system

Abstract

The system according to the embodiment aims to automate the creation of minutes and task management, thereby improving business efficiency. [Solution] A system according to an embodiment includes an information acquisition unit, a task organization unit, a voice input unit, and an instruction unit. The information acquisition unit acquires information from minutes or a communication tool. The task organization unit extracts and organizes tasks based on the information acquired by the information acquisition unit. The voice input unit uses a voice recognition function to instantly store tasks that arise in a database. The instruction unit issues instructions based on the tasks extracted and organized by the task organization unit.

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] With conventional technology, creating minutes and managing tasks requires a lot of time and effort, leaving room for improvement in efficiency.

[0005] The system according to the embodiment aims to automate the creation of minutes and task management, thereby improving business efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes an information acquisition unit, a task organization unit, a voice input unit, and an instruction unit. The information acquisition unit acquires information from minutes or a communication tool. The task organization unit extracts and organizes tasks based on the information acquired by the information acquisition unit. The voice input unit uses a voice recognition function to store tasks that are generated immediately in a database. The instruction unit issues instructions based on the tasks extracted and organized by the task organization unit. [Effects of the Invention]

[0007] The system according to the embodiment automates the creation of minutes and task management, thereby improving work efficiency. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The business automation system according to an embodiment of the present invention automates tasks such as creating meeting minutes, managing tasks, and sending reminders. This system periodically acquires information from meeting minutes and communication tools and stores the tasks in a database. It automatically extracts and organizes tasks using a generation AI and issues instructions when necessary. For example, by using a voice recognition function, tasks can be instantly stored in a database, eliminating the need to open a task list. This system can be tested internally and sold externally as a package, potentially generating revenue. For example, the business automation system collects information from meeting minutes and chat tools and stores it in a database. Examples of such information include meeting minutes and chat messages recording project progress. Next, it uses a generation AI to analyze the collected information and extract and organize tasks. The generation AI analyzes the collected information and automatically extracts task content, deadlines, and assignees. For example, it extracts preparation tasks for the next meeting from meeting minutes and sets assignees and deadlines. Furthermore, it uses a voice recognition function to instantly store tasks that are generated in a database. For example, when a user speaks to their smartphone, "I want to create materials for the next meeting," the content is registered in the database. This eliminates the need to open the task list. Finally, instructions can be issued when necessary. For example, a reminder notification can be sent when a task deadline is approaching. This allows users to understand the progress of the task and take appropriate action. It is possible to increase revenue by testing this system in-house and then selling it externally as a package. For example, providing it to companies can improve business efficiency and increase revenue. In this way, the business automation system can streamline tasks such as creating meeting minutes, managing tasks, and sending reminders.

[0029] A task automation system according to an embodiment includes an information acquisition unit, a task organization unit, a voice input unit, and an instruction unit. The information acquisition unit acquires information from minutes or a communication tool. Examples of the minutes or communication tool include, but are not limited to, meeting minutes, Slack (registered trademark), and Teams (registered trademark). The information acquisition unit, for example, periodically collects meeting minutes and stores them in a database. The information acquisition unit can also collect messages recording project progress from a chat tool. For example, the information acquisition unit acquires messages using a Slack API and stores them in a database. The task organization unit analyzes the collected information using a generation AI and extracts task content, deadlines, and responsible parties. Examples of the generation AI include, but are not limited to, GPT-4 (registered trademark) and Gemini. The task organization unit, for example, extracts preparation tasks for the next meeting from meeting minutes and sets responsible parties and deadlines. The task organization unit can also analyze the progress of a project and automatically extract necessary tasks. For example, the generation AI analyzes the contents of the minutes using natural language processing technology and extracts important tasks. The voice input unit uses a voice recognition function to register tasks that are thought up in a database. Examples of voice recognition functions include, but are not limited to, Google® Speech-to-Text and IBM Watson. For example, when a user speaks into a smartphone, saying, "I'm going to create materials for the next meeting," the voice input unit registers the content in a database. The voice input unit can also support multiple languages ​​using voice recognition technology. For example, the voice input unit recognizes voice input in multiple languages, such as English, Japanese, and French, and converts it into text data. The instruction unit sends a reminder notification when a task deadline approaches. Examples of reminder notifications include, but are not limited to, email notifications and in-app notifications. For example, the instruction unit sends a notification by email when a task deadline approaches. The instruction unit can also display a reminder notification within the app. For example, the instruction unit sends a reminder using a smartphone's notification function.As a result, the business automation system according to the embodiment can improve the efficiency of tasks such as creating minutes, managing tasks, and sending reminders.

[0030] The information acquisition unit can collect information from minutes or chat tools. For example, the information acquisition unit periodically collects meeting minutes and stores them in a database. The minutes include, for example, the contents of the meeting, decisions made, and action items. The information acquisition unit can also collect messages recording the progress of a project from a chat tool. For example, the information acquisition unit acquires messages using a Slack API and stores them in a database. This makes it possible to collect information from minutes and chat tools. Some or all of the above-mentioned processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can acquire messages using a Slack API and analyze the content of the messages using AI.

[0031] The task organizer can analyze the information collected using a generation AI and extract task content, deadlines, and responsible parties. For example, the task organizer can extract preparation tasks for the next meeting from meeting minutes and set responsible parties and deadlines. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. For example, the task organizer can analyze the content of the minutes using a generation AI and extract important tasks. The task organizer can also analyze the progress of a project and automatically extract necessary tasks. For example, the generation AI can analyze the progress of a project using natural language processing technology and extract task content, deadlines, and responsible parties. Thus, the generation AI can automatically extract task content, deadlines, and responsible parties. Some or all of the above-described processing in the task organizer can be performed using, for example, AI, or without AI. For example, the task organizer can analyze the content of the minutes using a generation AI and extract task content, deadlines, and responsible parties.

[0032] The voice input unit can use a voice recognition function to register tasks that come to mind in a database. For example, when a user speaks into a smartphone, saying, "Create materials for the next meeting," the voice input unit registers the content of the speech in the database. Examples of voice recognition functions include, but are not limited to, Google Speech-to-Text and IBM Watson. The voice input unit can also support multiple languages ​​using voice recognition technology. For example, the voice input unit recognizes voice input in multiple languages, such as English, Japanese, and French, and converts it into text data. This allows tasks that come to mind to be instantly registered in the database using the voice recognition function. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can analyze the user's voice using voice recognition technology and register the task in the database.

[0033] The instruction unit can send a reminder notification when the task deadline approaches. The instruction unit, for example, sends a notification by email when the task deadline approaches. Reminder notifications include, but are not limited to, email notifications and in-app notifications. The instruction unit can also display a reminder notification within an app, for example. For example, the instruction unit can send a reminder using a notification function of a smartphone. In this way, by sending a reminder notification when the task deadline approaches, the user can take appropriate action. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can monitor the task deadline using AI and send a reminder notification.

[0034] The task organizer can extract preparatory tasks required for the next meeting from the meeting minutes and set the responsible party and deadline. The task organizer, for example, extracts preparatory tasks for the next meeting from the meeting minutes and sets the responsible party and deadline. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The task organizer, for example, uses the generation AI to analyze the contents of the minutes and extract important tasks. The task organizer can also analyze the progress of a project and automatically extract necessary tasks. For example, the generation AI analyzes the progress of a project using natural language processing technology and extracts the task content, deadline, and responsible party. This allows the preparatory tasks for the next meeting to be automatically extracted from the meeting minutes and the responsible party and deadline to be set. Some or all of the above-described processing in the task organizer can be performed using, for example, AI, or without AI. For example, the task organizer can analyze the contents of the minutes using the generation AI and extract the task content, deadline, and responsible party.

[0035] The information acquisition unit can analyze past information acquisition history and select an appropriate acquisition method. For example, the information acquisition unit prioritizes acquisition of information sources that the user has frequently used in the past. The information acquisition unit can also suggest the most efficient acquisition method based on the user's past information acquisition history. Furthermore, the information acquisition unit can analyze patterns of information acquired by the user in the past and set the optimal acquisition timing. In this way, the optimal acquisition method can be selected by analyzing the past information acquisition history. Some or all of the above-mentioned processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the past information acquisition history into a generation AI and select the optimal acquisition method.

[0036] When acquiring information, the information acquisition unit can perform filtering based on the user's current project or area of ​​interest. For example, the information acquisition unit prioritizes acquiring information related to a project currently underway by the user. The information acquisition unit can also filter and acquire highly relevant information based on the user's area of ​​interest. Furthermore, the information acquisition unit can also acquire only necessary information based on keywords set by the user. This makes it possible to acquire highly relevant information by filtering information based on the user's current project or area of ​​interest. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the user's project information into a generation AI to perform filtering.

[0037] When acquiring information, the information acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the information acquisition unit prioritizes acquiring information related to that area. Furthermore, when the user is moving, the information acquisition unit can also acquire optimal information based on the user's current location. Furthermore, when the user is in a specific location, the information acquisition unit can also prioritize acquiring information related to that location. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the user's geographical location information into a generation AI to acquire highly relevant information.

[0038] The information acquisition unit can analyze the user's social media activity and acquire related information when acquiring information. The information acquisition unit can acquire related information based on, for example, information shared by the user on social media. The information acquisition unit can also preferentially acquire information shared by the user's social media followers and friends. Furthermore, the information acquisition unit can analyze the user's social media activity history and acquire highly relevant information. In this way, highly relevant information can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the information acquisition unit can be performed, for example, using AI or without using AI. For example, the information acquisition unit can input the user's social media activity data into a generation AI to acquire related information.

[0039] The task organization unit can adjust the level of detail of the organization based on the importance of the task when organizing the task. For example, the task organization unit organizes tasks with high importance in detail and organizes tasks with low importance in a simplified manner. The task organization unit can also determine the priority of the organization based on the importance of the task. Furthermore, the task organization unit can organize tasks with high importance including detailed information and organize tasks with low importance in a simplified manner. This enables efficient task organization by adjusting the level of detail of the organization based on the importance of the task. Some or all of the above-mentioned processing in the task organization unit may be performed using, for example, AI, or may be performed without using AI. For example, the task organization unit can input task importance data to a generation AI to adjust the level of detail of the organization.

[0040] The task organization unit can apply different organization algorithms depending on the task category when organizing tasks. For example, the task organization unit organizes project tasks and daily tasks using different algorithms. The task organization unit can also organize urgent tasks and long-term tasks using different algorithms. Furthermore, the task organization unit can organize team tasks and individual tasks using different algorithms. This enables efficient task organization by applying different organization algorithms depending on the task category. Some or all of the above-mentioned processing in the task organization unit may be performed using, for example, AI, or may be performed without using AI. For example, the task organization unit can input task category data into a generation AI and apply different organization algorithms.

[0041] When organizing tasks, the task organizing unit can determine the order of priority for organizing based on the submission dates of the tasks. For example, the task organizing unit prioritizes organizing tasks with upcoming submission deadlines. The task organizing unit can also postpone tasks with distant submission deadlines. Furthermore, the task organizing unit can dynamically adjust the order of priority for tasks based on the submission deadlines. This enables efficient task organization by determining the order of priority for organizing based on the submission dates of the tasks. Some or all of the above-described processing in the task organizing unit may be performed using, or without, AI, for example. For example, the task organizing unit can input task submission date data into a generation AI to determine the order of priority for organizing.

[0042] The task organization unit can adjust the order of organization based on the relevance of the tasks when organizing the tasks. For example, the task organization unit groups and organizes highly related tasks. The task organization unit can also organize less related tasks individually. Furthermore, the task organization unit can dynamically adjust the order of organization based on the relevance of the tasks. This enables efficient task organization by adjusting the order of organization based on the relevance of the tasks. Some or all of the above-described processing in the task organization unit may be performed using, for example, AI, or may be performed without using AI. For example, the task organization unit can input task relevance data into a generation AI and adjust the order of organization.

[0043] The voice input unit can select the optimal input method by referring to the user's past voice input history when inputting voice. For example, the voice input unit preferentially suggests voice commands that the user has used in the past. The voice input unit can also select the optimal input method from the user's past voice input history. Furthermore, the voice input unit can automatically recognize voice commands that the user has frequently used in the past. This makes it possible to select the optimal input method by referring to the user's past voice input history. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input the user's voice input history data into a generation AI to select the optimal input method.

[0044] The voice input unit can filter the input content based on the user's current situation when inputting voice. For example, if the user is in a meeting, the voice input unit can input only tasks related to the meeting. Furthermore, if the user is traveling, the voice input unit can input only tasks related to the travel. Furthermore, if the user is concentrating on a specific project, the voice input unit can input only tasks related to that project. In this way, by filtering the input content based on the user's current situation, highly relevant tasks can be input. Some or all of the above-mentioned processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input the user's situation data to a generation AI to filter the input content.

[0045] The voice input unit can prioritize processing highly relevant input content by taking into account the user's geographical location information when inputting voice. For example, when the user is in a specific area, the voice input unit prioritizes input of tasks related to that area. Furthermore, when the user is moving, the voice input unit can also input the most appropriate task based on the user's current location. Furthermore, when the user is in a specific location, the voice input unit can also prioritize input of tasks related to that location. In this way, highly relevant input content can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input the user's geographical location information to a generation AI and process highly relevant input content.

[0046] The voice input unit can analyze the user's social media activity during voice input and acquire related input content. The voice input unit can, for example, input related tasks based on information shared by the user on social media. The voice input unit can also preferentially input information shared by the user's social media followers and friends. Furthermore, the voice input unit can analyze the user's social media activity history and input highly relevant tasks. In this way, highly relevant input content can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the voice input unit can be performed, for example, using AI or without AI. For example, the voice input unit can input the user's social media activity data into a generation AI to acquire related input content.

[0047] The instruction unit can adjust the level of detail of the instruction based on the importance of the task when issuing the instruction. For example, the instruction unit can issue detailed instructions for tasks with high importance and brief instructions for tasks with low importance. The instruction unit can also determine the priority of the instructions based on the importance of the task. Furthermore, the instruction unit can issue instructions including detailed information for tasks with high importance and brief instructions for tasks with low importance. This enables efficient instruction issuing by adjusting the level of detail of the instructions based on the importance of the task. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input task importance data to the generation AI and adjust the level of detail of the instructions.

[0048] The instruction unit can apply different instruction algorithms depending on the task category when issuing instructions. For example, the instruction unit can apply different instruction algorithms to project tasks and daily tasks. The instruction unit can also apply different instruction algorithms to urgent tasks and long-term tasks. Furthermore, the instruction unit can apply different instruction algorithms to team tasks and individual tasks. This enables efficient instruction by applying different instruction algorithms depending on the task category. Some or all of the above-mentioned processing in the instruction unit can be performed using, for example, AI, or can be performed without using AI. For example, the instruction unit can input task category data into a generation AI and apply different instruction algorithms.

[0049] When issuing instructions, the instruction issuing unit can determine the priority of instructions based on the submission time of the task. For example, the instruction issuing unit can give priority to issuing instructions for tasks with upcoming submission deadlines. The instruction issuing unit can also postpone instructions for tasks with distant submission deadlines. Furthermore, the instruction issuing unit can dynamically adjust the priority of instructions based on the submission deadlines. This enables efficient instruction issuing by determining the priority of instructions based on the submission time of the task. Some or all of the above-mentioned processing in the instruction issuing unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction issuing unit can input task submission time data into a generation AI to determine the priority of instructions.

[0050] The instruction unit can adjust the order of instructions based on the relevance of the tasks when issuing instructions. For example, the instruction unit can group and issue instructions for highly related tasks. The instruction unit can also issue instructions for less related tasks individually. Furthermore, the instruction unit can dynamically adjust the order of instructions based on the relevance of the tasks. This enables efficient instruction issuing by adjusting the order of instructions based on the relevance of the tasks. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input task relevance data into a generation AI and adjust the order of instructions.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The task organization unit can analyze the user's past task completion history and propose an efficient task organization method. For example, it can analyze the patterns of tasks that the user completed quickly in the past and prioritize similar tasks. It can also identify tasks that the user struggled with in the past and provide detailed guidelines for those tasks. It can also propose an optimal task schedule based on the user's past task completion times. This makes it possible to realize efficient task organization by utilizing the user's past task completion history.

[0053] The instruction unit can analyze the user's past instruction history and suggest the optimal instruction method. For example, it can analyze patterns of instructions that the user has responded to quickly in the past and prioritize the use of similar instruction methods. It can also identify instructions that the user has confused in the past and add detailed explanations to those instructions. It can also suggest the optimal timing to give instructions based on the time it took the user to respond to instructions in the past. This makes it possible to use the user's past instruction history to achieve efficient instruction giving.

[0054] The information acquisition unit can acquire the user's calendar information and adjust the timing of information acquisition based on the schedule. For example, if the user is in a meeting, information acquisition can be refrained from. The user can also concentrate information acquisition during their free time. Furthermore, if the user has an important appointment approaching, it is also possible to prioritize the acquisition of information related to that appointment. In this way, efficient information collection can be achieved by adjusting the timing of information acquisition based on the user's schedule.

[0055] The voice input unit can analyze the user's past voice input history and suggest optimal voice commands. For example, it can prioritize suggesting voice commands that the user has used frequently in the past. It can also select the optimal input method from the user's past voice input history. It can also identify voice commands that the user has input incorrectly in the past and provide detailed guidelines for those commands. This makes it possible to realize efficient voice input by utilizing the user's past voice input history.

[0056] The information acquisition unit can acquire the user's geographical location information and prioritize acquisition of highly relevant information based on the acquired geographical location information. For example, when the user is in a specific area, information related to that area is prioritized. Also, when the user is moving, optimal information can be acquired based on the user's current location. Furthermore, when the user is in a specific location, it is also possible to prioritize acquisition of information related to that location. This allows for efficient information collection by prioritized acquisition of highly relevant information based on the user's geographical location information.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The information acquisition unit acquires information from minutes or communication tools. Minutes include meeting minutes, and communication tools include Slack and Teams. The information acquisition unit periodically collects meeting minutes and stores them in a database. It can also collect messages recording the progress of projects from chat tools. For example, it can use the Slack API to acquire messages and store them in a database. Step 2: The task organization unit uses a generation AI to analyze the collected information and extract the task content, deadline, and person in charge. Generation AI includes GPT-4 and Gemini. For example, it can extract preparation tasks for the next meeting from meeting minutes and set the person in charge and deadline. It can also analyze the progress of a project and automatically extract necessary tasks. The generation AI uses natural language processing technology to analyze the content of the minutes and extract important tasks. Step 3: The voice input unit uses a voice recognition function to register the task that comes to mind in a database. Voice recognition functions include Google Speech-to-Text and IBM Watson. For example, if a user speaks into their smartphone and says, "Create materials for the next meeting," the content is registered in the database. The voice input unit can also support multiple languages ​​using voice recognition technology. For example, it recognizes voice input in multiple languages, such as English, Japanese, and French, and converts it into text data. Step 4: The instruction unit sends a reminder notification when the task deadline approaches. Reminder notifications include email notifications and in-app notifications. For example, a notification can be sent by email when the task deadline approaches. Reminder notifications can also be displayed within the app. For example, a reminder can be sent using the notification function of a smartphone.

[0059] (Example 2) The business automation system according to an embodiment of the present invention automates tasks such as creating meeting minutes, managing tasks, and sending reminders. This system periodically acquires information from meeting minutes and communication tools and stores the tasks in a database. It automatically extracts and organizes tasks using a generation AI and issues instructions when necessary. For example, by using a voice recognition function, tasks can be instantly stored in a database, eliminating the need to open a task list. This system can be tested internally and sold externally as a package, potentially generating revenue. For example, the business automation system collects information from meeting minutes and chat tools and stores it in a database. Examples of such information include meeting minutes and chat messages recording project progress. Next, it uses a generation AI to analyze the collected information and extract and organize tasks. The generation AI analyzes the collected information and automatically extracts task content, deadlines, and assignees. For example, it extracts preparation tasks for the next meeting from meeting minutes and sets assignees and deadlines. Furthermore, it uses a voice recognition function to instantly store tasks that are generated in a database. For example, when a user speaks to their smartphone, "I want to create materials for the next meeting," the content is registered in the database. This eliminates the need to open the task list. Finally, instructions can be issued when necessary. For example, a reminder notification can be sent when a task deadline is approaching. This allows users to understand the progress of the task and take appropriate action. It is possible to increase revenue by testing this system in-house and then selling it externally as a package. For example, providing it to companies can improve business efficiency and increase revenue. In this way, the business automation system can streamline tasks such as creating meeting minutes, managing tasks, and sending reminders.

[0060] The task automation system according to the embodiment includes an information acquisition unit, a task organization unit, a voice input unit, and an instruction unit. The information acquisition unit acquires information from minutes or a communication tool. Examples of the minutes or communication tool include, but are not limited to, meeting minutes, Slack, and Teams. The information acquisition unit, for example, periodically collects meeting minutes and stores them in a database. The information acquisition unit can also collect messages recording project progress from a chat tool. For example, the information acquisition unit acquires messages using a Slack API and stores them in a database. The task organization unit analyzes the collected information using a generation AI and extracts task content, deadlines, and responsible parties. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The task organization unit, for example, extracts preparation tasks for the next meeting from meeting minutes and sets responsible parties and deadlines. The task organization unit can also analyze the progress of a project and automatically extract necessary tasks. For example, the generation AI analyzes the contents of meeting minutes using natural language processing technology and extracts important tasks. The voice input unit uses a voice recognition function to register tasks that are thought up in a database. Examples of voice recognition functions include, but are not limited to, Google Speech-to-Text and IBM Watson. For example, when a user speaks into a smartphone, saying, "I'm going to create materials for the next meeting," the voice input unit registers the content in a database. The voice input unit can also support multiple languages ​​using voice recognition technology. For example, the voice input unit recognizes voice input in multiple languages, such as English, Japanese, and French, and converts it into text data. The instruction unit sends a reminder notification when a task deadline approaches. Examples of reminder notifications include, but are not limited to, email notifications and in-app notifications. For example, the instruction unit sends a notification by email when a task deadline approaches. The instruction unit can also display a reminder notification within the app. For example, the instruction unit sends a reminder using a smartphone's notification function.As a result, the business automation system according to the embodiment can improve the efficiency of tasks such as creating minutes, managing tasks, and sending reminders.

[0061] The information acquisition unit can collect information from minutes or chat tools. For example, the information acquisition unit periodically collects meeting minutes and stores them in a database. The minutes include, for example, the contents of the meeting, decisions made, and action items. The information acquisition unit can also collect messages recording the progress of a project from a chat tool. For example, the information acquisition unit acquires messages using a Slack API and stores them in a database. This makes it possible to collect information from minutes and chat tools. Some or all of the above-mentioned processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can acquire messages using a Slack API and analyze the content of the messages using AI.

[0062] The task organizer can analyze the information collected using a generation AI and extract task content, deadlines, and responsible parties. For example, the task organizer can extract preparation tasks for the next meeting from meeting minutes and set responsible parties and deadlines. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. For example, the task organizer can analyze the content of the minutes using a generation AI and extract important tasks. The task organizer can also analyze the progress of a project and automatically extract necessary tasks. For example, the generation AI can analyze the progress of a project using natural language processing technology and extract task content, deadlines, and responsible parties. Thus, the generation AI can automatically extract task content, deadlines, and responsible parties. Some or all of the above-described processing in the task organizer can be performed using, for example, AI, or without AI. For example, the task organizer can analyze the content of the minutes using a generation AI and extract task content, deadlines, and responsible parties.

[0063] The voice input unit can use a voice recognition function to register tasks that come to mind in a database. For example, when a user speaks into a smartphone, saying, "Create materials for the next meeting," the voice input unit registers the content of the speech in the database. Examples of voice recognition functions include, but are not limited to, Google Speech-to-Text and IBM Watson. The voice input unit can also support multiple languages ​​using voice recognition technology. For example, the voice input unit recognizes voice input in multiple languages, such as English, Japanese, and French, and converts it into text data. This allows tasks that come to mind to be instantly registered in the database using the voice recognition function. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can analyze the user's voice using voice recognition technology and register the task in the database.

[0064] The instruction unit can send a reminder notification when the task deadline approaches. The instruction unit, for example, sends a notification by email when the task deadline approaches. Reminder notifications include, but are not limited to, email notifications and in-app notifications. The instruction unit can also display a reminder notification within an app, for example. For example, the instruction unit can send a reminder using a notification function of a smartphone. In this way, by sending a reminder notification when the task deadline approaches, the user can take appropriate action. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can monitor the task deadline using AI and send a reminder notification.

[0065] The task organizer can extract preparatory tasks required for the next meeting from the meeting minutes and set the responsible party and deadline. The task organizer, for example, extracts preparatory tasks for the next meeting from the meeting minutes and sets the responsible party and deadline. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The task organizer, for example, uses the generation AI to analyze the contents of the minutes and extract important tasks. The task organizer can also analyze the progress of a project and automatically extract necessary tasks. For example, the generation AI analyzes the progress of a project using natural language processing technology and extracts the task content, deadline, and responsible party. This allows the preparatory tasks for the next meeting to be automatically extracted from the meeting minutes and the responsible party and deadline to be set. Some or all of the above-described processing in the task organizer can be performed using, for example, AI, or without AI. For example, the task organizer can analyze the contents of the minutes using the generation AI and extract the task content, deadline, and responsible party.

[0066] The information acquisition unit can estimate the user's emotions and adjust the timing of information acquisition based on the estimated user emotions. For example, when the user is feeling stressed, the information acquisition unit reduces the frequency of information acquisition to reduce the user's burden. Furthermore, when the user is relaxed, the information acquisition unit can increase the frequency of information acquisition and collect detailed information. Furthermore, when the user is in a hurry, the information acquisition unit can prioritize and quickly process only important information. This adjusts the timing of information acquisition according to the user's emotions, thereby reducing the user's burden. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information acquisition unit can input the user's emotion data into the generation AI to adjust the timing of information acquisition.

[0067] The information acquisition unit can analyze past information acquisition history and select an appropriate acquisition method. For example, the information acquisition unit prioritizes acquisition of information sources that the user has frequently used in the past. The information acquisition unit can also suggest the most efficient acquisition method based on the user's past information acquisition history. Furthermore, the information acquisition unit can analyze patterns of information acquired by the user in the past and set the optimal acquisition timing. In this way, the optimal acquisition method can be selected by analyzing the past information acquisition history. Some or all of the above-mentioned processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the past information acquisition history into a generation AI and select the optimal acquisition method.

[0068] When acquiring information, the information acquisition unit can perform filtering based on the user's current project or area of ​​interest. For example, the information acquisition unit prioritizes acquiring information related to a project currently underway by the user. The information acquisition unit can also filter and acquire highly relevant information based on the user's area of ​​interest. Furthermore, the information acquisition unit can also acquire only necessary information based on keywords set by the user. This makes it possible to acquire highly relevant information by filtering information based on the user's current project or area of ​​interest. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the user's project information into a generation AI to perform filtering.

[0069] The information acquisition unit can estimate the user's emotions and determine the priority of information to be acquired based on the estimated user's emotions. For example, when the user is feeling stressed, the information acquisition unit postpones information of low importance. Furthermore, when the user is relaxed, the information acquisition unit can also prioritize acquiring detailed information. Furthermore, when the user is in a hurry, the information acquisition unit can quickly acquire only important information. Thus, by determining the priority of information to be acquired according to the user's emotions, important information can be acquired preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the information acquisition unit can input the user's emotion data into the generation AI to determine the priority of information.

[0070] When acquiring information, the information acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, when the user is in a specific area, the information acquisition unit prioritizes acquiring information related to that area. Furthermore, when the user is moving, the information acquisition unit can also acquire optimal information based on the user's current location. Furthermore, when the user is in a specific location, the information acquisition unit can also prioritize acquiring information related to that location. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the information acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the information acquisition unit can input the user's geographical location information into a generation AI to acquire highly relevant information.

[0071] The information acquisition unit can analyze the user's social media activity and acquire related information when acquiring information. The information acquisition unit can acquire related information based on, for example, information shared by the user on social media. The information acquisition unit can also preferentially acquire information shared by the user's social media followers and friends. Furthermore, the information acquisition unit can analyze the user's social media activity history and acquire highly relevant information. In this way, highly relevant information can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the information acquisition unit can be performed, for example, using AI or without using AI. For example, the information acquisition unit can input the user's social media activity data into a generation AI to acquire related information.

[0072] The task organization unit can estimate the user's emotions and adjust the task organization method based on the estimated user emotions. For example, if the user is feeling stressed, the task organization unit can simplify and organize tasks. Furthermore, if the user is relaxed, the task organization unit can also organize tasks in detail. Furthermore, if the user is in a hurry, the task organization unit can prioritize and organize important tasks. This adjusts the task organization method according to the user's emotions, thereby reducing the user's burden. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the task organization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the task organization unit can input the user's emotion data into the generation AI and adjust the task organization method.

[0073] The task organization unit can adjust the level of detail of the organization based on the importance of the task when organizing the task. For example, the task organization unit organizes tasks with high importance in detail and organizes tasks with low importance in a simplified manner. The task organization unit can also determine the priority of the organization based on the importance of the task. Furthermore, the task organization unit can organize tasks with high importance including detailed information and organize tasks with low importance in a simplified manner. This enables efficient task organization by adjusting the level of detail of the organization based on the importance of the task. Some or all of the above-mentioned processing in the task organization unit may be performed using, for example, AI, or may be performed without using AI. For example, the task organization unit can input task importance data to a generation AI to adjust the level of detail of the organization.

[0074] The task organization unit can apply different organization algorithms depending on the task category when organizing tasks. For example, the task organization unit organizes project tasks and daily tasks using different algorithms. The task organization unit can also organize urgent tasks and long-term tasks using different algorithms. Furthermore, the task organization unit can organize team tasks and individual tasks using different algorithms. This enables efficient task organization by applying different organization algorithms depending on the task category. Some or all of the above-mentioned processing in the task organization unit may be performed using, for example, AI, or may be performed without using AI. For example, the task organization unit can input task category data into a generation AI and apply different organization algorithms.

[0075] The task organization unit can estimate the user's emotions and determine the priority of tasks based on the estimated user emotions. For example, when the user is feeling stressed, the task organization unit postpones less important tasks. Furthermore, when the user is relaxed, the task organization unit can also prioritize detailed tasks. Furthermore, when the user is in a hurry, the task organization unit can quickly process important tasks. Thus, by determining the priority of tasks according to the user's emotions, important tasks can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the task organization unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the task organization unit can input the user's emotion data into the generation AI to determine the priority of tasks.

[0076] When organizing tasks, the task organizing unit can determine the order of priority for organizing based on the submission dates of the tasks. For example, the task organizing unit prioritizes organizing tasks with upcoming submission deadlines. The task organizing unit can also postpone tasks with distant submission deadlines. Furthermore, the task organizing unit can dynamically adjust the order of priority for tasks based on the submission deadlines. This enables efficient task organization by determining the order of priority for organizing based on the submission dates of the tasks. Some or all of the above-described processing in the task organizing unit may be performed using, or without, AI, for example. For example, the task organizing unit can input task submission date data into a generation AI to determine the order of priority for organizing.

[0077] The task organization unit can adjust the order of organization based on the relevance of the tasks when organizing the tasks. For example, the task organization unit groups and organizes highly related tasks. The task organization unit can also organize less related tasks individually. Furthermore, the task organization unit can dynamically adjust the order of organization based on the relevance of the tasks. This enables efficient task organization by adjusting the order of organization based on the relevance of the tasks. Some or all of the above-described processing in the task organization unit may be performed using, for example, AI, or may be performed without using AI. For example, the task organization unit can input task relevance data into a generation AI and adjust the order of organization.

[0078] The voice input unit can estimate the user's emotion and adjust the accuracy of the voice input based on the estimated user's emotion. For example, when the user is stressed, the voice input unit increases the accuracy of the voice input to prevent erroneous input. Furthermore, when the user is relaxed, the voice input unit can set the accuracy of the voice input to normal. Furthermore, when the user is in a hurry, the voice input unit can increase the accuracy of the voice input to allow for quick input. This adjusts the accuracy of the voice input according to the user's emotion, thereby preventing erroneous input. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without AI. For example, the voice input unit can input the user's emotion data into the generation AI and adjust the accuracy of the voice input.

[0079] The voice input unit can select the optimal input method by referring to the user's past voice input history when inputting voice. For example, the voice input unit preferentially suggests voice commands that the user has used in the past. The voice input unit can also select the optimal input method from the user's past voice input history. Furthermore, the voice input unit can automatically recognize voice commands that the user has frequently used in the past. This makes it possible to select the optimal input method by referring to the user's past voice input history. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input the user's voice input history data into a generation AI to select the optimal input method.

[0080] The voice input unit can filter the input content based on the user's current situation when inputting voice. For example, if the user is in a meeting, the voice input unit can input only tasks related to the meeting. Furthermore, if the user is traveling, the voice input unit can input only tasks related to the travel. Furthermore, if the user is concentrating on a specific project, the voice input unit can input only tasks related to that project. In this way, by filtering the input content based on the user's current situation, highly relevant tasks can be input. Some or all of the above-mentioned processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input the user's situation data to a generation AI to filter the input content.

[0081] The voice input unit can estimate the user's emotions and prioritize voice inputs based on the estimated user emotions. For example, when the user is stressed, the voice input unit postpones voice inputs of lower importance. Furthermore, when the user is relaxed, the voice input unit can also prioritize detailed voice inputs. Furthermore, when the user is in a hurry, the voice input unit can quickly process important voice inputs. Thus, by prioritizing voice inputs according to the user's emotions, important voice inputs can be prioritized and processed. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the voice input unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the voice input unit can input user emotion data into the generation AI to determine the priority of voice inputs.

[0082] The voice input unit can prioritize processing highly relevant input content by taking into account the user's geographical location information when inputting voice. For example, when the user is in a specific area, the voice input unit prioritizes input of tasks related to that area. Furthermore, when the user is moving, the voice input unit can also input the most appropriate task based on the user's current location. Furthermore, when the user is in a specific location, the voice input unit can also prioritize input of tasks related to that location. In this way, highly relevant input content can be prioritized by taking into account the user's geographical location information. Some or all of the above-described processing in the voice input unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice input unit can input the user's geographical location information to a generation AI and process highly relevant input content.

[0083] The voice input unit can analyze the user's social media activity during voice input and acquire related input content. The voice input unit can, for example, input related tasks based on information shared by the user on social media. The voice input unit can also preferentially input information shared by the user's social media followers and friends. Furthermore, the voice input unit can analyze the user's social media activity history and input highly relevant tasks. In this way, highly relevant input content can be acquired by analyzing the user's social media activity. Some or all of the above-described processing in the voice input unit can be performed, for example, using AI or without AI. For example, the voice input unit can input the user's social media activity data into a generation AI to acquire related input content.

[0084] The instruction unit can estimate the user's emotions and adjust the way instructions are given based on the estimated user emotions. For example, if the user is feeling stressed, the instruction unit can give concise and clear instructions. Furthermore, if the user is relaxed, the instruction unit can also give detailed instructions. Furthermore, if the user is in a hurry, the instruction unit can also give quick instructions. This reduces the burden on the user by adjusting the way instructions are given according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the instruction unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the instruction unit can input the user's emotion data into the generation AI and adjust the way instructions are given.

[0085] The instruction unit can adjust the level of detail of the instruction based on the importance of the task when issuing the instruction. For example, the instruction unit can issue detailed instructions for tasks with high importance and brief instructions for tasks with low importance. The instruction unit can also determine the priority of the instructions based on the importance of the task. Furthermore, the instruction unit can issue instructions including detailed information for tasks with high importance and brief instructions for tasks with low importance. This enables efficient instruction issuing by adjusting the level of detail of the instructions based on the importance of the task. Some or all of the above-mentioned processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input task importance data to the generation AI and adjust the level of detail of the instructions.

[0086] The instruction unit can apply different instruction algorithms depending on the task category when issuing instructions. For example, the instruction unit can apply different instruction algorithms to project tasks and daily tasks. The instruction unit can also apply different instruction algorithms to urgent tasks and long-term tasks. Furthermore, the instruction unit can apply different instruction algorithms to team tasks and individual tasks. This enables efficient instruction by applying different instruction algorithms depending on the task category. Some or all of the above-mentioned processing in the instruction unit can be performed using, for example, AI, or can be performed without using AI. For example, the instruction unit can input task category data into a generation AI and apply different instruction algorithms.

[0087] The instruction unit can estimate the user's emotions and determine the priority of instructions based on the estimated user's emotions. For example, when the user is stressed, the instruction unit can postpone instructions with lower importance. Furthermore, when the user is relaxed, the instruction unit can also prioritize detailed instructions. Furthermore, when the user is in a hurry, the instruction unit can quickly process important instructions. Thus, by determining the priority of instructions according to the user's emotions, important instructions can be processed preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the instruction unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the instruction unit can input the user's emotion data into the generation AI to determine the priority of instructions.

[0088] When issuing instructions, the instruction issuing unit can determine the priority of instructions based on the submission time of the task. For example, the instruction issuing unit can give priority to issuing instructions for tasks with upcoming submission deadlines. The instruction issuing unit can also postpone instructions for tasks with distant submission deadlines. Furthermore, the instruction issuing unit can dynamically adjust the priority of instructions based on the submission deadlines. This enables efficient instruction issuing by determining the priority of instructions based on the submission time of the task. Some or all of the above-mentioned processing in the instruction issuing unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction issuing unit can input task submission time data into a generation AI to determine the priority of instructions.

[0089] The instruction unit can adjust the order of instructions based on the relevance of the tasks when issuing instructions. For example, the instruction unit can group and issue instructions for highly related tasks. The instruction unit can also issue instructions for less related tasks individually. Furthermore, the instruction unit can dynamically adjust the order of instructions based on the relevance of the tasks. This enables efficient instruction issuing by adjusting the order of instructions based on the relevance of the tasks. Some or all of the above-described processing in the instruction unit may be performed using, for example, AI, or may be performed without using AI. For example, the instruction unit can input task relevance data into a generation AI and adjust the order of instructions. === Hard Collateral 1-1 === Each of the multiple elements, including the information acquisition unit, task organization unit, voice input unit, and instruction unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the information acquisition unit acquires information from minutes or a chat tool using the communication I / F 44 of the smart device 14 and stores the information in the database 24 of the data processing device 12. The task organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information using a generation AI to extract the task content, deadline, and responsible person. The voice input unit recognizes voice using, for example, the microphone 38B of the smart device 14, converts the voice into text data by the specific processing unit 290 of the data processing device 12, and registers the text data in the database 24. The instruction unit sends a reminder notification using, for example, the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the information acquisition unit, task organization unit, voice input unit, and instruction unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the information acquisition unit acquires information from minutes or a chat tool using the communication I / F 44 of the smart glasses 214 and stores the information in the database 24 of the data processing device 12. The task organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI to extract the task content, deadline, and responsible person. The voice input unit recognizes voice using, for example, the microphone 238 of the smart glasses 214, converts the voice into text data by the specific processing unit 290 of the data processing device 12, and registers the text data in the database 24. The instruction unit sends a reminder notification using, for example, the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the information acquisition unit, task organization unit, voice input unit, and instruction unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the information acquisition unit acquires information from minutes or a chat tool using the communication I / F 44 of the headset terminal 314 and stores the information in the database 24 of the data processing device 12. The task organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI to extract the task content, deadline, and person in charge. The voice input unit recognizes voice using, for example, the microphone 238 of the headset terminal 314, converts the voice into text data by the specific processing unit 290 of the data processing device 12, and registers the text data in the database 24. The instruction unit sends a reminder notification using, for example, the speaker 240 of the headset terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the information acquisition unit, task organization unit, voice input unit, and instruction unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the information acquisition unit acquires information from minutes or a chat tool using the communication I / F 44 of the robot 414 and stores the information in the database 24 of the data processing device 12. The task organization unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected information using a generation AI to extract the task content, deadline, and person in charge. The voice input unit recognizes voice using, for example, the microphone 238 of the robot 414, converts the voice into text data by the specific processing unit 290 of the data processing device 12, and registers the text data in the database 24. The instruction unit sends a reminder notification using, for example, the speaker 240 of the robot 414.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The information acquisition unit can acquire the user's biometric information and adjust the timing of information acquisition based on the acquired biometric information. For example, it can monitor the user's heart rate and electrodermal activity, and reduce the frequency of information acquisition when the user's stress level is high and increase the frequency of information acquisition when the user is relaxed. It can also analyze the user's biometric information and acquire important information during times when the user is most focused. Furthermore, it can take the user's sleep patterns into consideration and refrain from acquiring information during rest periods. In this way, adjusting the timing of information acquisition based on the user's biometric information reduces the burden on the user and enables efficient information collection.

[0092] The task organization unit can analyze the user's past task completion history and propose an efficient task organization method. For example, it can analyze the patterns of tasks that the user completed quickly in the past and prioritize similar tasks. It can also identify tasks that the user struggled with in the past and provide detailed guidelines for those tasks. It can also propose an optimal task schedule based on the user's past task completion times. This makes it possible to realize efficient task organization by utilizing the user's past task completion history.

[0093] The voice input unit can analyze the tone and speed of the user's voice, estimate the user's emotions, and adjust the accuracy of the voice input. For example, if the user is speaking in a hurry, the accuracy of the voice input can be increased to input quickly. On the other hand, if the user is speaking relaxed, the input can be performed with normal accuracy. Furthermore, if the user is feeling stressed, the accuracy of the voice input can be further increased to prevent input errors. In this way, by adjusting the accuracy of the voice input based on the tone and speed of the user's voice, input errors can be prevented and efficient voice input can be achieved.

[0094] The instruction unit can analyze the user's past instruction history and suggest the optimal instruction method. For example, it can analyze patterns of instructions that the user has responded to quickly in the past and prioritize the use of similar instruction methods. It can also identify instructions that the user has confused in the past and add detailed explanations to those instructions. It can also suggest the optimal timing to give instructions based on the time it took the user to respond to instructions in the past. This makes it possible to use the user's past instruction history to achieve efficient instruction giving.

[0095] The information acquisition unit can acquire the user's calendar information and adjust the timing of information acquisition based on the schedule. For example, if the user is in a meeting, information acquisition can be refrained from. The user can also concentrate information acquisition during their free time. Furthermore, if the user has an important appointment approaching, it is also possible to prioritize the acquisition of information related to that appointment. In this way, efficient information collection can be achieved by adjusting the timing of information acquisition based on the user's schedule.

[0096] The task organizing unit can estimate the user's emotions and dynamically adjust task priorities based on the estimated user emotions. For example, if the user is feeling stressed, it can postpone less important tasks. Also, if the user is relaxed, it can prioritize detailed tasks. Furthermore, if the user is in a hurry, it can quickly process important tasks. In this way, efficient task management can be achieved by dynamically adjusting task priorities according to the user's emotions.

[0097] The voice input unit can analyze the user's past voice input history and suggest optimal voice commands. For example, it can prioritize suggesting voice commands that the user has used frequently in the past. It can also select the optimal input method from the user's past voice input history. It can also identify voice commands that the user has input incorrectly in the past and provide detailed guidelines for those commands. This makes it possible to realize efficient voice input by utilizing the user's past voice input history.

[0098] The instruction unit can estimate the user's emotions and adjust the way instructions are given based on the estimated user emotions. For example, if the user is feeling stressed, concise and clear instructions can be given. If the user is relaxed, detailed instructions can be given. Furthermore, if the user is in a hurry, instructions can be given quickly. In this way, by adjusting the way instructions are given based on the user's emotions, the burden on the user can be reduced and efficient instruction giving can be achieved.

[0099] The information acquisition unit can acquire the user's geographical location information and prioritize acquisition of highly relevant information based on the acquired geographical location information. For example, when the user is in a specific area, information related to that area is prioritized. Also, when the user is moving, optimal information can be acquired based on the user's current location. Furthermore, when the user is in a specific location, it is also possible to prioritize acquisition of information related to that location. This allows for efficient information collection by prioritized acquisition of highly relevant information based on the user's geographical location information.

[0100] The task organization unit can estimate the user's emotions and adjust the task organization method based on the estimated user emotions. For example, if the user is feeling stressed, the tasks can be simplified and organized. If the user is relaxed, detailed task organization can be performed. Furthermore, if the user is in a hurry, it is also possible to organize important tasks as a priority. In this way, by adjusting the task organization method according to the user's emotions, the burden on the user can be reduced and efficient task organization can be achieved.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The information acquisition unit acquires information from minutes or communication tools. Minutes include meeting minutes, and communication tools include Slack and Teams. The information acquisition unit periodically collects meeting minutes and stores them in a database. It can also collect messages recording the progress of projects from chat tools. For example, it can use the Slack API to acquire messages and store them in a database. Step 2: The task organization unit uses a generation AI to analyze the collected information and extract the task content, deadline, and person in charge. Generation AI includes GPT-4 and Gemini. For example, it can extract preparation tasks for the next meeting from meeting minutes and set the person in charge and deadline. It can also analyze the progress of a project and automatically extract necessary tasks. The generation AI uses natural language processing technology to analyze the content of the minutes and extract important tasks. Step 3: The voice input unit uses a voice recognition function to register the task that comes to mind in a database. Voice recognition functions include Google Speech-to-Text and IBM Watson. For example, if a user speaks into their smartphone and says, "Create materials for the next meeting," the content is registered in the database. The voice input unit can also support multiple languages ​​using voice recognition technology. For example, it recognizes voice input in multiple languages, such as English, Japanese, and French, and converts it into text data. Step 4: The instruction unit sends a reminder notification when the task deadline approaches. Reminder notifications include email notifications and in-app notifications. For example, a notification can be sent by email when the task deadline approaches. Reminder notifications can also be displayed within the app. For example, a reminder can be sent using the notification function of a smartphone.

[0103] 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0117] 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.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also 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 perform the same process as the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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. an information acquisition unit that acquires information from minutes or communication tools; a task organizing unit that extracts and organizes tasks based on the information acquired by the information acquiring unit; a voice input unit that uses a voice recognition function to instantly store generated tasks in a database; an instruction issuing unit that issues instructions based on the tasks extracted and organized by the task organizing unit; A system characterized by:

2. The information acquisition unit Collect information from meeting notes or chat tools 2. The system of claim 1.

3. The task organizing unit Analyze the collected information using generative AI to extract task details, deadlines, and responsible parties.

2. The system of claim 1.

4. The voice input unit Use the voice recognition function to register tasks that come to mind in a database 2. The system of claim 1.

5. The instruction unit is Send reminders when a task is due 2. The system of claim 1.

6. The task organizing unit Extract preparation tasks required for the next meeting from the meeting minutes and set the person in charge and deadline 2. The system of claim 1.

7. The information acquisition unit Estimates the user's emotions and adjusts the timing of information acquisition based on the estimated user emotions.

2. The system of claim 1.

8. The information acquisition unit Analyze past information acquisition history and select the appropriate acquisition method 2. The system of claim 1.

9. The information acquisition unit Filter information as it is retrieved based on the user's current projects and interests 2. The system of claim 1.

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