system

Abstract

The system according to this embodiment aims to enable non-engineer users to improve work efficiency. [Solution] The system according to the embodiment comprises a reception unit, a proposal unit, a generation unit, and an execution unit. The reception unit receives input from the user, including a business operation screen and PC logs. The proposal unit analyzes the information received by the reception unit and proposes ideas for efficiency improvements. The generation unit generates program code based on the ideas proposed by the proposal unit. The execution unit executes the program code generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, automation by programming has not advanced at non-engineer sites, and there is room for efficiency improvement.

[0005] The system according to the embodiment aims to enable non-engineer users to achieve work efficiency improvement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a proposal unit, a generation unit, and an execution unit. The reception unit receives input from the user, including a business operation screen and PC logs. The proposal unit analyzes the information received by the reception unit and proposes ideas for efficiency improvements. The generation unit generates program code based on the ideas proposed by the proposal unit. The execution unit executes the program code generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment enables even non-engineer users to improve work efficiency. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system for promoting automation through programming even in non-engineer environments. This system allows the AI ​​to perform tasks from suggesting efficiency improvements to implementing programs by providing the AI ​​with the user's tedious task operation screen and PC logs as prompts. For example, the user provides the AI ​​with the user's tedious task operation screen and PC logs. In this case, the user records the operation screen of a specific task and collects the PC operation log. For example, tasks such as data entry and routine report creation are targeted. This information is input to the AI ​​agent as a prompt. Next, the AI ​​agent analyzes the provided information and suggests efficiency improvement ideas. The AI ​​agent analyzes the operation screen and PC logs and identifies which parts can be automated. For example, it may suggest automating part of data entry or automating routine report creation. This allows the user to have a concrete image of how efficiency can be improved. Furthermore, based on the suggested ideas, the AI ​​generates and implements program code. The AI ​​agent generates program code such as Python, GAS, or VBA based on the suggested efficiency improvement ideas. For example, a Python script to automate data entry or a GAS script to automate report creation is generated. The generated program code is executed by an AI agent, enabling the automation of tasks. This makes it easy for non-engineers to automate tasks. Users simply provide screenshots of tedious tasks and PC logs, and the AI ​​agent proposes efficiency improvements and generates and implements program code. This improves work efficiency and enables automation through programming, even for non-engineers. As a result, the AI ​​agent system makes it easy for non-engineers to automate tasks.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, a proposal unit, a generation unit, and an execution unit. The reception unit receives input from the user, including, but is not limited to, data entry and routine report creation. The reception unit, for example, records the user's operation screen for a specific task and collects PC operation logs. The proposal unit analyzes the information received by the reception unit and proposes ideas for efficiency improvements. The proposal unit, for example, analyzes the operation screen and PC logs to identify which parts can be automated. For example, the proposal unit proposes automating part of the data entry or automating routine report creation. The generation unit generates program code based on the ideas proposed by the proposal unit. The generation unit generates program code such as Python, GAS, or VBA. For example, the generation unit generates a Python script to automate data entry or a GAS script to automate report creation. The execution unit executes the program code generated by the generation unit. The execution unit, for example, executes the generated program code to automate business processes. As a result, the AI ​​agent system according to this embodiment proposes efficiency improvement ideas based on the user's business operation screen and PC logs, and generates and executes program code, enabling even non-engineers to automate business processes.

[0030] The reception desk receives input from users of their work operations screens and PC logs. Work operations screens from users include, but are not limited to, data entry and the creation of standardized reports. The reception desk, for example, records users' work operations screens and collects PC operation logs. Specifically, it captures the operation screen in real time as the user performs their work and records the details of their operations. This includes operations such as mouse movements, clicks, and keyboard input. PC logs include application startup and shutdown, file access history, and network usage. This data is important for understanding the user's workflow in detail and forms the basis for subsequent analysis and recommendations. The reception desk securely collects this data and stores it in a central database. Appropriate security measures are in place to protect user privacy during data collection. For example, data is encrypted during transmission and encrypted again during storage. An interface is also provided that allows users to review the scope and content of the data being collected and stop collection if necessary. This allows the reception department to efficiently and securely collect users' work screens and PC logs, providing foundational data for subsequent analysis and proposals.

[0031] The Proposal Department analyzes information received by the Reception Department and proposes ideas for efficiency improvements. For example, the Proposal Department analyzes user interface data and PC logs to identify which parts can be automated. Specifically, the Proposal Department uses AI to analyze captured data of user interface data and PC logs to analyze the user's workflow in detail. The AI ​​uses pattern recognition technology to identify repetitive operations and routine tasks and determines whether these tasks can be automated. For example, it may propose automating part of data entry or the creation of routine reports. The Proposal Department provides concrete ideas to optimize the user's workflow, thereby improving efficiency. Furthermore, the Proposal Department can propose more effective efficiency ideas by referring to past data and the workflows of other users. For example, it can analyze data from other users performing similar tasks and make proposals based on successful automation examples. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the Proposal Department can provide concrete and practical ideas to streamline the user's work and support the automation of their operations.

[0032] The generation unit generates program code based on ideas proposed by the proposal unit. The generation unit generates program code in languages ​​such as Python, GAS, and VBA. Specifically, based on efficiency improvements provided by the proposal unit, the generation unit selects the appropriate programming language and generates code for automation. For example, it generates Python scripts to automate data entry and GAS scripts to automate report creation. The generation unit uses AI to generate code and create programs optimized for the user's workflow. The AI ​​uses natural language processing technology to understand the proposal and generate appropriate code. For example, if a user inputs "I want to automate data entry," the AI ​​analyzes the request and generates specific code to automate data entry. The generation unit also tests the generated code to ensure it works correctly. This allows the generation unit to provide high-quality program code to streamline the user's work. Furthermore, the generation unit has a function to automatically generate code comments and documentation so that users can easily understand and modify the generated code. This allows the generation unit to help users customize the code and adjust it to their business needs.

[0033] The execution unit executes the program code generated by the generation unit. For example, the execution unit executes the generated program code to automate business processes. Specifically, the execution unit executes generated Python scripts or GAS scripts to automate user tasks. For example, it executes a script to automate data entry, automating data entry tasks that users previously performed manually. It also executes a script to automate the creation of standardized reports, automatically generating reports that users previously created manually each time. The execution unit verifies that the generated program code works correctly and performs error handling and logging as needed. This allows the execution unit to streamline user tasks and achieve business automation. Furthermore, the execution unit also has the function of providing feedback on execution results to the user and reporting on the progress of tasks and the effectiveness of automation. For example, the execution unit provides the user with a report showing the results of the executed scripts, indicating which tasks were automated and how much time was saved. In addition, the execution unit can continuously improve the accuracy and effectiveness of the entire system by collecting user feedback and providing it to the generation unit and proposal unit. This allows the execution unit to play a crucial role in streamlining users' work and achieving automation.

[0034] The reception department can analyze the user's past business operation history and select the optimal reception method. For example, the reception department prioritizes operations that the user has frequently performed in the past. The reception department can also suggest the most efficient reception method based on the user's past operation history. Furthermore, the reception department can analyze the user's operation patterns and determine the optimal reception timing. This enables efficient information gathering by selecting the optimal reception method based on the user's past operation history. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI.

[0035] The reception unit can filter the received work operation screens and PC logs based on the user's current work status and areas of interest. For example, the reception unit prioritizes receiving information related to the work the user is currently working on. The reception unit can also filter highly relevant information based on the user's areas of interest. Furthermore, the reception unit can receive only the necessary information depending on the user's work status. This allows for the efficient collection of only the necessary information by filtering information based on the user's work status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0036] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving business operation screens and PC logs. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. The reception unit can also filter the most relevant information based on the user's location. Furthermore, if the user is on the move, the reception unit can receive necessary information based on their current location. This allows for efficient collection of necessary information by prioritizing the receipt of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without the use of AI.

[0037] The reception unit can analyze the user's social media activity and receive relevant information when receiving business operation screens and PC logs. For example, the reception unit can receive relevant business operation screens and PC logs based on information shared by the user on social media. The reception unit can also prioritize receiving information related to the user's areas of interest from their social media activity. Furthermore, the reception unit can analyze the content of the user's social media posts and filter the necessary information. This allows for the efficient collection of necessary information by receiving relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0038] The proposal department can adjust the level of detail in its proposals based on the importance of the task. For example, it can provide detailed proposals for high-priority tasks and concise proposals for low-priority tasks. It can also gradually adjust the level of detail in its proposals according to the importance of the task. This allows for appropriate proposals by adjusting the level of detail according to the importance of the task. Some or all of the above-described processes in the proposal department may be performed using AI, or not.

[0039] The proposal department can apply different proposal algorithms depending on the category of work when making proposals. For example, for data entry tasks, the proposal department can propose automation of data entry. For report creation tasks, the proposal department can also propose automatic template generation. For communication tasks, the proposal department can also propose the introduction of chatbots. By applying different proposal algorithms depending on the category of work, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0040] The proposal department can determine the priority of proposals based on the execution time of the tasks. For example, the proposal department will prioritize proposals for urgent tasks. It can also make proposals earlier for tasks with an upcoming execution date. Furthermore, it can postpone proposals for tasks with a distant execution date. By determining the priority of proposals based on the execution time of the tasks, it becomes possible to make proposals at the appropriate time. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0041] The proposal department can adjust the order of proposals based on the relevance of the tasks. For example, the proposal department can prioritize proposals for highly relevant tasks. Conversely, it can postpone proposals for less relevant tasks. Furthermore, the proposal department can adjust the order of proposals in stages according to the relevance of the tasks. By adjusting the order of proposals based on the relevance of the tasks, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0042] The generation unit can adjust the level of detail of the program code based on the importance of the task during generation. For example, the generation unit generates detailed program code for high-importance tasks. It can also generate concise program code for low-importance tasks. Furthermore, the generation unit can adjust the level of detail of the program code in stages according to the importance of the task. By adjusting the level of detail of the program code according to the importance of the task, appropriate program code is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0043] The generation unit can apply different program code generation algorithms depending on the category of the task during generation. For example, for data entry tasks, the generation unit generates program code related to the automation of data entry. The generation unit can also generate program code related to the automatic generation of templates for report creation tasks. Furthermore, the generation unit can generate program code related to the implementation of chatbots for communication tasks. By applying different program code generation algorithms depending on the category of the task, more appropriate program code is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0044] The generation unit can determine the priority of program code based on the execution timing of tasks during generation. For example, the generation unit will prioritize generating program code for urgent tasks. It can also generate program code earlier for tasks with an upcoming execution date. Furthermore, it can postpone generating program code for tasks with a distant execution date. By determining the priority of program code based on the execution timing of tasks, it becomes possible to generate program code at the appropriate time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0045] The generation unit can adjust the order of program code based on the relevance of the tasks during generation. For example, the generation unit will prioritize generating program code for highly relevant tasks. It can also postpone generating program code for less relevant tasks. Furthermore, the generation unit can adjust the order of program code in stages according to the relevance of the tasks. By adjusting the order of program code based on the relevance of the tasks, more appropriate program code can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0046] The execution unit can analyze the user's past business execution history during execution to select the optimal execution method. For example, the execution unit may prioritize execution methods that the user has successfully used in the past. The execution unit can also propose the most efficient execution method based on the user's past execution history. Furthermore, the execution unit can analyze the user's execution patterns and determine the optimal execution timing. This enables efficient execution by selecting the optimal execution method based on the user's past execution history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI.

[0047] The execution unit can customize the means of execution at runtime based on the user's current work situation. For example, the execution unit provides an execution method related to the work the user is currently working on. The execution unit can also suggest the optimal means of execution according to the user's work situation. Furthermore, the execution unit can analyze the user's current work situation and customize the necessary means of execution. This allows for more appropriate execution by customizing the means of execution based on the user's current work situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI.

[0048] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, if the user is in a specific region, the execution unit will provide an execution method relevant to that region. The execution unit can also suggest the most relevant execution method based on the user's location information. Furthermore, if the user is on the move, the execution unit can provide the necessary execution method based on their current location. This allows for more appropriate execution by selecting the optimal execution method based on the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI.

[0049] The execution unit can analyze the user's social media activity during execution and propose means of execution. For example, the execution unit can propose relevant execution methods based on information shared by the user on social media. The execution unit can also provide means of execution related to the user's areas of interest based on their social media activity. Furthermore, the execution unit can analyze the content of the user's social media posts and propose necessary means of execution. This allows for more appropriate execution by proposing relevant means of execution based on the user's social media activity. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The reception department can analyze a user's past business operation history and select the optimal reception method. For example, it can prioritize operations that the user has frequently performed in the past. Furthermore, the reception department can suggest the most efficient reception method based on the user's past operation history. In addition, the reception department can analyze the user's operation patterns and determine the optimal reception timing. This allows for efficient information gathering by selecting the optimal reception method based on the user's past operation history.

[0052] The proposal department can adjust the level of detail in a proposal based on the importance of the task. For example, they can provide detailed proposals for high-priority tasks, and concise proposals for low-priority tasks. Furthermore, they can adjust the level of detail in a stepwise manner according to the importance of the task. This allows for the creation of appropriate proposals by adjusting the level of detail according to the importance of the task.

[0053] The generation unit can apply different program code generation algorithms depending on the category of the task during generation. For example, for data entry tasks, it can generate program code related to automating data entry. The generation unit can also generate program code related to the automatic generation of templates for report creation tasks. Furthermore, for communication tasks, it can generate program code related to the implementation of chatbots. By applying different program code generation algorithms depending on the category of the task, more appropriate program code is generated.

[0054] The execution unit can analyze the user's past business execution history during execution to select the optimal execution method. For example, it can prioritize execution methods that the user has successfully used in the past. The execution unit can also suggest the most efficient execution method based on the user's past execution history. Furthermore, the execution unit can analyze the user's execution patterns and determine the optimal execution timing. This allows for efficient execution by selecting the optimal execution method based on the user's past execution history.

[0055] The execution unit can customize the execution method based on the user's current work situation during execution. For example, it can provide an execution method related to the task the user is currently working on. The execution unit can also suggest the optimal execution method according to the user's work situation. Furthermore, the execution unit can analyze the user's current work situation and customize the necessary execution method. This allows for more appropriate execution by customizing the execution method based on the user's current work situation.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reception desk receives input from users of their work operations screens and PC logs. Examples of work operations screens from users include, but are not limited to, data entry and the creation of standardized reports. The reception desk may, for example, record the user's operation screen for a specific task and collect PC operation logs. Step 2: The proposal department analyzes the information received by the reception department and proposes ideas for efficiency improvements. For example, the proposal department analyzes the user interface and PC logs to identify which parts can be automated. For instance, the proposal department might propose automating part of the data entry process or automating the creation of standardized reports. Step 3: The generation unit generates program code based on the ideas proposed by the proposal unit. The generation unit generates program code in languages ​​such as Python, GAS, and VBA. For example, the generation unit generates a Python script to automate data entry or a GAS script to automate report creation. Step 4: The execution unit executes the program code generated by the generation unit. For example, the execution unit executes the generated program code to automate business processes.

[0058] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system for promoting automation through programming even in non-engineer environments. This system allows the AI ​​to perform tasks from suggesting efficiency improvements to implementing programs by providing the AI ​​with the user's tedious task operation screen and PC logs as prompts. For example, the user provides the AI ​​with the user's tedious task operation screen and PC logs. In this case, the user records the operation screen of a specific task and collects the PC operation log. For example, tasks such as data entry and routine report creation are targeted. This information is input to the AI ​​agent as a prompt. Next, the AI ​​agent analyzes the provided information and suggests efficiency improvement ideas. The AI ​​agent analyzes the operation screen and PC logs and identifies which parts can be automated. For example, it may suggest automating part of data entry or automating routine report creation. This allows the user to have a concrete image of how efficiency can be improved. Furthermore, based on the suggested ideas, the AI ​​generates and implements program code. The AI ​​agent generates program code such as Python, GAS, or VBA based on the suggested efficiency improvement ideas. For example, a Python script to automate data entry or a GAS script to automate report creation is generated. The generated program code is executed by an AI agent, enabling the automation of tasks. This makes it easy for non-engineers to automate tasks. Users simply provide screenshots of tedious tasks and PC logs, and the AI ​​agent proposes efficiency improvements and generates and implements program code. This improves work efficiency and enables automation through programming, even for non-engineers. As a result, the AI ​​agent system makes it easy for non-engineers to automate tasks.

[0059] The AI ​​agent system according to this embodiment comprises a reception unit, a proposal unit, a generation unit, and an execution unit. The reception unit receives input from the user, including, but is not limited to, data entry and routine report creation. The reception unit, for example, records the user's operation screen for a specific task and collects PC operation logs. The proposal unit analyzes the information received by the reception unit and proposes ideas for efficiency improvements. The proposal unit, for example, analyzes the operation screen and PC logs to identify which parts can be automated. For example, the proposal unit proposes automating part of the data entry or automating routine report creation. The generation unit generates program code based on the ideas proposed by the proposal unit. The generation unit generates program code such as Python, GAS, or VBA. For example, the generation unit generates a Python script to automate data entry or a GAS script to automate report creation. The execution unit executes the program code generated by the generation unit. The execution unit, for example, executes the generated program code to automate business processes. As a result, the AI ​​agent system according to this embodiment proposes efficiency improvement ideas based on the user's business operation screen and PC logs, and generates and executes program code, enabling even non-engineers to automate business processes.

[0060] The reception desk receives input from users of their work operations screens and PC logs. Work operations screens from users include, but are not limited to, data entry and the creation of standardized reports. The reception desk, for example, records users' work operations screens and collects PC operation logs. Specifically, it captures the operation screen in real time as the user performs their work and records the details of their operations. This includes operations such as mouse movements, clicks, and keyboard input. PC logs include application startup and shutdown, file access history, and network usage. This data is important for understanding the user's workflow in detail and forms the basis for subsequent analysis and recommendations. The reception desk securely collects this data and stores it in a central database. Appropriate security measures are in place to protect user privacy during data collection. For example, data is encrypted during transmission and encrypted again during storage. An interface is also provided that allows users to review the scope and content of the data being collected and stop collection if necessary. This allows the reception department to efficiently and securely collect users' work screens and PC logs, providing foundational data for subsequent analysis and proposals.

[0061] The Proposal Department analyzes information received by the Reception Department and proposes ideas for efficiency improvements. For example, the Proposal Department analyzes user interface data and PC logs to identify which parts can be automated. Specifically, the Proposal Department uses AI to analyze captured data of user interface data and PC logs to analyze the user's workflow in detail. The AI ​​uses pattern recognition technology to identify repetitive operations and routine tasks and determines whether these tasks can be automated. For example, it may propose automating part of data entry or the creation of routine reports. The Proposal Department provides concrete ideas to optimize the user's workflow, thereby improving efficiency. Furthermore, the Proposal Department can propose more effective efficiency ideas by referring to past data and the workflows of other users. For example, it can analyze data from other users performing similar tasks and make proposals based on successful automation examples. The Proposal Department can also collect user feedback and continuously improve the accuracy and effectiveness of its proposals. In this way, the Proposal Department can provide concrete and practical ideas to streamline the user's work and support the automation of their operations.

[0062] The generation unit generates program code based on ideas proposed by the proposal unit. The generation unit generates program code in languages ​​such as Python, GAS, and VBA. Specifically, based on efficiency improvements provided by the proposal unit, the generation unit selects the appropriate programming language and generates code for automation. For example, it generates Python scripts to automate data entry and GAS scripts to automate report creation. The generation unit uses AI to generate code and create programs optimized for the user's workflow. The AI ​​uses natural language processing technology to understand the proposal and generate appropriate code. For example, if a user inputs "I want to automate data entry," the AI ​​analyzes the request and generates specific code to automate data entry. The generation unit also tests the generated code to ensure it works correctly. This allows the generation unit to provide high-quality program code to streamline the user's work. Furthermore, the generation unit has a function to automatically generate code comments and documentation so that users can easily understand and modify the generated code. This allows the generation unit to help users customize the code and adjust it to their business needs.

[0063] The execution unit executes the program code generated by the generation unit. For example, the execution unit executes the generated program code to automate business processes. Specifically, the execution unit executes generated Python scripts or GAS scripts to automate user tasks. For example, it executes a script to automate data entry, automating data entry tasks that users previously performed manually. It also executes a script to automate the creation of standardized reports, automatically generating reports that users previously created manually each time. The execution unit verifies that the generated program code works correctly and performs error handling and logging as needed. This allows the execution unit to streamline user tasks and achieve business automation. Furthermore, the execution unit also has the function of providing feedback on execution results to the user and reporting on the progress of tasks and the effectiveness of automation. For example, the execution unit provides the user with a report showing the results of the executed scripts, indicating which tasks were automated and how much time was saved. In addition, the execution unit can continuously improve the accuracy and effectiveness of the entire system by collecting user feedback and providing it to the generation unit and proposal unit. This allows the execution unit to play a crucial role in streamlining users' work and achieving automation.

[0064] The reception desk can estimate the user's emotions and adjust the timing of receiving data from the work operation screen and PC logs based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the reception timing to help them relax. Conversely, if the user is relaxed, the reception desk can speed up the reception timing to efficiently collect information. Furthermore, if the user is in a hurry, the reception desk can process the information immediately. By adjusting the reception timing according to the user's emotions, information can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0065] The reception department can analyze the user's past business operation history and select the optimal reception method. For example, the reception department prioritizes operations that the user has frequently performed in the past. The reception department can also suggest the most efficient reception method based on the user's past operation history. Furthermore, the reception department can analyze the user's operation patterns and determine the optimal reception timing. This enables efficient information gathering by selecting the optimal reception method based on the user's past operation history. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI.

[0066] The reception unit can filter the received work operation screens and PC logs based on the user's current work status and areas of interest. For example, the reception unit prioritizes receiving information related to the work the user is currently working on. The reception unit can also filter highly relevant information based on the user's areas of interest. Furthermore, the reception unit can receive only the necessary information depending on the user's work status. This allows for the efficient collection of only the necessary information by filtering information based on the user's work status and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0067] The reception desk can estimate the user's emotions and, based on the estimated emotions, determine the priority of the work operation screens and PC logs to be received. For example, if the user is stressed, the reception desk will postpone receiving less important information. Conversely, if the user is relaxed, the reception desk can prioritize receiving more important information. Furthermore, if the user is in a hurry, the reception desk can immediately receive the most important information. In this way, by prioritizing information according to the user's emotions, important information can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0068] The reception unit can prioritize receiving highly relevant information by considering the user's geographical location when receiving business operation screens and PC logs. For example, if the user is in a specific region, the reception unit will prioritize receiving information related to that region. The reception unit can also filter the most relevant information based on the user's location. Furthermore, if the user is on the move, the reception unit can receive necessary information based on their current location. This allows for efficient collection of necessary information by prioritizing the receipt of highly relevant information based on the user's geographical location. Some or all of the above processing in the reception unit may be performed using AI, for example, or without the use of AI.

[0069] The reception unit can analyze the user's social media activity and receive relevant information when receiving business operation screens and PC logs. For example, the reception unit can receive relevant business operation screens and PC logs based on information shared by the user on social media. The reception unit can also prioritize receiving information related to the user's areas of interest from their social media activity. Furthermore, the reception unit can analyze the content of the user's social media posts and filter the necessary information. This allows for the efficient collection of necessary information by receiving relevant information based on the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0070] The suggestion function can estimate the user's emotions and adjust the way efficiency ideas are presented based on those emotions. For example, if the user is relaxed, the suggestion function will provide suggestions with detailed explanations. If the user is in a hurry, the suggestion function can provide concise and to-the-point suggestions. If the user is excited, the suggestion function can also provide suggestions with visually stimulating effects. By adjusting the way efficiency ideas are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0071] The proposal department can adjust the level of detail in its proposals based on the importance of the task. For example, it can provide detailed proposals for high-priority tasks and concise proposals for low-priority tasks. It can also gradually adjust the level of detail in its proposals according to the importance of the task. This allows for appropriate proposals by adjusting the level of detail according to the importance of the task. Some or all of the above-described processes in the proposal department may be performed using AI, or not.

[0072] The proposal department can apply different proposal algorithms depending on the category of work when making proposals. For example, for data entry tasks, the proposal department can propose automation of data entry. For report creation tasks, the proposal department can also propose automatic template generation. For communication tasks, the proposal department can also propose the introduction of chatbots. By applying different proposal algorithms depending on the category of work, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0073] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion function will provide short, concise suggestions. If the user is relaxed, the suggestion function can provide longer suggestions with more detailed explanations. If the user is excited, the suggestion function can also provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The proposal department can determine the priority of proposals based on the execution time of the tasks. For example, the proposal department will prioritize proposals for urgent tasks. It can also make proposals earlier for tasks with an upcoming execution date. Furthermore, it can postpone proposals for tasks with a distant execution date. By determining the priority of proposals based on the execution time of the tasks, it becomes possible to make proposals at the appropriate time. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0075] The proposal department can adjust the order of proposals based on the relevance of the tasks. For example, the proposal department can prioritize proposals for highly relevant tasks. Conversely, it can postpone proposals for less relevant tasks. Furthermore, the proposal department can adjust the order of proposals in stages according to the relevance of the tasks. By adjusting the order of proposals based on the relevance of the tasks, more appropriate proposals can be made. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI.

[0076] The generation unit can estimate the user's emotions and adjust the way the generated program code is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate program code with detailed comments. If the user is in a hurry, the generation unit can also generate program code with concise comments. If the user is excited, the generation unit can also generate program code with visually stimulating effects. By adjusting the way the program code is expressed according to the user's emotions, more appropriate program code can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The generation unit can adjust the level of detail of the program code based on the importance of the task during generation. For example, the generation unit generates detailed program code for high-importance tasks. It can also generate concise program code for low-importance tasks. Furthermore, the generation unit can adjust the level of detail of the program code in stages according to the importance of the task. By adjusting the level of detail of the program code according to the importance of the task, appropriate program code is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0078] The generation unit can apply different program code generation algorithms depending on the category of the task during generation. For example, for data entry tasks, the generation unit generates program code related to the automation of data entry. The generation unit can also generate program code related to the automatic generation of templates for report creation tasks. Furthermore, the generation unit can generate program code related to the implementation of chatbots for communication tasks. By applying different program code generation algorithms depending on the category of the task, more appropriate program code is generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0079] The generation unit can estimate the user's emotions and adjust the length of the program code it generates based on those emotions. For example, if the user is in a hurry, the generation unit will generate short, concise program code. If the user is relaxed, the generation unit can also generate longer program code with detailed explanations. If the user is excited, the generation unit can also generate program code with visually stimulating effects. By adjusting the length of the program code according to the user's emotions, more appropriate program code can be generated. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The generation unit can determine the priority of program code based on the execution timing of tasks during generation. For example, the generation unit will prioritize generating program code for urgent tasks. It can also generate program code earlier for tasks with an upcoming execution date. Furthermore, it can postpone generating program code for tasks with a distant execution date. By determining the priority of program code based on the execution timing of tasks, it becomes possible to generate program code at the appropriate time. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0081] The generation unit can adjust the order of program code based on the relevance of the tasks during generation. For example, the generation unit will prioritize generating program code for highly relevant tasks. It can also postpone generating program code for less relevant tasks. Furthermore, the generation unit can adjust the order of program code in stages according to the relevance of the tasks. By adjusting the order of program code based on the relevance of the tasks, more appropriate program code can be generated. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0082] The execution unit can estimate the user's emotions and adjust the execution method of the program code based on the estimated emotions. For example, if the user is relaxed, the execution unit may provide an execution method that includes detailed explanations. If the user is in a hurry, the execution unit may provide a concise and quick execution method. If the user is excited, the execution unit may provide an execution method that includes visually stimulating effects. This allows for more appropriate execution by adjusting the execution method of the program code according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The execution unit can analyze the user's past business execution history during execution to select the optimal execution method. For example, the execution unit may prioritize execution methods that the user has successfully used in the past. The execution unit can also propose the most efficient execution method based on the user's past execution history. Furthermore, the execution unit can analyze the user's execution patterns and determine the optimal execution timing. This enables efficient execution by selecting the optimal execution method based on the user's past execution history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI.

[0084] The execution unit can customize the means of execution at runtime based on the user's current work situation. For example, the execution unit provides an execution method related to the work the user is currently working on. The execution unit can also suggest the optimal means of execution according to the user's work situation. Furthermore, the execution unit can analyze the user's current work situation and customize the necessary means of execution. This allows for more appropriate execution by customizing the means of execution based on the user's current work situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI.

[0085] The execution unit can estimate the user's emotions and determine the execution priority of program code based on the estimated emotions. For example, if the user is stressed, the execution unit will postpone executions of lower importance. Conversely, if the user is relaxed, the execution unit can prioritize executions of higher importance. Furthermore, if the user is in a hurry, the execution unit can immediately perform the most important executions. This allows for more appropriate timing of execution by determining execution priorities according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, if the user is in a specific region, the execution unit will provide an execution method relevant to that region. The execution unit can also suggest the most relevant execution method based on the user's location information. Furthermore, if the user is on the move, the execution unit can provide the necessary execution method based on their current location. This allows for more appropriate execution by selecting the optimal execution method based on the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI.

[0087] The execution unit can analyze the user's social media activity during execution and propose means of execution. For example, the execution unit can propose relevant execution methods based on information shared by the user on social media. The execution unit can also provide means of execution related to the user's areas of interest based on their social media activity. Furthermore, the execution unit can analyze the content of the user's social media posts and propose necessary means of execution. This allows for more appropriate execution by proposing relevant means of execution based on the user's social media activity. Some or all of the above processing in the execution unit may be performed using AI, for example, or without using AI.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The suggestion function can estimate the user's emotions and customize the content of suggestions based on those emotions. For example, if the user is feeling stressed, the suggestion function can suggest relaxation methods or breaks to reduce stress. If the user is relaxed, the suggestion function can suggest more challenging tasks. Furthermore, if the user is excited, the suggestion function can suggest stimulating tasks to maintain that excitement. By customizing the content of suggestions according to the user's emotions, more appropriate suggestions can be made.

[0090] The generation unit can estimate the user's emotions and adjust the difficulty of the program code it generates based on those emotions. For example, if the user is relaxed, the generation unit will generate advanced program code that includes detailed comments and explanations. If the user is in a hurry, the generation unit can also generate concise and fast program code. Furthermore, if the user is excited, the generation unit can generate program code with visually appealing effects. By adjusting the difficulty of the program code according to the user's emotions, more appropriate program code can be generated.

[0091] The execution unit can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the user is relaxed, the execution unit provides detailed feedback. If the user is in a hurry, the execution unit can provide concise and to-the-point feedback. Furthermore, if the user is excited, the execution unit can provide feedback with visually stimulating effects. By adjusting the feedback method according to the user's emotions, more appropriate feedback becomes possible.

[0092] The reception desk can estimate the user's emotions and adjust the type of information it receives based on those estimates. For example, if the user is stressed, the reception desk will prioritize receiving information that helps reduce stress. If the user is relaxed, the reception desk may prioritize receiving more challenging information. Furthermore, if the user is excited, the reception desk may prioritize receiving stimulating information to maintain that excitement. By adjusting the type of information received according to the user's emotions, more appropriate information gathering becomes possible.

[0093] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the suggestion function will delay suggestions until the stress subsides. If the user is relaxed, the suggestion function can make suggestions immediately. Furthermore, if the user is excited, the suggestion function can adjust the timing of suggestions to maintain that excitement. By adjusting the timing of suggestions according to the user's emotions, more appropriate suggestions can be made.

[0094] The reception department can analyze a user's past business operation history and select the optimal reception method. For example, it can prioritize operations that the user has frequently performed in the past. Furthermore, the reception department can suggest the most efficient reception method based on the user's past operation history. In addition, the reception department can analyze the user's operation patterns and determine the optimal reception timing. This allows for efficient information gathering by selecting the optimal reception method based on the user's past operation history.

[0095] The proposal department can adjust the level of detail in a proposal based on the importance of the task. For example, they can provide detailed proposals for high-priority tasks, and concise proposals for low-priority tasks. Furthermore, they can adjust the level of detail in a stepwise manner according to the importance of the task. This allows for the creation of appropriate proposals by adjusting the level of detail according to the importance of the task.

[0096] The generation unit can apply different program code generation algorithms depending on the category of the task during generation. For example, for data entry tasks, it can generate program code related to automating data entry. The generation unit can also generate program code related to the automatic generation of templates for report creation tasks. Furthermore, for communication tasks, it can generate program code related to the implementation of chatbots. By applying different program code generation algorithms depending on the category of the task, more appropriate program code is generated.

[0097] The execution unit can analyze the user's past business execution history during execution to select the optimal execution method. For example, it can prioritize execution methods that the user has successfully used in the past. The execution unit can also suggest the most efficient execution method based on the user's past execution history. Furthermore, the execution unit can analyze the user's execution patterns and determine the optimal execution timing. This allows for efficient execution by selecting the optimal execution method based on the user's past execution history.

[0098] The execution unit can customize the execution method based on the user's current work situation during execution. For example, it can provide an execution method related to the task the user is currently working on. The execution unit can also suggest the optimal execution method according to the user's work situation. Furthermore, the execution unit can analyze the user's current work situation and customize the necessary execution method. This allows for more appropriate execution by customizing the execution method based on the user's current work situation.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The reception desk receives input from users of their work operations screens and PC logs. Examples of work operations screens from users include, but are not limited to, data entry and the creation of standardized reports. The reception desk may, for example, record the user's operation screen for a specific task and collect PC operation logs. Step 2: The proposal department analyzes the information received by the reception department and proposes ideas for efficiency improvements. For example, the proposal department analyzes the user interface and PC logs to identify which parts can be automated. For instance, the proposal department might propose automating part of the data entry process or automating the creation of standardized reports. Step 3: The generation unit generates program code based on the ideas proposed by the proposal unit. The generation unit generates program code in languages ​​such as Python, GAS, and VBA. For example, the generation unit generates a Python script to automate data entry or a GAS script to automate report creation. Step 4: The execution unit executes the program code generated by the generation unit. For example, the execution unit executes the generated program code to automate business processes.

[0101] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0104] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives input from the user, such as a business operation screen and PC logs. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit and proposes ideas for efficiency improvements. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates program code based on the proposed ideas. The execution unit is implemented by the control unit 46A of the smart device 14 and executes the generated program code. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, and execution unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives input from the user, such as a business operation screen and PC logs. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit and proposes ideas for efficiency improvements. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates program code based on the proposed ideas. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes the generated program code. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives input from the user, such as a business operation screen and PC logs. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit and proposes ideas for efficiency improvements. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates program code based on the proposed ideas. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes the generated program code. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 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.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the reception unit, proposal unit, generation unit, and execution unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives input from the user, such as a work operation screen and PC logs. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information received by the reception unit and proposes ideas for efficiency improvements. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates program code based on the proposed ideas. The execution unit is implemented by the control unit 46A of the robot 414 and executes the generated program code. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A reception area that accepts user input of work operation screens and PC logs, The proposal department analyzes the information received by the reception department and proposes ideas for improving efficiency, A generation unit that generates program code based on the ideas proposed by the aforementioned proposal unit, The system comprises an execution unit that executes the program code generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving data from the work operation screen and PC logs based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past business operation history to select the optimal reception method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When receiving work operation screens and PC logs, filtering is performed based on the user's current work status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the business operation screens and PC logs to be processed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When receiving business operation screens and PC logs, the system prioritizes receiving information that is highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When receiving data from the business operation screen and PC logs, the system analyzes the user's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned proposal section is, It estimates the user's emotions and adjusts how efficiency ideas are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the work. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned proposal section is, When making a proposal, prioritize the proposal based on the timeline for when the work will be performed. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the work. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is This process estimates user emotions and adjusts how the generated program code is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the level of detail in the program code is adjusted based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, different program code generation algorithms are applied depending on the category of the task. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the program code generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the program code priority is determined based on the execution timing of the business processes. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the order of program code is adjusted based on the relevance of the business processes. The system described in Appendix 1, characterized by the features described herein. (Note 20) The execution unit is, It estimates the user's emotions and adjusts how the program code is executed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The execution unit is, During execution, the system analyzes the user's past work execution history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The execution unit is, At runtime, the execution method is customized based on the user's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The execution unit is, It estimates the user's emotions and determines the execution priority of program code based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The execution unit is, At runtime, the optimal execution method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The execution unit is, During execution, the system analyzes the user's social media activity and suggests implementation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts user input of work operation screens and PC logs, The proposal department analyzes the information received by the reception department and proposes ideas for improving efficiency, A generation unit that generates program code based on the ideas proposed by the aforementioned proposal unit, The system comprises an execution unit that executes the program code generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of receiving data from the work operation screen and PC logs based on the estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past business operation history to select the optimal reception method. The system according to feature 1.

4. The aforementioned reception unit is When receiving work operation screens and PC logs, filtering is performed based on the user's current work status and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and determines the priority of the business operation screens and PC logs to be processed based on the estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When receiving business operation screens and PC logs, the system prioritizes receiving information that is highly relevant based on the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When receiving data from the business operation screen and PC logs, the system analyzes the user's social media activity and collects relevant information. The system according to feature 1.

8. The aforementioned proposal section is, It estimates the user's emotions and adjusts how efficiency ideas are expressed based on those estimated emotions. The system according to feature 1.

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