System

Abstract

An object of the system according to the embodiment is to find an effective strategy for a situation in which the user has a heavy task or is not motivated.SOLUTION: A system according to an embodiment includes a strategy providing unit, a task dividing unit, a work rhythm proposing unit, and a feedback unit. The strategy providing unit provides a personalized strategy based on the information input by the user. The task divider subdivides the task based on the strategy provided by the strategy provider. The work rhythm proposing section proposes an optimum work rhythm based on the tasks subdivided by the task dividing section. The feedback section provides feedback on the achievement of the minor goal based on the work rhythm proposed by the work rhythm proposal section.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 had the problem of making it difficult for users to find effective strategies for dealing with heavy tasks or unmotivated situations.

[0005] The system according to the embodiment aims to help users find effective strategies for dealing with heavy tasks and unmotivated situations. [Means for solving the problem]

[0006] The system according to the embodiment includes a strategy providing unit, a task dividing unit, a work rhythm proposing unit, and a feedback unit. The strategy providing unit provides individual strategies based on information input by a user. The task dividing unit divides tasks into smaller tasks based on the strategies provided by the strategy providing unit. The work rhythm proposing unit proposes an optimal work rhythm based on the tasks divided by the task dividing unit. The feedback unit provides feedback on the achievement of sub-goals based on the work rhythm proposed by the work rhythm proposing unit. [Effects of the Invention]

[0007] The system according to the embodiment allows users to find effective strategies for heavy tasks and unmotivated situations. [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 AI ​​support system according to an embodiment of the present invention provides individualized strategies for users to deal with heavy tasks or situations where they feel unmotivated, and can break down tasks into smaller pieces based on user input, provide advice on goal setting, suggest optimal work rhythms, set achievable sub-goals, and provide motivational feedback each time a goal is achieved.

[0029] An AI support system according to an embodiment includes a strategy provider, a task divider, a work rhythm suggester, and a feedback unit. The strategy provider provides individual strategies based on user input. For example, if a user inputs, "I have too much homework and I'm not motivated," the generation AI provides specific advice such as, "Let's start with easy problems first." If a user inputs, "I don't know how to proceed with a large project," the generation AI provides advice such as, "Divide the project into several small tasks and set deadlines for each." If a user inputs, "I can't maintain concentration," the generation AI provides specific guidelines such as, "Try the Pomodoro technique, where you work for 25 minutes and then take a 5-minute break." The task divider divides tasks into smaller tasks based on the strategy provided by the strategy provider. For example, the generation AI provides advice on how to divide tasks into smaller tasks and on goal setting based on the user's input. The work rhythm suggester proposes an optimal work rhythm based on the tasks divided by the task divider. For example, the generation AI proposes guidelines for finding an optimal work rhythm based on the user's input. The feedback unit provides feedback on the achievement of sub-goals based on the work rhythm proposed by the work rhythm suggestion unit. For example, the generation AI sets achievable sub-goals for the user and provides positive feedback such as "Congratulations! Let's try harder next time!" each time the user achieves one. This allows the AI ​​support system according to the embodiment to enable users to work efficiently. For example, students can receive specific advice on how to efficiently complete their homework, and working adults can obtain guidelines for effectively managing projects. Furthermore, feeling a sense of accomplishment improves motivation and increases the willingness to continue working.

[0030] The strategy providing unit can analyze the user's past behavioral history and prioritize suggest the most effective strategy. For example, the strategy providing unit stores the history of tasks the user has performed in the past in a database and identifies the most effective strategy based on that data. For example, it analyzes how tasks were performed and how much time was allocated to tasks that were successful in the past and suggests a strategy to use again in a similar situation. This allows the user to work efficiently by analyzing the user's past behavioral history and prioritize suggesting the most effective strategy.

[0031] The strategy providing unit can monitor the user's current psychological state in real time and dynamically change the strategy accordingly. The strategy providing unit, for example, uses heart rate and facial expression recognition technology to monitor the user's psychological state in real time. For example, if the stress level is high, the unit suggests a task to help the user relax. In this way, the user can efficiently proceed with their work by monitoring the user's current psychological state in real time and dynamically changing the strategy accordingly.

[0032] The strategy providing unit can provide strategies specialized for different age groups and occupations, and give optimal advice according to the user's attributes. The strategy providing unit builds a system that provides optimal strategies based on, for example, the user's age group and occupation. For example, it provides a strategy specialized for studying to students, and a strategy specialized for work to working adults. This allows the user to work efficiently by providing strategies specialized for different age groups and occupations and giving optimal advice according to the user's attributes.

[0033] The strategy providing unit can use a visual interface when providing a strategy to enable the user to intuitively understand. The strategy providing unit, for example, uses a visual interface when providing a strategy to enable the user to intuitively understand. For example, the strategy providing unit displays the progress of a task using graphs and charts. This allows the user to intuitively understand the strategy when providing a strategy using a visual interface, thereby enabling the user to efficiently proceed with work.

[0034] The task division unit can analyze the user's past task completion data and propose the most effective task division method. For example, the task division unit stores the user's past task completion data in a database and identifies the most effective task division method based on that data. For example, it proposes a task division method that was successful in the past again. In this way, by analyzing the user's past task completion data and proposing the most effective task division method, the user can proceed with their work efficiently.

[0035] The task division unit can automatically set priorities based on the importance and urgency of tasks. The task division unit, for example, analyzes the importance and urgency of tasks and builds a system that automatically sets priorities. For example, it preferentially suggests tasks with high importance and urgency. This allows users to efficiently proceed with their work by automatically setting priorities based on the importance and urgency of tasks.

[0036] The task division unit provides task division methods specialized for different industries and occupations, and can provide optimal advice according to the user's occupation. For example, the task division unit registers task division methods specialized for different industries and occupations in a database, and provides optimal advice according to the user's occupation. For example, it proposes a task division method specialized for the medical industry. This allows the user to work efficiently by providing task division methods specialized for different industries and occupations and providing optimal advice according to the user's occupation.

[0037] The task division unit can use visual tools to enable the user to intuitively grasp the progress when dividing a task. For example, the task division unit uses visual tools to enable the user to intuitively grasp the progress when dividing a task. For example, the task division unit displays the progress of a task using a Gantt chart. This allows the user to intuitively grasp the progress when dividing a task using visual tools, thereby enabling the user to efficiently proceed with work.

[0038] The work rhythm suggestion unit can prioritize and suggest the most effective work rhythm based on the user's past work data. The work rhythm suggestion unit, for example, stores the user's past work data in a database and identifies the most effective work rhythm based on that data. For example, it may suggest a work rhythm that was successful in the past again. This allows the user to work efficiently by prioritizedly suggesting the most effective work rhythm based on the user's past work data.

[0039] The work rhythm suggestion unit can provide work rhythms specialized for different cultural spheres and regions and propose optimal guidelines that suit the user's lifestyle. For example, the work rhythm suggestion unit registers work rhythms specialized for different cultural spheres and regions in a database and provides optimal guidelines that suit the user's lifestyle. For example, in cultural spheres where people take a break after lunch, the work rhythm suggestion unit proposes that rhythm. In this way, by providing work rhythms specialized for different cultural spheres and regions and proposing optimal guidelines that suit the user's lifestyle, the user can work efficiently.

[0040] The work rhythm suggestion unit can provide a user with a relaxing environment by using music and environmental sounds when proposing a work rhythm. The work rhythm suggestion unit, for example, constructs a system that provides a user with a relaxing environment by using music and environmental sounds when proposing a work rhythm. For example, it suggests music to improve concentration. In this way, by providing a user with a relaxing environment by using music and environmental sounds when proposing a work rhythm, the user can proceed with work efficiently.

[0041] The feedback unit can analyze the user's past goal achievement data and suggest the most effective method for setting sub-goals. For example, the feedback unit stores the user's past goal achievement data in a database and identifies the most effective method for setting sub-goals based on that data. For example, the feedback unit re-suggests a sub-goal setting method that was successful in the past. In this way, by analyzing the user's past goal achievement data and suggesting the most effective method for setting sub-goals, the user can proceed with their work efficiently.

[0042] The feedback unit can dynamically change the content of the feedback depending on the degree of achievement of the sub-goal. For example, the feedback unit builds a system that analyzes the degree of achievement of the sub-goal in real time and dynamically changes the content of the feedback based on the results. For example, if the degree of achievement is high, positive feedback is provided. This allows the user to work efficiently by dynamically changing the content of the feedback depending on the degree of achievement of the sub-goal.

[0043] The feedback unit can provide sub-goal setting methods specialized for different age groups and occupations, and provide optimal advice according to the user's attributes. For example, the feedback unit registers sub-goal setting methods specialized for different age groups and occupations in a database, and provides optimal advice according to the user's attributes. For example, it suggests a sub-goal setting method specialized for learning to students. This allows the user to work efficiently by providing sub-goal setting methods specialized for different age groups and occupations, and providing optimal advice according to the user's attributes.

[0044] The feedback unit can visually display the degree of achievement of the sub-goal, allowing the user to intuitively grasp the progress. The feedback unit, for example, builds a system that visually displays the degree of achievement of the sub-goal, allowing the user to intuitively grasp the progress. For example, the degree of achievement is displayed using a graph or chart. In this way, the degree of achievement of the sub-goal is visually displayed, allowing the user to intuitively grasp the progress, thereby allowing the user to efficiently proceed with the work.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The strategy provider can also monitor the user's health status and provide optimal strategies based on that information. For example, it can analyze the user's sleep data and suggest lighter tasks if the user is not getting enough rest. It can also provide strategies for incorporating short exercise sessions if a lack of exercise is detected. It can also provide advice for optimizing energy levels based on the timing and content of meals.

[0047] The strategy provider can also provide strategies for making tasks more enjoyable based on the user's hobbies and interests. For example, for a user who likes music, the strategy provider can suggest listening to their favorite music while working. For a user who likes games, the strategy provider can provide a method for gamifying tasks and visualizing progress. Furthermore, for a user who likes reading, the strategy provider can provide a strategy for setting aside short reading breaks between tasks.

[0048] The strategy provider can also leverage the user's social network to suggest collaboration and support. For example, it can suggest working with friends or colleagues who have the same task. It can also encourage information sharing and support in online communities and forums. It can also provide a way to keep the user motivated by reporting progress to family and friends.

[0049] The task division unit can also analyze the user's schedule and propose an optimal task division method. For example, it can analyze the user's daily schedule, find free time, and divide tasks accordingly. It can also analyze the user's weekly schedule and propose a long-term task division method. It can also analyze the user's monthly schedule and propose a task division method for managing the progress of a large project.

[0050] The work rhythm suggestion unit can also analyze the user's biorhythm and suggest an optimal work rhythm. For example, it can analyze the user's sleep patterns and identify the time periods when the user can concentrate best. It can also analyze the user's meal timings and assign important tasks to times when the user's energy level is high. It can also analyze the user's exercise habits and suggest working in a refreshed state after exercise.

[0051] The processing flow of the first embodiment will be briefly explained below.

[0052] Step 1: The strategy provider provides an individual strategy based on the user's input. For example, if the user inputs, "I have too much homework and I'm not motivated," the AI ​​generator will provide specific advice such as, "Let's start with easy problems first." Similarly, if the user inputs, "I don't know how to proceed with a large project," the AI ​​generator will provide advice such as, "Divide the project into several small tasks and set deadlines for each." Furthermore, if the user inputs, "I can't maintain concentration," the AI ​​generator will provide specific guidelines such as, "Try the Pomodoro technique, where you work for 25 minutes and then take a 5-minute break." Step 2: The task division unit divides the task into smaller pieces based on the strategy provided by the strategy provision unit. For example, the generation AI provides advice on how to divide the task into smaller pieces and on goal setting based on the user's information input. Step 3: The work rhythm suggestion unit proposes an optimal work rhythm based on the tasks divided by the task division unit. For example, the generation AI proposes guidelines for finding the optimal work rhythm based on the user's information input. Step 4: The feedback unit provides feedback on the achievement of sub-goals based on the work rhythm proposed by the work rhythm suggestion unit. For example, the generation AI sets achievable sub-goals for the user and provides positive feedback such as "Congratulations! Let's try harder next time" each time the user achieves one.

[0053] (Example 2) The AI ​​support system according to an embodiment of the present invention provides individualized strategies for users to deal with heavy tasks or situations where they feel unmotivated, and can break down tasks into smaller pieces based on user input, provide advice on goal setting, suggest optimal work rhythms, set achievable sub-goals, and provide motivational feedback each time a goal is achieved.

[0054] An AI support system according to an embodiment includes a strategy provider, a task divider, a work rhythm suggester, and a feedback unit. The strategy provider provides individual strategies based on user input. For example, if a user inputs, "I have too much homework and I'm not motivated," the generation AI provides specific advice such as, "Let's start with easy problems first." If a user inputs, "I don't know how to proceed with a large project," the generation AI provides advice such as, "Divide the project into several small tasks and set deadlines for each." If a user inputs, "I can't maintain concentration," the generation AI provides specific guidelines such as, "Try the Pomodoro technique, where you work for 25 minutes and then take a 5-minute break." The task divider divides tasks into smaller tasks based on the strategy provided by the strategy provider. For example, the generation AI provides advice on how to divide tasks into smaller tasks and on goal setting based on the user's input. The work rhythm suggester proposes an optimal work rhythm based on the tasks divided by the task divider. For example, the generation AI proposes guidelines for finding an optimal work rhythm based on the user's input. The feedback unit provides feedback on the achievement of sub-goals based on the work rhythm proposed by the work rhythm suggestion unit. For example, the generation AI sets achievable sub-goals for the user and provides positive feedback such as "Congratulations! Let's try harder next time!" each time the user achieves one. This allows the AI ​​support system according to the embodiment to enable users to work efficiently. For example, students can receive specific advice on how to efficiently complete their homework, and working adults can obtain guidelines for effectively managing projects. Furthermore, feeling a sense of accomplishment improves motivation and increases the willingness to continue working.

[0055] The strategy providing unit can analyze the user's past behavioral history and prioritize suggest the most effective strategy. For example, the strategy providing unit stores the history of tasks the user has performed in the past in a database and identifies the most effective strategy based on that data. For example, it analyzes how tasks were performed and how much time was allocated to tasks that were successful in the past and suggests a strategy to use again in a similar situation. This allows the user to work efficiently by analyzing the user's past behavioral history and prioritize suggesting the most effective strategy.

[0056] The strategy providing unit can monitor the user's current psychological state in real time and dynamically change the strategy accordingly. The strategy providing unit, for example, uses heart rate and facial expression recognition technology to monitor the user's psychological state in real time. For example, if the stress level is high, the unit suggests a task to help the user relax. In this way, the user can efficiently proceed with their work by monitoring the user's current psychological state in real time and dynamically changing the strategy accordingly.

[0057] The strategy providing unit can use the emotion estimation function to provide a strategy based on the user's emotions and elicit positive emotions. For example, the strategy providing unit uses the emotion estimation function to analyze the user's emotions in real time and provide a strategy that elicits positive emotions. For example, if the user is feeling anxious, the strategy providing unit suggests a task that reminds the user of a successful experience. In this way, the emotion estimation function can be used to provide a strategy based on the user's emotions and elicit positive emotions, allowing the user to proceed with the work efficiently.

[0058] The strategy providing unit can provide strategies specialized for different age groups and occupations, and give optimal advice according to the user's attributes. The strategy providing unit builds a system that provides optimal strategies based on, for example, the user's age group and occupation. For example, it provides a strategy specialized for studying to students, and a strategy specialized for work to working adults. This allows the user to work efficiently by providing strategies specialized for different age groups and occupations and giving optimal advice according to the user's attributes.

[0059] The strategy providing unit can use a visual interface when providing a strategy to enable the user to intuitively understand. The strategy providing unit, for example, uses a visual interface when providing a strategy to enable the user to intuitively understand. For example, the strategy providing unit displays the progress of a task using graphs and charts. This allows the user to intuitively understand the strategy when providing a strategy using a visual interface, thereby enabling the user to efficiently proceed with work.

[0060] The strategy providing unit can use the emotion estimation function to suggest a strategy at a timing when the user is likely to accept the strategy. The strategy providing unit, for example, uses the emotion estimation function to build a system that suggests a strategy at a timing when the user is likely to accept the strategy. For example, the strategy providing unit suggests a strategy when the user is relaxed. In this way, the emotion estimation function is used to suggest a strategy at a timing when the user is likely to accept the strategy, allowing the user to proceed with work efficiently.

[0061] The task division unit can analyze the user's past task completion data and propose the most effective task division method. For example, the task division unit stores the user's past task completion data in a database and identifies the most effective task division method based on that data. For example, it proposes a task division method that was successful in the past again. In this way, by analyzing the user's past task completion data and proposing the most effective task division method, the user can proceed with their work efficiently.

[0062] The task division unit can automatically set priorities based on the importance and urgency of tasks. The task division unit, for example, analyzes the importance and urgency of tasks and builds a system that automatically sets priorities. For example, it preferentially suggests tasks with high importance and urgency. This allows users to efficiently proceed with their work by automatically setting priorities based on the importance and urgency of tasks.

[0063] The task division unit can use the emotion estimation function to propose a task division method that will most motivate the user. For example, the task division unit uses the emotion estimation function to build a system that proposes a task division method that will most motivate the user. For example, the task division unit preferentially proposes a task division method that will motivate the user. In this way, by using the emotion estimation function to propose a task division method that will most motivate the user, the user can proceed with work efficiently.

[0064] The task division unit provides task division methods specialized for different industries and occupations, and can provide optimal advice according to the user's occupation. For example, the task division unit registers task division methods specialized for different industries and occupations in a database, and provides optimal advice according to the user's occupation. For example, it proposes a task division method specialized for the medical industry. This allows the user to work efficiently by providing task division methods specialized for different industries and occupations and providing optimal advice according to the user's occupation.

[0065] The task division unit can use visual tools to enable the user to intuitively grasp the progress when dividing a task. For example, the task division unit uses visual tools to enable the user to intuitively grasp the progress when dividing a task. For example, the task division unit displays the progress of a task using a Gantt chart. This allows the user to intuitively grasp the progress when dividing a task using visual tools, thereby enabling the user to efficiently proceed with work.

[0066] The task division unit can use the emotion estimation function to propose a task division method that causes the user the least stress. The task division unit, for example, uses the emotion estimation function to build a system that proposes a task division method that causes the user the least stress. For example, the task division unit divides tasks when the user is relaxed. In this way, the emotion estimation function is used to propose a task division method that causes the user the least stress, allowing the user to work efficiently.

[0067] The work rhythm suggestion unit can prioritize and suggest the most effective work rhythm based on the user's past work data. The work rhythm suggestion unit, for example, stores the user's past work data in a database and identifies the most effective work rhythm based on that data. For example, it may suggest a work rhythm that was successful in the past again. This allows the user to work efficiently by prioritizedly suggesting the most effective work rhythm based on the user's past work data.

[0068] The work rhythm proposing unit can use the emotion estimation function to propose a work rhythm that allows the user to concentrate best. The work rhythm proposing unit, for example, uses the emotion estimation function to build a system that proposes a work rhythm that allows the user to concentrate best. For example, it suggests that the user should perform concentrated work during a time period when they have positive emotions. In this way, by using the emotion estimation function to propose a work rhythm that allows the user to concentrate best, the user can proceed with work efficiently.

[0069] The work rhythm suggestion unit can provide work rhythms specialized for different cultural spheres and regions and propose optimal guidelines that suit the user's lifestyle. For example, the work rhythm suggestion unit registers work rhythms specialized for different cultural spheres and regions in a database and provides optimal guidelines that suit the user's lifestyle. For example, in cultural spheres where people take a break after lunch, the work rhythm suggestion unit proposes that rhythm. In this way, by providing work rhythms specialized for different cultural spheres and regions and proposing optimal guidelines that suit the user's lifestyle, the user can work efficiently.

[0070] The work rhythm suggestion unit can provide a user with a relaxing environment by using music and environmental sounds when proposing a work rhythm. The work rhythm suggestion unit, for example, constructs a system that provides a user with a relaxing environment by using music and environmental sounds when proposing a work rhythm. For example, it suggests music to improve concentration. In this way, by providing a user with a relaxing environment by using music and environmental sounds when proposing a work rhythm, the user can proceed with work efficiently.

[0071] The work rhythm proposal unit can use the emotion estimation function to propose a work rhythm that allows the user to be most relaxed. The work rhythm proposal unit, for example, uses the emotion estimation function to build a system that proposes a work rhythm that allows the user to be most relaxed. For example, the work rhythm proposal unit suggests that the user take a break during a time period when the user is relaxed. In this way, the emotion estimation function is used to propose a work rhythm that allows the user to be most relaxed, allowing the user to work efficiently.

[0072] The feedback unit can analyze the user's past goal achievement data and suggest the most effective method for setting sub-goals. For example, the feedback unit stores the user's past goal achievement data in a database and identifies the most effective method for setting sub-goals based on that data. For example, the feedback unit re-suggests a sub-goal setting method that was successful in the past. In this way, by analyzing the user's past goal achievement data and suggesting the most effective method for setting sub-goals, the user can proceed with their work efficiently.

[0073] The feedback unit can dynamically change the content of the feedback depending on the degree of achievement of the sub-goal. For example, the feedback unit builds a system that analyzes the degree of achievement of the sub-goal in real time and dynamically changes the content of the feedback based on the results. For example, if the degree of achievement is high, positive feedback is provided. This allows the user to work efficiently by dynamically changing the content of the feedback depending on the degree of achievement of the sub-goal.

[0074] The feedback unit can use the emotion estimation function to provide feedback that makes the user feel the most positive emotion. For example, the feedback unit uses the emotion estimation function to build a system that provides feedback that makes the user feel the most positive emotion. For example, feedback that makes the user feel joy is provided preferentially. This allows the user to efficiently proceed with their work by using the emotion estimation function to provide feedback that makes the user feel the most positive emotion.

[0075] The feedback unit can provide sub-goal setting methods specialized for different age groups and occupations, and provide optimal advice according to the user's attributes. For example, the feedback unit registers sub-goal setting methods specialized for different age groups and occupations in a database, and provides optimal advice according to the user's attributes. For example, it suggests a sub-goal setting method specialized for learning to students. This allows the user to work efficiently by providing sub-goal setting methods specialized for different age groups and occupations, and providing optimal advice according to the user's attributes.

[0076] The feedback unit can visually display the degree of achievement of the sub-goal, allowing the user to intuitively grasp the progress. The feedback unit, for example, builds a system that visually displays the degree of achievement of the sub-goal, allowing the user to intuitively grasp the progress. For example, the degree of achievement is displayed using a graph or chart. In this way, the degree of achievement of the sub-goal is visually displayed, allowing the user to intuitively grasp the progress, thereby allowing the user to efficiently proceed with the work.

[0077] The feedback unit can use the emotion estimation function to provide feedback that most motivates the user. The feedback unit, for example, uses the emotion estimation function to build a system that provides feedback that most motivates the user. For example, feedback that the user has a positive emotion is preferentially provided. This allows the user to work efficiently by providing feedback that most motivates the user using the emotion estimation function.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The strategy provider can also monitor the user's health status and provide optimal strategies based on that information. For example, it can analyze the user's sleep data and suggest lighter tasks if the user is not getting enough rest. It can also provide strategies for incorporating short exercise sessions if a lack of exercise is detected. It can also provide advice for optimizing energy levels based on the timing and content of meals.

[0080] The strategy provider can also provide strategies for making tasks more enjoyable based on the user's hobbies and interests. For example, for a user who likes music, the strategy provider can suggest listening to their favorite music while working. For a user who likes games, the strategy provider can provide a method for gamifying tasks and visualizing progress. Furthermore, for a user who likes reading, the strategy provider can provide a strategy for setting aside short reading breaks between tasks.

[0081] The strategy provider can also leverage the user's social network to suggest collaboration and support. For example, it can suggest working with friends or colleagues who have the same task. It can also encourage information sharing and support in online communities and forums. It can also provide a way to keep the user motivated by reporting progress to family and friends.

[0082] The strategy provider can estimate the user's emotions and suggest relaxation techniques to reduce stress. For example, it can provide deep breathing or meditation guides. It can also suggest ambient sounds or music that will help the user relax. Furthermore, it can suggest stretching or light exercise to do during short breaks, thereby reducing stress and improving work efficiency.

[0083] The strategy provider can also increase motivation by estimating the user's emotions and providing positive feedback. For example, it can provide words or messages that make the user feel a sense of accomplishment. It can also increase the user's sense of self-efficacy by reminding them of past successful experiences. Furthermore, it can elicit positive emotions by providing feedback that makes the user feel grateful.

[0084] The task division unit can also analyze the user's schedule and propose an optimal task division method. For example, it can analyze the user's daily schedule, find free time, and divide tasks accordingly. It can also analyze the user's weekly schedule and propose a long-term task division method. It can also analyze the user's monthly schedule and propose a task division method for managing the progress of a large project.

[0085] The task division unit can also estimate the user's emotions and suggest a task division method that will motivate the user. For example, it can assign important tasks to times when the user is feeling positive. It can also assign lighter tasks to times when the user is relaxed. Furthermore, if the user is feeling stressed, it can suggest a method to divide tasks into smaller pieces to make them feel a greater sense of accomplishment.

[0086] The work rhythm suggestion unit can also analyze the user's biorhythm and suggest an optimal work rhythm. For example, it can analyze the user's sleep patterns and identify the time periods when the user can concentrate best. It can also analyze the user's meal timings and assign important tasks to times when the user's energy level is high. It can also analyze the user's exercise habits and suggest working in a refreshed state after exercise.

[0087] The work rhythm suggestion unit can estimate the user's emotions and suggest the most relaxing work rhythm. For example, it can suggest taking a break during a time when the user is relaxed. If the user is feeling stressed, it can also suggest taking frequent short breaks. It can also suggest concentrating on work during a time when the user is feeling positive.

[0088] The feedback unit can estimate the user's emotions and provide feedback that elicits the most positive emotions. For example, it can provide words or messages that make the user feel a sense of accomplishment. It can also increase the user's sense of self-efficacy by reminding the user of past successful experiences. Furthermore, it can elicit positive emotions by providing feedback that makes the user feel grateful.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The strategy provider provides an individual strategy based on the user's input. For example, if the user inputs, "I have too much homework and I'm not motivated," the AI ​​generator will provide specific advice such as, "Let's start with easy problems first." Similarly, if the user inputs, "I don't know how to proceed with a large project," the AI ​​generator will provide advice such as, "Divide the project into several small tasks and set deadlines for each." Furthermore, if the user inputs, "I can't maintain concentration," the AI ​​generator will provide specific guidelines such as, "Try the Pomodoro technique, where you work for 25 minutes and then take a 5-minute break." Step 2: The task division unit divides the task into smaller pieces based on the strategy provided by the strategy provision unit. For example, the generation AI provides advice on how to divide the task into smaller pieces and on goal setting based on the user's information input. Step 3: The work rhythm suggestion unit proposes an optimal work rhythm based on the tasks divided by the task division unit. For example, the generation AI proposes guidelines for finding the optimal work rhythm based on the user's information input. Step 4: The feedback unit provides feedback on the achievement of sub-goals based on the work rhythm proposed by the work rhythm suggestion unit. For example, the generation AI sets achievable sub-goals for the user and provides positive feedback such as "Congratulations! Let's try harder next time" each time the user achieves one.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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, in order to avoid confusion and to 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.

[0157] 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]

[0158] 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 strategy providing unit that provides an individual strategy based on information input by a user; a task dividing unit that divides a task into smaller tasks based on the strategy provided by the strategy providing unit; a work rhythm proposing unit that proposes an optimal work rhythm based on the tasks divided by the task dividing unit; a feedback unit that provides feedback regarding achievement of a sub-goal based on the work rhythm proposed by the work rhythm proposal unit. A system characterized by:

2. The strategy providing unit Monitor the user's current psychological state in real time and dynamically change your strategy accordingly.

2. The system of claim 1.

3. The task division unit Analyze users' past task completion data and suggest the most effective task division method 2. The system of claim 1.

4. The work rhythm proposal unit Analyzing the user's biometric data and suggesting the optimal work rhythm 2. The system of claim 1.

5. The feedback unit Dynamically change the feedback depending on the degree of achievement of sub-goals 2. The system of claim 1.

6. The strategy providing unit Using emotion estimation, we provide strategies based on the user's emotions to elicit positive emotions.

2. The system of claim 1.

7. The task division unit Using emotion estimation, we propose a task division method that motivates users the most.

2. The system of claim 1.

8. The work rhythm proposal unit Using emotion estimation function, we suggest a work rhythm that allows users to concentrate best.

2. The system of claim 1.

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