System

Abstract

An object of the system according to the embodiment is to efficiently digitize the work of the local government and reduce the burden on the staff.SOLUTION: A system includes a business start part, a digitization determination part, a business analysis part, and a priority specification part. The work wash-out unit washes out the work of the staff member. The digitization determination unit determines the possibility of digitization of the work screened out by the work screening unit. The business analysis unit analyzes in detail the business for which the possibility of digitization has been determined by the digitization determination unit. The priority specification unit specifies a priority of digitization of the business analyzed by the business analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not provide sufficient processes for efficiently digitizing local government operations, and there is room for improvement in reducing the burden on staff and improving work efficiency.

[0005] The system according to the embodiment aims to efficiently digitize the operations of local governments and reduce the burden on staff. [Means for solving the problem]

[0006] The system according to the embodiment includes a task identification unit, a digitalization determination unit, a task analysis unit, and a priority determination unit. The task identification unit identifies the tasks of employees. The digitalization determination unit determines the possibility of digitalizing the tasks identified by the task identification unit. The task analysis unit analyzes in detail the tasks determined by the digitalization determination unit to be possible for digitalization. The priority determination unit determines the priority of digitalization for the tasks analyzed by the task analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently digitize the operations of local governments and reduce the burden on staff. [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) The business optimization system according to an embodiment of the present invention is a system that automatically identifies the tasks performed by employees, and uses a generation AI to determine the possibility of digitalization and identify priorities. As a result, the business optimization system can efficiently digitize the tasks of employees and improve business efficiency.

[0029] The business optimization system according to the embodiment includes a business task identification unit, a digitalization determination unit, a business task analysis unit, and a priority determination unit. The business task identification unit identifies employee tasks. For example, it lists the tasks performed daily by employees and organizes the workflow. The business task identification unit can also analyze business logs and log data to discover hidden business processes and inefficient procedures. The digitalization determination unit determines the possibility of digitalizing the identified tasks. For example, it evaluates the technical feasibility and cost-effectiveness of the tasks. The digitalization determination unit can also record employee tasks in real time using voice input or natural language processing, and use a generation AI to evaluate the possibility of digitalization on the spot. The business task analysis unit performs a detailed analysis of tasks determined to be suitable for digitalization. For example, it evaluates the frequency and importance of the tasks and the employee's burden. The business task analysis unit can also perform a detailed analysis of the tasks using video recording and video analysis. The priority determination unit determines the priority of digitalization for the analyzed tasks. For example, it determines the priority based on the importance of the tasks and the amount of resources consumed. The priority determination unit can also analyze feedback and satisfaction data from residents to prioritize tasks that are directly related to resident services. This allows the business optimization system according to the embodiment to efficiently digitize staff tasks and improve work efficiency. For example, digitizing tasks can reduce the manual work of staff, allowing them to focus on more important tasks. In order to improve the quality of resident services, the priority of tasks can also be determined based on feedback from residents.

[0030] The task identification unit can analyze employee work diaries and log data to automatically discover hidden business processes and inefficient procedures. For example, the task identification unit uses generation AI to analyze employee work diaries and log data to automatically discover hidden business processes and inefficient procedures. For example, it uses natural language processing technology to analyze text data from work diaries to identify tasks that occur frequently but are not recorded. The task identification unit also analyzes employee work log data to automatically discover inefficient procedures and duplicated work. For example, it identifies procedures in which the same data is entered multiple times and unnecessary approval processes. Furthermore, the task identification unit uses generation AI to automatically generate business process flowcharts based on the work diaries and log data, visually showing inefficient parts. For example, it highlights parts that are done manually. This automatically discovers hidden business processes and inefficient procedures, improving work efficiency.

[0031] The digitization determination unit records employee work content in real time using voice input and natural language processing, and the generation AI can evaluate the feasibility of digitization on the spot. For example, the digitization determination unit records employee work content in real time using voice input as employees perform their work, and the generation AI evaluates the feasibility of digitization on the spot. For example, it uses voice recognition technology to convert the work content into text data and determine whether it can be digitized. The digitization determination unit also uses natural language processing technology to analyze employee work content in real time and evaluate the feasibility of digitization. For example, it analyzes work content in real time and automatically identifies parts that can be digitized. The digitization determination unit also builds a system in which employee work content is recorded in real time using voice input and natural language processing as employees perform their work, and the generation AI evaluates the feasibility of digitization on the spot. For example, it records work content using a voice input device, and the generation AI determines whether it can be digitized. This allows work content to be recorded in real time and the feasibility of digitization to be quickly evaluated.

[0032] The business analysis department can video record the work content of employees and have a generative AI analyze the video to determine the feasibility of digitalization. For example, the business analysis department can video record the work content of employees and have a generative AI analyze the video to determine the feasibility of digitalization. For example, work procedures are recorded as video data and the generative AI identifies the parts that can be automated. The business analysis department can also analyze the video-recorded work content and build a system where the generative AI evaluates the feasibility of digitalization. For example, manual tasks are extracted from the video data and a determination is made as to whether they can be automated. The business analysis department can also introduce a process whereby the work content of employees is video-recorded and a generative AI analyzes the video to determine the feasibility of digitalization. For example, points for improving work efficiency are identified based on the video data. This makes it possible to video record work content and analyze the video to determine the feasibility of digitalization.

[0033] The Business Analysis Department compares business processes between different municipalities and can introduce digitalization methods that have been successful in other municipalities. For example, the Business Analysis Department compares business processes between different municipalities and introduces digitalization methods that have been successful in other municipalities. For example, it collects success stories from other municipalities and has the generation AI propose the optimal method. The Business Analysis Department also analyzes digitalization methods that have been successful in other municipalities and has the generation AI propose a process to apply to the municipality itself. For example, it selects the optimal method based on data from success stories. The Business Analysis Department also compares business processes between different municipalities and builds a system for the generation AI to introduce digitalization methods that have been successful in other municipalities. For example, it customizes digitalization methods based on success stories. This makes it possible to introduce digitalization methods that have been successful in other municipalities and improve business efficiency.

[0034] The priority identification unit can use the generation AI to analyze the time consumption and resource usage of tasks in detail and identify the tasks that consume the most resources. The priority identification unit, for example, uses the generation AI to analyze the time consumption and resource usage of tasks in detail and identify the tasks that consume the most resources. For example, it analyzes time consumption data for each task and identifies the tasks that consume the most resources. The priority identification unit also analyzes the resource usage of tasks in detail and builds a system in which the generation AI identifies the tasks that consume the most resources. For example, it determines the priority of tasks based on resource usage data. The priority identification unit also introduces a process in which the generation AI analyzes the time consumption and resource usage of tasks in detail and identifies the tasks that consume the most resources. For example, it prioritizes digitization of tasks that consume the most resources. This makes it possible to identify the tasks that consume the most resources and efficiently advance digitalization.

[0035] The priority identification unit can use generation AI to analyze feedback and satisfaction data from residents and prioritize tasks that are directly related to resident services. For example, the priority identification unit can use generation AI to analyze feedback and satisfaction data from residents and evaluate the importance of tasks. For example, it can identify tasks that are directly related to resident services based on resident opinions. The priority identification unit also uses generation AI to analyze feedback data from residents and build a system that prioritizes tasks that are directly related to resident services. For example, it can determine the priority of tasks based on satisfaction data. The priority identification unit also uses generation AI to analyze feedback and satisfaction data from residents and introduce a process that prioritizes tasks that are directly related to resident services. For example, it can make proposals for task improvements that reflect resident opinions. This makes it possible to prioritize the digitalization of tasks that are directly related to resident services and improve resident satisfaction.

[0036] The priority identification unit refers to the best practices of other local governments and companies, and the generation AI can propose optimal priorities based on them. The priority identification unit, for example, refers to the best practices of other local governments and companies, and the generation AI proposes business priorities. For example, the optimal digitalization priorities are determined based on success cases. The priority identification unit also uses the generation AI to analyze the best practices of other local governments and companies, and build a system that proposes business priorities. For example, the priority identification unit determines business priorities based on data from success cases. The priority identification unit also introduces a process that refers to the best practices of other local governments and companies, and the generation AI proposes business priorities. For example, the generation AI proposes digitalization priorities based on success cases. This makes it possible to propose optimal priorities based on the best practices of other local governments and companies, and improve business efficiency.

[0037] The priority identification unit can visualize the priorities of tasks and enable employees to intuitively understand them using an interactive dashboard. The priority identification unit, for example, visualizes the priorities of tasks and enables employees to intuitively understand them using an interactive dashboard. For example, it displays the priorities of tasks in graphs and charts. The priority identification unit also uses generative AI to build a dashboard that visualizes the priorities of tasks. For example, it visually displays the importance of tasks and resource consumption. The priority identification unit also introduces a process for visualizing the priorities of tasks and enables employees to intuitively understand them using an interactive dashboard. For example, it displays the priorities of tasks in a color-coded manner. This makes it possible to visualize the priorities of tasks and enables employees to intuitively understand them.

[0038] The business analysis department can use generative AI to analyze business procedures in detail and propose specific methods for automating the manual parts. For example, the business analysis department uses generative AI to analyze business procedures in detail and propose specific methods for automating the manual parts. For example, it generates a script for automating data entry work. The business analysis department also analyzes business procedures in detail and builds a system in which generative AI proposes specific methods for automation. For example, it proposes tools for automating manual work. The business analysis department also introduces a process in which generative AI analyzes business procedures in detail and proposes specific methods for automating the manual parts. For example, it designs a workflow for automating manual work. This proposes specific methods for automating the manual parts, making it possible to improve business efficiency.

[0039] The business analysis department can simulate the effects of automation and use generative AI to build a model to predict those effects before implementation. The business analysis department, for example, simulates the effects of automation and uses generative AI to build a model to predict those effects before implementation. For example, it simulates the time reduction effect of automation. The business analysis department also uses generative AI to develop a system to build a model to simulate the effects of automation. For example, it predicts the cost reduction effect of automation. The business analysis department also introduces a process to simulate the effects of automation and build a model to predict those effects before implementation using generative AI. For example, it simulates the business efficiency improvement effect of automation. This makes it possible to simulate the effects of automation and build a model to predict those effects before implementation.

[0040] The Business Analysis Department can compare tasks to be automated with examples from different municipalities and companies, and introduce the most effective automation method. For example, the Business Analysis Department can compare tasks to be automated with examples from different municipalities and companies, and introduce the most effective automation method. For example, it can refer to automation methods that have been successful in other municipalities. The Business Analysis Department can also use generative AI to analyze examples from different municipalities and companies, and build a system to introduce the most effective automation method. For example, it can select an automation method based on successful examples. The Business Analysis Department can also compare tasks to be automated with examples from different municipalities and companies, and introduce a process to introduce the most effective automation method. For example, it can customize an automation method based on successful examples. This makes it possible to compare with examples from different municipalities and companies, and introduce the most effective automation method.

[0041] The Business Analysis Department uses generative AI to develop metrics for quantitatively evaluating the effects of automation, and can continuously monitor the effects after implementation. For example, the Business Analysis Department uses generative AI to develop metrics for quantitatively evaluating the effects of automation, and can continuously monitor the effects after implementation. For example, they quantitatively evaluate the effects of time reduction and cost reduction. The Business Analysis Department also uses generative AI to build a system that develops metrics for quantitatively evaluating the effects of automation. For example, they quantitatively evaluate the effects of improving business efficiency. The Business Analysis Department also uses generative AI to develop metrics for quantitatively evaluating the effects of automation, and can introduce a process to continuously monitor the effects after implementation. For example, they monitor the effects of automation based on quantitative evaluation data. This makes it possible to quantitatively evaluate the effects of automation and continuously monitor the effects after implementation.

[0042] The priority identification unit can use the generation AI to quantitatively evaluate the workload of employees, identify tasks that are heavy burdens, and prioritize their improvement. The priority identification unit, for example, uses the generation AI to quantitatively evaluate the workload of employees, identify tasks that are heavy burdens, and prioritize their improvement. For example, it quantifies the degree of burden for each task and identifies points for improvement. The priority identification unit also builds a system that quantitatively evaluates the workload of employees, and the generation AI identifies tasks that are heavy burdens. For example, it makes suggestions for improving tasks based on data on the degree of burden. The priority identification unit also uses the generation AI to quantitatively evaluate the workload of employees, identify tasks that are heavy burdens, and introduce a process for improving them on a priority basis. For example, it determines the priority of tasks based on the degree of burden. This makes it possible to quantitatively evaluate the workload of employees, identify tasks that are heavy burdens, and prioritize their improvement.

[0043] The priority identification unit can use the generation AI to automatically detect bottlenecks in business processes and make improvement proposals. The priority identification unit can, for example, use the generation AI to automatically detect bottlenecks in business processes and make improvement proposals. For example, it analyzes business flows and identifies bottlenecks. The priority identification unit also builds a system that automatically detects bottlenecks in business processes, and the generation AI makes improvement proposals. For example, it makes proposals for improving business efficiency based on bottleneck data. The priority identification unit also uses the generation AI to introduce a process that automatically detects bottlenecks in business processes and makes improvement proposals. For example, it identifies bottlenecks and proposes improvement measures. This makes it possible to automatically detect bottlenecks in business processes and make improvement proposals.

[0044] The Priority Identification Unit uses the generation AI to analyze success stories from other local governments and companies, and can introduce optimal methods to reduce the burden on employees. For example, the Priority Identification Unit uses the generation AI to analyze success stories from other local governments and companies, and can introduce optimal methods to reduce the burden on employees. For example, it proposes optimal work improvement methods based on data from success stories. The Priority Identification Unit also uses the generation AI to analyze success stories from other local governments and companies, and builds a system to introduce methods to reduce the burden on employees. For example, it selects methods to improve work efficiency based on success stories. The Priority Identification Unit also uses the generation AI to analyze success stories from other local governments and companies, and can introduce a process to introduce optimal methods to reduce the burden on employees. For example, it makes proposals for work improvement based on success stories. This makes it possible to analyze success stories from other local governments and companies, and introduce optimal methods to reduce the burden on employees.

[0045] The priority identification unit can use the generation AI to compare different business processes and introduce the most efficient process. The priority identification unit, for example, uses the generation AI to compare different business processes and introduce the most efficient process. For example, it analyzes business flow data and identifies an efficient process. The priority identification unit also compares different business processes and builds a system in which the generation AI introduces the most efficient process. For example, it identifies points for improving business efficiency and proposes an optimal process. The priority identification unit also uses the generation AI to compare different business processes and introduce the most efficient process. For example, it selects an efficient process based on business flow data. This makes it possible to compare different business processes and introduce the most efficient process.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The task identification unit can analyze employee work diaries and log data to automatically discover hidden business processes and inefficient procedures. For example, it uses generation AI to analyze employee work diaries and log data to automatically discover hidden business processes and inefficient procedures. For example, it uses natural language processing technology to analyze text data from work diaries to identify tasks that occur frequently but are not recorded. The task identification unit also analyzes employee work log data to automatically discover inefficient procedures and duplicate work. For example, it identifies procedures in which the same data is entered multiple times and unnecessary approval processes. Furthermore, the task identification unit uses generation AI to automatically generate business process flowcharts based on the work diaries and log data, visually showing inefficient parts. For example, it highlights parts that are done manually. This makes it possible to automatically discover hidden business processes and inefficient procedures and improve work efficiency.

[0048] The digitization assessment department records employee work content in real time using voice input and natural language processing, and the generation AI can evaluate the feasibility of digitization on the spot. For example, as employees perform their work, they can record their work content in real time using voice input, and the generation AI can evaluate the feasibility of digitization on the spot. For example, they can use voice recognition technology to convert the work content into text data and determine whether it can be digitized. The digitization assessment department also uses natural language processing technology to analyze employee work content in real time and evaluate the feasibility of digitization. For example, they can analyze work content in real time and automatically identify parts that can be digitized. The digitization assessment department also builds a system in which, as employees perform their work, they can record their work content in real time using voice input and natural language processing, and the generation AI can evaluate the feasibility of digitization on the spot. For example, they can record work content using a voice input device, and the generation AI can determine whether it can be digitized. This allows work content to be recorded in real time and the feasibility of digitization to be quickly evaluated.

[0049] The Work Analysis Department can video record the work of employees and have a generative AI analyze the video to determine the feasibility of digitalization. For example, the work of employees can be video recorded and a generative AI can analyze the video to determine the feasibility of digitalization. For example, work procedures can be recorded as video data and a generative AI can identify parts that can be automated. The Work Analysis Department can also build a system in which the video-recorded work content is analyzed and a generative AI can evaluate the feasibility of digitalization. For example, manual tasks can be extracted from the video data and a determination can be made as to whether they can be automated. The Work Analysis Department can also introduce a process in which the work of employees can be video recorded and a generative AI can analyze the video to determine the feasibility of digitalization. For example, points for improving work efficiency can be identified based on the video data. This makes it possible to video record work content and analyze the video to determine the feasibility of digitalization.

[0050] The Business Analysis Department compares business processes between different municipalities and can introduce digitalization methods that have been successful in other municipalities. For example, by comparing business processes between different municipalities and introducing digitalization methods that have been successful in other municipalities. For example, by collecting success stories from other municipalities, the generation AI proposes the optimal method. The Business Analysis Department also analyzes digitalization methods that have been successful in other municipalities and the generation AI proposes a process to apply to the municipality itself. For example, the generation AI selects the optimal method based on data from success stories. The Business Analysis Department also compares business processes between different municipalities and builds a system for the generation AI to introduce digitalization methods that have been successful in other municipalities. For example, the generation AI customizes digitalization methods based on success stories. This makes it possible to introduce digitalization methods that have been successful in other municipalities and improve business efficiency.

[0051] The priority identification unit can use the generation AI to perform a detailed analysis of the time consumption and resource usage of each task, and identify the task that consumes the most resources. For example, the generation AI can be used to perform a detailed analysis of the time consumption and resource usage of each task, and identify the task that consumes the most resources. For example, the time consumption data for each task can be analyzed to identify the task that consumes the most resources. The priority identification unit can also perform a detailed analysis of the resource usage of each task, and build a system in which the generation AI can identify the task that consumes the most resources. For example, the priority identification unit can determine the priority of tasks based on resource usage data. The priority identification unit can also introduce a process in which the generation AI can perform a detailed analysis of the time consumption and resource usage of each task, and identify the task that consumes the most resources. For example, the task that consumes the most resources can be given priority for digitalization. This allows the task that consumes the most resources to be identified, and digitalization can be advanced efficiently.

[0052] The priority identification unit can use generation AI to analyze feedback and satisfaction data from residents and prioritize tasks that are directly related to resident services. For example, it can analyze feedback and satisfaction data from residents using generation AI to evaluate the importance of tasks. For example, it can identify tasks that are directly related to resident services based on resident opinions. The priority identification unit also uses generation AI to analyze feedback data from residents and build a system that prioritizes tasks that are directly related to resident services. For example, it can determine the priority of tasks based on satisfaction data. The priority identification unit also uses generation AI to analyze feedback and satisfaction data from residents and introduce a process that prioritizes tasks that are directly related to resident services. For example, it can make proposals for task improvements that reflect resident opinions. This makes it possible to prioritize the digitalization of tasks that are directly related to resident services and improve resident satisfaction.

[0053] The priority identification unit refers to the best practices of other local governments and companies, and the generation AI can propose optimal priorities based on them. For example, the generation AI refers to the best practices of other local governments and companies and proposes work priorities. For example, it determines optimal digitalization priorities based on success cases. The priority identification unit also uses the generation AI to analyze the best practices of other local governments and companies and build a system that proposes work priorities. For example, it determines work priorities based on data from success cases. The priority identification unit also introduces a process that refers to the best practices of other local governments and companies, and the generation AI proposes work priorities. For example, it determines digitalization priorities based on success cases. This makes it possible to propose optimal priorities based on the best practices of other local governments and companies, thereby improving work efficiency.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The Work Identification Department identifies the work of employees. For example, they list the work that employees do on a daily basis and organize the work flow. They can also analyze work diaries and log data to discover hidden work processes and inefficient procedures. Step 2: The Digitalization Assessment Department determines the digitization potential of the identified tasks. For example, it evaluates the technical feasibility and cost-effectiveness of the tasks. Additionally, the content of employees' work can be recorded in real time using voice input and natural language processing, and the generative AI can assess the digitization potential on the spot. Step 3: The Business Analysis Department conducts a detailed analysis of the tasks identified as suitable for digitalization. For example, it evaluates the frequency and importance of the tasks, as well as the burden on employees. It can also use video recordings and video analysis to analyze the tasks in detail. Step 4: The priority determination unit determines the priority for digitalization of the analyzed tasks. For example, it determines the priority based on the importance of the task and the amount of resources consumed. It can also analyze feedback and satisfaction data from residents to prioritize tasks that are directly related to resident services.

[0056] (Example 2) The business optimization system according to an embodiment of the present invention is a system that automatically identifies the tasks performed by employees, and uses a generation AI to determine the possibility of digitalization and identify priorities. As a result, the business optimization system can efficiently digitize the tasks of employees and improve business efficiency.

[0057] The business optimization system according to the embodiment includes a business task identification unit, a digitalization determination unit, a business task analysis unit, and a priority determination unit. The business task identification unit identifies employee tasks. For example, it lists the tasks performed daily by employees and organizes the workflow. The business task identification unit can also analyze business logs and log data to discover hidden business processes and inefficient procedures. The digitalization determination unit determines the possibility of digitalizing the identified tasks. For example, it evaluates the technical feasibility and cost-effectiveness of the tasks. The digitalization determination unit can also record employee tasks in real time using voice input or natural language processing, and use a generation AI to evaluate the possibility of digitalization on the spot. The business task analysis unit performs a detailed analysis of tasks determined to be suitable for digitalization. For example, it evaluates the frequency and importance of the tasks and the employee's burden. The business task analysis unit can also perform a detailed analysis of the tasks using video recording and video analysis. The priority determination unit determines the priority of digitalization for the analyzed tasks. For example, it determines the priority based on the importance of the tasks and the amount of resources consumed. The priority determination unit can also analyze feedback and satisfaction data from residents to prioritize tasks that are directly related to resident services. This allows the business optimization system according to the embodiment to efficiently digitize staff tasks and improve work efficiency. For example, digitizing tasks can reduce the manual work of staff, allowing them to focus on more important tasks. In order to improve the quality of resident services, the priority of tasks can also be determined based on feedback from residents.

[0058] The task identification unit can analyze employee work diaries and log data to automatically discover hidden business processes and inefficient procedures. For example, the task identification unit uses generation AI to analyze employee work diaries and log data to automatically discover hidden business processes and inefficient procedures. For example, it uses natural language processing technology to analyze text data from work diaries to identify tasks that occur frequently but are not recorded. The task identification unit also analyzes employee work log data to automatically discover inefficient procedures and duplicated work. For example, it identifies procedures in which the same data is entered multiple times and unnecessary approval processes. Furthermore, the task identification unit uses generation AI to automatically generate business process flowcharts based on the work diaries and log data, visually showing inefficient parts. For example, it highlights parts that are done manually. This automatically discovers hidden business processes and inefficient procedures, improving work efficiency.

[0059] The digitization determination unit records employee work content in real time using voice input and natural language processing, and the generation AI can evaluate the feasibility of digitization on the spot. For example, the digitization determination unit records employee work content in real time using voice input as employees perform their work, and the generation AI evaluates the feasibility of digitization on the spot. For example, it uses voice recognition technology to convert the work content into text data and determine whether it can be digitized. The digitization determination unit also uses natural language processing technology to analyze employee work content in real time and evaluate the feasibility of digitization. For example, it analyzes work content in real time and automatically identifies parts that can be digitized. The digitization determination unit also builds a system in which employee work content is recorded in real time using voice input and natural language processing as employees perform their work, and the generation AI evaluates the feasibility of digitization on the spot. For example, it records work content using a voice input device, and the generation AI determines whether it can be digitized. This allows work content to be recorded in real time and the feasibility of digitization to be quickly evaluated.

[0060] The digitization determination unit uses the emotion estimation function to evaluate the stress and burden felt by employees while working, and can prioritize digitization of tasks that are particularly burdensome. The digitization determination unit, for example, uses the emotion estimation function to evaluate the stress and burden felt by employees while working, and prioritizes digitization of tasks that are particularly burdensome. For example, it analyzes the employee's facial expressions and voice to quantify the stress level. The digitization determination unit also collects emotional data when employees work, and the generation AI identifies tasks that are particularly burdensome based on that data. For example, it prioritizes digitization of tasks with high emotional scores. The digitization determination unit also uses the emotion estimation function to monitor the stress and burden felt by employees while working in real time, and builds a system that prioritizes digitization of tasks that are particularly burdensome. For example, it determines the priority of digitization based on the emotional data. This makes it possible to prioritize digitization of tasks that are particularly burdensome, thereby reducing employee stress.

[0061] The business analysis department can video record the work content of employees and have a generative AI analyze the video to determine the feasibility of digitalization. For example, the business analysis department can video record the work content of employees and have a generative AI analyze the video to determine the feasibility of digitalization. For example, work procedures are recorded as video data and the generative AI identifies the parts that can be automated. The business analysis department can also analyze the video-recorded work content and build a system where the generative AI evaluates the feasibility of digitalization. For example, manual tasks are extracted from the video data and a determination is made as to whether they can be automated. The business analysis department can also introduce a process whereby the work content of employees is video-recorded and a generative AI analyzes the video to determine the feasibility of digitalization. For example, points for improving work efficiency are identified based on the video data. This makes it possible to video record work content and analyze the video to determine the feasibility of digitalization.

[0062] The Business Analysis Department compares business processes between different municipalities and can introduce digitalization methods that have been successful in other municipalities. For example, the Business Analysis Department compares business processes between different municipalities and introduces digitalization methods that have been successful in other municipalities. For example, it collects success stories from other municipalities and has the generation AI propose the optimal method. The Business Analysis Department also analyzes digitalization methods that have been successful in other municipalities and has the generation AI propose a process to apply to the municipality itself. For example, it selects the optimal method based on data from success stories. The Business Analysis Department also compares business processes between different municipalities and builds a system for the generation AI to introduce digitalization methods that have been successful in other municipalities. For example, it customizes digitalization methods based on success stories. This makes it possible to introduce digitalization methods that have been successful in other municipalities and improve business efficiency.

[0063] The work analysis department can use the emotion estimation function to monitor the emotions of employees when they perform their work in real time and make work improvement proposals that will elicit positive emotions. For example, the work analysis department can use the emotion estimation function to monitor the emotions of employees when they perform their work in real time and make work improvement proposals that will elicit positive emotions. For example, it can identify areas for work improvement based on emotion data. The work analysis department can also collect employee emotion data in real time and build a system in which a generative AI makes work improvement proposals that will elicit positive emotions. For example, it can make proposals to improve work that has a low emotion score. The work analysis department can also use the emotion estimation function to introduce a process in which it monitors the emotions of employees when they perform their work in real time and makes work improvement proposals that will elicit positive emotions. For example, it can identify areas for improving work efficiency based on emotion data. This makes it possible to monitor employee emotions in real time and make work improvement proposals that will elicit positive emotions.

[0064] The priority identification unit can use the generation AI to analyze the time consumption and resource usage of tasks in detail and identify the tasks that consume the most resources. The priority identification unit, for example, uses the generation AI to analyze the time consumption and resource usage of tasks in detail and identify the tasks that consume the most resources. For example, it analyzes time consumption data for each task and identifies the tasks that consume the most resources. The priority identification unit also analyzes the resource usage of tasks in detail and builds a system in which the generation AI identifies the tasks that consume the most resources. For example, it determines the priority of tasks based on resource usage data. The priority identification unit also introduces a process in which the generation AI analyzes the time consumption and resource usage of tasks in detail and identifies the tasks that consume the most resources. For example, it prioritizes digitization of tasks that consume the most resources. This makes it possible to identify the tasks that consume the most resources and efficiently advance digitalization.

[0065] The priority identification unit can use generation AI to analyze feedback and satisfaction data from residents and prioritize tasks that are directly related to resident services. For example, the priority identification unit can use generation AI to analyze feedback and satisfaction data from residents and evaluate the importance of tasks. For example, it can identify tasks that are directly related to resident services based on resident opinions. The priority identification unit also uses generation AI to analyze feedback data from residents and build a system that prioritizes tasks that are directly related to resident services. For example, it can determine the priority of tasks based on satisfaction data. The priority identification unit also uses generation AI to analyze feedback and satisfaction data from residents and introduce a process that prioritizes tasks that are directly related to resident services. For example, it can make proposals for task improvements that reflect resident opinions. This makes it possible to prioritize the digitalization of tasks that are directly related to resident services and improve resident satisfaction.

[0066] The priority identification unit uses the emotion estimation function to collect emotional data when employees perform their work, and can prioritize digitization of tasks that are emotionally stressful. For example, the priority identification unit uses the emotion estimation function to collect emotional data when employees perform their work, and prioritize digitization of tasks that are emotionally stressful. For example, it identifies tasks with high emotion scores and determines the priority for digitization. The priority identification unit also collects employee emotion data and builds a system in which a generation AI identifies tasks that are emotionally stressful. For example, it makes suggestions for digitization of tasks based on the emotion data. The priority identification unit also uses the emotion estimation function to collect emotional data when employees perform their work, and introduces a process to prioritize digitization of tasks that are emotionally stressful. For example, it determines the priority of tasks based on the emotion scores. This makes it possible to prioritize digitization of tasks that are emotionally stressful, thereby reducing employee stress.

[0067] The priority identification unit refers to the best practices of other local governments and companies, and the generation AI can propose optimal priorities based on them. The priority identification unit, for example, refers to the best practices of other local governments and companies, and the generation AI proposes business priorities. For example, the optimal digitalization priorities are determined based on success cases. The priority identification unit also uses the generation AI to analyze the best practices of other local governments and companies, and build a system that proposes business priorities. For example, the priority identification unit determines business priorities based on data from success cases. The priority identification unit also introduces a process that refers to the best practices of other local governments and companies, and the generation AI proposes business priorities. For example, the generation AI proposes digitalization priorities based on success cases. This makes it possible to propose optimal priorities based on the best practices of other local governments and companies, and improve business efficiency.

[0068] The priority identification unit can visualize the priorities of tasks and enable employees to intuitively understand them using an interactive dashboard. The priority identification unit, for example, visualizes the priorities of tasks and enables employees to intuitively understand them using an interactive dashboard. For example, it displays the priorities of tasks in graphs and charts. The priority identification unit also uses generative AI to build a dashboard that visualizes the priorities of tasks. For example, it visually displays the importance of tasks and resource consumption. The priority identification unit also introduces a process for visualizing the priorities of tasks and enables employees to intuitively understand them using an interactive dashboard. For example, it displays the priorities of tasks in a color-coded manner. This makes it possible to visualize the priorities of tasks and enables employees to intuitively understand them.

[0069] The priority identification unit uses the emotion estimation function to dynamically adjust the priority of tasks based on the emotion data of employees, thereby always maintaining an optimal workflow. The priority identification unit, for example, uses the emotion estimation function to dynamically adjust the priority of tasks based on the emotion data of employees, thereby always maintaining an optimal workflow. For example, tasks with high emotion scores are prioritized for digitization. The priority identification unit also collects employee emotion data in real time, and builds a system in which a generation AI dynamically adjusts the priority of tasks. For example, the priority of tasks is automatically changed based on the emotion data. The priority identification unit also uses the emotion estimation function to dynamically adjust the priority of tasks based on the emotion data of employees, thereby introducing a process to always maintain an optimal workflow. For example, the priority of tasks is updated in real time based on the emotion score. This allows the priority of tasks to be dynamically adjusted based on the emotion data of employees, thereby always maintaining an optimal workflow.

[0070] The business analysis department can use generative AI to analyze business procedures in detail and propose specific methods for automating the manual parts. For example, the business analysis department uses generative AI to analyze business procedures in detail and propose specific methods for automating the manual parts. For example, it generates a script for automating data entry work. The business analysis department also analyzes business procedures in detail and builds a system in which generative AI proposes specific methods for automation. For example, it proposes tools for automating manual work. The business analysis department also introduces a process in which generative AI analyzes business procedures in detail and proposes specific methods for automating the manual parts. For example, it designs a workflow for automating manual work. This proposes specific methods for automating the manual parts, making it possible to improve business efficiency.

[0071] The business analysis department can simulate the effects of automation and use generative AI to build a model to predict those effects before implementation. The business analysis department, for example, simulates the effects of automation and uses generative AI to build a model to predict those effects before implementation. For example, it simulates the time reduction effect of automation. The business analysis department also uses generative AI to develop a system to build a model to simulate the effects of automation. For example, it predicts the cost reduction effect of automation. The business analysis department also introduces a process to simulate the effects of automation and build a model to predict those effects before implementation using generative AI. For example, it simulates the business efficiency improvement effect of automation. This makes it possible to simulate the effects of automation and build a model to predict those effects before implementation.

[0072] The work analysis department can use the emotion estimation function to predict how employees will feel about automated tasks and select an automation method that will elicit positive emotions. For example, the work analysis department uses the emotion estimation function to predict how employees will feel about automated tasks and select an automation method that will elicit positive emotions. For example, it selects an automation method with a high emotion score. The work analysis department also builds a system in which a generative AI selects an automation method based on employee emotion data. For example, it proposes an automation method that elicits positive emotions. The work analysis department also uses the emotion estimation function to introduce a process for predicting how employees will feel about automated tasks and selecting an automation method that will elicit positive emotions. For example, it selects an automation method based on the emotion data. This makes it possible to select an automation method that will cause employees to feel positive emotions about automated tasks.

[0073] The Business Analysis Department can compare tasks to be automated with examples from different municipalities and companies, and introduce the most effective automation method. For example, the Business Analysis Department can compare tasks to be automated with examples from different municipalities and companies, and introduce the most effective automation method. For example, it can refer to automation methods that have been successful in other municipalities. The Business Analysis Department can also use generative AI to analyze examples from different municipalities and companies, and build a system to introduce the most effective automation method. For example, it can select an automation method based on successful examples. The Business Analysis Department can also compare tasks to be automated with examples from different municipalities and companies, and introduce a process to introduce the most effective automation method. For example, it can customize an automation method based on successful examples. This makes it possible to compare with examples from different municipalities and companies, and introduce the most effective automation method.

[0074] The Business Analysis Department uses generative AI to develop metrics for quantitatively evaluating the effects of automation, and can continuously monitor the effects after implementation. For example, the Business Analysis Department uses generative AI to develop metrics for quantitatively evaluating the effects of automation, and can continuously monitor the effects after implementation. For example, they quantitatively evaluate the effects of time reduction and cost reduction. The Business Analysis Department also uses generative AI to build a system that develops metrics for quantitatively evaluating the effects of automation. For example, they quantitatively evaluate the effects of improving business efficiency. The Business Analysis Department also uses generative AI to develop metrics for quantitatively evaluating the effects of automation, and can introduce a process to continuously monitor the effects after implementation. For example, they monitor the effects of automation based on quantitative evaluation data. This makes it possible to quantitatively evaluate the effects of automation and continuously monitor the effects after implementation.

[0075] The business analysis department can use the emotion estimation function to monitor employees' emotional reactions to automated tasks in real time and adjust the automation method as needed. For example, the business analysis department uses the emotion estimation function to monitor employees' emotional reactions to automated tasks in real time and adjust the automation method as needed. For example, the automation method is changed if the emotion score is low. The business analysis department also collects employee emotion data in real time and builds a system in which the generative AI adjusts the automation method. For example, the automation method is optimized based on the emotion data. The business analysis department also uses the emotion estimation function to introduce a process in which employees' emotional reactions to automated tasks in real time and adjust the automation method as needed. For example, the automation method is dynamically changed based on the emotion score. This allows employees to monitor their emotional reactions to automated tasks in real time and adjust the automation method as needed.

[0076] The priority identification unit can use the generation AI to quantitatively evaluate the workload of employees, identify tasks that are heavy burdens, and prioritize their improvement. The priority identification unit, for example, uses the generation AI to quantitatively evaluate the workload of employees, identify tasks that are heavy burdens, and prioritize their improvement. For example, it quantifies the degree of burden for each task and identifies points for improvement. The priority identification unit also builds a system that quantitatively evaluates the workload of employees, and the generation AI identifies tasks that are heavy burdens. For example, it makes suggestions for improving tasks based on data on the degree of burden. The priority identification unit also uses the generation AI to quantitatively evaluate the workload of employees, identify tasks that are heavy burdens, and introduce a process for improving them on a priority basis. For example, it determines the priority of tasks based on the degree of burden. This makes it possible to quantitatively evaluate the workload of employees, identify tasks that are heavy burdens, and prioritize their improvement.

[0077] The priority identification unit can use the generation AI to automatically detect bottlenecks in business processes and make improvement proposals. The priority identification unit can, for example, use the generation AI to automatically detect bottlenecks in business processes and make improvement proposals. For example, it analyzes business flows and identifies bottlenecks. The priority identification unit also builds a system that automatically detects bottlenecks in business processes, and the generation AI makes improvement proposals. For example, it makes proposals for improving business efficiency based on bottleneck data. The priority identification unit also uses the generation AI to introduce a process that automatically detects bottlenecks in business processes and makes improvement proposals. For example, it identifies bottlenecks and proposes improvement measures. This makes it possible to automatically detect bottlenecks in business processes and make improvement proposals.

[0078] The priority identification unit can use the emotion estimation function to optimize work processes based on employee emotion data and introduce techniques to reduce stress. The priority identification unit, for example, uses the emotion estimation function to optimize work processes based on employee emotion data and introduce techniques to reduce stress. For example, it improves work with a high emotion score. The priority identification unit also collects employee emotion data and builds a system in which a generation AI optimizes work processes. For example, it makes suggestions for improving work efficiency based on the emotion data. The priority identification unit also uses the emotion estimation function to introduce a process for optimizing work processes based on employee emotion data and introducing techniques to reduce stress. For example, it determines the priority of work based on the emotion score. This makes it possible to optimize work processes based on employee emotion data and introduce techniques to reduce stress.

[0079] The Priority Identification Unit uses the generation AI to analyze success stories from other local governments and companies, and can introduce optimal methods to reduce the burden on employees. For example, the Priority Identification Unit uses the generation AI to analyze success stories from other local governments and companies, and can introduce optimal methods to reduce the burden on employees. For example, it proposes optimal work improvement methods based on data from success stories. The Priority Identification Unit also uses the generation AI to analyze success stories from other local governments and companies, and builds a system to introduce methods to reduce the burden on employees. For example, it selects methods to improve work efficiency based on success stories. The Priority Identification Unit also uses the generation AI to analyze success stories from other local governments and companies, and can introduce a process to introduce optimal methods to reduce the burden on employees. For example, it makes proposals for work improvement based on success stories. This makes it possible to analyze success stories from other local governments and companies, and introduce optimal methods to reduce the burden on employees.

[0080] The priority identification unit can use the generation AI to compare different business processes and introduce the most efficient process. The priority identification unit, for example, uses the generation AI to compare different business processes and introduce the most efficient process. For example, it analyzes business flow data and identifies an efficient process. The priority identification unit also compares different business processes and builds a system in which the generation AI introduces the most efficient process. For example, it identifies points for improving business efficiency and proposes an optimal process. The priority identification unit also uses the generation AI to compare different business processes and introduce the most efficient process. For example, it selects an efficient process based on business flow data. This makes it possible to compare different business processes and introduce the most efficient process.

[0081] The priority identification unit uses the emotion estimation function to dynamically adjust business processes based on employee emotion data, thereby always maintaining an optimal business environment. The priority identification unit, for example, uses the emotion estimation function to dynamically adjust business processes based on employee emotion data, thereby always maintaining an optimal business environment. For example, it improves tasks with low emotion scores. The priority identification unit also collects employee emotion data in real time, and builds a system in which a generation AI dynamically adjusts business processes. For example, it makes suggestions for improving business efficiency based on the emotion data. The priority identification unit also uses the emotion estimation function to dynamically adjust business processes based on employee emotion data, thereby introducing a process to always maintain an optimal business environment. For example, it determines the priority of tasks based on the emotion scores. This makes it possible to dynamically adjust business processes based on employee emotion data, thereby always maintaining an optimal business environment.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The task identification unit can analyze employee work diaries and log data to automatically discover hidden business processes and inefficient procedures. For example, it uses generation AI to analyze employee work diaries and log data to automatically discover hidden business processes and inefficient procedures. For example, it uses natural language processing technology to analyze text data from work diaries to identify tasks that occur frequently but are not recorded. The task identification unit also analyzes employee work log data to automatically discover inefficient procedures and duplicate work. For example, it identifies procedures in which the same data is entered multiple times and unnecessary approval processes. Furthermore, the task identification unit uses generation AI to automatically generate business process flowcharts based on the work diaries and log data, visually showing inefficient parts. For example, it highlights parts that are done manually. This makes it possible to automatically discover hidden business processes and inefficient procedures and improve work efficiency.

[0084] The digitization assessment department records employee work content in real time using voice input and natural language processing, and the generation AI can evaluate the feasibility of digitization on the spot. For example, as employees perform their work, they can record their work content in real time using voice input, and the generation AI can evaluate the feasibility of digitization on the spot. For example, they can use voice recognition technology to convert the work content into text data and determine whether it can be digitized. The digitization assessment department also uses natural language processing technology to analyze employee work content in real time and evaluate the feasibility of digitization. For example, they can analyze work content in real time and automatically identify parts that can be digitized. The digitization assessment department also builds a system in which, as employees perform their work, they can record their work content in real time using voice input and natural language processing, and the generation AI can evaluate the feasibility of digitization on the spot. For example, they can record work content using a voice input device, and the generation AI can determine whether it can be digitized. This allows work content to be recorded in real time and the feasibility of digitization to be quickly evaluated.

[0085] The digitization determination unit uses the emotion estimation function to evaluate the stress and burden felt by employees while working, and can prioritize digitization of tasks that are particularly burdensome. For example, the emotion estimation function can be used to evaluate the stress and burden felt by employees while working, and prioritize digitization of tasks that are particularly burdensome. For example, the emotion estimation function can be used to analyze employees' facial expressions and voices to quantify stress levels. The digitization determination unit also collects emotional data when employees work, and the generation AI uses that data to identify tasks that are particularly burdensome. For example, tasks with high emotional scores can be prioritized for digitization. The digitization determination unit can also use the emotion estimation function to monitor the stress and burden felt by employees while working in real time, and build a system that prioritizes digitization of tasks that are particularly burdensome. For example, it can determine the priority of digitization based on the emotional data. This makes it possible to prioritize digitization of tasks that are particularly burdensome, thereby reducing employee stress.

[0086] The Work Analysis Department can video record the work of employees and have a generative AI analyze the video to determine the feasibility of digitalization. For example, the work of employees can be video recorded and a generative AI can analyze the video to determine the feasibility of digitalization. For example, work procedures can be recorded as video data and a generative AI can identify parts that can be automated. The Work Analysis Department can also build a system in which the video-recorded work content is analyzed and a generative AI can evaluate the feasibility of digitalization. For example, manual tasks can be extracted from the video data and a determination can be made as to whether they can be automated. The Work Analysis Department can also introduce a process in which the work of employees can be video recorded and a generative AI can analyze the video to determine the feasibility of digitalization. For example, points for improving work efficiency can be identified based on the video data. This makes it possible to video record work content and analyze the video to determine the feasibility of digitalization.

[0087] The Business Analysis Department compares business processes between different municipalities and can introduce digitalization methods that have been successful in other municipalities. For example, by comparing business processes between different municipalities and introducing digitalization methods that have been successful in other municipalities. For example, by collecting success stories from other municipalities, the generation AI proposes the optimal method. The Business Analysis Department also analyzes digitalization methods that have been successful in other municipalities and the generation AI proposes a process to apply to the municipality itself. For example, the generation AI selects the optimal method based on data from success stories. The Business Analysis Department also compares business processes between different municipalities and builds a system for the generation AI to introduce digitalization methods that have been successful in other municipalities. For example, the generation AI customizes digitalization methods based on success stories. This makes it possible to introduce digitalization methods that have been successful in other municipalities and improve business efficiency.

[0088] The business analysis department can use the emotion estimation function to monitor the emotions of employees when they perform their work in real time and make business improvement proposals that will elicit positive emotions. For example, the emotion estimation function can be used to monitor the emotions of employees when they perform their work in real time and make business improvement proposals that will elicit positive emotions. For example, points for business improvement can be identified based on emotion data. The business analysis department also collects employee emotion data in real time and builds a system in which a generative AI makes business improvement proposals that will elicit positive emotions. For example, it makes proposals to improve work that has a low emotion score. The business analysis department also uses the emotion estimation function to introduce a process in which the emotions of employees when they perform their work in real time and make business improvement proposals that will elicit positive emotions. For example, points for improving business efficiency can be identified based on emotion data. This makes it possible to monitor employee emotions in real time and make business improvement proposals that will elicit positive emotions.

[0089] The priority identification unit can use the generation AI to perform a detailed analysis of the time consumption and resource usage of each task, and identify the task that consumes the most resources. For example, the generation AI can be used to perform a detailed analysis of the time consumption and resource usage of each task, and identify the task that consumes the most resources. For example, the time consumption data for each task can be analyzed to identify the task that consumes the most resources. The priority identification unit can also perform a detailed analysis of the resource usage of each task, and build a system in which the generation AI can identify the task that consumes the most resources. For example, the priority identification unit can determine the priority of tasks based on resource usage data. The priority identification unit can also introduce a process in which the generation AI can perform a detailed analysis of the time consumption and resource usage of each task, and identify the task that consumes the most resources. For example, the task that consumes the most resources can be given priority for digitalization. This allows the task that consumes the most resources to be identified, and digitalization can be advanced efficiently.

[0090] The priority identification unit can use generation AI to analyze feedback and satisfaction data from residents and prioritize tasks that are directly related to resident services. For example, it can analyze feedback and satisfaction data from residents using generation AI to evaluate the importance of tasks. For example, it can identify tasks that are directly related to resident services based on resident opinions. The priority identification unit also uses generation AI to analyze feedback data from residents and build a system that prioritizes tasks that are directly related to resident services. For example, it can determine the priority of tasks based on satisfaction data. The priority identification unit also uses generation AI to analyze feedback and satisfaction data from residents and introduce a process that prioritizes tasks that are directly related to resident services. For example, it can make proposals for task improvements that reflect resident opinions. This makes it possible to prioritize the digitalization of tasks that are directly related to resident services and improve resident satisfaction.

[0091] The priority identification unit can use the emotion estimation function to collect emotional data when employees perform their work and prioritize digitization of tasks that are emotionally stressful. For example, the emotion estimation function can be used to collect emotional data when employees perform their work and prioritize digitization of tasks that are emotionally stressful. For example, tasks with high emotion scores can be identified and priorities for digitization can be determined. The priority identification unit also collects emotion data from employees and builds a system in which a generation AI identifies tasks that are emotionally stressful. For example, it can make proposals for digitization of tasks based on the emotion data. The priority identification unit also uses the emotion estimation function to collect emotional data when employees perform their work and introduce a process to prioritize digitization of tasks that are emotionally stressful. For example, it can determine the priority of tasks based on the emotion scores. This makes it possible to prioritize digitization of tasks that are emotionally stressful and reduce employee stress.

[0092] The priority identification unit refers to the best practices of other local governments and companies, and the generation AI can propose optimal priorities based on them. For example, the generation AI refers to the best practices of other local governments and companies and proposes work priorities. For example, it determines optimal digitalization priorities based on success cases. The priority identification unit also uses the generation AI to analyze the best practices of other local governments and companies and build a system that proposes work priorities. For example, it determines work priorities based on data from success cases. The priority identification unit also introduces a process that refers to the best practices of other local governments and companies, and the generation AI proposes work priorities. For example, it determines digitalization priorities based on success cases. This makes it possible to propose optimal priorities based on the best practices of other local governments and companies, thereby improving work efficiency.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The Work Identification Department identifies the work of employees. For example, they list the work that employees do on a daily basis and organize the work flow. They can also analyze work diaries and log data to discover hidden work processes and inefficient procedures. Step 2: The Digitalization Assessment Department determines the digitization potential of the identified tasks. For example, it evaluates the technical feasibility and cost-effectiveness of the tasks. Additionally, the content of employees' work can be recorded in real time using voice input and natural language processing, and the generative AI can assess the digitization potential on the spot. Step 3: The Business Analysis Department conducts a detailed analysis of the tasks identified as suitable for digitalization. For example, it evaluates the frequency and importance of the tasks, as well as the burden on employees. It can also use video recordings and video analysis to analyze the tasks in detail. Step 4: The priority determination unit determines the priority for digitalization of the analyzed tasks. For example, it determines the priority based on the importance of the task and the amount of resources consumed. It can also analyze feedback and satisfaction data from residents to prioritize tasks that are directly related to resident services.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, 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 robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 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 work identification department that identifies the work of employees, a digitalization determination unit that determines the possibility of digitalizing the business operations identified by the business operation identification unit; a task analysis unit that performs detailed analysis of tasks determined by the digitization determination unit to have the potential for digitalization; a priority specification unit that specifies the priority of digitization of the business analyzed by the business analysis unit. A system characterized by:

2. The business identification unit Analyze the employee's work diary and log data to automatically discover hidden business processes and inefficient procedures.

2. The system of claim 1.

3. The digitization determination unit The employee's work content is recorded in real time using voice input and natural language processing, and a generative AI evaluates the possibility of digitalization on the spot.

2. The system of claim 1.

4. The digitization determination unit Evaluate the stress and burden felt by employees during their work, and prioritize digitizing the work that places the greatest burden on them.

2. The system of claim 1.

5. The business analysis unit The work of the employee is video-recorded, and the generating AI analyzes the video to determine the possibility of digitization.

2. The system of claim 1.

6. The business analysis unit Compare business processes across different municipalities and adopt digitalization methods that have been successful in other municipalities 2. The system of claim 1.

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