System

Abstract

An object of a system according to an embodiment is to efficiently manage a schedule or a task using speech recognition and support daily life.SOLUTION: A system includes a speech recognition unit, a schedule management unit, a task management unit, and a diary recording unit. The voice recognition unit recognizes a voice. The schedule management unit manages a schedule on the basis of the voice recognized by the voice recognition unit. The task management unit manages the task based on the schedule managed by the schedule management unit. The diary recording unit records a diary based on the task managed by the task management unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately managed schedules and tasks using voice recognition, making it difficult to provide efficient daily support.

[0005] The system according to the embodiment aims to efficiently manage schedules and tasks using voice recognition and to support daily life. [Means for solving the problem]

[0006] The system according to the embodiment includes a voice recognition unit, a schedule management unit, a task management unit, and a diary recording unit. The voice recognition unit recognizes voice. The schedule management unit manages schedules based on the voice recognized by the voice recognition unit. The task management unit manages tasks based on the schedules managed by the schedule management unit. The diary recording unit records a diary based on the tasks managed by the task management unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage schedules and tasks using voice recognition, and can support daily life. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The personal AI secretary of an embodiment of the present invention is a system that utilizes voice recognition and generative AI to support users' daily lives. This system operates 24 hours a day, 365 days a year, and provides a wide range of functions, such as schedule management, task management, and diary recording. As a result, the personal AI secretary improves the user's work efficiency and saves time, making their life more comfortable.

[0029] A personal AI secretary according to an embodiment includes a voice recognition unit, a schedule management unit, a task management unit, and a diary recording unit. The voice recognition unit recognizes a user's voice. For example, the voice recognition unit converts the user's speech into text using a voice recognition algorithm. The voice recognition unit can input voice using a device such as a smartphone or a microphone. The voice recognition unit can also remove environmental sounds using noise canceling technology to improve the accuracy of voice recognition. The schedule management unit manages a schedule based on the voice recognized by the voice recognition unit. For example, the schedule management unit analyzes the content of the user's conversations and emails and automatically adds them to the schedule. The schedule management unit can also learn the user's past behavioral patterns and predict and suggest future behavior. The schedule management unit can also automatically prioritize the schedule and highlight important appointments. The task management unit manages tasks based on the schedule managed by the schedule management unit. For example, the task management unit adds the user's tasks to a list and sets priorities. The task management unit can also automatically adjust task priorities according to the user's emotional state using an emotion estimation function. The task management unit can also monitor the progress of tasks in real time and issue alerts if delays occur. The diary recording unit records a diary based on the tasks managed by the task management unit. For example, the diary recording unit summarizes the user's daily events and comments and records them as a diary. The diary recording unit can also provide feedback based on the user's emotions using an emotion estimation function. The diary recording unit can also analyze the contents of the diary and automatically highlight important events and changes in emotions. This allows the personal AI secretary of the embodiment to centrally manage schedules, tasks, and diary entries based on the user's voice. For example, by recognizing the user's voice and automatically adding them to the schedule, important appointments are never overlooked. Task priorities can also be automatically set, allowing for efficient work. Furthermore, diary recording can promote self-development, and flexible customization can accommodate individual needs.

[0030] The speech recognition unit can learn the characteristics of a user's voice and improve the accuracy of speech recognition. For example, the speech recognition unit collects everyday conversation data to learn the characteristics of the user's voice and individually optimizes the speech recognition model. For example, the speech recognition unit analyzes the user's pronunciation and intonation patterns to improve the accuracy of speech recognition. The speech recognition unit also customizes the speech recognition model based on the characteristics of the user's voice and builds a speech recognition system optimized for a specific user. For example, the speech recognition unit adjusts the speech recognition algorithm to match the tone and speed of the user's voice. The speech recognition unit also continuously learns the characteristics of the user's voice and develops a system that improves the accuracy of speech recognition. For example, to respond to changes in the user's voice, the speech data is regularly updated and the model is retrained. In this way, the speech recognition unit learns the characteristics of the user's voice and improves the accuracy of speech recognition.

[0031] The schedule management unit learns the user's past behavioral patterns and can predict and make suggestions about future behavior. For example, the schedule management unit collects the user's past behavioral data, and the generation AI learns those patterns. For example, the schedule management unit analyzes the user's schedule and task history, and predicts and makes suggestions about future behavior. The schedule management unit also develops an algorithm for the generation AI to predict future behavior based on the user's past behavioral patterns. For example, it automatically suggests tasks and appointments that the user frequently performs. The schedule management unit also builds a system that learns the user's behavioral patterns and the generation AI predicts and makes suggestions about future behavior. For example, it presents the next task or appointment to be performed based on the user's past behavioral data. This allows the system to learn the user's past behavioral patterns and predict and make suggestions about future behavior.

[0032] The schedule management unit can automatically set schedule priorities and highlight important appointments. For example, the schedule management unit builds a system in which a generation AI automatically sets schedule priorities and highlights important appointments. For example, important appointments such as meetings and deadlines are displayed in different colors. The schedule management unit also develops an algorithm that automatically sets schedule priorities and highlights important appointments. For example, it automatically identifies appointments with high importance based on the user's past behavioral patterns. The schedule management unit also develops a system in which a generation AI sets schedule priorities and highlights important appointments. For example, it displays important appointments prominently on the calendar based on the user's instructions. This makes it possible to automatically set schedule priorities and highlight important appointments.

[0033] The schedule management unit can automatically readjust related tasks and appointments when a schedule change occurs. The schedule management unit builds a system in which the generation AI automatically readjusts related tasks and appointments when a schedule change occurs. For example, if a meeting time is changed, the deadline for related tasks is automatically adjusted. The schedule management unit also develops an algorithm in which the generation AI detects schedule changes and automatically readjusts related tasks and appointments. For example, when a user's schedule is changed, it avoids overlaps with other appointments. The schedule management unit also develops a system in which the generation AI automatically readjusts related tasks and appointments when a schedule change occurs. For example, it reflects schedule changes based on the user's instructions. This makes it possible to automatically readjust related tasks and appointments when a schedule change occurs.

[0034] The schedule management unit can link with other calendar apps and task management tools. For example, the schedule management unit builds a system that links the schedule management function with other calendar apps and task management tools. For example, it synchronizes with Google (registered trademark) Calendar and Microsoft Outlook. The schedule management unit also develops an API for linking with other calendar apps and task management tools and integrates the schedule management function. For example, it centrally manages the user's schedule. The schedule management unit also develops a system that links the schedule management function with other calendar apps and task management tools. For example, it integrates data from multiple calendar apps and displays it to the user. This centralizes schedule management by linking with other calendar apps and task management tools.

[0035] The schedule management unit adds a schedule sharing function, allowing schedules to be shared with teams or families. The schedule management unit, for example, adds a schedule sharing function and builds a system for sharing schedules with teams or families. For example, all family schedules are integrated into one calendar. The schedule management unit also develops a function for sharing schedules with teams or families, making it easier to adjust schedules. For example, schedules are adjusted using a shared calendar. The schedule management unit also adds a schedule sharing function and develops a system for sharing schedules with teams or families. For example, a notification is sent when an event is added to a shared calendar. In this way, adding the schedule sharing function makes it possible to share schedules with teams or families.

[0036] The task management unit can monitor the progress of tasks in real time and issue alerts if delays occur. For example, the task management unit builds a system that monitors the progress of tasks in real time and issues alerts if delays occur. For example, it sends a notification when a task deadline approaches. The task management unit also develops an algorithm that allows the generation AI to monitor the progress of tasks and issue alerts if delays occur. For example, it displays the progress of tasks in a graph and detects delays. The task management unit also develops a system that monitors the progress of tasks in real time and issues alerts if delays occur. For example, it notifies the user by email if a delay occurs based on their instructions. This makes it possible to monitor the progress of tasks in real time and issue alerts if delays occur.

[0037] The task management unit can analyze task dependencies and propose an efficient task order. The task management unit, for example, builds a system that analyzes task dependencies and proposes an efficient task order. For example, it analyzes the context of tasks and proposes the optimal order. The task management unit also develops an algorithm that uses a generative AI to analyze task dependencies and propose an efficient task order. For example, it displays task dependencies in a graph and calculates the optimal order. The task management unit also develops a system that analyzes task dependencies and proposes an efficient task order. For example, it automatically adjusts the task order based on user instructions. This makes it possible to analyze task dependencies and propose an efficient task order.

[0038] The task management unit can link with project management tools. For example, the task management unit builds a system that links the task management function with project management tools. For example, by syncing with Trello or Asana. The task management unit also develops an algorithm that allows the generation AI to link the task management function with project management tools. For example, by automatically importing tasks from project management tools. The task management unit also develops a system that links the task management function with project management tools. For example, by centrally managing tasks from project management tools. This centralizes task management by linking with project management tools.

[0039] The task management unit can display the progress of tasks on a visual dashboard. For example, the task management unit builds a system that displays the progress of tasks on a visual dashboard. For example, it displays the progress of tasks in graphs or charts. The task management unit also develops an algorithm that enables the generation AI to display the progress of tasks on a visual dashboard. For example, it updates the progress of tasks in real time. The task management unit also develops a system that displays the progress of tasks on a visual dashboard. For example, it visualizes the progress of tasks based on user instructions. As a result, by displaying the progress of tasks on a visual dashboard, the progress can be grasped at a glance.

[0040] The task management unit can analyze the importance and urgency of tasks and automatically set priorities. For example, the task management unit builds a system in which a generation AI analyzes the importance and urgency of tasks and automatically sets priorities. For example, it displays tasks with high importance first. The task management unit also develops an algorithm that analyzes the importance and urgency of tasks and automatically sets priorities. For example, it adjusts task priorities based on user instructions. The task management unit also develops a system in which a generation AI analyzes the importance and urgency of tasks and automatically sets priorities. For example, it displays the importance and urgency of tasks in a graph and sets priorities. This makes it possible to analyze the importance and urgency of tasks and automatically set priorities.

[0041] The task management unit can automatically search for and provide information necessary to solve a task from related databases and materials. For example, the task management unit builds a system in which the generation AI automatically searches for and provides information necessary to solve a task from related databases and materials. For example, it searches for related materials based on keywords specified by the user. The task management unit also develops an algorithm that automatically searches for information necessary to solve a task and provides it to the user. For example, it provides information based on past project data and reference literature. The task management unit also develops a system in which the generation AI automatically searches for and provides information necessary to solve a task from related databases and materials. For example, it searches for necessary information in real time based on user instructions. This makes it possible to automatically search for and provide information necessary to solve a task.

[0042] The task management unit can link the priority setting function with other task management tools. For example, the task management unit builds a system that links the priority setting function with other task management tools. For example, by syncing with Trello or Asana. The task management unit also develops an algorithm that enables the generation AI to link the priority setting function with other task management tools. For example, by automatically importing tasks from a task management tool. The task management unit also develops a system that links the priority setting function with other task management tools. For example, by centrally managing tasks from a task management tool. In this way, task management is centralized by linking the priority setting function with other task management tools.

[0043] The task management unit can customize the information provision function according to the user's preferences. For example, the task management unit builds a system that customizes the information provision function according to the user's preferences. For example, it sets the format and content of information that the user prefers. The task management unit also develops an algorithm that allows the generation AI to customize the information provision function according to the user's preferences. For example, it adjusts the information provision method based on the user's past selection history. The task management unit also develops a system that customizes the information provision function according to the user's preferences. For example, it provides information based on the information priority specified by the user. This allows the information provision function to be customized according to the user's preferences.

[0044] The diary recording unit can analyze the contents of the diary and highlight important events and changes in emotions. For example, the diary recording unit can analyze the contents of the diary and build a system that automatically highlights important events and changes in emotions. For example, highlighting can be done based on specific keywords or emotion scores. The diary recording unit can also develop an algorithm that uses a generative AI to analyze the contents of the diary and automatically highlight important events and changes in emotions. For example, it can analyze the user's emotional state and highlight changes in emotions. The diary recording unit can also develop a system that analyzes the contents of the diary and automatically highlight important events and changes in emotions. For example, it can automatically highlight important events specified by the user. This makes it possible to analyze the contents of the diary and automatically highlight important events and changes in emotions.

[0045] The diary recording unit can analyze a user's behavioral patterns and emotional tendencies based on the diary records. The diary recording unit, for example, builds a system that analyzes a user's behavioral patterns and emotional tendencies based on the diary records. For example, it analyzes the contents of the diary and displays changes in behavioral patterns and emotions in a graph. The diary recording unit also develops an algorithm that uses the generation AI to analyze a user's behavioral patterns and emotional tendencies based on the diary records. For example, it analyzes the contents of the diary and identifies behavioral patterns and emotional tendencies. The diary recording unit also develops a system that analyzes a user's behavioral patterns and emotional tendencies based on the diary records. For example, it analyzes behavioral patterns and emotional tendencies based on the user's instructions. This makes it possible to analyze a user's behavioral patterns and emotional tendencies based on the diary records.

[0046] The diary recording unit can link the diary recording function with other life log tools. For example, the diary recording unit builds a system that links the diary recording function with other life log tools. For example, by synchronizing with a fitness tracker or a sleep tracker. The diary recording unit also develops an algorithm that uses a generative AI to link the diary recording function with other life log tools. For example, by automatically importing data from life log tools. The diary recording unit also develops a system that links the diary recording function with other life log tools. For example, by centrally managing data from life log tools. This makes it possible to centrally manage life logs by linking the diary recording function with other life log tools.

[0047] The diary recording unit can convert the contents of the diary into visual notes or mind maps. The diary recording unit, for example, builds a system that converts the contents of the diary into visual notes. For example, it displays the contents of the diary using diagrams or icons. The diary recording unit also develops an algorithm that uses a generation AI to convert the contents of the diary into a mind map. For example, it organizes the contents of the diary using related keywords or phrases. The diary recording unit also develops a system that converts the contents of the diary into visual notes or mind maps. For example, it visually displays the contents of the diary based on the user's instructions. In this way, by converting the contents of the diary into visual notes or mind maps, information can be organized visually.

[0048] The customization department can optimize the algorithm of the generative AI according to the needs of the user. For example, the customization department builds a system that individually optimizes the algorithm of the generative AI according to the needs of the user. For example, it develops an algorithm specialized for a specific task. The customization department also develops an algorithm that allows the generative AI to optimize the algorithm according to the needs of the user. For example, it adjusts the algorithm based on the user's past behavioral data. The customization department also develops a system that individually optimizes the algorithm of the generative AI according to the needs of the user. For example, it customizes the algorithm based on the user's instructions. This makes it possible to individually optimize the algorithm of the generative AI according to the needs of the user.

[0049] The customization department can reflect the results of customization in real time and continuously improve based on user feedback. The customization department, for example, builds a system that reflects the results of customization in real time and continuously improves based on user feedback. For example, it adjusts the algorithm by reflecting user opinions. The customization department also develops an algorithm that the generative AI reflects the results of customization in real time and improves based on user feedback. For example, it retrains the algorithm based on user feedback. The customization department also develops a system that reflects the results of customization in real time and continuously improves based on user feedback. For example, it dynamically adjusts the algorithm based on user instructions. This allows the results of customization to be reflected in real time and continuously improve based on user feedback.

[0050] The customization unit can link the customization function with other AI tools and applications. For example, the customization unit builds a system that links the customization function with other AI tools and applications. For example, by synchronizing with a voice assistant or a smart home device. The customization unit also develops an algorithm that allows the generation AI to link the customization function with other AI tools and applications. For example, by automatically importing settings from other AI tools. The customization unit also develops a system that links the customization function with other AI tools and applications. For example, by centrally managing data from other AI tools. This improves the flexibility of the system by linking the customization function with other AI tools and applications.

[0051] The customization unit can change the interface design and operation method according to the user's preferences. For example, the customization unit builds a system that changes the interface design and operation method according to the user's preferences. For example, it sets the user's preferred colors and layout. The customization unit also develops an algorithm that enables the generation AI to change the interface design and operation method according to the user's preferences. For example, it adjusts the interface based on the user's past selection history. The customization unit also develops a system that changes the interface design and operation method according to the user's preferences. For example, it customizes the interface based on a design specified by the user. This makes it possible to change the interface design and operation method according to the user's preferences.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The personal AI secretary can further include a health management unit. The health management unit monitors the user's health status and provides appropriate advice. For example, the health management unit can analyze the user's sleep patterns and suggest optimal sleep times. The health management unit can also analyze the user's dietary records and suggest nutritionally balanced meal plans. Furthermore, the health management unit can collect the user's exercise data and provide an appropriate exercise plan. This allows for more efficient health management for the user.

[0054] The personal AI secretary can further include a learning support unit. The learning support unit monitors the user's learning progress and suggests effective learning methods. For example, the learning support unit analyzes the user's learning history and creates an optimal learning schedule. The learning support unit can also evaluate the user's level of understanding and suggest necessary supplementary learning content. Furthermore, the learning support unit can provide relevant learning resources based on the user's interests. This improves the user's learning efficiency.

[0055] The personal AI secretary can further include a travel planning unit. The travel planning unit supports the user's travel planning and proposes the optimal travel plan. For example, the travel planning unit may propose travel destinations and accommodations based on the user's preferences and budget. The travel planning unit may also propose the optimal means of transportation and tourist spots based on the user's schedule. Furthermore, the travel planning unit may manage the user's activities during the trip and provide real-time support. This will further enhance the user's travel experience.

[0056] The personal AI secretary can further include a hobby support unit. The hobby support unit supports the user's hobby activities and provides related information and resources. For example, the hobby support unit may provide the latest information and events related to the user's hobby. The hobby support unit may also suggest training plans to improve the user's skills. Furthermore, the hobby support unit may introduce communities and groups related to the user's hobby and provide opportunities for interaction. This will further enrich the user's hobby activities.

[0057] The personal AI secretary can further include a household finance management unit. The household finance management unit monitors the user's income and expenditures and suggests effective household finance management methods. For example, the household finance management unit analyzes the user's income and expenses and provides advice on saving. The household finance management unit can also create a savings plan based on the user's goals. Furthermore, the household finance management unit can analyze the user's spending patterns and make suggestions on reducing wasteful spending. This allows the user to manage their household finances more efficiently.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The voice recognition unit recognizes the user's voice. For example, the voice recognition unit converts the user's speech into text using a voice recognition algorithm. The voice recognition unit can also input voice using a device such as a smartphone or microphone. Furthermore, the voice recognition unit can use noise canceling technology to remove ambient sounds and improve the accuracy of voice recognition. Step 2: The schedule management unit manages the schedule based on the voice recognized by the voice recognition unit. For example, the schedule management unit analyzes the content of the user's conversations and emails and automatically adds them to the schedule. The schedule management unit can also learn the user's past behavioral patterns and predict and make suggestions about future behavior. Furthermore, the schedule management unit can automatically set schedule priorities and highlight important appointments. Step 3: The task management unit manages tasks based on the schedule managed by the schedule management unit. For example, the task management unit adds the user's tasks to a list and sets priorities. The task management unit can also automatically adjust task priorities according to the user's emotional state using an emotion estimation function. Furthermore, the task management unit can monitor the progress of tasks in real time and issue alerts if delays occur. Step 4: The diary recording unit records the diary based on the tasks managed by the task management unit. For example, the diary recording unit summarizes the user's daily events and comments and records them as a diary. The diary recording unit can also provide feedback based on the user's emotions using an emotion estimation function. Furthermore, the diary recording unit can analyze the contents of the diary and automatically highlight important events and changes in emotions.

[0060] (Example 2) The personal AI secretary of an embodiment of the present invention is a system that utilizes voice recognition and generative AI to support users' daily lives. This system operates 24 hours a day, 365 days a year, and provides a wide range of functions, such as schedule management, task management, and diary recording. As a result, the personal AI secretary improves the user's work efficiency and saves time, making their life more comfortable.

[0061] A personal AI secretary according to an embodiment includes a voice recognition unit, a schedule management unit, a task management unit, and a diary recording unit. The voice recognition unit recognizes a user's voice. For example, the voice recognition unit converts the user's speech into text using a voice recognition algorithm. The voice recognition unit can input voice using a device such as a smartphone or a microphone. The voice recognition unit can also remove environmental sounds using noise canceling technology to improve the accuracy of voice recognition. The schedule management unit manages a schedule based on the voice recognized by the voice recognition unit. For example, the schedule management unit analyzes the content of the user's conversations and emails and automatically adds them to the schedule. The schedule management unit can also learn the user's past behavioral patterns and predict and suggest future behavior. The schedule management unit can also automatically prioritize the schedule and highlight important appointments. The task management unit manages tasks based on the schedule managed by the schedule management unit. For example, the task management unit adds the user's tasks to a list and sets priorities. The task management unit can also automatically adjust task priorities according to the user's emotional state using an emotion estimation function. The task management unit can also monitor the progress of tasks in real time and issue alerts if delays occur. The diary recording unit records a diary based on the tasks managed by the task management unit. For example, the diary recording unit summarizes the user's daily events and comments and records them as a diary. The diary recording unit can also provide feedback based on the user's emotions using an emotion estimation function. The diary recording unit can also analyze the contents of the diary and automatically highlight important events and changes in emotions. This allows the personal AI secretary of the embodiment to centrally manage schedules, tasks, and diary entries based on the user's voice. For example, by recognizing the user's voice and automatically adding them to the schedule, important appointments are never overlooked. Task priorities can also be automatically set, allowing for efficient work. Furthermore, diary recording can promote self-development, and flexible customization can accommodate individual needs.

[0062] The speech recognition unit can learn the characteristics of a user's voice and improve the accuracy of speech recognition. For example, the speech recognition unit collects everyday conversation data to learn the characteristics of the user's voice and individually optimizes the speech recognition model. For example, the speech recognition unit analyzes the user's pronunciation and intonation patterns to improve the accuracy of speech recognition. The speech recognition unit also customizes the speech recognition model based on the characteristics of the user's voice and builds a speech recognition system optimized for a specific user. For example, the speech recognition unit adjusts the speech recognition algorithm to match the tone and speed of the user's voice. The speech recognition unit also continuously learns the characteristics of the user's voice and develops a system that improves the accuracy of speech recognition. For example, to respond to changes in the user's voice, the speech data is regularly updated and the model is retrained. In this way, the speech recognition unit learns the characteristics of the user's voice and improves the accuracy of speech recognition.

[0063] The schedule management unit learns the user's past behavioral patterns and can predict and make suggestions about future behavior. For example, the schedule management unit collects the user's past behavioral data, and the generation AI learns those patterns. For example, the schedule management unit analyzes the user's schedule and task history, and predicts and makes suggestions about future behavior. The schedule management unit also develops an algorithm for the generation AI to predict future behavior based on the user's past behavioral patterns. For example, it automatically suggests tasks and appointments that the user frequently performs. The schedule management unit also builds a system that learns the user's behavioral patterns and the generation AI predicts and makes suggestions about future behavior. For example, it presents the next task or appointment to be performed based on the user's past behavioral data. This allows the system to learn the user's past behavioral patterns and predict and make suggestions about future behavior.

[0064] The task management unit can use the emotion estimation function to automatically adjust task priorities according to the user's emotional state. For example, the task management unit uses the emotion estimation function to analyze the user's emotional state in real time and take action based on the results. For example, if the user is feeling stressed, the task management unit makes suggestions for relaxation. The task management unit also develops a system that analyzes the user's emotional state and takes action according to the emotion. For example, if the user is tired, the task management unit displays a message encouraging the user to take a break. The task management unit also uses the emotion estimation function to build an algorithm that takes action according to the user's emotional state. For example, if the user is feeling positive, the task management unit displays an encouraging message. This makes it possible to automatically adjust task priorities according to the user's emotional state.

[0065] The diary recording unit can use the emotion estimation function to provide feedback according to the user's emotions. For example, the diary recording unit uses the emotion estimation function to analyze the user's emotional state and suggest music or entertainment based on the results. For example, if the user feels like relaxing, relaxing music is suggested. The diary recording unit also develops a system that analyzes the user's emotional state and suggests music or entertainment according to the emotion. For example, if the user feels like cheering up, upbeat music is suggested. The diary recording unit also uses the emotion estimation function to build an algorithm that suggests music or entertainment according to the user's emotions. For example, if the user feels sad, an uplifting movie is suggested. This makes it possible to provide feedback according to the user's emotions.

[0066] The schedule management unit can automatically set schedule priorities and highlight important appointments. For example, the schedule management unit builds a system in which a generation AI automatically sets schedule priorities and highlights important appointments. For example, important appointments such as meetings and deadlines are displayed in different colors. The schedule management unit also develops an algorithm that automatically sets schedule priorities and highlights important appointments. For example, it automatically identifies appointments with high importance based on the user's past behavioral patterns. The schedule management unit also develops a system in which a generation AI sets schedule priorities and highlights important appointments. For example, it displays important appointments prominently on the calendar based on the user's instructions. This makes it possible to automatically set schedule priorities and highlight important appointments.

[0067] The schedule management unit can automatically readjust related tasks and appointments when a schedule change occurs. The schedule management unit builds a system in which the generation AI automatically readjusts related tasks and appointments when a schedule change occurs. For example, if a meeting time is changed, the deadline for related tasks is automatically adjusted. The schedule management unit also develops an algorithm in which the generation AI detects schedule changes and automatically readjusts related tasks and appointments. For example, when a user's schedule is changed, it avoids overlaps with other appointments. The schedule management unit also develops a system in which the generation AI automatically readjusts related tasks and appointments when a schedule change occurs. For example, it reflects schedule changes based on the user's instructions. This makes it possible to automatically readjust related tasks and appointments when a schedule change occurs.

[0068] The schedule management unit can use the emotion estimation function to propose a schedule that takes into account the user's stress level. The schedule management unit, for example, uses the emotion estimation function to analyze the user's stress level and builds a system that proposes a schedule based on the results. For example, if the user is feeling stressed, the system suggests more rest time. The schedule management unit also analyzes the user's stress level and develops an algorithm that proposes a schedule based on the user's emotions. For example, the system incorporates time in the schedule for the user to relax. The schedule management unit also uses the emotion estimation function to develop a system that proposes a schedule that takes into account the user's stress level. For example, if the user is tired, the system preferentially suggests lighter tasks. This makes it possible to propose a schedule that takes into account the user's stress level.

[0069] The schedule management unit can link with other calendar apps and task management tools. For example, the schedule management unit builds a system that links the schedule management function with other calendar apps and task management tools. For example, it synchronizes with Google (registered trademark) Calendar and Microsoft Outlook. The schedule management unit also develops an API for linking with other calendar apps and task management tools and integrates the schedule management function. For example, it centrally manages the user's schedule. The schedule management unit also develops a system that links the schedule management function with other calendar apps and task management tools. For example, it integrates data from multiple calendar apps and displays it to the user. This centralizes schedule management by linking with other calendar apps and task management tools.

[0070] The schedule management unit adds a schedule sharing function, allowing schedules to be shared with teams or families. The schedule management unit, for example, adds a schedule sharing function and builds a system for sharing schedules with teams or families. For example, all family schedules are integrated into one calendar. The schedule management unit also develops a function for sharing schedules with teams or families, making it easier to adjust schedules. For example, schedules are adjusted using a shared calendar. The schedule management unit also adds a schedule sharing function and develops a system for sharing schedules with teams or families. For example, a notification is sent when an event is added to a shared calendar. In this way, adding the schedule sharing function makes it possible to share schedules with teams or families.

[0071] The schedule management unit can use the emotion estimation function to change the reminder notification method according to the user's emotion. For example, the schedule management unit uses the emotion estimation function to analyze the user's emotional state and build a system that changes the reminder notification method based on the results. For example, if the user is feeling stressed, a gentle notification sound is used. The schedule management unit also develops an algorithm that analyzes the user's emotional state and changes the reminder notification method according to the emotion. For example, if the user is relaxed, a light notification sound is used. The schedule management unit also uses the emotion estimation function to develop a system that changes the reminder notification method according to the user's emotion. For example, if the user is concentrating, a vibration notification is used. This makes it possible to change the reminder notification method according to the user's emotion.

[0072] The task management unit can monitor the progress of tasks in real time and issue alerts if delays occur. For example, the task management unit builds a system that monitors the progress of tasks in real time and issues alerts if delays occur. For example, it sends a notification when a task deadline approaches. The task management unit also develops an algorithm that allows the generation AI to monitor the progress of tasks and issue alerts if delays occur. For example, it displays the progress of tasks in a graph and detects delays. The task management unit also develops a system that monitors the progress of tasks in real time and issues alerts if delays occur. For example, it notifies the user by email if a delay occurs based on their instructions. This makes it possible to monitor the progress of tasks in real time and issue alerts if delays occur.

[0073] The task management unit can analyze task dependencies and propose an efficient task order. The task management unit, for example, builds a system that analyzes task dependencies and proposes an efficient task order. For example, it analyzes the context of tasks and proposes the optimal order. The task management unit also develops an algorithm that uses a generative AI to analyze task dependencies and propose an efficient task order. For example, it displays task dependencies in a graph and calculates the optimal order. The task management unit also develops a system that analyzes task dependencies and proposes an efficient task order. For example, it automatically adjusts the task order based on user instructions. This makes it possible to analyze task dependencies and propose an efficient task order.

[0074] The task management unit can use the emotion estimation function to suggest a task management method that increases the user's motivation. For example, the task management unit uses the emotion estimation function to analyze the user's emotional state and build a system that suggests a task management method that increases the user's motivation based on the results. For example, if the user is feeling motivated, it suggests a difficult task. The task management unit also analyzes the user's emotional state and develops an algorithm that suggests a task management method according to the emotion. For example, if the user is tired, it preferentially suggests easy tasks. The task management unit also uses the emotion estimation function to develop a system that suggests a task management method that increases the user's motivation. For example, if the user is feeling positive, it suggests a challenging task. This makes it possible to suggest a task management method that increases the user's motivation.

[0075] The task management unit can link with project management tools. For example, the task management unit builds a system that links the task management function with project management tools. For example, by syncing with Trello or Asana. The task management unit also develops an algorithm that allows the generation AI to link the task management function with project management tools. For example, by automatically importing tasks from project management tools. The task management unit also develops a system that links the task management function with project management tools. For example, by centrally managing tasks from project management tools. This centralizes task management by linking with project management tools.

[0076] The task management unit can display the progress of tasks on a visual dashboard. For example, the task management unit builds a system that displays the progress of tasks on a visual dashboard. For example, it displays the progress of tasks in graphs or charts. The task management unit also develops an algorithm that enables the generation AI to display the progress of tasks on a visual dashboard. For example, it updates the progress of tasks in real time. The task management unit also develops a system that displays the progress of tasks on a visual dashboard. For example, it visualizes the progress of tasks based on user instructions. As a result, by displaying the progress of tasks on a visual dashboard, the progress can be grasped at a glance.

[0077] The task management unit can use the emotion estimation function to automatically adjust task priorities according to the user's emotions. The task management unit, for example, uses the emotion estimation function to analyze the user's emotional state and builds a system that automatically adjusts task priorities based on the results. For example, if the user is feeling stressed, easier tasks are preferentially suggested. The task management unit also develops an algorithm that analyzes the user's emotional state and automatically adjusts task priorities according to the emotions. For example, if the user is relaxed, more difficult tasks are suggested. The task management unit also uses the emotion estimation function to develop a system that automatically adjusts task priorities according to the user's emotions. For example, if the user is feeling positive, more challenging tasks are suggested. This makes it possible to automatically adjust task priorities according to the user's emotions.

[0078] The task management unit can analyze the importance and urgency of tasks and automatically set priorities. For example, the task management unit builds a system in which a generation AI analyzes the importance and urgency of tasks and automatically sets priorities. For example, it displays tasks with high importance first. The task management unit also develops an algorithm that analyzes the importance and urgency of tasks and automatically sets priorities. For example, it adjusts task priorities based on user instructions. The task management unit also develops a system in which a generation AI analyzes the importance and urgency of tasks and automatically sets priorities. For example, it displays the importance and urgency of tasks in a graph and sets priorities. This makes it possible to analyze the importance and urgency of tasks and automatically set priorities.

[0079] The task management unit can automatically search for and provide information necessary to solve a task from related databases and materials. For example, the task management unit builds a system in which the generation AI automatically searches for and provides information necessary to solve a task from related databases and materials. For example, it searches for related materials based on keywords specified by the user. The task management unit also develops an algorithm that automatically searches for information necessary to solve a task and provides it to the user. For example, it provides information based on past project data and reference literature. The task management unit also develops a system in which the generation AI automatically searches for and provides information necessary to solve a task from related databases and materials. For example, it searches for necessary information in real time based on user instructions. This makes it possible to automatically search for and provide information necessary to solve a task.

[0080] The task management unit can use the emotion estimation function to select an information provision method according to the user's emotional state. The task management unit, for example, uses the emotion estimation function to analyze the user's emotional state and builds a system that selects an information provision method based on the results. For example, if the user is feeling stressed, concise information is provided. The task management unit also develops an algorithm that analyzes the user's emotional state and selects an information provision method according to the emotion. For example, if the user is relaxed, detailed information is provided. The task management unit also uses the emotion estimation function to develop a system that selects an information provision method according to the user's emotional state. For example, if the user is feeling positive, proactive suggestions are made. This makes it possible to select an information provision method according to the user's emotional state.

[0081] The task management unit can link the priority setting function with other task management tools. For example, the task management unit builds a system that links the priority setting function with other task management tools. For example, by syncing with Trello or Asana. The task management unit also develops an algorithm that enables the generation AI to link the priority setting function with other task management tools. For example, by automatically importing tasks from a task management tool. The task management unit also develops a system that links the priority setting function with other task management tools. For example, by centrally managing tasks from a task management tool. In this way, task management is centralized by linking the priority setting function with other task management tools.

[0082] The task management unit can customize the information provision function according to the user's preferences. For example, the task management unit builds a system that customizes the information provision function according to the user's preferences. For example, it sets the format and content of information that the user prefers. The task management unit also develops an algorithm that allows the generation AI to customize the information provision function according to the user's preferences. For example, it adjusts the information provision method based on the user's past selection history. The task management unit also develops a system that customizes the information provision function according to the user's preferences. For example, it provides information based on the information priority specified by the user. This allows the information provision function to be customized according to the user's preferences.

[0083] The task management unit can use the emotion estimation function to adjust the timing of information presentation according to the user's emotion. For example, the task management unit uses the emotion estimation function to analyze the user's emotional state and build a system that adjusts the timing of information presentation based on the results. For example, if the user is relaxed, important information is presented. The task management unit also analyzes the user's emotional state and develops an algorithm that adjusts the timing of information presentation according to the emotion. For example, if the user is concentrating, notifications are withheld. The task management unit also uses the emotion estimation function to develop a system that adjusts the timing of information presentation according to the user's emotional state. For example, if the user is feeling positive, proactive suggestions are made. This makes it possible to adjust the timing of information presentation according to the user's emotion.

[0084] The diary recording unit can analyze the contents of the diary and highlight important events and changes in emotions. For example, the diary recording unit can analyze the contents of the diary and build a system that automatically highlights important events and changes in emotions. For example, highlighting can be done based on specific keywords or emotion scores. The diary recording unit can also develop an algorithm that uses a generative AI to analyze the contents of the diary and automatically highlight important events and changes in emotions. For example, it can analyze the user's emotional state and highlight changes in emotions. The diary recording unit can also develop a system that analyzes the contents of the diary and automatically highlight important events and changes in emotions. For example, it can automatically highlight important events specified by the user. This makes it possible to analyze the contents of the diary and automatically highlight important events and changes in emotions.

[0085] The diary recording unit can analyze a user's behavioral patterns and emotional tendencies based on the diary records. The diary recording unit, for example, builds a system that analyzes a user's behavioral patterns and emotional tendencies based on the diary records. For example, it analyzes the contents of the diary and displays changes in behavioral patterns and emotions in a graph. The diary recording unit also develops an algorithm that uses the generation AI to analyze a user's behavioral patterns and emotional tendencies based on the diary records. For example, it analyzes the contents of the diary and identifies behavioral patterns and emotional tendencies. The diary recording unit also develops a system that analyzes a user's behavioral patterns and emotional tendencies based on the diary records. For example, it analyzes behavioral patterns and emotional tendencies based on the user's instructions. This makes it possible to analyze a user's behavioral patterns and emotional tendencies based on the diary records.

[0086] The diary recording unit can use the emotion estimation function to provide feedback according to the user's emotions. The diary recording unit, for example, uses the emotion estimation function to analyze the user's emotional state and build a system that provides feedback based on the results. For example, if the user has positive emotions, an encouraging message is displayed. The diary recording unit also develops an algorithm that analyzes the user's emotional state and provides feedback according to the emotions. For example, if the user is feeling stressed, a suggestion for relaxation is made. The diary recording unit also uses the emotion estimation function to develop a system that provides feedback according to the user's emotional state. For example, if the user has negative emotions, a positive suggestion is made. This makes it possible to provide feedback according to the user's emotions.

[0087] The diary recording unit can link the diary recording function with other life log tools. For example, the diary recording unit builds a system that links the diary recording function with other life log tools. For example, by synchronizing with a fitness tracker or a sleep tracker. The diary recording unit also develops an algorithm that uses a generative AI to link the diary recording function with other life log tools. For example, by automatically importing data from life log tools. The diary recording unit also develops a system that links the diary recording function with other life log tools. For example, by centrally managing data from life log tools. This makes it possible to centrally manage life logs by linking the diary recording function with other life log tools.

[0088] The diary recording unit can convert the contents of the diary into visual notes or mind maps. The diary recording unit, for example, builds a system that converts the contents of the diary into visual notes. For example, it displays the contents of the diary using diagrams or icons. The diary recording unit also develops an algorithm that uses a generation AI to convert the contents of the diary into a mind map. For example, it organizes the contents of the diary using related keywords or phrases. The diary recording unit also develops a system that converts the contents of the diary into visual notes or mind maps. For example, it visually displays the contents of the diary based on the user's instructions. In this way, by converting the contents of the diary into visual notes or mind maps, information can be organized visually.

[0089] The diary recording unit can use the emotion estimation function to suggest a diary recording method according to the user's emotions. The diary recording unit, for example, uses the emotion estimation function to analyze the user's emotional state and builds a system that suggests a diary recording method based on the results. For example, if the user is feeling positive emotions, the system suggests recording feelings of gratitude. The diary recording unit also develops an algorithm that analyzes the user's emotional state and suggests a diary recording method according to the emotions. For example, if the user is feeling stressed, the system suggests recording an event that helps the user relax. The diary recording unit also uses the emotion estimation function to develop a system that suggests a diary recording method according to the user's emotional state. For example, if the user is feeling negative emotions, the system suggests recording a positive event. This makes it possible to suggest a diary recording method according to the user's emotions.

[0090] The customization department can optimize the algorithm of the generative AI according to the needs of the user. For example, the customization department builds a system that individually optimizes the algorithm of the generative AI according to the needs of the user. For example, it develops an algorithm specialized for a specific task. The customization department also develops an algorithm that allows the generative AI to optimize the algorithm according to the needs of the user. For example, it adjusts the algorithm based on the user's past behavioral data. The customization department also develops a system that individually optimizes the algorithm of the generative AI according to the needs of the user. For example, it customizes the algorithm based on the user's instructions. This makes it possible to individually optimize the algorithm of the generative AI according to the needs of the user.

[0091] The customization department can reflect the results of customization in real time and continuously improve based on user feedback. The customization department, for example, builds a system that reflects the results of customization in real time and continuously improves based on user feedback. For example, it adjusts the algorithm by reflecting user opinions. The customization department also develops an algorithm that the generative AI reflects the results of customization in real time and improves based on user feedback. For example, it retrains the algorithm based on user feedback. The customization department also develops a system that reflects the results of customization in real time and continuously improves based on user feedback. For example, it dynamically adjusts the algorithm based on user instructions. This allows the results of customization to be reflected in real time and continuously improve based on user feedback.

[0092] The customization unit can use the emotion estimation function to make customization suggestions according to the user's emotions. The customization unit, for example, uses the emotion estimation function to analyze the user's emotional state and builds a system that makes customization suggestions based on the results. For example, if the user is feeling stressed, the customization unit suggests relaxing settings. The customization unit also develops an algorithm that analyzes the user's emotional state and makes customization suggestions according to the emotions. For example, if the user is feeling positive, the customization unit suggests challenging settings. The customization unit also uses the emotion estimation function to develop a system that makes customization suggestions according to the user's emotional state. For example, if the user is feeling negative, the customization unit suggests positive settings. This makes it possible to make customization suggestions according to the user's emotions.

[0093] The customization unit can link the customization function with other AI tools and applications. For example, the customization unit builds a system that links the customization function with other AI tools and applications. For example, by synchronizing with a voice assistant or a smart home device. The customization unit also develops an algorithm that allows the generation AI to link the customization function with other AI tools and applications. For example, by automatically importing settings from other AI tools. The customization unit also develops a system that links the customization function with other AI tools and applications. For example, by centrally managing data from other AI tools. This improves the flexibility of the system by linking the customization function with other AI tools and applications.

[0094] The customization unit can change the interface design and operation method according to the user's preferences. For example, the customization unit builds a system that changes the interface design and operation method according to the user's preferences. For example, it sets the user's preferred colors and layout. The customization unit also develops an algorithm that enables the generation AI to change the interface design and operation method according to the user's preferences. For example, it adjusts the interface based on the user's past selection history. The customization unit also develops a system that changes the interface design and operation method according to the user's preferences. For example, it customizes the interface based on a design specified by the user. This makes it possible to change the interface design and operation method according to the user's preferences.

[0095] The customization unit can use the emotion estimation function to provide customization options according to the user's emotions. For example, the customization unit uses the emotion estimation function to analyze the user's emotional state and build a system that provides customization options based on the results. For example, if the user feels like relaxing, the customization unit suggests a relaxing setting. The customization unit also analyzes the user's emotional state and develops an algorithm that provides customization options according to the emotion. For example, if the user has positive emotions, the customization unit suggests a challenging setting. The customization unit also uses the emotion estimation function to develop a system that provides customization options according to the user's emotional state. For example, if the user has negative emotions, the customization unit suggests a positive setting. This makes it possible to provide customization options according to the user's emotions.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The personal AI secretary can further include a health management unit. The health management unit monitors the user's health status and provides appropriate advice. For example, the health management unit can analyze the user's sleep patterns and suggest optimal sleep times. The health management unit can also analyze the user's dietary records and suggest nutritionally balanced meal plans. Furthermore, the health management unit can collect the user's exercise data and provide an appropriate exercise plan. This allows for more efficient health management for the user.

[0098] The personal AI secretary can further include a learning support unit. The learning support unit monitors the user's learning progress and suggests effective learning methods. For example, the learning support unit analyzes the user's learning history and creates an optimal learning schedule. The learning support unit can also evaluate the user's level of understanding and suggest necessary supplementary learning content. Furthermore, the learning support unit can provide relevant learning resources based on the user's interests. This improves the user's learning efficiency.

[0099] The personal AI secretary can further include a travel planning unit. The travel planning unit supports the user's travel planning and proposes the optimal travel plan. For example, the travel planning unit may propose travel destinations and accommodations based on the user's preferences and budget. The travel planning unit may also propose the optimal means of transportation and tourist spots based on the user's schedule. Furthermore, the travel planning unit may manage the user's activities during the trip and provide real-time support. This will further enhance the user's travel experience.

[0100] The personal AI secretary can further include a hobby support unit. The hobby support unit supports the user's hobby activities and provides related information and resources. For example, the hobby support unit may provide the latest information and events related to the user's hobby. The hobby support unit may also suggest training plans to improve the user's skills. Furthermore, the hobby support unit may introduce communities and groups related to the user's hobby and provide opportunities for interaction. This will further enrich the user's hobby activities.

[0101] The personal AI secretary can further include a household finance management unit. The household finance management unit monitors the user's income and expenditures and suggests effective household finance management methods. For example, the household finance management unit analyzes the user's income and expenses and provides advice on saving. The household finance management unit can also create a savings plan based on the user's goals. Furthermore, the household finance management unit can analyze the user's spending patterns and make suggestions on reducing wasteful spending. This allows the user to manage their household finances more efficiently.

[0102] The personal AI secretary can also use its emotion estimation function to suggest relaxation methods according to the user's emotions. For example, if the user is feeling stressed, it can suggest relaxing music or meditation. If the user is tired, it can also suggest refreshing activities. Furthermore, if the user is feeling positive, it can also suggest activities to maintain that emotion. This makes it possible to provide relaxation methods according to the user's emotional state.

[0103] The personal AI secretary can also use its emotion estimation function to suggest communication methods that correspond to the user's emotions. For example, if the user is feeling negative, it can display an encouraging message. If the user is feeling positive, it can also display a message of gratitude. Furthermore, if the user is feeling stressed, it can suggest communication methods that will help them relax. This makes it possible to provide communication methods that correspond to the user's emotional state.

[0104] The personal AI secretary can also use its emotion estimation function to suggest entertainment that matches the user's emotions. For example, if the user wants to relax, it can suggest relaxing movies and music. If the user wants to cheer up, it can suggest upbeat music or action movies. Furthermore, if the user is feeling sad, it can suggest comedy movies to lift their spirits. This allows it to provide entertainment that matches the user's emotional state.

[0105] The personal AI secretary can also use its emotion estimation function to provide feedback according to the user's emotions. For example, if the user is feeling positive, it can display an encouraging message. If the user is feeling stressed, it can also make suggestions to help them relax. Furthermore, if the user is feeling negative, it can make positive suggestions. This allows it to provide feedback according to the user's emotions.

[0106] The personal AI secretary can also use its emotion estimation function to automatically adjust task priorities according to the user's emotions. For example, if the user is feeling stressed, it can prioritize easy tasks. If the user is relaxed, it can also suggest more difficult tasks. Furthermore, if the user is feeling positive, it can also suggest more challenging tasks. This makes it possible to automatically adjust task priorities according to the user's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The voice recognition unit recognizes the user's voice. For example, the voice recognition unit converts the user's speech into text using a voice recognition algorithm. The voice recognition unit can also input voice using a device such as a smartphone or microphone. Furthermore, the voice recognition unit can use noise canceling technology to remove ambient sounds and improve the accuracy of voice recognition. Step 2: The schedule management unit manages the schedule based on the voice recognized by the voice recognition unit. For example, the schedule management unit analyzes the content of the user's conversations and emails and automatically adds them to the schedule. The schedule management unit can also learn the user's past behavioral patterns and predict and make suggestions about future behavior. Furthermore, the schedule management unit can automatically set schedule priorities and highlight important appointments. Step 3: The task management unit manages tasks based on the schedule managed by the schedule management unit. For example, the task management unit adds the user's tasks to a list and sets priorities. The task management unit can also automatically adjust task priorities according to the user's emotional state using an emotion estimation function. Furthermore, the task management unit can monitor the progress of tasks in real time and issue alerts if delays occur. Step 4: The diary recording unit records the diary based on the tasks managed by the task management unit. For example, the diary recording unit summarizes the user's daily events and comments and records them as a diary. The diary recording unit can also provide feedback based on the user's emotions using an emotion estimation function. Furthermore, the diary recording unit can analyze the contents of the diary and automatically highlight important events and changes in emotions.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0146] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0148] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0149] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0150] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a speech recognition unit that recognizes speech; a schedule management unit that manages schedules based on the voice recognized by the voice recognition unit; a task management unit that manages tasks based on the schedule managed by the schedule management unit; a diary recording unit that records a diary based on the tasks managed by the task management unit. A system characterized by:

2. The voice recognition unit Learns the characteristics of the user's voice and improves the accuracy of voice recognition 2. The system of claim 1.

3. The schedule management unit Learns the user's past behavior patterns, predicts future behavior, and makes suggestions 2. The system of claim 1.

4. The task management unit Automatically adjust task priorities according to the user's emotional state 2. The system of claim 1.

5. The diary recording unit Providing emotional feedback to users 2. The system of claim 1.

Images