System

Abstract

An object of a system according to an embodiment is to generate an optimum program based on a task list and track a progress status of a task in real time.SOLUTION: A system includes a task list analysis part, a program generation part, a cooperative work proposal part, and a progress state tracking part. The task list analysis unit analyzes the task list. The program generation unit generates an optimum program based on the task list analyzed by the task list analysis unit. The collaborative work proposal unit proposes collaborative work with a person for a task that cannot be completely processed by the program generated by the program generation unit. The progress tracker may track a progress of the task in real time using the IoT sensor.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques do not adequately generate an optimal program based on a task list and track the progress of tasks in real time, leaving room for improvement.

[0005] The system according to the embodiment aims to generate an optimal program based on a task list and to track the progress of the tasks in real time. [Means for solving the problem]

[0006] A system according to an embodiment includes a task list analysis unit, a program generation unit, a collaboration suggestion unit, and a progress tracking unit. The task list analysis unit analyzes a task list. The program generation unit generates an optimal program based on the task list analyzed by the task list analysis unit. The collaboration suggestion unit suggests collaboration with humans for tasks that the program generated by the program generation unit cannot fully handle. The progress tracking unit tracks task progress in real time using IoT sensors. [Effects of the Invention]

[0007] The system according to the embodiment can generate an optimal program based on the task list and track the progress of the tasks in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) An integrated office work support system according to an embodiment of the present invention is a system that supports a client in performing tasks in an office. This system is provided by combining artificial intelligence (AI), the Internet of Things (IoT), and electronic payment services. This allows the integrated office work support system to efficiently support a client in performing various tasks in an office.

[0029] An integrated office work support system according to an embodiment includes a task list analysis unit, a program generation unit, a collaboration proposal unit, and a progress tracking unit. The task list analysis unit analyzes a task list submitted by a client. For example, the task list analysis unit analyzes the content of tasks using text analysis technology. The task list analysis unit can also evaluate the importance and urgency of tasks using data mining technology. The task list analysis unit can also prioritize tasks based on the client's past work history. The program generation unit generates an optimal program based on the task list analyzed by the task list analysis unit. For example, the program generation unit generates an optimal program for each task using a generation AI (e.g., a text generation AI or a multimodal generation AI). The program generation unit can also automatically propose necessary resources (e.g., databases and APIs) based on the content of the task and incorporate them into the program. The program generation unit can also propose an optimal program by referring to success stories of other clients. The collaboration proposal unit proposes collaboration with humans for tasks that the program generated by the program generation unit cannot fully handle. For example, the collaboration suggestion unit evaluates the complexity of a task and automatically matches the most suitable expert. The collaboration suggestion unit can also monitor the progress of the collaboration in real time and propose task redistribution as necessary. The collaboration suggestion unit can also use an emotion estimation function to analyze the emotional state of members working together and propose improvements to communication. The progress tracking unit uses IoT sensors to track the progress of a task in real time. For example, the progress tracking unit can record the progress of a task in detail and automatically generate reports based on the progress data. The progress tracking unit can also monitor the progress of a task in real time and issue an alert if an abnormality is detected. The progress tracking unit can also use the emotion estimation function to analyze the emotional state of a client and automatically adjust the optimal work environment (music, lighting, etc.).As a result, the integrated office work support system according to the embodiment can efficiently support client tasks and track progress in real time. For example, clients can grasp the progress of tasks at a glance and work efficiently. Furthermore, when payment is required, payments can be made quickly using an electronic payment service.

[0030] The task list analysis unit analyzes the task list, automatically sets task priorities, and can generate an optimal schedule. In the task list analysis unit, for example, the generation AI analyzes the task list and evaluates the importance and urgency of each task to set priorities. For example, the task list is scheduled to prioritize tasks with upcoming deadlines. In addition, the generation AI automatically generates an optimal schedule based on the contents of the task list. For example, it considers task dependencies and arranges tasks in an efficient order. In addition, the generation AI analyzes the task list and proposes an optimal schedule based on the client's past work history and performance data. For example, it generates a schedule that matches the client's work pace. This makes it possible to automatically set task priorities and generate an optimal schedule.

[0031] The program generation unit can automatically suggest required resources based on the content of the task and incorporate them into the program. In the program generation unit, for example, the generation AI analyzes a task list and automatically suggests databases and APIs required for each task. For example, it selects an appropriate database for a data entry task. In addition, the program generation unit automatically incorporates required resources into the program based on the content of the task. For example, it incorporates an appropriate API for a report creation task. In addition, the program generation unit analyzes a task list using the generation AI and suggests optimal resources based on the client's past work history and resource usage data. For example, it prioritizes suggesting databases that the client frequently uses. This allows required resources to be automatically suggested and incorporated into the program.

[0032] The collaboration proposal unit can evaluate the complexity of a task and automatically match the most suitable expert. For example, the collaboration proposal unit builds a system in which a generative AI evaluates the complexity of a task and automatically matches the most suitable expert. For example, it matches a data scientist for a data analysis task. The collaboration proposal unit also evaluates the skill sets of experts based on the content of the task and suggests the most suitable expert. For example, it matches a designer for creative design work. The collaboration proposal unit also evaluates the complexity of a task using a generative AI and matches the most suitable expert based on the client's past work history. For example, it suggests experts who have successfully completed the same type of task. This makes it possible to evaluate the complexity of a task and automatically match the most suitable expert.

[0033] The progress tracking unit can monitor the progress of a task in real time and issue an alert if an abnormality is detected. For example, the progress tracking unit builds a system in which IoT sensors monitor the progress of a task in real time and issue an alert if an abnormality is detected. For example, a notification is sent if the task is behind schedule. The progress tracking unit also develops a system in which IoT sensors monitor the progress of a task in real time and issue an alert if an abnormality is detected. For example, an alert is issued if the progress of a task stops. The progress tracking unit also builds a system in which IoT sensors monitor the progress of a task in real time and issue an alert if an abnormality is detected. For example, a notification is also sent if the progress of a task is faster than scheduled. This makes it possible to monitor the progress of a task in real time and issue an alert if an abnormality is detected.

[0034] The progress tracking unit not only tracks the progress of a task, but also simultaneously records environmental data and can suggest the optimal work environment. For example, the progress tracking unit will build a system in which IoT sensors track the progress of a task while simultaneously recording environmental data (temperature, humidity, lighting, etc.). For example, the unit will suggest the optimal environment based on the work environment data. The progress tracking unit will also develop a system in which the task progress and environmental data are simultaneously recorded and the optimal work environment is suggested. For example, the progress tracking unit will monitor whether the temperature and humidity are within appropriate ranges. The progress tracking unit will also use IoT sensors to simultaneously record the task progress and environmental data and suggest the optimal work environment. For example, the unit will suggest adjusting the brightness of the lighting. This makes it possible to track the progress of a task and suggest the optimal work environment.

[0035] The program generation unit can analyze a task list and generate a program that groups similar tasks and processes them in bulk. In the program generation unit, for example, a generation AI analyzes a task list and automatically groups similar tasks. For example, multiple data entry tasks are grouped into one group. The program generation unit also generates a program that processes the grouped tasks in bulk. For example, multiple queries to the same database are processed by one program. In the program generation unit, a generation AI analyzes a task list and groups similar tasks based on the client's past work history. For example, report creation tasks of the same type are grouped into one group. This makes it possible to generate a program that groups similar tasks and processes them in bulk.

[0036] The collaborative work proposal unit can monitor the progress of the collaborative work in real time and propose task redistribution as necessary. For example, the collaborative work proposal unit uses a generation AI to monitor the progress of the collaborative work in real time and propose redistribution according to the progress of the tasks. For example, it may assign delayed tasks to other members. The collaborative work proposal unit also builds a system in which the generation AI proposes task redistribution based on the progress of the collaborative work. For example, it may redistribute tasks if the task load is uneven. The collaborative work proposal unit also uses a generation AI to monitor the progress of the collaborative work in real time and propose optimal redistribution based on the client's past work history. For example, it may prioritize allocating tasks that the client is good at. This allows the progress of the collaborative work to be monitored in real time and propose task redistribution as necessary.

[0037] The collaborative work proposal unit can automatically assemble a team combining experts from different fields based on the content of the task. For example, the collaborative work proposal unit uses a generation AI to analyze the content of the task and automatically assemble a team combining experts from different fields. For example, combining a data scientist with a designer. The collaborative work proposal unit also uses the generation AI to select the most suitable experts and assemble a team depending on the complexity of the task. For example, combining multiple experts for a complex project. The collaborative work proposal unit also uses the generation AI to assemble the optimal team based on the content of the task by referring to the client's past work history. For example, it recreates team compositions that have been successful in the past. This makes it possible to automatically assemble a team combining experts from different fields.

[0038] The collaborative work proposal unit can analyze past collaborative work data and propose the most efficient work process. In the collaborative work proposal unit, for example, the generation AI analyzes past collaborative work data and proposes the most efficient work process. For example, proposals are made based on the work processes of successful projects. The collaborative work proposal unit also builds a system in which the generation AI automatically extracts and proposes efficient work processes based on past data. For example, it analyzes work time and resource usage. In addition, the collaborative work proposal unit uses the generation AI to analyze past collaborative work data and propose the optimal work process based on the client's work history. For example, it prioritizes proposals for work processes in which the client excels. This makes it possible to analyze past collaborative work data and propose the most efficient work process.

[0039] The progress tracking unit can record the progress of tasks in detail and automatically generate reports based on the progress data. For example, the progress tracking unit uses IoT sensors to record the progress of tasks in real time and automatically generate reports based on that data. For example, it records the start and end times of each task and visualizes the progress. The progress tracking unit also uses IoT sensors to record the progress of tasks in detail and automatically generate weekly or monthly reports based on the progress data. For example, the report reflects the task completion rate and delay status. The progress tracking unit also builds a system in which IoT sensors monitor the progress of tasks in real time and automatically generate reports based on the progress data. For example, it displays the task progress in graphs or charts. This makes it possible to record the progress of tasks in detail and automatically generate reports based on the progress data.

[0040] The progress tracking unit can monitor the progress of a task in real time and provide a benchmark by comparing it with the progress data of other clients. The progress tracking unit, for example, builds a system in which IoT sensors track the progress of a task and provide a benchmark by comparing it with the progress data of other clients. For example, the progress of other clients performing the same task is displayed. The progress tracking unit also develops a system in which the progress of a task is tracked and provided a benchmark by comparing it with the progress data of other clients. For example, the progress of a client is evaluated by comparing it with an average progress speed. The progress tracking unit also uses IoT sensors to track the progress of a task and provide a benchmark by comparing it with the progress data of other clients. For example, a benchmark is set based on the progress data of clients in the same industry. This makes it possible to monitor the progress of a task in real time and provide a benchmark by comparing it with the progress data of other clients.

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

[0042] The task list analyzer not only analyzes client tasks, but can also visually display task dependencies. For example, the task list analyzer can display task dependencies in a graph format, allowing clients to understand the task flow at a glance. The task list analyzer can also suggest the optimal task order based on task dependencies. For example, it can create a schedule that prioritizes tasks with strong dependencies. The task list analyzer can also analyze task dependencies, identify task bottlenecks, and suggest improvement measures. This allows clients to visually understand task dependencies and progress with tasks efficiently.

[0043] When analyzing a client's tasks, the task list analysis unit can also perform a risk assessment of the tasks. For example, the task list analysis unit can identify risk factors for the tasks and suggest that high-risk tasks be processed first. The task list analysis unit can also suggest risk mitigation measures based on the task risk assessment. For example, allocating additional resources to high-risk tasks. The task list analysis unit can also perform a risk assessment of the tasks and notify the client of high-risk tasks. This allows the client to understand the risks of the tasks and take appropriate measures.

[0044] The program generation unit can not only suggest required resources based on the content of the task, but also suggest the optimal way to use the resources. For example, the program generation unit can suggest the optimal way to query a database, supporting efficient data retrieval. The program generation unit can also suggest the optimal way to use APIs, supporting efficient data integration. For example, it can suggest a way to minimize the number of API calls. The program generation unit can also suggest the optimal way to use resources, improving the work efficiency of the client. This allows the client to use resources efficiently and complete tasks quickly.

[0045] The collaborative work proposal unit not only evaluates the complexity of a task and matches the most suitable expert, but also adjusts the expert's schedule. For example, the collaborative work proposal unit automatically adjusts the expert's schedule and assigns tasks at the optimal timing. The collaborative work proposal unit can also reevaluate task priorities based on the expert's schedule and propose an efficient schedule. For example, it rearranges tasks to match the time slots when the expert is available. The collaborative work proposal unit can also adjust the expert's schedule to ensure smooth progress of the task. This enables clients to efficiently manage tasks taking into account the expert's schedule.

[0046] The progress tracking unit not only monitors the progress of tasks in real time and issues alerts when an abnormality is detected, but also identifies the cause of the abnormality and proposes a solution. For example, the progress tracking unit can identify the cause of a delay in task progress and propose adding appropriate resources. The progress tracking unit can also identify the cause of a halt in task progress and propose steps to resume it. For example, if necessary data is missing, it can propose a method for obtaining the data. The progress tracking unit can also identify the cause of the abnormality and notify the client, thereby supporting a prompt response. This allows the client to understand the cause of the abnormality and take appropriate measures.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The task list analysis unit analyzes the task list submitted by the client. For example, the task list analysis unit uses text analysis technology to analyze the content of the tasks and data mining technology to evaluate the importance and urgency of the tasks. It can also set task priorities based on the client's past work history. Step 2: The program generation unit generates the optimal program based on the task list analyzed by the task list analysis unit. For example, it uses generation AI to generate the optimal program for each task, automatically suggests the necessary resources, and incorporates them into the program. It can also refer to the success stories of other clients to suggest the optimal program. Step 3: The collaboration suggestion unit proposes collaboration with humans for tasks that the program generated by the program generation unit cannot fully handle. For example, it evaluates the complexity of the task and automatically matches the most suitable expert. It also monitors the progress of the collaboration in real time and suggests redistribution of tasks as necessary. Furthermore, it can use an emotion estimation function to analyze the emotional state of members during collaboration and make suggestions for improving communication. Step 4: The progress tracking unit uses IoT sensors to track the progress of tasks in real time. For example, it can record detailed task progress and automatically generate reports based on the progress data. It can also monitor task progress in real time and issue alerts if anomalies are detected. Furthermore, it can use emotion estimation to analyze the client's emotional state and automatically adjust the optimal working environment.

[0049] (Example 2) An integrated office work support system according to an embodiment of the present invention is a system that supports a client in performing tasks in an office. This system is provided by combining artificial intelligence (AI), the Internet of Things (IoT), and electronic payment services. This allows the integrated office work support system to efficiently support a client in performing various tasks in an office.

[0050] An integrated office work support system according to an embodiment includes a task list analysis unit, a program generation unit, a collaboration proposal unit, and a progress tracking unit. The task list analysis unit analyzes a task list submitted by a client. For example, the task list analysis unit analyzes the content of tasks using text analysis technology. The task list analysis unit can also evaluate the importance and urgency of tasks using data mining technology. The task list analysis unit can also prioritize tasks based on the client's past work history. The program generation unit generates an optimal program based on the task list analyzed by the task list analysis unit. For example, the program generation unit generates an optimal program for each task using a generation AI (e.g., a text generation AI or a multimodal generation AI). The program generation unit can also automatically propose necessary resources (e.g., databases and APIs) based on the content of the task and incorporate them into the program. The program generation unit can also propose an optimal program by referring to success stories of other clients. The collaboration proposal unit proposes collaboration with humans for tasks that the program generated by the program generation unit cannot fully handle. For example, the collaboration suggestion unit evaluates the complexity of a task and automatically matches the most suitable expert. The collaboration suggestion unit can also monitor the progress of the collaboration in real time and propose task redistribution as necessary. The collaboration suggestion unit can also use an emotion estimation function to analyze the emotional state of members working together and propose improvements to communication. The progress tracking unit uses IoT sensors to track the progress of a task in real time. For example, the progress tracking unit can record the progress of a task in detail and automatically generate reports based on the progress data. The progress tracking unit can also monitor the progress of a task in real time and issue an alert if an abnormality is detected. The progress tracking unit can also use the emotion estimation function to analyze the emotional state of a client and automatically adjust the optimal work environment (music, lighting, etc.).As a result, the integrated office work support system according to the embodiment can efficiently support client tasks and track progress in real time. For example, clients can grasp the progress of tasks at a glance and work efficiently. Furthermore, when payment is required, payments can be made quickly using an electronic payment service.

[0051] The task list analysis unit analyzes the task list, automatically sets task priorities, and can generate an optimal schedule. In the task list analysis unit, for example, the generation AI analyzes the task list and evaluates the importance and urgency of each task to set priorities. For example, the task list is scheduled to prioritize tasks with upcoming deadlines. In addition, the generation AI automatically generates an optimal schedule based on the contents of the task list. For example, it considers task dependencies and arranges tasks in an efficient order. In addition, the generation AI analyzes the task list and proposes an optimal schedule based on the client's past work history and performance data. For example, it generates a schedule that matches the client's work pace. This makes it possible to automatically set task priorities and generate an optimal schedule.

[0052] The program generation unit can automatically suggest required resources based on the content of the task and incorporate them into the program. In the program generation unit, for example, the generation AI analyzes a task list and automatically suggests databases and APIs required for each task. For example, it selects an appropriate database for a data entry task. In addition, the program generation unit automatically incorporates required resources into the program based on the content of the task. For example, it incorporates an appropriate API for a report creation task. In addition, the program generation unit analyzes a task list using the generation AI and suggests optimal resources based on the client's past work history and resource usage data. For example, it prioritizes suggesting databases that the client frequently uses. This allows required resources to be automatically suggested and incorporated into the program.

[0053] The collaboration proposal unit can evaluate the complexity of a task and automatically match the most suitable expert. For example, the collaboration proposal unit builds a system in which a generative AI evaluates the complexity of a task and automatically matches the most suitable expert. For example, it matches a data scientist for a data analysis task. The collaboration proposal unit also evaluates the skill sets of experts based on the content of the task and suggests the most suitable expert. For example, it matches a designer for creative design work. The collaboration proposal unit also evaluates the complexity of a task using a generative AI and matches the most suitable expert based on the client's past work history. For example, it suggests experts who have successfully completed the same type of task. This makes it possible to evaluate the complexity of a task and automatically match the most suitable expert.

[0054] The progress tracking unit can monitor the progress of a task in real time and issue an alert if an abnormality is detected. For example, the progress tracking unit builds a system in which IoT sensors monitor the progress of a task in real time and issue an alert if an abnormality is detected. For example, a notification is sent if the task is behind schedule. The progress tracking unit also develops a system in which IoT sensors monitor the progress of a task in real time and issue an alert if an abnormality is detected. For example, an alert is issued if the progress of a task stops. The progress tracking unit also builds a system in which IoT sensors monitor the progress of a task in real time and issue an alert if an abnormality is detected. For example, a notification is also sent if the progress of a task is faster than scheduled. This makes it possible to monitor the progress of a task in real time and issue an alert if an abnormality is detected.

[0055] The progress tracking unit not only tracks the progress of a task, but also simultaneously records environmental data and can suggest the optimal work environment. For example, the progress tracking unit will build a system in which IoT sensors track the progress of a task while simultaneously recording environmental data (temperature, humidity, lighting, etc.). For example, the unit will suggest the optimal environment based on the work environment data. The progress tracking unit will also develop a system in which the task progress and environmental data are simultaneously recorded and the optimal work environment is suggested. For example, the progress tracking unit will monitor whether the temperature and humidity are within appropriate ranges. The progress tracking unit will also use IoT sensors to simultaneously record the task progress and environmental data and suggest the optimal work environment. For example, the unit will suggest adjusting the brightness of the lighting. This makes it possible to track the progress of a task and suggest the optimal work environment.

[0056] The progress tracking unit can use the emotion estimation function to monitor the emotional state of the client in real time and suggest relaxation methods when stress increases. The progress tracking unit, for example, uses the emotion estimation function to monitor the emotional state of the client in real time and suggest relaxation methods when stress increases. For example, it can suggest deep breathing or a short break. The progress tracking unit also builds a system that monitors the emotional state of the client in real time and suggests relaxation methods when stress increases. For example, it can play relaxing music. The progress tracking unit also monitors the emotional state of the client in real time based on the emotion estimation data and suggests relaxation methods when stress increases. For example, it can suggest stretching or light exercise. In this way, the client's emotional state can be monitored in real time and relaxation methods can be suggested when stress increases.

[0057] The program generation unit can analyze a task list and generate a program that groups similar tasks and processes them in bulk. In the program generation unit, for example, a generation AI analyzes a task list and automatically groups similar tasks. For example, multiple data entry tasks are grouped into one group. The program generation unit also generates a program that processes the grouped tasks in bulk. For example, multiple queries to the same database are processed by one program. In the program generation unit, a generation AI analyzes a task list and groups similar tasks based on the client's past work history. For example, report creation tasks of the same type are grouped into one group. This makes it possible to generate a program that groups similar tasks and processes them in bulk.

[0058] The program generation unit uses the emotion estimation function to propose a task order that will most motivate the client, thereby promoting efficient task completion. The program generation unit, for example, uses the emotion estimation function to propose a task order that will most motivate the client. For example, it prioritizes tasks that the client likes. The program generation unit also analyzes the client's emotional state and proposes a task order that will increase motivation. For example, it starts with easy tasks and gradually increases the difficulty. The program generation unit also proposes a task order that will maintain the client's motivation based on the emotion estimation data. For example, it places tasks that are likely to give the client a sense of accomplishment in the middle. This makes it possible to propose a task order that will most motivate the client, thereby promoting efficient task completion.

[0059] The collaborative work proposal unit can monitor the progress of the collaborative work in real time and propose task redistribution as necessary. For example, the collaborative work proposal unit uses a generation AI to monitor the progress of the collaborative work in real time and propose redistribution according to the progress of the tasks. For example, it may assign delayed tasks to other members. The collaborative work proposal unit also builds a system in which the generation AI proposes task redistribution based on the progress of the collaborative work. For example, it may redistribute tasks if the task load is uneven. The collaborative work proposal unit also uses a generation AI to monitor the progress of the collaborative work in real time and propose optimal redistribution based on the client's past work history. For example, it may prioritize allocating tasks that the client is good at. This allows the progress of the collaborative work to be monitored in real time and propose task redistribution as necessary.

[0060] The collaborative work suggestion unit can use the emotion estimation function to analyze the emotional states of members working together and make suggestions for improving communication. The collaborative work suggestion unit, for example, uses the emotion estimation function to monitor the emotional states of members working together in real time and make suggestions for improving communication. For example, it can suggest ways to relax when stress levels rise. The collaborative work suggestion unit also builds a system that analyzes members' emotional states and makes suggestions for improving communication. For example, it can make suggestions to increase positive feedback. The collaborative work suggestion unit also analyzes the emotional states of members working together based on the emotion estimation data and makes suggestions for improving communication. For example, it can send encouraging messages to members who are feeling down. This makes it possible to analyze the emotional states of members working together and make suggestions for improving communication.

[0061] The collaborative work proposal unit can automatically assemble a team combining experts from different fields based on the content of the task. For example, the collaborative work proposal unit uses a generation AI to analyze the content of the task and automatically assemble a team combining experts from different fields. For example, combining a data scientist with a designer. The collaborative work proposal unit also uses the generation AI to select the most suitable experts and assemble a team depending on the complexity of the task. For example, combining multiple experts for a complex project. The collaborative work proposal unit also uses the generation AI to assemble the optimal team based on the content of the task by referring to the client's past work history. For example, it recreates team compositions that have been successful in the past. This makes it possible to automatically assemble a team combining experts from different fields.

[0062] The collaborative work proposal unit can analyze past collaborative work data and propose the most efficient work process. In the collaborative work proposal unit, for example, the generation AI analyzes past collaborative work data and proposes the most efficient work process. For example, proposals are made based on the work processes of successful projects. The collaborative work proposal unit also builds a system in which the generation AI automatically extracts and proposes efficient work processes based on past data. For example, it analyzes work time and resource usage. In addition, the collaborative work proposal unit uses the generation AI to analyze past collaborative work data and propose the optimal work process based on the client's work history. For example, it prioritizes proposals for work processes in which the client excels. This makes it possible to analyze past collaborative work data and propose the most efficient work process.

[0063] The collaborative work proposal unit uses the emotion estimation function to provide real-time feedback on the emotional states of members working together, thereby maintaining the motivation of the team. The collaborative work proposal unit, for example, uses the emotion estimation function to build a system that provides real-time feedback on the emotional states of members working together. For example, it visualizes and displays emotion scores. The collaborative work proposal unit also provides real-time feedback on the emotional states of members and makes suggestions to maintain the team's motivation. For example, it sends encouraging messages when positive emotions drop. The collaborative work proposal unit also builds a system that provides real-time feedback on the emotional states of members working together, thereby maintaining the team's motivation, based on the emotion estimation data. For example, it appoints a member with a high emotion score as a leader. This allows real-time feedback on the emotional states of members working together to maintain the team's motivation.

[0064] The progress tracking unit can record the progress of tasks in detail and automatically generate reports based on the progress data. For example, the progress tracking unit uses IoT sensors to record the progress of tasks in real time and automatically generate reports based on that data. For example, it records the start and end times of each task and visualizes the progress. The progress tracking unit also uses IoT sensors to record the progress of tasks in detail and automatically generate weekly or monthly reports based on the progress data. For example, the report reflects the task completion rate and delay status. The progress tracking unit also builds a system in which IoT sensors monitor the progress of tasks in real time and automatically generate reports based on the progress data. For example, it displays the task progress in graphs or charts. This makes it possible to record the progress of tasks in detail and automatically generate reports based on the progress data.

[0065] The progress tracking unit can monitor the progress of a task in real time and provide a benchmark by comparing it with the progress data of other clients. The progress tracking unit, for example, builds a system in which IoT sensors track the progress of a task and provide a benchmark by comparing it with the progress data of other clients. For example, the progress of other clients performing the same task is displayed. The progress tracking unit also develops a system in which the progress of a task is tracked and provided a benchmark by comparing it with the progress data of other clients. For example, the progress of a client is evaluated by comparing it with an average progress speed. The progress tracking unit also uses IoT sensors to track the progress of a task and provide a benchmark by comparing it with the progress data of other clients. For example, a benchmark is set based on the progress data of clients in the same industry. This makes it possible to monitor the progress of a task in real time and provide a benchmark by comparing it with the progress data of other clients.

[0066] The progress tracking unit can use the emotion estimation function to analyze the emotional state of the client and automatically adjust the optimal work environment. The progress tracking unit, for example, uses the emotion estimation function to analyze the emotional state of the client in real time and build a system that automatically adjusts the optimal work environment. For example, music is played according to the emotional state. The progress tracking unit also develops a system that analyzes the emotional state of the client and automatically adjusts the optimal work environment. For example, lighting brightness is adjusted according to the emotional state. The progress tracking unit also analyzes the emotional state of the client in real time based on the emotion estimation data and automatically adjusts the optimal work environment. For example, temperature and humidity are adjusted according to the emotional state. In this way, the client's emotional state can be analyzed and the optimal work environment can be automatically adjusted.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The task list analyzer not only analyzes client tasks, but can also visually display task dependencies. For example, the task list analyzer can display task dependencies in a graph format, allowing clients to understand the task flow at a glance. The task list analyzer can also suggest the optimal task order based on task dependencies. For example, it can create a schedule that prioritizes tasks with strong dependencies. The task list analyzer can also analyze task dependencies, identify task bottlenecks, and suggest improvement measures. This allows clients to visually understand task dependencies and progress with tasks efficiently.

[0069] When analyzing a client's tasks, the task list analysis unit can also perform a risk assessment of the tasks. For example, the task list analysis unit can identify risk factors for the tasks and suggest that high-risk tasks be processed first. The task list analysis unit can also suggest risk mitigation measures based on the task risk assessment. For example, allocating additional resources to high-risk tasks. The task list analysis unit can also perform a risk assessment of the tasks and notify the client of high-risk tasks. This allows the client to understand the risks of the tasks and take appropriate measures.

[0070] The program generation unit can not only suggest required resources based on the content of the task, but also suggest the optimal way to use the resources. For example, the program generation unit can suggest the optimal way to query a database, supporting efficient data retrieval. The program generation unit can also suggest the optimal way to use APIs, supporting efficient data integration. For example, it can suggest a way to minimize the number of API calls. The program generation unit can also suggest the optimal way to use resources, improving the work efficiency of the client. This allows the client to use resources efficiently and complete tasks quickly.

[0071] The collaborative work proposal unit not only evaluates the complexity of a task and matches the most suitable expert, but also adjusts the expert's schedule. For example, the collaborative work proposal unit automatically adjusts the expert's schedule and assigns tasks at the optimal timing. The collaborative work proposal unit can also reevaluate task priorities based on the expert's schedule and propose an efficient schedule. For example, it rearranges tasks to match the time slots when the expert is available. The collaborative work proposal unit can also adjust the expert's schedule to ensure smooth progress of the task. This enables clients to efficiently manage tasks taking into account the expert's schedule.

[0072] The progress tracking unit not only monitors the progress of tasks in real time and issues alerts when an abnormality is detected, but also identifies the cause of the abnormality and proposes a solution. For example, the progress tracking unit can identify the cause of a delay in task progress and propose adding appropriate resources. The progress tracking unit can also identify the cause of a halt in task progress and propose steps to resume it. For example, if necessary data is missing, it can propose a method for obtaining the data. The progress tracking unit can also identify the cause of the abnormality and notify the client, thereby supporting a prompt response. This allows the client to understand the cause of the abnormality and take appropriate measures.

[0073] The progress tracking unit can use the emotion estimation function to monitor the client's emotional state in real time and suggest relaxation methods when stress increases. For example, the progress tracking unit can analyze the client's emotional state and suggest deep breathing or a short break when stress increases. The progress tracking unit can also suggest playing relaxing music based on the client's emotional state. For example, it can automatically select and play music that helps the client relax. The progress tracking unit can also monitor the client's emotional state in real time based on the emotion estimation data and suggest stretching or light exercise when stress increases. In this way, the client's emotional state can be monitored in real time and relaxation methods can be suggested when stress increases.

[0074] The program generation unit can use the emotion estimation function to suggest a task order that will most motivate the client and promote efficient task completion. For example, the program generation unit analyzes the client's emotional state and suggests a task order that will increase motivation. For example, it may prioritize tasks that the client likes. The program generation unit can also suggest a task order that will maintain the client's motivation based on the emotion estimation data. For example, it may start with easy tasks and gradually increase the difficulty. The program generation unit can also suggest placing tasks that will likely give the client a sense of accomplishment midway based on the client's emotional state. This makes it possible to suggest a task order that will most motivate the client and promote efficient task completion.

[0075] The collaborative work suggestion unit can use the emotion estimation function to analyze the emotional states of members working together and make suggestions for improving communication. For example, the collaborative work suggestion unit can use the emotion estimation function to monitor the emotional states of members working together in real time and make suggestions for improving communication. For example, it can suggest ways to relax when stress levels rise. The collaborative work suggestion unit can also build a system that analyzes members' emotional states and makes suggestions for improving communication. For example, it can make suggestions to increase positive feedback. The collaborative work suggestion unit can also analyze the emotional states of members working together based on the emotion estimation data and make suggestions for improving communication. For example, it can send an encouraging message to a member who is feeling depressed. This makes it possible to analyze the emotional states of members working together and make suggestions for improving communication.

[0076] The progress tracking unit can use the emotion estimation function to analyze the emotional state of the client and automatically adjust the optimal work environment. For example, the progress tracking unit can use the emotion estimation function to build a system that analyzes the emotional state of the client in real time and automatically adjusts the optimal work environment. For example, music can be played according to the emotional state. The progress tracking unit can also develop a system that analyzes the emotional state of the client and automatically adjusts the optimal work environment. For example, the brightness of the lighting can be adjusted according to the emotional state. The progress tracking unit can also analyze the emotional state of the client in real time based on the emotion estimation data and automatically adjust the optimal work environment. For example, the temperature and humidity can be adjusted according to the emotional state. In this way, the client's emotional state can be analyzed and the optimal work environment can be automatically adjusted.

[0077] The collaboration suggestion unit can use the emotion estimation function to provide real-time feedback on the emotional states of members working together to maintain the motivation of the team. For example, the collaboration suggestion unit can use the emotion estimation function to build a system that provides real-time feedback on the emotional states of members working together. For example, the emotion scores can be visualized and displayed. The collaboration suggestion unit can also provide real-time feedback on members' emotional states and make suggestions to maintain the team's motivation. For example, sending an encouraging message when positive emotions drop. The collaboration suggestion unit can also build a system that provides real-time feedback on the emotional states of members working together to maintain the team's motivation based on the emotion estimation data. For example, a member with a high emotion score can be appointed as a leader. This allows real-time feedback on the emotional states of members working together to maintain the team's motivation.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The task list analysis unit analyzes the task list submitted by the client. For example, the task list analysis unit uses text analysis technology to analyze the content of the tasks and data mining technology to evaluate the importance and urgency of the tasks. It can also set task priorities based on the client's past work history. Step 2: The program generation unit generates the optimal program based on the task list analyzed by the task list analysis unit. For example, it uses generation AI to generate the optimal program for each task, automatically suggests the necessary resources, and incorporates them into the program. It can also refer to the success stories of other clients to suggest the optimal program. Step 3: The collaboration suggestion unit proposes collaboration with humans for tasks that the program generated by the program generation unit cannot fully handle. For example, it evaluates the complexity of the task and automatically matches the most suitable expert. It also monitors the progress of the collaboration in real time and suggests redistribution of tasks as necessary. Furthermore, it can use an emotion estimation function to analyze the emotional state of members during collaboration and make suggestions for improving communication. Step 4: The progress tracking unit uses IoT sensors to track the progress of tasks in real time. For example, it can record detailed task progress and automatically generate reports based on the progress data. It can also monitor task progress in real time and issue alerts if anomalies are detected. Furthermore, it can use emotion estimation to analyze the client's emotional state and automatically adjust the optimal working environment.

[0080] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0081] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0082] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0085] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0086] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0087] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0088] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0089] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0090] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0091] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0092] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0093] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0094] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0095] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0097] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0100] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0101] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0102] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0103] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0104] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0105] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0106] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0110] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0112] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0121] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0124] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0130] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0131] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0132] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0133] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0134] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0135] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0136] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0137] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0138] 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.

[0139] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0140] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0141] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0142] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0143] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0144] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0145] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0146] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0147] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a task list analysis unit that analyzes the task list; a program generation unit that generates an optimal program based on the task list analyzed by the task list analysis unit; a collaborative work suggestion unit that suggests collaborative work with a human for a task that cannot be completely processed by the program generated by the program generation unit; A progress tracking unit that tracks the progress of a task in real time using an IoT sensor. A system characterized by:

2. The task list analysis unit Analyzing the task list, automatically prioritizing the tasks, and generating an optimal schedule 2. The system of claim 1.

3. The program generation unit Based on the content of the task, the necessary resources are automatically suggested and incorporated into the program.

2. The system of claim 1.

4. The collaborative work proposal unit Evaluate the complexity of the task and automatically match the most suitable expert 2. The system of claim 1.

5. The progress tracking unit Monitors the client's emotional state in real time and suggests relaxation methods if stress levels rise 2. The system of claim 1.

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