System

Abstract

A system in accordance with an embodiment aims to allow users to efficiently manage large tasks and track progress.SOLUTION: A system includes a task input unit, a task segmentation unit, an advice providing unit, a progress check unit, and a message transmission unit. The task input unit allows a user to input a task. The task fragmentation unit analyzes the task input by the task input unit and divides the task into as small actions as possible. The advice providing unit provides specific advice for each step divided by the task fragmenting unit. The progress checking unit may check, in real time, a process in which a user performs a task. The message transmission unit transmits a message encouraging the user in accordance with the progress checked by the progress check unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have made it difficult for users to efficiently manage large tasks and track their progress.

[0005] The system according to the embodiment aims to help users efficiently manage large tasks and track their progress. [Means for solving the problem]

[0006] The system according to the embodiment includes a task input unit, a task subdivision unit, an advice providing unit, a progress check unit, and a message sending unit. The task input unit allows a user to input a task. The task subdivision unit analyzes the task input by the task input unit and divides it into as small actions as possible. The advice providing unit provides specific advice for each step divided by the task subdivision unit. The progress check unit checks the progress of the user as they progress through the task in real time. The message sending unit sends a message of encouragement to the user according to the progress checked by the progress check unit. [Effects of the Invention]

[0007] An embodiment of the system allows users to efficiently manage large tasks and track progress. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The task breakdown AI system according to an embodiment of the present invention is a system that breaks down a task that a user finds difficult into smaller steps, making it easier to tackle. This allows the task breakdown AI system to efficiently progress through a task that the user finds difficult.

[0029] A task breakdown AI system according to an embodiment includes a task input unit, a task subdivision unit, a progress check unit, and a message sending unit. The task input unit receives a task input from a user. For example, the user may input "clean the room." The task input unit may also receive a task such as "write a report." The task subdivision unit analyzes the task input by the task input unit and divides it into as small actions as possible. For example, the task "clean the room" may be divided into specific steps such as "throw away trash," "sweep the floor," and "tidy up the desk." The task subdivision unit also provides specific advice for each step. For example, for the step "throw away trash," the advice may be "first, prepare a trash bag, collect trash from all over the room, and throw it away." The progress check unit monitors the progress of the user as they complete the task in real time. For example, each time the user completes a step, the progress is recorded and the user is prompted to proceed to the next step. The progress check unit also sends encouraging messages to the user based on their progress. For example, by sending a message such as "Good job! Let's sweep the floor next," the user's motivation is maintained and they are guided to complete the task. The message sending unit sends a message encouraging the user according to the progress checked by the progress checking unit. For example, it sends a message such as "Good luck with the next step!" This allows the task breakdown AI system according to the embodiment to efficiently progress through tasks that the user finds difficult.

[0030] When a user inputs a task, the task input unit allows the generation AI to refer to past task history and automatically complete the input of similar tasks. For example, when a user inputs "clean the room," the task input unit allows the generation AI to refer to past task history and automatically complete similar tasks such as "throw out the trash," "sweep the floor," and "tidy up the desk." In addition, when a user inputs "write a report," the task input unit allows the generation AI to refer to past task history and automatically complete similar tasks such as "collect materials," "create an outline," and "write the main text." This reduces the effort required for users to input tasks.

[0031] The task input unit allows the user to input tasks by voice, which the generation AI can convert into text using voice recognition technology. For example, when the user inputs "clean the room" by voice, the task input unit uses voice recognition technology to convert it into text "clean the room." Also, when the user inputs "write a report" by voice, the task input unit uses voice recognition technology to convert it into text "write a report." This allows the user to input tasks by voice.

[0032] The task input unit adds a function that allows users to attach images or videos when entering tasks, allowing the generation AI to analyze visual information as well. For example, if a user enters "clean the room" and attaches a photo of the room, the generation AI will analyze the image and suggest specific cleaning steps. Also, if a user enters "write a report" and attaches a related video, the generation AI will analyze the video and suggest specific steps for gathering materials. This allows users to enter tasks using visual information.

[0033] The task input unit can seamlessly synchronize task input from different devices. For example, if a user inputs "clean the room" on a smartphone and continues inputting on a tablet, the generation AI will seamlessly synchronize and consistently manage the task progress. Also, if a user inputs "write a report" on a PC and continues inputting on a smartphone, the generation AI will seamlessly synchronize and consistently manage the task progress. This allows users to input tasks from different devices.

[0034] When the generation AI breaks down a task, the task breakdown unit can refer to the user's past success stories and suggest optimal steps. For example, if the user inputs "clean the room," the task breakdown unit will refer to past success stories and suggest optimal steps such as "throw out the trash," "sweep the floor," and "tidy up the desk." Similarly, if the user inputs "write a report," the task breakdown unit will refer to past success stories and suggest optimal steps such as "gather materials," "create an outline," and "write the main text." This allows the generation AI to suggest optimal steps by utilizing the user's past success stories.

[0035] The task breakdown unit enables the generation AI to customize specific advice for each step according to the user's skill level. For example, when a user inputs "clean the room," the task breakdown unit allows the generation AI to provide specific advice such as "cleaning methods for beginners" or "cleaning techniques for advanced users" according to the user's skill level. Similarly, when a user inputs "write a report," the task breakdown unit allows the generation AI to provide specific advice such as "material gathering methods for beginners" or "techniques for writing a paper for advanced users" according to the user's skill level. This makes it possible to provide specific advice according to the user's skill level.

[0036] When the generation AI breaks down a task, the task breakdown unit can refer to the success stories of other users and incorporate best practices. For example, when a user inputs "clean the room," the generation AI will refer to the success stories of other users and incorporate best practices such as "throw out the trash," "sweep the floor," and "tidy up the desk." Similarly, when a user inputs "write a report," the generation AI will refer to the success stories of other users and incorporate best practices such as "gather materials," "create an outline," and "write the main text." This allows the generation AI to utilize the success stories of other users to suggest optimal steps.

[0037] The task breakdown unit allows the generation AI to provide visual guides and video tutorials for each step. For example, if a user inputs "clean the room," the generation AI will provide visual guides and video tutorials for steps such as "throw out the trash," "sweep the floor," and "tidy up the desk." Similarly, if a user inputs "write a report," the task breakdown unit will provide visual guides and video tutorials for steps such as "gather materials," "create an outline," and "write the main text." This allows the user to use visual guides to progress through the task.

[0038] When the generation AI checks the progress, the progress check unit refers to the user's schedule and calendar information and can suggest the next step at the optimal time. For example, if the user inputs "Clean the room," the generation AI will refer to the user's calendar information and suggest a step such as "Next, sweep the floor" when there is free time. Alternatively, if the user inputs "Write a report," the generation AI will refer to the user's schedule and suggest a step such as "Next, create an outline" at the appropriate time. This allows the generation AI to suggest the next step at the optimal time based on the user's schedule.

[0039] When the generation AI checks the progress of a task, the progress check unit can compare it with the progress of other users and send a message that stimulates a competitive spirit. For example, when a user inputs "clean my room," the generation AI compares it with the progress of other users and sends a message that stimulates a competitive spirit, such as "other users have progressed this far!". Alternatively, when a user inputs "write a report," the progress check unit compares it with the progress of other users and sends a message that stimulates a competitive spirit, such as "other users have finished up to this part!". In this way, a message that stimulates a competitive spirit can be sent by comparing the progress of other users.

[0040] When the generation AI checks the progress, the progress check unit can suggest music or podcasts that suit the user's preferences, optimizing the work environment. For example, when a user inputs "cleaning the room," the progress check unit suggests music that suits the user's preferences and sends a message such as "listening to this music while cleaning will be fun!". Alternatively, when a user inputs "writing a report," the progress check unit suggests podcasts that suit the user's preferences and sends a message such as "listening to this podcast while working will help you concentrate!". This makes it possible to suggest music or podcasts that suit the user's preferences and optimize the work environment.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] When a user inputs a task, the task input unit can automatically complete any ambiguous parts of the task using natural language processing technology. For example, if a user inputs "tidy up the room," the generation AI will automatically complete details such as "which room to tidy up" and "how much to tidy up." Similarly, if a user inputs "write a report," the generation AI will automatically complete details such as "what topic to write about" and "how many pages to write." This further reduces the effort required for users to input tasks.

[0043] When the generation AI breaks down a task, the task segmentation unit can refer to the user's past failures and suggest steps to avoid failure. For example, if a user inputs "clean the room," the generation AI will refer to past failures and suggest steps to avoid failure, such as "prepare a garbage bag before throwing out the trash." Similarly, if a user inputs "write a report," the generation AI will refer to past failures and suggest steps to avoid failure, such as "check a reliable source of information before collecting materials." This helps the user avoid repeating past mistakes.

[0044] When the generation AI checks the progress of a task, the progress check unit monitors the user's health condition and can suggest breaks at appropriate times. For example, if a user inputs "clean the room," the generation AI monitors the user's heart rate and fatigue level, and when the heart rate rises, it sends a message such as "take a short break." Alternatively, if a user inputs "write a report," the generation AI monitors the user's fatigue level and when the fatigue level rises, it sends a message such as "relax and try again." This allows the generation AI to suggest breaks at appropriate times based on the user's health condition.

[0045] When the generation AI checks the progress of a task, the progress check unit can suggest rewards based on the user's preferences. For example, if a user inputs "Clean the room," the generation AI will suggest a reward based on the user's preferences, such as "Watch your favorite movie after you finish cleaning." Or, if a user inputs "Write a report," the generation AI will suggest a reward based on the user's preferences, such as "Let's have a nice cup of coffee after you finish the report." This can increase the user's motivation.

[0046] When the generation AI breaks down a task, the task breakdown unit can refer to the user's past success stories and suggest optimal steps. For example, if a user inputs "clean the room," the generation AI will refer to past success stories and suggest optimal steps such as "throw out the trash," "sweep the floor," and "tidy up the desk." Similarly, if a user inputs "write a report," the task breakdown unit will refer to past success stories and suggest optimal steps such as "gather materials," "create an outline," and "write the main text." This allows the generation AI to suggest optimal steps by utilizing the user's past success stories.

[0047] When a user inputs a task, the task input unit allows the generation AI to refer to past task history and automatically complete the input of similar tasks. For example, if a user inputs "clean the room," the generation AI will refer to past task history and automatically complete similar tasks such as "throw out the trash," "sweep the floor," and "tidy up the desk." In addition, when a user inputs "write a report," the task input unit allows the generation AI to refer to past task history and automatically complete similar tasks such as "collect materials," "create an outline," and "write the main text." This reduces the effort required for users to input tasks.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: In the task input section, the user inputs a task. For example, the user inputs a task such as "clean the room" or "write a report." Step 2: The task breakdown unit analyzes the task entered by the task input unit and breaks it down into as small actions as possible. For example, a task such as "cleaning the room" can be broken down into specific steps such as "throwing out the trash," "sweeping the floor," and "tidying up the desk." Step 3: The advice provider provides specific advice for each step divided by the task segmentation unit. For example, for the step "throw away the trash," the advice provided would be "first prepare a trash bag, then collect the trash from all over the room and throw it away." Step 4: The progress checker monitors the user's progress in real time as they complete the task. For example, each time the user completes a step, it records the progress and prompts the user to proceed to the next step. Step 5: The message sending unit sends a message encouraging the user according to the progress checked by the progress checking unit. For example, it sends a message such as "Good luck with the next step!"

[0050] (Example 2) The task breakdown AI system according to an embodiment of the present invention is a system that breaks down a task that a user finds difficult into smaller steps, making it easier to tackle. This allows the task breakdown AI system to efficiently progress through a task that the user finds difficult.

[0051] A task breakdown AI system according to an embodiment includes a task input unit, a task subdivision unit, a progress check unit, and a message sending unit. The task input unit receives a task input from a user. For example, the user may input "clean the room." The task input unit may also receive a task such as "write a report." The task subdivision unit analyzes the task input by the task input unit and divides it into as small actions as possible. For example, the task "clean the room" may be divided into specific steps such as "throw away trash," "sweep the floor," and "tidy up the desk." The task subdivision unit also provides specific advice for each step. For example, for the step "throw away trash," the advice may be "first, prepare a trash bag, collect trash from all over the room, and throw it away." The progress check unit monitors the progress of the user as they complete the task in real time. For example, each time the user completes a step, the progress is recorded and the user is prompted to proceed to the next step. The progress check unit also sends encouraging messages to the user based on their progress. For example, by sending a message such as "Good job! Let's sweep the floor next," the user's motivation is maintained and they are guided to complete the task. The message sending unit sends a message encouraging the user according to the progress checked by the progress checking unit. For example, it sends a message such as "Good luck with the next step!" This allows the task breakdown AI system according to the embodiment to efficiently progress through tasks that the user finds difficult.

[0052] When a user inputs a task, the task input unit allows the generation AI to refer to past task history and automatically complete the input of similar tasks. For example, when a user inputs "clean the room," the task input unit allows the generation AI to refer to past task history and automatically complete similar tasks such as "throw out the trash," "sweep the floor," and "tidy up the desk." In addition, when a user inputs "write a report," the task input unit allows the generation AI to refer to past task history and automatically complete similar tasks such as "collect materials," "create an outline," and "write the main text." This reduces the effort required for users to input tasks.

[0053] The task input unit allows the user to input tasks by voice, which the generation AI can convert into text using voice recognition technology. For example, when the user inputs "clean the room" by voice, the task input unit uses voice recognition technology to convert it into text "clean the room." Also, when the user inputs "write a report" by voice, the task input unit uses voice recognition technology to convert it into text "write a report." This allows the user to input tasks by voice.

[0054] The task input unit uses the emotion estimation function to analyze the emotion a user expresses when entering a task, and can instantly display an encouraging message for tasks associated with negative emotions. For example, if the emotion estimation function detects a negative emotion when a user enters "clean the room," the task input unit displays an encouraging message such as "You'll feel refreshed once you start cleaning!". Also, if the emotion estimation function detects a negative emotion when a user enters "write a report," the task input unit displays an encouraging message such as "Let's start by gathering the materials!" This can reduce the user's negative emotions.

[0055] The task input unit adds a function that allows users to attach images or videos when entering tasks, allowing the generation AI to analyze visual information as well. For example, if a user enters "clean the room" and attaches a photo of the room, the generation AI will analyze the image and suggest specific cleaning steps. Also, if a user enters "write a report" and attaches a related video, the generation AI will analyze the video and suggest specific steps for gathering materials. This allows users to enter tasks using visual information.

[0056] The task input unit can seamlessly synchronize task input from different devices. For example, if a user inputs "clean the room" on a smartphone and continues inputting on a tablet, the generation AI will seamlessly synchronize and consistently manage the task progress. Also, if a user inputs "write a report" on a PC and continues inputting on a smartphone, the generation AI will seamlessly synchronize and consistently manage the task progress. This allows users to input tasks from different devices.

[0057] The task input unit can use the emotion estimation function to analyze the emotions of the user when entering a task in real time and provide an interface design to elicit positive emotions. For example, when the user enters "clean the room," the task input unit provides an interface that displays bright colors and encouraging messages so that the emotion estimation function can elicit positive emotions. Also, when the user enters "write a report," the task input unit provides an interface that displays interactive guides and encouraging messages so that the emotion estimation function can elicit positive emotions. This can elicit positive emotions from the user.

[0058] When the generation AI breaks down a task, the task breakdown unit can refer to the user's past success stories and suggest optimal steps. For example, if the user inputs "clean the room," the task breakdown unit will refer to past success stories and suggest optimal steps such as "throw out the trash," "sweep the floor," and "tidy up the desk." Similarly, if the user inputs "write a report," the task breakdown unit will refer to past success stories and suggest optimal steps such as "gather materials," "create an outline," and "write the main text." This allows the generation AI to suggest optimal steps by utilizing the user's past success stories.

[0059] The task breakdown unit enables the generation AI to customize specific advice for each step according to the user's skill level. For example, when a user inputs "clean the room," the task breakdown unit allows the generation AI to provide specific advice such as "cleaning methods for beginners" or "cleaning techniques for advanced users" according to the user's skill level. Similarly, when a user inputs "write a report," the task breakdown unit allows the generation AI to provide specific advice such as "material gathering methods for beginners" or "techniques for writing a paper for advanced users" according to the user's skill level. This makes it possible to provide specific advice according to the user's skill level.

[0060] The task segmentation unit uses the emotion estimation function to analyze the user's emotions toward each step and can provide special encouraging messages for steps associated with negative emotions. For example, if a user inputs "clean the room" and has negative emotions toward a specific step, the task segmentation unit can provide an encouraging message such as "Once you finish this step, your room will be tidy!". Similarly, if a user inputs "write a report" and has negative emotions toward a specific step, the task segmentation unit can provide an encouraging message such as "Once you finish this part, most of the report will be complete!". This can reduce the user's negative emotions and support the progress of the task.

[0061] When the generation AI breaks down a task, the task breakdown unit can refer to the success stories of other users and incorporate best practices. For example, when a user inputs "clean the room," the generation AI will refer to the success stories of other users and incorporate best practices such as "throw out the trash," "sweep the floor," and "tidy up the desk." Similarly, when a user inputs "write a report," the generation AI will refer to the success stories of other users and incorporate best practices such as "gather materials," "create an outline," and "write the main text." This allows the generation AI to utilize the success stories of other users to suggest optimal steps.

[0062] The task breakdown unit allows the generation AI to provide visual guides and video tutorials for each step. For example, if a user inputs "clean the room," the generation AI will provide visual guides and video tutorials for steps such as "throw out the trash," "sweep the floor," and "tidy up the desk." Similarly, if a user inputs "write a report," the task breakdown unit will provide visual guides and video tutorials for steps such as "gather materials," "create an outline," and "write the main text." This allows the user to use visual guides to progress through the task.

[0063] The task segmentation unit uses the emotion estimation function to analyze the user's emotions for each step in real time and provide advice to elicit positive emotions. For example, if the user inputs "clean your room," the task segmentation unit can elicit positive emotions for a specific step by providing advice such as "Once you finish this step, your room will be tidy!". Alternatively, if the user inputs "write a report," the task segmentation unit can elicit positive emotions for a specific step by providing advice such as "Once you finish this part, most of the report will be complete!". This can elicit positive emotions from the user and support the progress of the task.

[0064] When the generation AI checks the progress of a task, the progress check unit monitors the user's biometric information and can send encouraging messages at appropriate times. For example, if the user inputs "clean the room," the generation AI monitors the user's heart rate and stress level, and sends a message such as "take a short break" when the heart rate rises. Alternatively, if the user inputs "write a report," the progress check unit monitors the user's stress level and sends a message such as "relax and try again" when the stress level rises. This allows encouraging messages to be sent at appropriate times based on the user's biometric information.

[0065] When the generation AI checks the progress, the progress check unit refers to the user's schedule and calendar information and can suggest the next step at the optimal time. For example, if the user inputs "Clean the room," the generation AI will refer to the user's calendar information and suggest a step such as "Next, sweep the floor" when there is free time. Alternatively, if the user inputs "Write a report," the generation AI will refer to the user's schedule and suggest a step such as "Next, create an outline" at the appropriate time. This allows the generation AI to suggest the next step at the optimal time based on the user's schedule.

[0066] The progress check unit can use the emotion estimation function to analyze the user's emotional state in real time and customize an encouraging message to elicit positive emotions. For example, the user inputs "clean my room," and the emotion estimation function analyzes the user's emotional state in real time, customizing an encouraging message such as "Once you finish this step, your room will be tidy!". Alternatively, the progress check unit can input "write a report," and the emotion estimation function analyzes the user's emotional state in real time, customizing an encouraging message such as "Once you finish this part, most of the report will be complete!". This makes it possible to provide an encouraging message to elicit positive emotions according to the user's emotional state.

[0067] When the generation AI checks the progress of a task, the progress check unit can compare it with the progress of other users and send a message that stimulates a competitive spirit. For example, when a user inputs "clean my room," the generation AI compares it with the progress of other users and sends a message that stimulates a competitive spirit, such as "other users have progressed this far!". Alternatively, when a user inputs "write a report," the progress check unit compares it with the progress of other users and sends a message that stimulates a competitive spirit, such as "other users have finished up to this part!". In this way, a message that stimulates a competitive spirit can be sent by comparing the progress of other users.

[0068] When the generation AI checks the progress, the progress check unit can suggest music or podcasts that suit the user's preferences, optimizing the work environment. For example, when a user inputs "cleaning the room," the progress check unit suggests music that suits the user's preferences and sends a message such as "listening to this music while cleaning will be fun!". Alternatively, when a user inputs "writing a report," the progress check unit suggests podcasts that suit the user's preferences and sends a message such as "listening to this podcast while working will help you concentrate!". This makes it possible to suggest music or podcasts that suit the user's preferences and optimize the work environment.

[0069] The progress check unit can use the emotion estimation function to analyze the user's emotional state in real time and provide a customized encouraging message to elicit positive emotions. For example, when the user inputs "clean my room," the emotion estimation function analyzes the user's emotional state in real time and provides a customized encouraging message such as "Once you finish this step, your room will be tidy!". Alternatively, when the user inputs "write a report," the emotion estimation function analyzes the user's emotional state in real time and provides a customized encouraging message such as "Once you finish this part, most of the report will be complete!". In this way, a customized encouraging message to elicit positive emotions can be provided according to the user's emotional state.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] When a user inputs a task, the task input unit can automatically complete any ambiguous parts of the task using natural language processing technology. For example, if a user inputs "tidy up the room," the generation AI will automatically complete details such as "which room to tidy up" and "how much to tidy up." Similarly, if a user inputs "write a report," the generation AI will automatically complete details such as "what topic to write about" and "how many pages to write." This further reduces the effort required for users to input tasks.

[0072] When the generation AI breaks down a task, the task segmentation unit can refer to the user's past failures and suggest steps to avoid failure. For example, if a user inputs "clean the room," the generation AI will refer to past failures and suggest steps to avoid failure, such as "prepare a garbage bag before throwing out the trash." Similarly, if a user inputs "write a report," the generation AI will refer to past failures and suggest steps to avoid failure, such as "check a reliable source of information before collecting materials." This helps the user avoid repeating past mistakes.

[0073] When the generation AI checks the progress of a task, the progress check unit monitors the user's health condition and can suggest breaks at appropriate times. For example, if a user inputs "clean the room," the generation AI monitors the user's heart rate and fatigue level, and when the heart rate rises, it sends a message such as "take a short break." Alternatively, if a user inputs "write a report," the generation AI monitors the user's fatigue level and when the fatigue level rises, it sends a message such as "relax and try again." This allows the generation AI to suggest breaks at appropriate times based on the user's health condition.

[0074] The task input unit uses the emotion estimation function to analyze the emotion a user expresses when entering a task, and can display a message that further motivates the user for tasks associated with positive emotions. For example, if the emotion estimation function detects a positive emotion when a user enters "clean the room," it displays a message such as "Great! Let's keep it up!". Also, if the emotion estimation function detects a positive emotion when a user enters "write a report," it displays a message such as "Keep it up!". This can further increase the user's positive emotions.

[0075] When the generation AI breaks down a task, the task subdivision unit can adjust the difficulty of the task according to the user's current mood and physical condition. For example, if a user inputs "clean the room," the generation AI will consider the user's mood and physical condition and suggest starting with an easier step. Alternatively, if a user inputs "write a report," the generation AI will consider the user's mood and physical condition and suggest starting with a step that can be completed in a short amount of time. This supports the user's progress in the task according to their mood and physical condition.

[0076] When the generation AI checks the progress of a task, the progress check unit can suggest rewards based on the user's preferences. For example, if a user inputs "Clean the room," the generation AI will suggest a reward based on the user's preferences, such as "Watch your favorite movie after you finish cleaning." Or, if a user inputs "Write a report," the generation AI will suggest a reward based on the user's preferences, such as "Let's have a nice cup of coffee after you finish the report." This can increase the user's motivation.

[0077] The task input unit uses the emotion estimation function to analyze the emotion a user feels when entering a task, and can suggest relaxing activities for tasks that involve negative emotions. For example, if the emotion estimation function detects a negative emotion when a user enters "clean the room," it will suggest an activity such as "do some stretching before cleaning." Similarly, if the emotion estimation function detects a negative emotion when a user enters "write a report," it will suggest an activity such as "take a deep breath before starting to write." This can help reduce the user's negative emotions.

[0078] When the generation AI breaks down a task, the task breakdown unit can refer to the user's past success stories and suggest optimal steps. For example, if a user inputs "clean the room," the generation AI will refer to past success stories and suggest optimal steps such as "throw out the trash," "sweep the floor," and "tidy up the desk." Similarly, if a user inputs "write a report," the task breakdown unit will refer to past success stories and suggest optimal steps such as "gather materials," "create an outline," and "write the main text." This allows the generation AI to suggest optimal steps by utilizing the user's past success stories.

[0079] The progress check unit can use the emotion estimation function to analyze the user's emotional state in real time and provide a customized encouraging message to elicit positive emotions. For example, if the user inputs "clean my room," the emotion estimation function analyzes the user's emotional state in real time and provides a customized encouraging message such as "Once you finish this step, your room will be tidy!". Alternatively, if the user inputs "write a report," the emotion estimation function analyzes the user's emotional state in real time and provides a customized encouraging message such as "Once you finish this part, most of the report will be complete!". In this way, customized encouraging messages can be provided to elicit positive emotions according to the user's emotional state.

[0080] When a user inputs a task, the task input unit allows the generation AI to refer to past task history and automatically complete the input of similar tasks. For example, if a user inputs "clean the room," the generation AI will refer to past task history and automatically complete similar tasks such as "throw out the trash," "sweep the floor," and "tidy up the desk." In addition, when a user inputs "write a report," the task input unit allows the generation AI to refer to past task history and automatically complete similar tasks such as "collect materials," "create an outline," and "write the main text." This reduces the effort required for users to input tasks.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: In the task input section, the user inputs a task. For example, the user inputs a task such as "clean the room" or "write a report." Step 2: The task breakdown unit analyzes the task entered by the task input unit and breaks it down into as small actions as possible. For example, a task such as "cleaning the room" can be broken down into specific steps such as "throwing out the trash," "sweeping the floor," and "tidying up the desk." Step 3: The advice provider provides specific advice for each step divided by the task segmentation unit. For example, for the step "throw away the trash," the advice provided would be "first prepare a trash bag, then collect the trash from all over the room and throw it away." Step 4: The progress checker monitors the user's progress in real time as they complete the task. For example, each time the user completes a step, it records the progress and prompts the user to proceed to the next step. Step 5: The message sending unit sends a message encouraging the user according to the progress checked by the progress checking unit. For example, it sends a message such as "Good luck with the next step!"

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a task input section where a user inputs a task; a task subdivision unit that analyzes the task input by the task input unit and divides it into as small actions as possible; an advice providing unit that provides specific advice for each step divided by the task subdivision unit; a progress check unit that checks the progress of the user's task in real time; a message sending unit that sends a message encouraging the user according to the progress checked by the progress checking unit. A system characterized by:

2. The task input unit Add a feature that allows users to attach images or videos when entering tasks, and the generative AI will also analyze visual information.

2. The system of claim 1.

3. The task subdivision unit When the generative AI breaks down a task, it refers to the user's past success stories and suggests optimal steps.

2. The system of claim 1.

4. The progress check unit When the generating AI checks the progress of the task, it monitors the user's biometric information and sends the encouraging message at the appropriate time.

2. The system of claim 1.

5. The task input unit Using the emotion estimation function, the emotions expressed when the user enters a task are analyzed, and the encouraging message is immediately displayed for tasks with negative emotions.

2. The system of claim 1.

Images