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 optimizes the task. The voice input unit analyzes a voice instruction and executes an operation.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 optimizes tasks. The voice input unit analyzes voice instructions and executes operations. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) The BizSync Pro system, an embodiment of the present invention, utilizes AI to support business automation and task management. This system aims to efficiently handle complex tasks and optimize business processes. It also supports voice input, allowing users to automatically schedule and cancel meetings with a single voice command. 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 business processes. For example, by automating daily tasks and routine work, it reduces the burden on employees. The business automation unit also automatically processes tasks such as issuing invoices, managing payments, and checking inventory. For example, the business automation unit automates issuance of invoices to streamline payment management. The business automation unit also automates inventory checks to optimize inventory management. The task management unit optimizes tasks. For example, the task management unit monitors the progress of a project in real time and automatically sets task priorities. The task management unit also analyzes project data to propose optimal task assignments and schedules. For example, the task management unit monitors the progress of a project in real time and automatically sets task priorities. The task management unit also analyzes project data to propose optimal task assignments and schedules. The voice input unit analyzes voice instructions and performs operations. For example, a user can issue voice instructions to automatically perform operations such as reserving or canceling a meeting. The voice input unit also analyzes voice instructions and automatically updates the calendar. For example, the voice input unit analyzes a voice instruction such as "Book a meeting for tomorrow at 10:00" and automatically updates the calendar. This enables the BizSync Pro system according to the embodiment to improve work efficiency, optimize task management, and operate via voice input.

[0030] The task automation unit can analyze a user's past work history and predict and automatically suggest the next task to be performed. For example, the task automation unit analyzes a user's past work history, and the generation AI learns from past data to predict the next task to be performed. For example, the generation AI automatically suggests the next task to be performed based on the time and frequency of past task completions. The task automation unit also considers the priority and importance of tasks when analyzing a user's work history and predicts the next task to be performed. For example, it prioritizes the suggestions of tasks with high importance. The task automation unit also predicts the next task to be performed based on the user's past work history and automatically adds it to the schedule. For example, it automatically incorporates tasks that need to be performed regularly into the schedule. This improves the user's work efficiency.

[0031] The process automation unit can monitor the progress of work in real time and automatically issue an alert if an abnormality occurs. For example, the process automation unit builds a system in which a generation AI monitors the progress of work in real time and automatically issues an alert if an abnormality occurs. For example, an alert is issued if the progress of a task is delayed. In addition, to monitor the progress of work in real time, the process automation unit has the generation AI periodically check the status of each task and issue an alert if an abnormality occurs. For example, an alert is issued if a task is not completed within the deadline. In addition, the process automation unit introduces an anomaly detection algorithm so that the generation AI can monitor the progress of work and issue an alert if an abnormality occurs. For example, an alert is issued if an abnormal pattern is detected. This makes it possible to quickly detect and respond to abnormalities in work.

[0032] The business automation department can use generative AI to automate customer response and customer support. For example, the business automation department uses generative AI to build a system that automates customer response and customer support. For example, it introduces a chatbot that automatically responds to customer inquiries. In addition, to expand the scope of business automation, the business automation department uses generative AI to automate customer response and customer support tasks. For example, it analyzes the content of customer inquiries and automatically generates appropriate responses. In addition, the business automation department uses generative AI to analyze customer data and provide personalized responses in order to automate customer response and customer support. For example, it suggests appropriate responses based on the customer's past inquiry history. This improves the efficiency of customer response and customer support.

[0033] The business automation department can analyze business processes in different industries and provide industry-specific automation solutions. For example, the business automation department analyzes business processes in different industries and uses generative AI to learn data for each industry in order to provide industry-specific automation solutions. For example, it analyzes business processes in the manufacturing and medical industries. The business automation department also uses generative AI to analyze business processes in different industries and builds a system that provides industry-specific automation solutions. For example, it optimizes business flows for each industry. The business automation department also collects industry-specific data to analyze business processes in different industries and proposes industry-specific automation solutions. For example, it provides automation solutions based on industry-specific best practices. This makes it possible to provide industry-specific automation solutions.

[0034] The task management unit can use generative AI to analyze task progress in detail and propose preventive measures before delays occur. For example, the task management unit uses generative AI to build a system that analyzes task progress in detail and proposes preventive measures before delays occur. For example, it analyzes task progress data in real time and detects signs of delays. In addition, to analyze task progress in detail, the task management unit periodically checks the status of each task using generative AI and proposes preventive measures before delays occur. For example, it re-prioritizes tasks. In addition, the task management unit uses generative AI to analyze task progress and considers task dependencies and resource usage to propose preventive measures before delays occur. For example, it proposes resource redistribution. This makes it possible to prevent task delays before they occur.

[0035] The task management unit can analyze the skill sets of team members and assign optimal tasks. For example, the task management unit uses a generation AI to analyze the skill sets of team members and build a system that assigns optimal tasks. For example, tasks are assigned based on each member's skills and experience. In addition, to analyze the skill sets of team members, the generation AI collects each member's historical data and performance data and assigns optimal tasks. For example, tasks that require specific skills are assigned to appropriate members. In addition, the task management unit uses a generation AI to analyze the skill sets of team members and create a skill matrix to assign optimal tasks. For example, the skill level of each member is visualized and task assignment is optimized. This makes it possible to assign tasks according to the skills of team members.

[0036] The task management unit can use a generating AI to manage tasks taking into account an individual's life events and health condition. For example, the task management unit uses a generating AI to build a system that manages tasks taking into account an individual's life events and health condition. For example, the task management unit adjusts task schedules based on the user's calendar and health data. In addition, to expand the scope of task management, the task management unit uses a generating AI to analyze an individual's life events and health condition and optimize task priorities and schedules. For example, tasks are adjusted when there is an important life event. In addition, the task management unit uses a generating AI to collect and analyze user data to manage tasks taking into account an individual's life events and health condition. For example, tasks are reduced when the user's health condition deteriorates. This enables flexible task management according to the individual's situation.

[0037] The task management unit can link with different project management tools to achieve integrated task management. For example, the task management unit uses generation AI to link with different project management tools and build a system that achieves integrated task management. For example, it collects task data from multiple tools and manages it centrally. In addition, to link with different project management tools, the generation AI uses each tool's API to integrate task data. For example, it links with tools such as Trello and Asana. In addition, the task management unit uses generation AI to link with different project management tools and synchronizes task data to achieve integrated task management. For example, it updates the task status of each tool in real time. This makes it possible to integrate multiple project management tools for task management.

[0038] The voice input unit can use a generation AI to learn and individually optimize a user's speech patterns to improve the accuracy of voice input. For example, the voice input unit uses a generation AI to build a system that learns and individually optimizes a user's speech patterns to improve the accuracy of voice input. For example, the voice input unit collects user speech data and customizes a voice recognition model. Furthermore, the voice input unit uses a generation AI to analyze each user's voice data and individually optimize it to learn the user's speech patterns. For example, a voice recognition model corresponding to a specific accent or dialect is created. Furthermore, the voice input unit uses a generation AI to learn and individually optimize a user's speech patterns to improve the accuracy of voice input. For example, the voice input unit analyzes voice data in real time and dynamically adjusts the voice recognition model to learn and individually optimize the user's speech patterns to improve the accuracy of voice input. For example, the voice recognition accuracy is improved to match the user's speaking style. This improves the accuracy of voice input and increases user convenience.

[0039] The voice input unit can use a generation AI to analyze voice instructions and provide a multitasking function that executes multiple tasks simultaneously. For example, the voice input unit builds a system in which the generation AI analyzes voice instructions and provides a multitasking function that executes multiple tasks simultaneously. For example, multiple calendar events are created simultaneously based on voice instructions. In addition, to analyze the voice instructions, the generation AI analyzes the voice data in real time and executes multiple tasks simultaneously. For example, a reminder is set at the same time as a meeting reservation. In addition, the voice input unit uses a generation AI to analyze the voice instructions and integrates voice recognition with a task management system to execute multiple tasks simultaneously. For example, task priorities are set based on voice instructions and executed simultaneously. This improves work efficiency by executing multiple tasks simultaneously.

[0040] The voice input unit uses generation AI to expand the range of operations via voice input, allowing smart home devices to be operated. For example, the voice input unit uses generation AI to expand the range of operations via voice input and build a system to operate smart home devices. For example, adjusting lighting and temperature based on voice instructions. In addition, to expand the range of operations via voice input, the voice input unit uses generation AI to link with smart home devices and operate the devices based on voice instructions. For example, turning home appliances on and off by voice. In addition, the voice input unit uses generation AI to integrate voice recognition with smart home systems to expand the range of operations via voice input and operate smart home devices. For example, operating a security system based on voice instructions. This expands the range of operations via voice input and makes it possible to operate smart home devices.

[0041] The voice input unit can use a generation AI to provide a voice input function that supports different languages ​​and dialects. For example, the voice input unit uses a generation AI to build a system that provides a voice input function that supports different languages ​​and dialects. For example, a voice recognition model that supports multiple languages, such as English, French, and Chinese, is created. In addition, to support different languages ​​and dialects, the voice input unit uses a generation AI to learn voice data for each language and dialect and optimize the voice input function. For example, a voice recognition model that supports dialects in a specific region is created. In addition, the voice input unit uses a generation AI to analyze voice data in real time and dynamically adjust the voice recognition model to provide a voice input function that supports different languages ​​and dialects. For example, the voice recognition accuracy is improved according to the user's language settings. This enables voice input that supports different languages ​​and dialects.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The BizSync Pro system can also acquire a user's health data and adjust work priorities based on their health condition. For example, if a user feels tired, the system will prioritize lighter tasks. If the user's health condition is good, the system can assign important tasks. Furthermore, it can analyze the user's health data over the long term and suggest optimal work schedules based on health trends. This allows for flexible work management according to the user's health condition.

[0044] The BizSync Pro system can also analyze a user's past work history to predict and automatically suggest the next task. For example, it can automatically suggest the next task based on the time and frequency of past task completions. It can also prioritize and suggest tasks with high importance, taking into account the priority and importance of each task. Furthermore, by automatically incorporating tasks that need to be performed periodically into the schedule, it can improve user work efficiency.

[0045] The BizSync Pro system can also analyze business processes in different industries and provide industry-specific automation solutions. For example, it can analyze business processes in the manufacturing and medical industries to optimize business flows for each industry. It can also provide automation solutions based on industry-specific best practices. This allows for the provision of industry-specific automation solutions and improved business efficiency.

[0046] The BizSync Pro system can also be equipped with the ability to automate customer support and customer service using generative AI. For example, a chatbot can be introduced to automatically respond to customer inquiries. It can also analyze customer inquiries and automatically generate appropriate responses. Furthermore, analyzing customer data and providing personalized responses can improve the efficiency of customer support and customer service.

[0047] The BizSync Pro system can also use generative AI to perform detailed analysis of task progress and propose preventative measures before delays occur. For example, it can analyze task progress data in real time to detect signs of delays. It can also prevent delays by reprioritizing tasks. Furthermore, it can prevent task delays by proposing resource redistribution, taking into account task dependencies and resource utilization.

[0048] The BizSync Pro system can also be equipped with a function that uses generative AI to manage tasks taking into account an individual's life events and health status. For example, it can adjust task schedules based on the user's calendar and health data. It can also adjust tasks when there is an important life event. Furthermore, by reducing tasks when a person's health condition worsens, flexible task management can be achieved according to the individual's situation.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The Business Automation Department automates business processes. For example, automating everyday tasks and routine work reduces the burden on employees. The Business Automation Department also automates tasks such as issuing invoices, managing payments, and checking inventory. For example, the Business Automation Department automates issuing invoices to streamline payment management. The Business Automation Department also automates inventory checking to optimize inventory management. Step 2: The task management unit optimizes tasks. For example, it monitors the progress of a project in real time and automatically sets task priorities. The task management unit also analyzes project data and proposes optimal task allocation and scheduling. For example, the task management unit monitors the progress of a project in real time and automatically sets task priorities. The task management unit also analyzes project data and proposes optimal task allocation and scheduling. Step 3: The voice input unit analyzes the voice instructions and executes the operation. For example, when the user issues voice instructions, operations such as reserving or canceling a meeting are automatically executed. The voice input unit also analyzes the voice instructions and automatically reflects them in the calendar. For example, the voice input unit analyzes the voice instruction "Reserve a meeting for tomorrow at 10 o'clock" and automatically reflects it in the calendar.

[0051] (Example 2) The BizSync Pro system, an embodiment of the present invention, utilizes AI to support business automation and task management. This system aims to efficiently handle complex tasks and optimize business processes. It also supports voice input, allowing users to automatically schedule and cancel meetings with a single voice command. This allows the BizSync Pro system to improve business performance and reduce time and stress.

[0052] 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 business processes. For example, by automating daily tasks and routine work, it reduces the burden on employees. The business automation unit also automatically processes tasks such as issuing invoices, managing payments, and checking inventory. For example, the business automation unit automates issuance of invoices to streamline payment management. The business automation unit also automates inventory checks to optimize inventory management. The task management unit optimizes tasks. For example, the task management unit monitors the progress of a project in real time and automatically sets task priorities. The task management unit also analyzes project data to propose optimal task assignments and schedules. For example, the task management unit monitors the progress of a project in real time and automatically sets task priorities. The task management unit also analyzes project data to propose optimal task assignments and schedules. The voice input unit analyzes voice instructions and performs operations. For example, a user can issue voice instructions to automatically perform operations such as reserving or canceling a meeting. The voice input unit also analyzes voice instructions and automatically updates the calendar. For example, the voice input unit analyzes a voice instruction such as "Book a meeting for tomorrow at 10:00" and automatically updates the calendar. This enables the BizSync Pro system according to the embodiment to improve work efficiency, optimize task management, and operate via voice input.

[0053] The task automation unit can analyze a user's past work history and predict and automatically suggest the next task to be performed. For example, the task automation unit analyzes a user's past work history, and the generation AI learns from past data to predict the next task to be performed. For example, the generation AI automatically suggests the next task to be performed based on the time and frequency of past task completions. The task automation unit also considers the priority and importance of tasks when analyzing a user's work history and predicts the next task to be performed. For example, it prioritizes the suggestions of tasks with high importance. The task automation unit also predicts the next task to be performed based on the user's past work history and automatically adds it to the schedule. For example, it automatically incorporates tasks that need to be performed regularly into the schedule. This improves the user's work efficiency.

[0054] The process automation unit can monitor the progress of work in real time and automatically issue an alert if an abnormality occurs. For example, the process automation unit builds a system in which a generation AI monitors the progress of work in real time and automatically issues an alert if an abnormality occurs. For example, an alert is issued if the progress of a task is delayed. In addition, to monitor the progress of work in real time, the process automation unit has the generation AI periodically check the status of each task and issue an alert if an abnormality occurs. For example, an alert is issued if a task is not completed within the deadline. In addition, the process automation unit introduces an anomaly detection algorithm so that the generation AI can monitor the progress of work and issue an alert if an abnormality occurs. For example, an alert is issued if an abnormal pattern is detected. This makes it possible to quickly detect and respond to abnormalities in work.

[0055] The task automation unit can use the emotion estimation function to monitor the user's stress level and automatically suggest tasks for relaxation when stress increases. For example, the task automation unit uses the emotion estimation function to build a system that monitors the user's stress level and automatically suggests tasks for relaxation when stress increases. For example, it suggests short breaks or activities for relaxation. In addition, to monitor the user's stress level, the task automation unit uses the emotion estimation function to analyze facial expressions and voice tones and suggests tasks for relaxation when stress increases. For example, it suggests deep breathing and stretching. In addition, the task automation unit uses the emotion estimation function to monitor the user's stress level in real time and automatically add tasks for relaxation to the schedule when stress increases. For example, it suggests time to listen to music for relaxation. This reduces the user's stress and improves work efficiency.

[0056] The business automation department can use generative AI to automate customer response and customer support. For example, the business automation department uses generative AI to build a system that automates customer response and customer support. For example, it introduces a chatbot that automatically responds to customer inquiries. In addition, to expand the scope of business automation, the business automation department uses generative AI to automate customer response and customer support tasks. For example, it analyzes the content of customer inquiries and automatically generates appropriate responses. In addition, the business automation department uses generative AI to analyze customer data and provide personalized responses in order to automate customer response and customer support. For example, it suggests appropriate responses based on the customer's past inquiry history. This improves the efficiency of customer response and customer support.

[0057] The business automation department can analyze business processes in different industries and provide industry-specific automation solutions. For example, the business automation department analyzes business processes in different industries and uses generative AI to learn data for each industry in order to provide industry-specific automation solutions. For example, it analyzes business processes in the manufacturing and medical industries. The business automation department also uses generative AI to analyze business processes in different industries and builds a system that provides industry-specific automation solutions. For example, it optimizes business flows for each industry. The business automation department also collects industry-specific data to analyze business processes in different industries and proposes industry-specific automation solutions. For example, it provides automation solutions based on industry-specific best practices. This makes it possible to provide industry-specific automation solutions.

[0058] The task automation unit uses the emotion estimation function to propose task automation in accordance with the user's emotions and can design a task flow that elicits positive emotions. The task automation unit, for example, uses the emotion estimation function to build a system that proposes task automation in accordance with the user's emotions. For example, if the user is feeling stressed, the task automation unit proposes a task to help the user relax. In addition, in order to propose task automation in accordance with the user's emotions, the task automation unit uses the emotion estimation function to analyze the user's emotional state in real time and design a task flow that elicits positive emotions. For example, the task automation unit prioritizes the proposal of tasks that bring joy to the user. In addition, the task automation unit uses the emotion estimation function to collect and analyze user emotion data in order to propose task automation in accordance with the user's emotions and design a task flow that elicits positive emotions. For example, tasks that make the user feel satisfied are automatically added to the schedule. This elicits positive emotions from the user and improves work efficiency.

[0059] The task management unit can use generative AI to analyze task progress in detail and propose preventive measures before delays occur. For example, the task management unit uses generative AI to build a system that analyzes task progress in detail and proposes preventive measures before delays occur. For example, it analyzes task progress data in real time and detects signs of delays. In addition, to analyze task progress in detail, the task management unit periodically checks the status of each task using generative AI and proposes preventive measures before delays occur. For example, it re-prioritizes tasks. In addition, the task management unit uses generative AI to analyze task progress and considers task dependencies and resource usage to propose preventive measures before delays occur. For example, it proposes resource redistribution. This makes it possible to prevent task delays before they occur.

[0060] The task management unit can analyze the skill sets of team members and assign optimal tasks. For example, the task management unit uses a generation AI to analyze the skill sets of team members and build a system that assigns optimal tasks. For example, tasks are assigned based on each member's skills and experience. In addition, to analyze the skill sets of team members, the generation AI collects each member's historical data and performance data and assigns optimal tasks. For example, tasks that require specific skills are assigned to appropriate members. In addition, the task management unit uses a generation AI to analyze the skill sets of team members and create a skill matrix to assign optimal tasks. For example, the skill level of each member is visualized and task assignment is optimized. This makes it possible to assign tasks according to the skills of team members.

[0061] The task management unit can monitor the motivation of team members using the emotion estimation function and reallocate appropriate tasks when motivation drops. The task management unit, for example, uses the emotion estimation function to monitor the motivation of team members and build a system that reallocates appropriate tasks when motivation drops. For example, it assigns interesting tasks to members whose motivation drops. In addition, the task management unit uses the emotion estimation function to analyze facial expressions and voice tones to monitor the motivation of team members and reallocates appropriate tasks when motivation drops. For example, it suggests tasks that will increase motivation. In addition, the task management unit uses the emotion estimation function to monitor the motivation of team members in real time and collects and analyzes motivation data to reallocate appropriate tasks when motivation drops. For example, it assigns support tasks to members whose motivation drops. This maintains the motivation of team members and improves work efficiency.

[0062] The task management unit can use a generating AI to manage tasks taking into account an individual's life events and health condition. For example, the task management unit uses a generating AI to build a system that manages tasks taking into account an individual's life events and health condition. For example, the task management unit adjusts task schedules based on the user's calendar and health data. In addition, to expand the scope of task management, the task management unit uses a generating AI to analyze an individual's life events and health condition and optimize task priorities and schedules. For example, tasks are adjusted when there is an important life event. In addition, the task management unit uses a generating AI to collect and analyze user data to manage tasks taking into account an individual's life events and health condition. For example, tasks are reduced when the user's health condition deteriorates. This enables flexible task management according to the individual's situation.

[0063] The task management unit can link with different project management tools to achieve integrated task management. For example, the task management unit uses generation AI to link with different project management tools and build a system that achieves integrated task management. For example, it collects task data from multiple tools and manages it centrally. In addition, to link with different project management tools, the generation AI uses each tool's API to integrate task data. For example, it links with tools such as Trello and Asana. In addition, the task management unit uses generation AI to link with different project management tools and synchronizes task data to achieve integrated task management. For example, it updates the task status of each tool in real time. This makes it possible to integrate multiple project management tools for task management.

[0064] The task management unit can use the emotion estimation function to analyze the emotional state of the entire team and manage tasks to maintain an emotionally positive environment. The task management unit, for example, uses the emotion estimation function to analyze the emotional state of the entire team and builds a system for task management to maintain an emotionally positive environment. For example, the task management unit collects emotional data of team members and suggests tasks that elicit positive emotions. The task management unit also uses the emotion estimation function to monitor the emotional data of each member in real time to analyze the emotional state of the entire team and manage tasks to maintain an emotionally positive environment. For example, it assigns support tasks to members whose emotions are low. The task management unit also uses the emotion estimation function to analyze the emotional state of the entire team and adjusts task priorities and schedules based on the emotional data to manage tasks to maintain an emotionally positive environment. For example, it prioritizes tasks that increase emotions. This keeps the emotional state of the entire team positive and improves work efficiency.

[0065] The voice input unit can use a generation AI to learn and individually optimize a user's speech patterns to improve the accuracy of voice input. For example, the voice input unit uses a generation AI to build a system that learns and individually optimizes a user's speech patterns to improve the accuracy of voice input. For example, the voice input unit collects user speech data and customizes a voice recognition model. Furthermore, the voice input unit uses a generation AI to analyze each user's voice data and individually optimize it to learn the user's speech patterns. For example, a voice recognition model corresponding to a specific accent or dialect is created. Furthermore, the voice input unit uses a generation AI to learn and individually optimize a user's speech patterns to improve the accuracy of voice input. For example, the voice input unit analyzes voice data in real time and dynamically adjusts the voice recognition model to learn and individually optimize the user's speech patterns to improve the accuracy of voice input. For example, the voice recognition accuracy is improved to match the user's speaking style. This improves the accuracy of voice input and increases user convenience.

[0066] The voice input unit can use a generation AI to analyze voice instructions and provide a multitasking function that executes multiple tasks simultaneously. For example, the voice input unit builds a system in which the generation AI analyzes voice instructions and provides a multitasking function that executes multiple tasks simultaneously. For example, multiple calendar events are created simultaneously based on voice instructions. In addition, to analyze the voice instructions, the generation AI analyzes the voice data in real time and executes multiple tasks simultaneously. For example, a reminder is set at the same time as a meeting reservation. In addition, the voice input unit uses a generation AI to analyze the voice instructions and integrates voice recognition with a task management system to execute multiple tasks simultaneously. For example, task priorities are set based on voice instructions and executed simultaneously. This improves work efficiency by executing multiple tasks simultaneously.

[0067] The voice input unit can use the emotion estimation function to estimate emotions from the user's voice and provide appropriate feedback according to the emotions. The voice input unit, for example, uses the emotion estimation function to build a system that estimates emotions from the user's voice and provides appropriate feedback according to the emotions. For example, if the user is feeling stressed, the voice input unit makes suggestions to help the user relax. The voice input unit also analyzes voice data using the emotion estimation function to estimate emotions from the user's voice and provides feedback according to the emotions. For example, if the user is feeling happy, a positive message is displayed. The voice input unit also collects and analyzes voice data using the emotion estimation function to estimate emotions from the user's voice in real time and provide appropriate feedback according to the emotions. For example, support is provided if the user is feeling anxious. As a result, user satisfaction is improved by providing feedback according to the user's emotions.

[0068] The voice input unit uses generation AI to expand the range of operations via voice input, allowing smart home devices to be operated. For example, the voice input unit uses generation AI to expand the range of operations via voice input and build a system to operate smart home devices. For example, adjusting lighting and temperature based on voice instructions. In addition, to expand the range of operations via voice input, the voice input unit uses generation AI to link with smart home devices and operate the devices based on voice instructions. For example, turning home appliances on and off by voice. In addition, the voice input unit uses generation AI to integrate voice recognition with smart home systems to expand the range of operations via voice input and operate smart home devices. For example, operating a security system based on voice instructions. This expands the range of operations via voice input and makes it possible to operate smart home devices.

[0069] The voice input unit can use a generation AI to provide a voice input function that supports different languages ​​and dialects. For example, the voice input unit uses a generation AI to build a system that provides a voice input function that supports different languages ​​and dialects. For example, a voice recognition model that supports multiple languages, such as English, French, and Chinese, is created. In addition, to support different languages ​​and dialects, the voice input unit uses a generation AI to learn voice data for each language and dialect and optimize the voice input function. For example, a voice recognition model that supports dialects in a specific region is created. In addition, the voice input unit uses a generation AI to analyze voice data in real time and dynamically adjust the voice recognition model to provide a voice input function that supports different languages ​​and dialects. For example, the voice recognition accuracy is improved according to the user's language settings. This enables voice input that supports different languages ​​and dialects.

[0070] The voice input unit can use the emotion estimation function to analyze the user's emotion at the time of voice input and suggest an operation according to the emotion. The voice input unit, for example, uses the emotion estimation function to build a system that analyzes the user's emotion at the time of voice input and suggests an operation according to the emotion. For example, if the user is feeling stressed, an operation to relax the user is suggested. Furthermore, the voice input unit uses the emotion estimation function to analyze voice data in real time to analyze the user's emotion and suggest an operation according to the emotion. For example, if the user is feeling happy, a positive operation is suggested. Furthermore, the voice input unit uses the emotion estimation function to analyze the user's emotion at the time of voice input and collects and analyzes voice data to suggest an operation according to the emotion. For example, if the user is feeling anxious, a support operation is suggested. This makes it possible to suggest an operation according to the user's emotion.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The BizSync Pro system can also estimate a user's emotions and adjust work priorities based on those emotions. For example, if a user is feeling stressed, the system can prioritize relaxing tasks. If a user is highly motivated, the system can assign challenging tasks. Furthermore, by collecting user emotional data and analyzing long-term emotional trends, the system can suggest optimal work schedules. This allows for flexible work management that adapts to the user's emotional state.

[0073] The BizSync Pro system can also acquire a user's health data and adjust work priorities based on their health condition. For example, if a user feels tired, the system will prioritize lighter tasks. If the user's health condition is good, the system can assign important tasks. Furthermore, it can analyze the user's health data over the long term and suggest optimal work schedules based on health trends. This allows for flexible work management according to the user's health condition.

[0074] The BizSync Pro system can also analyze a user's past work history to predict and automatically suggest the next task. For example, it can automatically suggest the next task based on the time and frequency of past task completions. It can also prioritize and suggest tasks with high importance, taking into account the priority and importance of each task. Furthermore, by automatically incorporating tasks that need to be performed periodically into the schedule, it can improve user work efficiency.

[0075] The BizSync Pro system can also analyze business processes in different industries and provide industry-specific automation solutions. For example, it can analyze business processes in the manufacturing and medical industries to optimize business flows for each industry. It can also provide automation solutions based on industry-specific best practices. This allows for the provision of industry-specific automation solutions and improved business efficiency.

[0076] The BizSync Pro system can also be equipped with the ability to automate customer support and customer service using generative AI. For example, a chatbot can be introduced to automatically respond to customer inquiries. It can also analyze customer inquiries and automatically generate appropriate responses. Furthermore, analyzing customer data and providing personalized responses can improve the efficiency of customer support and customer service.

[0077] The BizSync Pro system can also use emotion estimation to monitor a user's stress level and automatically suggest tasks to help them relax when stress levels rise. For example, it can suggest short breaks or relaxing activities. It can also analyze facial expressions and voice tones to suggest deep breathing or stretching. Furthermore, it can suggest relaxing music to listen to, reducing stress and improving work efficiency.

[0078] The BizSync Pro system also uses emotion estimation to suggest work automation based on the user's emotions, and is equipped with the ability to design workflows that elicit positive emotions. For example, if a user is feeling stressed, it can suggest tasks to help them relax. It can also prioritize tasks that bring joy to the user. Furthermore, by collecting and analyzing user emotional data, it is possible to design workflows that elicit positive emotions.

[0079] The BizSync Pro system can also use generative AI to perform detailed analysis of task progress and propose preventative measures before delays occur. For example, it can analyze task progress data in real time to detect signs of delays. It can also prevent delays by reprioritizing tasks. Furthermore, it can prevent task delays by proposing resource redistribution, taking into account task dependencies and resource utilization.

[0080] The BizSync Pro system can also use emotion estimation to monitor team members' motivation and reassign appropriate tasks when motivation drops. For example, it can assign interesting tasks to members who are feeling unmotivated. It can also analyze facial expressions and voice tone to suggest tasks that will increase motivation. Furthermore, by collecting and analyzing motivation data, it can assign support tasks to members who are feeling unmotivated.

[0081] The BizSync Pro system can also be equipped with a function that uses generative AI to manage tasks taking into account an individual's life events and health status. For example, it can adjust task schedules based on the user's calendar and health data. It can also adjust tasks when there is an important life event. Furthermore, by reducing tasks when a person's health condition worsens, flexible task management can be achieved according to the individual's situation.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The Business Automation Department automates business processes. For example, automating everyday tasks and routine work reduces the burden on employees. The Business Automation Department also automates tasks such as issuing invoices, managing payments, and checking inventory. For example, the Business Automation Department automates issuing invoices to streamline payment management. The Business Automation Department also automates inventory checking to optimize inventory management. Step 2: The task management unit optimizes tasks. For example, it monitors the progress of a project in real time and automatically sets task priorities. The task management unit also analyzes project data and proposes optimal task allocation and scheduling. For example, the task management unit monitors the progress of a project in real time and automatically sets task priorities. The task management unit also analyzes project data and proposes optimal task allocation and scheduling. Step 3: The voice input unit analyzes the voice instructions and executes the operation. For example, when the user issues voice instructions, operations such as reserving or canceling a meeting are automatically executed. The voice input unit also analyzes the voice instructions and automatically reflects them in the calendar. For example, the voice input unit analyzes the voice instruction "Reserve a meeting for tomorrow at 10 o'clock" and automatically reflects it in the calendar.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. The Business Automation Department, which automates business processes, a task management section that optimizes tasks; a voice input unit that analyzes voice instructions and executes operations. A system characterized by:

2. The business automation unit Analyzes the user's past work history, predicts the next task, and automatically suggests it 2. The system of claim 1.

3. The business automation unit The progress of the above operations is monitored in real time, and an alert is automatically issued if an abnormality occurs.

2. The system of claim 1.

4. The business automation unit Monitors the user's stress level and automatically suggests relaxation tasks when stress levels rise 2. The system of claim 1.

5. The business automation unit Automate customer interactions and support with generative AI 2. The system of claim 1.

6. The business automation unit Analyzing business processes in different industries and providing industry-specific automation solutions 2. The system of claim 1.

7. The business automation unit Propose automation solutions based on user emotions and design workflows that elicit positive emotions 2. The system of claim 1.

8. The task management unit Using generative AI to analyze the progress of the task in detail and suggest preventative measures before delays occur.

2. The system of claim 1.

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