system

Abstract

The system according to this embodiment aims to efficiently manage many tasks and prevent omissions and forgetfulness. [Solution] The system according to the embodiment comprises a voice analysis unit, a task management unit, a learning unit, and an image analysis unit. The voice analysis unit analyzes the user's speech. The task management unit manages tasks based on the information analyzed by the voice analysis unit. The learning unit learns the user's hobbies and preferences. The image analysis unit reads images using a camera.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that omissions and forgetfulness easily occur when managing many tasks, and it is difficult to manage tasks efficiently.

[0005] The system according to the embodiment aims to efficiently manage many tasks and prevent omissions and forgetfulness.

Means for Solving the Problems

[0006] The system according to the embodiment includes a voice analysis unit, a task management unit, a learning unit, and an image analysis unit. The voice analysis unit analyzes a user's utterance. The task management unit manages tasks based on the information analyzed by the voice analysis unit. The learning unit learns a user's hobbies and preferences. The image analysis unit reads an image with a camera. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage many tasks and prevent omissions and forgetfulness. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The earphone-integrated schedule and task management system according to an embodiment of the present invention is a system that analyzes user speech and manages tasks and schedules. This system is equipped with a generative AI that saves the user's voice and automatically records and categorizes speech in daily life, thereby preventing tasks from being missed or forgotten. For example, simply by wearing the earphones and going about daily life, the earphones listen to the user's voice, and the generative AI analyzes the content. For example, if the user says, "Tomorrow's meeting is at 10 o'clock," that information is automatically recorded and added to the schedule. Next, the generative AI manages the user's tasks and schedule based on the recorded information. For example, if the user says, "I'm having dinner with a friend next Friday," that information is automatically added to the task list and a reminder is sent. Furthermore, the generative AI learns the user's hobbies and preferences and suggests things they might like. For example, if the user says, "I like movies," the generative AI can suggest movies that match the user's preferences. The earphones are also equipped with a camera that can read images and text. For example, if the user takes a picture of a restaurant menu, that information is automatically imported into the app and added to the list. This system allows users to manage tasks and schedules in their daily lives without any special operations. After initial setup, the generating AI learns and functions as a highly accurate personal assistant. This prevents tasks from being overlooked or forgotten, reducing mental and physical stress. The integrated earphone schedule and task management system analyzes user speech and images to provide task management and hobby suggestions.

[0029] The earphone-integrated schedule and task management system according to this embodiment comprises a voice analysis unit, a task management unit, a learning unit, and an image analysis unit. The voice analysis unit analyzes the user's speech. The voice analysis unit analyzes the user's speech using, for example, a generative AI and provides the information to the task management unit. The voice analysis unit can convert the user's speech into text data using, for example, speech recognition technology. The voice analysis unit can also analyze the content of the speech using natural language processing technology and evaluate the importance of the tasks. For example, if the user says, "Tomorrow's meeting is at 10 o'clock," the voice analysis unit analyzes that information and adds it to the schedule. The task management unit manages tasks based on the information analyzed by the voice analysis unit. The task management unit determines the priority of tasks and issues reminders using, for example, a generative AI. For example, if the user says, "I'm having dinner with a friend next Friday," the task management unit adds that information to the task list and issues a reminder. The task management unit manages the user's schedule and can prioritize reminders for important tasks. The learning unit learns the user's hobbies and preferences. For example, the learning unit uses generative AI to analyze the user's statements and actions to learn their hobbies and preferences. For example, if the user says "I like movies," the learning unit uses that information to suggest movies that match the user's preferences. The learning unit can learn the user's hobbies and preferences and suggest things they like. The image analysis unit reads images with the camera. For example, the image analysis unit uses generative AI to analyze the images read by the camera and add them to a list. For example, if the user takes a picture of a restaurant menu, the image analysis unit analyzes that information and adds it to a list. The image analysis unit can use image recognition technology to extract text information from images and reflect it in tasks and schedules. As a result, the earphone-integrated schedule and task management system according to this embodiment can analyze the user's statements and images to manage tasks and suggest hobbies.

[0030] The voice analysis unit analyzes the user's speech. For example, it uses generative AI to analyze the user's speech and provides the information to the task management unit. Specifically, the voice analysis unit uses high-precision speech recognition technology to convert the user's speech into text data in real time. This speech recognition technology has a noise-canceling function, which removes ambient noise and can accurately capture the user's speech. Furthermore, the voice analysis unit makes full use of natural language processing technology to analyze the content of the speech in detail and evaluate the importance and urgency of the task. For example, if the user says, "Tomorrow's meeting is at 10 o'clock," the voice analysis unit converts that speech into text data, extracts the date and time of the meeting, and adds it to the schedule. Also, if the user says, "I'm having dinner with a friend next Friday," the unit analyzes that information, adds it to the task list, and sends a reminder at the appropriate time. The voice analysis unit can also use generative AI to refer to background information and related information about the speech in order to understand the context and intent of the user's speech. For example, if a user says, "When is the deadline for the next project?", the voice analysis unit will refer to past schedules and task information and provide an appropriate answer. This allows the voice analysis unit to accurately and quickly analyze the user's speech and provide the task management unit with the necessary information.

[0031] The task management unit manages tasks based on information analyzed by the voice analysis unit. For example, the task management unit uses generative AI to determine task priorities and send reminders. Specifically, the task management unit centrally manages the user's schedule and task list and has a function to prioritize reminders for important tasks. For example, if a user says, "I'm having dinner with a friend next Friday," that information is added to the task list and a reminder is sent the day before the meal. The task management unit can also automatically adjust task priorities based on the user's past actions and statements. For example, if a user says, "Tomorrow's meeting is important," that meeting is reminded of as a priority over other tasks. Furthermore, the task management unit calculates the optimal reminder timing based on the user's schedule and supports the user in completing tasks most efficiently. For example, if a user says, "I'll prepare for the presentation at 3 PM," a reminder is sent before that preparation time so that the user doesn't forget to start preparing. The task management unit can learn the user's statements and behavior patterns using generative AI and continuously improve the accuracy and timing of reminders. This allows the task management department to efficiently manage users' schedules and support them in ensuring that important tasks are completed reliably.

[0032] The learning unit learns the user's hobbies and preferences. For example, it uses generative AI to analyze the user's statements and actions to learn their hobbies and preferences. Specifically, the learning unit analyzes the user's statements and behavioral history in detail to understand the user's interests and concerns. For example, if a user says "I like movies," it will suggest movies that match the user's preferences based on that information. Also, if a user says "I recently started jogging," it can suggest jogging-related events and recommended routes based on that information. The learning unit can continuously learn the user's statements and behavioral patterns using generative AI and respond to changes in the user's hobbies and preferences. For example, if a user starts a new hobby, it will quickly learn that information and provide information related to the new hobby. Furthermore, based on the user's past statements and behavioral history, the learning unit can also suggest new hobbies and activities that the user might be interested in. For example, if a user says "I like music," it will suggest new artists and concert information based on that information. By learning the user's hobbies and preferences in detail and making suggestions that match the user's lifestyle, the learning unit can improve user satisfaction.

[0033] The image analysis unit reads images from the camera. For example, it uses generative AI to analyze the images read by the camera and add them to a list. Specifically, the image analysis unit uses high-precision image recognition technology to analyze text information and objects within images captured by the camera. For example, if a user photographs a restaurant menu, the unit can extract the text information from the image and add it to a list. Furthermore, the image analysis unit can recognize specific objects or scenes from images taken by the user and automatically generate related tasks and schedules. For example, if a user photographs a whiteboard at a meeting, the unit can analyze the text information within the image and reflect it in the meeting minutes or task list. Also, if a user photographs a tourist destination, the unit can suggest information about the tourist destination and recommended sightseeing routes based on the image. By using generative AI to analyze information within images in detail and reflecting it in the user's task management and schedule, the image analysis unit improves user convenience. Furthermore, the image analysis unit can learn from the user's past image data and make suggestions based on the user's preferences and behavioral patterns. This allows the image analysis unit to utilize information obtained from images, in addition to user statements and actions, to provide more accurate task management and schedule suggestions.

[0034] The voice analysis unit can analyze the background sounds of a user's speech during voice analysis and supplement the context of the speech. For example, if a user speaks in a cafe, the voice analysis unit recognizes the cafe environment from the background sounds and supplements the context of the speech. For example, if a user speaks in a conference room, the voice analysis unit recognizes the meeting situation from the background sounds and evaluates the importance of the speech. For example, if a user speaks at home, the voice analysis unit recognizes a relaxed environment from the background sounds and analyzes the content of the speech in detail. By analyzing background sounds, the context of the speech can be supplemented, enabling more accurate task management. Some or all of the above processing in the voice analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice analysis unit can input the user's voice data and background sound data into a generative AI and have the generative AI perform the supplementation of the speech context.

[0035] The voice analysis unit can analyze the frequency and patterns of user utterances during voice analysis and determine task priorities. For example, the voice analysis unit can prioritize registering tasks that the user frequently mentions. For example, the voice analysis unit can analyze user utterance patterns and prioritize reminders for important tasks. For example, the voice analysis unit can prioritize managing tasks that the user mentions during specific time periods. This allows for the priority management of important tasks by analyzing the frequency and patterns of utterances. Some or all of the above processing in the voice analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice analysis unit can input user utterance data into a generative AI and have the generative AI perform an analysis of the frequency and patterns of utterances.

[0036] The voice analysis unit can evaluate the relevance of utterances while considering the user's geographical location information during voice analysis. For example, if the user is in the office, the voice analysis unit will prioritize evaluating work-related utterances. If the user is at home, the voice analysis unit will prioritize evaluating private utterances. If the user is traveling, the voice analysis unit will prioritize evaluating travel-related utterances. By considering geographical location information, the relevance of utterances can be evaluated, enabling more accurate task management. Some or all of the above processing in the voice analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the evaluation of utterance relevance.

[0037] The voice analysis unit can supplement the intent of a statement by referring to the user's social media activity during voice analysis. For example, the voice analysis unit can supplement the intent of a statement based on information shared by the user on social media. For example, the voice analysis unit can evaluate the relevance of a statement based on the user's social media activity history. For example, the voice analysis unit can supplement the content of a statement based on the user's interactions with friends on social media. This allows for more accurate task management by supplementing the intent of a statement by referring to social media activity. Some or all of the above processing in the voice analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice analysis unit can input the user's social media activity data into a generative AI and have the generative AI perform the supplementation of the intent of the statement.

[0038] The task management unit can analyze a user's past task completion history and select the optimal task management method during task management. For example, the task management unit can select the optimal reminder method based on the user's past task completion history. For example, the task management unit can propose an efficient task management method based on the user's past task completion history. For example, the task management unit can analyze a user's past task completion history and dynamically change task priorities. This allows the optimal task management method to be selected by analyzing past task completion history. Some or all of the above processes in the task management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the task management unit can input the user's past task completion history data into a generative AI and have the generative AI select the optimal task management method.

[0039] The task management unit can dynamically change task priorities while managing tasks, taking into account the user's current schedule. For example, the task management unit dynamically changes task priorities based on the user's current schedule. For example, the task management unit adjusts task reminder timings to match the user's schedule. For example, the task management unit re-evaluates the importance of tasks, taking the user's schedule into account. This allows for dynamic changes to task priorities by considering the current schedule. Some or all of the above processes in the task management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the task management unit can input user schedule data into a generative AI and have the generative AI perform dynamic changes to task priorities.

[0040] The task management unit can evaluate the relevance of tasks while considering the user's geographical location information. For example, if the user is in the office, the task management unit will prioritize displaying work-related tasks. If the user is at home, the task management unit will prioritize displaying personal tasks. If the user is traveling, the task management unit will prioritize displaying travel-related tasks. By considering geographical location information, the task management unit can evaluate the relevance of tasks and enable more accurate task management. Some or all of the above processing in the task management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the task management unit can input the user's geographical location information into a generative AI and have the generative AI perform the evaluation of task relevance.

[0041] The task management unit can supplement task content by referring to the user's social media activity during task management. For example, the task management unit can supplement task content based on information shared by the user on social media. For example, the task management unit can evaluate the relevance of tasks based on the user's social media activity history. For example, the task management unit can supplement task content based on the user's interactions with friends on social media. This allows for more accurate task management by supplementing task content through reference to social media activity. Some or all of the above processes in the task management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the task management unit can input the user's social media activity data into a generative AI and have the generative AI perform the task content supplementation.

[0042] The learning unit can analyze the user's past hobbies and preferences during the learning process and predict future preferences. For example, the learning unit can analyze the user's past hobbies and predict future preferences. For example, the learning unit can suggest new hobbies based on the user's past preferences. For example, the learning unit can analyze the user's past preferences and make suggestions that match future preferences. In this way, by analyzing past hobbies and preferences, it becomes possible to predict future preferences and make more appropriate suggestions. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input data on the user's past hobbies and preferences into a generative AI and have the generative AI perform predictions of future preferences.

[0043] The learning unit can optimize the learning algorithm during learning by considering the user's lifestyle patterns. For example, the learning unit optimizes the learning algorithm based on the user's lifestyle patterns. For example, the learning unit adjusts the timing of providing learning data to match the user's lifestyle patterns. For example, the learning unit dynamically changes the learning algorithm by considering the user's lifestyle patterns. This optimizes the learning algorithm by considering lifestyle patterns, enabling more effective learning. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input user lifestyle pattern data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0044] The learning unit can customize its suggestions during the learning process by taking into account the user's geographical location. For example, if the user is traveling, the learning unit will make suggestions related to their travel destination. If the user is at home, the learning unit will make suggestions that can be enjoyed at home. If the user is at the office, the learning unit will make suggestions related to work. By taking geographical location into account, the learning unit can customize its suggestions and make more appropriate recommendations. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the user's geographical location information into a generative AI and have the generative AI perform the customization of the suggestions.

[0045] The learning unit can supplement its suggestions by referencing the user's social media activity during the learning process. For example, the learning unit can supplement suggestions based on information the user has shared on social media. For example, the learning unit can customize suggestions based on the user's social media activity history. For example, the learning unit can supplement suggestions based on the user's interactions with friends on social media. This allows the learning unit to supplement suggestions by referencing social media activity, enabling more appropriate suggestions. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI and have the generative AI perform the supplementation of suggestions.

[0046] The image analysis unit can extract text information from images during image analysis and reflect it in tasks and schedules. For example, the image analysis unit can extract the date and time of a meeting from an image and reflect it in the schedule. For example, the image analysis unit can extract a task list from an image and add it to the task list. For example, the image analysis unit can extract event information from an image and reflect it in the schedule. In this way, by extracting text information from images, it is possible to reflect it in tasks and schedules. Some or all of the above processing in the image analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the image analysis unit can input image data into a generative AI and have the generative AI perform the extraction of text information.

[0047] The image analysis unit can supplement the analysis results by considering the location and time the image was taken during image analysis. For example, the image analysis unit can suggest relevant tasks based on the location where the image was taken. For example, the image analysis unit can reflect the time the image was taken in the schedule. For example, the image analysis unit can supplement the analysis results based on the location and time the image was taken. In this way, by considering the location and time the image was taken, the analysis results can be supplemented and more accurate information can be provided. Some or all of the above processing in the image analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the image analysis unit can input the image metadata into a generative AI and have the generative AI perform the supplementation of the analysis results.

[0048] The image analysis unit can evaluate the relevance of images while considering the user's geographical location information during image analysis. For example, if the user is traveling, the image analysis unit will prioritize displaying images of the travel destination. For example, if the user is at home, the image analysis unit will prioritize displaying images taken at home. For example, if the user is at the office, the image analysis unit will prioritize displaying work-related images. By considering geographical location information, the relevance of images can be evaluated, enabling the provision of more accurate information. Some or all of the above processing in the image analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the image analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the evaluation of image relevance.

[0049] The image analysis unit can supplement the intent of an image by referring to the user's social media activity during image analysis. For example, the image analysis unit can supplement the intent based on images shared by the user on social media. For example, the image analysis unit can evaluate the relevance of an image based on the user's social media activity history. For example, the image analysis unit can supplement the intent of an image based on the user's interactions with friends on social media. This allows for supplementing the intent of an image by referring to social media activity, enabling the provision of more accurate information. Some or all of the above processing in the image analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the image analysis unit can input the user's social media activity data into a generative AI and have the generative AI perform the supplementation of the intent of the image.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The earphone-integrated schedule and task management system can also be equipped with a music analysis unit that analyzes the user's musical preferences. For example, the music analysis unit uses sensors built into the earphones to analyze the user's musical preferences and suggest music they might like. For instance, if the user wants to relax, the music analysis unit can suggest relaxing music. Similarly, if the user wants to concentrate, the music analysis unit can suggest music that enhances concentration. This allows the system to analyze the user's musical preferences and suggest appropriate music.

[0052] The earphone-integrated schedule and task management system can also be equipped with a reading analysis unit that analyzes the user's reading preferences. For example, the reading analysis unit uses a camera built into the earphones to analyze the user's reading content and suggest books they might like. For instance, if the user wants to relax, the reading analysis unit can suggest relaxing books. For example, if the user wants to deepen their knowledge, the reading analysis unit can suggest specialized books. This allows the system to analyze the user's reading preferences and suggest appropriate books.

[0053] The earphone-integrated schedule and task management system can also be equipped with a travel analysis unit that analyzes the user's travel preferences. For example, the travel analysis unit uses a camera built into the earphones to analyze the user's travel destinations and suggest preferred destinations. For instance, if the user wants to relax, the travel analysis unit can suggest relaxing destinations. Similarly, if the user is seeking adventure, the travel analysis unit can suggest active destinations. This allows the system to analyze the user's travel preferences and suggest appropriate destinations.

[0054] The earphone-integrated schedule and task management system can also be equipped with a driving analysis unit that analyzes the user's driving habits. The driving analysis unit, for example, uses sensors built into the earphones to analyze the user's driving habits and encourage safe driving. For example, the driving analysis unit can remind the user to take a break if they are driving for extended periods. For example, the driving analysis unit can provide safe driving advice if the user frequently uses sudden braking. This allows for the analysis of the user's driving habits and promotes safe driving.

[0055] The earphone-integrated schedule and task management system can also be equipped with a shopping analysis unit that analyzes the user's shopping preferences. For example, the shopping analysis unit uses a camera built into the earphones to analyze the user's purchases and suggest products they might like. For instance, if the user wants to relax, the shopping analysis unit can suggest products that promote relaxation. Similarly, if the user is health-conscious, the shopping analysis unit can suggest health-related products. This allows for the analysis of the user's shopping preferences and the suggestion of appropriate products.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The voice analysis unit analyzes the user's speech. For example, it uses generative AI to analyze the user's speech and speech recognition technology to convert the speech into text data. It can also use natural language processing technology to analyze the content of the speech and evaluate the importance of the task. For example, if the user says, "Tomorrow's meeting is at 10 o'clock," the unit analyzes that information and adds it to the schedule. Step 2: The task management unit manages tasks based on the information analyzed by the voice analysis unit. For example, it uses a generation AI to determine task priorities and send reminders. If a user says, "I'm having dinner with a friend next Friday," that information is added to the task list and a reminder is sent. The task management unit can manage the user's schedule and prioritize reminders for important tasks. Step 3: The learning unit learns the user's hobbies and preferences. For example, it uses generative AI to analyze the user's statements and actions to learn their hobbies and preferences. If the user says "I like movies," the learning unit will use that information to suggest movies that match the user's preferences. The learning unit can learn the user's hobbies and preferences and suggest things that the user will like. Step 4: The image analysis unit reads images from the camera. For example, it uses a generation AI to analyze the images read by the camera and add them to a list. If a user takes a picture of a restaurant menu, the unit analyzes that information and adds it to the list. Image recognition technology can be used to extract text information from images and reflect it in tasks and schedules.

[0058] (Example of form 2) The earphone-integrated schedule and task management system according to an embodiment of the present invention is a system that analyzes user speech and manages tasks and schedules. This system is equipped with a generative AI that saves the user's voice and automatically records and categorizes speech in daily life, thereby preventing tasks from being missed or forgotten. For example, simply by wearing the earphones and going about daily life, the earphones listen to the user's voice, and the generative AI analyzes the content. For example, if the user says, "Tomorrow's meeting is at 10 o'clock," that information is automatically recorded and added to the schedule. Next, the generative AI manages the user's tasks and schedule based on the recorded information. For example, if the user says, "I'm having dinner with a friend next Friday," that information is automatically added to the task list and a reminder is sent. Furthermore, the generative AI learns the user's hobbies and preferences and suggests things they might like. For example, if the user says, "I like movies," the generative AI can suggest movies that match the user's preferences. The earphones are also equipped with a camera that can read images and text. For example, if the user takes a picture of a restaurant menu, that information is automatically imported into the app and added to the list. This system allows users to manage tasks and schedules in their daily lives without any special operations. After initial setup, the generating AI learns and functions as a highly accurate personal assistant. This prevents tasks from being overlooked or forgotten, reducing mental and physical stress. The integrated earphone schedule and task management system analyzes user speech and images to provide task management and hobby suggestions.

[0059] The earphone-integrated schedule and task management system according to this embodiment comprises a voice analysis unit, a task management unit, a learning unit, and an image analysis unit. The voice analysis unit analyzes the user's speech. The voice analysis unit analyzes the user's speech using, for example, a generative AI and provides the information to the task management unit. The voice analysis unit can convert the user's speech into text data using, for example, speech recognition technology. The voice analysis unit can also analyze the content of the speech using natural language processing technology and evaluate the importance of the tasks. For example, if the user says, "Tomorrow's meeting is at 10 o'clock," the voice analysis unit analyzes that information and adds it to the schedule. The task management unit manages tasks based on the information analyzed by the voice analysis unit. The task management unit determines the priority of tasks and issues reminders using, for example, a generative AI. For example, if the user says, "I'm having dinner with a friend next Friday," the task management unit adds that information to the task list and issues a reminder. The task management unit manages the user's schedule and can prioritize reminders for important tasks. The learning unit learns the user's hobbies and preferences. For example, the learning unit uses generative AI to analyze the user's statements and actions to learn their hobbies and preferences. For example, if the user says "I like movies," the learning unit uses that information to suggest movies that match the user's preferences. The learning unit can learn the user's hobbies and preferences and suggest things they like. The image analysis unit reads images with the camera. For example, the image analysis unit uses generative AI to analyze the images read by the camera and add them to a list. For example, if the user takes a picture of a restaurant menu, the image analysis unit analyzes that information and adds it to a list. The image analysis unit can use image recognition technology to extract text information from images and reflect it in tasks and schedules. As a result, the earphone-integrated schedule and task management system according to this embodiment can analyze the user's statements and images to manage tasks and suggest hobbies.

[0060] The voice analysis unit analyzes the user's speech. For example, it uses generative AI to analyze the user's speech and provides the information to the task management unit. Specifically, the voice analysis unit uses high-precision speech recognition technology to convert the user's speech into text data in real time. This speech recognition technology has a noise-canceling function, which removes ambient noise and can accurately capture the user's speech. Furthermore, the voice analysis unit makes full use of natural language processing technology to analyze the content of the speech in detail and evaluate the importance and urgency of the task. For example, if the user says, "Tomorrow's meeting is at 10 o'clock," the voice analysis unit converts that speech into text data, extracts the date and time of the meeting, and adds it to the schedule. Also, if the user says, "I'm having dinner with a friend next Friday," the unit analyzes that information, adds it to the task list, and sends a reminder at the appropriate time. The voice analysis unit can also use generative AI to refer to background information and related information about the speech in order to understand the context and intent of the user's speech. For example, if a user says, "When is the deadline for the next project?", the voice analysis unit will refer to past schedules and task information and provide an appropriate answer. This allows the voice analysis unit to accurately and quickly analyze the user's speech and provide the task management unit with the necessary information.

[0061] The task management unit manages tasks based on information analyzed by the voice analysis unit. For example, the task management unit uses generative AI to determine task priorities and send reminders. Specifically, the task management unit centrally manages the user's schedule and task list and has a function to prioritize reminders for important tasks. For example, if a user says, "I'm having dinner with a friend next Friday," that information is added to the task list and a reminder is sent the day before the meal. The task management unit can also automatically adjust task priorities based on the user's past actions and statements. For example, if a user says, "Tomorrow's meeting is important," that meeting is reminded of as a priority over other tasks. Furthermore, the task management unit calculates the optimal reminder timing based on the user's schedule and supports the user in completing tasks most efficiently. For example, if a user says, "I'll prepare for the presentation at 3 PM," a reminder is sent before that preparation time so that the user doesn't forget to start preparing. The task management unit can learn the user's statements and behavior patterns using generative AI and continuously improve the accuracy and timing of reminders. This allows the task management department to efficiently manage users' schedules and support them in ensuring that important tasks are completed reliably.

[0062] The learning unit learns the user's hobbies and preferences. For example, it uses generative AI to analyze the user's statements and actions to learn their hobbies and preferences. Specifically, the learning unit analyzes the user's statements and behavioral history in detail to understand the user's interests and concerns. For example, if a user says "I like movies," it will suggest movies that match the user's preferences based on that information. Also, if a user says "I recently started jogging," it can suggest jogging-related events and recommended routes based on that information. The learning unit can continuously learn the user's statements and behavioral patterns using generative AI and respond to changes in the user's hobbies and preferences. For example, if a user starts a new hobby, it will quickly learn that information and provide information related to the new hobby. Furthermore, based on the user's past statements and behavioral history, the learning unit can also suggest new hobbies and activities that the user might be interested in. For example, if a user says "I like music," it will suggest new artists and concert information based on that information. By learning the user's hobbies and preferences in detail and making suggestions that match the user's lifestyle, the learning unit can improve user satisfaction.

[0063] The image analysis unit reads images from the camera. For example, it uses generative AI to analyze the images read by the camera and add them to a list. Specifically, the image analysis unit uses high-precision image recognition technology to analyze text information and objects within images captured by the camera. For example, if a user photographs a restaurant menu, the unit can extract the text information from the image and add it to a list. Furthermore, the image analysis unit can recognize specific objects or scenes from images taken by the user and automatically generate related tasks and schedules. For example, if a user photographs a whiteboard at a meeting, the unit can analyze the text information within the image and reflect it in the meeting minutes or task list. Also, if a user photographs a tourist destination, the unit can suggest information about the tourist destination and recommended sightseeing routes based on the image. By using generative AI to analyze information within images in detail and reflecting it in the user's task management and schedule, the image analysis unit improves user convenience. Furthermore, the image analysis unit can learn from the user's past image data and make suggestions based on the user's preferences and behavioral patterns. This allows the image analysis unit to utilize information obtained from images, in addition to user statements and actions, to provide more accurate task management and schedule suggestions.

[0064] The voice analysis unit can estimate the user's emotions and evaluate the importance of their statements based on the estimated emotions. For example, if the user is nervous, the voice analysis unit will highly value the importance of their statements and immediately register them as tasks. For example, if the user is relaxed, the voice analysis unit will analyze the content of their statements in detail and evaluate their importance. For example, if the user is in a hurry, the voice analysis unit will quickly evaluate the importance based on the frequency and content of their statements. This allows for the prioritization of important tasks by evaluating the importance of statements based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the voice analysis unit may be performed using AI, for example, or without AI. For example, the voice analysis unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation.

[0065] The voice analysis unit can analyze the background sounds of a user's speech during voice analysis and supplement the context of the speech. For example, if a user speaks in a cafe, the voice analysis unit recognizes the cafe environment from the background sounds and supplements the context of the speech. For example, if a user speaks in a conference room, the voice analysis unit recognizes the meeting situation from the background sounds and evaluates the importance of the speech. For example, if a user speaks at home, the voice analysis unit recognizes a relaxed environment from the background sounds and analyzes the content of the speech in detail. By analyzing background sounds, the context of the speech can be supplemented, enabling more accurate task management. Some or all of the above processing in the voice analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice analysis unit can input the user's voice data and background sound data into a generative AI and have the generative AI perform the supplementation of the speech context.

[0066] The voice analysis unit can analyze the frequency and patterns of user utterances during voice analysis and determine task priorities. For example, the voice analysis unit can prioritize registering tasks that the user frequently mentions. For example, the voice analysis unit can analyze user utterance patterns and prioritize reminders for important tasks. For example, the voice analysis unit can prioritize managing tasks that the user mentions during specific time periods. This allows for the priority management of important tasks by analyzing the frequency and patterns of utterances. Some or all of the above processing in the voice analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice analysis unit can input user utterance data into a generative AI and have the generative AI perform an analysis of the frequency and patterns of utterances.

[0067] The voice analysis unit can estimate the user's emotions and filter the analysis results of the utterances based on the estimated emotions. For example, if the user is stressed, the voice analysis unit can filter only the important utterances and register them as tasks. For example, if the user is relaxed, the voice analysis unit can filter the detailed utterances and register them as tasks. For example, if the user is in a hurry, the voice analysis unit can prioritize filtering and registering concise utterances as tasks. This allows for the priority management of important tasks by filtering the analysis results of utterances based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the voice analysis unit may be performed using a generative AI, or not using a generative AI. For example, the voice analysis unit can input the user's voice data into a generative AI and have the generative AI perform emotion estimation and utterance filtering.

[0068] The voice analysis unit can evaluate the relevance of utterances while considering the user's geographical location information during voice analysis. For example, if the user is in the office, the voice analysis unit will prioritize evaluating work-related utterances. If the user is at home, the voice analysis unit will prioritize evaluating private utterances. If the user is traveling, the voice analysis unit will prioritize evaluating travel-related utterances. By considering geographical location information, the relevance of utterances can be evaluated, enabling more accurate task management. Some or all of the above processing in the voice analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the evaluation of utterance relevance.

[0069] The voice analysis unit can supplement the intent of a statement by referring to the user's social media activity during voice analysis. For example, the voice analysis unit can supplement the intent of a statement based on information shared by the user on social media. For example, the voice analysis unit can evaluate the relevance of a statement based on the user's social media activity history. For example, the voice analysis unit can supplement the content of a statement based on the user's interactions with friends on social media. This allows for more accurate task management by supplementing the intent of a statement by referring to social media activity. Some or all of the above processing in the voice analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the voice analysis unit can input the user's social media activity data into a generative AI and have the generative AI perform the supplementation of the intent of the statement.

[0070] The task management unit can estimate the user's emotions and adjust the task reminder method based on the estimated emotions. For example, if the user is stressed, the task management unit will provide less frequent reminders. For example, if the user is relaxed, the task management unit will provide detailed reminders. For example, if the user is in a hurry, the task management unit will provide concise reminders. This allows for more effective task management by adjusting the reminder method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task management unit may be performed using a generative AI, or not. For example, the task management unit can input user emotion data into a generative AI and have the generative AI adjust the reminder method.

[0071] The task management unit can analyze a user's past task completion history and select the optimal task management method during task management. For example, the task management unit can select the optimal reminder method based on the user's past task completion history. For example, the task management unit can propose an efficient task management method based on the user's past task completion history. For example, the task management unit can analyze a user's past task completion history and dynamically change task priorities. This allows the optimal task management method to be selected by analyzing past task completion history. Some or all of the above processes in the task management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the task management unit can input the user's past task completion history data into a generative AI and have the generative AI select the optimal task management method.

[0072] The task management unit can dynamically change task priorities while managing tasks, taking into account the user's current schedule. For example, the task management unit dynamically changes task priorities based on the user's current schedule. For example, the task management unit adjusts task reminder timings to match the user's schedule. For example, the task management unit re-evaluates the importance of tasks, taking the user's schedule into account. This allows for dynamic changes to task priorities by considering the current schedule. Some or all of the above processes in the task management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the task management unit can input user schedule data into a generative AI and have the generative AI perform dynamic changes to task priorities.

[0073] The task management unit can estimate the user's emotions and adjust the task display method based on the estimated emotions. For example, if the user is stressed, the task management unit provides a simple display method. For example, if the user is relaxed, the task management unit provides a display method that includes detailed information. For example, if the user is in a hurry, the task management unit provides a display method that gets straight to the point. By adjusting the task display method based on the user's emotions, more effective task management becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the task management unit may be performed using a generative AI, for example, or without a generative AI. For example, the task management unit can input user emotion data into a generative AI and have the generative AI adjust the task display method.

[0074] The task management unit can evaluate the relevance of tasks while considering the user's geographical location information. For example, if the user is in the office, the task management unit will prioritize displaying work-related tasks. If the user is at home, the task management unit will prioritize displaying personal tasks. If the user is traveling, the task management unit will prioritize displaying travel-related tasks. By considering geographical location information, the task management unit can evaluate the relevance of tasks and enable more accurate task management. Some or all of the above processing in the task management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the task management unit can input the user's geographical location information into a generative AI and have the generative AI perform the evaluation of task relevance.

[0075] The task management unit can supplement task content by referring to the user's social media activity during task management. For example, the task management unit can supplement task content based on information shared by the user on social media. For example, the task management unit can evaluate the relevance of tasks based on the user's social media activity history. For example, the task management unit can supplement task content based on the user's interactions with friends on social media. This allows for more accurate task management by supplementing task content through reference to social media activity. Some or all of the above processes in the task management unit may be performed using, for example, a generative AI, or without a generative AI. For example, the task management unit can input the user's social media activity data into a generative AI and have the generative AI perform the task content supplementation.

[0076] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit will select training data related to relaxation. For example, if the user is stressed, the learning unit will select training data related to stress reduction. For example, if the user is excited, the learning unit will select training data to calm the excitement. This allows for more effective learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI, or not using a generative AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data.

[0077] The learning unit can analyze the user's past hobbies and preferences during the learning process and predict future preferences. For example, the learning unit can analyze the user's past hobbies and predict future preferences. For example, the learning unit can suggest new hobbies based on the user's past preferences. For example, the learning unit can analyze the user's past preferences and make suggestions that match future preferences. In this way, by analyzing past hobbies and preferences, it becomes possible to predict future preferences and make more appropriate suggestions. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the learning unit can input data on the user's past hobbies and preferences into a generative AI and have the generative AI perform predictions of future preferences.

[0078] The learning unit can optimize the learning algorithm during learning by considering the user's lifestyle patterns. For example, the learning unit optimizes the learning algorithm based on the user's lifestyle patterns. For example, the learning unit adjusts the timing of providing learning data to match the user's lifestyle patterns. For example, the learning unit dynamically changes the learning algorithm by considering the user's lifestyle patterns. This optimizes the learning algorithm by considering lifestyle patterns, enabling more effective learning. Some or all of the above processes in the learning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the learning unit can input user lifestyle pattern data into a generative AI and have the generative AI perform the optimization of the learning algorithm.

[0079] The learning unit can estimate the user's emotions and adjust the suggestions based on the estimated emotions. For example, if the user is relaxed, the learning unit will make suggestions related to relaxation. For example, if the user is stressed, the learning unit will make suggestions related to stress reduction. For example, if the user is excited, the learning unit will make suggestions to calm the excitement. By adjusting the suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using a generative AI, or not using a generative AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI adjust the suggestions.

[0080] The learning unit can customize its suggestions during the learning process by taking into account the user's geographical location. For example, if the user is traveling, the learning unit will make suggestions related to their travel destination. If the user is at home, the learning unit will make suggestions that can be enjoyed at home. If the user is at the office, the learning unit will make suggestions related to work. By taking geographical location into account, the learning unit can customize its suggestions and make more appropriate recommendations. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or it may be performed without a generative AI. For example, the learning unit can input the user's geographical location information into a generative AI and have the generative AI perform the customization of the suggestions.

[0081] The learning unit can supplement its suggestions by referencing the user's social media activity during the learning process. For example, the learning unit can supplement suggestions based on information the user has shared on social media. For example, the learning unit can customize suggestions based on the user's social media activity history. For example, the learning unit can supplement suggestions based on the user's interactions with friends on social media. This allows the learning unit to supplement suggestions by referencing social media activity, enabling more appropriate suggestions. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input the user's social media activity data into a generative AI and have the generative AI perform the supplementation of suggestions.

[0082] The image analysis unit can estimate the user's emotions and filter the image analysis results based on the estimated emotions. For example, if the user is stressed, the image analysis unit will filter and analyze only the important images. For example, if the user is relaxed, the image analysis unit will perform a detailed image analysis. For example, if the user is in a hurry, the image analysis unit will perform a concise image analysis. This allows for the priority management of important information by filtering the image analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the image analysis unit can input user emotion data into a generative AI and have the generative AI perform filtering of the image analysis results.

[0083] The image analysis unit can extract text information from images during image analysis and reflect it in tasks and schedules. For example, the image analysis unit can extract the date and time of a meeting from an image and reflect it in the schedule. For example, the image analysis unit can extract a task list from an image and add it to the task list. For example, the image analysis unit can extract event information from an image and reflect it in the schedule. In this way, by extracting text information from images, it is possible to reflect it in tasks and schedules. Some or all of the above processing in the image analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the image analysis unit can input image data into a generative AI and have the generative AI perform the extraction of text information.

[0084] The image analysis unit can supplement the analysis results by considering the location and time the image was taken during image analysis. For example, the image analysis unit can suggest relevant tasks based on the location where the image was taken. For example, the image analysis unit can reflect the time the image was taken in the schedule. For example, the image analysis unit can supplement the analysis results based on the location and time the image was taken. In this way, by considering the location and time the image was taken, the analysis results can be supplemented and more accurate information can be provided. Some or all of the above processing in the image analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the image analysis unit can input the image metadata into a generative AI and have the generative AI perform the supplementation of the analysis results.

[0085] The image analysis unit can estimate the user's emotions and adjust the image display method based on the estimated emotions. For example, if the user is stressed, the image analysis unit provides a simple display method. For example, if the user is relaxed, the image analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the image analysis unit provides a display method that gets straight to the point. By adjusting the image display method based on the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the image analysis unit may be performed using a generative AI, or not using a generative AI. For example, the image analysis unit can input user emotion data into a generative AI and have the generative AI adjust the image display method.

[0086] The image analysis unit can evaluate the relevance of images while considering the user's geographical location information during image analysis. For example, if the user is traveling, the image analysis unit will prioritize displaying images of the travel destination. For example, if the user is at home, the image analysis unit will prioritize displaying images taken at home. For example, if the user is at the office, the image analysis unit will prioritize displaying work-related images. By considering geographical location information, the relevance of images can be evaluated, enabling the provision of more accurate information. Some or all of the above processing in the image analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the image analysis unit can input the user's geographical location information into a generative AI and have the generative AI perform the evaluation of image relevance.

[0087] The image analysis unit can supplement the intent of an image by referring to the user's social media activity during image analysis. For example, the image analysis unit can supplement the intent based on images shared by the user on social media. For example, the image analysis unit can evaluate the relevance of an image based on the user's social media activity history. For example, the image analysis unit can supplement the intent of an image based on the user's interactions with friends on social media. This allows for supplementing the intent of an image by referring to social media activity, enabling the provision of more accurate information. Some or all of the above processing in the image analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the image analysis unit can input the user's social media activity data into a generative AI and have the generative AI perform the supplementation of the intent of the image.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] The earphone-integrated schedule and task management system can also include a health management unit that monitors the user's health status. For example, the health management unit uses sensors built into the earphones to measure the user's heart rate and body temperature, and evaluate their health. For instance, if the user's heart rate is high, the health management unit can determine that they are experiencing stress and suggest tasks to help them relax. Similarly, if the user's body temperature is high, the health management unit can suggest they are unwell and remind them to rest. This allows for monitoring the user's health status and enables appropriate task management and suggestions.

[0090] The earphone-integrated schedule and task management system can also be equipped with a sleep analysis unit that analyzes the user's sleep patterns. The sleep analysis unit, for example, uses sensors built into the earphones to monitor the user's sleep state and evaluate sleep quality. For example, if the user's sleep is light, the sleep analysis unit can suggest relaxing music. For example, if the user's sleep duration is short, the sleep analysis unit can remind the user to go to bed earlier. This allows for analysis of the user's sleep patterns, enabling appropriate task management and suggestions.

[0091] The earphone-integrated schedule and task management system can also be equipped with an exercise analysis unit that analyzes the user's activity level. The exercise analysis unit, for example, uses sensors built into the earphones to measure the user's steps and activity level, and evaluates the quality of exercise. For example, if the user's activity level is low, the exercise analysis unit can suggest tasks to encourage exercise. For example, if the user's activity level is high, the exercise analysis unit can remind the user to rest. This allows for analysis of the user's activity level and enables appropriate task management and suggestions.

[0092] The earphone-integrated schedule and task management system can also be equipped with a meal analysis unit that analyzes the user's diet. The meal analysis unit, for example, uses a camera built into the earphones to photograph the user's meals and evaluate their nutritional balance. If the user's diet is unbalanced, the meal analysis unit can suggest a more balanced meal. If the user is eating too much, the meal analysis unit can remind them to eat an appropriate amount. This allows for analysis of the user's diet and enables appropriate task management and suggestions.

[0093] The earphone-integrated schedule and task management system can also include a stress analysis unit that analyzes the user's stress level. The stress analysis unit measures the user's stress level using sensors built into the earphones, for example, and evaluates the degree of stress. For example, if the user's stress level is high, the stress analysis unit can suggest tasks to help them relax. For example, if the user's stress level is low, the stress analysis unit can suggest tasks to improve their concentration. This allows for analysis of the user's stress level and enables appropriate task management and suggestions.

[0094] The earphone-integrated schedule and task management system can also be equipped with a music analysis unit that analyzes the user's musical preferences. For example, the music analysis unit uses sensors built into the earphones to analyze the user's musical preferences and suggest music they might like. For instance, if the user wants to relax, the music analysis unit can suggest relaxing music. Similarly, if the user wants to concentrate, the music analysis unit can suggest music that enhances concentration. This allows the system to analyze the user's musical preferences and suggest appropriate music.

[0095] The earphone-integrated schedule and task management system can also be equipped with a reading analysis unit that analyzes the user's reading preferences. For example, the reading analysis unit uses a camera built into the earphones to analyze the user's reading content and suggest books they might like. For instance, if the user wants to relax, the reading analysis unit can suggest relaxing books. For example, if the user wants to deepen their knowledge, the reading analysis unit can suggest specialized books. This allows the system to analyze the user's reading preferences and suggest appropriate books.

[0096] The earphone-integrated schedule and task management system can also be equipped with a travel analysis unit that analyzes the user's travel preferences. For example, the travel analysis unit uses a camera built into the earphones to analyze the user's travel destinations and suggest preferred destinations. For instance, if the user wants to relax, the travel analysis unit can suggest relaxing destinations. Similarly, if the user is seeking adventure, the travel analysis unit can suggest active destinations. This allows the system to analyze the user's travel preferences and suggest appropriate destinations.

[0097] The earphone-integrated schedule and task management system can also be equipped with a driving analysis unit that analyzes the user's driving habits. The driving analysis unit, for example, uses sensors built into the earphones to analyze the user's driving habits and encourage safe driving. For example, the driving analysis unit can remind the user to take a break if they are driving for extended periods. For example, the driving analysis unit can provide safe driving advice if the user frequently uses sudden braking. This allows for the analysis of the user's driving habits and promotes safe driving.

[0098] The earphone-integrated schedule and task management system can also be equipped with a shopping analysis unit that analyzes the user's shopping preferences. For example, the shopping analysis unit uses a camera built into the earphones to analyze the user's purchases and suggest products they might like. For instance, if the user wants to relax, the shopping analysis unit can suggest products that promote relaxation. Similarly, if the user is health-conscious, the shopping analysis unit can suggest health-related products. This allows for the analysis of the user's shopping preferences and the suggestion of appropriate products.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The voice analysis unit analyzes the user's speech. For example, it uses generative AI to analyze the user's speech and speech recognition technology to convert the speech into text data. It can also use natural language processing technology to analyze the content of the speech and evaluate the importance of the task. For example, if the user says, "Tomorrow's meeting is at 10 o'clock," the unit analyzes that information and adds it to the schedule. Step 2: The task management unit manages tasks based on the information analyzed by the voice analysis unit. For example, it uses a generation AI to determine task priorities and send reminders. If a user says, "I'm having dinner with a friend next Friday," that information is added to the task list and a reminder is sent. The task management unit can manage the user's schedule and prioritize reminders for important tasks. Step 3: The learning unit learns the user's hobbies and preferences. For example, it uses generative AI to analyze the user's statements and actions to learn their hobbies and preferences. If the user says "I like movies," the learning unit will use that information to suggest movies that match the user's preferences. The learning unit can learn the user's hobbies and preferences and suggest things that the user will like. Step 4: The image analysis unit reads images from the camera. For example, it uses a generation AI to analyze the images read by the camera and add them to a list. If a user takes a picture of a restaurant menu, the unit analyzes that information and adds it to the list. Image recognition technology can be used to extract text information from images and reflect it in tasks and schedules.

[0101] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0102] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0103] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0104] Each of the multiple elements described above, including the voice analysis unit, task management unit, learning unit, and image analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the voice analysis unit detects the user's speech using the microphone 38B of the smart device 14, and the AI ​​generated by the control unit 46A analyzes the speech. The task management unit is implemented in the specific processing unit 290 of the data processing unit 12, and manages tasks and provides reminders based on the analyzed information. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, and learns the user's hobbies and preferences and suggests items that the user likes. The image analysis unit reads images using the camera 42 of the smart device 14, analyzes them by the control unit 46A, and adds them to a list. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0111] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0112] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0113] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0114] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0115] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0116] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the voice analysis unit, task management unit, learning unit, and image analysis unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the voice analysis unit detects the user's speech using the microphone 238 of the smart glasses 214, and the AI ​​generated by the control unit 46A analyzes the speech. The task management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and manages tasks and provides reminders based on the analyzed information. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and learns the user's hobbies and preferences and suggests items that the user likes. The image analysis unit reads images using the camera 42 of the smart glasses 214, analyzes them by the control unit 46A, and adds them to a list. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the voice analysis unit, task management unit, learning unit, and image analysis unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the voice analysis unit detects the user's speech using the microphone 238 of the headset terminal 314, and the AI ​​generated by the control unit 46A analyzes the speech. The task management unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and manages tasks and provides reminders based on the analyzed information. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and learns the user's hobbies and preferences and suggests items that the user likes. The image analysis unit reads images using the camera 42 of the headset terminal 314, analyzes them by the control unit 46A, and adds them to a list. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0145] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0146] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0147] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0148] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the voice analysis unit, task management unit, learning unit, and image analysis unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the voice analysis unit detects the user's speech using the microphone 238 of the robot 414, and the control unit 46A generates an AI that analyzes the speech. The task management unit is implemented in the specific processing unit 290 of the data processing unit 12, and manages tasks and provides reminders based on the analyzed information. The learning unit is implemented in the specific processing unit 290 of the data processing unit 12, and learns the user's hobbies and preferences and suggests items that the user likes. The image analysis unit reads images using the camera 42 of the robot 414, analyzes them using the control unit 46A, and adds them to a list. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0154] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0156] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0157] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0158] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0162] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0163] 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.

[0164] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0165] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0166] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0167] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0169] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0170] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0171] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0172] (Note 1) A voice analysis unit that analyzes the user's speech, A task management unit manages tasks based on the information analyzed by the aforementioned voice analysis unit, A learning unit that learns the user's hobbies and preferences, It comprises an image analysis unit that reads images from a camera, A system characterized by the following features. (Note 2) The aforementioned voice analysis unit, It estimates the user's emotions and evaluates the importance of their statements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned voice analysis unit, During speech analysis, the system analyzes the background sounds of the user's speech to supplement the context of the speech. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned voice analysis unit, During voice analysis, the frequency and patterns of user utterances are analyzed to determine task priorities. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned voice analysis unit, It estimates the user's emotions and filters the analysis results of the statements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned voice analysis unit, During voice analysis, the relevance of utterances is evaluated by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned voice analysis unit, During voice analysis, the system supplements the intent behind the speech by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned task management unit, It estimates the user's emotions and adjusts the task reminder method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned task management unit, During task management, the system analyzes the user's past task completion history to select the optimal task management method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned task management unit, When managing tasks, dynamically change task priorities while considering the user's current schedule. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned task management unit, It estimates the user's emotions and adjusts how tasks are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned task management unit, When managing tasks, the relevance of tasks is evaluated by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned task management unit, When managing tasks, refer to the user's social media activity to supplement the task details. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, During the learning process, the system analyzes the user's past hobbies and preferences and predicts their future preferences. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During training, the learning algorithm is optimized by considering the user's lifestyle patterns. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned learning unit, During training, the suggestions are customized to take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, During learning, the system supplements its suggestions by referencing the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned image analysis unit, It estimates the user's emotions and filters the image analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned image analysis unit, During image analysis, text information within the image is extracted and used to inform tasks and schedules. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned image analysis unit, During image analysis, the analysis results are supplemented by considering the location and time the image was taken. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned image analysis unit, It estimates the user's emotions and adjusts how images are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned image analysis unit, When analyzing images, the relevance of images is evaluated by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned image analysis unit, During image analysis, the user's social media activity is referenced to supplement the image's intent. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A voice analysis unit that analyzes the user's speech, A task management unit manages tasks based on the information analyzed by the aforementioned voice analysis unit, A learning unit that learns the user's hobbies and preferences, It comprises an image analysis unit that reads images from a camera, A system characterized by the following features.

2. The aforementioned voice analysis unit, It estimates the user's emotions and evaluates the importance of their statements based on those estimated emotions. The system according to feature 1.

3. The aforementioned voice analysis unit, During speech analysis, the system analyzes the background sounds of the user's speech to supplement the context of the speech. The system according to feature 1.

4. The aforementioned voice analysis unit, During voice analysis, the frequency and patterns of user utterances are analyzed to determine task priorities. The system according to feature 1.

5. The aforementioned voice analysis unit, It estimates the user's emotions and filters the analysis results of the statements based on the estimated user emotions. The system according to feature 1.

6. The aforementioned voice analysis unit, During voice analysis, the relevance of utterances is evaluated by considering the user's geographical location. The system according to feature 1.

7. The aforementioned voice analysis unit, During voice analysis, the system supplements the intent behind the speech by referencing the user's social media activity. The system according to feature 1.

8. The aforementioned task management unit, It estimates the user's emotions and adjusts the task reminder method based on the estimated user emotions. The system according to feature 1.

9. The aforementioned task management unit, During task management, the system analyzes the user's past task completion history to select the optimal task management method. The system according to feature 1.

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