system

Abstract

The system according to this embodiment aims to effectively utilize the user's information and reflect their unique values ​​and preferences. [Solution] The system according to the embodiment comprises an information shaping agent, a Cobrain agent, an Action agent, a Creative agent, and an iPaaS agent. The information shaping agent extracts information held by the user. The Cobrain agent stores the information extracted by the information shaping agent in memory and retrieves appropriate information to think. The Action agent creates an execution plan conceived by the Cobrain agent. The Creative agent breaks down complex tasks into simple tasks based on the execution plan created by the Action agent. The iPaaS agent provides a UI / UX that connects the tasks broken down by the Creative agent with other agents and the user.

Description

Technical Field

[0004] ,

[0006] , , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the information possessed by the user has not been effectively utilized, and the user's unique values and preferences have not been sufficiently reflected, leaving room for improvement.

[0005] The system according to the embodiment aims to effectively utilize the information possessed by the user and reflect the user's unique values and preferences. [[ID=4)]

Means for Solving the Problems

[0006] The system according to this embodiment comprises an information shaping agent, a Cobrain agent, an Action agent, a Creative agent, and an iPaaS agent. The information shaping agent extracts information from the user. The Cobrain agent stores the information extracted by the information shaping agent in memory and retrieves appropriate information to think. The Action agent creates an execution plan conceived by the Cobrain agent. The Creative agent breaks down complex tasks into simple tasks based on the execution plan created by the Action agent. The iPaaS agent provides a UI / UX that connects the tasks broken down by the Creative agent with other agents and the user. [Effects of the Invention]

[0007] The system according to this embodiment can effectively utilize the user's information and reflect their unique values ​​and preferences. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. 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, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The information processing system according to an embodiment of the present invention is a system that processes user information by dividing it among multiple agents and improves the accuracy of generated AI by embedding the user's unique values, preferences, and knowledge into the agents. The information processing system allows users to easily obtain the information they truly need, and the agents become more user-friendly the more they are used. First, the information shaping agent extracts information from the user from mobile devices, PCs, wearable devices, etc. Next, the Cobrain agent stores the extracted information in memory and retrieves appropriate information to think. Furthermore, the Action agent creates an execution plan including specific operations and instructs appropriate services and their operation procedures. The Creative agent breaks down complex tasks into simple tasks and reduces them to tasks that are easy to execute. Finally, the iPaaS agent provides a UI / UX that connects the user with other agents. Through this mechanism, users can easily obtain the information they need, and the agents become closer to the user's preferences the more they are used. For example, if a user frequently searches for specific information, that information will be provided preferentially. In addition, more appropriate information and services will be provided based on the user's behavior patterns and preferences. This enables information processing systems to efficiently extract, store, create execution plans for, break down tasks, and provide user-friendly (UI / UX) information from users.

[0029] The information processing system according to this embodiment comprises an information shaping agent, a Cobrain agent, an Action agent, a Creative agent, and an iPaaS agent. The information shaping agent extracts information held by the user. This information includes, but is not limited to, text data, image data, and audio data. The information shaping agent can extract information from, for example, mobile devices, personal computers, and wearable devices. The Cobrain agent stores the information extracted by the information shaping agent in memory and retrieves appropriate information to think. For memory, for example, a database or cloud storage is used, but is not limited to this example. The Cobrain agent can, for example, select information based on the user's request and prioritize retrieving frequently used information. The Action agent creates an execution plan conceived by the Cobrain agent. The execution plan includes, for example, a list of tasks, a schedule, and resource allocation, but is not limited to this example. The Action agent can, for example, list tasks and create a schedule based on the user's request. The Creative agent breaks down complex tasks into simpler tasks based on the execution plan created by the Action agent. Complex tasks include, but are not limited to, project management, data analysis, and marketing strategy. The Creative agent can, for example, break down project management into tasks and data analysis into simple steps. The iPaaS agent provides a UI / UX that connects the tasks broken down by the Creative agent to other agents and users. UI / UX includes, but is not limited to, user interface design and methods for improving the user experience. The iPaaS agent can, for example, design the user interface and provide feedback to improve the user experience.As a result, the information processing system according to this embodiment can efficiently extract, store, create execution plans for, decompose tasks, and provide UI / UX based on the user's information.

[0030] The information shaping agent extracts information from the user. This information includes, but is not limited to, text data, image data, and audio data. The information shaping agent can extract information from, for example, mobile devices, personal computers, and wearable devices. Specifically, from mobile devices, it extracts photos taken by the user, recorded voice memos, and text messages. From personal computers, it extracts document files, emails, and web browser history. From wearable devices, it extracts health data, location information, and voice commands. The information shaping agent integrates this data to provide foundational data for analyzing the user's behavior and preferences. Furthermore, the information shaping agent preprocesses the extracted data, removing noise and standardizing data formats to facilitate subsequent processing. For example, it standardizes the resolution of image data, removes noise from audio data, and standardizes the format of text data. This allows the information shaping agent to efficiently extract diverse information from the user and prepare it in a format that is easy for subsequent agents to use.

[0031] The Cobrain agent stores information extracted by the information shaping agent in memory and retrieves appropriate information to think. Memory can be, but is not limited to, databases or cloud storage. For example, the Cobrain agent can select information based on user requests and prioritize retrieving frequently used information. Specifically, it indexes information stored in databases and uses search algorithms to quickly retrieve necessary information. Cloud storage allows for efficient management of large amounts of data and scalable support as needed. The Cobrain agent analyzes the user's past behavior and search history and prioritizes providing information that the user frequently accesses or considers highly important. For example, it can cache documents, images, and audio data that the user frequently refers to for quick access. Furthermore, the Cobrain agent uses machine learning algorithms to learn user preferences and patterns, providing optimal information tailored to the user's requests. This enables the Cobrain agent to respond quickly and accurately to user requests, achieving efficient information management and delivery.

[0032] The Action agent creates an execution plan devised by the Cobrain agent. This execution plan includes, but is not limited to, a list of tasks, a schedule, and resource allocation. For example, the Action agent can list tasks and create a schedule based on user requests. Specifically, when a user inputs their desired goals and deadlines, the Action agent lists the specific tasks needed to achieve those goals and allocates the necessary time and resources to each task. Furthermore, the Action agent sets task priorities and creates a schedule for efficient task progress. For example, it can integrate with project management tools to monitor task progress in real time and adjust the schedule as needed. The Action agent also optimizes resource allocation and plans to maximize the use of the user's resources. In this way, the Action agent supports users in achieving their goals and enables efficient task management and schedule creation.

[0033] The Creative agent breaks down complex tasks into simpler tasks based on the execution plan created by the Action agent. Complex tasks include, but are not limited to, project management, data analysis, and marketing strategy. For example, the Creative agent can break down project management into tasks and data analysis into simple steps. Specifically, in project management, it grasps the overall picture of the project and lists the tasks required for each phase. In data analysis, it breaks it down into steps such as data collection, preprocessing, analysis, and reporting of results, and clarifies the work required for each step. When breaking down these tasks, the Creative agent takes into account the user's skills and resources to perform the optimal task breakdown. For example, it assigns tasks considering the user's areas of expertise and available tools. The Creative agent also monitors the progress of tasks and redistributes or adjusts tasks as needed. In this way, the Creative agent can efficiently manage complex tasks and help users perform their work smoothly.

[0034] The iPaaS agent provides a UI / UX that connects the user with other agents, based on tasks broken down by the Creative agent. UI / UX includes, but is not limited to, user interface design and methods for improving the user experience. For example, the iPaaS agent can design the user interface and provide feedback to improve the user experience. Specifically, it designs an intuitive interface and improves it based on user operation history and feedback. For example, it can adjust button placement, color, font size, etc., to ensure user-friendly operation. Furthermore, the iPaaS agent analyzes user operation history to understand frequently used functions and operation patterns, providing a more user-friendly interface. In addition, the iPaaS agent provides an interface for smooth collaboration with other agents, enabling users to efficiently utilize multiple agents. For example, it seamlessly integrates with information shaping agents, Cobrain agents, Action agents, and Creative agents, ensuring a consistent user experience. In this way, the iPaaS agent provides users with an intuitive and user-friendly interface and facilitates smooth collaboration between agents, thereby improving user work efficiency.

[0035] Information shaping agents can extract user information from mobile devices, personal computers, wearable devices, and other sources. For example, an information shaping agent can extract text messages and call history from a mobile device. It can also extract emails and documents from a personal computer. Furthermore, it can extract health data and activity logs from wearable devices. This makes it possible to extract user information from a variety of devices. Some or all of the above-described processes in the information shaping agent may be performed using AI, for example, or without AI. For example, an information shaping agent can input text messages obtained from a mobile device into a generating AI and have the generating AI perform analysis of the text messages.

[0036] The Cobrain agent can store information extracted by the information shaping agent in memory and retrieve appropriate information to think. For example, the Cobrain agent can store information in a database and search for and retrieve the necessary information. The Cobrain agent can also store information using cloud storage and retrieve it via remote access. Furthermore, the Cobrain agent can select information based on user requests and prioritize the retrieval of frequently used information. This enables the extracted information to be properly stored and the necessary information to be used for thinking. Some or all of the above processes in the Cobrain agent may be performed using AI, for example, or without AI. For example, the Cobrain agent can input information stored in a database into a generating AI and have the generating AI perform information retrieval and retrieval.

[0037] The Action agent can create execution plans devised by the Cobrain agent. For example, the Action agent can create a list of tasks and set a schedule for each task. The Action agent can also plan resource allocation and secure the necessary resources. Furthermore, the Action agent can list tasks and create schedules based on user requests. This makes it possible to create an appropriate execution plan. Some or all of the above processes in the Action agent may be performed using AI, for example, or not using AI. For example, the Action agent can input a list of tasks into a generating AI and have the generating AI create the schedule.

[0038] The Creative agent can break down complex tasks into simpler tasks based on the execution plan created by the Action agent. For example, the Creative agent can break down project management into tasks and define the details of each task. It can also break down data analysis into simple steps and clarify the procedures for each step. Furthermore, the Creative agent can break down a marketing strategy into specific actions and translate them into easily executable tasks. This makes it possible to break down complex tasks into simpler ones. Some or all of the above processes in the Creative agent may be performed using AI, for example, or not. For example, the Creative agent can input project management tasks into a generating AI and have the generating AI perform the task breakdown.

[0039] The iPaaS agent can provide a UI / UX that connects other agents and users to tasks broken down by the Creative agent. For example, the iPaaS agent can design user interfaces and provide feedback to improve the user experience. It can also provide guidelines to support user operations and simplify operating procedures. Furthermore, the iPaaS agent can provide interfaces to facilitate collaboration with other agents and promote information sharing. This makes it possible to provide a UI / UX that connects other agents and users. Some or all of the above processes in the iPaaS agent may be performed using AI, for example, or not using AI. For example, the iPaaS agent can input a user interface design into a generative AI and have the generative AI perform design optimization.

[0040] An information shaping agent can analyze a user's past information usage history and select the optimal extraction method. For example, the information shaping agent can prioritize the extraction of information sources that the user has frequently used in the past. It can also analyze the format of information (text, images, videos, etc.) used by the user in the past and extract information in the most optimal format. Furthermore, the information shaping agent can predict and extract information used during specific time periods based on the user's past usage history. This makes it possible to select the optimal extraction method based on the user's past information usage history. Some or all of the above processing in the information shaping agent may be performed using AI, for example, or without AI. For example, the information shaping agent can input the user's past information usage history into a generating AI and have the generating AI select the optimal extraction method.

[0041] The information shaping agent can filter information based on the user's current activities and areas of interest during extraction. For example, if the user is working, the information shaping agent can prioritize extracting work-related information. It can also extract information related to a user's hobby if the user is searching for information about that hobby. Furthermore, if the user is working on a specific project, the information shaping agent can extract information related to that project. This makes it possible to filter information based on the user's current activities and areas of interest. Some or all of the above processing in the information shaping agent may be performed using AI, for example, or without AI. For example, the information shaping agent can input data on the user's current activities and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0042] The information shaping agent can prioritize extracting highly relevant information by considering the user's geographical location during information extraction. For example, if the user is in a specific region, the information shaping agent can prioritize extracting information related to that region. Furthermore, if the user is traveling, the information shaping agent can prioritize extracting information related to the travel destination. Additionally, if the user is participating in a specific event, the information shaping agent can prioritize extracting information related to that event. This makes it possible to prioritize the extraction of highly relevant information based on the user's geographical location. Some or all of the above processing in the information shaping agent may be performed using AI, for example, or without AI. For example, the information shaping agent can input the user's geographical location information into a generating AI and have the generating AI perform the extraction of highly relevant information.

[0043] The information shaping agent can analyze a user's social media activity and extract relevant information during the information extraction process. For example, the information shaping agent can extract relevant information based on information shared by the user on social media. It can also extract relevant information based on information about accounts followed by the user on social media. Furthermore, the information shaping agent can extract relevant information based on information about groups the user participates in on social media. This makes it possible to extract relevant information based on the user's social media activity. Some or all of the above processing in the information shaping agent may be performed using AI, for example, or without AI. For example, the information shaping agent can input the user's social media activity data into a generating AI and have the generating AI perform the extraction of relevant information.

[0044] The Cobrain agent can adjust the level of detail in memory based on the importance of the information. For example, the Cobrain agent can store important information in detail and less important information in a simplified manner. It can also prioritize the storage of highly important information and postpone the storage of less important information. Furthermore, the Cobrain agent can change the way information is stored according to its importance. This makes it possible to adjust the level of detail in memory based on the importance of the information. Some or all of the above processes in the Cobrain agent may be performed using AI, for example, or without AI. For example, the Cobrain agent can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in memory.

[0045] The Cobrain agent can apply different memory algorithms depending on the category of information when storing it. For example, the Cobrain agent can store text information using a text-based algorithm. It can also store image information using an image-based algorithm. Furthermore, it can store video information using a video-based algorithm. This makes it possible to apply different memory algorithms depending on the category of information. Some or all of the above processing in the Cobrain agent may be performed using AI, for example, or without AI. For example, the Cobrain agent can input information category data into a generating AI and have the generating AI perform the application of the memory algorithm.

[0046] The Cobrain agent can prioritize information storage based on when the information was submitted. For example, the Cobrain agent can prioritize storing recently submitted information. It can also postpone storing older information. Furthermore, the Cobrain agent can change how it stores information depending on when it was submitted. This makes it possible to prioritize storage based on when the information was submitted. Some or all of the above processes in the Cobrain agent may be performed using AI, for example, or not using AI. For example, the Cobrain agent can input information submission time data into a generating AI and have the generating AI perform the determination of storage priorities.

[0047] The Cobrain agent can adjust the order in which information is stored based on its relevance. For example, the Cobrain agent can prioritize storing highly relevant information. It can also postpone storing less relevant information. Furthermore, the Cobrain agent can change how it stores information depending on its relevance. This makes it possible to adjust the order in which information is stored based on its relevance. Some or all of the above processes in the Cobrain agent may be performed using AI, for example, or without AI. For example, the Cobrain agent can input information relevance data into a generating AI and have the generating AI perform the adjustment of the order in which information is stored.

[0048] The Action agent can adjust the level of detail in an execution plan based on the importance of the tasks when creating the plan. For example, the Action agent can plan important tasks in detail and less important tasks in a simplified manner. It can also prioritize high-importance tasks and postpone low-importance tasks. Furthermore, the Action agent can change how tasks are planned depending on their importance. This makes it possible to adjust the level of detail in the plan based on the importance of the tasks. Some or all of the above processes in the Action agent may be performed using AI, for example, or not using AI. For example, the Action agent can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the plan.

[0049] The Action agent can apply different planning algorithms depending on the task category when creating an execution plan. For example, the Action agent can plan work tasks using a work-based algorithm, household tasks using a household-based algorithm, and hobby tasks using a hobby-based algorithm. This makes it possible to apply different planning algorithms depending on the task category. Some or all of the above processing in the Action agent may be performed using AI, for example, or without AI. For example, the Action agent can input task category data into a generating AI and have the generating AI perform the application of the planning algorithm.

[0050] The Action agent can prioritize tasks based on their due dates when creating an execution plan. For example, the Action agent can prioritize tasks with upcoming due dates. It can also postpone tasks with later due dates. Furthermore, the Action agent can change how tasks are planned depending on their due dates. This makes it possible to prioritize plans based on task due dates. Some or all of the above processes in the Action agent may be performed using AI, for example, or not. For example, the Action agent can input task due date data into a generating AI and have the generating AI determine the plan priorities.

[0051] The Action agent can adjust the order of tasks in an execution plan based on their relevance when creating the plan. For example, the Action agent can prioritize tasks that are highly relevant. It can also postpone tasks that are less relevant. Furthermore, the Action agent can change how tasks are planned depending on their relevance. This makes it possible to adjust the order of tasks based on their relevance. Some or all of the above processes in the Action agent may be performed using AI, for example, or not using AI. For example, the Action agent can input task relevance data into a generating AI and have the generating AI perform the adjustment of the order of tasks.

[0052] The Creative agent can adjust the level of detail in task breakdown based on the importance of each task. For example, it can break down important tasks in detail and less important tasks in a simplified manner. It can also prioritize the breakdown of high-importance tasks and postpone lower-importance tasks. Furthermore, the Creative agent can change the method of task breakdown depending on importance. This makes it possible to adjust the level of detail in the breakdown based on the importance of each task. Some or all of the above processes in the Creative agent may be performed using AI, for example, or not. For example, the Creative agent can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the breakdown.

[0053] The Creative agent can apply different decomposition algorithms depending on the task category when decomposing tasks. For example, the Creative agent can decompose work tasks using a work-based algorithm. It can also decompose household tasks using a household-based algorithm. Furthermore, it can decompose hobby tasks using a hobby-based algorithm. This makes it possible to apply different decomposition algorithms depending on the task category. Some or all of the above processing in the Creative agent may be performed using AI, for example, or not using AI. For example, the Creative agent can input task category data into a generating AI and have the generating AI perform the application of the decomposition algorithm.

[0054] The Creative Agent can determine the priority of task decomposition based on the task's submission deadline. For example, the Creative Agent can prioritize decomposing tasks with upcoming submission deadlines. It can also postpone tasks with later submission deadlines. Furthermore, the Creative Agent can change the task decomposition method depending on the submission deadline. This makes it possible to determine the priority of decomposition based on the task's submission deadline. Some or all of the above processes in the Creative Agent may be performed using AI, for example, or not. For example, the Creative Agent can input task submission deadline data into a generating AI and have the generating AI determine the decomposition priority.

[0055] The Creative agent can adjust the order of task decomposition based on the relevance of the tasks. For example, the Creative agent can prioritize the decomposition of highly relevant tasks. It can also postpone less relevant tasks. Furthermore, the Creative agent can change the method of task decomposition depending on the relevance. This makes it possible to adjust the order of decomposition based on the relevance of the tasks. Some or all of the above processes in the Creative agent may be performed using AI, for example, or not using AI. For example, the Creative agent can input task relevance data into a generating AI and have the generating AI perform the adjustment of the decomposition order.

[0056] The iPaaS agent can select the optimal display method when displaying UI / UX by referring to the user's past operation history. For example, the iPaaS agent can prioritize displaying interface designs that the user has frequently used in the past. Furthermore, the iPaaS agent can predict specific operation patterns from the user's past operation history and select the optimal display method. In addition, the iPaaS agent can prioritize displaying operation methods (touch, voice, etc.) that the user has used in the past. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above processing in the iPaaS agent may be performed using AI, for example, or without AI. For example, the iPaaS agent can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0057] The iPaaS agent can select the optimal display method when displaying UI / UX, taking into account the user's device information. For example, if the user is using a smartphone, the iPaaS agent can provide a display method that is adapted to the screen size. Furthermore, if the user is using a tablet, the iPaaS agent can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the iPaaS agent can provide a concise and highly visible display method. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above processing in the iPaaS agent may be performed using AI, for example, or without AI. For example, the iPaaS agent can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0058] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0059] The information shaping agent can analyze a user's past information usage history and select the optimal extraction method. For example, it can prioritize the extraction of information sources that the user has frequently used in the past. It can also analyze the format of information the user has used in the past (text, images, videos, etc.) and extract information in the most optimal format. Furthermore, it can predict and extract information that the user will use at a specific time period based on their past usage history. This makes it possible to select the optimal extraction method based on the user's past information usage history. Some or all of the above processing in the information shaping agent may be performed using AI or not. For example, the information shaping agent can input the user's past information usage history into a generating AI and have the generating AI select the optimal extraction method.

[0060] The Cobrain agent can adjust the level of detail in memory based on the importance of the information. For example, important information can be memorized in detail, while less important information can be memorized in a simplified manner. It can also prioritize the memorization of highly important information, while delaying the memorization of less important information. Furthermore, the method of memorizing information can be changed according to its importance. This makes it possible to adjust the level of detail in memory based on the importance of the information. Some or all of the above processes in the Cobrain agent may be performed using AI, or they may be performed without AI. For example, the Cobrain agent can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in memory.

[0061] The Action agent can adjust the level of detail in an execution plan based on the importance of the tasks when creating the plan. For example, important tasks can be planned in detail, while less important tasks can be planned more simply. It can also prioritize highly important tasks and postpone less important tasks. Furthermore, the method of planning tasks can be changed depending on their importance. This allows for adjustment of the level of detail in the plan based on the importance of the tasks. Some or all of the above processes in the Action agent may be performed using AI or not. For example, the Action agent can input task importance data into a generating AI and have the generating AI adjust the level of detail in the plan.

[0062] The Creative agent can apply different decomposition algorithms depending on the task category when decomposing tasks. For example, work tasks can be decomposed using a work-based algorithm, household tasks can be decomposed using a household-based algorithm, and hobby tasks can be decomposed using a hobby-based algorithm. This makes it possible to apply different decomposition algorithms depending on the task category. Some or all of the above processing in the Creative agent may be performed using AI or not. For example, the Creative agent can input task category data into a generating AI and have the generating AI perform the application of the decomposition algorithm.

[0063] The iPaaS agent can select the optimal display method when displaying UI / UX, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above processing in the iPaaS agent may be performed using AI or not. For example, the iPaaS agent can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The information shaping agent extracts information from the user. This information includes text data, image data, and audio data. The information shaping agent can extract information from mobile devices, personal computers, wearable devices, etc. Step 2: The Cobrain agent stores the information extracted by the information shaping agent in memory and retrieves the appropriate information to think. Databases or cloud storage are used for memory. The Cobrain agent can select information based on the user's requests and prioritize retrieving frequently used information. Step 3: The Action agent creates an execution plan devised by the Cobrain agent. The execution plan includes a list of tasks, a schedule, and resource allocation. The Action agent can list tasks and create a schedule based on user requests. Step 4: The Creative agent breaks down complex tasks into simpler tasks based on the execution plan created by the Action agent. Complex tasks include project management, data analysis, and marketing strategy. The Creative agent can break down project management into tasks and data analysis into simpler steps. Step 5: The iPaaS agent provides a UI / UX that connects the tasks broken down by the Creative agent to other agents and users. UI / UX includes user interface design and methods for improving the user experience. The iPaaS agent can design the user interface and provide feedback to improve the user experience.

[0066] (Example of form 2) The information processing system according to an embodiment of the present invention is a system that processes user information by dividing it among multiple agents and improves the accuracy of generated AI by embedding the user's unique values, preferences, and knowledge into the agents. The information processing system allows users to easily obtain the information they truly need, and the agents become more user-friendly the more they are used. First, the information shaping agent extracts information from the user from mobile devices, PCs, wearable devices, etc. Next, the Cobrain agent stores the extracted information in memory and retrieves appropriate information to think. Furthermore, the Action agent creates an execution plan including specific operations and instructs appropriate services and their operation procedures. The Creative agent breaks down complex tasks into simple tasks and reduces them to tasks that are easy to execute. Finally, the iPaaS agent provides a UI / UX that connects the user with other agents. Through this mechanism, users can easily obtain the information they need, and the agents become closer to the user's preferences the more they are used. For example, if a user frequently searches for specific information, that information will be provided preferentially. In addition, more appropriate information and services will be provided based on the user's behavior patterns and preferences. This enables information processing systems to efficiently extract, store, create execution plans for, break down tasks, and provide user-friendly (UI / UX) information from users.

[0067] The information processing system according to this embodiment comprises an information shaping agent, a Cobrain agent, an Action agent, a Creative agent, and an iPaaS agent. The information shaping agent extracts information held by the user. This information includes, but is not limited to, text data, image data, and audio data. The information shaping agent can extract information from, for example, mobile devices, personal computers, and wearable devices. The Cobrain agent stores the information extracted by the information shaping agent in memory and retrieves appropriate information to think. For memory, for example, a database or cloud storage is used, but is not limited to this example. The Cobrain agent can, for example, select information based on the user's request and prioritize retrieving frequently used information. The Action agent creates an execution plan conceived by the Cobrain agent. The execution plan includes, for example, a list of tasks, a schedule, and resource allocation, but is not limited to this example. The Action agent can, for example, list tasks and create a schedule based on the user's request. The Creative agent breaks down complex tasks into simpler tasks based on the execution plan created by the Action agent. Complex tasks include, but are not limited to, project management, data analysis, and marketing strategy. The Creative agent can, for example, break down project management into tasks and data analysis into simple steps. The iPaaS agent provides a UI / UX that connects the tasks broken down by the Creative agent to other agents and users. UI / UX includes, but is not limited to, user interface design and methods for improving the user experience. The iPaaS agent can, for example, design the user interface and provide feedback to improve the user experience.As a result, the information processing system according to this embodiment can efficiently extract, store, create execution plans for, decompose tasks, and provide UI / UX based on the user's information.

[0068] The information shaping agent extracts information from the user. This information includes, but is not limited to, text data, image data, and audio data. The information shaping agent can extract information from, for example, mobile devices, personal computers, and wearable devices. Specifically, from mobile devices, it extracts photos taken by the user, recorded voice memos, and text messages. From personal computers, it extracts document files, emails, and web browser history. From wearable devices, it extracts health data, location information, and voice commands. The information shaping agent integrates this data to provide foundational data for analyzing the user's behavior and preferences. Furthermore, the information shaping agent preprocesses the extracted data, removing noise and standardizing data formats to facilitate subsequent processing. For example, it standardizes the resolution of image data, removes noise from audio data, and standardizes the format of text data. This allows the information shaping agent to efficiently extract diverse information from the user and prepare it in a format that is easy for subsequent agents to use.

[0069] The Cobrain agent stores information extracted by the information shaping agent in memory and retrieves appropriate information to think. Memory can be, but is not limited to, databases or cloud storage. For example, the Cobrain agent can select information based on user requests and prioritize retrieving frequently used information. Specifically, it indexes information stored in databases and uses search algorithms to quickly retrieve necessary information. Cloud storage allows for efficient management of large amounts of data and scalable support as needed. The Cobrain agent analyzes the user's past behavior and search history and prioritizes providing information that the user frequently accesses or considers highly important. For example, it can cache documents, images, and audio data that the user frequently refers to for quick access. Furthermore, the Cobrain agent uses machine learning algorithms to learn user preferences and patterns, providing optimal information tailored to the user's requests. This enables the Cobrain agent to respond quickly and accurately to user requests, achieving efficient information management and delivery.

[0070] The Action agent creates an execution plan devised by the Cobrain agent. This execution plan includes, but is not limited to, a list of tasks, a schedule, and resource allocation. For example, the Action agent can list tasks and create a schedule based on user requests. Specifically, when a user inputs their desired goals and deadlines, the Action agent lists the specific tasks needed to achieve those goals and allocates the necessary time and resources to each task. Furthermore, the Action agent sets task priorities and creates a schedule for efficient task progress. For example, it can integrate with project management tools to monitor task progress in real time and adjust the schedule as needed. The Action agent also optimizes resource allocation and plans to maximize the use of the user's resources. In this way, the Action agent supports users in achieving their goals and enables efficient task management and schedule creation.

[0071] The Creative agent breaks down complex tasks into simpler tasks based on the execution plan created by the Action agent. Complex tasks include, but are not limited to, project management, data analysis, and marketing strategy. For example, the Creative agent can break down project management into tasks and data analysis into simple steps. Specifically, in project management, it grasps the overall picture of the project and lists the tasks required for each phase. In data analysis, it breaks it down into steps such as data collection, preprocessing, analysis, and reporting of results, and clarifies the work required for each step. When breaking down these tasks, the Creative agent takes into account the user's skills and resources to perform the optimal task breakdown. For example, it assigns tasks considering the user's areas of expertise and available tools. The Creative agent also monitors the progress of tasks and redistributes or adjusts tasks as needed. In this way, the Creative agent can efficiently manage complex tasks and help users perform their work smoothly.

[0072] The iPaaS agent provides a UI / UX that connects the user with other agents, based on tasks broken down by the Creative agent. UI / UX includes, but is not limited to, user interface design and methods for improving the user experience. For example, the iPaaS agent can design the user interface and provide feedback to improve the user experience. Specifically, it designs an intuitive interface and improves it based on user operation history and feedback. For example, it can adjust button placement, color, font size, etc., to ensure user-friendly operation. Furthermore, the iPaaS agent analyzes user operation history to understand frequently used functions and operation patterns, providing a more user-friendly interface. In addition, the iPaaS agent provides an interface for smooth collaboration with other agents, enabling users to efficiently utilize multiple agents. For example, it seamlessly integrates with information shaping agents, Cobrain agents, Action agents, and Creative agents, ensuring a consistent user experience. In this way, the iPaaS agent provides users with an intuitive and user-friendly interface and facilitates smooth collaboration between agents, thereby improving user work efficiency.

[0073] Information shaping agents can extract user information from mobile devices, personal computers, wearable devices, and other sources. For example, an information shaping agent can extract text messages and call history from a mobile device. It can also extract emails and documents from a personal computer. Furthermore, it can extract health data and activity logs from wearable devices. This makes it possible to extract user information from a variety of devices. Some or all of the above-described processes in the information shaping agent may be performed using AI, for example, or without AI. For example, an information shaping agent can input text messages obtained from a mobile device into a generating AI and have the generating AI perform analysis of the text messages.

[0074] The Cobrain agent can store information extracted by the information shaping agent in memory and retrieve appropriate information to think. For example, the Cobrain agent can store information in a database and search for and retrieve the necessary information. The Cobrain agent can also store information using cloud storage and retrieve it via remote access. Furthermore, the Cobrain agent can select information based on user requests and prioritize the retrieval of frequently used information. This enables the extracted information to be properly stored and the necessary information to be used for thinking. Some or all of the above processes in the Cobrain agent may be performed using AI, for example, or without AI. For example, the Cobrain agent can input information stored in a database into a generating AI and have the generating AI perform information retrieval and retrieval.

[0075] The Action agent can create execution plans devised by the Cobrain agent. For example, the Action agent can create a list of tasks and set a schedule for each task. The Action agent can also plan resource allocation and secure the necessary resources. Furthermore, the Action agent can list tasks and create schedules based on user requests. This makes it possible to create an appropriate execution plan. Some or all of the above processes in the Action agent may be performed using AI, for example, or not using AI. For example, the Action agent can input a list of tasks into a generating AI and have the generating AI create the schedule.

[0076] The Creative agent can break down complex tasks into simpler tasks based on the execution plan created by the Action agent. For example, the Creative agent can break down project management into tasks and define the details of each task. It can also break down data analysis into simple steps and clarify the procedures for each step. Furthermore, the Creative agent can break down a marketing strategy into specific actions and translate them into easily executable tasks. This makes it possible to break down complex tasks into simpler ones. Some or all of the above processes in the Creative agent may be performed using AI, for example, or not. For example, the Creative agent can input project management tasks into a generating AI and have the generating AI perform the task breakdown.

[0077] The iPaaS agent can provide a UI / UX that connects other agents and users to tasks broken down by the Creative agent. For example, the iPaaS agent can design user interfaces and provide feedback to improve the user experience. It can also provide guidelines to support user operations and simplify operating procedures. Furthermore, the iPaaS agent can provide interfaces to facilitate collaboration with other agents and promote information sharing. This makes it possible to provide a UI / UX that connects other agents and users. Some or all of the above processes in the iPaaS agent may be performed using AI, for example, or not using AI. For example, the iPaaS agent can input a user interface design into a generative AI and have the generative AI perform design optimization.

[0078] An information shaping agent can estimate a user's emotions and adjust the timing of information extraction based on the estimated emotions. For example, if a user is stressed, the information shaping agent can delay information extraction and wait until the user relaxes. Alternatively, if a user is focused, the information shaping agent can extract information quickly to avoid interrupting the user's work. Furthermore, if a user is tired, the information shaping agent can minimize information extraction to reduce the user's burden. This makes it possible to adjust the timing of information extraction according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information shaping agent may be performed using AI or not. For example, the information shaping agent can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] An information shaping agent can analyze a user's past information usage history and select the optimal extraction method. For example, the information shaping agent can prioritize the extraction of information sources that the user has frequently used in the past. It can also analyze the format of information (text, images, videos, etc.) used by the user in the past and extract information in the most optimal format. Furthermore, the information shaping agent can predict and extract information used during specific time periods based on the user's past usage history. This makes it possible to select the optimal extraction method based on the user's past information usage history. Some or all of the above processing in the information shaping agent may be performed using AI, for example, or without AI. For example, the information shaping agent can input the user's past information usage history into a generating AI and have the generating AI select the optimal extraction method.

[0080] The information shaping agent can filter information based on the user's current activities and areas of interest during extraction. For example, if the user is working, the information shaping agent can prioritize extracting work-related information. It can also extract information related to a user's hobby if the user is searching for information about that hobby. Furthermore, if the user is working on a specific project, the information shaping agent can extract information related to that project. This makes it possible to filter information based on the user's current activities and areas of interest. Some or all of the above processing in the information shaping agent may be performed using AI, for example, or without AI. For example, the information shaping agent can input data on the user's current activities and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0081] An information shaping agent can estimate a user's emotions and determine the priority of information to extract based on the estimated emotions. For example, if a user is stressed, the information shaping agent can prioritize extracting information that promotes relaxation. Similarly, if a user is focused, it can prioritize extracting information relevant to their work. Furthermore, if a user is tired, it can prioritize extracting information that is easy to understand. This makes it possible to prioritize information extracted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information shaping agent may be performed using AI, or not. For example, the information shaping agent can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0082] The information shaping agent can prioritize extracting highly relevant information by considering the user's geographical location during information extraction. For example, if the user is in a specific region, the information shaping agent can prioritize extracting information related to that region. Furthermore, if the user is traveling, the information shaping agent can prioritize extracting information related to the travel destination. Additionally, if the user is participating in a specific event, the information shaping agent can prioritize extracting information related to that event. This makes it possible to prioritize the extraction of highly relevant information based on the user's geographical location. Some or all of the above processing in the information shaping agent may be performed using AI, for example, or without AI. For example, the information shaping agent can input the user's geographical location information into a generating AI and have the generating AI perform the extraction of highly relevant information.

[0083] The information shaping agent can analyze a user's social media activity and extract relevant information during the information extraction process. For example, the information shaping agent can extract relevant information based on information shared by the user on social media. It can also extract relevant information based on information about accounts followed by the user on social media. Furthermore, the information shaping agent can extract relevant information based on information about groups the user participates in on social media. This makes it possible to extract relevant information based on the user's social media activity. Some or all of the above processing in the information shaping agent may be performed using AI, for example, or without AI. For example, the information shaping agent can input the user's social media activity data into a generating AI and have the generating AI perform the extraction of relevant information.

[0084] The Cobrain agent can estimate the user's emotions and adjust how information is stored based on the estimated emotions. For example, if the user is relaxed, the Cobrain agent can store detailed information. If the user is in a hurry, the Cobrain agent can store concise information. Furthermore, if the user is excited, the Cobrain agent can store visually stimulating information. This makes it possible to adjust how information is stored according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Cobrain agent may be performed using AI or not. For example, the Cobrain agent can input user emotion data into a generative AI and have the generative AI adjust how information is stored.

[0085] The Cobrain agent can adjust the level of detail in memory based on the importance of the information. For example, the Cobrain agent can store important information in detail and less important information in a simplified manner. It can also prioritize the storage of highly important information and postpone the storage of less important information. Furthermore, the Cobrain agent can change the way information is stored according to its importance. This makes it possible to adjust the level of detail in memory based on the importance of the information. Some or all of the above processes in the Cobrain agent may be performed using AI, for example, or without AI. For example, the Cobrain agent can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in memory.

[0086] The Cobrain agent can apply different memory algorithms depending on the category of information when storing it. For example, the Cobrain agent can store text information using a text-based algorithm. It can also store image information using an image-based algorithm. Furthermore, it can store video information using a video-based algorithm. This makes it possible to apply different memory algorithms depending on the category of information. Some or all of the above processing in the Cobrain agent may be performed using AI, for example, or without AI. For example, the Cobrain agent can input information category data into a generating AI and have the generating AI perform the application of the memory algorithm.

[0087] The Cobrain agent can estimate a user's emotions and prioritize information to remember based on those emotions. For example, if a user is stressed, the Cobrain agent can prioritize information that helps them relax. Similarly, if a user is focused, it can prioritize information related to their work. Furthermore, if a user is tired, it can prioritize information that is easy to understand. This allows the agent to prioritize information to remember according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Cobrain agent may be performed using AI or not. For example, the Cobrain agent can input user emotion data into a generative AI and have the generative AI determine the priority of information.

[0088] The Cobrain agent can prioritize information storage based on when the information was submitted. For example, the Cobrain agent can prioritize storing recently submitted information. It can also postpone storing older information. Furthermore, the Cobrain agent can change how it stores information depending on when it was submitted. This makes it possible to prioritize storage based on when the information was submitted. Some or all of the above processes in the Cobrain agent may be performed using AI, for example, or not using AI. For example, the Cobrain agent can input information submission time data into a generating AI and have the generating AI perform the determination of storage priorities.

[0089] The Cobrain agent can adjust the order in which information is stored based on its relevance. For example, the Cobrain agent can prioritize storing highly relevant information. It can also postpone storing less relevant information. Furthermore, the Cobrain agent can change how it stores information depending on its relevance. This makes it possible to adjust the order in which information is stored based on its relevance. Some or all of the above processes in the Cobrain agent may be performed using AI, for example, or without AI. For example, the Cobrain agent can input information relevance data into a generating AI and have the generating AI perform the adjustment of the order in which information is stored.

[0090] The Action agent can estimate the user's emotions and adjust the presentation of the execution plan based on the estimated emotions. For example, if the user is nervous, the Action agent can provide a simple and highly visible presentation. If the user is relaxed, the Action agent can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, the Action agent can provide a presentation that gets straight to the point. This makes it possible to adjust the presentation of the execution plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Action agent may be performed using AI or not using AI. For example, the Action agent can input user emotion data into the generative AI and have the generative AI adjust the presentation of the execution plan.

[0091] The Action agent can adjust the level of detail in an execution plan based on the importance of the tasks when creating the plan. For example, the Action agent can plan important tasks in detail and less important tasks in a simplified manner. It can also prioritize high-importance tasks and postpone low-importance tasks. Furthermore, the Action agent can change how tasks are planned depending on their importance. This makes it possible to adjust the level of detail in the plan based on the importance of the tasks. Some or all of the above processes in the Action agent may be performed using AI, for example, or not using AI. For example, the Action agent can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the plan.

[0092] The Action agent can apply different planning algorithms depending on the task category when creating an execution plan. For example, the Action agent can plan work tasks using a work-based algorithm, household tasks using a household-based algorithm, and hobby tasks using a hobby-based algorithm. This makes it possible to apply different planning algorithms depending on the task category. Some or all of the above processing in the Action agent may be performed using AI, for example, or without AI. For example, the Action agent can input task category data into a generating AI and have the generating AI perform the application of the planning algorithm.

[0093] The Action agent can estimate the user's emotions and adjust the length of the execution plan based on the estimated emotions. For example, if the user is in a hurry, the Action agent can provide a short, concise execution plan. If the user is relaxed, the Action agent can provide a longer execution plan with detailed explanations. Furthermore, if the user is excited, the Action agent can provide an execution plan with visually stimulating effects. This makes it possible to adjust the length of the execution plan according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Action agent may be performed using AI or not. For example, the Action agent can input user emotion data into the generative AI and have the generative AI adjust the length of the execution plan.

[0094] The Action agent can prioritize tasks based on their due dates when creating an execution plan. For example, the Action agent can prioritize tasks with upcoming due dates. It can also postpone tasks with later due dates. Furthermore, the Action agent can change how tasks are planned depending on their due dates. This makes it possible to prioritize plans based on task due dates. Some or all of the above processes in the Action agent may be performed using AI, for example, or not. For example, the Action agent can input task due date data into a generating AI and have the generating AI determine the plan priorities.

[0095] The Action agent can adjust the order of tasks in an execution plan based on their relevance when creating the plan. For example, the Action agent can prioritize tasks that are highly relevant. It can also postpone tasks that are less relevant. Furthermore, the Action agent can change how tasks are planned depending on their relevance. This makes it possible to adjust the order of tasks based on their relevance. Some or all of the above processes in the Action agent may be performed using AI, for example, or not using AI. For example, the Action agent can input task relevance data into a generating AI and have the generating AI perform the adjustment of the order of tasks.

[0096] The Creative Agent can estimate the user's emotions and adjust the task breakdown method based on the estimated emotions. For example, if the user is stressed, the Creative Agent can provide a simple and highly visual breakdown method. If the user is relaxed, the Creative Agent can provide a breakdown method that includes detailed information. Furthermore, if the user is in a hurry, the Creative Agent can provide a breakdown method that gets straight to the point. This makes it possible to adjust the task breakdown method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Creative Agent may be performed using AI or not using AI. For example, the Creative Agent can input user emotion data into a generative AI and have the generative AI adjust the task breakdown method.

[0097] The Creative agent can adjust the level of detail in task breakdown based on the importance of each task. For example, it can break down important tasks in detail and less important tasks in a simplified manner. It can also prioritize the breakdown of high-importance tasks and postpone lower-importance tasks. Furthermore, the Creative agent can change the method of task breakdown depending on importance. This makes it possible to adjust the level of detail in the breakdown based on the importance of each task. Some or all of the above processes in the Creative agent may be performed using AI, for example, or not. For example, the Creative agent can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the breakdown.

[0098] The Creative agent can apply different decomposition algorithms depending on the task category when decomposing tasks. For example, the Creative agent can decompose work tasks using a work-based algorithm. It can also decompose household tasks using a household-based algorithm. Furthermore, it can decompose hobby tasks using a hobby-based algorithm. This makes it possible to apply different decomposition algorithms depending on the task category. Some or all of the above processing in the Creative agent may be performed using AI, for example, or not using AI. For example, the Creative agent can input task category data into a generating AI and have the generating AI perform the application of the decomposition algorithm.

[0099] The Creative Agent can estimate the user's emotions and determine the priority of tasks to break down based on the estimated emotions. For example, if the user is stressed, the Creative Agent can prioritize tasks that promote relaxation. Similarly, if the user is focused, the Creative Agent can prioritize tasks related to the work itself. Furthermore, if the user is tired, the Creative Agent can prioritize tasks that are easy to understand. This makes it possible to determine the priority of tasks to break down according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Creative Agent may be performed using AI or not. For example, the Creative Agent can input user emotion data into a generative AI and have the generative AI determine the task prioritization.

[0100] The Creative Agent can determine the priority of task decomposition based on the task's submission deadline. For example, the Creative Agent can prioritize decomposing tasks with upcoming submission deadlines. It can also postpone tasks with later submission deadlines. Furthermore, the Creative Agent can change the task decomposition method depending on the submission deadline. This makes it possible to determine the priority of decomposition based on the task's submission deadline. Some or all of the above processes in the Creative Agent may be performed using AI, for example, or not. For example, the Creative Agent can input task submission deadline data into a generating AI and have the generating AI determine the decomposition priority.

[0101] The Creative agent can adjust the order of task decomposition based on the relevance of the tasks. For example, the Creative agent can prioritize the decomposition of highly relevant tasks. It can also postpone less relevant tasks. Furthermore, the Creative agent can change the method of task decomposition depending on the relevance. This makes it possible to adjust the order of decomposition based on the relevance of the tasks. Some or all of the above processes in the Creative agent may be performed using AI, for example, or not using AI. For example, the Creative agent can input task relevance data into a generating AI and have the generating AI perform the adjustment of the decomposition order.

[0102] The iPaaS agent can estimate the user's emotions and adjust the UI / UX display based on the estimated emotions. For example, if the user is stressed, the iPaaS agent can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, the iPaaS agent can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, the iPaaS agent can provide a simple and highly visible interface to facilitate the input process. This makes it possible to adjust the UI / UX display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the iPaaS agent may be performed using AI, or not. For example, the iPaaS agent can input user emotion data into a generative AI and have the generative AI adjust the UI / UX display.

[0103] The iPaaS agent can select the optimal display method when displaying UI / UX by referring to the user's past operation history. For example, the iPaaS agent can prioritize displaying interface designs that the user has frequently used in the past. Furthermore, the iPaaS agent can predict specific operation patterns from the user's past operation history and select the optimal display method. In addition, the iPaaS agent can prioritize displaying operation methods (touch, voice, etc.) that the user has used in the past. This makes it possible to select the optimal display method based on the user's past operation history. Some or all of the above processing in the iPaaS agent may be performed using AI, for example, or without AI. For example, the iPaaS agent can input the user's past operation history data into a generating AI and have the generating AI select the optimal display method.

[0104] The iPaaS agent can estimate the user's emotions and adjust the UI / UX operation procedures based on the estimated emotions. For example, if the user is tense, the iPaaS agent can provide simple and highly visible operation procedures. If the user is relaxed, the iPaaS agent can also provide operation procedures that include detailed information. Furthermore, if the user is in a hurry, the iPaaS agent can provide operation procedures that get straight to the point. This makes it possible to adjust the UI / UX operation procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the iPaaS agent may be performed using AI or not using AI. For example, the iPaaS agent can input user emotion data into a generative AI and have the generative AI perform the adjustment of operation procedures.

[0105] The iPaaS agent can select the optimal display method when displaying UI / UX, taking into account the user's device information. For example, if the user is using a smartphone, the iPaaS agent can provide a display method that is adapted to the screen size. Furthermore, if the user is using a tablet, the iPaaS agent can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the iPaaS agent can provide a concise and highly visible display method. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above processing in the iPaaS agent may be performed using AI, for example, or without AI. For example, the iPaaS agent can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] An information shaping agent can estimate a user's emotions and adjust how information is extracted based on those emotions. For example, if a user is stressed, the information shaping agent can prioritize extracting information that promotes relaxation. If the user is focused, it can quickly extract information relevant to their work. Furthermore, if the user is tired, it can extract information that is easy to understand. This allows for adjusting the information extraction method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI may include, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above-described processes in the information shaping agent may be performed using AI or not. For example, the information shaping agent can input user emotion data into a generative AI and have the generative AI adjust the information extraction method.

[0108] The Cobrain agent can estimate a user's emotions and adjust how information is stored based on those emotions. For example, if the user is relaxed, it can store detailed information. If the user is in a hurry, it can store concise information. Furthermore, if the user is excited, it can store visually stimulating information. This allows the agent to adjust how information is stored according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above processing in the Cobrain agent may be performed using AI or not. For example, the Cobrain agent can input user emotion data into a generative AI and have the generative AI adjust how information is stored.

[0109] The Action agent can estimate the user's emotions and adjust the presentation of the execution plan based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible presentation. If the user is relaxed, it can provide a presentation that includes detailed information. Furthermore, if the user is in a hurry, it can provide a presentation that gets straight to the point. This makes it possible to adjust the presentation of the execution plan according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the Action agent may be performed using AI or not. For example, the Action agent can input user emotion data into a generative AI and have the generative AI adjust the presentation of the execution plan.

[0110] The Creative agent can estimate the user's emotions and adjust the task breakdown method based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visual breakdown method. If the user is relaxed, it can provide a breakdown method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a breakdown method that gets straight to the point. This makes it possible to adjust the task breakdown method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the Creative agent may be performed using AI or not. For example, the Creative agent can input the user's emotion data into a generative AI and have the generative AI adjust the task breakdown method.

[0111] The iPaaS agent can estimate the user's emotions and adjust the UI / UX display based on those emotions. For example, if the user is stressed, it can provide an interface with calming colors to reduce visual stress. If the user is enjoying themselves, it can provide an interface with bright colors to make the input process more enjoyable. Furthermore, if the user is tired, it can provide a simple and highly visible interface to facilitate the input process. This makes it possible to adjust the UI / UX display according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the iPaaS agent may be performed using AI or not. For example, the iPaaS agent can input user emotion data into a generative AI and have the generative AI adjust the UI / UX display.

[0112] The information shaping agent can analyze a user's past information usage history and select the optimal extraction method. For example, it can prioritize the extraction of information sources that the user has frequently used in the past. It can also analyze the format of information the user has used in the past (text, images, videos, etc.) and extract information in the most optimal format. Furthermore, it can predict and extract information that the user will use at a specific time period based on their past usage history. This makes it possible to select the optimal extraction method based on the user's past information usage history. Some or all of the above processing in the information shaping agent may be performed using AI or not. For example, the information shaping agent can input the user's past information usage history into a generating AI and have the generating AI select the optimal extraction method.

[0113] The Cobrain agent can adjust the level of detail in memory based on the importance of the information. For example, important information can be memorized in detail, while less important information can be memorized in a simplified manner. It can also prioritize the memorization of highly important information, while delaying the memorization of less important information. Furthermore, the method of memorizing information can be changed according to its importance. This makes it possible to adjust the level of detail in memory based on the importance of the information. Some or all of the above processes in the Cobrain agent may be performed using AI, or they may be performed without AI. For example, the Cobrain agent can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in memory.

[0114] The Action agent can adjust the level of detail in an execution plan based on the importance of the tasks when creating the plan. For example, important tasks can be planned in detail, while less important tasks can be planned more simply. It can also prioritize highly important tasks and postpone less important tasks. Furthermore, the method of planning tasks can be changed depending on their importance. This allows for adjustment of the level of detail in the plan based on the importance of the tasks. Some or all of the above processes in the Action agent may be performed using AI or not. For example, the Action agent can input task importance data into a generating AI and have the generating AI adjust the level of detail in the plan.

[0115] The Creative agent can apply different decomposition algorithms depending on the task category when decomposing tasks. For example, work tasks can be decomposed using a work-based algorithm, household tasks can be decomposed using a household-based algorithm, and hobby tasks can be decomposed using a hobby-based algorithm. This makes it possible to apply different decomposition algorithms depending on the task category. Some or all of the above processing in the Creative agent may be performed using AI or not. For example, the Creative agent can input task category data into a generating AI and have the generating AI perform the application of the decomposition algorithm.

[0116] The iPaaS agent can select the optimal display method when displaying UI / UX, taking into account the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. This makes it possible to select the optimal display method based on the user's device information. Some or all of the above processing in the iPaaS agent may be performed using AI or not. For example, the iPaaS agent can input the user's device information into a generating AI and have the generating AI select the optimal display method.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The information shaping agent extracts information from the user. This information includes text data, image data, and audio data. The information shaping agent can extract information from mobile devices, personal computers, wearable devices, etc. Step 2: The Cobrain agent stores the information extracted by the information shaping agent in memory and retrieves the appropriate information to think. Databases or cloud storage are used for memory. The Cobrain agent can select information based on the user's requests and prioritize retrieving frequently used information. Step 3: The Action agent creates an execution plan devised by the Cobrain agent. The execution plan includes a list of tasks, a schedule, and resource allocation. The Action agent can list tasks and create a schedule based on user requests. Step 4: The Creative agent breaks down complex tasks into simpler tasks based on the execution plan created by the Action agent. Complex tasks include project management, data analysis, and marketing strategy. The Creative agent can break down project management into tasks and data analysis into simpler steps. Step 5: The iPaaS agent provides a UI / UX that connects the tasks broken down by the Creative agent to other agents and users. UI / UX includes user interface design and methods for improving the user experience. The iPaaS agent can design the user interface and provide feedback to improve the user experience.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0121] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] Each of the multiple elements described above, including the information shaping agent, Cobrain agent, Action agent, Creative agent, and iPaaS agent, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the information shaping agent is implemented by the computer 36 of the smart device 14 and extracts information held by the user. The Cobrain agent is implemented by the specific processing unit 290 of the data processing device 12 and stores the extracted information in memory, retrieves appropriate information, and thinks. The Action agent is implemented by the specific processing unit 290 of the data processing device 12 and creates an execution plan. The Creative agent is implemented by the control unit 46A of the smart device 14 and breaks down complex tasks into simple tasks. The iPaaS agent is implemented by the control unit 46A of the smart device 14 and provides a UI / UX that connects the user with other agents. The correspondence between each part and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0129] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0130] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0131] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0132] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0133] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the information shaping agent, Cobrain agent, Action agent, Creative agent, and iPaaS agent, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the information shaping agent is implemented by the computer 36 of the smart glasses 214 and extracts information held by the user. The Cobrain agent is implemented by the specific processing unit 290 of the data processing device 12 and stores the extracted information in memory, retrieves appropriate information, and thinks. The Action agent is implemented by the specific processing unit 290 of the data processing device 12 and creates an execution plan. The Creative agent is implemented by the control unit 46A of the smart glasses 214 and breaks down complex tasks into simple tasks. The iPaaS agent is implemented by the control unit 46A of the smart glasses 214 and provides a UI / UX that connects the user with other agents. The correspondence between each part and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the information shaping agent, Cobrain agent, Action agent, Creative agent, and iPaaS agent, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the information shaping agent is implemented by the computer 36 of the headset terminal 314 and extracts information held by the user. The Cobrain agent is implemented by the specific processing unit 290 of the data processing device 12 and stores the extracted information in memory, retrieves appropriate information, and thinks. The Action agent is implemented by the specific processing unit 290 of the data processing device 12 and creates an execution plan. The Creative agent is implemented by the control unit 46A of the headset terminal 314 and breaks down complex tasks into simple tasks. The iPaaS agent is implemented by the control unit 46A of the headset terminal 314 and provides a UI / UX that connects the user with other agents. The correspondence between each part and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0163] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0164] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0165] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0166] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0167] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0169] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0171] Each of the multiple elements described above, including the information shaping agent, Cobrain agent, Action agent, Creative agent, and iPaaS agent, is implemented in at least one of the robot 414 and the data processing device 12. For example, the information shaping agent is implemented by the computer 36 of the robot 414 and extracts information held by the user. The Cobrain agent is implemented by the specific processing unit 290 of the data processing device 12 and stores the extracted information in memory, retrieves appropriate information, and thinks. The Action agent is implemented by the specific processing unit 290 of the data processing device 12 and creates an execution plan. The Creative agent is implemented by the control unit 46A of the robot 414 and breaks down complex tasks into simple tasks. The iPaaS agent is implemented by the control unit 46A of the robot 414 and provides a UI / UX that connects the user with other agents. The correspondence between each part and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0172] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0174] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0175] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0176] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0180] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0181] 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.

[0182] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0183] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0184] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0185] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0187] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0188] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0189] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0190] (Note 1) An information shaping agent that extracts information from the user, The Cobrain agent stores the information extracted by the aforementioned information shaping agent in memory and retrieves the appropriate information to think, An Action agent that creates an execution plan devised by the aforementioned Cobrain agent, A Creative agent that breaks down complex tasks into simpler tasks based on the execution plan created by the Action agent, The system comprises an iPaaS agent that provides a UI / UX connecting the user with other agents for tasks broken down by the aforementioned Creative agent. A system characterized by the following features. (Note 2) The aforementioned information shaping agent is Extract user information from mobile devices, PCs, wearable devices, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned Cobrain agent, The information extracted by the aforementioned information shaping agent is stored in memory, and appropriate information is retrieved to perform thinking. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned Action agent, The aforementioned Cobrain agent generates an execution plan. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Creative agent, Based on the execution plan created by the aforementioned Action agent, complex tasks are broken down into simpler tasks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The iPaaS agent described above is The aforementioned Creative agent provides a UI / UX that connects the tasks broken down by the Creative agent with other agents and the user. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned information shaping agent is It estimates the user's emotions and adjusts the timing of information extraction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned information shaping agent is Analyze the user's past information usage history and select the optimal extraction method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned information shaping agent is When extracting information, filtering is performed based on the user's current activity status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information shaping agent is It estimates the user's emotions and determines the priority of information to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned information shaping agent is When extracting information, the system prioritizes extracting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information shaping agent is When extracting information, the system analyzes the user's social media activity and extracts relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned Cobrain agent, It estimates the user's emotions and adjusts how information is stored based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned Cobrain agent, When storing information, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned Cobrain agent, When storing information, different storage algorithms are applied depending on the category of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned Cobrain agent, It estimates the user's emotions and determines the priority of information to remember based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned Cobrain agent, When memorizing information, the priority of memory is determined based on when the information was presented. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned Cobrain agent, When storing information, the order of memory is adjusted based on the relationships between the pieces of information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned Action agent, It estimates the user's emotions and adjusts how the execution plan is represented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned Action agent, When creating an action plan, adjust the level of detail in the plan based on the importance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned Action agent, When creating an execution plan, apply different planning algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned Action agent, The system estimates the user's emotions and adjusts the length of the execution plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned Action agent, When creating an action plan, prioritize tasks based on their submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned Action agent, When creating an execution plan, adjust the order of tasks based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned Creative agent, It estimates the user's emotions and adjusts how tasks are broken down based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Creative agent, When breaking down tasks, adjust the level of detail in the breakdown based on the importance of each task. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Creative agent, When breaking down tasks, apply different decomposition algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Creative agent, Estimate user emotions and prioritize tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Creative agent, When breaking down tasks, prioritize the breakdown based on the task submission method. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Creative agent, When breaking down tasks, adjust the order of decomposition based on the relationships between tasks. The system described in Appendix 1, characterized by the features described herein. (Note 31) The iPaaS agent described above is It estimates the user's emotions and adjusts the UI / UX display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The iPaaS agent described above is When displaying UI / UX, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The iPaaS agent described above is It estimates the user's emotions and adjusts the UI / UX operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The iPaaS agent described above is When displaying UI / UX, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. An information shaping agent that extracts information from the user, The Cobrain agent stores the information extracted by the aforementioned information shaping agent in memory and retrieves the appropriate information to think, An Action agent that creates an execution plan devised by the aforementioned Cobrain agent, A Creative agent that breaks down complex tasks into simpler tasks based on the execution plan created by the Action agent, The system comprises an iPaaS agent that provides a UI / UX connecting the user with other agents for tasks broken down by the aforementioned Creative agent. A system characterized by the following features.

2. The aforementioned information shaping agent is Extract user information from mobile devices, PCs, wearable devices, etc. The system according to feature 1.

3. The aforementioned Cobrain agent, The information extracted by the aforementioned information shaping agent is stored in memory, and appropriate information is retrieved to perform thinking. The system according to feature 1.

4. The aforementioned Action agent, The aforementioned Cobrain agent generates an execution plan. The system according to feature 1.

5. The aforementioned Creative agent, Based on the execution plan created by the aforementioned Action agent, complex tasks are broken down into simpler tasks. The system according to feature 1.

6. The iPaaS agent described above is The aforementioned Creative agent provides a UI / UX that connects the tasks broken down by the Creative agent with other agents and the user. The system according to feature 1.

7. The aforementioned information shaping agent is It estimates the user's emotions and adjusts the timing of information extraction based on the estimated user emotions. The system according to feature 1.

8. The aforementioned information shaping agent is Analyze the user's past information usage history and select the optimal extraction method. The system according to feature 1.

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