System

Abstract

An object of a system according to an embodiment is to efficiently perform automation and task management of work.SOLUTION: A system according to an embodiment includes a task automation unit, a task management unit, and a speech input unit. The task automation unit automates a task. The task management unit manages a task. The voice input unit receives a voice input.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 technologies do not provide sufficient efficiency in automating business processes or managing tasks, and there is room for improvement.

[0005] The system according to the embodiment aims to automate business operations and manage tasks efficiently. [Means for solving the problem]

[0006] The system according to the embodiment includes a task automation unit, a task management unit, and a voice input unit. The task automation unit automates tasks. The task management unit manages tasks. The voice input unit accepts voice input. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently automate business operations and manage tasks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The BizSync Pro system according to an embodiment of the present invention is a system that supports business automation and task management. This system utilizes AI to efficiently process tasks and optimize business processes. This allows the BizSync Pro system to improve business performance and reduce time and stress.

[0029] The BizSync Pro system according to an embodiment includes a business automation unit, a task management unit, and a voice input unit. The business automation unit automates everyday tasks and routine work. For example, the business automation unit automatically replies to emails. The business automation unit can also automatically organize data. The business automation unit can also automatically create reports. The task management unit monitors project progress in real time. For example, the task management unit automatically sets task priorities. The task management unit can also display task progress in graphs. The task management unit can also notify task completion statuses. The voice input unit analyzes voice instructions and automatically schedules or cancels meetings. For example, when the voice input unit receives a voice instruction such as "Schedule a meeting for tomorrow at 10:00," it automatically adds the meeting to the calendar. When the voice input unit receives a voice instruction such as "Cancel the meeting," it can automatically delete the meeting from the calendar. The voice input unit can also convert voice instructions into text and record it. As a result, the BizSync Pro system according to the embodiment can improve business efficiency and optimize business processes. For example, the business automation unit reduces the burden on employees. The task management unit improves the work efficiency of the entire team. The voice input unit simplifies schedule management.

[0030] The task automation unit can analyze a user's past task history and predict and automate future tasks. For example, the task automation unit can analyze a user's past email sending history and find specific patterns. For example, if a user has the habit of sending a specific report every Monday, the generation AI can learn that pattern and automate the next sending. The task automation unit can also analyze a user's past task completion history and predict and automate future tasks. For example, it can automate data aggregation work performed at the end of each month. The task automation unit can also analyze a user's past meeting schedule and predict and automate future meetings. For example, it can automatically schedule regular meetings every Friday. This allows the user's tasks to be automated efficiently.

[0031] The task automation unit can learn the user's work patterns and suggest automated tasks at the optimal timing. For example, the task automation unit can learn the user's work patterns and automate tasks that are concentrated during specific time periods. For example, it can automate the email check that occurs every morning at 9:00 and notify only important emails. The task automation unit can also learn the user's work patterns and send reminders at the optimal timing. For example, it can send a reminder 10 minutes before a meeting starts. The task automation unit can also learn the user's work patterns and adjust task priorities at the optimal timing. For example, it can suggest that important tasks be processed first. This improves the user's work efficiency.

[0032] The task management unit can analyze task progress in real time and issue alerts before delays occur. For example, the task management unit uses a generation AI to monitor task progress in real time and issue alerts for tasks that are behind schedule. For example, if a project deadline is approaching, the generation AI will send a reminder. The task management unit can also display task progress in a graph to visually indicate signs of delay. For example, a delay is indicated by the progress bar turning red. The task management unit can also analyze task progress and identify the cause of delays. For example, if a specific task is a bottleneck, it will suggest that that task be processed as a priority. This helps prevent task delays.

[0033] The task management unit can analyze task dependencies and automatically set the optimal task order. For example, the task management unit uses a generation AI to analyze task dependencies and automatically set the optimal task order. For example, if a specific task depends on other tasks, the task order is set taking that dependency into consideration. The task management unit can also display and visually show task dependencies in a graph. For example, task dependencies are shown with arrows. The task management unit can also analyze task dependencies and suggest the optimal task order. For example, it can suggest that important tasks be processed first. This allows the task order to be optimized.

[0034] The voice input unit can analyze voice instructions and provide a multitasking function that processes multiple tasks at once. For example, if a user issues a voice instruction such as "Prepare materials for tomorrow's meeting and also compile this week's sales data," the generation AI will analyze the instruction and process both tasks simultaneously. The voice input unit can also automatically set a schedule for processing multiple tasks at once. For example, preparing for a meeting and compiling data can be done in parallel. The voice input unit can also optimally allocate resources to process multiple tasks at once. For example, it can automatically collect necessary data and streamline task processing. This improves work efficiency by processing multiple tasks simultaneously.

[0035] The voice input unit can learn the user's voice instruction history and predict and automate the user's instruction patterns. For example, the voice input unit can learn the user's past voice instruction history and identify specific patterns. For example, if a user instructs the system to "create a weekly report" every Monday, the generation AI can learn that pattern and predict and automate the next instruction. The voice input unit can also learn the user's instruction patterns and execute voice instructions at the optimal timing. For example, if the user instructs the system to "check today's schedule" every morning at 9:00, the system will automatically check the schedule at that time. The voice input unit can also learn the user's instruction patterns and prepare predicted instructions in advance. For example, it can automatically prepare necessary materials before a meeting. This improves work efficiency by predicting and automating user instruction patterns.

[0036] The voice input unit can analyze voice instructions in different languages ​​and provide a multilingual voice input function. For example, if a user issues a voice instruction in English such as "Schedule a meeting for tomorrow at 10 AM," the generation AI can analyze the instruction and automatically add the meeting to the calendar. Alternatively, if a user issues a voice instruction in Spanish such as "Programa una reunion para manana a las 10 AM," the generation AI can analyze the instruction and automatically add the meeting to the calendar. Alternatively, if a user issues a voice instruction in Japanese such as "Schedule a meeting for tomorrow at 10 AM," the generation AI can analyze the instruction and automatically add the meeting to the calendar. This improves user convenience by providing a multilingual voice input function.

[0037] The voice input unit can analyze voice instructions and provide visual feedback based on the voice instructions. For example, when a user issues a voice instruction such as "Prepare the materials for tomorrow's meeting," the generation AI analyzes the instruction and displays the preparation status of the materials as visual feedback. Also, when a user issues a voice instruction such as "Summarize this week's sales data," the generation AI can analyze the instruction and display the compiled status of the sales data as visual feedback. Also, when a user issues a voice instruction such as "Tell me the progress of the project," the voice input unit can analyze the instruction and display the progress of the project as visual feedback. In this way, providing visual feedback based on voice instructions improves the user's understanding.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The BizSync Pro system can also be equipped with a health management unit that monitors the user's health data. For example, the health management unit monitors the user's heart rate and blood pressure in real time and issues an alert if an abnormality is detected. The health management unit can also analyze the user's exercise volume and sleep patterns to evaluate their health condition. For example, if it detects a lack of exercise, it can send a reminder to encourage exercise. The health management unit can also provide individual health advice based on the user's health data. For example, it can provide advice on how to relax when stress is high. This can support the user's health management.

[0040] The BizSync Pro system can also be equipped with a break suggestion unit that analyzes the user's schedule and suggests optimal break times. For example, if the user has been working continuously for a long period of time, the break suggestion unit will suggest taking a break at an appropriate time. The break suggestion unit can also learn the user's work patterns and automatically set optimal break times. For example, it could suggest a short break at 3:00 p.m. every day. The break suggestion unit can also determine the need for a break based on the user's health data. For example, it could suggest a relaxation break if the user's heart rate is high. This can improve the user's health and work efficiency.

[0041] The BizSync Pro system can also include a project analysis section that supports users' project management. For example, the project analysis section performs detailed analysis of the project's progress and identifies bottlenecks. The project analysis section can also make suggestions for optimizing project resource allocation. For example, it can suggest concentrating resources on specific tasks. The project analysis section can also evaluate project risks and provide advice for risk management. For example, it can suggest preventive measures for high-risk tasks. This can improve the project's success rate.

[0042] The BizSync Pro system can also include a learning support unit that supports users' learning. For example, the learning support unit creates a learning plan for the user to acquire new skills. The learning support unit can also monitor the user's learning progress and provide appropriate feedback. For example, it can send a reminder if the user's learning progress is behind. The learning support unit can also provide learning materials that suit the user's learning style. For example, it can provide visual learning materials to users who are good at visual learning. This can improve the user's learning efficiency.

[0043] The BizSync Pro system can also be equipped with a security management unit to further strengthen user data security. For example, the security management unit encrypts user data to prevent unauthorized access. The security management unit can also monitor user data access history and issue alerts if abnormal access is detected. For example, it can notify users if access occurs during unusual hours. The security management unit can also regularly back up user data to prevent data loss. This strengthens user data security.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The Business Automation Department automates everyday tasks and routine work. For example, the Business Automation Department can automate email replies, automatically organize data, and automatically create reports. Step 2: The task manager monitors the progress of the project in real time. For example, the task manager can automatically prioritize tasks, display task progress in a graph, and notify you of task completion status. Step 3: The voice input unit analyzes the voice instructions and automatically schedules or cancels meetings. For example, when the voice input unit receives a voice instruction such as "Schedule a meeting for tomorrow at 10 o'clock," it can automatically add the meeting to the calendar, and when it receives a voice instruction such as "Cancel the meeting," it can automatically delete the meeting from the calendar. The voice input unit can also convert the voice instructions into text and record it.

[0046] (Example 2) The BizSync Pro system according to an embodiment of the present invention is a system that supports business automation and task management. This system utilizes AI to efficiently process tasks and optimize business processes. This allows the BizSync Pro system to improve business performance and reduce time and stress.

[0047] The BizSync Pro system according to an embodiment includes a business automation unit, a task management unit, and a voice input unit. The business automation unit automates everyday tasks and routine work. For example, the business automation unit automatically replies to emails. The business automation unit can also automatically organize data. The business automation unit can also automatically create reports. The task management unit monitors project progress in real time. For example, the task management unit automatically sets task priorities. The task management unit can also display task progress in graphs. The task management unit can also notify task completion statuses. The voice input unit analyzes voice instructions and automatically schedules or cancels meetings. For example, when the voice input unit receives a voice instruction such as "Schedule a meeting for tomorrow at 10:00," it automatically adds the meeting to the calendar. When the voice input unit receives a voice instruction such as "Cancel the meeting," it can automatically delete the meeting from the calendar. The voice input unit can also convert voice instructions into text and record it. As a result, the BizSync Pro system according to the embodiment can improve business efficiency and optimize business processes. For example, the business automation unit reduces the burden on employees. The task management unit improves the work efficiency of the entire team. The voice input unit simplifies schedule management.

[0048] The task automation unit can analyze a user's past task history and predict and automate future tasks. For example, the task automation unit can analyze a user's past email sending history and find specific patterns. For example, if a user has the habit of sending a specific report every Monday, the generation AI can learn that pattern and automate the next sending. The task automation unit can also analyze a user's past task completion history and predict and automate future tasks. For example, it can automate data aggregation work performed at the end of each month. The task automation unit can also analyze a user's past meeting schedule and predict and automate future meetings. For example, it can automatically schedule regular meetings every Friday. This allows the user's tasks to be automated efficiently.

[0049] The task automation unit can learn the user's work patterns and suggest automated tasks at the optimal timing. For example, the task automation unit can learn the user's work patterns and automate tasks that are concentrated during specific time periods. For example, it can automate the email check that occurs every morning at 9:00 and notify only important emails. The task automation unit can also learn the user's work patterns and send reminders at the optimal timing. For example, it can send a reminder 10 minutes before a meeting starts. The task automation unit can also learn the user's work patterns and adjust task priorities at the optimal timing. For example, it can suggest that important tasks be processed first. This improves the user's work efficiency.

[0050] The task automation unit can use emotion estimation to detect a user's stress level and increase the number of automated tasks when stress levels are high. For example, the task automation unit can analyze a user's tone of voice and facial expressions to estimate their stress level. For example, if a user sounds tired, the generative AI can automate tasks for that day. The task automation unit can also collect a user's biometric data (heart rate and electrodermal activity) using sensors to estimate their stress level. For example, it can calculate stress levels based on heart rate fluctuations. The task automation unit can also adjust the number of automated tasks according to the user's stress level. For example, when stress levels are high, routine tasks can be automated to reduce the burden. This can reduce the user's stress.

[0051] The task management unit can analyze task progress in real time and issue alerts before delays occur. For example, the task management unit uses a generation AI to monitor task progress in real time and issue alerts for tasks that are behind schedule. For example, if a project deadline is approaching, the generation AI will send a reminder. The task management unit can also display task progress in a graph to visually indicate signs of delay. For example, a delay is indicated by the progress bar turning red. The task management unit can also analyze task progress and identify the cause of delays. For example, if a specific task is a bottleneck, it will suggest that that task be processed as a priority. This helps prevent task delays.

[0052] The task management unit can analyze task dependencies and automatically set the optimal task order. For example, the task management unit uses a generation AI to analyze task dependencies and automatically set the optimal task order. For example, if a specific task depends on other tasks, the task order is set taking that dependency into consideration. The task management unit can also display and visually show task dependencies in a graph. For example, task dependencies are shown with arrows. The task management unit can also analyze task dependencies and suggest the optimal task order. For example, it can suggest that important tasks be processed first. This allows the task order to be optimized.

[0053] The task management unit can use emotion estimation to analyze the user's motivation and adjust task priorities when motivation is low. For example, the task management unit can analyze the user's voice tone and facial expressions to adjust task priorities when motivation is low. For example, if the user sounds tired, the generation AI will postpone less important tasks. The task management unit can also collect the user's biometric data (heart rate and electrodermal activity) using sensors to analyze motivation. For example, it can calculate motivation based on heart rate fluctuations. The task management unit can also adjust task priorities according to the user's motivation. For example, when motivation is low, it can suggest prioritizing easier tasks. This allows task priorities to be adjusted according to the user's motivation.

[0054] The voice input unit can analyze voice instructions and provide a multitasking function that processes multiple tasks at once. For example, if a user issues a voice instruction such as "Prepare materials for tomorrow's meeting and also compile this week's sales data," the generation AI will analyze the instruction and process both tasks simultaneously. The voice input unit can also automatically set a schedule for processing multiple tasks at once. For example, preparing for a meeting and compiling data can be done in parallel. The voice input unit can also optimally allocate resources to process multiple tasks at once. For example, it can automatically collect necessary data and streamline task processing. This improves work efficiency by processing multiple tasks simultaneously.

[0055] The voice input unit can learn the user's voice instruction history and predict and automate the user's instruction patterns. For example, the voice input unit can learn the user's past voice instruction history and identify specific patterns. For example, if a user instructs the system to "create a weekly report" every Monday, the generation AI can learn that pattern and predict and automate the next instruction. The voice input unit can also learn the user's instruction patterns and execute voice instructions at the optimal timing. For example, if the user instructs the system to "check today's schedule" every morning at 9:00, the system will automatically check the schedule at that time. The voice input unit can also learn the user's instruction patterns and prepare predicted instructions in advance. For example, it can automatically prepare necessary materials before a meeting. This improves work efficiency by predicting and automating user instruction patterns.

[0056] The voice input unit can use the emotion estimation function to estimate the user's emotion from the tone of their voice and respond accordingly. The voice input unit, for example, analyzes the user's tone of voice to estimate their emotion. For example, if the user sounds tired, the generation AI suggests a task to help them relax. The voice input unit can also analyze the user's tone of voice and respond accordingly. For example, if the user sounds angry, the generation AI provides advice to help them stay calm. The voice input unit can also analyze the user's tone of voice and provide feedback according to their emotion. For example, if the user sounds happy, the generation AI provides feedback that shares that emotion. This improves work efficiency by responding according to the user's emotion.

[0057] The voice input unit can analyze voice instructions in different languages ​​and provide a multilingual voice input function. For example, if a user issues a voice instruction in English such as "Schedule a meeting for tomorrow at 10 AM," the generation AI can analyze the instruction and automatically add the meeting to the calendar. Alternatively, if a user issues a voice instruction in Spanish such as "Programa una reunion para manana a las 10 AM," the generation AI can analyze the instruction and automatically add the meeting to the calendar. Alternatively, if a user issues a voice instruction in Japanese such as "Schedule a meeting for tomorrow at 10 AM," the generation AI can analyze the instruction and automatically add the meeting to the calendar. This improves user convenience by providing a multilingual voice input function.

[0058] The voice input unit can analyze voice instructions and provide visual feedback based on the voice instructions. For example, when a user issues a voice instruction such as "Prepare the materials for tomorrow's meeting," the generation AI analyzes the instruction and displays the preparation status of the materials as visual feedback. Also, when a user issues a voice instruction such as "Summarize this week's sales data," the generation AI can analyze the instruction and display the compiled status of the sales data as visual feedback. Also, when a user issues a voice instruction such as "Tell me the progress of the project," the voice input unit can analyze the instruction and display the progress of the project as visual feedback. In this way, providing visual feedback based on voice instructions improves the user's understanding.

[0059] The voice input unit can use the emotion estimation function to prioritize voice instructions based on the user's emotions and respond optimally. The voice input unit, for example, analyzes the user's voice tone and prioritizes voice instructions based on emotions. For example, if the user speaks in a tired voice, the generation AI prioritizes a task for relaxation. The voice input unit can also analyze the user's voice tone and respond based on emotions. For example, if the user speaks in an angry voice, the generation AI provides advice on how to stay calm. The voice input unit can also analyze the user's voice tone and provide feedback based on emotions. For example, if the user speaks in a happy voice, the generation AI provides feedback that shares that emotion. This improves work efficiency by prioritizing voice instructions based on the user's emotions.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The BizSync Pro system can also be equipped with a health management unit that monitors the user's health data. For example, the health management unit monitors the user's heart rate and blood pressure in real time and issues an alert if an abnormality is detected. The health management unit can also analyze the user's exercise volume and sleep patterns to evaluate their health condition. For example, if it detects a lack of exercise, it can send a reminder to encourage exercise. The health management unit can also provide individual health advice based on the user's health data. For example, it can provide advice on how to relax when stress is high. This can support the user's health management.

[0062] The BizSync Pro system can also include a music provider that estimates the user's emotions and selects music based on the estimated emotions. For example, if the user sounds tired, the music provider can provide relaxing music. If the user sounds happy, the music provider can provide music to further uplift the mood. The music provider can also provide music to enhance concentration when the user wants to concentrate. This improves work efficiency by providing music that matches the user's emotions.

[0063] The BizSync Pro system can also be equipped with a break suggestion unit that analyzes the user's schedule and suggests optimal break times. For example, if the user has been working continuously for a long period of time, the break suggestion unit will suggest taking a break at an appropriate time. The break suggestion unit can also learn the user's work patterns and automatically set optimal break times. For example, it could suggest a short break at 3:00 p.m. every day. The break suggestion unit can also determine the need for a break based on the user's health data. For example, it could suggest a relaxation break if the user's heart rate is high. This can improve the user's health and work efficiency.

[0064] The BizSync Pro system can also include a task adjustment unit that estimates the user's emotions and adjusts the difficulty of tasks based on the estimated emotions. For example, if the user sounds tired, the task adjustment unit can prioritize assigning easier tasks. Furthermore, if the user feels stressed, the task adjustment unit can suggest tasks to reduce stress. Furthermore, if the user is highly motivated, the task adjustment unit can assign more difficult tasks. This improves work efficiency by adjusting tasks according to the user's emotions.

[0065] The BizSync Pro system can also include a project analysis section that supports users' project management. For example, the project analysis section performs detailed analysis of the project's progress and identifies bottlenecks. The project analysis section can also make suggestions for optimizing project resource allocation. For example, it can suggest concentrating resources on specific tasks. The project analysis section can also evaluate project risks and provide advice for risk management. For example, it can suggest preventive measures for high-risk tasks. This can improve the project's success rate.

[0066] The BizSync Pro system can also include a communication adjustment unit that estimates the user's emotions and adjusts the communication method based on the estimated emotions. For example, if the user sounds tired, the communication adjustment unit can send a concise and easy-to-understand message. If the user sounds angry, the communication adjustment unit can also provide advice on how to stay calm. If the user sounds happy, the communication adjustment unit can also provide positive feedback. This improves work efficiency by communicating in accordance with the user's emotions.

[0067] The BizSync Pro system can also include a learning support unit that supports users' learning. For example, the learning support unit creates a learning plan for the user to acquire new skills. The learning support unit can also monitor the user's learning progress and provide appropriate feedback. For example, it can send a reminder if the user's learning progress is behind. The learning support unit can also provide learning materials that suit the user's learning style. For example, it can provide visual learning materials to users who are good at visual learning. This can improve the user's learning efficiency.

[0068] The BizSync Pro system can also include a refreshment suggestion unit that estimates the user's emotions and suggests activities to refresh based on the estimated emotions. For example, if the user sounds tired, the refreshment suggestion unit can suggest a short walk. If the user is feeling stressed, the refreshment suggestion unit can also suggest a relaxation activity. If the user is highly motivated, the refreshment suggestion unit can also suggest a challenging activity. This improves work efficiency by suggesting refreshments based on the user's emotions.

[0069] The BizSync Pro system can also be equipped with a security management unit to further strengthen user data security. For example, the security management unit encrypts user data to prevent unauthorized access. The security management unit can also monitor user data access history and issue alerts if abnormal access is detected. For example, it can notify users if access occurs during unusual hours. The security management unit can also regularly back up user data to prevent data loss. This strengthens user data security.

[0070] The BizSync Pro system may also include a notification customization unit that estimates a user's emotions and provides customized notifications based on the estimated emotions. For example, if the user sounds tired, the notification customization unit may send only important notifications. If the user feels stressed, the notification customization unit may also reduce the frequency of notifications. If the user is highly motivated, the notification customization unit may also send notifications containing positive feedback. This improves work efficiency by providing customized notifications based on the user's emotions.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: The Business Automation Department automates everyday tasks and routine work. For example, the Business Automation Department can automate email replies, automatically organize data, and automatically create reports. Step 2: The task manager monitors the progress of the project in real time. For example, the task manager can automatically prioritize tasks, display task progress in a graph, and notify you of task completion status. Step 3: The voice input unit analyzes the voice instructions and automatically schedules or cancels meetings. For example, when the voice input unit receives a voice instruction such as "Schedule a meeting for tomorrow at 10 o'clock," it can automatically add the meeting to the calendar, and when it receives a voice instruction such as "Cancel the meeting," it can automatically delete the meeting from the calendar. The voice input unit can also convert the voice instructions into text and record it.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. Business Automation Department and Task management department, a voice input unit; A system characterized by:

2. The business automation unit Analyze users' past work history, predict future work, and automate it 2. The system of claim 1.

3. The task management unit Analyze task progress in real time and get alerts before delays occur 2. The system of claim 1.

4. The voice input unit Analyzes voice commands and provides multitasking functionality to handle multiple tasks at once 2. The system of claim 1.

5. The business automation unit Detect user stress levels and automate more tasks when stress levels are high 2. The system of claim 1.

Images