example, by reviewing reports created by the AI ​​and making necessary corrections, the quality of work can be maintained. This system improves the satisfaction of part-time employees. Part-time workers can have more flexibility in their work style, making it easier to balance work and personal life. Furthermore, reviewing the roles of low-productivity employees improves work efficiency. For example, assigning part-time workers to employees with high workloads improves the division of work and overall productivity. Furthermore, identifying work flows leads to leveling out work and preventing reliance on individuals. Detailed analysis of work flows and having AI analyze them further standardizes work.This allows work to be carried out efficiently without relying on specific employees. For example, by having AI handle routine tasks such as data entry and report creation, work can be prevented from becoming dependent on individuals and overall work efficiency can be improved. This allows the system to improve employee productivity and work efficiency.

[0063] A back-office business support system according to an embodiment includes a collection unit, an analysis unit, an extraction unit, an assignment unit, and a response unit. The collection unit collects information indicating each employee's work. The collection unit can collect, for example, information such as details of the tasks performed daily by employees, progress status, and the person in charge. The collection unit acquires data from, for example, a business management system or project management tool used by employees. The collection unit can also collect information manually entered by employees. For example, the collection unit collects the work details written by employees in daily and weekly reports. The analysis unit analyzes each employee's work flow based on the information collected by the collection unit. For example, the analysis unit analyzes each step of the work flow in detail to identify which parts can be AI-enabled. For example, the analysis unit identifies tasks that involve a lot of routine work, such as data entry and standard report creation. The analysis unit uses AI to perform analysis to improve the efficiency of the work flow. For example, the analysis unit identifies bottlenecks in the work flow and proposes improvements. The extraction unit extracts AI-enabled tasks from the work flow analyzed by the analysis unit. The extraction unit extracts tasks that can be handled by AI, such as data entry and standard report creation. The extraction unit uses AI to prioritize tasks that can be handled by AI based on the importance and frequency of the tasks. The assignment unit assigns employees to reduced-hours workers based on the tasks that can be handled by AI extracted by the extraction unit. The assignment unit assigns reduced-hours workers to employees with roles that require a high workload, for example. The assignment unit uses AI to select and assign the most suitable reduced-hours workers. The response unit uses AI to handle tasks outside of the hours during which the reduced-hours workers assigned by the assignment unit work. For example, the response unit automatically processes tasks outside of the hours during which the reduced-hours workers work. The response unit uses AI to monitor the progress of tasks in real time and make necessary adjustments. This allows the back-office business support system according to the embodiment to improve employee productivity and streamline business operations. For example, the collection unit collects information such as details of the tasks performed daily by employees, progress status, and the person in charge. The analysis unit analyzes the workflow of each employee based on the information collected by the collection unit.The extraction unit extracts tasks that can be handled by AI from the workflow analyzed by the analysis unit. The assignment unit assigns reduced-hours workers based on the AI-compatible tasks extracted by the extraction unit. The response unit has AI handle tasks outside of the hours when the reduced-hours workers assigned by the assignment unit work. As a result, the back-office business support system according to the embodiment can improve employee productivity and streamline business operations.

[0064] The collection unit can collect information indicating work. For example, the collection unit collects information such as details of tasks performed daily by employees, progress, and individuals in charge. The collection unit acquires data, for example, from a work management system or project management tool used by employees. The collection unit can also collect information manually entered by employees. For example, the collection unit collects the work content written by employees in daily or weekly reports. This allows the collection unit to efficiently collect information indicating work. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data acquired from the work management system into AI, which analyzes the data and extracts information indicating work.

[0065] The analysis unit can analyze the workflow and extract tasks that can be adapted for AI. For example, the analysis unit can analyze each step of the workflow in detail and identify which parts can be adapted for AI. For example, the analysis unit can identify tasks that involve a lot of routine work, such as data entry and standard report creation. The analysis unit uses AI to perform analysis to improve the efficiency of the workflow. For example, the analysis unit can identify bottlenecks in the workflow and suggest areas for improvement. This allows the analysis unit to analyze the workflow in detail and identify tasks that can be adapted for AI. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input workflow data into AI, which analyzes the data and identifies tasks that can be adapted for AI.

[0066] The extraction unit can extract tasks that can be handled by AI. The extraction unit extracts tasks that can be handled by AI, such as data entry and routine report creation. The extraction unit uses AI to preferentially extract tasks that can be handled by AI based on the importance and frequency of the tasks. This allows the extraction unit to efficiently extract tasks that can be handled by AI. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input business flow data into AI, which analyzes the data and extracts tasks that can be handled by AI.

[0067] The assignment department can assign reduced-hours workers. For example, the assignment department assigns reduced-hours workers to employees with roles that involve a heavy workload. The assignment department uses AI to select and assign the most suitable reduced-hours workers. This allows the assignment department to efficiently assign reduced-hours workers. Some or all of the above-mentioned processing in the assignment department may be performed using AI, for example, or may be performed without using AI. For example, the assignment department can input business flow data into AI, which analyzes the data to select and assign the most suitable reduced-hours workers.

[0068] The response unit can execute the tasks handled by AI. For example, the response unit uses AI to automatically process tasks outside of the hours when part-time workers are working. The response unit uses AI to monitor the progress of tasks in real time and make necessary adjustments. This allows the response unit to efficiently execute the tasks handled by AI. Some or all of the above-mentioned processing in the response unit may be performed using AI, for example, or may be performed without using AI. For example, the response unit can input business flow data into AI, which analyzes the data and executes the tasks.

[0069] Furthermore, the back-office business support system includes an adjustment unit that monitors the work progress of the part-time employees and the AI ​​in real time and makes necessary adjustments. The adjustment unit, for example, monitors the work progress of the part-time employees and the AI ​​in real time. The adjustment unit uses AI to monitor the work progress in real time and makes necessary adjustments. For example, the adjustment unit can monitor the work progress and make schedule changes or reallocate resources. The adjustment unit can monitor the work progress in real time and make necessary adjustments using AI. For example, the adjustment unit can monitor the work progress and make schedule changes or reallocate resources. This allows the adjustment unit to monitor the work progress of the part-time employees and the AI ​​in real time and make necessary adjustments. Some or all of the above-described processing in the adjustment unit may be performed using AI, for example, or may be performed without using AI. For example, the adjustment unit can input the work progress into AI, which analyzes the data and makes necessary adjustments.

[0070] The back-office business support system further includes a provision unit that uses AI to identify improvements to the business process based on the work results of the part-time employees and the AI ​​and provides the identified improvements. The provision unit, for example, identifies improvements to the business process based on the work results of the part-time employees and the AI. The provision unit uses AI to identify improvements to the business process and provides the identified improvements. For example, the provision unit can identify bottlenecks in the business process and make suggestions for improving efficiency. The provision unit uses AI to identify improvements to the business process and provide the identified improvements. For example, the provision unit can identify bottlenecks in the business process and make suggestions for improving efficiency. As a result, the provision unit can identify improvements to the business process based on the work results of the part-time employees and the AI ​​and provide the identified improvements. Some or all of the above-described processing in the provision unit may be performed using AI, for example, or without AI. For example, the provision unit can input data on the work results into AI, which can analyze the data to identify improvements to the business process and provide the identified improvements.

[0071] The extraction unit can use AI to analyze each step of a business flow and identify which parts are AI-compatible. The extraction unit, for example, analyzes each step of a business flow in detail and identifies AI-compatible business. The extraction unit uses AI to analyze each step of a business flow in detail and identify which parts are AI-compatible. For example, the extraction unit analyzes data entry work in detail and identifies parts that are AI-compatible. The extraction unit uses AI to analyze each step of a business flow in detail and identify which parts are AI-compatible. For example, the extraction unit analyzes data entry work in detail and identifies parts that are AI-compatible. In this way, the extraction unit can analyze each step of a business flow in detail and identify AI-compatible business. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input business flow data into AI, and the AI ​​can analyze the data to identify AI-compatible business.

[0072] The assignment unit can assign reduced-hours workers to employees whose roles involve a high weighting of the extracted work. The assignment unit, for example, assigns the most suitable reduced-hours worker to employees whose roles involve a high weighting of work. The assignment unit uses AI to select and assign the most suitable reduced-hours worker to employees whose roles involve a high weighting of work. For example, the assignment unit assigns reduced-hours workers to employees whose roles involve a high weighting of work. The assignment unit uses AI to select and assign the most suitable reduced-hours worker to employees whose roles involve a high weighting of work. For example, the assignment unit assigns reduced-hours workers to employees whose roles involve a high weighting of work. This allows the assignment unit to assign the most suitable reduced-hours worker to employees whose roles involve a high weighting of work. Some or all of the above-described processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input business flow data into AI, which analyzes the data to select and assign the most suitable reduced-hours worker.

[0073] The response unit can record the results of the work handled by the AI. For example, the response unit records the results of the work handled by the AI ​​in detail and makes necessary corrections. The response unit uses AI to record the results of the work handled by the AI ​​in detail and makes necessary corrections. For example, the response unit records reports created by the AI ​​in detail and makes necessary corrections. The response unit uses AI to record the results of the work handled by the AI ​​in detail and makes necessary corrections. For example, the response unit records reports created by the AI ​​in detail and makes necessary corrections. In this way, the response unit can record the results of the work handled by the AI ​​in detail and make necessary corrections. Some or all of the above-mentioned processing in the response unit may be performed using AI, or may be performed without using AI, for example. For example, the response unit can input data on the work results into AI, which analyzes the data, records the work results, and makes necessary corrections.

[0074] The analysis unit can analyze the workflow in detail and promote standardization of the work. The analysis unit, for example, analyzes the workflow in detail and promotes standardization of the work. The analysis unit uses AI to analyze the workflow in detail and promote standardization of the work. For example, the analysis unit uses AI to analyze the workflow in detail and promote standardization of the work. For example, the analysis unit uses AI to analyze the workflow in detail and promote standardization of the work. This allows the analysis unit to analyze the workflow in detail and promote standardization of the work. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input workflow data into AI, which analyzes the data and promotes standardization of the work.

[0075] Furthermore, the collection unit can estimate the user's emotions and adjust the timing of collecting business information based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of collecting business information based on the estimated user emotions. The collection unit uses AI to estimate the user's emotions and adjusts the timing of collecting business information based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit delays the collection of business information and collects it at a time when the user is relaxed. The collection unit uses AI to estimate the user's emotions and adjusts the timing of collecting business information based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit delays the collection of business information and collects it at a time when the user is relaxed. In this way, the collection unit can estimate the user's emotions and adjust the timing of collecting business information based on the estimated user emotions. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input user emotion data into AI, and the AI ​​can analyze the data and adjust the timing of collecting business information.

[0076] The collection unit can analyze each employee's past work history and select the optimal collection method. The collection unit, for example, analyzes each employee's past work history and selects the optimal collection method. The collection unit uses AI to analyze each employee's past work history and select the optimal collection method. For example, the collection unit analyzes each employee's past work history and prioritizes frequently used data collection methods. The collection unit uses AI to analyze each employee's past work history and select the optimal collection method. For example, the collection unit analyzes each employee's past work history and prioritizes frequently used data collection methods. This allows the collection unit to analyze each employee's past work history and select the optimal collection method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input work history data into AI, which analyzes the data and selects the optimal collection method.

[0077] The collection unit can filter the work information based on the employee's current project or area of ​​interest when collecting the work information. For example, the collection unit can filter the work information based on the employee's current project or area of ​​interest when collecting the work information. The collection unit can use AI to filter the work information based on the employee's current project or area of ​​interest when collecting the work information. For example, the collection unit can prioritize collecting information related to projects the employee is currently working on. The collection unit can use AI to filter the work information based on the employee's current project or area of ​​interest when collecting the work information. For example, the collection unit can prioritize collecting information related to projects the employee is currently working on. This allows the collection unit to filter the work information based on the employee's current project or area of ​​interest when collecting the work information. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or can be performed without using AI. For example, the collection unit can input work information data into AI, which can analyze the data and perform filtering.

[0078] The collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. For example, the collection unit prioritizes collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. The collection unit uses AI to prioritize collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. For example, if an employee is in a specific area, the collection unit prioritizes collecting information related to that area. The collection unit uses AI to prioritize collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. For example, if an employee is in a specific area, the collection unit prioritizes collecting information related to that area. In this way, the collection unit can prioritize collecting highly relevant information by taking into account the geographical location information of employees when collecting business information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input geographical location information data into AI, which analyzes the data and prioritizes collecting highly relevant information.

[0079] The collection unit can analyze employees' social media activities and collect related information when collecting business information. For example, the collection unit can analyze employees' social media activities and collect related information when collecting business information. The collection unit can use AI to analyze employees' social media activities and collect related information when collecting business information. For example, the collection unit can analyze employees' social media activities and collect related business information. The collection unit can use AI to analyze employees' social media activities and collect related information when collecting business information. For example, the collection unit can analyze employees' social media activities and collect related business information. In this way, the collection unit can analyze employees' social media activities and collect related information when collecting business information. Some or all of the above-mentioned processing in the collection unit can be performed using AI, for example, or can be performed without using AI. For example, the collection unit can input social media activity data into AI, which can analyze the data and collect related information.

[0080] The analysis unit can estimate a user's emotions and adjust a workflow analysis method based on the estimated user emotions. For example, the analysis unit can estimate a user's emotions and adjust a workflow analysis method based on the estimated user emotions. The analysis unit can use AI to estimate a user's emotions and adjust a workflow analysis method based on the estimated user emotions. For example, if a user is feeling stressed, the analysis unit can reduce the level of detail in the analysis and provide a concise analysis result. The analysis unit can use AI to estimate a user's emotions and adjust a workflow analysis method based on the estimated user emotions. For example, if a user is feeling stressed, the analysis unit can reduce the level of detail in the analysis and provide a concise analysis result. This allows the analysis unit to estimate a user's emotions and adjust a workflow analysis method based on the estimated user emotions. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input user emotion data into AI, which can analyze the data and adjust a workflow analysis method.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the task when analyzing the business flow. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the task when analyzing the business flow. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the task when analyzing the business flow. For example, the analysis unit performs a detailed analysis for a task with high importance. The analysis unit uses AI to adjust the level of detail of the analysis based on the importance of the task when analyzing the business flow. For example, the analysis unit performs a detailed analysis for a task with high importance. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the task when analyzing the business flow. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input business flow data into AI, which analyzes the data and adjusts the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the task when analyzing the workflow. For example, the analysis unit applies different analysis algorithms depending on the category of the task when analyzing the workflow. The analysis unit uses AI to apply different analysis algorithms depending on the category of the task when analyzing the workflow. For example, the analysis unit applies a specific algorithm to data entry tasks to perform analysis. The analysis unit uses AI to apply different analysis algorithms depending on the category of the task when analyzing the workflow. For example, the analysis unit applies a specific algorithm to data entry tasks to perform analysis. This allows the analysis unit to apply different analysis algorithms depending on the category of the task when analyzing the workflow. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input workflow data into AI, which analyzes the data and applies different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. The analysis unit uses AI to estimate the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, the analysis unit provides a simple, highly visible display method when the user is feeling stressed. The analysis unit uses AI to estimate the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions. For example, the analysis unit provides a simple, highly visible display method when the user is feeling stressed. This allows the analysis unit to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input user emotion data into AI, which analyzes the data and adjusts the display method of the analysis results.

[0084] The analysis unit can determine the analysis priority based on the submission time of tasks when analyzing the workflow. The analysis unit, for example, determines the analysis priority based on the submission time of tasks when analyzing the workflow. The analysis unit uses AI to determine the analysis priority based on the submission time of tasks when analyzing the workflow. For example, the analysis unit prioritizes analyzing tasks with an upcoming submission deadline. The analysis unit uses AI to determine the analysis priority based on the submission time of tasks when analyzing the workflow. For example, the analysis unit prioritizes analyzing tasks with an upcoming submission deadline. This allows the analysis unit to determine the analysis priority based on the submission time of tasks when analyzing the workflow. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input workflow data into AI, which analyzes the data and determines the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of tasks when analyzing a business flow. The analysis unit, for example, adjusts the order of analysis based on the relevance of tasks when analyzing a business flow. The analysis unit uses AI to adjust the order of analysis based on the relevance of tasks when analyzing a business flow. For example, the analysis unit prioritizes analysis of highly related tasks. The analysis unit uses AI to adjust the order of analysis based on the relevance of tasks when analyzing a business flow. For example, the analysis unit prioritizes analysis of highly related tasks. This allows the analysis unit to adjust the order of analysis based on the relevance of tasks when analyzing a business flow. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input business flow data into AI, which analyzes the data and adjusts the order of analysis.

[0086] The extraction unit can estimate a user's emotion and determine the priority of tasks to be extracted based on the estimated user's emotion. For example, the extraction unit can estimate a user's emotion and determine the priority of tasks to be extracted based on the estimated user's emotion. The extraction unit can use AI to estimate a user's emotion and determine the priority of tasks to be extracted based on the estimated user's emotion. For example, when a user is feeling stressed, the extraction unit can prioritize extracting tasks with high importance. The extraction unit can use AI to estimate a user's emotion and determine the priority of tasks to be extracted based on the estimated user's emotion. For example, when a user is feeling stressed, the extraction unit can prioritize extracting tasks with high importance. This allows the extraction unit to estimate a user's emotion and determine the priority of tasks to be extracted based on the estimated user's emotion. Some or all of the above-described processing in the extraction unit can be performed using AI, for example, or without AI. For example, the extraction unit can input user's emotion data into AI, and the AI ​​can analyze the data and determine the priority of tasks to be extracted.

[0087] The extraction unit can improve the accuracy of extraction by taking into account the interrelationships between tasks during extraction. The extraction unit, for example, takes into account the interrelationships between tasks and extracts related tasks collectively. The extraction unit uses AI to take into account the interrelationships between tasks and extracts related tasks collectively. For example, the extraction unit analyzes the interrelationships between tasks and selects an optimal extraction method. The extraction unit uses AI to take into account the interrelationships between tasks and extracts related tasks collectively. For example, the extraction unit analyzes the interrelationships between tasks and selects an optimal extraction method. This allows the extraction unit to improve the accuracy of extraction by taking into account the interrelationships between tasks. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input data on the interrelationships between tasks into AI, which analyzes the data to improve the accuracy of extraction.

[0088] The extraction unit can estimate a user's emotion and adjust the display method of the extracted task based on the estimated user emotion. For example, the extraction unit can estimate a user's emotion and adjust the display method of the extracted task based on the estimated user emotion. The extraction unit can use AI to estimate a user's emotion and adjust the display method of the extracted task based on the estimated user emotion. For example, the extraction unit can provide a simple, highly visible display method when the user is feeling stressed. The extraction unit can use AI to estimate a user's emotion and adjust the display method of the extracted task based on the estimated user emotion. For example, the extraction unit can provide a simple, highly visible display method when the user is feeling stressed. This allows the extraction unit to estimate a user's emotion and adjust the display method of the extracted task based on the estimated user emotion. Some or all of the above-described processing in the extraction unit can be performed using AI, for example, or without AI. For example, the extraction unit can input user emotion data into AI, and the AI ​​can analyze the data and adjust the display method of the extracted task.

[0089] The extraction unit can perform extraction taking into account the geographical distribution of tasks. For example, the extraction unit takes into account the geographical distribution of tasks and prioritizes extracting related tasks. The extraction unit uses AI to take into account the geographical distribution of tasks and prioritizes extracting related tasks. For example, the extraction unit analyzes the geographical distribution of tasks and selects an optimal extraction method. The extraction unit uses AI to take into account the geographical distribution of tasks and prioritizes extracting related tasks. For example, the extraction unit analyzes the geographical distribution of tasks and selects an optimal extraction method. This allows the extraction unit to perform extraction taking into account the geographical distribution of tasks. Some or all of the above-described processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input geographical distribution data into AI, and the AI ​​can analyze the data and perform extraction.

[0090] The extraction unit can improve the accuracy of extraction by referring to literature related to the business during extraction. The extraction unit, for example, refers to literature related to the business and selects the optimal extraction method. The extraction unit uses AI to refer to literature related to the business and select the optimal extraction method. For example, the extraction unit improves the accuracy of extraction based on literature related to the business. The extraction unit uses AI to refer to literature related to the business and selects the optimal extraction method. For example, the extraction unit improves the accuracy of extraction based on literature related to the business. In this way, the extraction unit can improve the accuracy of extraction by referring to literature related to the business. Some or all of the above-mentioned processing in the extraction unit may be performed using AI, for example, or may be performed without using AI. For example, the extraction unit can input data from related literature into AI, which analyzes the data to improve the accuracy of extraction.

[0091] The assignment unit can estimate the user's emotions and adjust the assignment method for reduced-hours workers based on the estimated user emotions. The assignment unit, for example, estimates the user's emotions and adjusts the assignment method for reduced-hours workers based on the estimated user emotions. The assignment unit uses AI to estimate the user's emotions and adjusts the assignment method for reduced-hours workers based on the estimated user emotions. For example, if the user is feeling stressed, the assignment unit assigns reduced-hours workers to tasks with less strain. The assignment unit uses AI to estimate the user's emotions and adjusts the assignment method for reduced-hours workers based on the estimated user emotions. For example, if the user is feeling stressed, the assignment unit assigns reduced-hours workers to tasks with less strain. This allows the assignment unit to estimate the user's emotions and adjust the assignment method for reduced-hours workers based on the estimated user emotions. Some or all of the above-described processing in the assignment unit may be performed, for example, using AI, or may be performed without using AI. For example, the assignment unit can input the user's emotional data into the AI, which can then analyze the data and adjust the assignment method.

[0092] When assigning work, the assignment unit can have employees with high work loads take on part of the work. For example, the assignment unit assigns part-time workers to employees with high work loads to have them take on part of the work. The assignment unit uses AI to assign part-time workers to employees with high work loads to have them take on part of the work. For example, the assignment unit assigns part-time workers to employees with high work loads to have them take on part of the work. The assignment unit uses AI to assign part-time workers to employees with high work loads to have them take on part of the work. In this way, the assignment unit can have employees with high work loads take on part of the work. Some or all of the above-mentioned processing in the assignment unit may be performed using, for example, AI, or may be performed without using AI. For example, the assignment unit can input work load data into AI, which analyzes the data and adjusts the assignment method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, assignment unit, response unit, adjustment unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects employee work information via the control unit 46A of the smart device 14, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the work flow via the specific processing unit 290 of the data processing device 12, and the extraction unit extracts work that can be handled by AI via the specific processing unit 290 of the data processing device 12. The assignment unit assigns employees working reduced hours via the specific processing unit 290 of the data processing device 12, and the response unit uses AI to handle the work via the control unit 46A of the smart device 14. The adjustment unit monitors work progress in real time via the specific processing unit 290 of the data processing device 12, and the provision unit identifies and provides improvements to the work process via the specific processing unit 290 of the data processing device 12. The collection unit estimates the user's emotions using the control unit 46A of the smart device 14, and adjusts the timing of collecting business information. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, assignment unit, response unit, adjustment unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects employee work information via the control unit 46A of the smart glasses 214, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the work flow via the specific processing unit 290 of the data processing device 12, and the extraction unit extracts AI-compatible work via the specific processing unit 290 of the data processing device 12. The assignment unit assigns employees working reduced hours via the specific processing unit 290 of the data processing device 12, and the response unit uses AI to handle the work via the control unit 46A of the smart glasses 214. The adjustment unit monitors work progress in real time via the specific processing unit 290 of the data processing device 12, and the provision unit identifies and provides improvements to the work process via the specific processing unit 290 of the data processing device 12. The collection unit estimates the user's emotions using the control unit 46A of the smart glasses 214, and adjusts the timing of collecting business information. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, assignment unit, response unit, adjustment unit, and provision unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the collection unit collects employee work information via the control unit 46A of the headset terminal 314, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the work flow via the specific processing unit 290 of the data processing device 12, and the extraction unit extracts AI-compatible work tasks via the specific processing unit 290 of the data processing device 12. The assignment unit assigns employees working reduced hours via the specific processing unit 290 of the data processing device 12, and the response unit uses AI to handle the work tasks via the control unit 46A of the headset terminal 314. The adjustment unit monitors work progress in real time via the specific processing unit 290 of the data processing device 12, and the provision unit identifies and provides improvements to the work process via the specific processing unit 290 of the data processing device 12. The collection unit estimates the user's emotions using the control unit 46A of the headset type terminal 314, and adjusts the timing of collecting business information. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, extraction unit, assignment unit, response unit, adjustment unit, and provision unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects employee work information via the control unit 46A of the robot 414, and the information is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the work flow via the specific processing unit 290 of the data processing device 12, and the extraction unit extracts work that can be handled by AI via the specific processing unit 290 of the data processing device 12. The assignment unit assigns employees working reduced hours via the specific processing unit 290 of the data processing device 12, and the response unit has AI handle the work via the control unit 46A of the robot 414. The adjustment unit monitors work progress in real time via the specific processing unit 290 of the data processing device 12, and the provision unit identifies and provides improvements to the work process via the specific processing unit 290 of the data processing device 12. The collection unit estimates the user's emotions using the control unit 46A of the robot 414, and adjusts the timing of collecting business information.

[0093] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0094] The back-office business support system can further include a skill evaluation unit that evaluates employees' skill levels. The skill evaluation unit analyzes each employee's past work history and performance data to evaluate their skill level. For example, the skill evaluation unit can evaluate an employee's skill level based on the success rate of projects the employee has worked on in the past and the speed at which the employee completes tasks. The skill evaluation unit can also take into account the training the employee has received and the qualifications they have acquired. This allows the skill evaluation unit to accurately evaluate employees' skill levels and assign them appropriate tasks. Furthermore, the skill evaluation unit can propose training plans to improve employees' skills based on the evaluation results.

[0095] The collection unit can monitor employees' health conditions and collect health data. For example, the collection unit can obtain data from wearable devices that measure employees' heart rates and stress levels. The collection unit can also collect self-reported health data entered by employees. This allows the collection unit to understand employees' health conditions in real time and provide necessary support. For example, if an employee is experiencing high stress, the collection unit can make suggestions to reduce their workload.

[0096] When analyzing the workflow, the analysis unit can take into account the motivation level of employees. For example, if an employee's motivation is high, the analysis unit can suggest more challenging work. Also, if an employee's motivation is low, the analysis unit can make suggestions to reduce the workload and improve motivation. This allows the analysis unit to improve work efficiency while maintaining employee motivation. Furthermore, the analysis unit can identify and suggest areas for improvement in the workflow based on employee motivation data.

[0097] The extraction unit can take into account the employee's work-life balance when determining the priority of tasks. For example, if an employee has difficulty working during a specific time period due to family circumstances, the extraction unit can prioritize and extract tasks that can be performed during that time period. The extraction unit can also extract tasks taking into account the employee's desired working hours and vacation plans. This allows the extraction unit to improve work efficiency while maintaining the employee's work-life balance. Furthermore, the extraction unit can adjust the priority of tasks based on employee feedback.

[0098] The Assignment Department can assign tasks taking into consideration the employee's career path. For example, the Assignment Department can prioritize tasks that align with the employee's desired career path. The Assignment Department can also assign tasks that allow employees to acquire new skills. This allows the Assignment Department to improve work efficiency while supporting the employee's career growth. Furthermore, the Assignment Department can propose appropriate training plans based on the employee's career goals.

[0099] The response department can evaluate the results of tasks handled by AI and improve the AI's performance based on the evaluation results. For example, the response department can evaluate the quality of reports created by AI and identify areas for improvement. The response department can also evaluate the progress of tasks handled by AI and make suggestions for efficiency improvements. This allows the response department to continuously improve AI performance and increase the efficiency of tasks. Furthermore, the response department can update the AI's learning data based on the evaluation results to improve accuracy.

[0100] The adjustment unit can estimate the emotions of employees and adjust the progress of work based on the estimated emotions. For example, if an employee is feeling high stress, the adjustment unit can slow down the progress of work or assign the work to another employee. Also, if an employee is relaxed, the adjustment unit can speed up the progress of work. In this way, the adjustment unit can improve the efficiency of work while taking into account the emotions of employees. Furthermore, the adjustment unit can adjust the progress of work in real time based on the emotional data of employees.

[0101] The data provision department can collect employee feedback and identify areas for improvement in business processes based on the feedback. For example, the data provision department can collect problems and areas for improvement that employees have identified with business processes. The data provision department can also identify bottlenecks in business processes based on employee feedback and make suggestions for improving efficiency. This allows the data provision department to continuously improve business processes by utilizing employee feedback. Furthermore, the data provision department can also propose measures to increase employee satisfaction based on the feedback results.

[0102] When extracting tasks, the extraction unit can improve the accuracy of the extraction by taking into account the employee's past performance data. For example, the extraction unit can preferentially extract tasks in which the employee has performed well in the past. The extraction unit can also extract tasks in a way that avoids tasks in which the employee has struggled in the past. In this way, the extraction unit can improve the accuracy of task extraction by utilizing the employee's past performance data. Furthermore, the extraction unit can optimize the method of extracting tasks based on the employee's performance data.

[0103] The assignment department can estimate the emotions of employees and adjust the way work is assigned based on the estimated emotions. For example, if an employee is feeling high stress, the assignment department can assign them less demanding work. Also, if an employee is relaxed, the assignment department can assign them more challenging work. This allows the assignment department to improve work efficiency while taking into account the emotions of employees. Furthermore, the assignment department can adjust the way work is assigned in real time based on employee emotion data.

[0104] The processing flow of the second embodiment will be briefly explained below.

[0105] Step 1: The collection unit collects information indicating the work of each employee. For example, the collection unit can collect information such as details of the tasks that employees perform daily, their progress, and the person in charge. The collection unit obtains data from the work management system or project management tool used by employees. It can also collect information manually entered by employees. For example, it collects the work details that employees write in their daily or weekly reports. Step 2: The analysis department analyzes each employee's workflow based on the information collected by the collection department. The analysis department analyzes each step of the workflow in detail and identifies which parts can be adapted for AI. For example, it identifies tasks that involve a lot of routine work, such as data entry and standard report creation. The analysis department uses AI to perform analysis to improve the efficiency of the workflow. For example, it identifies bottlenecks in the workflow and suggests areas for improvement. Step 3: The extraction unit extracts AI-compatible tasks from the workflow analyzed by the analysis unit. The extraction unit extracts tasks that can be handled by AI, such as data entry and standard report creation. The extraction unit uses AI to prioritize AI-compatible tasks based on the importance and frequency of the tasks. Step 4: The assignment department assigns reduced-hours workers based on the tasks that can be handled by AI extracted by the extraction department. The assignment department assigns reduced-hours workers to employees with roles that require a high workload. The assignment department uses AI to select and assign the most suitable reduced-hours workers. Step 5: The Response Department uses AI to handle work outside of the hours when the reduced-hours worker assigned by the Assignment Department works. The Response Department automatically handles work outside of the hours when the reduced-hours worker works. The Response Department uses AI to monitor the progress of work in real time and make any necessary adjustments.

[0106] 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.

[0107] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0108] 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.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] 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.

[0122] 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.

[0123] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] 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.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0134] 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.

[0135] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0136] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0137] 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.

[0138] 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.

[0139] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0140] 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.

[0141] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 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.

[0155] 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.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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."

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects information indicating the work of each employee; an analysis unit that analyzes the workflow of each employee based on the information collected by the collection unit; An extraction unit that extracts AI-compatible tasks from the workflow analyzed by the analysis unit; An assignment unit that assigns reduced-hours workers based on the AI-compatible tasks extracted by the extraction unit; A response unit in which AI performs work outside the time periods during which the reduced-hours worker assigned by the assignment unit works; Equipped with A system characterized by:

2. Includes a coordination department that monitors the work progress of employees working reduced hours and AI in real time and makes necessary adjustments.

2. The system of claim 1.

3. Based on the work results of employees working reduced hours and AI, AI identifies areas for improvement in business processes and provides the identified improvements.

2. The system of claim 1.

4. The extraction unit AI analyzes each step of the business flow and identifies which parts can be handled by AI.

2. The system of claim 1.

5. The assignment unit Assign reduced-hours workers to employees whose roles involve a large proportion of the extracted work.

2. The system of claim 1.

6. The corresponding part is Record the results of tasks handled by AI 2. The system of claim 1.

7. The analysis unit Analyze the workflow in detail and standardize the work 2. The system of claim 1.

8. The collecting unit To estimate a user's emotions and adjust the timing of collecting business information based on the estimated user's emotions.

2. The system of claim 1.

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

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